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Antibodies
October 2024
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Antibodies 101
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Antibodies 101
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Introduction to Antibodies 101
I
Dear Reader,
f you study proteins, it’s likely you’ll need to use antibodies in
your research. You may have even ordered a recombinant
monoclonal antibody from Addgene! Our ready-to-use antibody
collection launched in 2022, and we’ve been excited to see it grow steadily
ever since then. It now includes a range of neuroscience antibodies, general
use antibodies, and antibodies specific to difficult-to-target cell surface and
secreted proteins. We also have a large collection of antibody plasmids
available.
As we developed our antibody collection, we also developed our collection
of antibody educational resources, including blog posts aimed at providing
practical advice for using antibodies in the lab. In this, the very first edition of
our Antibodies 101 eBook, we have compiled our antibodies blog posts into a
single, downloadable resource.
We hope you will find this eBook useful for learning about antibodies,
training others, selecting an antibody, designing assays, and of course,
troubleshooting your experiments.
If you have any comments, questions, or suggestions for this eBook, please
send an email to news@addgene.org, and we’ll be happy to help in any way
we can.
Happy reading!
The Addgene Team
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Contents
Introduction to Antibodies 101
3
Chapter 1
What is an Antibody?7
Introduction to Antibodies8
Isotypes 17
Plasmid-Based Recombinant Monoclonal Antibodies22
Fc Effector Functions
Buffers, Storage, and Conjugates
Conjugation
27
31
35
Chapter 2 Antibodies and Affinity Reagents
39
Monoclonal Antibodies40
Polyclonal Antibodies45
Secondary Antibodies
Chimeric Antibodies55
Affinity Reagents Single-Chain Fragment Variables 64
Fab Fragments 71
Year of the Camelids75
Chapter 3
Finding the Right Antibody for Your Experiment
Epitope Availability80
Selecting the Right Antibody84
Choosing the Right Isotype
The Antibody Data Hub
Epitope Tags 100
Of Myc and Men104
Antibody Validation A Control for All Seasons118
50
59
79
89
95
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Chapter 4
Labeling An Introduction to Immunoprecipitation126
Fixing and Permeabilizing for Immunofluorescence133
Multiplex Immunofluorescence
Avoiding the Mouse-on-Mouse Mess in IHC146
SunTag and Fluorescent Imaging 150
Chapter 5
Detecting
The Basics of Western Blotting155
How to Strip and Reprobe a Western Blot162
Technical Design of a Western Blot
Troubleshooting and Optimizing a Western Blot181
ELISA (Enzyme-Linked Immunosorbent Assay) 197
The Four ELISAs and When to Use Them
203
Great Results Start with Great Standard Curves
209
Chapter 6
Capturing and Purifying 215
An Introduction to Immunoprecipitation216
ChIP221
Affinity Tags
225
To Each HIS Own
229
125
139
154
167
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Chapter 7
Sorting 234
Flow Cytometry235
Validating Antibodies for Use in Flow Cytometry243
Reading a Flow Plot
251
Introduction to Gating
259
Flow Cytometry Controls 266
Designing Your First Flow Panel270
Flow Compensation
278
Yes, No, and Everything in Between
283
Beyond Surface Labeling288
Conventional vs. Spectral Flow Cytometry
Chapter 8
Making Antibodies
Producing Recombinant Antibodies301
295
300
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Introduction to Antibodies
Y
Aliyah Weinstein, January 2021
ou may have heard the term antibody tossed around in the
news or in the lab. But what exactly is an antibody, and how is a
component of the immune system useful as a research reagent?
Let’s find out!
What is an antibody?
Antibodies, also known as immunoglobulins, are ~150 kDa, Y-shaped proteins
that are both a natural part of the immune system and a tool that can be used
for a variety of research applications. Within the immune system, antibodies
are produced by B cells. They bind to proteins on the surface of extracellular
pathogens such as parasites or microbes, or to proteins expressed on the
surface of cells that have been infected with a microbe, to trigger immune
cascades that clear these infections. Anything that generates an antibody
response in the immune system is referred to as an antigen.
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The ability of antibodies to bind proteins is useful for research applications as
well, because they allow scientists to target specific proteins they’re interested
in. Once a protein is targeted with an antibody, you can visualize the protein via
fluorescence or chemiluminescence, precipitate the protein out of solution, or
isolate cells expressing this protein. Read on to learn more about antibodies and
how to use them in the lab!
Parts of an antibody
Antibodies have two regions: the Fab, or antigen-binding region, and the Fc, or
crystallizable region. Two immunoglobulin (Ig) heavy chains and two Ig light chains
make up each antibody molecule. The Fc region and part of the Fab regions are
made from the Ig heavy chains, and the rest of the Fab regions are completed
with the Ig light chains. The variable regions of the heavy and light chain
determine what antigen the antibody recognizes and binds to.
The constant region of the heavy chain determines the antibody’s isotype —
that is, one of five broad groups of antibodies (IgM, IgD, IgA, IgG, or IgE). In the
context of the immune system, each isotype is expressed at different times of
the immune response and initiates different immune cascades. In the research
setting, antibodies of different isotypes can be used together in the same
Figure 1: Labeled diagram of an antibody including Fc, Fab, heavy chain, light chain, constant region,
variable region.
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experiment because the reagents used to detect the presence of an antibody
depend partially on its isotype.
The variable region of an antibody does not recognize the entirety of the target
protein: instead, it recognizes a small portion of the protein, known as an epitope.
Epitopes are between 5–8 amino acids long, with a typical length of five or six
amino acids (Cruse et al., 2004). Because proteins are much larger than this, it
may contain many epitopes that antibodies can recognize. Imagine you’re doing a
word search puzzle. You’re not always going to spot the whole, long word right off
the bat, but you might see a few letters and be able to guess whether that string
of letters is a part of the word you’re searching for. Antibodies may recognize a
linearized sequence of amino acids, or only in their 3D conformation.
Types of antibodies and production
In the immune system, antibodies are produced by B cells. But there are many
ways that antibodies can be produced for research purposes, some of which take
advantage of natural immune system production and many more engineered
methods. Each type of antibody and production strategy has advantages and
disadvantages compared to each other.
Polyclonal antibodies
These antibodies are derived from animals, such as rabbits or goats; the animals
are injected with the antigen that you want the antibody to recognize. This
triggers an immune response within the animal, and its B cells will churn out
antibodies against the injected protein. Several weeks later, blood from the
animal is drawn and the antibodies are extracted. These antibodies recognize a
variety of epitopes on the target protein, so they are referred to as polyclonal.
Polyclonal antibodies have the advantage of more robustly recognizing the target
protein because the antibodies will recognize multiple epitopes on the protein.
However, each animal will make antibodies against different protein epitopes,
and so variability between lots is a big concern. You can find more details about
polyclonal antibodies in the Polyclonal Antibodies section of this eBook.
Monoclonal antibodies
On the other hand, monoclonal antibodies are a homogenous population
of antibodies that all recognize the same epitope of the target protein. Most
commonly, these antibodies are produced from hybridomas. Hybridomas are
formed by fusing B cells that have been extracted from animals injected with
protein antigen to myeloma cells. The hybridomas are then subcloned by limiting
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dilution, which produces an immortalized, clonal population of cells that produce
a single variant of antibody. Monoclonal antibodies are less likely to cross-react
with other similar proteins due to recognizing a single epitope of the target
protein. However, small mutations in the DNA of the hybridoma can occur, known
as genetic drift, meaning that over time, the antibody you expect it to produce is
not necessarily what you are getting. Learn more about monoclonal antibodies in
the Monoclonal Antibodies section.
Recombinant antibodies
Similar to monoclonal antibodies, recombinant antibodies are a homogeneous
population. However, unlike monoclonal antibodies, which come from
naturally-occurring immunoglobulin genes, recombinant antibodies are plasmidbased. Their sequences can be optimized for specificity and sensitivity, for
example, by changing certain amino acids to improve its stability. Additionally,
the choice of vector can impact the expression level of an antibody (Ayyar et al.,
2017).
Single-chain variable fragment (ScFv)
Because the entire antibody molecule is not necessary for antigen binding, the
variable regions alone can be generated as a fusion protein. An ScFv is made
up of the variable regions of the heavy and light chains fused together to form
a single protein that can recognize the target protein (Wang et al., 2013). These
proteins are also plasmid-derived. However, a disadvantage to ScFv is their
lower affinity for their target protein compared to whole antibodies (CrivianuGaita 2016). Get more details on ScFvs in the chapter on Single Chain Fragment
Variables (scFvs).
Figure 2: Comparison between the IgG antibody and scFv.
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Figure 3: Comparison of the Hcab and nanobody.
Nanobodies
Nanobodies are the variable heavy chain fragment of heavy chain camelid
antibodies (Hcab) (Arbabi-Ghahroudi et al., 2017). Although they’re based on the
immune system of llamas, synthetic nanobodies, or synbodies, can be produced
from plasmids, saving time and money. Nanobodies and synbodies are just
as specific for their target antigen as canonical antibodies. They also have the
advantage of being significantly smaller, meaning they can recognize epitopes
that full antibodies would not physically be able to reach. While a full antibody is
~150 kDa, a nanobody is 12–15 kDa.
Research applications for antibodies
You can use antibodies for experiments in a variety of fields, including
neuroscience and immunology. They can be used for qualitative and quantitative
measurement of protein expression in cell lysates, whole cells, or tissue
samples. Many experiments that use antibodies use a primary antibody, which
recognizes your protein of interest, and a secondary antibody, which recognizes
the primary antibody and provides the method of detection. You’ll also want to
use the appropriate controls for your experiment to account for potential nonspecific binding of the antibody. Let’s dive in and take a look at some of these
applications.
Western blot
Western blots are used to detect the presence of proteins in samples containing
a mixture of proteins. The proteins are separated based on molecular weight by
SDS-PAGE, and are subsequently transferred to a membrane. A primary antibody
recognizing the protein of interest is added, and will bind to that protein on the
membrane. Then, addition of a secondary antibody allows the protein to be
detected by chemiluminescence or fluorescence. Western blots are frequently
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Figure 4: Western blot showing GFP expression in cells that have been transfected with a plasmid
encoding GFP. Actin, a common internal control for western blots, shows that a similar concentration of
cell lysate was added to each well. Image from Sun et al., 2010.
used to compare relative levels of protein expression between cell types or
treatment conditions. They can be used quantitatively if you also load the gel with
appropriate controls for normalizing the protein concentration. To get even more
details, check out The Basics of Western Blotting.
Enzyme-linked immunosorbent assay (ELISA)
Similarly, ELISA is used to detect the presence of a single protein from a
heterogeneous mixture — for example, cell lysate or media from cultured cells.
ELISA can be used to quantitatively measure the amount of protein present. The
reaction takes place in a 96-well plate, where primary and secondary antibodies
are used to capture the protein of interest and detect its presence. Then, the
amount of light that can pass through each well — the optical density (OD) — of
the known and experimental wells is measured.
To find the exact amount of protein in your sample, you’ll compare your samples
to a standard curve. This curve is generated using a serial dilution of a known
amount of protein that is also detected in the same way. The standard curve
that is created from the OD and concentration of the known samples is used to
calculate the concentration of protein in the experimental samples. Get more
details on ELISAs in our section ELISA (Enzyme-linked Immunosorbent Assay).
Immunoprecipitation
Sometimes, instead of just detecting the presence of protein within a sample,
you’ll want to extract your protein of interest from the mixture. Antibodies can do
this, too! In this method, antibodies bound to microscopic beads grab the
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protein of interest and pull it out of the solution. Once the unbound protein
is washed away, the protein of interest is dissociated from the bead and the
antibody using a low pH buffer. Then, the purified protein can be used in
experiments to compare the relative amount of protein present in a series of
samples, such as western blots or ELISAs, or for other experiments to study
protein-protein interactions.
Flow cytometry
Flow cytometry is used to detect proteins on the surface of or inside whole cells.
Antibodies — typically, primary antibodies that are conjugated to a fluorophore,
to avoid the use of secondary antibodies in this experiment — are incubated
with a mixture of cells. The technique of using fluorescently-labeled antibodies
to detect proteins is referred to as labeling at Addgene. Some people also refer
to it as staining. This panel of antibodies is used to both identify the cell subsets
of interest from among the mixture and to measure the relative amount of your
protein of interest on or in these cells: antibodies can bind to proteins on the cell
surface, or the cell membrane can be permeabilized to allow antibodies to bind to
intracellular proteins.
The fluorescence signal is detected by the flow cytometer and functions as a
proxy for protein expression. Because the signal comes from the antibodyfluorophore conjugate, and is not expressed by the cell itself, there is a lot of
flexibility in which proteins you can investigate in each panel. An advantage of
flow cytometry is the ability to use multiple fluorescent signals (up to a dozen or
Figure 5: Immunofluorescence staining for CD31 (green), CARD14 (red), and pNFkB (blue) in lesional
skin from a psoriasis patient, showing an inflammatory phenotype. Each protein was detected using an
unconjugated primary antibody, then visualized using a fluorophore-conjugated secondary antibody or
Fab. Image from Harden et al., 2014.
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measure the relative or absolute amount of the expression of multiple proteins
on the same cell simultaneously as well as compare between samples. Learn
more about flow cytometry.
Cell and tissue labeling
Antibodies can be used to visualize tissues (immunohistochemistry) or cells
(immunocytochemistry). This could be cells or tissue slices mounted to a slide, or
whole organs. Because the tissue architecture is preserved, this technique allows
you to not only visualize the presence of specific proteins, but their localization
within the cell or tissue as well. Similar to flow cytometry, you’ll use antibodies to
label for markers of one or more specific cell types, as well as for your protein of
interest, in order to best contextualize its localization. These markers could be
visualized using fluorophore-conjugated antibodies (i.e., immunofluorescence), or
by a colorimetric reaction facilitated by an enzymatic reaction.
In vivo uses
For both research and therapeutic purposes, antibodies can be used to
affect cellular processes in vivo, and these are referred to as functional grade
antibodies. Some of the research and therapeutic uses overlap: antibodies can
bind to and inhibit the function of a protein to stop its effect within the body, or
they can be used to activate cellular pathways by binding to cell surface receptors.
These applications are typically used for investigating signaling pathways or
developing therapeutics.
Experimentally, antibodies conjugated to fluorescent tags or other markers can
be injected into an animal, to directly label circulating cells or other cells that are
accessible from the bloodstream. Then, via a blood draw or organ harvest, the
labeled cells can be analyzed using techniques such as flow cytometry.
Conclusion
Whew! Now you know that antibodies can be used for a lot of purposes! I hope
you appreciate how many types of antibodies are out there, and how many
different research applications they can be used for. Addgene’s Antibody Plasmid
Collection includes plasmids that express antibodies, nanobodies, and ScFvs,
as well as tools for developing and producing plasmid-based antibodies. We
also distribute ready-to-use recombinant antibodies to help facilitate scientific
research! n
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References
Arbabi-Ghahroudi M (2017) Camelid Single-Domain Antibodies: Historical Perspective and Future Outlook. Front
Immunol 8:. https://doi.org/10.3389/fimmu.2017.01589
Ayyar BV, Arora S, Ravi SS (2017) Optimizing antibody expression: The nuts and bolts. Methods 116:51–62. https://doi.
org/10.1016/j.ymeth.2017.01.009
Crivianu-Gaita V, Thompson M (2016) Aptamers, antibody scFv, and antibody Fab’ fragments: An overview and
comparison of three of the most versatile biosensor biorecognition elements. Biosensors and Bioelectronics 85:32–45.
https://doi.org/10.1016/j.bios.2016.04.091
Cruse JM, Lewis RE, Wang H, Geziena MT, Steven GE, Lorna J. (2004) ANTIGENS, IMMUNOGENS, VACCINES, AND
IMMUNIZATION. In: Immunology Guidebook. Elsevier, pp 17–45. https://doi.org/10.1016/B978-012198382-6/50026-1
Wang R, Xiang S, Feng Y, Srinivas S, Zhang Y, Lin M, Wang S (2013) Engineering production of functional scFv antibody
in E. coli by co-expressing the molecule chaperone Skp. Front Cell Infect Microbiol 3:. https://doi.org/10.3389/
fcimb.2013.00072
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Isotypes
T
Beth Kenkel, October 2021
here are a lot of ways to classify antibodies: monoclonal,
polyclonal, scFvs, nanobodies, and the list goes on. But have
you heard of an antibody isotype? An isotype determines several
key characteristics of an antibody as well as the role it plays in an immune
response. But what is an isotype? How many are there? Why do they matter?
And how do antibodies switch isotypes?
What is an isotype?
An isotype is a class of antibody that’s determined by its heavy-chain constant
region (see Introduction to Antibodies for a refresher). There are five
antibody isotypes that each have a unique heavy-chain constant region: IgM,
IgD, IgG, IgE, and IgA.
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Figure 1: Diagram of an antibody labeled with Fc, Fab, heavy chain, light chain, constant region, and
variable region.
Why are antibody isotypes important?
To your immune system, an antibody’s isotype is important because it determines
what immune cells and molecules are recruited by the antibody to help destroy
and remove a pathogen. Different isotypes also appear at different stages of an
immune response. There are three main pathogen clearing functions or effector
functions of the constant regions of antibodies, although not all isotypes have the
same ability to activate each of these functions:
1. Recruitment of immune cells that express receptors that recognize the Fc
portion of different antibody isotypes.
2. Binding to complement system proteins, which can initiate a cascade that
helps attack extracellular pathogens.
3. Transportation of antibodies to places they can’t reach on their own. The
antibody’s heavy-chain constant domains, or Fc region, can be bound by
special receptors that transport antibodies through cells and into different
body compartments, such as into mucus, tears, or milk.
When using antibodies in the lab, it’s useful to know the isotype of an antibody
so you can select an appropriate isotype control. An isotype control has the same
constant heavy chain domain as your primary antibody but doesn’t bind your
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antigen of interest. Isotype controls are used to make sure the staining you see
is due to target-specific antibody binding rather than nonspecific background
staining. Isotype controls are used in antibody-based applications including
flow cytometry, immunohistochemistry, immunocytochemistry, ELISAs, and
western blotting.
The five antibody isotypes
Differences in their heavy chain constant regions result in several unique physical
characteristics of these five isotypes, such as the number and location of disulfide
bonds, the number of constant domains, and the hinge region length.
Now for some essential isotype nomenclature. Antibody heavy chain proteins
as well as the genes that encode those proteins are designated by the lower
case Greek letters μ, δ, γ, ε, and α. Some isotypes have different versions or
subclasses that have minor differences in their heavy chain constant domains.
These are typically numbered, i.e. IgG1, IgG2, etc.
Let’s dive in and meet each of the isotypes
IgM
This isotype is produced during a primary immune response because all B cells
begin by expressing IgM. IgM has a pentamer structure, which means each IgM
molecule has a binding capacity or valency of 10 antigens. An extra constant
domain replaces the hinge region in IgM antibodies. IgM has no subclasses and is
found mostly in blood.
IgD
While IgD is coexpressed with IgM on the surface of most immature B cells, its
function is unknown. It has a monomer structure with a valency of two and has no
subclasses.
IgG
IgG is the most abundant isotype in blood, but it’s also found in tissues. IgG is
the predominant isotype during a secondary immune response, which is when
the immune system encounters an antigen for a second or subsequent time. It
is expressed as a monomer with a valency of two. There are four IgG subclasses,
numbered based on their abundance in blood: IgG1, IgG2, IgG3, and IgG4.
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IgE
IgE antibodies help protect against parasites but also play a role in allergies. They
are found bound to IgE receptors on the surface of basophils and mast cells,
two types of white blood cells involved in allergic reactions. There are no IgE
subclasses and they are expressed as monomers with a valency of two.
IgA
IgA antibodies are found in blood, where it’s the second most common antibody
after IgG, but it’s the most prevalent antibody in secretions (e.g. tears, saliva,
mucus) where it protects mucosal membranes. This isotype is most commonly a
dimer with a valency of four. There are two IgA subclasses: IgA1 and IgA2.
Isotype switching
Isotype switching occurs when a B cell changes which heavy chain constant
domain it pairs with its variable chain domain. B cells start by co-expressing IgM
and IgD antibodies, but later in an immune response, the variable region of an
antibody is paired with any of the other isotype heavy chain constant domains.
This switching helps B cells adapt during the course of an immune response since
it also changes the effector function of the antibody.
Figure 2: Isotype switching requires DNA recombination of the antibody gene locus. B cells start by coexpressing IgM and IgD isotypes antibodies which are encoded by the heavy-chain constant genes μ and
δ. These are the first two genes in the heavy-chain constant domain gene cluster. DNA recombination
can delete intervening heavy-chain constant domain genes, placing the variable domain genes next
to a different constant domain gene. This process is called isotype switching and results in the B cell
expressing an antibody with the same variable domain but a different heavy-chain constant domain.
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Isotype switching is caused by a specialized type of DNA recombination. Heavy
chain constant genes are clustered together downstream of the variable region
gene in this order: μ, δ, γ, ε, and α. An antibody variable gene is initially expressed
with the IgM and IgD constant domains because they are the first constant
domain genes in the cluster. However, when B cells switch to expressing a
different isotype, the intervening DNA sequence is deleted, which places the
variable region next to a different constant domain gene. For example, if a B cell
switches to producing IgE antibodies, the gene segments for IgM, IgD, and IgG are
deleted. n
References
Janeway CA Jr, Travers P, Walport M, et al. Immunobiology: The Immune System in Health and Disease. 5th edition.
New York: Garland Science; 2001. Structural variation in immunoglobulin constant regions. https://www.ncbi.nlm.nih.
gov/books/NBK27106/
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Plasmid-Based Recombinant
Monoclonal Antibodies: What
They Are and Why You Should
Be Excited About Them
I
Melina Fan, May 2021
f you were born before 1985, you might remember going to the store
and buying CDs when you wanted to hear a piece of music. It was tough
to share or remix the songs, the CDs could get scratched over time, and
it was difficult to keep track of growing collections of music. I still have a shelf
full of CDs that have been gathering dust since the digital music revolution hit.
I predict a similar revolution for antibodies. Recombinant antibody technology
makes it easy for scientists to share, remix, store, and track these essential
components of the scientific toolbox.
What are recombinant antibodies?
Antibodies are used by scientists around the world to detect, purify, quantify,
deplete, and visualize proteins of interest (Greenfield, 2014). Traditionally,
they have been made as either polyclonal antibodies or monoclonal
hybridomas, but those techniques have several drawbacks. Polyclonal
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Figure 1: (Top) Polyclonal antibodies are produced in animals and consist of a mixture of antibodies
recognizing many different epitopes. (Middle) Monoclonal antibodies produced from hybridomas are
usually a single antibody recognizing one epitope. (Bottom) Recombinant monoclonal antibodies are
encoded in plasmids and produce a single antibody recognizing one epitope.
antibodies produced in animals may be variable among animals and bleed dates,
and even clonal hybridoma cell lines can produce more than one monoclonal
antibody (mAb) (Bradbury et al., 2018). Hybridomas can lose gene expression
of the mAb-encoding genes or fail to revive after cryopreservation (Bradbury &
Plückthun, 2015a, b). Therefore, researchers from over 100 scientific institutions
have proposed a shift to recombinant DNA-based antibody technologies
(Bradbury & Plückthun, 2015a).
Recombinant antibodies are monoclonal antibodies that are generated in vitro
using synthetic genes that are typically expressed from a plasmid or from an
integrated sequence in a stable cell line. The genes encode the heavy chain and
light chain for the antibody and when translated into protein will assemble into
a fully functional antibody. These antibodies can be used just as you would use
antibodies made from animals or hybridomas.
How are recombinant antibodies made?
To make recombinant antibodies, you first need to know the sequence. Scientists
are getting better and better at identifying and cloning the genes for antibody
expression. If starting from a protein sample (for instance, affinity purified
antibodies from a patient’s blood), scientists can use mass spectrometry to
determine the amino acid sequences of the antibodies and then synthesize the
genes that encode those amino acids (Tran et al., 2016). Each antibody would then
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Figure 2: (Left) The light chain and heavy chain genes of the antibody are encoded in a plasmid. (Right)
An assembled antibody protein binds an antigen. Image from Fvasconcellos.
be tested for antigen binding. If starting from an established hybridoma line,
the antibody can be converted into recombinant form through DNA sequencing
of the line and subsequent cloning of the antibody chains (Crosnier et al.,
2010; Andrews et al., 2019). Finally, scientists can perform the entire process of
antibody selection and maturation in vitro by selecting for antigen binding from
libraries of recombinant antibodies. The selection process is often performed via
phage display, yeast display, ribosome display, or mammalian display (Tsuruta et
al., 2017). In vitro selection allows scientists to create antibodies to targets that
may not work well in animals because of similarities to host proteins.
After the antibody of interest has been cloned into an expression plasmid, the
plasmid can be introduced into host cells, such as bacterial, yeast, or mammalian
cells, for antibody production and subsequent purification.
Academic laboratories and companies have already begun creating plasmids
encoding affinity reagents. These include conventional heavy and light chain
recombinant monoclonal antibodies as well as other forms of antibody-based
(e.g., single-domain antibodies, scFvs) and non-antibody-based affinity reagents
(e.g., monobodies, DARPins) (Helma et al., 2015).
The many benefits of recombinant antibodies
Recombinant antibodies offer numerous advantages over polyclonal antibodies
and traditional hybridomas.
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First, the long-term stability, consistency between batches, and molecular
definition of recombinant antibodies are essential for good reproducibility. Nonrecombinant antibodies are notorious for being the source of irreproducible
data (Begley & Ellis, 2012; Baker, 2015). This contributes to an enormous amount
of wasted money and time, with an estimated $350 million per year lost on
low quality antibodies in the US alone (Bradbury & Plückthun, 2015a). With
molecularly defined antibodies that do not change over time, scientists will know
exactly what antibody they are using, and any validation experiments performed
will be useful in perpetuity.
Second, plasmids are easy to store and share, making recombinant antibodies
the most practical choice. Hybridomas are much tougher to ship, often lack
sequence data, and may genetically drift over time. Antibodies made from
animals are limited in supply, and producing antibodies from plasmids is much
kinder to our animal friends!
Finally, if we want better antibodies, we need to empower the scientific
community with the tools to engineer them. Given the ease with which
scientists can now improve genetically-encoded research tools, it is shocking
that the plasmids and sequences for antibodies are not shared. For instance,
through open plasmid sharing, the CRISPR community has created hundreds of
useful variants of Cas9. By providing access to the plasmids encoding antibodies,
scientists will be able to make higher-affinity antibodies, modify antibody
function, improve antibody stability, and design tools that we haven’t even
imagined yet. n
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References
Andrews, N. P., Boeckman, J. X., Manning, C., Nguyen, J. T., Bechtold, H., Dumitras, C., Gong, B., Nguyen, K., Van Der List,
D., Murray, K. D., Engebrecht, J., & Trimmer, J. S. (2019b). A toolbox of IgG subclass-switched recombinant monoclonal
antibodies for enhanced multiplex immunolabeling of brain. eLife, 8. https://doi.org/10.7554/elife.43322
Baker, M. (2015). Reproducibility crisis: Blame it on the antibodies. Nature, 521(7552), 274–276. https://doi.
org/10.1038/521274a
Begley, C. G., & Ellis, L. M. (2012b). Raise standards for preclinical cancer research. Nature, 483(7391), 531–533. https://
doi.org/10.1038/483531a
Bradbury, A., & Plückthun, A. (2015). Reproducibility: Standardize antibodies used in research. Nature, 518(7537), 27–
29. https://doi.org/10.1038/518027a
Bradbury, A., & Plückthun, A. (2015c). Antibodies: validate recombinants once. Nature, 520(7547), 295. https://doi.
org/10.1038/520295b
Bradbury, A., Trinklein, N. D., Thie, H., Wilkinson, I., Tandon, A. K., Anderson, S. W., Bladen, C. L., Jones, B. R., Aldred, S.
F., Bestagno, M., Burrone, Ó. R., Maynard, J. A., Ferrara, F., Trimmer, J. S., Görnemann, J., Glanville, J., Wolf, P., Frenzel, A.,
Wong, J. K. L., . . . Dübel, S. (2018). When monoclonal antibodies are not monospecific: Hybridomas frequently express
additional functional variable regions. MAbs, 10(4), 539–546. https://doi.org/10.1080/19420862.2018.1445456
Crosnier, C., Staudt, N., & Wright, G. J. (2010). A rapid and scalable method for selecting recombinant mouse monoclonal
antibodies. BMC Biology, 8(1). https://doi.org/10.1186/1741-7007-8-76
Greenfield, E.A. (Eds.), Antibodies: a Laboratory Manual, 2nd Edition. 2014. Cold Spring Harbor Laboratory Publications,
New York.
Helma, J., Cardoso, M. C., Muyldermans, S., & Leonhardt, H. (2015). Nanobodies and recombinant binders in cell biology.
the Journal of Cell Biology/ the Journal of Cell Biology, 209(5), 633–644. https://doi.org/10.1083/jcb.201409074
Tran, N. H., Rahman, M. Z., He, L., Lei, X., Shan, B., & Li, M. (2016). Complete de novo assembly of monoclonal antibody
sequences. Scientific Reports, 6(1). https://doi.org/10.1038/srep31730
Tsuruta, L. R., Dos, M. L., & Moro, A. M. (2018). Display technologies for the selection of monoclonal antibodies for
clinical use. In InTech eBooks. https://doi.org/10.5772/intechopen.70930
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Fc Effector Functions
W
Rachel Leeson, May 2023
hen it comes to using antibodies in the lab, we focus a lot
on the variable region and not so much on the constant,
or Fc, region. Sure, we all know that the Fc region provides
structure, determines isotypes, and provides a place for secondary antibodies
to bind. We also know that changing the Fc domain can impact an antibody’s
binding affinity (Janda et al., 2016; Torres & Casadevall, 2008) but… does it
really do anything?
Fc functions
The answer is, yes, it does! While the Fc domain’s functions, besides structure
and isotype determination, don’t affect antibody applications like ELISAs or
western blots, they are key in helping antibodies drive an effective immune
response against a pathogen. The Fc domain creates a link between the
antibody and other parts of the immune system, including the innate immune
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Figure 1: Antibody structure.
response, by binding to Fc receptors on other immune cells. (psst! Need a quick
review of the different immune responses? Check this out!)
Many types of immune cells, from mast cells to B cells, express Fc receptors that
bind to the Fc domain of an antigen-bound antibody and induce (or, in the case
of the Fc receptors FcγRIIB1 and FcγRIIB, inhibit) specific immune responses.
These receptors are specific to isotype class, or even subclass, and are part of the
reason why different isotypes drive different types of immune responses. Handily,
the receptors are named after the isotypes they interact with, so IgG isotypes bind
to Fcγ receptors; IgE isotypes bind to Fcε receptors; and so on and so forth. There
are a lot of different receptors, but their downstream effects can be sorted into
three main effector functions (Figure 2).
Antibody-dependent complement deposition
Antibodies can bind to a pathogen and physically prevent it from entering a cell,
a non-Fc-dependent effector function known as neutralization. But they can also
bind to a pathogen and mark it as a target for other immune cells, in a process
known as opsonization. In antibody-dependent complement deposition (ADCD),
IgG and IgM antibodies activate the immune system’s classical complement
pathway, starting a cascade of events that results in the pores being opened
in the pathogen’s cell membrane (Dunkelberger & Song, 2010). As cells cannot
survive when holes are punched in their membranes, the pathogens quickly lyse
and die.
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Figure 2: Fc effector functions in an immune response to a viral infection a) antibody-dependent
complement deposition, b) antibody-dependent cellular cytotoxicity, c) antibody-dependent cellular
phagocytosis. Figure adapted from van Erp, et al., 2019.
Antibody-dependent cellular cytotoxicity
Antibody-dependent cellular cytotoxicity (ADCC) is a slightly more direct method
than opsonization. In ADCC, IgG antibodies bind to a pathogen and natural killer
(NK) cells expressing Fcγ receptors bind to IgG and release cytotoxins that kill the
target cell. If the pathogen is a large parasite, ADCC can be induced through a very
similar interaction between IgE antibodies and eosinophils, where the eosinophils
release cytotoxins via degranulation.
Antibody-dependent cellular phagocytosis
So far, our antibodies have induced “stabbing” and “poisoning,” but let’s be
honest, the elimination style the immune system is most famous for is eating. In
antibody-dependent cellular phagocytosis (ADCP), Fcγ receptors on macrophages
bind IgG antibodies. The macrophages then engulf and digest the pathogen the
antibodies are bound to in a process known as phagocytosis.
Other functions
We’re still learning about Fc effector functions and all that antibodies can
do! For instance, some Fc receptors, once bound to an antibody, can act as
immune modulators, shaping the immune response by driving particular cells or
responses. And there are still antibody isotypes and Fc receptors that we don’t
know much about yet. Who knows what kinds of functions they may induce?
It’s true that Fc receptors don’t impact how antibodies work as experimental tools.
But they are intriguing from a therapeutic standpoint. Engineered monoclonal
antibodies that can drive specific Fc effector functions may be useful for inducing
immunity or treating diseases (Cao et al, 2022). So the next time you see an
antibody description that says the Fc domain provides structure and isotype
determination, remember that it can do so much more than that! n
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References
Cao, X., Chen, J., Li, B., Dang, J., Zhang, W., Zhong, X., Wang, C., Raoof, M., Sun, Z., Yu, J., Fakih, M. G., & Feng, M. (2022).
Promoting antibody-dependent cellular phagocytosis for effective macrophage-based cancer immunotherapy. Science
Advances, 8(11), eabl9171. https://doi.org/10.1126/sciadv.abl9171. PMID: 35302839
Clynes, R. A., Towers, T. L., Presta, L. G., & Ravetch, J. V. (2000). Inhibitory Fc receptors modulate in vivo cytotoxicity
against tumor targets. Nature Medicine, 6(4), 443–446. https://doi.org/10.1038/74704. PMID: 10742152.
Dunkelberger, J. R., & Song, W. C. (2010). Complement and its role in innate and adaptive immune responses. Cell
Research, 20(1), 34–50. https://doi.org/10.1038/cr.2009.139. PMID: 20010915
Janda, A., Bowen, A., Greenspan, N. S., & Casadevall, A. (2016). Ig Constant Region Effects on Variable Region Structure
and Function. Frontiers in Microbiology, 7, 22. https://doi.org/10.3389/fmicb.2016.00022. PMID: 26870003
van Erp, E. A., Luytjes, W., Ferwerda, G., & van Kasteren, P. B. (2019). Fc-Mediated Antibody Effector Functions
During Respiratory Syncytial Virus Infection and Disease. Frontiers in Immunology, 10, 548. https://doi.org/10.3389/
fimmu.2019.00548. PMID: 30967872
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Buffers, Storage, and
Conjugates
I
Rachel Leeson, February 2022
t’s not just the antibodies that matter when you’re prepping for your
experiment — there are a number of outside factors that need to
be considered as well. Here, we’ll touch on antibody storage, buffer
considerations, and give just the lightest of nods towards conjugates.
Storage
Once you get your exciting new antibody, the second thing you’ll want to do is
store it away safely. The first thing you should do is record the manufacturer,
lot number, and expiration date somewhere you can easily check it whenever
you use the antibody. This information is important, and you’ll need to track it
throughout all your experiments.
Now that that’s out of the way, you can put your antibody in short-term
storage (think days to weeks) at 4 °C. If you’ll only be using it every few
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months or even less frequently, the -20 °C is your best option, in aliquots no
smaller than 10 µL. You can leave them at room temperature when you’re
working with them, but avoid freeze-thaw cycles, as they’ll degrade your antibody.
At some point in your long and illustrious lab career, you will likely leave your
antibody out on the bench overnight. Do not panic! Typically, antibodies are okay
if they’re left out overnight, so mark the vial, add the incident to your records, and
consider the following questions as you move forward:
1. Was a light-sensitive conjugate on the antibody left exposed to light?
2. Has this antibody been left out, gone through a freeze-thaw-freeze cycle
before, or otherwise been subject to mishandling?
3. Is the experiment you’re planning to run with the antibodies time-sensitive,
using precious samples, very sensitive, or being compared to other data in a
manner that demands high accuracy and consistency?
If the answer to any of these questions is “yes,” then I would suggest either testing
the antibody through a standard curve, a quick experiment, or using a different
vial and saving the poor, abandoned antibody for a … less sensitive moment.
Buffers
You know what temperature to store your antibodies at, but it also matters
what they’re stored in — their buffer. Which is made of chemicals, which have
an unfortunate tendency to react with other chemicals and biological structures,
some of which you may need in your experiment.
If you are trying to conjugate your antibody in the lab, you’ll want to research
your conjugation reaction to understand if any of your buffer components could
affect the reaction. For instance, sodium azide, a common antimicrobial agent
added to buffers, can bind to amine groups present in some conjugates, blocking
the conjugation reaction. Many commercial antibodies come with sodium
azide already included in the buffer, which will be noted on the product sheet.
Thankfully, it can easily be removed from the buffer and then re-added after the
conjugation reaction is done.
However, antibodies conjugated to horseradish protein (HRP), primarily used in
western blots and ELISAs, cannot be stored in sodium azide at all, as it
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inhibits HRP. In those cases, thimerosal at a concentration of 0.01% w/v can be
used as an alternative antimicrobial.
Conjugates
Ah, and HRP brings us handily to the final topic of this section, the conjugates (or
signaling molecules)! Please keep in mind that this is just a brief introduction to
the wide, wild, and fascinating field of antibody conjugations.
Assay type
Westerns and ELISAs mostly use HRP, a chemiluminescent conjugate that can be
activated to emit light with a simple kit, and is readily available on any number of
secondary antibodies. And look — if it ain’t broke, don’t fix it. HRP is easy to find,
simple to use, not sensitive to light, and cheap. Unless there’s a specific reason
you can’t use it for your ELISA and western, I’d highly recommend sticking with
what works.
To balance this out, however, is pretty much every other assay you can do. Most
non-HRP conjugates work not by reacting to a chemical, but by emitting a specific
wavelength of light in response to excitation by a laser, which is then read by
the machine. These conjugates are light-sensitive, so they’ll need to be stored
either in a dark, opaque vial or wrapped in aluminum foil to prevent ambient light
from degrading them. You’ll also need to know the wavelength of laser needed
to excite them — and it’s a good idea to make sure that laser is available in your
machine before you make your purchase.
Multiplex assays
Multiplex assays involve multiple antibodies, each with a different conjugate,
that all need to be detected in the same assay. This is one of the most complex
subjects in antibody-based protocols — but thankfully this is an Antibody 101
article. If you follow the two basic guidelines below, you’ll likely be able to create a
usable, small panel.
First, signaling molecules vary in brightness. You don’t want a super bright
conjugate on an abundant target, as you risk getting so much signal that you’ll
flood out other signals. Research the brightness of your conjugates before buying
and try to inversely pair brightness with abundance whenever possible.
Second, you’ll need to consider signal overlap. Some conjugates signal at
wavelengths close together, which can cause the read-out machinery to lose
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Figure 1: Try to inversely pair fluorophore strength with protein abundance.
differentiation between the two signals. You’ll want to pick signaling molecules
that send out very different signals whenever possible, to get the most accurate
read from the machine you run your assay on.
Working with antibodies can get a bit complicated, but hopefully this helps you
successfully select — and keep! — antibodies that are a good match for your
experiments, and avoid the awkwardness of getting the right antibody with the
wrong everything else. n
References
Andrews, N. P., Boeckman, J. X., Manning, C., Nguyen, J. T., Bechtold, H., Dumitras, C., Gong, B., Nguyen, K., Van Der List,
D., Murray, K. D., Engebrecht, J., & Trimmer, J. S. (2019b). A toolbox of IgG subclass-switched recombinant monoclonal
antibodies for enhanced multiplex immunolabeling of brain. eLife, 8. https://doi.org/10.7554/elife.43322
Baker, M. (2015). Reproducibility crisis: Blame it on the antibodies. Nature, 521(7552), 274–276. https://doi.
org/10.1038/521274a
Begley, C. G., & Ellis, L. M. (2012b). Raise standards for preclinical cancer research. Nature, 483(7391), 531–533. https://
doi.org/10.1038/483531a
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Conjugation
W
Ashley Waldron, March 2024
hile the antibodies present throughout our bodies carry out
plenty of roles just the way they are, the research antibodies
in your refrigerator often need a little help to be useful.
Mainly because, well, antibodies are kind of hard to see. To solve this issue,
researchers conjugate various labels to antibodies that produce detectable
signals like light or color. In this section, we’ll go over some of the common
conjugates you may encounter and how conjugates and antibodies
come together.
Common conjugates in the lab
Fluorophores
Fluorophores allow you to detect your antibodies using techniques like
fluorescence microscopy, flow cytometry, or spectrophotometry. The range of
fluorophores available for antibody conjugation spans the visible spectrum, so
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they are great for multiplexing and can be used in a variety of assay types. Be
sure to check the spectral properties of the fluorophores you are considering to
make sure that they’ll be compatible with your instruments, each other, and your
target. I love FPbase’s Spectra Viewer tool for comparing different fluorophores.
We also touched on fluorophores as antibody conjugates in an earlier section,
where you can find some additional tips for working with these tools.
Enzymes
An equally, if not more, common conjugate class than fluorophores are
enzymes. The most common enzyme conjugates are Horseradish Peroxidase
(HRP) and Alkaline Phosphatase (AP). Various substrates for each of
these enzymes allow you to detect enzyme activity (and therefore your
antibody) either through a chromogenic or chemiluminescent reaction.
Chemiluminescence, which is light produced by a chemical reaction, can be
detected using specialized imaging instruments or X-ray film and lends itself
well to assays like western blots and ELISAs. Chromogenic reactions produce
a color change, so they are typically used in microscopy-based assays like
immunohistochemistry, though they are also a common choice for ELISAs.
Biotin
Biotin is used in a range of antibody-based assays. Biotin itself is not visible
or capable of producing a visible signal, but it has a strong affinity for avidin
and streptavidin, both of which can bind up to four biotin molecules and can
themselves be linked to a labeling molecule. These properties allow for efficient
signal amplification, making biotin-conjugated antibodies good options for
detecting low-abundance targets. Check out the section on Biotinylation to learn
more about biotin and its use in molecular biology.
Other conjugates to know
It would be overwhelming to try to cover all of the possible antibody conjugates
(especially if you start to consider clinical applications). But there are a few other
classes that we would be remiss not to at least mention. Oligonucleotides allow
for sensitive detection of low-abundance targets with significant multiplexing
potential. You will find antibody-oligonucleotide conjugates used in assays like
proximity ligation, immuno-PCR, and single-cell applications (Hegazy et al., 2020;
Niemeyer et al., 2007; Stoeckius et al., 2017). Metal isotopes are useful for mass
cytometry (Bendall et al., 2012). And, you can also conjugate antibodies directly
to beads for purification purposes.
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Figure 1: Examples of sites on antibodies used for conjugation. 1) Endogenous lysine residues, which can
be found throughout the antibody. 2) Endogenous cysteines, such as those that make up the disulfide
bonds between antibody chains. 3) Endogenous carbohydrates added to antibodies post-translationally.
4) Modifications made to antibodies, such as inclusion of non-canonical amino acids or peptide tags. 5)
Strong protein-protein interactions with other peptides. Created with BioRender.com.
Coupling up — antibody conjugation chemistry
Whatever the conjugate, the molecules must somehow be tightly linked to your
antibody. This chemical coupling can be achieved through several different
mechanisms. Most take advantage of chemical groups intrinsically present on
antibodies, such as the primary amine groups from lysines, sulfhydryl groups
from cysteines, or carbohydrates from glycosylation (Dennler et al., 2015).
However, there are certainly other approaches that involve modifying the
antibody sequence or that rely on strong protein-protein interactions with other
peptides, like an Fc-binding domain (Figure 1).
Many biologists can lead perfectly blissful, productive lives without thinking
about these chemical reactions. After all, there is an abundance of commercially
available conjugated antibodies that are ready to use off the shelf. So why
are we talking about this? Even though there are so many commercially
conjugated antibodies available, that never guarantees the antibody you need
will be conjugated, or that it will be conjugated to the conjugate of your choice.
Knowing you can conjugate an antibody yourself allows you to consider a
broader selection of antibodies for your experiments.
Many of the methods for antibody conjugation can be quite variable from
antibody to antibody or batch to batch. For example, take lysine conjugation,
which is commonly used for labeling research antibodies. Lysine residues are
found throughout antibody sequences, up to 80 per antibody (Mueller et al.,
1988). If some of those lysines are in the antigen binding region or even just
nearby, then conjugation could impact the antibody’s antigen recognition and
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affinity. Hopefully, a commercially available conjugated antibody will have been
verified to perform the same as its unconjugated version, but if you find yourself
doing your own conjugation, it is important to keep in mind that you will need to
confirm your antibodies’ performance after the reaction.
If you are looking to conjugate antibodies yourself, there are kits available
to make the process quick and easy. Before getting started, be sure to check
that your antibody’s buffer is compatible with your conjugation kit, since many
common antibody buffers and buffer additives can impair the reaction or the
functionality of the conjugate. For example, Addgene’s antibodies are provided
in a buffer with the antimicrobial agent sodium azide, which can interfere with
conjugation reactions. If needed, you can often remove the incompatible agents
by dialysis or gel filtration.
There is, of course, a lot more to explore in the world of antibody conjugation.
But hopefully, this introduction provides a good base from which to dive deeper!
n
References
Bendall, S. C., Nolan, G. P., Roederer, M., & Chattopadhyay, P. K. (2012). A deep profiler’s guide to cytometry. Trends in
Immunology, 33(7), 323–332. https://doi.org/10.1016/j.it.2012.02.010
Dennler, P., Fischer, E., & Schibli, R. (2015). Antibody conjugates: from heterogeneous populations to defined reagents.
Antibodies, 4(3), 197–224. https://doi.org/10.3390/antib4030197
Hegazy, M., Cohen-Barak, E., Koetsier, J. L., Najor, N. A., Arvanitis, C., Sprecher, E., Green, K. J., & Godsel, L. M. (2020).
Proximity ligation assay for detecting Protein-Protein interactions and protein modifications in cells and tissues in situ.
Current Protocols in Cell Biology, 89(1). https://doi.org/10.1002/cpcb.115
Mueller, B. M., Wrasidlo, W. A., & Reisfeld, R. A. (1988). Determination of the number of E-Amino groups available
for conjugation of effector molecules to monoclonal antibodies. Hybridoma, 7(5), 453–456. https://doi.org/10.1089/
hyb.1988.7.453
Niemeyer, C. M., Adler, M., & Wacker, R. (2007). Detecting antigens by quantitative immuno-PCR. Nature Protocols, 2(8),
1918–1930. https://doi.org/10.1038/nprot.2007.267
Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., &
Smibert, P. (2017). Simultaneous epitope and transcriptome measurement in single cells. Nature Methods, 14(9), 865–
868. https://doi.org/10.1038/nmeth.4380
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Antibodies and Affinity Reagents
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Monoclonal Antibodies
I
Aliyah Weinstein, June 2021
f you’re just getting started using antibodies in your experiments, you
may be curious about all of the different kinds of antibodies that are
available. One common type of antibody is a monoclonal antibody. But
what does that mean, and how do monoclonal antibodies differ from other
types of antibodies?
How monoclonal antibodies are made
“Monoclonal” refers to antibodies that are all of the same isotype and
specificity. In other words, monoclonal antibodies are derived from a single
B cell clone. One common way of producing monoclonal antibodies is from
a hybridoma, a fusion between an antibody-producing B cell and a myeloma
cell (National Research Council (US) Committee on Methods of Producing
Monoclonal Antibodies, 1999) (Figure 1). This creates an immortal cell
population that will continue to produce the specific antibody that the original
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original B cell produced. Because of this, all of the antibodies recognize a single
epitope on the target molecule. Although it’s possible to generate antibodies
against many types of molecules including proteins, lipids, and carbohydrates,
we’ll focus on antibodies that recognize proteins.
The antibody-producing B cells that are used to make hybridomas come from
animals that were immunized against the target protein. Animals are first injected
with a small peptide that represents a short, unique amino acid sequence found
in the protein, or are injected with the whole protein. After isolating the B cells
from an immunized animal, those B cells are fused to myeloma cells at a 1:1 ratio
and then cultured in a special medium called HAT (hypoxanthine-aminopterinthymidine). This medium selects against unfused myeloma cells and antibodyproducing B cells that cannot replicate, meaning that only hybridomas will survive
this step. Then, a limiting dilution is performed to isolate individual hybridomas
and ELISA is used to identify the hybridomas producing the most specific
antibodies. Finally, the selected hybridoma is expanded as a clonal population
(Holzlöhner, 2017).
Why choose monoclonal antibodies for your experiment
Monoclonal antibodies are particularly useful when you want to ensure that the
antibody binds to a specific region of your protein of interest. For example, if you
are studying a protein that may bind a ligand during your experiment, then you’d
Figure 1: Steps in the generation of hybridomas for monoclonal antibody production. Image created with
BioRender.com.
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want to make sure that you choose an antibody that recognizes an epitope far
from the ligand binding site (Mould, 2016). If your antibody recognizes a part
of your protein that may be masked while the ligand is bound, then you’ll get a
false negative signal — the antibody can’t recognize the protein even though it is
present in your sample.
Additionally, there is a lower likelihood that monoclonal antibodies will exhibit
off-target binding to unintended proteins. This is because monoclonal antibodies
recognize a single epitope, so it’s less likely that this amino acid sequence will
be shared across other proteins. Conversely, polyclonal antibodies — which are
a heterogeneous mixture of antibodies that recognize many different epitopes
on the same protein — are more likely to have off-target effects. This is because
there are more chances for these antibodies to recognize epitopes that are also
present in other proteins.
Because of the specificity of monoclonal antibodies, they also lead to more
reproducible experimental results. When you are originally selecting a hybridoma,
you can choose one that produces high-affinity/high-avidity antibodies. The same
hybridoma is then used to produce all lots of the antibody, meaning that the
antibodies are largely identical between lots.
Disadvantages of monoclonal antibodies
As mentioned above, genetic drift is one concern when using hybridomas to
produce monoclonal antibodies. Genetic drift refers to changes in the nucleic acid
sequence of the antibody-encoding genes over time as the hybridoma cells divide.
These changes can alter the antibody that is actually produced by the hybridoma,
meaning that there will be changes between lots over time. Specifically, the
antigen-binding sites of the antibody — the paratopes — may change and impact
the specificity and avidity of the antibodies produced by the hybridoma (Figure
2). Any genetic drive can be identified by sequencing the heavy and light chains.
To circumvent any possible genetic drift, scientists can freeze hybridoma cells
before culturing or they can clone the genes from the hybridoma into a plasmid
to create a recombinant antibody.
Another barrier to using monoclonal antibodies in the lab may be their cost.
Because of the steps involved in producing and validating monoclonal antibodies,
their cost is higher than that of polyclonal antibodies. For example, it takes
longer to develop a hybridoma than it does to immunize an animal and recover
polyclonal antibodies (Lipman, 2005). This is reflected in the antibody’s cost.
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Figure 2: Changes to the heavy chain or light chain genes within a hybridoma change the antibodies
produced by the hybridoma. Image from Bradbury, 2018.
Conclusion
Whether you choose to use a monoclonal antibody for your experiment versus
other types of antibodies depends on a variety of factors. Understanding the
advantages and disadvantages of monoclonal antibodies will help you make the
best decision for your experimental needs. n
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References
Bradbury, A. R. M., Trinklein, N. D., Thie, H., et al. (2018) When monoclonal antibodies are not monospecific: Hybridomas
frequently express additional functional variable regions. mAbs, 10(4), 539–546. https://doi.org/10.1080/19420862.20
18.1445456. PMID: 29485921.
Holzlöhner, P., & Hanack, K. (2017). Generation of Murine Monoclonal Antibodies by Hybridoma Technology. J Vis Exp.,
(119), 54832. https://doi.org/10.3791/54832. PMID: 28117810.
Lipman, N. S., Jackson, L. R., Trudel, L. J., & Weis-Garcia, F. (2005). Monoclonal versus polyclonal antibodies: distinguishing
characteristics, applications, and information resources. ILAR J., 46:258–268. https://doi.org/10.1093/ilar.46.3.258.
PMID: 15953833.
Mould, A. P., Askari, J. A., Byron, A., Takada, Y., Jowitt, T. A., & Humphries, M. J. (2016). Ligand-induced Epitope Masking:
Dissociation of integrin α5β1-fibronectin complexes only by monoclonal antibodies with an allosteric mode of action.
J Biol Chem., 291(40), 20993–21007. https://doi.org/10.1074/jbc.M116.736942. PMID: 27484800.
National Research Council (US) Committee on Methods of Producing Monoclonal Antibodies. (1999). Monoclonal
Antibody Production. National Academies Press (US). 1, Generation of Hybridomas: Permanent Cell Lines Secreting
Monoclonal Antibodies. Available from: https://www.ncbi.nlm.nih.gov/books/NBK100203/. PMID: 22934324.
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Polyclonal Antibodies
W
Aliyah Weinstein, July 2021
hen you’re searching for an antibody to use in your next
experiment, you’ll probably notice a lot of options to choose
from. In this article we’ll cover polyclonal antibodies, one
of the many different types of antibodies available (others you’ll encounter
include monoclonal and recombinant antibodies). By the end, you’ll have a
better understanding of what makes polyclonal antibodies unique and what
experiments you should choose them for.
How polyclonal antibodies are produced
Polyclonal antibodies are a heterogeneous mixture of many antibodies that
recognize the same protein. In the immune system, antibodies are produced
by B cells. Each individual B cell produces antibodies that all recognize the
same region, or epitope, of the target protein. These antibodies are also all
the same isotype. But together, all the B cell clones in the immune system
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make different isotypes of antibodies that recognize many different epitopes
of the target protein. Whereas production of monoclonal antibodies starts
with selecting just one of these B cell clones for further antibody production,
polyclonal antibodies include any antibodies produced during the immune
response.
To generate polyclonal antibodies, an animal (such as a rabbit or a goat) is
injected with an immunogen. This might be the full-length protein that you want
to generate antibodies against, or a unique peptide sequence derived from
this protein. At the same time, an adjuvant such as KLH or Freund’s adjuvant is
injected, which stimulates the overall immune response. Once the initial antibody
Figure 1: Polyclonal antibodies are generated by injecting an animal with an immunogen, then isolating
and purifying the antibodies produced from its serum several weeks later. Created with BioRender.com.
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response starts to wane about a month later, the animal is given additional
booster immunizations every 2–3 weeks to increase the antibody titer. The titer
is tested following each immunization until the desired level of antibodies is
reached — typically within 2–4 months.
After the animal has the desired level of antibodies in its bloodstream, the
antibodies are retrieved first by drawing blood from the immunized animal. The
red blood cells and the serum are separated, and the serum — which contains
the antibodies — is further processed to isolate the antibodies. Most commonly,
antibodies of the IgG isotype are used for research purposes.
Isolating polyclonal antibodies from the serum
There are a few different ways the antibodies can be isolated from the serum.
Protein A/G purification
Protein A or Protein G purification is one common method. These proteins bind
the IgG heavy chain. Running the serum over a column of resin crosslinked to
protein A/G will bind the IgG antibodies present in the serum, whether specific to
the immunogen or not, while any other proteins pass through. However, protein
A and protein G each have different affinities for IgG subtypes from different
animals, so it’s important to choose the reagent that works for your species.
Affinity purification
Only up to 5% of the IgG antibodies produced by an animal are specific for
the immunogen. A different type of purification can be performed to isolate
antibodies only recognizing your protein of interest: affinity-purified antibodies
are isolated from the serum by their ability to bind the target antigen. In this case,
the serum is run over a column containing the target protein, and any antibody
recognizing the target protein remains bound to the column while the rest of the
proteins are eluted. Then, the antibodies are dissociated from the column and
from the target protein.
Pre-adsorption
Finally, polyclonal antibodies may be pre-adsorbed to remove antibodies that
could cross-react with antibodies of another species. Pre-adsorbed polyclonal
antibodies are designed for experiments where antibodies raised in several
different species are present. One common example is using pre-adsorbed
secondary antibodies (which recognize other antibodies) in experiments that
require multiple antibodies that each detect a different antigen.
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To pre-adsorb the antibodies, pass them through a column containing
immobilized serum protein from other species. Antibodies that don’t crossreact with the serum proteins pass through the column, resulting in a polyclonal
antibody population that is enriched for antibodies that recognize your target
protein without causing high background due to cross-reactivity.
When to use polyclonal antibodies
One advantage of polyclonal antibodies is that they are inexpensive. Their short
production time and the bulk purification steps keep the cost low compared to
other antibody formats. This low barrier to entry makes polyclonal antibodies a
common reagent in many labs.
Polyclonal antibodies also have a high sensitivity to the target protein due to
their ability to recognize many different epitopes of the target protein. While this
can increase background staining (see next section), it may be advantageous if
you are trying to detect a protein in low abundance or whose conformational
state may change. If your protein might become slightly denatured during your
experimental protocol, using a polyclonal antibody increases the likelihood that
one of the clones will still recognize your protein. For the same reason, they’re
also useful in experiments where the availability of some regions of the protein
may be masked by crosslinking (Chromatin immunoprecipitation, ChIP) or fixation
(Immunohistochemistry, IHC).
Because of their increased sensitivity, polyclonal antibodies are particularly
useful for certain research applications. Many secondary antibodies are
polyclonal, as their function is to amplify the signal from a primary antibody;
the ability of polyclonal antibodies to bind many epitopes of their target protein
thereby greatly increasing signal amplification.
When to stay away from polyclonal antibodies
The most prevalent issue with polyclonal antibodies is their batch-to-batch
variability. Once an animal has undergone a terminal bleed to acquire all of its
serum, there is no more of that exact polyclonal antibody composition available.
If you’re going to need a large quantity of antibody for your experiment, you
should either order several vials of the same lot of polyclonal antibody, or select a
monoclonal or recombinant antibody instead. Otherwise, you might experience
variability in the strength of staining and background noise. If you do use different
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lots of your polyclonal antibody, you’ll want to use the same positive control
across experiments to be aware of signal strength.
In general, polyclonal antibodies produce a higher level of background noise
compared to monoclonal or recombinant antibodies. This is because the
polyclonal antibody population recognizes many different epitopes, increasing the
likelihood of cross-reactivity with other proteins. As with any experiment, when
using a polyclonal antibody, you’ll want to be sure to choose the appropriate
controls to be sure the staining you are seeing is real. It’s important to include a
negative control using serum from the same species your polyclonal antibody was
generated in or a polyclonal isotype control, to account for nonspecific binding.
Now that you understand the pros and cons of choosing polyclonal antibodies
for your experiment and how they compare to other antibody options out there,
you’re set up for a successful next experiment! n
References
Ascoli, C. A., & Aggeler, B. (2018). Overlooked benefits of using polyclonal antibodies. BioTechniques, 65(3), 127–136.
https://doi.org/10.2144/btn-2018-0065
Fishman, J. B., & Berg, E. A. (2019). Antibody purification and storage. Cold Spring Harbor Protocols, 2019(5), pdb.
top099101. https://doi.org/10.1101/pdb.top099101
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Secondary Antibodies
I
Rachel Leeson, December 2021
f you’ve been learning about antibody techniques, you may already
have a good idea of the basics of immunoimaging. A scientist
conjugates an antibody with a signaling molecule, the antibody binds
to a protein, and then voila! Wherever the protein of interest is, a signal will
also be. This is the direct approach to immunoimaging assays. But if the direct
approach isn’t giving you enough signal to detect your protein of interest, it
may be time to look at the indirect approach — which means it’s time to talk
about secondary antibodies.
While a primary antibody binds to the protein of interest, a secondary antibody binds to the primary antibody. In the indirect approach, the signaling
molecule is conjugated to the secondary antibody instead of the primary
antibody. Since multiple secondary antibodies can bind to a single primary
antibody, this approach can greatly increase the sensitivity of an assay by
increasing the number of signaling molecules associated, indirectly, with each
protein of interest (Figure 1).
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Figure 1: The direct approach (left) versus the indirect approach (right). Primary antibodies are
represented in blue; secondary antibodies are represented in purple; signaling molecules are represented
in light green; and proteins are represented in orange. Created with BioRender.com.
How do secondary antibodies work?
Secondary antibodies are antibodies generated against antibody isotypes from
a specific species, such as the rabbit IgG class of antibodies. They do so by
targeting the Fc region of an antibody, the so-called ‘constant’ region, which is,
quite usefully, both conserved across and unique to a species. This Fc region (the
stem of an antibody’s Y) is large enough to allow multiple secondary antibodies
to bind to a primary antibody without interfering with the hypervariable region,
where an antibody binds to its target. While most secondary antibodies recognize
broad isotype classes, it is possible to get secondary antibodies generated against
isotype subclasses, such as mouse IgG2 or IgG4. This can be important for some
applications.
Assay use
While secondary antibodies are useful signal amplifiers, the flexibility of their
use varies across applications. Western blots, for instance, almost always use the
indirect approach, while flow cytometry’s standard approach is the direct method.
ELISAs, immunohistochemistry, and immunocytochemistry may all use either
the direct approach or indirect approach, depending on the requirements of the
specific assay. For the latter three, secondary antibodies are indicated when the
protein of interest is found in low amounts or if the conjugated signaling molecule
has a weak signal. For flow cytometry, secondary antibodies may be indicated on
occasion, usually after other signal amplification techniques have been ruled out.
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Ease of access
Signal amplification isn’t the only benefit of secondary antibodies. Finding the
right primary antibody for your assay and protein of interest is often one of
the most challenging components of any immunoimaging assay. Secondary
antibodies, on the other hand, are easy to source and are readily available
conjugated to a number of different signaling molecules. With the direct
approach, primary antibodies must be either sourced already conjugated or
conjugated in the lab. In the indirect approach, you can simply keep a stock of
conjugated secondary antibodies on hand and use them to easily test different
antibodies or different signaling molecules. Many labs find that a trusted stock of
secondary antibodies saves time and money when developing assays.
Protocol architecture
The basic (very basic!) architecture of the indirect approach is as follows:
1. Prepare the sample.
2. Incubate the sample with the primary (unconjugated) antibody.
3. Perform a series of wash steps to remove any unbound or excess antibody.
4. Incubate the sample again with a conjugated secondary antibody, which will
bind to the primary antibody.
5. Perform a second series of wash steps.
6. Activate the signaling molecule and perform the assay readout.
Of course, this is much easier said than done! Each technique, and sometimes
each individual assay, will need to be developed and optimized for the samples,
antibodies, activation, and assay readout used. You’ll note that the secondary
antibodies require their own incubation and wash steps, which means that
indirect methods are often significantly longer than direct ones.
Even though the secondary antibody incubation time is often shorter than that of
the primary antibody, due to increased binding affinity, many indirect antibody
protocols are designed to take place over multiple days. Increased run time is
considered one of the major drawbacks of the indirect method, lengthening
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an often already lengthy process. (And while I have it on good authority that a
complete western blot can be run in a single day, I really cannot recommend it
except in the most dire of circumstances.)
Multiplex assays
“Mo’ proteins, mo’ problems” may not be a common saying, but it probably should
be. In a multiplex assay — one with multiple proteins of interest — the secondary
antibody needs careful consideration before selection. If all the primary
antibodies used are mouse IgG antibodies, for instance, an anti-mouse secondary
will bind to all the primary antibodies indiscriminately, giving them the same
output signal. In these cases, it is best to use the direct method when possible,
or ensure that the primary antibodies are different species and/or isotypes. For
highly multiplexed assays, using secondary antibodies which recognize specific
isotype subclasses of primary antibodies can increase the possible number of
target proteins.
The exception to this rule is western blots. Instead of using signaling molecules
to identify each protein of interest, this gel-based method identifies proteins
by weight and size, relying on the signaling molecule solely as a means of
visualization. This means that if all the primary antibodies are the same isotype
from the same species, only one secondary antibody will be required regardless
of the number of protein targets, simplifying multiplex assays.
A brief note
Since the indirect method relies on multiple secondary antibodies attaching to
a single primary antibody, it is sometimes assumed that the direct method is
more quantitative than the indirect method. But if you use polyclonal antibodies,
multiple primary antibodies can and do attach to a single target protein, and
this assumption will be incorrect. Most immunoimaging assays do not allow for
absolute quantitative analysis of a sample. Instead, relative quantification can be
done using either the direct or the indirect method.
New methods
Time is a precious commodity in the lab, and eliminating the need for secondary
antibodies would save a good deal of it. Researchers are continuously looking for
ways to improve direct methods and avoid the indirect ones. Some western blots,
for example, use a primary antibody labeled with a probe, eliminating the need
for a secondary antibody. Other immunohistochemistry advances have found
ways to increase signals from conjugated primary antibodies. The introduction of
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antibody fragments, such as single-domain antibodies, has introduced new
options for increasing signal strength.
For the time being, though, secondary antibodies remain the go-to option for
signal amplification while providing the needed flexibility in assay development.
Researchers who understand how and why to use secondary antibodies will find
themselves able to approach immunoimaging assays more confidently, collect
a broader array of data, and occasionally ask themselves and others that most
perplexing of questions: “Who moved my goat anti-rabbit?!”* n
*a secondary antibody generated in goat against rabbit immunoglobulins.
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Chimeric Antibodies
Y
Meghan Rego, April 2023
ou are a scientist looking to determine how Protein A and Protein
B interact. You read extensive research on the two proteins and
come up with a great experimental plan that requires indirect
staining of both targets in your specimen. You scour the literature and find
an ideal antibody against Protein A and an ideal antibody against Protein B.
The antibodies are both extensively published, knockout validated, and work
in your application. But…they are the same isotype and therefore cannot be
indirectly probed simultaneously on the sample. What to do?
If you’ve ever found yourself facing a similar problem, then isotype-converted,
or chimeric, antibodies may be the right solution for you. Read on to learn
more.
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What is an isotype?
Antibodies are composed of light chains and heavy chains made up of variable
regions and constant regions. Variable regions are unique to each antibody and
convey specificity to the target, while constant regions are identical for antibodies
within certain classification groups called isotypes.
Why does isotype matter?
Isotype matters when co-straining using the indirect method since a secondary
antibody reactive to a certain isotype will bind to all members of that group. If
you use two antibodies that are the same isotype, then the secondary antibody
will bind to both and you will not be able to distinguish between the two targets.
Chimeric antibodies can help
A chimeric antibody is made by changing an antibody’s natural isotype to that of a
different group (Figure 1). This process, called isotope conversion, gives users the
flexibility needed for multiplexing. In the case above, you can’t co-stain with your
desired antibodies because the pair belong to the same isotype and will react
with the same secondary. If you were to change one of the antibody’s isotypes,
however, you could then use two different secondaries for your antibody pair and
visualize both targets in parallel (Figure 2). For example, if both of your antibodies
are rat IgG1, then you could convert your anti-Protein B antibody to a mouse
IgG2a Fc. The resulting chimeric antibody would have the original variable regions
from the parental rat antibody but the Fc of a mouse IgG2a. This would allow you
to use a red fluorophore-tagged anti-rat IgG1 secondary to detect Protein A and
Figure 1: In the isotype conversion process, the variable regions of a parental antibody (blue, rat) are
cloned into a backbone expressing the constant regions from an alternative isotype (purple, mouse).
Depending on the cloning process, all constant regions, just the heavy chain constant regions, or solely
the Fc region may be changed.
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Figure 2: Two primary antibodies with the same isotype cannot be used for multiplexed indirect staining
methods since the secondary antibody used will react to both primary antibodies and the targets will be
indistinguishable (top panel). If one of the antibodies is converted to a different isotype, then two different
secondary antibodies can be used with different fluorescent labels and both targets can be successfully
visualized (bottom panel).
a green fluorophore-tagged anti-mouse IgG2a secondary to detect Protein B, and
successfully visualize both proteins.
Can any antibody be converted?
Isotype conversion is a unique characteristic of recombinant antibodies.
Recombinant antibodies are plasmid-based and are created synthetically
or derived from hybridoma antibody sequences that have been cloned into
plasmids. Because their sequences are known and encoded on a plasmid, they
can be easily manipulated into other tools. In the case of isotype conversion,
the variable regions of a specific antibody are cloned into a plasmid backbone
containing all constant regions, just the heavy chain constant regions, or solely
the Fc region of a different species and/or isotype (Liddell, 2013). In most cases,
the resulting chimeric antibody retains the binding specificity to the original target
and can be used with secondary antibodies against the new Fc region. In addition
to increasing multiplexing capabilities, this process is frequently used to reduce
immunogenicity and thereby enhance the therapeutic potential of antibody
medicines. In this process, antibodies derived from non-human species like
mice are “humanized” by replacing as much of the non-antigen binding mouse
sequence as possible with human antibody sequences.
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Figure 3: Recommended secondary antibodies for rat IgG1 and mouse 1gG2a primary antibodies used in
a multiplexed imaging experiment.
Additional considerations
Although isotype conversion is routinely used to increase the utility of
recombinant antibody tools, the resulting chimera may have different binding
properties (Morelock et al., 1994). While in the majority of cases the differences
are subtle, chimeric antibodies should be revalidated before use.
In addition, chimeric antibodies will inevitably retain some sequence from their
parental species which can be bound by broadly reactive secondary antibodies
to that parental species. While this will not affect single-plexed experiments, it
needs to be addressed when multiplexing. It is imperative that you use narrowly
selective isotype-specific secondary antibodies when multiplexing with chimeric
antibodies. In the case above, you should use an anti-rat IgG1 secondary antibody
to bind to the anti-Protein A antibody and an anti-mouse IgG2a secondary
antibody to bind to the anti-Protein B antibody (Figure 3). n
References
Liddell, E. (2013). Antibodies. In Elsevier eBooks (pp. 245–265). https://doi.org/10.1016/b978-0-08-097037-0.00017-8
Morelock, M. M., Rothlein, R., Bright, S., Robinson, M. K., Graham, E. T., Sabo, J. P., Owens, R. M., King, D. J., Norris, S. H.,
& Scher, D. S. (1994). Isotype choice for chimeric antibodies affects binding properties. Journal of Biological Chemistry,
269(17), 13048–13055. https://doi.org/10.1016/s0021-9258(18)99982-5. PMID 7909805.
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Affinity Reagents
A
ffinity reagents are molecular tools that have the ability to
specifically recognize and bind proteins. They’re used in a
number of research and clinical applications. Most of the time,
when someone speaks about an affinity reagent, they’re talking about a
standard antibody, the ~150 kDa protein made by immune systems. Instead,
we’re going to focus on what I’m going to call “alternative affinity reagents” —
everything from camelid antibodies to antibody fragments to tools completely
unrelated to antibodies.
Why use an alternative affinity reagent?
The main advantage of alternate affinity reagents is their size. Antibodies are
large enough that their bulk can be a barrier at times. Smaller, slimmer affinity
reagents can have deeper penetration into a tissue, useful for applications like
immunohistochemistry (IHC), or be able to access an epitope folded into
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a space that would size-exclude an antibody. In imaging applications, smaller
affinity reagents mean higher resolution. Many of the affinity reagents below are
also small enough to be expressed in bacterial and other cell systems.
Since the majority of these alternative reagents are created in the lab, instead of
generated in vivo, they have higher reproducibility and consistency in the final
product. Because the constant (non-epitope binding) region is the first thing
to go when downsizing an affinity reagent, most of the alternative reagents
have reduced immunogenicity. Effector functions are eliminated, as is antibody
recognition, since antibody-specific antibodies bind to and recognize the constant
region. Nonspecific binding of antibodies to cell receptors for the antibody
constant region is also reduced.
Disadvantages
Every rose has its thorns, and every affinity reagent has its downsides. These
reduced-in-size options often have reduced binding strength, half-lives, and/or
specificity as well. They’re more difficult to source, may not be as readily available
for a large number of targets, and can require specific conjugates. And, of course,
once you have them, you’ll need to either re-develop or re-optimize your assay for
an entirely new affinity reagent.
Types of affinity reagents
(Listed in no particular order)
Fab fragments are created through enzymatic cleavage of the variable region
Figure 1: A comparison of an antibody (left) to several alternative affinity reagents.
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(the antibody region that binds to the antigen) from existing antibodies. Each
cleaved antibody will produce two ~50 kDa Fab fragments, one for each arm of
the antibody’s ‘Y’. They are often used in clinical applications. Fab fragments can
be created in the lab in situations where a suitable antibody has been identified
but a smaller or less immunogenic version is needed.
Single-chain fragment variables (scFvs) are ~27 kDa engineered fusion proteins
similar to a Fab fragment but created through engineering instead of cleavage.
They are comprised of the variable heavy and light chain regions, connected by a
short linker peptide. The lack of constant region makes them less immunogenic,
but they have lower affinity and longevity compared to antibodies, along with a
higher likelihood of aggregation (Bates & Power, 2019). They are small enough to
be produced in bacteria and are generally cheaper to make than antibodies.
Minibodies are created by binding together two scFvs with a CH3 linker,
resulting in an ~50 kDa protein. Minibodies are bispecific, meaning that each
scFv can target a different antigen; binding of each can be either simultaneous or
independent of the other (Shahied et al., 2004). They are often used for clinical
applications, including imaging.
Diabodies are ~60 kDa proteins formed by two Fab fragments bound by short
peptide linkers. They are bispecific, with a lot of flexibility in the orientation of the
two fragments. However, rigidity can be introduced through mutation, allowing
diabodies to be used for assembling protein nanostructures. Diabodies readily cocrystalize, a major advantage over antibodies (Kwon et al., 2019).
Single-domain antibodies (sdAbs), sold under the commercial name
nanobodies, are derived from a camelid antibody lacking a light chain, called a
heavy chain antibody (Muyldermans, 2013). They have the unique distinction of
being the only affinity reagent on this list derived from nature. Single-domain
antibodies are extremely small at 12–15 kDa but can have weak signals due to
their monovalent nature. They are stable up to 80 °C and can refold correctly after
denaturing.
Designed ankyrin repeat proteins (DARPins) are genetically engineered small
affinity proteins, around 14–15 kDas in size. These are antibody mimetics and are
not closely related to antibodies. Instead, they are derived from a class of binding
proteins known as ankyrin repeat proteins. DARPins typically consist of 3–4
repeats, have high specificity and binding affinity, can be produced in E. coli, and
are both soluble and easily engineered.
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Monobodies are ~10 kDa synthetic binding proteins which use the fibronectin
type III domain as their scaffolding instead of the antibody constant region.
They are generated using combinatorial libraries, and high specificity has been
reported for a number of targets. Due to their size and lack of disulfide bonds,
monobodies can be easily expressed in transfected eukaryotic cells, allowing for
them to be used as intracellular inhibitors.
Aptamers are not proteins at all. These high-affinity RNA molecules can bind to
a wide variety of targets, including proteins, peptides, amino acids, drugs, metal
ions, and cells. They can be used either with or as fluorophores in a wide variety
of colors.
To antibody or not to antibody?
That is the question… or is it? This article isn’t meant to convert anyone to
downsizing their affinity reagents, trendy as that may sound. Validated antibodies
are still an excellent option for many applications, and frankly, if it ain’t broke,
don’t fix it!
That being said, it’s important to know all your options when planning and
running experiments. You never know what problems will arise in your research
or what data you may need to collect. Understanding available alternatives
to antibodies can allow you to be flexible in your approach to affinity reagent
applications. Whatever affinity reagents you use, one thing is clear: it’s an exciting
time to be a scientist looking for that perfect fit! n
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References
Grumezescu A. M. (2018). Drug targeting and stimuli sensitive drug delivery systems. Elsevier Science & Technology
Books.
Bates, A., & Power, C. (2019). David vs. Goliath: The Structure, Function, and Clinical Prospects of Antibody Fragments.
Antibodies, 8(2), 28. https://doi.org/10.3390/antib8020028. PMID: 31544834.
Shahied, L., Tang, Y., Alpaugh, R. K., Somer, R. A., Greenspon, D., & Weiner, L. M. (2004). Bispecific Minibodies Targeting
HER2/neu and CD16 Exhibit Improved Tumor Lysis When Placed in a Divalent Tumor Antigen Binding Format. Journal
of Biological Chemistry, 279(52), 53907–53914. https://doi.org/10.1074/jbc.m407888200. PMID 15471859.
Kwon, N., Kim, Y., & Lee, J. (2019). Structural diversity and flexibility of diabodies. Methods, 154, 136–142. https://doi.
org/10.1016/j.ymeth.2018.09.005. PMID 30261312.
Muyldermans, S. (2013). Nanobodies: Natural Single-Domain antibodies. Annual Review of Biochemistry, 82(1), 775–
797. https://doi.org/10.1146/annurev-biochem-063011-092449. PMID 23495938.
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Single-Chain Fragment
Variables
W
Beth Kenkel, June 2021
hen you think of antibodies, you probably think of
monoclonal antibodies (mAbs). mAbs have been around since
1975 thanks to the Noble Prize-winning work of Milstein and
Köhler, who developed hybridoma technology (Leavy, 2016). But what about
single chain fragment variables (scFvs)? scFvs weren’t developed for another
decade when in 1988 they were cloned by two separate labs (Bird et al., 1988;
Huston et al., 1988). scFvs are the smallest unit of an antibody molecule
that can bind antigen. While they may seem unfamiliar, scFvs are often the
building blocks for engineering proteins. So let’s get acquainted with scFvs.
What is an scFv?
scFVs are a type of recombinant antibody. They are ~25 kDa single
polypeptides that contain the variable light chain (VL) and variable heavy chain
(VH) of an antibody. These two chains are connected by a flexible linker
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Figure 1: Comparison between the IgG antibody and scFv.
peptide that is usually 15–20 amino acids long and made up of glycine and serine
with dispersed hydrophilic residues for increased solubility (Monnier et al., 2013).
The linker keeps the C-terminus of one variable domain and the N-terminus of
the other domain at a distance that favors proper folding and formation of the
antigen-binding site while also minimizing oligomerization of the scFv. While
you might assume that the variable domains of an scFv would mirror that of an
antibody (VL-linker-VH), both VL-linker-VH and VH-linker-VL configurations can
generate functional scFvs; however, some individual scFvs performing better in
one configuration than the other (Sandomenico et al., 2020).
What are the advantages and disadvantages of a
scFv vs. a mAb?
Advantages:
scFv’s advantages are rooted in their size. Being smaller means scFvs are easier
and cheaper to make because they can be expressed in bacteria, while mAbs
generally require mammalian expression systems. scFvs are also small enough
to be screened for with in vitro display methods such as phage display (Bradbury
et al., 2011). In vitro selection avoids animal immunization and also allows for
generations of scFvs against two antigens that are impossible to generate scFvs
against in vivo: antigens found in the body or self antigens, and toxins that are
lethal to animals.
In the clinic, scFvs’ size also provides advantages over antibodies (Ahmad et al.,
2012; Bates & Power, 2019): rapid blood clearance, which is useful for imaging
applications; better tissue penetration, which is useful for therapeutic and
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immunogenicity when administered in vivo imaging applications; and reduced
due to their lack of an Fc region.
Disadvantages:
Compared to antibodies, scFvs tend to have lower affinities, lower longterm stability, and a higher likelihood to aggregate due to their small size
(Bates & Power, 2019). Their rapid clearance from blood can be a drawback
for therapeutic applications where longer retention times often increase
therapeutic efficacy (Ahmad et al., 2012).
What are the advantages and disadvantages of a
scFv vs. a mAb?
scFv sequences have traditionally been cloned by amplifying the VL and VH
antibody sequences of hybridomas (Khantasup et al., 2015), but phage display is
a popular way to generate scFvs.
For phage display, a pooled library of scFvs is displayed on the coats of
bacteriophages, a process which links genotype and phenotype. Incubating
the phage display library with plate-bound antigen helps select for high-affinity
scFvs. Bound phage are then proliferated by infecting bacteria for additional
Figure 2: Phage display starts with a pooled library of scFvs that are displayed on the coats of
bacteriophages. This library is incubating with plate-bound antigen to select for scFvs with high-affinity
binding. Bound phage are then proliferated by infecting bacteria for additional rounds of screening.
Created with BioRender.com.
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rounds of panning and scFv characterization. Some phage display libraries
screen antibody genes from B cells of immunized animals, but it’s possible
to avoid animal use by using semi-synthetic or entirely synthetic VL and VH
sequences. Phage display also allows for screening scFv generated from the B
cell antibody genes from diseased or vaccinated humans.
How do you produce scFvs?
While it’s possible to produce scFvs in mammalian, yeast, plant, and insect
cells, they are most often expressed from a plasmid in bacteria (Ahmad et al.,
2012). One challenge of bacterial expression is the proper formation of the
disulfide bond between the VL and VH domains. This bond provides stability
and solubility for the scFv (Gąciarz & Ruddock, 2017), but requires an oxidizing
environment to form. When expressed in the non-oxidizing cytoplasm of
bacteria, scFvs accumulate in insoluble inclusion bodies. While it’s possible to
re-solubilize and re-fold scFvs from inclusion bodies, it’s time-consuming and
laborious.
To avoid this, one or more of these approaches are often used for scFv
expression in bacteria:
1. Targeting to the oxidizing periplasm of bacteria to allow for proper disulfide
bond formation.
2. Expression in strains of bacteria that are redox mutants so they have a more
oxidizing cytoplasm.
3. Expression in the presence of molecular chaperones that help scFvs fold
properly in the cytoplasm (Sandomenico et al., 2020).
4. Conversion to a cysteine-free format that’s easier to express in the cytoplasm
of E. coli, although this is not possible for all scFvs (Proba et al., 1998).
What are scFvs used for?
scFvs are used in many of the ways antibodies are used. scFvs are even used
to screen for new antibodies. To do this, antibody variable domain sequences
are expressed as scFvs and screened with in vitro assays to select for strong
binders. Then the sequences for the variable domains of the selected scFvs are
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inserted into a vector encoding the antibody constant domain scaffold to
convert the scFv into an antibody.
The ability to combine scFvs with other protein domains, much like Lego pieces,
makes them better suited than antibodies to be used as building blocks for
engineered proteins. This flexibility is largely due to scFvs being smaller than
antibodies. scFvs are often genetically fused to other proteins, which has led to
their application for basic and translational research.
Basic research applications of scFvs:
•
•
The SunTag system uses an scFv to amplify the fluorescence intensity of a
tagged protein. SunTag has two components: 1) a protein of interest that’s
tagged with 10–24 copies of the short epitope GCN4, also called a scaffold;
and 2) a GCN4 binding scFv that’s fused to GFP. When the multiple copies of
the scFv-GFP fusion bind the SunTag scaffold, the intensity of the fluorescent
signal is boosted and enables single-molecule tracking in living cells.
The HA frankenbody is an HA tag detection probe. It contains an HA-binding
scFv that’s fused to a fluorescent protein such as GFP. The HA frankenbody
works just like an antibody-based probe, but is easier and cheaper to
generate since it’s genetically encoded.
Translational applications of scFvs:
•
•
Just like antibodies, there are many scFv-based therapies in clinical trials or
already in the clinic (Bates & Power, 2019). Many of the scFvs designed for
oncology applications are reformatted as bispecific molecules, which bind
CD3 and a tumour-specific antigen. When these molecules, called Bispecific
T-cell engagers (BiTE®s), bind CD3 on T cells and a tumor-specific antigen,
it brings T cells to a tumor site. scFvs can also be fused to cellular toxins,
radioisotopes, cytokines, and enzymes for cancer, autoimmune, and/or
inflammatory therapeutic applications. Antibody fragments such as scFvs are
also used for eye diseases, such as age-related macular degeneration (AMD)
since direct injection to the eye results in high drug concentrations in the eye
with minimal systemic side effects.
scFvs are part of engineered chimeric antigen receptors (CARs). CARs are a
chimera of scFvs and T-cell receptors and are expressed on T cells to make
CAR T cells. The extracellular scFv portion of the receptors recognizes an
antigen of interest while the intracellular T-cell receptor portion of the CAR
facilitates signal transduction and release of cytotoxic granules from the T
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cell. When a CAR has an scFv that binds cancer-related antigens, binding of
the scFv part of the receptor can activate T cells to kill cancer cells. scFv’s
small size also allows for their delivery by viral vectors like AAV, which could
be useful for delivering scFvs that inhibit HIV (Deal & Balazs, 2015). n
References
Ahmad, Z. A., Yeap, S. K., Ali, A. M., Ho, W. Y., Alitheen, N. B. M., & Hamid, M. (2012). SCFV Antibody: principles and
clinical application. Clinical and Developmental Immunology, 2012, 1–15. https://doi.org/10.1155/2012/980250
Bates, A., & Power, C. A. (2019). David vs. Goliath: The Structure, Function, and Clinical Prospects of Antibody Fragments.
Antibodies, 8(2), 28. https://doi.org/10.3390/antib8020028
Bird, R. E., Hardman, K. D., Jacobson, J. W., Johnson, S., Kaufman, B. M., Lee, S., Lee, T., Pope, S. H., Riordan, G. S.,
& Whitlow, M. (1988). Single-Chain Antigen-Binding proteins. Science, 242(4877), 423–426. https://doi.org/10.1126/
science.3140379
Bradbury, A. R. M., Sidhu, S., Dübel, S., & McCafferty, J. (2011). Beyond natural antibodies: the power of in vitro display
technologies. Nature Biotechnology, 29(3), 245–254. https://doi.org/10.1038/nbt.1791
Deal, C. E., & Balazs, A. B. (2015). Vectored antibody gene delivery for the prevention or treatment of HIV infection.
Current Opinion in HIV and AIDS, 10(3), 190–197. https://doi.org/10.1097/coh.0000000000000145
Gąciarz, A., & Ruddock, L. W. (2017). Complementarity determining regions and frameworks contribute to the disulfide
bond independent folding of intrinsically stable scFv. PloS One, 12(12), e0189964. https://doi.org/10.1371/journal.
pone.0189964
Huston, J. S., Levinson, D., Mudgett-Hunter, M., Tai, M. S., Novotný, J., Margolies, M. N., Ridge, R. J., Bruccoleri, R. E.,
Haber, E., & Crea, R. (1988). Protein engineering of antibody binding sites: recovery of specific activity in an anti-digoxin
single-chain Fv analogue produced in Escherichia coli. Proceedings of the National Academy of Sciences of the United
States of America, 85(16), 5879–5883. https://doi.org/10.1073/pnas.85.16.5879
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Leavy, O. (2016). The birth of monoclonal antibodies. Nature Immunology, 17(S1), S13. https://doi.org/10.1038/ni.3608
Monnier, P., Vigouroux, R., & Tassew, N. (2013). In vivo applications of single chain FV (Variable Domain) (SCFV)
fragments. Antibodies, 2(4), 193–208. https://doi.org/10.3390/antib2020193
Khantasup, K., Chantima, W., Sangma, C., Poomputsa, K., & Dharakul, T. (2015). Design and Generation of Humanized
Single-chain Fv Derived from Mouse Hybridoma for Potential Targeting Application. Monoclonal Antibodies in
Immunodiagnosis and Immunotherapy, 34(6), 404–417. https://doi.org/10.1089/mab.2015.0036
Proba, K., Wörn, A., Honegger, A., & Plückthun, A. (1998). Antibody scFv fragments without disulfide bonds, made
by molecular evolution. Journal of Molecular Biology, 275(2), 245–253. https://doi.org/10.1006/jmbi.1997.1457
Sandomenico, A., Sivaccumar, J. P., & Ruvo, M. (2020). Evolution of Escherichia coli Expression System in Producing
Antibody Recombinant Fragments. International Journal of Molecular Sciences, 21(17), 6324. https://doi.org/10.3390/
ijms21176324
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Fab Fragments
I
Ashley Waldron, August 2024
magine an antibody. Do you immediately visualize a Y-shaped protein
reminiscent of the Addgene mascot Abi? If so, you are not alone. Fullsized antibodies dominate the world of research affinity reagents, and
for good reason. However, sometimes you want a tool that is a little more
compact — more of an antibody fragment rather than the whole thing. Fab
fragments are exactly that! Here, we’ll go over what a Fab fragment is and
when you might use it in the lab.
What is a Fab Fragment?
Consider the classic IgG antibody (Figure 1): it is composed of four peptide
chains (two identical heavy chains and two identical light chains) each with a
constant region and a variable region. The chains are covalently linked to one
another by disulfide bonds between the two heavy chains and between one
heavy chain and one light chain. When assembled, the four chains make a
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Figure 1: IgG antibody structure with important features labeled. V = Variable domain. L = Light chain. C =
Constant domain. H = Heavy chain. Created with BioRender.com.
Y-shaped protein that can be divided into the Fragment Crystallizable region (or
the “Fc”) and two Fragment Antigen Binding regions (or “Fabs”). The Fc region is
the “tail” of the antibody and comprises portions of the constant regions of the
two heavy chains. In the lab, this is the region that determines what secondary
antibody you should use when performing indirect immunoassays. The Fab
regions, on the other hand, are the “arms” of the Y and comprise the entire light
chain and a variable and constant region of the heavy chain. As the name implies,
these Fabs are the regions of the antibody that actually bind antigens.
The modularity of antibodies means that it is relatively easy to physically divide
up the different regions, both through genetic and proteolytic approaches.
This comes in handy when, for example, you want to swap out one isotype for
another. But it also allows you to use just a fragment of the antibody in isolation
from another portion. For example, Fab fragments are molecules composed of
just the Fab portion of the antibody. Fab fragments are useful when you just need
an antibody’s antigen binding function but not the Fc region (more on when that
might be later).
Depending on how an antibody is broken up, there are a few commonly available
different fragments that retain antigen binding (Figure 2):
•
•
F(ab’)2 — These fragments are produced by proteolytic digestion that retains
an antibody’s disulfide bonds within the hinge region. The resulting product is
a mini-Y with both Fab regions still attached to one another. Size: ~110 kDa.
Fab’ — These fragments are produced by mild reduction of F(ab’)2 fragments,
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which reduces the hinge region disulfide bonds, resulting in two independent
Fab fragments with small tails of the hinge region. Size: ~55 kDa.
Fab — These fragments are produced by proteolytic digestion above the hinge
region, which results in two independent Fab fragments. Size: ~50 kDa.
Fv — These fragments are composed of just the variable domains of the heavy
and light chains of an antibody. Arguably not a “Fab” fragment, Fvs are often
produced recombinantly and engineered to be joined by a linker peptide,
resulting in a product called a single-chain Fragment Variable (scFv). Size: ~25
kDa.
Why Fabs?
Antibody fragments have a handful of advantages over full antibodies. Their
smaller size allows them to get into samples more efficiently and can also improve
localization accuracy when used in super-resolution microscopy by reducing
the distance between the target and reporter. They have fewer nonspecific
binding opportunities due to the lack of an Fc region. The lack of Fc also simplifies
analyses when used in structural studies and reduces immunogenicity when used
in vivo.
Of course, the lack of an Fc portion means that most of the common anti-IgG
secondary antibodies you already have in your refrigerator will not recognize
a Fab fragment. In order to detect a Fab fragment, it either needs to be
directly conjugated to a reporter or you need a light chain specific conjugated
secondary antibody. These requirements limit the flexibility of and demand for
Fab fragments and subsequently Fab fragments intended for use as primary
antibodies are relatively uncommon. Though, if you want to use Fab fragments as
primary antibodies, you can make your own antibody fragments as needed.
Figure 1: IgG antibody structure with important features labeled. V = Variable domain. L = Light chain. C =
Constant domain. H = Heavy chain. Created with BioRender.com.
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In contrast, anti-IgG antibodies, commonly used as secondary antibodies, have
much broader applicability. It is fairly easy to find these in a Fab fragment format.
Reporter-conjugated anti-IgG Fab fragments make good secondaries for cases
where you need to preform primary-secondary antibody complexes before
applying them to samples, such as when performing immunohistochemistry (IHC)
in a sample where your secondary antibody recognizes endogenous antigens.
In these cases, even though one full antibody may be able to get into the tissue,
two full antibodies bound together may be too large. Using a complex that is one
full antibody bound to a Fab fragment gives you a better chance of getting the
complex into the sample. Alternatively, Fab fragments can be used as a block in
similar scenarios. By applying unconjugated Fab fragments that are the same
species as your secondary antibody to your sample prior to adding your primary
and secondary antibodies, you block the antigens that the secondary would
recognize and reduce nonspecific binding.
At this point, you may be asking yourself, what type of Fab fragment are we
talking about here? Many of the Fab fragments described above can be used for
similar applications and have similar advantages, though with some caveats.
First off, F(ab’)2 fragments are bivalent, so may have higher avidity than
monovalent Fab’ or Fab fragments. Higher avidity means that F(ab’)2 fragments
may have overall stronger interaction with their antigens than a Fab or Fab’
fragment. But this bivalency may cause problems for blocking purposes, since the
fragment could bind not only the endogenous proteins you are trying to block but
your primary antibody as well.
Next, Fab and Fab’ fragments are almost interchangeable, but the extra bit of
heavy chain on a Fab’ can act as a convenient site for conjugation.
Finally, Fvs are the most amenable to recombinant expression and will have the
highest tissue penetration. However, they tend to be less stable and have lower
binding affinity than some of the larger fragments.
Think of antibody fragments as new components in your antibody toolbox. As you
consider which tools to use, be sure to think about what pieces of the antibody
you do and don’t need, and how the protein will interact with other components
of your experiment. These considerations will help you narrow down which
antibody format to use for your specific scenario. n
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Year of the Camelids
T
Ashley Waldron, May 2024
he UN General Assembly has declared 2024 the International
Year of Camelids. The declaration is intended to raise awareness
of the economic and cultural importance of these animals to
human populations around the world. Here at Addgene, we love camelids too,
though not just for the reasons the UN describes. In honor of the Camelid
family, I wanted to take a moment to revisit some of the ways these animals
have impacted humans through biomedical research.
The Camelid family is composed of camels, llamas, and alpacas (and their
undomesticated counterparts) — not exactly the species that jump to mind
when we think “biomedical research”. The family entered the biomedical
field’s spotlight after researchers discovered heavy-chain only antibodies
in the serum of a camel and soon after found similar antibodies in llamas
and alpacas (Figure 1). At the time, researchers were already on a quest for
smaller forms of antibodies and had begun developing Fab fragments and
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Figure 1: Comparison of a heavy-chain only antibody and single-domain antibody.
scFvs. But the potential for an even smaller, single-domain antibody fragment was
exciting, and it wasn’t long until the field was off and running with camelid-derived
single-domain antibodies (sdAb), commonly known as nanobodies (ArbabiGhahroudi, 2017)!
We’ve talked about sdAbs a number of times on this eBook and have described
how they compare to other antibodies and affinity reagents. But for a quick
refresher, some of the advantages of sdAbs over conventional antibodies include:
•
•
•
•
Excellent tissue penetration, thanks to their small size (12–15 kDa).
Ease of cloning and expression from plasmids, due to only needing one open
reading frame.
Ability to access unique epitopes with high affinity, due to a greater ability to
target concave epitopes.
Suitability for in vivo experiments, due to their simple structure.
sdAbs in research
These advantages have contributed to sdAbs becoming valuable tools for
diverse applications. For example, RANbodies can be used as an alternative to
conventional primary antibodies, and there are unique benefits to using sdAbs as
secondary antibodies. sdAbs, however, really shine in vivo, like “chromobodies,”
which are single-domain antibodies genetically fused to fluorescent proteins and
used to visualize target antigens in living cells or tissues. sdAbs can also be used
to manipulate cellular functions, like in this early example we highlighted
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back in 2015, where sdAbs were used to create GFP scaffolds for transcriptional
activation.
sdAbs in the clinic
sdAbs have been finding their way out of the lab and into the clinic as well.
The first sdAb-based therapeutic was approved in 2018 and there are many
others in clinical trials (Jin et al., 2023). In addition to therapeutics, there is a
lot of excitement around using sdAbs in diagnostic applications. For example,
fluorescently and radioactively labeled sdAbs have become appealing candidates
for the detection of different cancers (Jin et al., 2023).
So where do all these sdAbs come from? In the early years of sdAbs development,
the process started with immunizing a camelid (usually llamas or alpacas).
But because sdAbs are so amenable to recombinant expression (and because
maintaining a llama facility is out of reach and unappealing to many labs), the
field is moving towards using synthetic single-domain antibody libraries to
generate new sdAbs. There has also been some interesting work done developing
transgenic mice that express camelid variable domains (aka nanomice), which
gives researchers the “best of both worlds” — a familiar lab model and a source
of sdAbs (Figure 2) (Xu et al., 2021). The single-domain antibody field has come a
long way since the serendipitous discovery of heavy-chain only antibodies in
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Figure 2: Genetically modified mice offer an alternative method to producing single-domain antibodies
without the need for a species that naturally produces heavy-chain only antibodies. (A) Overview of
genetic modifications made to generate the nanomouse. (VHH = variable domains of heavy chain only
antibodies) (B) Nanomice express hybrid heavy-chain only antibodies that are the source of mousederived single-domain antibodies. Image created in BioRender.com and adapted from Xu et al., 2021.
a camel. And I suspect we will only continue to see their impact grow as new
sdAbs become easier to generate and more widely adopted. So thank you to the
camelids who inspired this versatile family of tools, the applications they have
already enabled, and the discoveries yet to come. n
References
Arbabi-Ghahroudi, M. (2017). Camelid Single-Domain Antibodies: Historical perspective and future outlook. Frontiers
in Immunology, 8. https://doi.org/10.3389/fimmu.2017.01589
Jin, B., Odongo, S., Radwanska, M., & Magez, S. (2023). Nanobodies: A Review of generation, Diagnostics and Therapeutics.
International Journal of Molecular Sciences, 24(6), 5994. https://doi.org/10.3390/ijms24065994
Xu, J., Xu, K., Jung, S., Conte, A., Lieberman, J., Muecksch, F., Lorenzi, J. C. C., Park, S., Schmidt, F., Wang, Z., Huang,
Y., Luo, Y., Nair, M. S., Wang, P., Schulz, J. E., Tessarollo, L., Bylund, T., Chuang, G., Olia, A. S., . . . Casellas, R. (2021).
Nanobodies from camelid mice and llamas neutralize SARS-CoV-2 variants. Nature, 595(7866), 278–282. https://doi.
org/10.1038/s41586-021-03676-z
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Epitope Availability
I
Rachel Leeson, January 2022
f you work in a lab that regularly does immunoimaging, there’s likely a
large collection of antibodies in your lab. Perhaps you’re even in that
mythical place where antibodies are well-organized, documented, and
easy to find (though we might need to see it to believe it). As you browse
the collection, you’ll likely find some proteins of interest for which you have
several antibodies in stock. Why are there so many options for a single
target protein?
Epitope availability
The answer is simple: epitope availability. An antibody can only bind to a
target, or epitope, that it can access. If the epitope is hidden, perhaps in the
pocket of a folded protein, or blocked by something bound to the protein,
the antibody can’t bind to it. As proteins change configurations or binding
partners, the epitopes available for binding change as well. If this change
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hides the epitope that the antibodies used to recognize the protein, your antibody
will no longer be effective. On the other hand, if it opens up an epitope that was
previously hidden, an antibody might suddenly work that didn’t before. This
is most often an issue with monoclonal antibodies, which only recognize one
epitope. (It can sometimes be an issue with polyclonal antibodies, so it’s good
to keep epitope availability in mind whenever you’re working with antibodies).
Conformation changes usually fall into one of two categories: ones induced by the
researcher and ones that happen naturally.
Sample denaturing
First, let’s think about induced conformation changes. Several types of assays,
such as ELISAs and westerns, frequently denature the proteins as part of the
sample prep. This involves using heat and/or chemicals to break some of the
bonds keeping the protein folded, changing it from its native state to a less folded,
or denatured, one. This enables absorbing of the proteins to the microtiter walls
for ELISAs and a more accurate separation of proteins by mass:charge ratio in an
SDS-PAGE gel. As the protein unfolds, new epitopes may appear, while others may
get hidden. Monoclonal antibodies suitable for a western or ELISA may therefore
often be specific to denatured versions, or even to just that particular assay.
Biological processes
If you’re feeling relieved that you’re working with native proteins instead of
denatured ones — well, don’t relax just yet. Proteins often change shapes and
Figure 1: A native protein (left) is denatured, allowing the antibody to bind to a previously hidden epitope
(right). Created with BioRender.com.
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epitope accessibility naturally, by binding to other proteins, changing locations, or
even just as part of performing a specific function. A transmembrane protein that
is being used as a marker for flow cytometry, for example, can only be recognized
by antibodies that bind to the epitopes that are available on the outside of the
cell. The same protein may have different epitopes available if it is being selected
out of a complex mix of proteins that have been extracted from a cell, instead of
when it is attached to a cell membrane.
Picking the right antibody
It can get even more complex if you’re looking for proteins that are interacting
with one another, or if you’re looking for a protein of interest while it’s in a
transition state or specific configuration. So how do you ensure that you have the
right antibody for the protein as it exists in your assay?
First, try to understand, to the best of your ability, what factors may be affecting
the protein’s configuration in your assay. Did you have a denaturing step? Do you
want to capture the protein bound to anything? Is it a transmembrane protein,
or is it know to translocate or fold in certain conditions? It can help to visualize an
abstract protein during your sample prep process, imagining how it starts and if
any steps could affect its configuration. You don’t necessarily need to know where
the epitopes are, just if the available epitopes could be affected by, or critical to,
the assay.
Next, look for antibodies that have been validated for the type of assay that you
are performing. If they’re not available, the next best thing is a protein that has
been validated for an assay that uses similar sample preparation — for instance,
if you cannot find an antibody validated for western blots, but find one validated
for ELISAs, it has a good chance of also working for a western because they both
typically use denatured protein samples. If, however, you are performing an
immunohistochemistry assay, you may want to rule out monoclonal antibodies
that have been validated solely for denaturing assays. Many antibodies will note if
they’re intended for a specific condition or configuration.
Validation
Once you’ve selected an antibody, or multiple antibodies, you’ll need to validate
them for your protein and its configuration in your assay. For some assays, you
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may only need to validate for recognizing the protein following your sample prep
steps, i.e., you may only need to validate on denatured proteins or on live cells.
But for others, you may need to validate that it recognizes the protein of interest
in specific configurations or conditions. In those cases, pay special attention
to designing your controls in validation, as you may need to test different
configurations and conditions to understand exactly what your
antibody recognizes.
Understanding how the available epitopes in any given protein might change
depending on the conditions and the assay can help you avoid a great deal of
frustration when designing and troubleshooting immunoimaging assays. It can
also elucidate information about what a given protein is doing or changing in
different conditions. Just remember to keep good records on which antibodies are
suitable for which assays and conditions when you’re building your
antibody library! n
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Selecting the Right Antibody
P
Meghan Rego, March 2022
icture this: you’ve been assigned an exciting new project aimed
at understanding how a critical cellular pathway is regulated.
You’ve read all the background papers you could get your hands
on, formulated a hypothesis, and planned out your key experiments.
Unsurprisingly, many of these experiments require antibodies. You run a
quick internet search for the required antibodies and find … hundreds of
options. With limited time and budget, you cannot test them all, so how do
you know which ones to try? Read on and we will provide you with some
advice on the antibody selection process.
Antibodies are not one-size fits all
When choosing an appropriate antibody, first look at the applicationspecific data provided on the manufacturer’s website. Because of how they
are developed, antibodies rarely work for every assay. Antibodies that are
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generated against a linear antigen tend to work very well for applications that
denature a protein to its primary (linear) structure using heat or acid, for example
in a western blot. If you were to try and use such an antibody in an assay for a
native (folded) protein, such as immunoprecipitation, the epitope may not be
accessible for binding and the antibody will not work. Whenever possible, choose
an antibody that has been validated in your specific application.
Consider also that there may be variations within an application. Take
immunohistochemistry (IHC), for instance. While an antibody may be validated
for IHC, not all IHC experiments are the same. Differences in sample processing,
fixation, antigen-retrieval, and permeabilization will influence how the antibody
interacts with its target and consequently the quality of the stain. Choose the
antibody that was successfully used in conditions that are the most similar to
yours. Take a look at the publications cited on the manufacturer’s website, which
may contain more detailed data on the assay methods. You might find that while
the website highlights a variation of your application, a publication successfully
used it in a similar way as you plan to.
To secondary or not to secondary?
The application will also determine whether you should use an antibody that
is directly conjugated to a detection substrate, such as a fluorophore, or a
conjugated secondary antibody that will bind to your primary antibody. If you
are trying to visualize the precise location of a protein in a tissue section, then
a directly-conjugated antibody will provide better resolution as these immune
complexes are smaller than a primary:secondaries:conjugates complex. If you
are trying to visualize a protein in low abundance on a western blot, a secondary
antibody is ideal as it will help to amplify the signal. Some applications use
either direct (primary-conjugated) or indirect (secondary) methods. In those
cases, remember that a secondary should be used when signal amplification
is needed.
Species
Next, check the species that the antibody cross-reacts with. Ideally, you will
find an antibody that has been validated in the species your protein of interest
comes from. If one is not available, don’t lose hope! It could still work. Take a
close look at the antigen or antigenic fragment that was used to develop the
antibody. Is this protein or protein region highly conserved across species? If the
proteins are highly homologous between your target species and the species
antibody cross-reacts to, then there is a good chance that the antibody could
meet your needs.
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Multiplex experiments
Finally, consider whether you will be using other antibodies in the experiment.
If you are planning to use multiple antibodies in a single experiment, then
you will need to make sure that you can easily distinguish each different type
of antibody and therefore each target protein. This can be accomplished by
using a panel of primary antibodies conjugated to different fluorophores or a
panel of primaries that was raised in different species or are unique isotypes. If
every antibody in the assay is a unique isotype or species, then you can choose
different fluorophore-conjugated secondary antibodies specific to each isotype
or species to easily discern the proteins in your assay.
Narrowing the field
After working your way through the above steps, you may find that you still
have a number of options. If this is the case, you can narrow them down in
a variety of ways. First, review the citations. An antibody that has been cited
thousands of times may be a safer bet than one that has never been used in a
publication. Talk with your peers. If someone in your field has used the antibody
before, you will not only have peace of mind as you make your purchase but
also someone to turn to for advice if you need to troubleshoot. What do you
know about the vendor and the vendor’s technical support? An antibody from a
vendor that you use frequently, with a responsive and knowledgeable technical
support team, is a safer bet than one from a large distributor that may not have
direct experience with the product. Note that many antibodies are sold under
different names through multiple vendors, so look at epitope target, source, and
other information to make sure you’re not trying to choose between the same
antibody sold under different names.
If all else is equal, feel free to make the decision by price point, shipping time,
or other factors. Just remember that these are secondary considerations to
validation, application specificity, and experimental considerations — no other
factors can make up for an antibody that doesn’t work for your application.
Antibodies are not one-size fits all
You now know that application validation is the most important factor in
antibody selection, but what does that mean? Not all validation is equal, and
you should think critically about what data is and is not shown on an antibody
website. In 2016, the International Working Group for Antibody Validation
proposed five pillars for antibody validation.
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Figure 1: Antibody Selection Flow Chart
The five pillars include genetic strategies that test expression in knockout or
knockdown tissues and cell lines; orthogonal strategies that compare antibody
assay results with those of antibody-independent assays such as mRNA data
from transcriptomics; independent antibody strategies that compare the
staining patterns of multiple antibodies against the same target; tagged protein
strategies that express a tagged version of the antigen and compare staining
of the antibody to that of the anti-tag antibody; and immunoprecipitationmass spectrometry strategies that isolate an antibody’s immune complexes
and confirm interacting partners by mass spectrometry (Uhlen et al., 2016). Be
sure that the antibody you choose has been validated using one or more of the
proposed pillars.
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Remember, validation does not mean that the antibody is guaranteed to work.
Antibody-based assays are complex and antibodies may perform differently in
different samples and experimental conditions. Be prepared to troubleshoot
and be sure to include proper positive and negative controls as you get your
assays up and running. n
References
Uhlen, M., Bandrowski, A., Carr, S., Edwards, A., Ellenberg, J., Lundberg, E., Rimm, D. L., Rodriguez, H., Hiltke, T., Snyder,
M., & Yamamoto, T. (2016). A proposal for validation of antibodies. Nature Methods, 13(10), 823–827. https://doi.
org/10.1038/nmeth.3995
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Choosing the Right Isotype
I
Meghan Rego, April 2023
f you are lucky in lab life, you will have a plethora of antibody options
for your experiment (all well-validated for your application, of course!)
When the stars are aligned and the lab gods are smiling down at you,
you may wonder, “Which antibody should I pick? Do I go for the rabbit or the
mouse? Is mouse IgG1 better or IgG2a? Will my choice affect my experiment?”
Here we will review some of the main factors that affect isotype choice.
Target and application should be considered
The first question you may have is whether a monoclonal or polyclonal
antibody is better for your experiment. Monoclonal antibodies contain
a single antibody clone and therefore a single isotype, while polyclonal
antibodies contain a mix of different isotypes and subclasses. They each have
their own advantages. Since monoclonal antibodies contain a single clone,
they have higher lot-to-lot specificity and tend towards more reproducible
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results. Since polyclonal antibodies contain a variety of clones and thus the
potential to bind many different sites on the target protein, they tend to have a
strong signal and to work in a variety of applications.
The desired specificity of your secondary antibody depends on whether you are
using polyclonal or monoclonal primary antibodies. Some secondary antibodies
are broad and recognize multiple isotypes from a species, like a general antimouse antibody. Others recognize specific isotopes within that species, like an
anti-mouse IgG antibody. Some are subclass-specific, like an anti-mouse
IgG2a antibody.
When using a monoclonal antibody, be sure to use a highly specific secondary
antibody, rather than a broadly reactive secondary (Figure 1). For example, an
anti-mouse IgG2a secondary would be a better choice for a mouse monoclonal
IgG2a than a general anti-mouse IgG. In a broadly specific secondary antibody,
only a fraction of the antibody molecules will recognize and bind to your primary
antibody. Manning et al. tested a variety of commercially available secondary
antibodies and found that across suppliers there was a detection bias of
IgG2a > IgG2b > IgG1 (Manning et al., 2012).
Since a polyclonal primary antibody contains a mix of different isotypes and
subclasses, use a broadly reactive secondary antibody to increase the chance of
primary/secondary antibody binding and therefore the signal strength (Figure 1).
Figure 1: Broadly reactive versus subclass-specific antibodies. Only a fraction of the antibody molecules
present in a broadly reactive secondary antibody will bind to a monoclonal primary antibody (A). To boost
signal, consider using subclass-specific secondary antibodies instead (B).
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Monoclonal primaries
If you do decide on a monoclonal antibody, you may wonder, “Will the isotype and
subclass affect my experiment?” Well, it depends on the application and target. If
you are running a basic western blot and probing a single target that expresses
well, then probably not. In this case, take a look at the secondary antibodies that
your lab has on hand and choose a primary antibody that will work with one of
these. After all, there’s no sense in spending valuable research dollars on a new
secondary antibody if you don’t need it.
If, on the other hand, your target is expressed at very low levels or your
application requires a high degree of affinity, then species choice could very
well matter. Take rabbit versus mouse monoclonal antibodies for example.
Rabbit monoclonal antibodies have higher affinity and specificity than mouse
monoclonal antibodies. Antibodies with higher affinities, like those from a rabbit,
bind a greater amount of antigen in a shorter period of time and would likely
perform better for probing poorly expressed targets. Similarly, rabbit-derived
antibodies may be the better choice for assays that require high-affinity binding,
such as immunoprecipitations.
Your sample species should also be considered when choosing an antibody.
In some applications, probing species-on-species, i.e., using a mouse-derived
primary antibody to stain a target protein in mouse tissues, causes a high
degree of background staining. This occurs because the anti-mouse secondary
antibodies used for detection bind to non-target immunoglobulins naturally
present in the tissue sample. There are species-on-species staining protocols that
use antibody fragments to block the endogenous immunoglobulins and reduce
background staining, but they do not always eliminate the issue (Lü & Partridge,
1998). Consequently, if you find yourself in a sticky species-on-species situation,
and other antibody options are available to you, it may be worth considering an
antibody raised in a different species.
Complexity is a key factor
Biological systems tend to be complex with a variety of proteins interacting.
Experiments tend to be equally complex with users trying to probe multiple
targets in parallel. In these cases, it is critical that users choose antibodies that are
easily distinguishable from each other. In a direct approach, that is simple — but
what about when you need to use an indirect (primary and secondary antibody)
approach?
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Table 1: Isotype considerations when choosing antibodies
Experiment
type
Considerations
Single Target
Abundance
Required affinity
Multiple targets
Recommendation
Low abundance
High affinity monoclonal
antibody (rabbit)
High abundance
Consider what lab has in stock
High
High affinity monoclonal
antibody (rabbit)
Low
Consider what lab has in stock
Sample type
Look for an antibody from a
different species than your
sample
Primary antibodies
Choose a unique isotype/
subclass per target
Try to use antibodies from more
distantly related species
Secondary antibodies
Should be subclass-specific or at
least isotype-specific
Try to use secondaries that all
come from the same host
If possible, choose cross
absorbed
Labeling for multiple targets
When labeling multiple targets indirectly, you must strategically plan primary/
secondary antibody pairing such that each target has a unique species or isotype
that can be probed with a distinctly conjugated secondary antibody. Consider
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too, that some secondary antibodies can cross-react with other species. For
example, some goat anti-mouse secondary antibodies recognize some rat
primary antibodies (Erickson et al., 1993). It is always better to choose primary
antibodies from more distantly related species and/or use narrowly specific
secondary antibodies rather than generally reactive ones. And make sure to check
for any reported cross-reactivity when planning your experiment.
When probing multiple targets in parallel, use the most narrowly specific
secondary antibodies available to minimize the risk of cross-reactivity. To further
limit species cross-reactivity, try to find secondary antibodies that are all derived
from the same host species and have been cross-adsorbed. The cross-adsorption
process filters out off-target antibodies, increasing specificity and reducing crossreactivity. For example, if you are using mouse IgG2a and rat IgG1 antibodies in
the same application, using a goat anti-mouse IgG2a cross-adsorbed secondary
and a goat anti-rat IgG1cross-adsorbed secondary would limit species crossreactivity and therefore reduce the background staining (Table 2). In short,
when staining multiple targets, look for primary antibodies raised in different,
and distantly related species, and secondary antibodies raised in the same
species that have been cross-adsorbed to reduce off-target signals. Use isotypeTable 2: Secondary antibody selections for a four-antibody panel, labeling in
parallel
Antibody
Primary antibody
isotype
Secondary antibody
Anti-protein A
Mouse IgG2a
Goat anti-mouse IgG2a
cross-adsorbed secondary
antibody
Anti-protein B
Rabbit IgG
Goat anti-rabbit IgG crossadsorbed secondary
antibody
Anti-protein C
Rat IgG1
Goat anti-rat IgG1 crossadsorbed secondary
antibody
Anti-protein D
Mouse IgG2b
Goat anti-mouse IgG2b
cross-adsorbed secondary
antibody
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or subtype-specific secondary antibodies when staining multiple targets with
monoclonal primary antibodies, and use broadly reactive secondary antibodies
when you’re using polyclonal primary antibodies.
We hope this has explained when and why antibody isotypes matter and that it
helps you choose the right antibody for your application! n
References
Lü, Q., & Partridge, T. (1998). A new blocking method for application of murine monoclonal antibody to
mouse tissue sections. The Journal of Histochemistry and Cytochemistry, 46(8), 977–983. https://doi.
org/10.1177/002215549804600813
Erickson, P. A., Lewis, G. P., & Fisher, S. K. (1993). Chapter 15 Postembedding Immunocytochemical Techniques for light
and electron microscopy. Methods in Cell Biology (pp. 283–310). https://doi.org/10.1016/s0091-679x(08)60255-1
Manning, C., Bundros, A. M., & Trimmer, J. S. (2012). Benefits and Pitfalls of secondary antibodies: Why choosing the
right secondary is of primary importance. PloS One, 7(6), e38313. https://doi.org/10.1371/journal.pone.0038313
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The Antibody Data Hub
I
Rachel Leeson, September 2024
f you’re using antibodies in your research, you’ve probably found
yourself staring at a browser full of tabs, each open to a different
antibody option. Or you may find yourself with only two options, but
very little data on which might work in your application. It can be quite difficult
to decide on which antibody to request for your experiment!
Addgene’s Antibody Data Hub
Addgene’s Antibody Data Hub can help with this decision. In the openaccess Data Hub, researchers interested in the Addgene antibody collection
can look at user-deposited data detailing how an antibody performed in an
experiment.
Antibody data reports can be searched and sorted by gene/target, name,
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Figure 1: The front page of the Addgene Data Hub.
application, pass/fail rating, sample species, antibody species, or whether the
results include knockout data.
Data reports
Each antibody submission is used to create a data report, which contains
experimental data showing how the antibody performed in the submitted
application, along with a brief description of the experiment. If that data was
included in a publication, you’ll find that information in the report as well.
For a more detailed understanding of the experimental conditions in which
Figure 2: Search options in the Antibody Data Hub.
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Figure 3: An antibody data report, showing the results of an immunocytochemistry experiment performed
with the anti-Lhx6.1 antibody.
the data was generated, you can scroll down the page to the materials and
methods section. This section will vary slightly depending on the application —
flow cytometry, western blot, immunohistochemistry, etc. — but in all cases, it
contains a detailed description of the experimental conditions.
Figure 4: The materials and methods section for the data report shown in Figure 3.
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Figure 5: Results for the data report shown in Figure 3. The antibody is rated “Pass”.
At the end of the report, you’ll find a results section, which will tell you if the
antibody worked under the conditions described above, and a short description
of how the antibody performed.
Curating the data reports
Just like our AAV Data Hub, each data submission we receive is checked by
Addgene’s curation team for accuracy and completeness. The information
is then formatted and published as a report with a DOI for citation. If you’re
considering requesting an Addgene antibody, the Data Hub can help you decide
if an antibody is a good candidate for your experiment. If you’ve requested and
used an Addgene antibody, we encourage you to submit data showing how the
antibody worked in your application. The submission process is easy, with simple,
Figure 6: Contributing data to the Data Hub is easy!
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descriptive fields, drop-down boxes when appropriate, and can be saved at any
point throughout the process, so you can work through it at your own pace. To
submit, simply click “Contribute Data.”
Here at Addgene, we love all data, including negative data, which can help
researchers save time and money when selecting an antibody! We encourage
everyone interested in the Addgene antibody collection to use and contribute to
the Antibody Data Hub. n
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Epitope Tags
T
Susanna Stroik, May 2023
he stress of finding a ‘good’ antibody is something we’ve all
experienced. Finding an antibody that works for your application,
specifically detects your protein, is species compatible, and doesn’t
come with a high background can be a huge challenge. Epitope tags eliminate
the need for target-specific antibodies and have been widely used across
species and applications. Here, we will review the most common tags, the
antibodies used to detect them, and how they can fix your antibody woes.
What are epitope tags?
Epitope tags are short peptides introduced at the N or C terminus of a protein
that are bound by antibodies for the purpose of purification or detection of
the tagged protein. These peptides consist of as little as eight or as many
as several hundred amino acids, depending on the tag, and are introduced
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via knock-in at a genomic locus or more commonly via cloning of recombinant
proteins.
Why use a tag?
If you are fortunate enough to study actin, then you may not need to read this
— but for many other proteins, quality antibodies do not exist, or they don’t
exist for a specific application, or they exist but are unreliable for a variety of
reasons (Baker, Nature). Tagging your protein allows you access to a variety of
antibodies that have almost certainly already been optimized for your application.
Depending on the tag, directly imaging or tracking your protein may also be
possible, without the need for an antibody. Tags can enable the separation of
detection of a mutant protein vs. endogenous wild-type (WT) levels of the same
protein when both are expressed in the same system. Tagging can also aid in
protein purification! Here, though, we are focusing on tagging for detection
purposes.
The tags
A number of epitope tags exist — all with individual optimizations and specific
monoclonal antibodies. Below we review the most commonly available tags and
their antibodies, along with some pros and cons.
FLAG
FLAG is a synthetically derived eight amino acid tag (DYKDDDDK) that can be
Figure 1: Applications of epitope tags.
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added multiple times (often 3x FLAG, but can be more!) to a protein’s termini
to increase detection. The tag can also be removed by treating the protein with
enterokinase, which recognizes the five amino acids on the C terminal of the tag.
Of the most common tags, FLAG is the most charged, with five negatively charged
and two positively charged amino acids. FLAG tag has been used with moderate
to high success rates across all applications. You can find FLAG antibodies in
Addgene’s catalog, including a mouse monoclonal (M2) as well as a
chimera (rabbit).
Myc
The Myc tag is a 10 amino acid peptide (EQKLISEEDL) derived from the
protooncogene of the same name. The tag has a net negative charge with
four negatively charged and one positively charged amino acids. Myc tags are
frequently used for western blot, flow cytometry, and immunofluorescence.
However, protein purification is not ideal with Myc, as low pH conditions are
required during purification, which may affect protein function.
Fun fact: The antibody to Myc was developed in 1985 (Evan, et al. 1985) and is still
the most prevalent clone used today! Goat, chicken, mouse, or rabbit? Addgene
has you covered with monoclonal antibodies to Myc for all of these species.
HA
The HA tag is a nine amino acid peptide (YPYDVPDYA) derived from human
influenza hemagglutinin. The tag has two negatively charged amino acids
for an overall negative charge. This tag has been used successfully for
immunoprecipitation, western blot, and immunofluorescence, with some success
for protein purification as well. This tag is not suitable for use in apoptotic cells,
as Caspase 3/7 both cleave after the DVPD sequence. Check out Addgene’s
monoclonal antibodies (mouse, rabbit, and chicken) for this tag!
GFP
GFP is more than a peptide — it’s a 238 amino acid protein that can also be
used as a tag! GFP was originally derived from jellyfish (Aequorea victoria)
and can serve as an epitope tag as well as a fluorescent marker. The GFP
tag is ideal for live cell imaging, FRET, and flow cytometry. It’s also useful for
immunofluorescence if an antibody can’t be used or to allow antibody labeling
to be skipped. It has the flexibility of also being used for immunoprecipitation,
purification, and western blot as monoclonal antibodies also exist for imaging.
The obvious con of GFP is its size — which can interfere with protein function and
localization. If you need monoclonal GFP antibodies or guidance on designing a
GFP tag, Addgene has you covered!
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Potential tag issues
Tags have a lot of major benefits — as discussed above — but there are a few
instances when they should be used with caution or not at all. The introduction
of a tag at the N or C terminus of a protein may disrupt the biological function
of some proteins. Protein folding issues, steric hindrances, and destabilization
can be brought about by the tag, so it’s important to validate tagged protein
functionality (Arribere et al, 2016). In rare cases, tags can also affect protein
localization and solubility, so be on the lookout for aggregation of your protein of
interest!
We hope this helps you on your epitope tagging journey! n
References
Evan, G. I., Lewis, G. K., Ramsay, G., & Bishop, J. M. (1985). Isolation of monoclonal antibodies specific for human c-myc
proto-oncogene product. Molecular and Cellular Biology, 5(12), 3610–3616. https://doi.org/10.1128/mcb.5.12.36103616.1985. PMID 3915782.
Terpe, K. (2003). Overview of tag protein fusions: from molecular and biochemical fundamentals to commercial
systems. Applied Microbiology and Biotechnology, 60(5), 523–533. https://doi.org/10.1007/s00253-002-1158-6. PMID
12536251.
Baker, M. (2015). Reproducibility crisis: Blame it on the antibodies. Nature, 521(7552), 274–276. https://doi.
org/10.1038/521274a. PMID 25993940.
Arribere, J. A., Cenik, E. S., Jain, N., Hess, G. T., Lee, C. H., Bassik, M. C., & Fire, A. (2016). Translation readthrough
mitigation. Nature, 534(7609), 719–723. https://doi.org/10.1038/nature18308. PMID 27281202.
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Of Myc and Men
D
Ashley Waldron, January 2023
o you ever wonder about the origins of some of the common
techniques or tools you use in the lab? Take for instance, the
commonly used Myc-tag. Who first started using it in protein
tagging experiments? Why Myc? When did the commonly used anti-c-Myc
[9E10] antibody come into play? We’ll dive into those questions in this section
as we explore ... the life and times of the myc tag.
Origins of protein tags
For about as long as the disciplines have existed, cell and molecular biologists
have been continuously pursuing new and better methods to peer into the
mysterious, microscopic world of cells. The development of antibodies as
tools allowed us to detect and capture proteins of interest to better study
them in vivo and in vitro, especially in the mid-late 20th century with the
advent of hybridoma technology and monoclonal antibodies. However, it was
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(and still is) infeasible to generate highly specific antibodies for every potential
new target and basically impossible if you wanted to distinguish between
endogenous and exogenous versions of the same target. In the 1980s, Munro
and Pelham (1984) set out to address this issue by developing generic strategies
for detecting cloned genes. The strategy they landed on was to recombinantly
append their proteins of interest with unrelated peptides that already had highquality antibodies available — aka peptide tagging.
Key to this strategy was the existence of high-quality antibodies for well-defined
peptides. Munro and Pelham’s first published peptide tagging experiment
involved a peptide from the neuropeptide substance P. This protein had a highly
specific antibody to the C-terminal five amino acids, meaning the tag could be
quite small. However, substance P has an amide group added to its C-terminus
post-translationally, which turned out to be critical for antibody recognition. As
such, the peptide could only be added to the C-terminus of proteins of interest,
and proteins tagged with it needed to be chemically treated before the antibody
could detect them (Munro & Pelham, 1984). In their paper, they noted that this
step “proved to be quite easy to achieve” — but I think we can all agree that it
sounds less than ideal.
Unexpected connections
Right around the same time that Munro and Pelham were devising their tagging
strategy, another group of researchers was working on isolating monoclonal
antibodies to better probe the functions of the human c-Myc gene (Evan et al.,
1985). Myc was one of the earliest identified oncogenes and was a target of
intense research. Evan et al. hoped that their new monoclonals would help the
field explore Myc’s role in cell proliferation and tumorigenesis.
Using two different synthetic peptides corresponding to linear sequences within
the middle of the human c-Myc protein or the C-terminus, Evan et al. (1985)
isolated six different monoclonal antibodies. Three bound to the mid-protein
peptide, while three bound to the C-terminal peptide. One of these C-terminal
antibodies was Myc1-9E10 (aka anti-c-Myc [9E10]). They noticed that Myc1-9E10
and the other C-terminal antibodies only recognized the human c-Myc protein
and did not recognize the mouse or chicken homologs. By comparing the
homologous sequences in these species, they found that the N-terminal half of
the antigen differs significantly between human and the other species, suggesting
that these antibodies recognize an epitope within that region.
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You’ll notice that “generate a reliable antibody for protein tagging approaches”
was not part of the goal when Evan’s team generated their Myc antibodies.
At first, anti-c-Myc [9E10] was just one of several new tools for unraveling the
multifaceted role Myc plays in human health and disease. But no science happens
in a vacuum!
Munro and Pelham learned of these new antibodies and identified Myc1-9E10 and
its target peptide as a suitable pair for protein tagging. In two studies investigating
mechanisms of endoplasmic reticulum localization, Munro and Pelham casually
introduced the Myc-tag (Munro & Pelham, 1986; Munro & Pelham, 1987). They
narrowed down the epitope recognized by Myc1-9E10 down to 10 amino acids
(EQKLISEEDL) — larger than the substance P peptide, but still quite small.
However, unlike the substance P tag, they found that the Myc epitope could be
recognized by its antibody without additional chemical modification steps and
when the epitope was within the tagged protein rather than at the C-terminus.
These features made this tag easier to use and more flexible — it could now be
used in way more applications.
Figure 1: Making a Protein Tag (Abridged)
Step 1: Locate a promising monoclonal antibody with high specificity. Step 2: Narrow down the specific
epitope that your antibody binds to. For example, Myc1-9E10 binds EQKLISEEDL (pronounced “equi-kliseed-le”). Step 3: Tag all the things!
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Myc1-9E10 and the Myc-tag today
Since Munro and Pelham introduced the Myc tag to the world, it has become
one of the most commonly used tags in molecular biology. And the Myc1-9E10
antibody remains a trusted antibody partner; as I write this, there are close to
10,000 citations for anti-Myc 9E10 in CiteAb, many of which are from this year.
But are we all really using the same antibody developed almost 40 years ago?
Well … kinda. In some cases, antibody providers are distributing monoclonal
antibodies derived from the same hybridoma developed by Evans et al. In other
cases, providers have moved to producing anti-Myc [9E10] as a recombinant
antibody. Researchers first sequenced the variable domains of Myc1-9E10 in the
late 1990s (Fuchs et al., 1997; Schiweck et al., 1997) with the goal of making the
antibody more amenable to protein engineering. As a result, you can now find
Myc1-9E10 in a variety of isotypes like rat IgG1, human IgG2, mouse IgE, goat
IgG, (and more!) as well as the original mouse IgG1. And the refinement of this
trusty tool doesn’t end there: you can also find anti-Myc [9E10] Fab fragments and
single-chain variable fragments.
All of the examples of new versions of Myc1-9E10 mentioned above basically
involve taking the Myc1-9E10 variable regions and sticking them onto different
scaffolds. But over the years, several alternative antibodies against the Myc
tag have been generated in an attempt to overcome some of Myc1-9E10’s
shortcomings. Wait, 9E10 isn’t the Mary Poppins of antibodies (absolutely perfect
in every way)? Unfortunately, no antibody is that perfect. One of the known
limitations was described by Schüchner et al. (2020), who published a paper
showing that the amino acids neighboring the Myc epitope influence 9E10’s
affinity for the epitope, a phenomenon known as sequence context sensitivity. In
their study, some of the newer Myc-tag antibodies appeared to be less sensitive
to sequence context.
So, do Schüchner et al.’s results mean the end of 9E10’s time in the saga of the
Myc tag? Hardly! 9E10’s accessibility and the variety of formats you can find it
in likely mean it will remain an important tool for the foreseeable future. These
newer findings simply add to our understanding of how this antibody works and
allow users to better troubleshoot and design experiments. It turns out that even
with the most popular antibodies, context is key. n
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References
Evan, G. I., Lewis, G. K., Ramsay, G., & Bishop, J. M. (1985). Isolation of monoclonal antibodies specific for human c-myc
proto-oncogene product. Molecular and Cellular Biology, 5(12), 3610–3616. https://doi.org/10.1128/mcb.5.12.3610
Fuchs, P., Breitling, F., Little, M., & Dübel, S. (1997). Primary Structure and Functional scFv Antibody Expression of an
Antibody Against the Human Protooncogen c-myc. Hybridoma, 16(3), 227–233. https://doi.org/10.1089/hyb.1997.16.227
Munro, S., & Pelham, H. (1984). Use of peptide tagging to detect proteins expressed from cloned genes: deletion mapping
functional domains of Drosophila hsp 70. EMBO Journal, 3(13), 3087–3093. https://doi.org/10.1002/j.1460-2075.1984.
tb02263.x
Munro, S., & Pelham, H. R. (1986). An hsp70-like protein in the ER: Identity with the 78 kd glucose-regulated protein
and immunoglobulin heavy chain binding protein. Cell, 46(2), 291–300. https://doi.org/10.1016/0092-8674(86)90746-4
Munro, S., & Pelham, H. R. (1987). A C-terminal signal prevents secretion of luminal ER proteins. Cell, 48(5), 899–907.
https://doi.org/10.1016/0092-8674(87)90086-9
Schiweck, W., Buxbaum, B., Schätzlein, C., Neiss, H. G., & Skerra, A. (1997). Sequence analysis and bacterial production
of the anti-c-myc antibody 9E10: the VH domain has an extended CDR-H3 and exhibits unusual solubility. FEBS Letters,
414(1), 33–38. https://doi.org/10.1016/s0014-5793(97)00983-6
Schüchner, S., Behm, C., Mudrak, I., & Ogris, E. (2020). The Myc tag monoclonal antibody 9E10 displays highly variable
epitope recognition dependent on neighboring sequence context. Science Signaling, 13(616). https://doi.org/10.1126/
scisignal.aax9730
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Antibody Validation
Y
Ashley Waldron, May 2022
our new antibody has arrived, hooray! Before you jump straight
into the exciting new experiments that you’ve been planning, it is
a good idea to pause and make sure that your antibody has been
appropriately validated so that you can have confidence in your results. But
what is appropriately validated? Here we will go over some of the general
considerations you should review before using an antibody.
Antibodies and reproducibility
First, let’s review why validation is so important. Antibodies are a critical
component of the researcher toolkit due to their ability to bind to molecular
targets with high affinity and specificity. However, there are countless
variables that impact an antibody’s affinity and specificity for a given target,
and it is all too common for antibodies to bind to off-target molecules or
even not to bind to their target molecule at all, leading to inconclusive results,
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erroneous conclusions, and wasted time. In fact, many have called out antibodies
as major culprits in the reproducibility crisis of biomedical research (Baker, 2015;
Bradbury & Plükthorn, 2015).
To combat this, a number of groups have come together over the past few years
to propose standards and recommendations for improving antibody use and
reliability (Roncador, et al., 2015; Taussig, et al., 2018; Uhlen, et al., 2016). The
major takeaway from these recommendations is: validate, validate, validate. This
doesn’t necessarily mean that you yourself have to perform extensive validation
experiments for every antibody that you receive; citing existing validation data
can be acceptable. However, it is your responsibility to determine what counts as
adequate validation. If you are not convinced by the existing data, you may need
to roll up your sleeves and perform some extra validation experiments yourself.
Goal of antibody validation
As you assess existing data or plan your own validation experiments, it’s
important to know what you are aiming to achieve. In general, these are three
core goals of antibody validation:
1. Confirm reactivity with the target antigen: Recognition of an immunogen does
not guarantee recognition of the target antigen. You want to make sure that
your antibody binds to what you expect it to bind to.
2. Show specificity for the target antigen: Just because an antibody can bind
to its target, does not mean that is all it will bind to — particularly when you
start using complex samples such as whole cell lysates or tissue sections. It is
important to confirm that your antibody is not binding to proteins outside of
your target protein.
3. Show suitability for an intended application: Remember, just because your
antibody performs as expected in one application does not mean that it will
perform as expected in another. You’ll need to validate that points one and
two hold true under your experimental conditions.
Strategies for antibody validation
There are many resources available that provide in-depth discussions on
strategies for antibody validation in different applications. Here, I want to
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Figure 1: Summary of the five IWGAV-proposed antibody validation pillars. Created with
BioRender.com. MS: Mass Spectrometry. WB: Western blot. IHC: Immunohistochemistry. ICC:
Immunocytochemistry. ELISA: Enzyme-linked immunosorbent assay. FC: Flow cytometry. IP:
Immunoprecipitation.
highlight the general suggestions given by the International Working Group on
Antibody Validation (IWGAV) (Uhlen et al., 2016). This group proposed five pillars
of antibody validation, which represent five experimental strategies researchers
can use to show that their antibody performs as expected. One of the benefits of
these pillars is that they are intentionally generalizable and not specific to certain
applications or antibody targets. That said, not every strategy will be suitable for
every antibody or for every application. The goal is to show that your antibody has
been validated using at least one of these strategies. Choosing which strategy to
use will depend on your intended application and available resources.
Orthogonal strategies
Compare target expression of antibody-independent approaches to your
antibody-dependent approach. Antibody-independent methods may include
analysis of transcriptomic or proteomic data from several different tissues or cell
types. If your antibody indeed binds to your target, then you would expect to see
good correlation between the levels of target detected in both methods.
These approaches have relatively low sensitivity, which can be problematic if you
are trying to show that your antibody can distinguish between two very similar
proteins. Additionally, they require variable expression of your target across
samples — it is hard to distinguish between specific, invariable expression and
non-specific, background signal, so you’ll need a range of expression. From this
data, you see that there is high RNA expression in the retina and in parts of the
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Figure 2: Orthogonal Strategies. A) Expected antibody binding to the brain and retina tissue but not in the
liver. B) RNA expression in the brain, retina, and liver. C) Antibody labeling strength in the brain, retina,
and liver. Created with BioRender.com.
brain, but almost none in the liver (Figure 2A). In general, RNA levels tend to
correspond to protein levels, so you perform immunohistochemistry with your
antibody on these tissues (Figure 2B). If that antibody works as expected, you will
see a strong antibody signal in the retinal sample, but little to no signal in the liver
sample, which you do (Figure 2C), so your antibody is validated!
Genetic strategies
This approach uses a gene knock-down or knock-out approach to show that an
antibody binds specifically to the expected protein. Common approaches include
using RNAi to knock-down expression of your target or using CRISPR/Cas9 to
create mutations that essentially eliminate expression of the target. Removing
(or reducing) the protein of interest in your sample, should lead to reduced or
eliminated antibody signal. If you see a similar antibody signal between your
control and your knock-out sample, you should be concerned that your antibody
is binding to something unexpected. This approach has high specificity, but you
need to be aware of any possible alternative translation start sites or alternative
splicing that could lead to some form of the target protein to be present and
recognizable by your antibody despite the manipulation. Additionally, not all
sample types are amenable to genetic manipulation, nor can all genes be easily
knocked down or out.
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Figure 3: Genetic strategy. A) A control cell line with antibodies binding. B) A mutant cell line with no
antibody binding. Created in BioRender.com.
For example, suppose you just received an antibody that you want to use to look
at the subcellular localization of a protein of interest. Conveniently, you found a
cell line in which the gene for your protein has been mutated using CRISPR/Cas9.
You check the mutation, and it causes a large deletion that removes the entire
exon that codes for the epitope recognized by your antibody.
You perform immunocytochemistry on these mutant cells as well as a non-mutant
line and see a great antibody signal in the non-mutant line but no signal in the
mutant line!
Recombinant strategies
This approach is essentially the opposite of the genetic strategy and looks for
increased antibody signal in samples that have had the target protein expression
experimentally increased. The simplest version would be to use cells that do not
express your protein of interest endogenously and transfect them with a plasmid
encoding your protein of interest. Then you should expect to see evidence of
antibody binding in the transfected cells but not in control cells. But (there is
always a ‘but’), you should be aware that overexpression of a protein can also
influence antibody binding by altering the relative concentration of potential
antibody binders, and so this approach may not be best if you are ultimately
planning to use your antibody to detect endogenous levels of the target in other
cells or tissues.
For example, suppose you want to show that your antibody binds to the expected
target protein.
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Figure 4: Recombinant strategies. A) Transfecting cells with your gene of interest or a negative control
gene before B) running a western blot. Created in BioRender.com.
You acquire a plasmid encoding your protein of interest and transfect it into your
favorite cell line. In parallel, you transfect cells with another plasmid not encoding
this protein. Then you lyse the cells and run a western blot with your antibody.
Your results show a lovely, strong band from the cells transfected with your
protein of interest and show just the faintest band from the cells transfected with
the other plasmid. Your antibody works!
Multiple (independent) antibodies strategies
These strategies use multiple, unique antibodies against the same protein, each
targeting a different epitope, and look for correlation between the results. Using
antibodies against different epitopes on the same target reduces the likelihood
that both antibodies would bind to the same off-target molecules, which gives
you more confidence when both antibodies exhibit similar results. Unfortunately,
it can be difficult to find epitope information for some antibodies, which makes
it hard to ensure that you have unique antibodies. An additional caveat is that
different antibodies may or may not perform well under the same conditions.
For example, suppose you want to sort cells based on the expression of a new
receptor that is of interest to you. You found a couple of antibodies that are
predicted to bind different epitopes of this protein.
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Figure 5: Multiple (independent) strategies. A) Testing antibodies with the same target protein but different
epitopes as well as an antibody with a different target and then B) generating a read out in a flow
cytometer. Created in BioRender.com.
You perform flow cytometry using both antibodies, as well as a third unrelated
antibody as a negative control. The two antibodies to your protein of interest
give you very similar results, while the third looks entirely different, giving you
confidence that your new antibodies will work for your experiments.
Capture MS strategies
This approach uses immunoprecipitation to capture proteins from a sample using
the antibody and then uses mass spectrometry to identify the captured proteins.
If the antibody is specific, then you expect that the bulk of the peptides identified
to be from the target protein. This approach is ideal for validating antibodies
for immunocapture applications and is amenable to high-throughput analyses;
however, it requires access to a mass spec facility and the ability to analyze the
resulting data, so it may not be the most accessible option for many laboratories.
Additionally, it can be difficult to distinguish between proteins bound by the
antibody vs. proteins bound to the target protein, so it’s important to understand
if protein:protein interactions could be a factor in your analysis.
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Figure 6: Capture MS strategies, in which proteins are captured by an antibody of interest and then
analyzed via MS. Created in BioRender.com.
For example, suppose you are planning an RNA-immunoprecipitation experiment
and want to validate that your antibody is specifically capturing your RNA-binding
protein of interest.
So you perform an immunoprecipitation with your antibody and send the
captured proteins off for mass spec analysis. When the results come back, it
shows that the majority of the protein in the sample was identified as your target
protein, allowing you to proceed with confidence.
Share your results
Ultimately, like all of science, validation experiments don’t mean anything
unless you share the results with others. If you share data generated using
your antibody, it’s best scientific practice to provide or reference the data that
convinced you of your antibody’s trustworthiness. You may also consider sharing
your validation attempts by other means, such as through antibody databases
like Antibodypedia, or through your antibody provider(s) if they accept and share
user data on the antibody webpages such as Addgene’s Data Hub. These outlets
can be particularly useful for sharing negative data. Negative data often goes
overlooked, but knowing that an antibody likely won’t work in a given application
is just as helpful as knowing when it will. And remember, antibodies are like
puppies. Just because an antibody doesn’t work in your application doesn’t mean
it’s a bad antibody, but more that it’s misunderstood and it could work great
in another context! So the next time you test an antibody (perhaps one from
Addgene!), consider sharing your results so that others in the community can
benefit and we can all help improve antibody reproducibility! n
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References
Baker, M. (2015b). Reproducibility crisis: Blame it on the antibodies. Nature, 521(7552), 274–276. https://doi.
org/10.1038/521274a
Bradbury, A., & Plückthun, A. (2015c). Reproducibility: Standardize antibodies used in research. Nature, 518(7537),
27–29. https://doi.org/10.1038/518027a
Roncador, G., Engel, P., Maestre, L., Anderson, A. P., Cordell, J., Cragg, M. S., Šerbec, V. Č., Jones, M., Lisnić, V. J., Kremer,
L., Li, D., Koch-Nolte, F., Pascual, N., Rodríguez-Barbosa, J., Torensma, R., Turley, H., Pulford, K., & Banham, A. H. (2015).
The European antibody network’s practical guide to finding and validating suitable antibodies for research. MAbs, 8(1),
27–36. https://doi.org/10.1080/19420862.2015.1100787
Taussig, M. J., Fonseca, C., & Trimmer, J. S. (2018). Antibody validation: a view from the mountains. New Biotechnology,
45, 1–8. https://doi.org/10.1016/j.nbt.2018.08.002
Uhlén, M., Bandrowski, A., Carr, S. A., Edwards, A., Ellenberg, J., Lundberg, E., Rimm, D. L., Rodriguez, H., Hiltke, T.,
Snyder, M., & Yamamoto, T. (2016). A proposal for validation of antibodies. Nature Methods, 13(10), 823–827. https://
doi.org/10.1038/nmeth.3995
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A Control for All Seasons
I
Meghan Rego, October 2023
n a world where so much is out of your hands, it’s helpful to focus on
something controllable, like experiments (and their controls!). This
will discuss the ins and outs of controls for biological experiments,
starting with general controls and then moving on to controls for antibodyrelated applications. After reading, you will have a better understanding of
the different types of controls and be able to use this information to design
thoughtfully and thoroughly controlled experiments.
Broadly useful controls
Positive and negative controls
Most biological experiments require positive and negative controls to
ensure proper interpretation of results. A positive control is generally a
sample or group that will have a desired response. For example, if you
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are studying the effect of a new tau protein kinase inhibitor on slowing diseases
progression in an Alzheimer’s mouse model, then you could treat one group
of mice, the experimental group, with the new kinase inhibitor and a second
group, the positive control group, with an alternative but well-studied kinase
known to prevent tau hyperphosphorylation. You could run brain samples
from both groups on a western blot and look for the molecular weight shifts
indicative of tau phosphorylation. By including the positive control, you will be
able to confirm that the testing protocols elicited the expected response and
have a baseline with which to compare the experimental group (Figure 1).
A negative control is a sample or group that is not subjected to the experimental
condition. In the example above, a negative control could be a third group of
mice that is subjected to the experimental protocol but does not receive either
protein kinase inhibitor. Since the negative control group does not receive the
drug, any response recorded would be due to natural biological variation. This
information is critical for correctly interpreting experimental results.
Replicates
Replicates are another critical control in biological experiments. Replicates
can be technical or biological and are included to improve the precision and
accuracy and therefore the statistical power of an experiment. Technical
Figure 1: A) Most biological experiments will benefit from positive (+) and negative (-) controls in addition
to experimental samples (?). B) The inclusion of biological replicates such as multiple mice in each group
and C) technical replicates or repeated measurements will increase the power of the results. In this case,
mouse samples from positive (+), negative (-) and experimental (?) groups were run on a western against a
protein standard (P) to check for tau molecular weight shifts.
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replicates are repeated measurements of the same sample and are included
to assess variability in the experimental protocol. In the experiment above, you
could repeat the tau western blot several times with the same samples and
compare the results across runs. If the results vary considerably, then the high
degree of variability in the process could make it difficult to obtain meaningful
results.
Biological replicates are distinct samples that are treated identically, used to
assess naturally occurring random biological variability. If one were studying the
effects of tau kinase inhibitors on mice, for example, it would be better to treat
and test multiple mice with the drug rather than rely on the results of a single
mouse. Biological replicates help you to determine which responses arise from
drug treatment versus and which are likely just natural differences between
individuals.
Recommended controls for antibody-based experiements
Primary antibody specificity
Antibody-based assays have their own unique controls that must be considered
(Burry, 2011). Perhaps the most critical are those that confirm primary antibody
specificity. The gold standard in demonstrating primary antibody specificity is to
test the primary antibody against wild-type and knockout samples. In this test,
wild-type and knockout samples are treated with the identical protocols that the
experimental samples undergo. If the antibody is specific, then the target signal
will be present in the wild-type samples and absent in the knockout (Figure 2A).
If knockout samples are unavailable, try substituting with knockdown
approaches using siRNA or shRNA. If this alternative is not possible due to a lack
of available materials, lethality of knockdown, or other factors, consider using
one of the alternative, albeit less ideal, verification approaches discussed in the
Validation chapter. The primary antibody control needs only to be run once for
each primary antibody that will be used (Burry, 2011). It does not need to be
repeated for each experimental run as long as the protocol remains the same.
Non-specific binding
Primary antibody specificity is further supported by a pre-immune serum or
isotype-specific control (Hewitt, 2014; Alexander, 2018). In this control, the
primary antibody is replaced with pre-immune serum from the animal the
antibody was produced in or an isotype-matched control antibody. This control
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Figure 2: Microscopy-based antibody experiments should include a primary antibody specificity control,
secondary antibody specificity control, label control, and cross-reactivity control when multiplexing. A)
Wild-type and knockout samples are the gold standard to demonstrate primary antibody specificity. B)
Secondary antibody-only staining will allow you to see nonspecific interactions between the sample and
the secondary antibody. C) A label control treats the sample to the full protocol without any antibodies
to determine the level of autofluorescence in the sample. D) When using multiple primary antibodies,
include controls where each primary is omitted but all secondaries included to uncover secondary
antibody cross-reactivity.
Figure 3: A pre-immune serum control addresses situations where a primary antibody not only binds
specifically to its target through the antigen binding site but also binds non-specifically through the
interactions of other antibody structures. In the Experimental sample, the antibody binds specifically to
its desired target (purple) and nonspecifically through the Fc region (orange). The pre-immune serum
control will not bind to the desired target but will bind nonspecifically through the Fc, allowing you to
detect this undesirable interaction.
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addresses situations where a primary antibody not only binds specifically to its
target through the antigen binding site but also binds non-specifically to other
molecules through the interactions of other antibody structures, such as the
Fc region. Fc-mediated binding interactions will be observable in pre-immune
serum a
‌ s well as with an isotype-matched control (Figure 3).
When non-specific interactions are suspected, use a pre-adsorption control to
confirm. In this method, the primary antibody is incubated with its antigen or a
small epitope-containing peptide to block the antigen-binding site. The sample
is then stained with the pre-adsorbed antibody. If a signal is detectable, it is
likely due to binding interactions outside of the antigen-binding site.
Secondary antibody specificity
In indirect staining approaches, secondary antibody specificity must also be
demonstrated. Secondary antibodies have a tendency to interact nonspecifically
with positively charged groups, such as aldehydes in fixed samples. To address
this, block positively charged groups with generally sticky proteins such as
bovine serum albumin.
Secondary antibodies can also react with endogenous antibodies naturally
present in the sample, for example at sites of inflammation. This tends to be
most problematic when staining cells or tissues that are the same species
as your primary antibody, like if you are using an anti-mouse IgG antibody
in mouse tissue. Such species-on-species staining can be partially addressed
with stringent staining protocols and the use of isotype-specific secondary
antibodies.
Baseline secondary signaling levels
In addition to the above-mentioned safeguards, you’ll need to test the
secondary antibody in the absence of the primary antibody to determine the
baseline level of nonspecific secondary antibody binding (Figure 2B). For this
control, samples are prepared following the standard protocol, but the primary
antibody is omitted during the incubation step. Samples are incubated with
‌secondary antibody as usual and the signal measured. If a nonspecific signal is
detected, the level of noise will determine whether it needs to be addressed.
In order to correctly interpret your results, the signal from your target needs
to be significantly higher than the background noise (Waters, 2009). If the
fluorescence from your target is extremely bright, then the noise likely will not
interfere. If the fluorescence from your target is weak, then it is best to reassess
the protocol and try to find a way to reduce, if not eliminate, the noise. This can
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be done either by modifying the blocking conditions or by substituting for a
more specific secondary antibody. The secondary antibody control should be
included with each experimental run (Burry, 2011).
Autofluorescence
Endogenous fluorescence of the sample, or autofluorescence, also interferes
with data interpretation and can be addressed with a label control. In a label
control, the samples are treated with the full experimental protocol with all
primary antibodies, secondary antibodies, and labels omitted (Figure 2C).
Any signal remaining is autofluorescence and should be considered in the
interpretation of results. Include the label control for all new samples, protocol
changes, or when high-background labeling occurs (Burry, 2011).
Complex experiments
Finally, additional controls may be required for more complex experiments. A
multiplexed experiment, for example, requires staining with multiple primary
and secondary antibodies. Ideally, secondary antibodies will be highly specific,
but in some cases they may cross-react and bind to more than their target
primary antibody. To address this, include a control wherein each primary
antibody is individually omitted from the staining but all secondary antibodies
are included (Figure 2D). If, for instance, you omitted a mouse IgG2a isotype
primary antibody but you still observe a signal from your anti-Mouse IgG2a
secondary antibody, then that secondary antibody is reacting non-specifically to
one of the other primary antibodies in the stain. In situations like this, you may
need to find alternative primary or secondary antibodies.
Application-specific controls
Some antibody-based applications have very specific controls, which we will
briefly touch on.
Western blotting
When comparing samples in different lanes in a western blot, you’ll need to
include a loading control. A loading control measures the protein level of a
target that is thought to be equal across all samples, such as a housekeeping
protein like GAPDH or tubulin. A loading control confirms equal loading across
samples and equal protein transfer from the gel to the membrane.
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A loading control is critical for data interpretation, especially when changes in
protein expression are the experimental readout. Always probe the loading
control on the same membrane that the target protein was measured on.
Flow cytometry
In addition to the controls listed above, viability and compensation controls are
recommended for flow cytometry. A viability control allows you to detect dead
or dying cells so that you can base your analysis on living cells only (for example,
propidium iodide only stains dead cells). Compensation controls address the
spectral overlap that occurs during multiplexing. Compensation controls are
single-stained samples for each fluorophore and are critical to analyze multiplex
flow cytometry experiments.
We hope that this will be a useful starting point for you when designing rigorous
experiments. While this blog touched upon some of the more critical controls
for antibody-based experiments, it is by no means an exhaustive list and we
encourage you to review the literature for your specific experimental technique
before starting. n
References
Burry R. W. (2011). Controls for immunocytochemistry: an update. The Journal of Histochemistry and Cytochemistry.,
59(1), 6–12. https://doi.org/10.1369/jhc.2010.956920. PMID: 20852036.
Hewitt, S. M., Baskin, D. G., Frevert, C. W., Stahl, W. L., & Rosa-Molinar, E. (2014). Controls for immunohistochemistry:
the Histochemical Society’s standards of practice for validation of immunohistochemical assays. The Journal of
Histochemistry and Cytochemistry., 62(10), 693–697. https://doi.org/10.1369/0022155414545224. PMID: 25023613.
Alexander, S. P. H., Roberts, R. E., Broughton, B. R. S., et al. (2018). Goals and practicalities of immunoblotting and
immunohistochemistry: A guide for submission to the British Journal of Pharmacology. British Journal of Pharmacology.,
175(3), 407–411. https://doi.org/10.1111/bph.14112. PMID: 29350411.
Waters J. C. (2009). Accuracy and precision in quantitative fluorescence microscopy. The Journal of Cell Biology., 185(7),
1135–1148. https://doi.org/10.1083/jcb.200903097. PMID: 19564400.
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Labeling
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Intro to Immunofluorescence
I
Ashley Waldron, November 2021
mmunofluorescence (IF), is an immunoassay that brings to light the
cellular world. The technique allows you to ask questions like: “Where
does my protein of interest live within a cell,” “Does this disease change
the architecture of my cells,” or “How does this mutation impact the types of
cells found in my tissue.” It is based on the same principles as other antibodybased assays, like an ELISA or western blot, but it allows you to visualize
a target of interest within an intact cell, tissue, or, in some cases, a whole
organism. But, as with any antibody-based technique, validation and proper
experimental design are essential. In this article, we’ll go through some of the
most important considerations to make when planning and performing an IF
experiment.
What is immunofluorescence?
Generally speaking, IF is an application in which antibodies that have been
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joined to fluorescent molecules are applied to a cell or tissue sample where they
bind to their targets, most commonly proteins. You can then use fluorescent
microscopy to look for those fluorescent molecules, knowing that where you
see fluorescence indicates your target’s location. Some other terms you might
see associated with IF are immunocytochemistry (ICC) or immunohistochemistry
(IHC). All three of these terms refer to the use of antibodies to visualize a target
within an intact sample, a cell in the case of ICC or a tissue in the case of IHC. IF is
used specifically when we visualize the antibodies using fluorescence (for some
background on fluorescence, try the Introduction to Fluorescence Microscopy
video from iBiology). IHC and ICC are essentially just more general terms that can
also refer to assays using non-fluorescent visualization methods.
Direct vs. indirect immunofluorescence
There are a couple different approaches to be familiar with when talking about
IF, direct and indirect. For direct IF, researchers use a single antibody to visualize
their target. These antibodies are generated against a specific antigen and then
conjugated with a fluorescent molecule allowing one to “directly” visualize their
target. In contrast, indirect IF requires two antibodies: a primary and a secondary.
For indirect IF, the primary antibody targets the antigen of interest but is not
attached to a fluorescent molecule, instead a secondary antibody that targets the
primary is conjugated to a fluorophore (Figure 1).
Figure 1: A protein gradient. The density of the bands, which increase as protein concentration increases,
show the protein concentrations are within the antibody’s dynamic range. Created with BioRender.com.
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Indirect IF is a much more common approach than direct IF, so we will focus
on this technique here. One of the reasons that indirect IF is more common is
that secondary antibodies are generally polyclonal, which means that multiple
secondary molecules can bind to a single primary, resulting in signal amplification.
Another reason is cost effectiveness. Generating and validating primary
antibodies is a time consuming and expensive process. Consider the vast number
of possible primary antibodies that exist and you can begin to see why generating
fluorophore-conjugated versions for each primary antibody could get out of hand.
Instead, it is more efficient to generate a handful of secondary antibodies that
target common IgGs and then conjugate them with an array of fluorophores. It is
common for labs to keep a few trusted secondary antibodies on hand to mix and
match with a larger collection of primary antibodies. This strategy gives you the
flexibility to target primaries produced in different species and to label targets
with different colors.
Multiplex immunofluorescence
Speaking of different colors, having a palette of secondary antibodies in a few
different species and colors makes it easy to perform multiplexed IF. By using
multiple primary antibodies made in different species on a single sample, you
can then use the corresponding secondary antibodies conjugated to distinct
fluorophores to visualize multiple targets at once. An advantage of multiplexing
is that you can ask how multiple proteins relate to one another in the same
sample. For this strategy to work, it is important to ensure that you are using
secondaries with fluorophores whose excitation and emission spectra do not
overlap. Multiplexed IF can also require some additional optimization as each
antibody performs differently and some antibodies impact the performance of
other antibodies. But, once you do find the right protocol, multiplex IF can give
you impactful and beautiful results.
Performing an immunofluorescence experiment
The process to perform IF can be broadly divided into the several steps outlined
below and in Figure 2. For a deeper dive into the steps and techniques, see Im et
al., 2018.
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1. Fixation
Good fixation maintains sample morphology with minimal impact to the
target epitope.
• There are multiple methods for fixing samples, each with its own pros and
cons.
• The method you use will depend on the target and sample type.
2. Sample preparation
•
•
•
This step can involve multiple smaller steps that all help to ensure that
antibodies can access their target and that you will be able to image them in
the end.
Again, the methods you use will depend on target and sample type.
Example steps include: permeabilization, antigen retrieval, and sectioning.
3. Blocking
•
•
Blocking reagent is used to reduce non-specific binding of antibodies.
You may need to optimize the type of blocking reagent used, the
concentration, or the incubation time depending on your target and sample
type.
4. Primary antibody
•
•
This is the first antibody applied to your sample, which will bind to your target
molecule.
You may need to optimize the concentration and incubation time.
5. Secondary antibody
•
•
Unsurprisingly, this is the second antibody you apply, which binds to the first
antibody and is conjugated to a fluorophore.
You may need to optimize the concentration and incubation time for this step
as well.
6. Preservation and imaging
•
Similar to sample preparation, this step can involve multiple smaller steps, but
the aim is to preserve sample integrity and fluorescence and to capture your
results.
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Figure 2: High-level overview of an indirect immunofluorescence procedure with key considerations about
each step. Image created with BioRender.com.
•
•
The methods used depend on the sample type, microscopy tools available,
and your experimental goals.
Example steps include: counterstaining, mounting sample on slides, and
confocal microscopy.
The first couple of steps are preliminary steps that are not exclusive to IF, but they
can have a significant impact on the outcome of your IF experiment. Many of the
details for each step depend on your antibody, target epitope, and sample type
(Im et al., 2018). To narrow down which specific methods to use, start by reading
the methods sections of papers that have successfully used your particular
antibodies and refer to the manufacturer’s recommendations.
As you optimize your protocol, always remember to include controls to show that
your antibodies are binding specifically. For positive controls, aim to use samples
that are known to express your target or have been manipulated to overexpress
it. For negative controls, use samples in which the target is known to be absent
naturally or in which it has been knocked-out or knocked-down. “No primary”,
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pre-absorption, and isotype controls are also commonly used in IF experiments.
Precisely which controls you perform will depend on your experimental context.
Choosing antibodies for your experiment
The antibodies you choose for your experiment will have a significant impact on
the protocol you use and the ultimate outcome of your assay. So how do you
choose good ones? Choosing a secondary is often much more straightforward
than choosing a primary, so we’ll start there. Some of the most important
considerations are to make sure your secondary 1) will recognize the species
of your primary antibody and 2) is conjugated to a fluorophore that suits your
experiment (i.e. the spectral properties match your available imaging instruments
and the spectral properties do not overlap with any other fluorescent molecules
in your sample).
When choosing a primary antibody, first, remember that antibodies recognize
very specific three-dimensional epitopes. Different sample processing steps can
alter epitopes in different ways, so antibodies that work in one context may not
work in another. Try to choose primary antibodies that have been validated in
either IF, IHC, or ICC. The biggest difference between IF and ICC or IHC relates
to the type of secondary antibody you use, so as long as the sample processing
steps used in a non-fluorescent IHC or ICC assay are similar to the ones you plan
to use, then the primary antibody will likely perform as expected in both.
How do you find validated antibodies? Many companies that produce and
distribute antibodies validate them in a variety of applications, including IF (or
IHC or ICC), and will share some of their results and corresponding protocols.
Other researchers may also have tested an antibody in IF and will share their
findings either through their publications or as a review on a company website. It
is important to review the data and protocols available for each antibody that you
consider in order to determine how reliable the antibody seems and what sort
of protocol you will need to use. (To learn more about antibody validation, this
commentary by Uhlen et al., 2016 is a good place to start.)
Reviewing available antibodies can seem overwhelming, especially when
there are dozens of products that all claim to target your protein of interest.
Thankfully, there are a number of websites compiling antibody data that can
help you compare antibodies from different companies, such as Validated
Antibody Database and CiteAb. The antibody you choose can make or break
your experiment, so take advantage of all the available resources and give
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yourself time to carefully review your options. If possible, try to select a couple of
antibodies that seem promising to compare in your own lab.
Even with all of the resources available, it may still be difficult to find an
antibody in which you are confident, especially if you work in an organism that is
underrepresented in the biomedical research community. You may have to take
some risks on antibodies. Once you have the antibodies in your lab, regardless of
how “risky” they may be, it is always important to verify their performance in your
hands and in your specific experiment. If you identify an antibody that performs
either really well or poorly in a given context, let your community know! Sharing
your experience, positive or negative, will help make antibodies more effective
and reliable in the future.
We hope that this section has shed some light on immunofluorescence. The
technique can provide you not only answers to your most pressing research
questions, but also stunning images (am I the only one with IF results as artwork
at home?). Take your time to research antibodies, protocols, and appropriate
controls and you will be well on your way to performing a successful IF
experiment. n
References
iBiology. (2020, December 13). Introduction to fluorescence Microscopy. iBiology. https://www.ibiology.org/talks/
fluorescence-microscopy/
Im, K., Mareninov, S., Diaz, M. F. P., & Yong, W. H. (2018). An Introduction to Performing Immunofluorescence Staining.
In Methods in molecular biology (pp. 299–311). https://doi.org/10.1007/978-1-4939-8935-5_26
Uhlen, M., Bandrowski, A., Carr, S., Edwards, A., Ellenberg, J., Lundberg, E., Rimm, D. L., Rodriguez, H., Hiltke, T., Snyder,
M., & Yamamoto, T. (2016). A proposal for validation of antibodies. Nature Methods, 13(10), 823–827. https://doi.
org/10.1038/nmeth.3995
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Fixing and Permeabilizing for
Immunofluorescence
E
Ashley Waldron, August 2022
arlier, we shared an introduction to immunofluorescence (IF) – a
common method for visualizing molecules of interest within a cell
or tissue. In that introduction, we broke down the method into
six general steps and outlined the considerations to be made during each
step. Now that you know the basic principles of IF, we’ll take a slightly deeper
dive into those steps and provide you with more details to help you plan and
optimize your own protocols. Here we’ll start with just the first couple of steps
— mainly, fixing and permeabilizing.
Fixation
What is the point?
Fixation allows you to freeze your samples in a given state or point in time.
You want to halt degradative processes while maintaining the structure of
your samples, the relationships between cellular components, and the
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accessibility of your target epitope(s). To achieve these goals, researchers
commonly use one of two types of fixatives: chemical cross-linkers and
organic solvents.
Chemical cross-linkers
Chemical cross-linkers work by (as the name suggests) cross-linking cellular
proteins. Cross-linking is a chemical reaction that covalently joins two molecules,
and it is great for preserving your samples and maintaining cellular and tissue
morphology. Formaldehyde is the most commonly used cross-linker for IF.
However, you might come across a couple other terms that can cause some
confusion.
•
•
Paraformaldehyde: Many IF protocols call for paraformaldehyde (PFA) as the
fixative. PFA is a polymer of formaldehyde that, on its own, is not a fixative.
However, when PFA is dissolved in solution and heated, it depolymerizes,
producing formaldehyde. Often people will call the resulting solution PFA, but
it’s really just formaldehyde.
Formalin: You may also come across formalin, which refers to a saturated
solution of formaldehyde (saturated being 37%). Formalin solutions
often contain methanol as well, in order to slow the polymerization of
formaldehyde.
Note: Formaldehyde in solution will naturally polymerize and lose its ability
to cross-link proteins. As such, you will get the best results if you use fresh
formaldehyde solutions.
Along with preserving your sample, cross-linking can block the epitopes of some
targets, preventing your primary antibody from binding (Figure 1). The level of
cross-linking that occurs depends on the incubation time and temperature, so
you may be able to recover good antibody binding by tweaking those parameters,
but in some cases your antibody may just not be suitable for use with this
type of fixation*.
Organic solvents
Organic solvents, generally acetone, methanol, or ethanol, work by dehydrating
and precipitating cellular components. This method of fixation is good for
epitopes that are sensitive to cross-linking, but it doesn’t preserve sample
structure quite as well as a cross-linker. This is because organic solvents can be
quite harsh — lipids and soluble proteins can be lost during fixation with these
chemicals, which impacts sample structure and could also cause your target to be
washed away. Furthermore, these chemicals can also alter the structure of
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Figure 1: Choice of fixation method impacts antibody binding. Formaldehyde cross-links the protein (red
lines), which maintains structural epitopes (orange region), but it can block epitopes hidden within the
folded protein (green region). Methanol dehydrates the protein, which can disrupt protein structure and
thus structural epitopes, but can reveal linear epitopes previously hidden within the protein. Created with
BioRender.com.
proteins, which could disrupt your target epitope (Figure 1). So, while organic
solvents may improve antibody binding for some targets, they can destroy it for
others. That said, organic solvents’ impact on lipids means that they are able to
permeabilize your sample while fixing it, which may be a good thing depending on
your experiment.
* There are other methods for recovering epitope availability following crosslinking fixation; they require an additional step generally referred to as antigen
retrieval. I chose not to get into antigen retrieval here because it’s not quite as
ubiquitously required for IF as fixing and permeabilizing.
Permeabilization
What is the point?
Antibodies are large proteins that need a little help crossing cell membranes as
they are not able to diffuse across natively. Permeabilization essentially punches
holes in the cell membranes of your sample to allow antibodies better access
to intracellular targets. If your target is already on the extracellular side of the
membrane, then this step may not be necessary. In fact, you may get cleaner
results by not permeabilizing as you’ll prevent binding to any target protein that is
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still on the inside of the cell. However, assuming you do need to permeabilize, you
have a few options for what reagents to use: organic solvents and detergents.
Organic solvents
As mentioned above, organic solvents can be used to simultaneously fix and
permeabilize a sample. But you can also use organic solvents after fixing your
sample with a cross-linker. This strategy gives you the benefits of both reagents
— cross-linkers better preserve sample morphology, while organic solvents
can reveal otherwise hidden epitopes. However, keep in mind that you will still
contend with many of the same drawbacks to each reagent type, so this approach
is not suitable for all antibodies.
Detergents
Another common family of permeabilization reagents are detergents, such
as Triton X-100, Tween-20, or saponin. Each detergent has different chemical
properties that will impact the level of permeabilization and membrane integrity
(Figure 2). For example, saponin interacts with cholesterol in cell membranes and
leaves many membrane-associated proteins in place. In contrast, Triton X-100
and Tween-20 are examples of non-ionic detergents that interact with lipids and
proteins in membranes and permeabilize non-selectively. This non-selectivity is
helpful when trying to pass tough-to-permeabilize membranes, and these
Figure 2: Examples of different permeabilization strategies and when to use them. A) Not permeabilizing
is suitable for antibodies that bind to the extracellular portion of a protein. B) Permeabilizing with the
selective detergent, saponin, keeps membrane-associated proteins in place, while allowing antibodies
into the cell where they could bind to intracellular epitopes. C) Permeabilizing with a non-ionic detergent,
like Triton X-100, can lose membrane-associated proteins, but it is better at permeabilizing organellar
membranes. Created with BioRender.com.
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detergents avoid some of the negative effects caused by organic solvents.
However, detergents can lyse cells or cause you to lose soluble proteins from the
sample, especially if left on for too long or used at too high a concentration.
How do I choose?!
Ok, so now you know some of the possible types of fixatives or permeabilizers,
but how do you decide which ones to use? Here are some tips for making those
decisions:
•
•
•
Always, always, always look at your antibody datasheet to see if it has been
tested in IF and if a protocol is available. When possible, try to start with
conditions and reagents that have already been used successfully.
If there isn’t information on how to use your specific antibody for IF, look for
general IF protocols for your sample type. Starting with a protocol that usually
works in your system and then optimizing from there is a good option.
Consider your target — if you are studying a membrane protein and you
want to minimize the risk of it being lost from your sample, start by fixing
with formaldehyde and permeabilizing with saponin. However, if you are not
worried about washing away your target (for example, some cytoskeletal
components) and you want to fix and permeabilize in one step, then methanol
treatment could be an effective strategy. (This article on fixation has some
more examples to help choose which fixative to use!)
Ultimately, the only way to know if a method will work is to try it. As you embark
on your next IF adventure, just remember that even though these early steps
don’t even involve antibodies, they are still critical to the outcome of your IF
experiment and deserve careful thought and attention. n
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References
Brown-Harding, H. (2020) Fixation artifacts and how to minimize them. FocalPlane. https://focalplane.biologists.
com/2020/07/07/fixation-artifacts-and-how-to-minimize-them/
Goldenthal, K. L., Hedman, K., Chen, J. W., August, J. T., & Willingham, M. C. (1985). Postfixation detergent treatment
for immunofluorescence suppresses localization of some integral membrane proteins. Journal of Histochemistry &
Cytochemistry 33:813–820. https://doi.org/10.1177/33.8.3894499. PMID: 3894499
Im, K., Mareninov, S., Diaz, M. F. P., & Yong, W. H. (2019). An Introduction to Performing Immunofluorescence Staining.
Methods in Molecular Biology. Springer New York, pp 299–311. https://doi.org/10.1007/978-1-4939-8935-5_26. PMID:
30539454
Rolls, G. (2022) Process of Tissue Fixation & the Nature of Fixatives in Histology. Leica Biosystems. https://www.
leicabiosystems.com/us/knowledge-pathway/fixation-and-fixatives-1-the-process-of-fixation-and-the-nature-offixatives/
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Multiplex
Immunofluorescence
Y
Mike Lacy, May 2023
ou may be familiar with immunofluorescence (IF, often referred
to as immunocytochemistry (ICC) when the sample is cultured cells
or immunohistochemistry (IHC) with tissues), where an antibody
binds a target protein in your sample, then a fluorescently labeled secondary
antibody binds that first antibody, then you use a microscope to see where
the target protein is (localization) and how much there is (quantification). But
what if you’ve got several interesting protein targets and you want to know if
they co-localize at the same place in the cell? Or you know that the presence
of several biomarkers together in the same tissue sample predicts a certain
cancer and want to look for them? Or maybe you just want to make some
really beautiful images? Multiplex IF may be just what you need!
How does it work?
There are several ways to achieve multiplex IF. One of the most common
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approaches is to use sequential cycles of primary and secondary antibody
labeling and imaging, repeating for each target. Alternatively, you can perform
simultaneous detection with a mixture of primary antibodies and appropriate
secondary antibodies. The general process is similar to standard IF, but with some
extra considerations (Im et al., 2019; McLaughlin, 2019). As with standard IF, you
will need to start with a fixed sample. Depending on the sample type, this might
be frozen or formalin-fixed, paraffin-embedded tissue sections, methanol- or
paraformaldehyde-fixed cell samples, or other preparations.
Sequential IF
For a sequential IF experiment (Figure 1A), you’ll block, add primary antibody to
bind the target, wash, add dye-conjugated secondary antibody that recognizes the
primary, wash again, then image. But here’s where the “sequential” part comes in.
Figure 1: Schematic of multiplex immunofluorescence approaches. A) In Sequential Multiplex IF, one
target is labeled and imaged at a time, inactivating the fluorescent secondary antibodies before labeling
the next target. B) For Simultaneous Multiplex IF, all the primary antibodies are combined in one step and
all the secondary antibodies are combined in one step.
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After you image the first target, you inactivate the fluorophores in the sample by
chemical treatment or photobleaching or strip out the antibodies, then start over
with the next target: add the new primary, wash, add secondary, image again,
inactivate, and repeat these cycles for all your targets. Of course, you’ll need to
align all these images once taken. To do so, you will need to include a marker like
the Hoechst stain for the nuclei. (See Brewer et al., 2022 for a sample protocol.)
Simultaneous IF
Depending on your needs, your sample, and targets, you might decide to do
simultaneous IF instead (Figure 1B). To label all your targets in one step, you
simply use a mixture of primary antibodies, wash, add a mixture of secondary
antibodies, and image all the different colors in one session — fast and easy! Just
make sure your primary antibodies are all of different species/isotypes and that
each secondary has a different color fluorophore that you can image separately
on your microscope. Abcam’s sample protocol is a good place to get started.
One important consideration when deciding whether you can do simultaneous or
sequential labeling is what conjugated secondary antibodies you have available.
If the only secondary antibodies you have on hand are conjugated with the same
dye (say, you have Goat Anti-Rabbit IgG and Donkey Anti-Mouse IgG, both with
Alexa Fluor® 488) then you can’t use them simultaneously because you won’t
know which label the signal is coming from. But by labeling sequentially with one,
inactivating the fluorophores, then labeling with the other, you can still record the
signal separately for each target.
Selecting your antibodies
When selecting antibodies for multiplexed IF, many of the same considerations
apply as for standard IF, but there are a few specific things to keep in mind. Most
importantly, make sure you are using primary antibodies from different species
or isotypes, so that the secondary antibodies don’t cross-react. Depending on
the abundance of your targets, it’s also possible to avoid secondary antibodies
altogether and multiplex with direct IF, using primary antibodies that are
conjugated to fluorophores. However, the primary-secondary workflow (a.k.a.
indirect IF) is more flexible and efficient, since you only need a few fluorescent
secondaries on hand in the lab, then mix and match with primaries for whichever
targets you like. Plus, indirect IF amplifies the signal since multiple secondary
antibodies will bind to each primary antibody.
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Figure 2: Examples of simultaneous multiplex IF labeling of rat brain samples from the James Trimmer
lab, using recombinant antibodies with different isotypes. (A) Neocortex labeled with anti-pan-Nav
(magenta), anti-CASPR (green), and anti-Kv2.1 (cyan). Scale bar = 150 μm. (B) Cerebellum labeled with
anti-GABA-AR β1 (white) and anti-GABA-AR β3 (green). Scale bar = 150 μm. (C) Hippocampus labeled
with anti-Kv2.1 (magenta), anti-AnkyrinG (green), and Hoechst nuclear stain (cyan). Scale bar = 150 μm.
Panels C1–C3 show magnified detail of the box in C. Scale bar for C1–C3 = 50 μm. Image adapted from
(Andrews et al., 2019) under CC-BY 4.0 license.
Look for primary antibodies that have been validated in IF applications (find
IHC-validated or ICC-validated antibodies from Addgene), or be prepared to
run some extra controls and validate the antibody yourself. If you can’t find a
suitable antibody against your target protein, consider fusing a protein tag to the
target and using an epitope-based strategy such as Anti-6xHis. You could also
consider strategies using other types of affinity reagents like fluorescently-labeled
single-domain antibodies or scFvs.
Many, many options for fluorescently conjugated secondary antibodies are
commercially available. Pick whichever combination of host and target species,
isotype specificity, and dye meets your needs and budget, or you can make your
own. For example, to label the rat brain sample shown in Figure 2A,
Andrews et al. used the combination of antibodies shown in Table 1.
If you need more signal, another strategy is to use a biotinylated secondary
antibody then label that with fluorescently-conjugated streptavidin to get more
fluorophores per antibody, or conjugate with an enzymatic label such as
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Table 1
Target (rat)
Primary antibody
Secondary antibody
Anti-protein A
anti-pan-Nav R-mAb K58/35R
(Mouse IgG2a)
Goat anti-mouse IgG2a
CASPR
anti-CASPR mAb K65/35
(Mouse IgG1)
Goat anti-mouse IgG1
Kv2.1 channel
anti-Kv2.1 KC (Rabbit IgG
polyclonal)
Goat anti-rabbit IgG
horseradish peroxidase (HRP). Experiments using enzymatic labeling can be
difficult to quantify accurately, but can be especially helpful to generate more
signal from low abundance targets.
Analysis and presentation
Pro tip!
If you find yourself stuck with red-green
images generated automatically from
the microscope software, you can always
recolor them! In Fiji, for example, you’d
open the offending red-green image,
go to Image>Color>Replace Red with
Magenta. Or you can use Color>Split
Channels and then Color>Merge
Channels into whatever combination of
colors you like.
Once you’ve captured your images, various software
programs will allow you to align the images of
each labeled target as different color channels
and analyze the results. Depending on the goal of
your experiment, you might need to quantify the
intensity of the different signals in a certain region
or calculate a colocalization factor to assess the
spatial relationship between the targets. Or, it might
be sufficient to describe or classify the labeled
structures visually — for example, confirming that
your target protein is expressed in the hippocampus,
or counting the number of puncta in each cell.
When making figures for a paper or presentation, make sure the images are
accessible to all viewers. Use a colorblind-friendly color palette for display (like
green-magenta-blue or orange-blue rather than red-green). Even better, show
each channel in individual panels, then the multicolor overlay; this helps all
viewers, especially if you have some complex patterns with several overlapping
targets to interpret. Check out (Jambor et al., 2021) for more guidelines for
creating clear and informative figures.
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Controls and validation
Can’t forget the controls! The necessary control experiments will vary depending
on your specific experimental design. Like selecting an antibody, some of
the requirements are similar to standard IF. Here, we’ll walk you through the
necessary types of controls for a multiplex experiment.
Check for non-specific interactions of the antibodies
Confirm your secondary antibody only labels sites where you expect the target
to be. You can test this by preparing a sample without the primary antibody,
or with a primary antibody of a different species/isotype; when you add the
secondary antibody, it should get completely washed away and leave no signal.
If you see lots of extra signal in these samples where you don’t expect to see it,
the secondary antibody may be binding non-specifically — try optimizing your
blocking and washing conditions or consider using a different antibody.
Prepare a positive control
You can prepare and label a sample that expresses your target(s) at a high level,
either naturally or by overexpression. You can also confirm the secondary works
as expected by using it to label a different protein known to be abundant in your
samples (with a primary antibody that has the same species and isotype as the
primary antibody in your experiment).
Confirm each individual target is stained correctly
Image a set of samples where you label only one target in each. Compare these
individually-labeled samples with the results of your multiplex-labeled sample
to see if the localization and intensity patterns are similar between the two
experiments. If not, it might be that the other antibodies are disrupting the
binding of later targets, the sample is being damaged by all the handling steps,
or there’s some unexpected cross-reactivity.
Keep in mind that you may need to optimize the conditions and concentrations
of all the different antibodies’, blocking, washing, and sample preparation steps,
in order to ensure your antibodies are performing at their best and that the
sample is not being degraded.
Whether you’re just getting started with two or three targets or you’re pushing
the limits like Addgene depositor Ronald Germain (Radtke et al., 2020
multiplexed over 65 targets!) IF is a versatile method with endless possibilities,
and there are always new and improved variations you could try.
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We hope this article helped you start planning for your next multiplex IF
experiment! Happy imaging! n
References
Andrews, N. P., Boeckman, J. X., Manning, C. F., Nguyen, J. T., Bechtold, H., Dumitras, C., Gong, B., Nguyen, K., Van
Der List, D., Murray, K. D., Engebrecht, J., & Trimmer, J. S. (2019). A toolbox of IgG subclass-switched recombinant
monoclonal antibodies for enhanced multiplex immunolabeling of brain. eLife, 8, e43322. https://doi.org/10.7554/
eLife.43322
Brewer, M., McDonough, L., Zhu, Y., Neumann, E., De Caestecker, M., Gutierrez, D., & Spraggins, J. (2022). Multiplex
Immunofluorescence on Fresh Frozen Tissue-V2 v2. protocols.io. https://doi.org/10.17504/protocols.io.bs68nhhw
Im, K., Mareninov, S., Diaz, M. F. P., & Yong, W. H. (2019). An Introduction to Performing Immunofluorescence Staining.
In W. H. Yong (Ed.), Biobanking (Vol. 1897, pp. 299–311). Springer New York. https://doi.org/10.1007/978-1-4939-89355_26
Jambor, H., Antonietti, A., Alicea, B., Audisio, T. L., Auer, S., Bhardwaj, V., Burgess, S. J., Ferling, I., Gazda, M. A., Hoeppner,
L. H., Ilangovan, V., Lo, H., Olson, M., Mohamed, S. Y., Sarabipour, S., Varma, A., Walavalkar, K., Wissink, E. M., &
Weissgerber, T. L. (2021). Creating clear and informative image-based figures for scientific publications. PLOS Biology,
19(3), e3001161. https://doi.org/10.1371/journal.pbio.3001161
McLaughlin, K. (2019). Multiplexing Immunohistochemistry. Materials and Methods, 9. https://doi.org/10.13070/
mm.en.9.2846
Radtke, A. J., Kandov, E., Lowekamp, B., Speranza, E., Chu, C. J., Gola, A., Thakur, N., Shih, R., Yao, L., Yaniv, Z. R., Beuschel,
R. T., Kabat, J., Croteau, J., Davis, J., Hernandez, J. M., & Germain, R. N. (2020). IBEX: A versatile multiplex optical imaging
approach for deep phenotyping and spatial analysis of cells in complex tissues. Proceedings of the National Academy
of Sciences, 117(52), 33455–33465. https://doi.org/10.1073/pnas.2018488117
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Avoiding the Mouse-on-Mouse
Mess in IHC
F
Ashley Waldron, September 2023
ighting with antibodies to produce immunohistochemistry images
that are crisp, bright, and lacking in non-specific staining can be a
challenge in the best of cases. But it can be particularly challenging
when your only antibody option is from the same species as your tissue
samples. The bad news: this situation is hard to avoid if your model system
is a mouse, given the wealth of commercially available mouse monoclonal
antibodies. The good news: there are strategies for mitigating this issue, and
we’ve highlighted a few in this section.
What’s the problem?
First, why are “mouse-on-mouse” (or any “species-on-species”)
immunochemistry (IHC) applications an issue? The culprits are endogenous
IgGs in your tissue samples. Most common IHC protocols use two antibodies
— one against your target of interest and a second against the first antibody.
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When endogenous IgGs of the same species as your primary antibody are
present, your secondary antibody recognizes both the endogenous IgGs and your
primary antibody, resulting in significant background staining (Figure 1). Not all
tissues will have the same levels of endogenous IgGs, so some researchers can
get away with species-on-species staining with no problem! But to be sure that
your secondary is not binding endogenous IgGs, you should include no primary
antibody controls in your experiments. If you do find high levels of background
staining by the secondary, then the following three strategies might help!
Strategies for avoiding background secondary binding
1. Direct immunohistochemistry (Figure 2A). How do you avoid having your
secondary antibody bind endogenous IgGs? Don’t use one. If it is possible, using
a primary antibody that is directly conjugated to your reporter circumvents the
issue. However, directly conjugated primary antibodies can be expensive and may
not provide enough signal to visualize low abundance targets.
2. Pre-formed primary and secondary antibody complexes (Figure 2B). Some
labs have found that you can significantly decrease background staining by
mixing your primary and secondary antibodies, blocking excess secondary with
serum from the same species as your primary/sample, and then applying this
mixture to your tissue (Tuson et al., 1990; Goodpaster et al., 2013). One issue with
this strategy is that the large complexes may not permeate through your tissue
samples as homogeneously as a single antibody. Using a secondary antibody that
is a Fab fragment, single-chain variable fragment (scFv), or a single-domain
antibody is one potential remedy for this problem within a problem.
Figure 1: Species-on-species staining can be a problem because the secondary antibody is not able to
distinguish between the primary antibody and endogenous IgGs in the tissue. Created with BioRender.com.
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3. Pre-block with Fab fragments (Figure 2C). Speaking of Fab fragments,
another strategy for species-on-species IHC protocols is to block endogenous
IgGs by pre-treating the tissue with unconjugated Fab fragments that recognize
the endogenous IgGs (Lu & Partridge, 1998). After washing away unbound
fragments, you can then apply your primary and secondary antibodies as normal.
Ideally, your secondary will only bind to your primary in this scenario because the
endogenous targets will have already been bound up by the Fab fragments. Note
that you should plan to use Fab fragments that are from the same host species
as your secondary antibody since 1) your secondary should not recognize Fab
fragments from the same species and 2) these fragments will be more likely to
bind (and block) the same epitopes recognized by the secondary.
Proper tissue processing and sample preparation can also help reduce the levels
of endogenous IgGs in your samples, but know that all is not lost if you still see
non-specific staining! Hopefully, these solutions will help get your experiments
back on track.
One final note: this section focuses on the species-on-species problem in the
context of IHC, but the problem can impact other techniques, like western blot
Figure 2: There are a few common solutions to avoid background staining when doing species-on-species
IHC. (A) One solution is to use direct IHC, which uses just a primary antibody that has your reporter (in
this case a fluorophore) directly attached to it. (B) Another solution is to combine your primary and
secondary antibodies before adding them to your sample so the secondary antibodies aren’t free to bind
endogenous IgGs. (C) A third solution is to use Fab fragments against the sample species to block the
binding sites on endogenous IgGs before you add your primary and secondary antibodies. Created with
BioRender.com.
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or immunocytochemistry (ICC). Not all of the solutions described here may be as
effective for other techniques, but it is important to be aware that the issue is not
unique to IHC. n
References
Goodpaster, T., & Randolph-Habecker, J. (2013). A flexible Mouse-On-Mouse immunohistochemical staining technique
adaptable to Biotin-Free reagents, immunofluorescence, and multiple antibody staining. Journal of Histochemistry and
Cytochemistry, 62(3), 197–204. https://doi.org/10.1369/0022155413511620
Lu, Q. L., & Partridge, T. A. (1998). A new blocking method for application of murine monoclonal antibody to mouse tissue
sections. Journal of Histochemistry and Cytochemistry, 46(8), 977–983. https://doi.org/10.1177/002215549804600813
Tuson, J. R., Pascoe, E. W., & Jacob, D. A. (1990). A novel immunohistochemical technique for demonstration of
specific binding of human monoclonal antibodies to human cryostat tissue sections. Journal of Histochemistry and
Cytochemistry, 38(7), 923–926. https://doi.org/10.1177/38.7.2355173
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SunTag and Fluorescent
Imaging
I
Mary Gearing, March 2017
n biology as in life, more is often better. More transcription factor
binding sites in a promoter lead to higher transcriptional activation.
Multiple nuclear localization signals (NLS) increase protein import into
the nucleus. In developing their SunTag technology, the Vale and Weissman
labs took this biological lesson and created a system to amplify fluorescent
signals. Named for the “stellar explosion SUperNova,” SunTag can help you
turn up the brightness in your fluorescent imaging experiments.
Fluorescent protein fusions
One of the easiest ways to track a given protein is to fuse it to a fluorescent
protein. You can then study where the protein is localized, and how its
localization and expression may change across various conditions. However,
this system is far from perfect. For example, if your fusion protein is
expressed at low levels, you have to increase your imaging time to get enough
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signal. This workaround risks cellular phototoxicity and eliminates the possibility
of long-term imaging studies. If you instead overexpress your protein at higher
levels, you risk observing artifacts only present when the protein is at a very high
concentration. Overexpressed proteins also have the potential to form aggregates
and may be toxic to the cell.
Here comes the SunTag
How does SunTag fix these problem? Instead of directly fusing a fluorescent
protein to your protein of interest, you instead fuse it to the synthetic SunTag
scaffold. This scaffold contains 10–24 copies of the short epitope GCN4. GCN4
recruits GFP fused to the cognate scFv antibody, which is expressed from a
separate plasmid. This system amplifies the intensity of the fluorescent signal
and enables single molecule tracking within living cells without affecting protein
function, thereby creating a single-molecule reporter of intracellular processes.
Initially, Tanenbaum et al. observed some GFP aggregation, which they reduced
by using superfolder GFP (sfGFP) with the small solubility tag GB1. In SunTag
nomenclature throughout the rest of this section, GFP refers to sfGFP-GB1.
Tanenbaum et al. examined the power of SunTag for single molecule imaging,
finding that plasma membrane-targeted CAAX-SunTag was 18-fold brighter than
sfGFP! The high SunTag signal allowed them to cut their laser power by over 80%
and still obtain a higher signal-to-noise ratio with a lower photobleaching rate.
Given the power of SunTag, they attempted single-molecule imaging deep inside
Figure 1. Comparing traditional GFP fusion proteins to SunTag fusion proteins. A traditional GFP fusion
fuses one copy of GFP to a protein of interest. Rather than fuse GFP to a protein, SunTag fusions contain
a synthetic scaffold that recruits GFP fused to the scFV antibody.
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the cell, in the nucleus and cytoplasm. Again, they found that SunTag marked
single molecules very effectively — they even managed to track run lengths of the
motor protein kinesin across microtubules.
Tanenbaum et al. then tested their hypothesis that the lower expression levels of
SunTag constructs would avoid negative effects on cell physiology. Having seen
that mitochondrial tracker GFP-mitoNEET can impair mitochondrial function, they
examined the effects of mitoNEET-SunTag-GFP. As expected, they obtained bright
images of mitochondria without organelle toxicity.
First generation (v1) SunTag is expressed at very low levels due to poor stability of
the GCN4 scaffold. To increase expression, Tanenbaum et al. modified the GCN4
sequence to increase its alpha-helical structure and stability, creating v4 SunTag.
Since the v4 system does not display protein aggregation, it’s recommended for
most imaging applications.
What can you do with SunTag?
Tanenbaum et al. performed a variety of different experiments in their paper, and
so can you! In almost any case you would use a traditional fluorescent protein (FP)
fusion, a SunTag fusion could be used as well.
Pros of SunTag compared to traditional FPs
•
•
•
•
Improved brightness and signal-to-noise ratio
Less chance of phototoxicity
Reduced photobleaching
Simplified, long-term single-molecule tracking
Caveats of SunTag
•
•
Very large size: a 24x scaffold fully occupied with GFP has a molecular weight
of 1,400 kDa vs. 24 kDa for GFP alone. Although traditional FP fusions can
also affect protein activity, half-life, or localization, these concerns are
greater with SunTag.
v1 SunTag exhibits some scaffold aggregation (v4 Suntag does not.)
It’s up to you to determine SunTag’s suitability in your experiments on a proteinby-protein basis. However, since many of Addgene’s SunTag plasmids have
coveted “blue flames,” it’s clear that the system is broadly applicable across
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many areas of research. Beyond fluorescence, SunTag can also be used to
improve CRISPR-based activation of target genes, but we’ll save that application
for another day! n
References
Tanenbaum, M. E., Gilbert, L. A., Qi, L. S., Weissman, J. S., & Vale, R. D. (2014). A Protein-Tagging system for signal
amplification in gene expression and fluorescence imaging. Cell, 159(3), 635–646. https://doi.org/10.1016/j.
cell.2014.09.039
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The Basics of Western Blotting
Y
Meghan Rego, March 2021
ou’ve gotten the plasmid encoding your protein of interest from
Addgene, transfected it into your target cells… now what? How
can you tell if the protein you are so keen to study is expressing
in your cells? Immunoblotting, or the western blot, or simply western,
is one of the simplest methods to detect the presence or absence of a
protein (Renart et al., 1979; Towbin et al., 1979; Burnett, 1981).
Introduction to western blotting
In a western, proteins are:
1. separated by size,
2. transferred to a membrane, and
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Figure 1: Overview of the western blot process. The protein of interest (purple) exists in a mix extracted
from cells or tissues. The mix of proteins is separated by size via SDS-PAGE alongside protein standards of
known molecular weight and then transferred to a membrane. The membrane is blocked to prevent nonspecific binding and then the protein of interest is stained with a primary antibody (red). Label-conjugated
secondary antibodies bind to the primary antibody to amplify the signal and allow the protein of interest
to be visualized.
3. detected using antibodies.
This process allows you to detect a single, specific protein within the complex
mix derived from cells or tissues. Westerns are useful not only to detect the
presence or absence of a protein, but can also determine if proteins are
being up-regulated or downregulated in a system; detect post-translational
modifications; quantify protein levels relative to standards; detect the cellular
location of proteins; and can be a readout for protein interaction studies such
as immunoprecipitation and pull-down assays.
Westerns are divided into two categories, native and denaturing. In a native
western, the protein’s secondary and tertiary structures remain intact, and the
protein is separated through a matrix by charge. In a denaturing western, the
protein is denatured to its primary structure and separated by size, with smaller
molecules moving more quickly through the matrix. For this section, we will be
focusing on denaturing westerns.
Separating a protein mix by size
Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) is the first
step of a western. To prepare the samples for SDS-PAGE, measure the protein
content and normalize to ensure equivalent loading. Denature the samples
to their primary amino acid sequence by boiling in the presence of a reducing
agent, typically containing thiols, to cleave disulfide bonds. Load the samples,
one sample per lane, onto the top of a resolving gel composed of the crosslinked
polymer acrylamide. In addition to the samples, a protein standard of known
molecular weight in a separate lane can help confirm the size of the protein of
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interest. An electric current applied to the gel causes the negatively charged
proteins to migrate toward the positive charge at the bottom of the gel and
separate by size. Smaller proteins encounter less resistance in the acrylamide
matrix and migrate faster through the gel. For better protein separation, vary
the acrylamide content of the gel. For small proteins, use a higher percentage of
acrylamide to increase migration resistance and improve separation. For large
proteins, reduce the percentage of acrylamide.
To further improve resolution, use a stacking gel on top of the resolving gel. A
stacking gel typically has a different ionic strength and lower pH and acrylamide
content than the resolving gel. Under these conditions, all of the proteins in a
sample migrate through the stacking gel at the same pace and enter the resolving
gel at the same time, where they are then separated by size. Gradient gels are
another great option to enhance resolution. In this setup, gels have an increasing
range of acrylamide content from top to bottom, allowing a mix of proteins with a
broad size range to be separated on a single gel.
Immobilizing proteins on a membrane
Once separated, the proteins are immobilized onto a membrane, usually
composed of nitrocellulose or polyvinylidene difluoride (PVDF) in a step called
protein transfer. While both nitrocellulose and PVDF are commonly used, PVDF
tends to be slightly more popular due to its durability and high binding capacity.
In addition, PVDF membranes can be repeatedly stripped of an antibody and reprobed with different antibodies, allowing multiple targets to be assessed from a
single western.
There are several variations of the protein transfer step, including wet, semi-dry,
and dry, but all follow the same general principle where the gel and membrane
are “sandwiched” together with blotting paper and an electric current is applied
causing proteins to migrate out of the gel and attach to the membrane.
In a wet transfer system, the sandwich is completely submerged in a tank filled
with a transfer buffer formulated to conduct the electric current. The wet transfer
process is very effective in transferring a broad range of protein sizes but takes
hours to overnight.
In contrast, a semi-dry transfer only requires enough buffer to saturate the
sandwich and typically takes less than an hour. However, large proteins typically
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Figure 2: Overview of using Sandwich ELISA to quantitatively measure protein concentration or antibody
specificity via a colorimetric reaction. Image from Boguszewska et al., 2019.
do not transfer as well using this method. Finally, commercially available dry
blotting systems efficiently transfer a range of protein sizes in minutes without
any transfer buffer. These systems, however, require you to purchase expensive
system-specific sandwiches.
After the transfer, some areas of the membrane remain unbound by protein.
These unbound regions are “sticky” and have the potential to bind nonspecifically
to the antibodies used for staining. To address this, the membranes are incubated
before staining in a buffer containing nonfat milk, bovine serum albumin, or other
proteins. The sticky areas on the membrane will bind to the proteins in the buffer,
blocking the membrane from binding to the antibodies during the stain. The ideal
blocking buffer depends on the antibodies and labels being used and may require
some trial and error (Mahmood & Yang, 2012).
Choosing your primary antibody
Once the membrane has been blocked, it is stained with a primary antibody
against your protein of interest. But which antibody should you use? You will likely
find several different antibody options targeting your protein of interest from a
variety of vendors, and you may even find plasmids encoding antibodies from
Addgene that you can make on your own.
To narrow your search, first pay attention to the validated applications listed
on the vendor’s website and choose an antibody that has been validated for
immunoblotting. Different immunoassays detect proteins in different states.
For example, in immunoprecipitation (IP), the antibody interacts with a protein
in its native state while a western blot detects denatured polypeptide chains.
The protein regions available for antibody binding, or epitopes, differ between a
protein in its native state versus the polypeptide chain. Consequently, antibodies
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validated for IP might not work for a western and vice versa. In addition to
validated applications, pay close attention to the controls listed on the vendor’s
website. Typically, the vendor will demonstrate that their antibody binds to
a protein of the correct size. Many vendors will show that the antibody binds
to transiently overexpressed protein, however, they should also demonstrate
that the antibody efficiently binds to endogenously expressed protein at
physiologically relevant levels. Some vendors will also test their antibody in a
variety of tissues to demonstrate that the protein staining matches the expected
expression patterns across tissues. Recently, knockdown or knockout lines have
become the gold standard in antibody validation. This level of validation ensures
that the antibody is staining the intended target.
Once you have chosen an antibody, read the data sheet thoroughly. Vendors
will frequently include suggestions for working concentrations, blocking buffers,
and incubation times. If the vendor includes publications that cited the antibody,
consider reviewing the materials and methods sections of those publications.
Finally, always verify that the chosen antibody works in your specific experimental
context (Pillai-Kastoori et al., 2019). Tissues or cell lines other than those used
by the vendor for validation could contain unintended cross-reactive proteins.
Similarly, if you vary the experimental conditions from those used by the vendor,
then it may lead to nonspecific binding.
Whenever possible, include positive and negative controls. Positive controls
typically include cell lines or tissues that are known to express the protein at
physiologically relevant levels (not overexpression systems) while negative
controls are those that do not naturally express the protein or have had gene
expression knocked down or knocked out. If you are interested in making your
own knockout line for validation, please view this tutorial from Stuart Orkin’s and
Daniel Bauer’s labs.
Visualize your protein of interest with the help of secondary
antibodies
In order to visualize proteins on the membrane, the antibodies are typically
conjugated to an enzyme, such as horseradish peroxidase (HRP), that emits light
upon reacting with a specific substrate. The emitted light, or chemiluminescence,
is detected on x-ray film or with specific imaging devices. In most cases, the
primary antibody, which binds the protein of interest, is not labeled. Instead, a
conjugated species-specific secondary antibody is used to visualize the proteins.
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In this setup, multiple secondary antibody molecules bind the primary antibody
and amplify the detection signal.
Analyzing the western blot
Once visible, it’s time to analyze your protein of interest. Analysis typically begins
by confirming that the protein is the expected size as compared to the protein
standards.
Protein abundance
The thickness of the band provides information about the relative abundance of
the protein in the sample. More protein causes a thicker band, while less protein
leads to a thinner band. It is important to note, however, that you must include a
loading control when assessing protein abundance or comparing samples across
lanes.
For a typical loading control, the membrane is probed with an antibody that
detects a ubiquitously expressed protein, such as actin. Actin levels should be
consistent across all samples on the gel and should be observed as a band of
consistent size and density present in all samples. Uneven sample loading or
incomplete protein transfer causes inconsistent levels of the loading control. In
these cases, the western should be repeated.
Finally, while westerns are a useful way to gauge protein abundance, they provide
qualitative measurements. If you require precise quantification of a protein,
consider using an alternative method such as an enzyme-linked immunosorbent
assay (ELISA) (Aydin, 2015).
Post-translational modifications
In addition to protein abundance, westerns detect changes to a protein’s posttranslational modifications such as phosphorylation, acetylation, methylation,
and ubiquitination. For example, to test if a protein is phosphorylated following a
specific experimental condition, you could look for a slight size shift in the protein
band before and after treatment.
Westerns are also a useful readout for a number of cellular assays. They can
determine protein interactions following immunoprecipitation and are a common
readout for subcellular fractionations, a procedure that isolates proteins from
different cellular compartments such as the mitochondria, cytoplasm, and
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nucleus. We hope that this section has provided you with a good overview of the
western blot. When planning a western, take care to include the proper positive
and negative controls, choose antibodies that are validated for your application,
and test the antibody in your specific system. n
References
Aydin, S. (2015). A short history, principles, and types of ELISA, and our laboratory experience with peptide/protein
analyses using ELISA. Peptides, 72, 4–15. https://doi.org/10.1016/j.peptides.2015.04.012
Burnette, W. (1981). “Western Blotting”: Electrophoretic transfer of proteins from sodium dodecyl sulfate-polyacrylamide
gels to unmodified nitrocellulose and radiographic detection with antibody and radioiodinated protein A. Analytical
Biochemistry, 112(2), 195–203. https://doi.org/10.1016/0003-2697(81)90281-5
Mahmood, T., & Yang, P. (2012). Western blot: Technique, theory, and trouble shooting. North American Journal of
Medical Sciences, 4(9), 429. https://doi.org/10.4103/1947-2714.100998
Pillai-Kastoori, L., Heaton, S., Shiflett, S. D., Roberts, A. C., Solache, A., & Schutz-Geschwender, A. R. (2019). Antibody
validation for Western blot: By the user, for the user. Journal of Biological Chemistry, 295(4), 926–939. https://doi.
org/10.1074/jbc.ra119.010472
Renart, J., Reiser, J., & Stark, G. R. (1979). Transfer of proteins from gels to diazobenzyloxymethyl-paper and detection
with antisera: a method for studying antibody specificity and antigen structure. Proceedings of the National Academy
of Sciences of the United States of America, 76(7), 3116–3120. https://doi.org/10.1073/pnas.76.7.3116
Towbin, H., Staehelin, T., & Gordon, J. (1979). Electrophoretic transfer of proteins from polyacrylamide gels to
nitrocellulose sheets: procedure and some applications. Proceedings of the National Academy of Sciences of the
United States of America, 76(9), 4350–4354. https://doi.org/10.1073/pnas.76.9.4350
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How to Strip and Reprobe a
Western Blot
W
Emily P. Bentley, August 2024
estern blots are a great tool to identify a protein of interest in
a complicated solution like cell lysate. But they can be a lot of
work — and what if you want to detect more than one protein
in your sample? Or what if something weird happened during your western
and your results look… funky? Do you need to start a whole new blot?
Never fear: membrane stripping is here!
Rather than start over, you can remove the detection antibodies from your
membrane, allowing you to probe the blot again, almost like new. This
approach preserves your sample and saves you from having to run a whole
new gel and membrane transfer. Let’s get started!
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Before you begin
In the best case, you know ahead of time that you want to probe your blot with
multiple antibodies. You’ll want to set up your blot with a polyvinylidene difluoride
(PVDF) membrane, which is sturdier and retains the blotted protein better than a
nitrocellulose membrane.
If you have already run your blot using nitrocellulose, you may decide you have
nothing to lose by trying to strip and reprobe. In this case, we recommend using
mild stripping solution (see below) to minimize loss of your sample from
the membrane.
Next, plan out the order of targets you plan to probe for. Even using PVDF,
some of your target protein will be removed from the membrane along with the
antibodies, especially with more stringent stripping methods. If you are planning
to detect multiple proteins through several rounds of stripping and reprobing,
maximize your signal by starting with the least abundant protein and working
toward the most abundant. If the abundance of your targets isn’t too different,
consider antibody affinity: stronger binding antibodies will require harsher
stripping to remove, so start with the weaker binding ones first to preserve
your sample.
Pro tip!
Because some protein is lost each time
you strip a blot, you should not compare
the strength of bands from different
rounds of detection.
Finally, stripping and reprobing works best
with western blots that use chemiluminescent
or fluorescent detection, like horseradish
peroxidase (HRP) or Alexa Fluor dyes, respectively.
Chromogenic detection produces colored
precipitates that permanently stain the membrane,
making that section of the blot unusable
for reprobing.
Preparing your western blot
Run your gel and membrane transfer as normal. Immediately after transfer, dry
the membrane to maximize protein retention. Following this step, most PVDF
membranes recommend re-wetting with methanol before rinsing with wash
buffer, as dry PVDF won’t evenly re-absorb aqueous buffer. Be sure to check the
manufacturer’s instructions for the membrane you are using.
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Dry nitrocellulose membranes are brittle, so proceed with caution. Unlike PVDF,
they can be returned directly to buffer without a re-wetting step.
Important: do not dry your membrane with antibodies on it if you plan to reprobe!
Just as your sample protein is retained better, antibodies will stick permanently to
a membrane that has been dried, no matter what stripping methods you throw at
it. Ideally, dry your membrane once before any detection steps to avoid retaining
residual antibodies. If you have already applied antibodies and need to dry the
membrane for storage, but plan to reprobe it later, strip it thoroughly first.
Then continue on with your western blot as normal!
Stripping your membrane
Alright, so you’ve dried your membrane and returned it to buffer, you’ve run
and imaged your western blot with your first target, and now you’re ready for
round two (even if round two is just another attempt at the same target!). In the
stripping step, you’ll remove the primary and secondary antibodies from your
membrane, clearing the way for the second blot. There are multiple ways to strip
blots, and the best choice will depend on your specific experiment.
Remember, stripping your membrane will remove some of your sample — and
harsher stripping means more sample loss. If you are still optimizing your system,
start with a mild stripping solution and move onto a stringent solution if the mild
is not effective.
You may choose to make your own stripping solution using a recipe like the
ones shared by Bio-Techne, ThermoFisher, or Millipore Sigma. We’ll share the
basic composition of these solutions below. Alternatively, companies including
ThermoFisher, Millipore Sigma, and Azure Biosystems offer pre-made stripping
solutions or kits.
Mild stripping
The mild stripping solution uses low pH to disrupt antibody binding to antigens.
For the mildest stripping solution, omit the Tween 20.
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Ingredient
Concentration or %
Glycine or Glycine-HCl
Glycine or Glycine-HCI
SDS
0.1%–1% (w/v)(Mouse IgG1)
Tween 20 (optional)
1% (v/v)
HCI
Adjust to pH 2.0–2.2
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Once your solution is made, you’ll want to:
1. Rinse the membrane in water or fresh buffer.
2. Cover the membrane in stripping solution and agitate for 5–30 minutes at
room temperature. The incubation time will depend on your blot — antibodies
that bind more strongly or that have saturated the blot may need longer
incubation for effective stripping. Some blots benefit from an additional 5–10
minutes of incubation at 37 °C.
3. Wash the membrane for 5–10 minutes 2–3x in fresh wash buffer such as PBS
or TBST.
Stringent stripping
The stringent stripping solution uses heat and detergent to denature and remove
antibodies but is also likely to remove more of
your target protein.
Pro tip!
βME is a reducing agent and gets
oxidized over time, so it should always
be added fresh to solutions. It is also
volatile and toxic (and smells bad!), so
be sure to use it under a fume hood.
1. Rinse the membrane in water or fresh buffer.
2. Cover the membrane in stripping solution and
agitate for 30–45 minutes at 50 °C.
3. Wash the membrane for 5–10 minutes 2–6x in
fresh wash buffer such as PBS or TBST. Note
that βME reacts with antibodies, so thorough
washing is critical.
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Ingredient
Concentration or %
Purpose
Tris HCI
62.5 mM
Buffer
SDS
2% (w/v)
Detergent
Β-mercaptoethanol (βME)
100–115 mM = 0.7%–0.8%
(v/v)
Breaks disulfide bonds
HCI
Adjust pH to 6.7–6.8
Adjust pH
Assess the stripping
Now it’s time to check to make sure the antibodies from your first round of
detection are actually gone. First, simply repeat the imaging step to check
if any secondary antibody remains, whether that involves adding fresh
chemiluminescent substrate or imaging your fluorescent probe. Then, if you don’t
see any signal, you’ll want to check for the primary antibody by re-blocking your
membrane and re-incubating with your secondary antibody. This is the one time
in western blotting that you’re looking for a “blank” result!
If some signal remains, you may want to repeat your stripping procedure,
perhaps using longer incubation times, or try the stringent solution if you started
with the mild one.
Reprobe
Once your membrane is clean of antibody, you’re ready to repeat your western
blot as normal. Be sure to rinse away the secondary antibody you used to assess
the stripping, and don’t forget to re-block your membrane!
Depending on the system, you may be able to strip and reprobe your western blot
several times — some researchers report up to ten cycles! Just remember that a
bit of sample is lost in each round of stripping, so it pays to plan ahead. n
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Technical Design of a Western
Blot
I
Rachel Leeson, August 2024
f you’ve ever run a western blot, or thought about running one, you’ll
know there’s a lot of choices to make when designing the experiment.
What detection method? What membrane? What should you block with?
It can be so overwhelming that you might just stick with the protocol your
labmate handed you — after all, it worked for them! Unfortunately, that
doesn’t guarantee it’ll work for your experiment. But if you understand the
technical aspects of a western blot, you’ll be able to understand how to
modify the protocol to one that works for you. There’s a lot to cover, so
let’s dive in!
Psst! Looking for a western blot protocol? Check out Addgene’s western blot
protocol and protocol video!
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Figure 1: The technical decisions to make when designing a western blot.
Antibodies
The most important part of your western is your antibodies, particularly your
primary antibodies. We always recommend using an antibody validated for your
target in your specific assay. If validated antibodies aren’t available or if your
experimental conditions are different than the validation assays, we recommend
performing your own validation assays. If you’re using previously published
antibodies, check the manuscript and supplementary figures for validation data,
or follow the references to find the original publication and ensure that it includes
validation data relevant to your application — and double-check that lot number
while you’re at it!
Polyclonal or monoclonal
Both polyclonal and monoclonal antibodies can work well for a western blot.
Polyclonal antibodies, which bind to multiple sites on an antigen, are cheap and
can help amplify a weak signal. However, they have high cross-reactivity, vary
significantly from lot to lot, and have to be re-validated each time a new lot is
purchased.
Monoclonal antibodies, on the other hand, may have lower signal, but they have
higher specificity, lower cross-reactivity, and are more consistent lot to lot.
The highest reproducibility comes from recombinant antibodies (rAbs), which
are produced from plasmids and have very little batch-to-batch variation.
Confirmation of sequence can be done from relatively cheap plasmid
sequencing. While rAb are monoclonal, recombinant multiclonal antibodies
— mixtures of different rAbs that target the same protein — can be used
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to reproduce the benefits of polyclonal antibodies. Because these mixtures
are defined by the manufacturer, each rAb in a mixture can be individually
validated. r‌ Abs are not as commonly available as monoclonal or polyclonal
antibodies, but it’s worth a look to see if they’re available for your target!
Incubation temperature and time
Incubating at a lower temperature reduces background noise but increases
the length of your assay. The most common options are 4 °C overnight or 1–2
hours at room temperature (RT). Most people choose to incubate their primary
antibody overnight at 4 °C and their secondary antibody for 1–2 hours at room
temperature, which allows them to reduce background while keeping the assay
run time to two days.
Indirect or direct detection method
Indirect detection is the most common way to run a western blot. It allows
you to detect relatively low levels of antigen, while providing flexibility in
assay design. Conjugated secondary antibodies are relatively cheap and easy
to source, making it easy to switch from one readout method to another or
change primary antibodies as needed. However, more antibodies mean more
optimization and likely higher background noise as well, and multiplexing is
challenging and limited.
Direct westerns typically rely on primary antibodies conjugated to fluorescent
proteins, instead of the more common chemiluminescent methods for detection
(though the enzymes used for chemiluminescence can be conjugated to primary
antibodies). Direct westerns are simpler and faster than indirect ones and
can be used in highly quantitative assays. On the other hand, they are not as
sensitive to lower amounts of protein, and can require more antibody or loading
sample, which may not be practical. Conjugated primary antibodies can be more
difficult to source, especially if you need a specific conjugate, and changing
your reporter molecule requires either re-conjugating or re-purchasing your
primary antibody with the new reporter. Finally, each new conjugate will have to
be tested to ensure it doesn’t impact the antibody’s binding to your protein of
interest.
Protein preparation
Before you can blot your proteins, you’ll need to lyse and denature them. If
you are intentionally running non-denatured proteins, in what is known as a
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native western blot, your loading buffer will not contain any denaturing agents.
However, you’ll still have to lyse your proteins. For this post, we are assuming
that you are running a denaturing blot.
There are a number of lysing methods and buffers to choose from. If you are
starting from tissue, you’ll likely want to use a mechanical method before your
enzyme lysis. For cell cultures, enzyme lysis is typically sufficient.
Lysis buffer
When choosing your lysis buffer, consider the subcellular location of your
protein. Proteins located in membrane-bound subcompartments, like the
mitochondria or nucleus, will likely require a harsher lysis buffer than proteins
easily available in the cytoplasm or whole-cell lysate. A harsher, SDS-containing
lysis buffer, like RIPA, should be considered if your protein is difficult to
solubilize, such as a membrane-bound protein. There are many different
lysis buffers available commercially, and most common buffers have recipes
available online if you prefer the DIY method. As some proteins aggregate at
95 °C, you may want to check for any available data on the best incubation
temperature for your specific protein(s) of interest.
Determining linear detection range
If you are quantifying your western blot data, you’ll need to determine the
linear detection range of your protein. The most common way to do this is via
a Bradford assay or BCA assay. Be sure to aliquot your sample(s) before adding
in your denaturing and/or loading buffers, as most Bradford assays are not
compatible with detergents like SDS, while BCA assays are not with denaturing
agents like DTT or β-mercaptoethanol.
Denaturing proteins
To denature your proteins, you’ll want to use SDS and either DTT or
β-mercaptoethanol (BME). DTT is a stronger reducing agent than BME and has
a less… distinctive smell. BME smells like rotting eggs but has a longer shelf life
when stored properly. Because BME is not as strong as DTT, you’ll have to use
different concentrations if you switch from one to the other.
Type of gel
The type of gel you’ll need depends mostly on the weight of the proteins you’re
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Table 1: Types of gels
Gel type
Protein
sizes
Running
buffer
Running
conditions
Pros
Cons
Anti-protein
A
6–400 kDa
Tris-glycine
100 V, 1–2
hours
Easy and
cheap to
handcast
Short
shelf-life;
can alter
proteins
due to high
pH
Bis-tris
6–400 kDa
MES for
proteins <
40 kDa
180 V, 30
minutes
Good shelf
life; runs at
neutral pH
(reduced
protein
alteration)
Expensive
buffers
Bis-tris
6–400 kDa
MOPS for
proteins >
30 kDA
200 V, 30
minutes
Good shelf
life; runs at
neutral pH
(reduced
protein
alteration)
Tristricine
2.5–40 kDa
Tristricine
30 V, 1 hour
or 100 V,
1–2 hours
Good
separation,
quality, and
stability
Limited
protein size
range
Tris-acetate
40–500 kDa
Tristricine
150 V, 1–3
hours
Good for
higherweight
proteins
Long
running
time
Expensive
buffers
interested in. Table 1 lists the most common gel chemistries for SDS-PAGE
running conditions, which will cover most western blot use cases. If you’re
running native (non-reduced) proteins or are looking to preserve specific protein
modifications, we recommend doing a little more research into gel chemistry
and/or specialty gel options.
It’s important to note that Bis and Tris gels run proteins in different patterns,
due to their differing chemistries. You therefore cannot compare Bis and Tris
gels or blots to each other.
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Table 2: Recommended gel percentages for various protein sizes
Protein size
Target proteins are
Gel percentage
50–250 kDa
Similar in size
8%
20–250 kDa
Similar in size
10%
10–100 kDa
Similar in size
12%
10–250 kDa
Different sizes
4–12% gradient
4–250 kDa
Different sizes
4–20% gradient
Gel percentage
Once you’ve selected your gel chemistry, you’ll need to pick the percentage. You
can get a single-percentage gel, which is cheap and useful when you’re looking
at proteins all roughly the same size, or a slightly more expensive gradient gel,
which will allow clear separation of proteins of different sizes.
Handcast or premade
If you need a gradient gel, the decision is probably already made: gradients are
extremely difficult to handcast, so unless you have an expert around, you’ll likely
want to purchase one.
If you need a single-percentage gel, you will have the option to cast your own
gel. Handcast gels are significantly cheaper and generate less waste. However,
they also add time to an already lengthy protocol, require the use of hazardous
chemicals, and are not always consistent. Precast gels, while expensive, reduce
protocol time, can be stored at 4 °C, and are very consistent gel-to-gel.
Ladder
Whatever gel type you select, don’t forget to save a lane for your standards
(ladder). You’ll want to select a pre-stained or unstained set of standards that
covers a size range appropriate for the target(s) and transfer method.
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Membranes
The two membrane options are PVDF and nitrocellulose. PVDF is a sturdy
membrane that can be stripped and reprobed multiple times. It doesn’t degrade
during long-term storage and has both high binding capacity and high protein
retention. It comes in a variety of pore sizes and protein capacity, allowing you
to optimize for smaller proteins or lower fluorescent backgrounds. However,
PVDF does add a few (short) steps to the transfer: you’ll need to pre-wet most
PVDF membranes before the transfer and allow them to dry afterwards.
Unless you buy a specialty low-background PVDF membrane, it also has higher
background noise than nitrocellulose membranes.
Pro tip!
You can visualize proteins on a PVDF
membrane without staining; simply wet
your membrane with 20% methanol and
place it on a light box after drying.
Nitrocellulose, on the other hand, doesn’t require
any pre-wetting and has lower background
than PVDF. It is a little quicker to use, with high
binding capacity, though it has lower retention
after binding and washes. It’s less sturdy and not
recommended for stripping and reprobing, unless
you buy specialty nitrocellulose membranes
specifically developed to allow stripping. And as
a safety note, nitrocellulose is flammable, so you
may want to avoid it if your lab does a lot of work
around a flame.
Pore size
Membranes come in a variety of pore sizes. If your protein of interest is small,
you may want to consider a smaller pore size (0.2 µm). Conversely, if your
protein of interest is large, look for a larger pore size (0.45 µm).
Types of transfers
Your transfer options are wet, semi-dry, or dry/electroblot.
A wet transfer is the most efficient method, meaning that it will transfer the
most protein from your gel to your membrane. It requires anywhere from 60
minutes to overnight to run, and is recommended for very large proteins. A
semi-dry transfer is faster — 3–30 minutes — and uses less buffer, but it is not
as efficient. It is recommended for small to medium proteins. Finally, there
are dry/electroblot transfer systems available from various manufacturers.
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These proprietary systems are fast (often < 10 minutes) and simple, utilizing
individually packaged membrane “sandwiches.” They work well for proteins of
most sizes and are worth considering if your proteins of interest vary wildly in
size. However, these systems are expensive, generate plastic waste, and limit
the amount of optimization that can be done.
Note that each type of transfer requires different equipment. Wet transfers can
be done in an insert in your gel box, while semi-dry transfers require a semi-dry
transfer cell. Dry/electroblot transfers only work in the proprietary hardware
sold by the manufacturer.
Blocking solutions
Your blocking options come in two categories: protein-based and chemicalbased. Before selecting one, check your antibody information. Many antibodies
will have a recommended blocking buffer and a recommended concentration.
That makes the choice easy!
Protein-based blockers include dry non-fat milk or serums like FBS, PBS, and
BSA. For nitrocellulose membranes, which require a lower concentration of
blocker, you can also use gelatin.
Pro tip!
If you get a speckled image, your
protein-based blocker is either degraded
or not completely mixed together.
Protein-based blockers are inexpensive and easily
made in the lab. They can be stored at your bench
during the procedure and at 4 °C when you’re not
running a western. They are not very stable and will
noticeably degrade after several months at 4 °C.
Commercially available chemical-based blockers,
while much more expensive, are consistent and
stable at room temperature for 1–2 years and are available in formulas specific
to your choice of detection methods. They are very useful for troubleshooting or
optimizing western blots. If you need to preserve sample, check the ingredients
before using, as some contain Tween-20, which can strip proteins from the
membrane.
There are also commercially available blockers that work as signal enhancers.
These blockers amplify signal without increasing background noise and can
reduce the amount of primary antibody required.
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Reporters
There are two main classes of reporters: chemiluminescence enzymes and
fluorescent proteins. Chemiluminescence enzymes are cheap, fast, shelf-stable
before activation, and light-insensitive. They can detect femtogram levels of
protein.
These enzymes cannot be used for multiplexing, and the solutions used to
provide the chemilumescence may be light-sensitive. They also have a poor
dynamic range, meaning that if your blot contains one protein in the femtogram
range and another in the microgram range, they cannot be read with equal
accuracy. Finally, they degrade quickly and need to be read fairly soon after the
reaction has started.
Types of chemiluminescence enzymes
The two most common enzymes are horseradish peroxidase protein (HRP) and
alkaline phosphates. HRPs are usually preferred because of their higher specific
activity, slightly lower cost, and smaller size. However, HRP is not compatible
with sodium azide, a common microbial agent used in antibody buffers, which
may be a concern if you’re conjugating your antibodies yourself. Alkaline
phosphates have a linear reaction rate and are compatible with antimicrobial
agents.
Chemiluminescence kits
When activating your enzyme, make sure to choose a chemilumescence kit
appropriately sensitive for the amount of protein you are expecting.
Fluorescent proteins
Fluorescent proteins, while more expensive, allow for multiplexing. They
have a ten-fold greater dynamic range than chemiluminescence enzymes,
making it easier to accurately visualize proteins of wildly different amounts
in the same blot, and better linearity within detection limits (important if you
want to quantify your blots). Fluorescent proteins are stable, so blots can
be stored for weeks to months without loss of signal. While not as sensitive
as chemiluminescence enzymes, they are more likely to be compatible with
stripping and probing.
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Imaging
At the final step of your western, you’ll have two choices: x-ray film or a CCD
camera system.
X-ray film is only compatible with chemiluminescence reporters and requires
access to a darkroom and a developer. Film is highly sensitive but has a limited
dynamic range, which means it may take multiple exposures for a blot with
varying signals on it. It requires long exposure times (up to thirty minutes!) to
reach the limit of detection. Film also needs to be digitized for quantitative work
— and with the advent of online lab notebooks, it’ll likely need to be digitized
even if you aren’t quantifying your blot.
CCD cameras have a much higher upfront cost, but require no consumables,
produce digital images, reach their limit of detection quickly (~1 minute), and
have a linear response over a broad dynamic range that typically spans 2–5
orders of magnitude, which includes a wide dynamic range for fluorescent
proteins. They do tend to have increased background with higher exposure
times, although newer models can compensate for this by binning.
Pro tip!
You only need a resolution of ~150 µm
to image a blot. This is because proteins
diffuse semiradially during the transfer,
making membranes inherently “fuzzier”
than gels.
This is probably the most limiting of steps, as
you may only have access to one or another.
Most institutes have moved towards CCD
cameras over the past few years.
Quantification
If you want to quantify the amounts of protein
in your blot, you have two options. It’s fairly
easy to decide!
If your protein of interest is available in pure form, you can use it to create a
standard curve on your blot to calculate the amount in your sample. (Do ensure
your blot is optimized so your standard curve is within your limit of detection!)
You’ll need to have enough lanes available for your standard curve, your
standard, and your sample(s), as you can only compare samples to a standard
curve generated on the same blot.
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If your protein of interest is not available in a pure form, you can perform relative
quantification, which measures protein amounts and compares the amount of
one protein to another. Since antibody binding varies wildly based on proteins,
and their differing amounts of available epitopes for that particular antibody, this
truly does provide a relative, not absolute, readout.
Normalization
Overall protein expression varies from sample to sample. To compare one
sample to another, you’ll need to normalize your protein of interest to overall
protein expression for each sample and protein. While ubiquitously expressed
“housekeeping” proteins — like GAPDH or actin — are often used, it is far more
accurate to use total protein loading.
Total protein loading measures overall protein expression and does not rely
on the assumption that a specific protein’s expression has not changed due
to ‌experimental conditions. You then normalize your protein of interest to
total protein expression. Total protein expression can be measured using a
protein stain like Coomassie Blue or Ponceau S. It is far less prone to error than
normalizing to so-called housekeeping genes (Aldridge et al., 2008).
Woah! That was a lot of information to cover. But don’t worry — the next time
you’re designing a western blot, you can check out our handy reference table to
help you quickly decide what approaches best suit your experimental needs.
Good luck and happy blotting! Many thanks to Addgenies Ashley Waldron,
Meghan Rego, and Amrita Rhodes for their help with this section. n
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Table 3: Optimizing the technical design of a western blot
If you want to…
Consider using…
But the method has/is…
Save time
Direct detection
Decreased signal
Nitrocellulose
Not recommended for strip
/reprobe
Semi-dry transfer
Decreased efficiency
Electroblot
Increased cost; decreased
CCD cameras
High initial (purchase) cost
Precast gel
Increased cost
Polyclonal antibodies
Higher non-specific binding;
lot-to-lot variability
Some recombinant
antibodies
Not always available; not
always cheaper.
Indirect detection
Increased time; higher
background
Chemiluminescent reporter
Smaller dynamic range; no
multiplexing
Protein-based blocker
Degrades quickly
Wet transfer
Increased time
Handcast gel
Increased time; have to
make as needed
Monoclonal antibodies
Increased cost
Save money
Increase specificity
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Table 3: Optimizing the technical design of a western blot (cont.)
If you want to…
Increase signal
Decrease background
Multiplex
Consider using…
But the method has/is…
Recombinant antibodies
Not always available; cost
varies widely
Polyclonal antibodies
Higher non-specific binding;
lot-to-lot variability
Recombinant multiclonal
antibodies
Not always available
Indirect detection method
Increased time; higher
background
Chemiluminescence reporter
Smaller dynamic range; no
multiplexing
Wet transfer
Increased time
Specialty chemical-based
buffers
Increased cost
X-ray film imaging
Increased time; increased
consumables cost
Direct detection method
Less sensitive; usually more
expensive
Nitrocellulose or lowfluorescence PVDF
membranes
Increased fragility
(nitrocellulose) or increased
cost (specialty PVDF)
Chemical buffers
Increased cost; may be
harsh
Fluorescent proteins
Less sensitive; more
expensive
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Table 3: Optimizing the technical design of a western blot (cont.)
If you want to…
Consider using…
But the method has/is…
Strip/Reprobe
PVDF membrane
Increased time
Calculate
absolute quantification
Running a standard curve
Uses many wells; purified
protein not always available
Calculate
relative
quantification
Normalizing to ubiquitously
expressed genes
Not as accurate; increased
antibody costs; increased
optimization
Normalizing to total protein
loading
Increased time; increased
reagents
References
Aldridge, G. M., Podrebarac, D. M., Greenough, W. T., & Weiler, I. J. (2008). The use of total protein stains as loading
controls: An alternative to high-abundance single protein controls in semi-quantitative immunoblotting. Journal of
Neuroscience Methods, 172(2), 250–254. https://doi.org/10.1016/j.jneumeth.2008.05.00
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Troubleshooting and
Optimizing a Western Blot
W
Rachel Leeson, September 2024
estern blots can be tricky to get right. If you’ve thoughtfully
done the technical design of your blot, but are still finding
yourself having issues, you’re in the right place! In this
section, we’ll talk about western blot optimization and troubleshooting.
Before you can optimize or troubleshoot a western, you’ll need to be familiar
with running a gradient, which will allow you to optimize the concentrations of
your various blot reagents. There are two types of gradients, protein gradients
and reagent gradients. Protein gradients use dilutions of your sample or
protein of interest to understand the dynamic or working range of a given
reagent (usually an antibody).
Alternatively, you can run a reagent gradient. In this approach, you’ll run the
same protein concentration in multiple lanes and cut up the membrane to
treat each lane with a different concentration of your reagent. Be aware that
this approach requires excellent attention to detail, since you’ll end up with a
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Figure 1: A protein gradient. The density of the bands, which increase as protein concentration increases,
show the protein concentrations are within the antibody’s dynamic range. Created with BioRender.com.
lot of moving parts that all look exactly alike.
Both gradients can be informative, so spend some time thinking about which type
of gradient can help answer your particular question before running one.
Good on gradients? Excellent! Now, let’s head west, intrepid reader!
Protein Prep
The first step in any western blot is to lyse your samples. There are a few issues
that can arise in this step: protein degradation, incomplete lysing/insoluble
proteins, and protein aggregation.
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Figure 2: Reagent gradient. Here, the lanes are lightly marked on the membrane (1) using the gel as a
guide. The gel is then removed from the membrane (2) and the membrane is cut so each lane is its own
piece (3). After incubation, blocking, and washing, the membrane is reassembled and imaged. In this
example, a 1:1,000 dilution gives a high signal with low background. Created with BioRender.com.
Protein degradation, which would result in low protein yield, can be reduced
by adding a protease inhibitor cocktail and phosphatase inhibitors to your lysis
buffer. Additionally, protein lysis should be done at 4 °C or on ice, whichever is
more practical.
If your lysing method is not resulting in your protein being available, check
its subcellular location and solubility. If it’s membrane-bound or located in a
subcellular compartment, you may want to try an SDS-containing detergent like
RIPA. You can try a few different lysing buffers and strengths. If that’s not working,
consider using a subcellular fractionation kit or a lysing protocol developed for
isolating proteins from a specific compartment. If your proteins are nuclear- or
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DNA-binding proteins, you may need to sonicate your lysates in order to release
the proteins from their binding.
If you’re seeing protein aggregation, you may want to consider changing your
incubation temperature. Some proteins aggregate at 95 °C, a common lysis
temperature, so consider a longer incubation (10–20 minutes) at 70 °C, or a truly
lengthy incubation (30–60 minutes) at 37 °C, if you suspect aggregation in
your prep.
If your proteins are the wrong size on your gel or blot, Table 1 (at the end of the
post) has a number of helpful suggestions.
Gels
Gel issues usually come down to voltage and time. Make sure to load an
appropriate protein ladder on your gel and keep an eye on it and the leading line
(dye front) visible in the sample buffer. You’ll want to run long enough to get good
separation in your ladder, particularly in the sizes around your protein of interest,
but you don’t want to run so long that your dye front or ladder runs off the gel.
Luckily, this is easily monitored in real time, as the dye front (and many ladders!)
are visible to the naked eye.
If you see a smiley face in your gel, or if you see smeared bands, your voltage is
likely too high. If you’re seeing a smiley face at an appropriate voltage (usually
10–15 V/cm of gel), your gel may be overheating. Try running it in a cold room or
putting ice packs in and/or around the gel box. If overheating remains an issue,
which can happen with large proteins, try lowering the voltage and using a longer
run time.
Samples can run into empty neighboring lanes, so load your gel to ensure your
samples and reference ladders are not next to empty wells. Most people will
simply load an extra ladder or technical replicate (i.e., leftover samples) to fill the
relevant wells. Similarly, if you load a gel and then forget to start it, the samples
can actually migrate into the buffer — so it’s better to start over if that happens.
Finally, if you’re seeing a weird issue and can’t figure out what’s causing it, try
making fresh running buffer. Old or improperly made running buffers can cause
all kinds of odd issues.
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If your gel is running properly but you’re not seeing sufficient resolution in the
bands, you may want to consult Tables 1 and 2 in our Technical Design of a
Western Blot section to make sure you have the optimal gel for your experiment.
Ladders
If you need very accurate protein measurements, consider using an unstained
protein ladder, which will run a bit truer to size than a stained ladder. Unstained
ladders contain either a
‌ protein tag or an IgG binding site, allowing you to
visualize them with a conjugated antibody. If you do not need such precise
measurements, you can use a colorimetric ladder, visible to the naked eye; a
fluorescent ladder compatible with your imaging system; or a combination of
the two. There are many commercially available options to choose from.
Transfer
Transfers are a little trickier than gels and may require multiple runs to optimize
or troubleshoot.
Before you start…
Mark your membrane! Membranes look the same from both sides, so it’s quite
easy to think lane 1 is‌lane 8. Common ways to keep membrane orientation
straight are to mark the upper left-hand corner with a pencil (not a pen — they
bleed!), cut off the upper left-hand corner, or place a double ladder on the
lefthand side. Whatever you do, be consistent and document it.
Did it work?
Save yourself time and trouble by confirming your transfer worked with
Ponceau S staining or other reversible protein staining immediately after your
transfer. If you are expecting low amounts of protein, make sure your reversible
staining method of choice is appropriately sensitive. If using a PVDF membrane,
you can dry the membrane, wet it with 20% methanol, and place over a light box
to visualize proteins after transfer.
Troubleshooting
The two most common (non-technical) issues with transferring are smaller
proteins going completely through the membrane or larger proteins not
transferring completely onto the membrane. If you’re not sure if your protein
of interest is over-transferring (going completely through your membrane to
the other side), you can check by using two membranes during your transfer
instead of one. If you see signal on both membranes, you know your protein is
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over-transferring! If you see very little signal, but you know you loaded a good
amount of sample of a large protein, you may be under-transferring.
Membrane pore size
Membranes come in different pore sizes for different proteins: try using a 0.2
µm (psi) membrane for small proteins (<15 kDa) and a 0.45 µm (p) membrane
for larger ones. Changing your membrane size is probably the easiest
troubleshooting step to take.
If you’re not sure if you need a smaller pore on your membrane, run a doublemembrane transfer with a psi membrane stacked behind the p membrane. If
your protein runs through the p membrane into the psi membrane, that’s a
good indication that the p membrane pores are too large.
Transfer method
Wet transfers are more efficient, so if you’re struggling to transfer a large
protein, consider using a wet transfer with a longer transfer time. Be aware
that the transfer works by electricity, so long transfer times increase the heat
of the system… and gels can overheat. Running your transfer in the cold room
or packing the box with ice packs can help keep the system cool, as can lower
voltages.
If your small protein is over-transferring, try using the faster semi-dry transfer,
which works better for small and medium proteins.
If your proteins are compatible with electroblots/dry transfer systems, and
you have one available, they are a good, if expensive, option for transferring
proteins of mixed sizes. If electroblots are not a good option for your
experiment, and wet and semi-dry are not working, you’ll want to try optimizing
your transfer buffer.
Adjusting the buffer
For wet and semi-dry transfers, you can adjust the amounts of alcohol and SDS
in your transfer buffers. To slow down smaller proteins and prevent them from
over-transferring, increase the amount of alcohol and decrease the amount of
SDS, with a decreased transfer time. Alcohol will slow down the migration of
smaller proteins through the membrane while increasing the ability of proteins
to stick to the membrane by stripping away the SDS.
To speed up larger proteins and prevent them from under-transferring, try
increasing the amount of SDS while decreasing the amount of alcohol.
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The increased SDS will increase the negative
charge on the protein, allowing it to move more
readily with the current.
Pro tip!
You need both alcohol and SDS for
your proteins to migrate… so don’t try
eliminating one component entirely.
If you are trying to transfer proteins of vastly
different sizes, you may need to optimize quite
a bit to find a membrane, transfer, and buffer
combination that works.
Background
If you’re seeing odd background patterns on your membrane, rather than
just high background, your issue is likely technical. White circles, for example,
could indicate that you had air trapped between the membrane and gel during
the transfer, while dark splotches could indicate dirty equipment, degraded
or improperly mixed blocking buffer, or insufficient rocking during incubation
steps.
Many errors can arise from technical issues. The good news is that such errors
are both commonly made and commonly discussed. If you’re seeing a weird
pattern in your background, try asking more experienced lab mates or looking it
up online. You’ll find many resources with examples, like BioRad’s Western Blot
Doctor page, or robust discussions, like ResearchGate’s western blot forum.
Blocking
Don’t skimp on the blocking time! Fill the time with smaller tasks, or simply
enjoy some well-deserved down time while waiting for your timer to ding. If
you’re shaving off a few minutes here and there, and seeing high background in
your blots, that could be why.
Troubleshooting
Blocking reduces the amount of background in your blot, so blocking issues
can either result in high background (insufficient blocking) or reduced signal
(too much blocking.) If you’re having issues with your blocking, first check your
antibody spec sheet. Some antibodies work better with specific blockers, and
the manufacturer will usually include recommended blocking conditions on the
spec sheet.
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If you’re still seeing high background after following the manufacturer’s
recommendations, you can try either longer blocking times or higher
concentrations of blocking buffer.
If you see a low signal, you can optimize blocking conditions by running a
gradient with different concentrations of your protein-based buffer (FBS, milk,
etc.) against the same amount of sample.
If you’re using chemical blockers, you can run a comparison of different
formulations instead of concentrations. Most labs use protein-based buffers, so
here’s a quick tip: Keep a bottle of chemical buffer around, which works well and
consistently, to help troubleshoot background issues. It’s a quick way to identify
(or rule out) blocking issues.
If your antibody information doesn’t contain blocker recommendations, you
may want to try different protein blockers to see if one works better than
another. For nitrocellulose membranes, you can even give gelatin a shot.
Remember that protein-based blockers degrade fairly quickly, even at 4 °C, so
when in doubt, make up a fresh batch.
Antibodies
We always recommend either using an antibody validated for your protein
and application of interest or validating an antibody yourself. But even with a
validated antibody, it can still take time for you to get it to work well.
Dilutions
Most antibodies come with a recommended dilution and/or range (typically
between 1:500 and 1:10,000 for primary antibodies). You’ll want to run a protein
gradient to test the antibody at the recommended dilution. If the antibody
dilution’s dynamic range encompasses your protein’s expected concentrations,
you’ll see the signal increase as the concentration of the protein increases
(Figure 3A).
This step is especially important for highly expressed proteins, including
“housekeeping” or ubiquitously expressed proteins used for normalization.
Many times, the expression levels of these proteins are out of the measurable
range of the dilution used in an assay, meaning that your normalization
calculations will not be correct, and you’ll need to adjust ‌your antibody dilution
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Figure 3: Testing the antibody’s dynamic range. A) The protein dilutions, representing expected
concentrations, are all within the antibody’s dynamic range. B) The protein dilutions, representing
expected concentrations, are outside of the antibody’s dynamic range. Specifically, the 1:2, 1:1, and No
dilution samples all have approximately equally dense bands. Created with BioRender.com.
to accurately measure these proteins. That being said, normalizing to total
protein expression is preferable, and more accurate, than normalizing to
ubiquitously expressed proteins.
Once you’ve run your protein gradient, compare your diluted samples. If
multiple dilutions are showing the same signal (Figure 3B), that means the
amount of protein loaded is outside the detectable range of that antibody
dilution. Adjust the antibody dilution until your protein dilutions are in the
dynamic linear range (i.e., expression decreases as protein concentration
decreases).
Since antibodies, especially polyclonal antibodies, can vary lot to lot, this
range needs to be empirically determined every time you order a new lot of
antibodies. If you’re planning to use a large amount of a specific antibody, plan
ahead and request as much antibody from
the same lot as you need or can get from the
manufacturer. You can also consider using
Pro tip!
monoclonal or recombinant antibodies, which
If your antibodies are coming directly
have much lower lot-to-lot variability.
from tissue or supernatant, and you’re
doing the purification and crossIf you’re running an already-developed assay,
adsorption in the lab, consider a dilution
and you have faint or no bands, it could be
range from 1:100–1:1,000. If your
that you need a higher antibody concentration
antibodies come from ascites fluid, try a
to detect low levels of proteins. In this case,
dilution range from 1:1,000–1:100,000.
it might be faster to run the same amount of
protein in every lane and cut up the membrane
to run with different concentrations of
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antibodies. This will let you know what the minimum antibody dilution is to
detect your level of protein. If you’re seeing a wide range of protein amounts
in your sample, you may need to optimize your antibody dilution further from
this starting point. You can also consider using polyclonal or recombinant
multiclonal antibodies to increase overall signal from the primary antibody.
Secondary antibodies
For secondary antibodies, the recommended dilution range is usually between
1:5,000 and 1:200,000. Check the manufacturer’s recommendations, and if
needed, optimize your secondary antibody concentration by running a protein
gradient.
If your background is high, and you’ve ruled out blocking issues, try running a
reagent gradient with your secondary antibody to see if lower concentrations
reduce background noise.
Temperature and time
You may need to optimize your antibody incubation temperature and time.
Though the most common conditions are 4 °C overnight or room temperature
for 1–2 hours, there is some evidence that antibodies may need longer to reach
their maximum binding, so feel free to trial even longer incubation times if you’d
like (Luo et al., 2011). This does come at the risk of higher backgrounds.
Once you’ve optimized antibody concentration, you can optimize i‌ncubation
conditions to improve binding and/or reduce background, including trying
longer incubations at both temperatures if needed.
Washes
Check your antibody manufacturer’s wash recommendations. Tris-buffered
saline (TBS) or phosphate-buffered saline (PBS) are the most commonly used
solutions for washing. If your background noise is high, you can consider adding
a detergent like Tween-20 to your washes — although this can be harsh and
inhibit some detection reactions.
Like blocking, you don’t want to skimp on your washing steps! It’s important to
let them wash for the full amount of time, every time.
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Imaging
The last step in a western blot tends to be the most straightforward. While there
are some things you’ll want to consider (see our Technical Design of a Western
Blot post), most imaging issues can be fixed in real-time by exposing for either
a shorter (for less exposure) or longer (for more exposure) time. If you’re seeing
high background or low signal during the imaging process, try adjusting your
exposure time first, since you can do that without having to re-run the western.
Remember that chemiluminescence methods, like horseradish peroxidase
protein (HRP), have a limited dynamic range based on the solution used to
expose them. You’ll need to make sure the sensitivity of your exposure solution
matches the expected amount of protein in your samples. If you have a wide
variety of protein amounts on the same blot, you may want to consider using a
different reporter or, if you don’t need to compare the protein amounts directly,
stripping and reprobing. Just make sure to go from least to most abundant
protein if you’re stripping, since you’ll lose protein with every strip cycle.
Troubleshooting the troubleshooting
There are many different troubleshooting and optimization steps available
for western blots, but it’s very unlikely you’ll be able to do them all for every
western blot assay you run. How do you know which steps to take?
As a rule of thumb, the more strictly you want to interpret your blot’s data, the
more time you’ll want to spend optimizing it. Blots for quantification generally
require more optimization than blots used to confirm overexpression. Before
starting a western blot, think about how you’ll interpret the data and what level
of accuracy you’ll need to do that confidently.
Once you’ve run your western, check out our handy Table 1 for help in deciding
which troubleshooting steps to take. It’s long but easy to use!
And remember, a western blot is only as good as its antibodies. Whenever
possible, use validated antibodies or validate them yourself, so you don’t spend
time troubleshooting an assay that’s fine with antibodies that are not.
We hope this helps you on your western blot journey. Best of luck and happy
gradients! n
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Table 1: Troubleshooting a western blot (*indicates a diagnostic-only step)
Issue
Potential cause
Troubleshooting suggestion
Proteins are the
wrong size
Proteins may be aggregating
Lyse at lower temperature for a
longer time
Protein degradation
Add protease inhibitors to lysate
Insufficient denaturing
conditions
Adjust lysis conditions: time,
temperature, and/or buffer to
increase lysis
Add sonication step to protein lysis
Weird background
patterns (dots, lines,
smudges, etc...)
Splice variants
Check literature
Posttranslational
modifications
Check literature
Highly charged amino acids
Run control protein (if available)
and note in analysis
Plasmid-based proteins:
plasmid sequence not
accurate
Check plasmid sequence for
frameshifts and stop codons
Technical errors
Search online/ask an expert
Review protocol videos for a visual
guide to good technique
High background
Antibody concentration too
high
Run a reagent gradient with the
primary antibody*
Inefficient blocking
Check antibody spec sheet for
buffer recommendations
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Table 1: Troubleshooting a western blot (*indicates a diagnostic-only step)
(cont.)
Issue
Potential cause
Troubleshooting suggestion
High background
Use a chemical blocking buffer
Increase blocking time and/or
concentration
Try a different protein-based buffer
(e.g., milk instead of BSA)
Nonspecific antibody binding
Run a reagent gradient with
primary antibody*
Decrease incubation time
Increase amount of protein/sample
loaded
Incubate primary antibody at 4 °C
overnight
Incomplete washing
Ensure wash is happening under
sufficient agitation (rocking)
Add Tween-20 to wash buffer
(can strip some protein from
membrane)
Increase time of wash step
Increase volume of wash buffer
Incomplete stripping (if
stripping/reprobing)
Check for antibody signaling after
each round of stripping*
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Table 1: Troubleshooting a western blot (*indicates a diagnostic-only step)
(cont.)
Issue
Potential cause
High background
Too little signal
Troubleshooting suggestion
Use a harsher stripping buffer
Large proteins: incomplete
transfer
Check transfer with Ponceau red
stain or other reversible stain*
Roll out all the bubbles in the
membrane sandwich
Ensure methanol concentration in
transfer buffer is <20%
Wet transfer
Increase transfer time
Increase voltage
Increase SDS/decrease alcohol in
transfer buffer
Small proteins: overtransferring
Transfer with two membranes*
Psi membrane
Semi-dry transfer
Decrease voltage
Decrease transfer time
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Table 1: Troubleshooting a western blot (*indicates a diagnostic-only step)
(cont.)
Issue
Potential cause
Troubleshooting suggestion
Too little signal
Decrease SDS/increase alcohol in
transfer buffer
Mixed proteins: transferring
issues
Transfer with two membranes*
Electroblot/dry transfer system
Different membrane pore size
Adjust SDS:alcohol ratio in transfer
buffer
Antibody dilutions too low
Run a reagent gradient for primary
antibody*
Run a reagent gradient for
secondary antibody*
Antibody not completely
binding
Increase incubation time
Not enough antibody binding
sites
Polyclonal or recombinant
multiclonal antibodies
Protein not properly lysed
Different lysing buffer/adjust lysing
buffer strength
Sonication-based lysing protocol
Subcellular fractional kit or lysing
protocol
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Table 1: Troubleshooting a western blot (*indicates a diagnostic-only step)
(cont.)
Issue
Too little signal
Potential cause
Troubleshooting suggestion
Not enough target
Run protein control or load more
lysate/sample*
Underexposed
Increase exposure time
Chemiluminescence: use a more
sensitive exposure kit
Too much signal
Antibody dilutions too high
Run a protein gradient*
Too much protein
Load less sample
Overexposed blot
Shorter exposure time
Chemiluminescence: use a less
sensitive exposure kit
No signal variation
in highly expressed
proteins
Antibody dilutions too high
Run a protein gradient*
Load less sample/lysate
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ELISA (Enzyme-Linked
Immunosorbent Assay)
A
Aliyah Weinstein, August 2021
ntibodies are used in many different experiments that require
scientists to detect proteins in their samples. One technique that
relies heavily on antibodies is ELISA, which stands for enzymelinked immunosorbent assay. ELISAs are used to detect proteins within a
96- or 384-well, flat-bottomed plate. The basic principle of an ELISA is that any
antigen within a sample adheres to the well (either directly or by binding to an
antibody that is coating the bottom of the well), and then its concentration is
detected through visualization of an enzymatic reaction.
Types of ELISA
There are four different types of ELISAs, which differ in the way the antigen is
detected. These are sandwich, competitive, direct, and indirect. Sandwich and
competitive ELISAs use antibodies coating the wells to capture antigen from a
sample, whereas antigen is bound directly to the wells in a direct and indirect
ELISA.
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Sandwich ELISA
Most commercially available ELISA kits are for a sandwich ELISA. This technique
works by first coating the wells of a flat-bottomed plate with a capture antibody
that is specific to the protein you are interested in detecting in your sample.
When an ELISA kit is ordered, the wells are typically already coated with the
antibody. Then, you add your sample to the wells, and the antibodies bind to
your protein of interest.
The next step is to detect the protein-antibody pair. In a sandwich ELISA,
the first step of this process is the addition of a detection antibody (typically
a monoclonal antibody) that also recognizes the same target protein. The
capture antibody and the detection antibody recognize different epitopes
on the target protein, so they do not compete with each other. This antibody
is also conjugated to another molecule, such as biotin. Next, a conjugated
substrate is added to the wells. In the case where the detection antibody is
biotin-conjugated, streptavidin (which has a high affinity for binding biotin), is
conjugated to the enzyme horseradish peroxidase (HRP). Finally, the substrate
of HRP is added to the wells. This reaction produces a colorimetric reaction
that reflects the concentration of your target protein in each well. The reaction
is stopped by the addition of hydrochloric acid (for more details on this, see
below).
The sandwich ELISA is best used to detect proteins for which multiple antibodies
that recognize different epitopes are available; this type of ELISA is extremely
specific and sensitive to the target protein due to the use of multiple antibodies.
Competitive ELISA
Similar to a sandwich ELISA, the competitive ELISA is also used to detect a single
protein within a mixed sample. However, in this case your protein of interest is
Figure 1: Overview of using Sandwich ELISA to quantitatively measure protein concentration or antibody
specificity via a colorimetric reaction. Image from Boguszewska et al., 2019.
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Figure 2: Overview of using Competitive ELISA to quantitatively measure protein concentration or
antibody specificity via a colorimetric reaction. Image from Boguszewska et al., 2019.
preincubated with a known amount of an antibody that recognizes it, while the
bottom of the wells are coated with another protein — known as a reference
antigen — that can also bind to that antibody. Then, the protein-antigen mixture
is added to the wells. Any free antibody (meaning it hasn’t bound to your protein
of interest) will bind to the competing antigen in the wells while the proteinantigen complexes will be washed away during the protocol. The amount of
antibody that binds in the well is an inverse measure of the amount of your
protein of interest in the original sample.
The competitive ELISA is best used for detecting antigens that are too small to
be captured between two antibodies in a sandwich ELISA.
While the most common type of competitive ELISA is a modification of the
sandwich ELISA as described here, the direct and indirect ELISA (more details
below) can also be adapted to a competitive format.
Direct ELISA
In a direct ELISA, purified proteins or the experimental sample are bound to the
wells of a plate instead of an antibody. Coating the wells with a purified protein
allows you to characterize properties of an antibody that recognizes
Figure 3: Overview of using Direct ELISA to quantitatively measure protein concentration or antibody
specificity via a colorimetric reaction. Image from Boguszewska et al., 2019.
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that protein, while coating the wells with an experimental sample allows for
detecting one protein from the sample. A direct ELISA has many fewer steps
than a sandwich or competitive ELISA because the protein of interest is then
detected by an enzyme-conjugated antibody. Next, its substrate is added and
the reaction can be visualized via a colorimetric reaction.
The direct ELISA is often used when there is only one antibody available for
your antigen as the other types of ELISAs require two different antibodies. As
mentioned above, it can also be used to characterize antibodies against the
antigen bound to the plate.
Indirect ELISA
The indirect ELISA is very similar to a direct ELISA, aside from the detection step.
Similarly, the protein antigen is coated onto the wells of the plate. However,
instead of detecting your protein using an enzyme-conjugated antibody, a
primary-secondary pair is used. This amplifies the signal as multiple secondary
antibodies can bind to a single primary antibody. The secondary antibody is
conjugated to an enzyme for visualization.
The indirect ELISA is used for similar experiments as the direct ELISA, the only
difference being the additional amplification step, which helps to visualize lowabundance proteins.
Detecting the concentration of proteins in an ELISA well
As mentioned above, the measurement of the concentration of proteins
in an ELISA well is based on a colorimetric reaction. Most commonly, this
is a blue reaction that results from the reaction of HRP with its substrate,
3,3’,5,5’-Tetramethylbenzidine (TMB). The addition of hydrochloric acid, which
Figure 4: Overview of using Indirect ELISA to quantitatively measure protein concentration or antibody
specificity via a colorimetric reaction. Image from Boguszewska et al., 2019.
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stops the reaction, turns the solution yellow. The absorbance of each well, which
is measured using a microplate reader, is directly proportional to the amount of
enzyme present in the well and is used to calculate the amount of your protein
of interest that is present.
Using a standard curve
To do that calculation, you’ll need to include a standard curve in your
experiment. These are wells on the same ELISA plate that include known
concentrations of your protein of interest. The optical density (OD) — the
measure of light absorbed by a solution — of these wells is plotted against
the known protein concentration. Importantly, you’ll want a portion of your
standard curve to plot to a linear equation (remember y = mx+b?).
The maximum OD that most microplate readers can detect is 4.0, so if you max
out your standard curve and it reaches an asymptote, you won’t be able to
accurately calculate the protein concentration of any experimental wells in that
range of optical densities. If your standard curve does not have a linear portion
or the OD of your experimental samples has reached 4.0, you can resolve
this issue when you repeat the experiment by diluting your standard curve,
performing a serial dilution of your experimental sample, and/or stopping the
HRP-TMB reaction more quickly. Just make sure to incorporate the dilution
factor in your calculation from OD to protein concentration if that’s the option
Figure 5: A standard curve is generated by plotting OD against known concentrations of your target
protein. The OD of experimental samples can then be plotted along this line to calculate the protein
concentration. Image from Jagarlamudi et al., 2015.
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you choose. Once you’ve identified the linear portion of your standard curve,
you can calculate the concentration of protein in the experimental wells based
on the equation of the line.
With these tips in mind, you’ll be prepared to run an ELISA in your lab! n
References
Boguszewska, K., Szewczuk, M., Urbaniak, S., & Karwowski, B. T. (2019). Review: immunoassays in DNA damage and
instability detection. Cellular and Molecular Life Sciences, 76(23), 4689–4704. https://doi.org/10.1007/s00018-01903239-6
Jagarlamudi, K. K., Moreau, L., Westberg, S., Rönnberg, H., & Eriksson, S. (2015). A New Sandwich ELISA for Quantification
of Thymidine Kinase 1 Protein Levels in Sera from Dogs with Different Malignancies Can Aid in Disease Management.
PloS One, 10(9), e0137871. https://doi.org/10.1371/journal.pone.0137871
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The Four ELISAs and When to
Use Them
A
Meghan Rego, July 2024
n enzyme-linked immunosorbent assay (ELISA) is a versatile
method used to quantify the level of target antigen in a sample.
While Engvall and Perlmann originally developed the ELISA assay
to measure antibody levels, scientists have since adapted it for a host of
different proteins and small molecules from a variety of sample types (Engvall
and Perlmann, 1971). Scientists value ELISAs for their accuracy, specificity, and
sensitivity (a good ELISA can detect picogram quantities of the target!) and use
them in numerous fields including research and development, diagnostics,
drug discovery, and food safety.
Part of the assay’s versatility lies in the user’s ability to modify the protocol
to best fit their specific needs. Herein, we’ll give you a rundown of the
different types of ELISA, advantages and disadvantages of each, and some
considerations when using the method.
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ELISA basics
All ELISAs follow the same overall process. First users coat a multiwell plate
with a capture reagent. The capture reagent may be an antigen or an antibody
depending on how the assay is set up. Then they block the capture reagent to
prevent non-specific interactions. Next comes the binding step where the target
antigen binds to antigen-specific antibodies. The antibodies are conjugated with a
reporter or tag that can then be detected.
Typically, the reporter is an enzyme such as horseradish peroxidase (HRP) or
alkaline phosphatase (AP). Fluorescent tags may also be used, but for simplicity
we will discuss the more common enzyme-based reporter methods. For
enzymatic signal detection, users provide a substrate that the enzyme converts
into a detectable product. Scientists compare the signal intensity of the sample
to that of a standard curve to extrapolate the concentration of antigen in the
sample. A variety of chromogenic, chemifluorescent, and chemiluminescent
substrates are available. Labs with limited equipment often opt for the
chromogenic substrates since they can be read on a standard plate reader as
opposed to chemifluorescent, and chemiluminescent methods that require
specialized equipment. Chromogenic methods are not as sensitive, however,
so those studying low abundance antigens may want to consider alternative
detection methods.
Figure 1: The four types of ELISA are direct, indirect, sandwich, and competitive. In a direct ELISA a
reporter enzyme conjugated antibody binds to antigen coated on the multiwell plate. In an indirect ELISA
a primary antibody first binds to the antigen and then a reporter enzyme conjugated secondary antibody
binds to the primary antibody. In a sandwich ELISA the multiwell plate is first coated with a capture
antibody that binds to the antigen in a sample and then a second detection antibody binds. The detection
antibody can be directly conjugated with a reporter enzyme or a conjugated secondary antibody can be
used. In a competitive ELISA a multiwell plate is coated with a competing antigen (green). The sample
(purple) is preincubated with a reporter enzyme labeled antibody and then added to the multiwell plate.
The more antigen in the sample, the less antibody available to bind to the competing antigen. In direct,
indirect, and sandwich ELISA the reporter signal is directly proportional to the level of antigen in the
sample whereas in a competitive ELISA the reporter signal is inversely proportional to the level of antigen
in the sample. This figure was created with BioRender.com.
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Direct ELISAs
A direct ELISA is the quickest and simplest of all varieties. In this method, users
immobilize the target antigen on the multiwell dish and incubate it with a reporter
conjugated primary antibody. The reporter reacts with a provided substrate and
produces a signal that is directly proportional to the level of antigen present in
the sample and is calculated by extrapolating from the standard curve. Because
the primary antibody is directly conjugated to the reporter, there is no need for
a secondary antibody incubation step, saving time. Moreover, since secondary
antibodies can cross-react with samples, eliminating them from the procedure
removes one potential source of cross-reactivity.
While the method is simple and saves time, it also has several drawbacks. Direct
ELISAs generally have higher levels of background staining. Antigen-containing
samples often include undesirable molecules that may bind non-specifically to
assay components and increase background staining. Direct ELISAs also lack the
signal amplification afforded by secondary antibodies and thus are not suitable
for low abundance proteins. Because the method uses a single antibody, it is
less specific than other ELISA methods. Users may also find it difficult to source
a commercially available and appropriately conjugated antibody. While labs can
conjugate antibodies themselves, the process can be laborious and expensive. In
some cases, conjugation methods can interfere with binding, and some
common antibody buffer components can interfere with conjugates.
Make sure to do your research before conjugating at the bench.
Indirect ELISAs
The indirect ELISA process is similar to that of a direct ELISA, but the indirect
method uses both an unconjugated primary antibody and a conjugated
secondary antibody. Incorporating a secondary antibody increases the sensitivity
of the assay, since multiple secondary antibodies will bind to a single primary
antibody and thereby amplify the signal. Since the primary antibody does not
need to be conjugated, and conjugated secondary antibodies are easy to find, it
is often easier for labs to source appropriate antibodies for an indirect ELISA than
for a direct. In addition, a lab can use the same secondary antibody for any ELISA
protocol that uses primary antibodies of the same isotype.
Specificity and timing are the major disadvantages of the indirect method. The
assay only uses one antigen-binding antibody, making it less specific than other
methods. In addition, the assay includes both primary and secondary incubation
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steps, increasing the workload and time commitment for users. This method
also requires more optimization and controls when developing, as secondary
antibodies can also be a source of cross-reactivity.
Sandwich ELISAs
In the most common type of ELISA, the sandwich ELISA, users first coat a multiwell
plate with a capture antibody and then add their antigen-containing sample. The
target antigen binds to the capture antibody and a series of wash steps removes
non-target proteins and molecules from the assay. The user then adds an
antigen-specific detection antibody.
Importantly, the detection antibody and the capture antibody must bind to
distinct, non-overlapping epitopes so that they do not interfere with each other.
The detection antibody may either be conjugated directly with the reporter
enzyme or used with a conjugated secondary antibody. The amount of signal
produced by the enzymatic reaction between the detection antibody and
substrate is proportional to the amount of antigen present in the sample and is
calculated by extrapolating from the standard curve.
Scientists often opt for sandwich ELISAs because they require two independent
antibodies for the capture and detection steps, which increases the specificity
of the assay. The assay is also flexible, as users can develop methods with either
directly conjugated detection antibodies or unconjugated detection antibodies
followed by conjugated secondary antibodies. Assay development, however, can
be challenging since scientists must first identify two independent antibodies
that work well together. Moreover, the procedure is longer than alternative ELISA
methods, especially when secondary antibodies are used.
Competitive ELISAs
Scientists typically use a competitive or inhibition ELISA when measuring small
molecules that cannot bind efficiently to two antibodies as would be required for
a sandwich ELISA. In this method, the target antigen competes with a reference
antigen for binding to a primary antibody.
While the precise setup of the method can vary, typically users first coat the
multiwell plate with a reference antigen and then they preincubate the antigencontaining sample with their conjugated primary antibody. Users next incubate
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the sample with the multiwell plate to allow the unbound primary antibody to
bind to the reference antigen. The more antigen in the sample, the more primary
antibody that will be bound, and the less antibody that will be left available
to bind to the reporter. Unlike for direct, indirect, and sandwich ELISAs, in a
competitive ELISA the signal intensity is inversely proportional to the amount of
antigen in the sample: more antigen means less antibody available to bind to the
reference antigen, producing a weaker signal.
The major drawbacks of the competitive ELISA method are similar to those of
the direct method. Namely, since the assay requires a single conjugated primary
antibody it is both less specific and can be difficult to develop if an appropriate
conjugated antibody is not readily available.
Additional considerations
No matter what ELISA method you choose, it is essential to include proper
Table 1: Overview of the advantages and disadvantages of each ELISA method.
Method
Advantages
Disadvantages
Direct
Fast and simple protocol
Reduced chance of crossreactivity
Higher level of background
staining
Less sensitivity
Less specificity
Limited availability of
conjugated antibodies
Indirect
High sensitivity
Good antibody availability
Longer protocol
Less specificity
Increased chance of crossreactivity
Sandwich
High specificity
High sensitivity
Longer protocol
Increased chance of crossreactivity
Challenging to develop assay
Competitive
Appropriate for small
molecules
Reduced chance of crossreactivity
Less sensitivity
Less specificity
Limited availability of
conjugated antibodies
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controls. As a positive control, include a sample that is known to contain the
target antigen. Some examples could be an endogenous sample of wild type cells,
purified protein, or a peptide. The positive control will confirm that the procedure
is working and should always be included. For a negative control, include a sample
that does not contain the target antigen, such as a knockout or knockdown cell
line or tissue samples where the target is not expressed. The negative control will
help you determine if there is non-specific binding or false positives. If testing a
recombinant protein, especially one with tags, be sure to include a sample that
expresses the endogenous protein, since folding of the recombinant protein may
differ from the native form, which can affect antibody binding.
We hope that you now feel empowered to choose the best ELISA for your needs.
Good luck and happy binding! n
References
Engvall, E., & Perlmann, P. (1971). Enzyme-linked immunosorbent assay (ELISA) quantitative assay of immunoglobulin
G. Immunochemistry, 8(9), 871–874. https://doi.org/10.1016/0019-2791(71)90454-x
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Great Results Start with Great
Standard Curves
W
Meghan Rego, August 2024
hat do a viral vector production facility, food allergy testing
lab, and the grad student down the hall from you have in
common? All of them rely on standard curves in their day-today work. Indeed, viral vector production facilities frequently use qPCR with
a standard curve to titer their viruses; food allergy labs use standard curves
with ELISAs to measure allergen concentrations in food; and your friend the
grad student relies on a standard in their bicinchoninic acid assay (BCA) to
normalize samples before running a western blot. Standard curves are critical
for a number of popular scientific applications, and the quality of a standard
curve can make or break an experiment. Here, we will provide an overview
of how to create and use a standard curve and provide some general
considerations for scientists planning to use them in their assays.
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What is a standard curve?
Scientists use standard curves to determine the concentration of a target
molecule in an unknown sample. To do this, you serially dilute a control sample
of known concentration and measure a specific response as a function of the
concentration. You then plot the concentration of the standards versus the
response and use that information to extrapolate the concentration of an
unknown sample. For example, a BCA assay uses a serial dilution of a control
protein, incubated alongside an unknown sample with the BCA reagent. The
absorbance of both is then measured on a spectrophotometer and concentration
versus absorbance is plotted for the known concentrations (standard curve),
generating an equation that allows you to determine the concentration of the
unknown sample.
How do I make the standards for a standard curve?
You create a standard curve by serially diluting a known control sample, called
a standard. The ideal standard curve has at least five dilutions, with each step
of the series diluted by the same factor. For example, Figure 1 depicts a 2-fold
dilution series. Each step of the series dilutes by 1:2 for a series that ranges
from 1:2–1:32. The specific dilution series used will depend on the expected
concentration of the unknown sample. The unknown should fall somewhere
in the middle of the standard curve. If you do not know what concentration to
expect, you may need to run the experiment a few times and optimize your
standard curve accordingly.
Figure 1: A 1 mg/mL control is diluted in a series ranging from 1:2 to 1:32. Each individual step in the
series is consistent at 1:2. When preparing a dilution series, use a new pipette tip for each step and mix
the samples well by vortexing or inversion. Created with BioRender.com.
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When choosing a standard, make sure that the sample is pure and free of any
contaminants that could affect the measurements. You’ll need to prepare the
standard, standard dilutions, and unknown sample(s) in the same buffer if
possible, or very similar buffers if not, since buffer components can affect the
final readout.
When preparing a standard curve, change pipette tips and mix thoroughly by
inversion or vortexing between each step of the series. To increase the accuracy
of the curve, avoid pipetting small volumes (< 2 µL) or volumes too large for a
standard micropipette (> 1,000 µL). The standard dilution series should be run
in duplicate or triplicate. The closeness of the data points generated by replicate
values provides useful information about the accuracy of the curve, which we
will discuss later.
Standard curves must be included every time the assay is run, ideally with
freshly prepared buffer. This is because standard curves vary depending on a
number of factors, including the user, equipment, assay incubation time, etc.,
which can vary even when the same protocol is being followed.
Finally, standard curves must be validated for each run. To validate, include a
positive control of known concentration in the experiment and use the standard
curve to calculate its experimental value. Though it varies from assay to assay,
typically the experimental value should be within 15% of the expected value.
How do I plot and use a standard curve?
To create a standard curve, you graph the measured response of the serially
diluted standard as a function of concentration on a scatter plot. For the BCA
example, the concentration of each serial dilution is plotted on the x-axis and
its absorbance is plotted on the y-axis. With the help of graphing software,
you then generate a trendline for the data. The appropriate trendline varies
between assays and may be linear or nonlinear. BCA assays have linear
trendlines while those for ELISAs and other enzymatic assays are sigmoidal.
Once the trendline is calculated, you can extrapolate the concentration of the
unknown sample based on its measured response. An example of this is shown
in Figure 2.
How “good” is my standard curve?
A poor standard curve leads to over or underestimation of an unknown
sample’s concentration. To determine the quality of a standard curve scientists
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Figure 2: The concentration of a serially diluted standard is plotted against its OD560 measurements and
the trendline y = 0.6561x + 0.019 is calculated. The concentration of an unknown sample is determined by
using the trendline to extrapolate its x value, 0.963 mg/mL, from its y value, 0.651 nm.
use graphing software to calculate the data’s coefficient of determination, R2.
R2 is the square of the correlation between the actual values and the predicate
values and measures how well the data points fit the trendline. R2 ranges
from 0 to 1, with 1 being a perfect fit and 0 indicating that there is no linear
relationship between the observed and predicted values. This could be because
there is no correlation or it could mean that the relationship is non-linear and
you should use a different method of analysis. Different fields and assays have
varying criteria for an acceptable R2 but for many scientific applications users
aim for an R2 above 0.95.
Additional considerations
When running assays that rely on a standard curve, it is critical that the
unknown sample’s concentration lies within the dynamic range of the curve. The
dynamic range is the linear span between the lowest and highest concentrations
that the curve can accurately measure.
Dynamic range depends on a number of factors, including the dilution series
of the standard and the minimum and maximum detection limits of the
instrument being used to measure the response. The dynamic range needs to
be wide enough to cover all possible concentrations of the unknown samples;
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however, it should not be too broad. A broad dynamic range covers a wider
span of concentrations but suffers from decreased specificity, especially as it
approaches the minimum and maximum thresholds. If the measurements for
the unknown sample fall outside of the dynamic range of the curve, then the
assay is invalid. If the unknown sample’s measurement is too low, redesign
the standard curve dilution series with lower dilution factors. If the unknown
sample’s measurement is too high, start with a higher concentration of standard
or make smaller dilutions.
As mentioned above, it is a good idea to run the standard dilution series in
duplicate or triplicate. Replicates allows you to calculate the curve’s coefficient
of variation, or %CV. The %CV of a sample is the standard deviation of each
replicate measurement divided by the mean of all the replicates multiplied by
100. To calculate the %CV of the standard curve, one averages the individual
%CVs for each point on the curve.‌The smaller the %CV, the more accurate the
curve. Though it varies based on the assay, the ideal %CV is less than 15%. You
should also run replicates for all of the samples in the assay, as this will increase
the accuracy of the data.
For assays involving sigmoidal curves, manufacturers often recommend using
advanced graphing software designed to plot complex trendlines. If you don’t
have access to this kind of software, don’t give up! Rather than plotting the
entire data set, plot only the linear portion. This allows you to use a simple
linear regression trendline, which may be good enough if your unknown lies
within the linear portion of the graph. It does restrict the dynamic range of your
standard curve, so if your sample does not fall within this range, it may be time
to reconsider your graphing program.
Summary
Few things in the lab are more disheartening than seeing a potentially exciting
result quashed by a bad standard curve. Take the following precautions to set
yourself up for success!
1. Study the instruments and specific assays you will be using ahead of time to
see what factors may impact the dynamic range of the assay. Try to minimize
these.
2. Review previous literature or results from labmates to get a sense of
the concentrations you might expect to see for the unknowns in your
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experiment. Carefully set up a standard curve dilution series that takes into
account the range of concentrations possible.
3. Include both a standard curve and a positive control in every run.
4. When setting up your dilution series use consistent dilution factors, change
pipette tips regularly, and mix samples very well.
5. Use duplicates or triplicates to increase the accuracy of your experiment.
6. Finally, carefully analyze the data and use the trendline that’s the best fit.
Good luck, and may your R2 never dip below 0.95! n
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Capturing and Purifying
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An Introduction to
Immunoprecipitation
I
Meghan Rego, December 2021
mmunoprecipitation (IP) uses immobilized antibodies, or
immunoglobulins, to isolate a specific protein out of a complex mix.
Using this technique, users can look for the presence or absence of a
protein, determine if a protein is up or downregulated, examine a protein’s
stability or post-translational modifications, or study how a target protein
interacts with other proteins or nucleic acids. Read on to learn more about
this versatile technique.
Immunoprecipitation overview
An immunoprecipitation reaction is typically carried out in one of two ways.
Both methods utilize the same basic strategy: immobilize the antibody or
antibody-target protein immune complexes, wash away unbound protein,
elute, and measure the target. However, the two different approaches allow
for optimized strategies for differing amounts of target proteins or antibody
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Figure 1: Steps of an IP: 1) Protein extracts from cells or tissues contain a complex mix of proteins seen as
green squares, purple circles, and blue triangles. 2) An antibody immobilized on a bead binds specifically
to the blue triangle protein but not the other. 3) The beads are collected by centrifugation or a magnet. 4)
Unbound proteins are washed away. 5) The target protein is eluted.
bonding strengths. In the first method, an antibody against a target protein is
immobilized, or tethered, on agarose or magnetic beads and then incubated
with a protein mix. During the incubation, the immobilized antibody binds to the
target protein, thereby tethering it to the beads. Unbound proteins in the mix are
removed through a series of wash steps and the target protein is then eluted. This
method is appropriate for most situations.
A slightly different method is desirable if the antibody is expected to bind weakly
to the target protein or if the target is present in low amounts. In this method,
the free antibody is incubated with the protein mix and antibody-target protein
immune complexes are allowed to form. Following incubation, beads are used to
immobilize the antibody-protein complexes, and as in the first method, unbound
proteins are washed away and the target protein eluted.
Immobilization of the primary antibody
Immobilization is the key to immunoprecipitation and simply refers to the
process of anchoring an antibody, often called the capture antibody, to agarose
or magnetic beads in a way that also allows the antibody to bind to the target
protein. One of the most common immobilization methods utilizes bacterial cell
wall proteins, Protein A and Protein G, which bind to the fragment crystallizable
region (Fc) of antibodies with a high affinity (Hjelm et al., 1972; Björck & Kronvall,
1984). Protein A, Protein G, or the recombinant Protein A/G are conjugated to
agarose or magnetic beads and allowed to bind to the antibody being used for
IP. These proteins bind only to the Fc portion of the immunoglobulin, leaving the
antigen binding sites free to capture the target protein. The beads can
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be collected by centrifugation (agarose beads) or a magnet (magnetic beads),
providing a solid support while unbound proteins are washed away.
While both Protein A and Protein G have an affinity for immunoglobulins, the
strength of the interaction varies between antibody host species and isotypes.
Consequently, users need to choose the protein type that is most compatible
with the host species and isotype of their primary antibody. To circumvent this,
a recombinant protein, Protein A/G, was developed, which can be used for most
antibodies compatible with either Protein A or G.
While commonly used, Protein A and Protein G are not suitable for all
applications, such as protein isolation from serum. In this case, Protein A and
Protein G will bind both the primary antibody and serum immunoglobulins
indiscriminately, causing competition for binding sites. In cases like these,
antibodies can be directly conjugated to agarose or magnetic beads with
commercially available chemical agents. Directly conjugating the primary
antibody has the additional benefit of permanent linkage. This means that the
primary antibody will not co-elute with the target protein, eliminating the risk
of the primary antibody interfering with downstream assays. This is particularly
advantageous if the antibody used for IP is derived from the same species as the
antibody that will be used for detection in a subsequent western blot.
When choosing beads, there are several factors to consider. Agarose beads
are sponge-like and vary in shape and structure. Their porous surface provides
a large area for binding, but antibodies conjugated within the pores are often
inaccessible to their targets. Magnetic beads are solid spheres and tend to be
much smaller than agarose beads. Proteins conjugate on the surface of magnetic
beads and are therefore completely accessible. The yields from magnetic beads
are equivalent, if not slightly higher, than those of agarose beads, despite the
size difference. Magnetic beads are particularly advantageous because they
do not require centrifugation between washes, which can cause loss of beads,
break weak protein interactions, and limit high-throughput procedures. However,
agarose beads are often a more cost-effective option.
Target protein/antibody interactions
To prepare for an IP, samples are typically lysed in a non-denaturing buffer
containing non-ionic detergents, such as NP-40 or triton-X, to preserve native
protein conformations and interactions. For difficult-to-release proteins, including
those in the nucleus, more stringent buffers may be needed. Since many factors,
including salt concentration, ionic strength, and pH, affect binding the lysis buffer
may need to be adjusted depending on the sample. It is important to remember
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that sample lysates will also contain proteases and phosphatases that may
degrade the target protein. To prevent this, include protease and phosphatase
inhibitors in the lysis buffer, keep samples on ice, and perform the IP at 4 oC.
When choosing the capture antibody for IP, be sure to choose one that recognizes
the target protein in its native conformation. Frequently, the antibodies used for a
standard, denaturing western blot will not be suitable for an IP, as proteins in an
IP are not denatured (learn more about antibodies here!)
When using Protein A/G to immobilize the antibody, it is recommended to
preclear the sample to remove any proteins that may interact nonspecifically
with the capture antibody or Protein A/G. To preclear, incubate the sample as you
would in an IP using a nonspecific antibody from the same host species as the
capture antibody. This will deplete the nonspecific proteins from the sample prior
to beginning the IP.
During the IP, the target protein in the sample binds to the capture antibody and
is immobilized. The ideal antibody concentration to use will vary between capture
antibodies and can be determined through titration. Unbound or weakly bound
proteins in the sample are removed during a series of wash steps following
the incubation step. The wash buffer has an optimal pH and ionic strength that
breaks weak interactions from residual non-specific proteins but leaves the
target protein/capture antibody intact. The wash step is repeated several times
to ensure non-specific proteins are completely removed. Wash steps should be
performed even with a pre-cleared sample.
Eluting the target protein for downstream testing
Once the non-specific proteins are removed through washing, the target protein
can be eluted. The elution method will vary depending on the specific target
protein:capture antibody interaction and/or the downstream application.
Typically, if the target protein will be detected in a western blot, then it can
be eluted by boiling the beads in SDS. (To learn more about western blotting,
check out The Basics of Western Blotting.) For mass spectrometry, elute in a
urea-containing lysis buffer. Alternatively, the protein can also be eluted using
a low-pH buffer such as 0.1 M glycine pH 2.5 and immediately neutralized with
Tris pH 8-8.5. Low-pH glycine is very effective at disrupting the antibody antigen
complexes without permanently altering the structure of the protein.
Controls
As with any experiment, it is critical to include proper positive and negative
controls for an IP. When possible, perform the IP in parallel with both a sample
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known to express the protein of interest, such as a sample transiently transfected
or stably infected with the protein of interest and a sample that does not.
Common negative controls include knockout cell lines or tissues that do not
express the protein. With this control set, you should see your target protein IP’d
in the positive control sample but not in the negative control, confirming that the
capture antibody is functioning as expected. In order to identify any non-specific
interactions, perform parallel IPs with beads only and an isotype control antibody.
Alternative applications
Oftentimes, an IP-appropriate antibody simply isn’t available for a specific target.
In cases such as this, an epitope such as c-Myc, GFP, or V5 can be used to tag
the protein of interest on the C- or N-terminus. In this variation, called a pulldown assay, an antibody against the tag is used to isolate the protein of interest.
The wide availability and high specificity of anti-epitope antibodies make this
approach appealing, but the disadvantages must be carefully considered before
using. For example, the tag may affect the confirmation of the target protein
or interfere with protein interactions of interest. Tagged proteins are typically
expressed at much higher levels than endogenous proteins. Data obtained from
experiments using a tagged protein may not be translatable to the endogenous
system and will need to be verified using alternative methods.
Finally, IP can also be used to study proteins that interact with nucleic acids. Labs
studying epigenetics routinely use the chromatin IP (ChIP) technique to identify
DNA binding proteins involved in histone modification. Similarly, RNA IP (RIP) can
be used to isolate proteins that bind to RNA, though the details of these methods
are beyond the scope of this ebook. n
References
Björck, L., & Kronvall, G. (1984). Purification and some properties of streptococcal protein G, a novel IgG-binding
reagent. The Journal of Immunology, 133(2), 969–974. https://doi.org/10.4049/jimmunol.133.2.969
Hjelm, H., Hjelm, K., & Sjöquist, J. (1972). Protein a from Staphylococcus aureus. Its isolation by affinity chromatography
and its use as an immunosorbent for isolation of immunoglobulins. FEBS Letters, 28(1), 73–76. https://doi.
org/10.1016/0014-5793(72)80680-x
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ChIP
C
Rachel Leeson, March 2022
hromatin immunoprecipitation (ChIP) is an extremely useful
technique that provides insight into protein:DNA interactions.
ChIP works by using antibodies to capture protein:DNA complexes
with antibodies specific to your protein(s) of interest. Once the complex is
captured, it is treated so that the protein and DNA are no longer bound, and
the DNA can then be isolated and studied through a variety of downstream
methods, such as qPCR or next-gen sequencing. ChIP is an incredibly useful
application for exploring these interactions, and can give valuable insight into
DNA expression and protein interactions, including epigenetic modifications.
But it’s not an approach for the faint of heart!
The process
ChIP can be done for either tissues or cells. The process is very similar for
both, though tissue samples do require an extra homogenization step.
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For this section, we’re going to focus on the ChIP process and not the upstream
sample collection process or the downstream analysis process. And ChIP itself
begins at ...
Crosslinking
Once you have your sample, the first step is to ensure your protein:DNA
complexes will remain bound to each other until you are ready to separate
them. Most proteins are not likely to remain attached to the DNA throughout
the sample processing and therefore will require crosslinking, which uses
formaldehyde or UV light to create strong but reversible bonds. This will keep
the protein:DNA complexes together until you want to break them. Crosslinking
is necessary for most ChIP experiences, but the increased binding can mask the
epitopes antibodies recognize. It is therefore best to either select antibodies
validated for ChIP or to validate your antibody on crosslinked proteins. In the case
of histone modifying proteins, which bind quite strongly to DNA, crosslinking may
not be necessary. This is known as Native ChIP.
If you’re working with a tissue sample, you’ll need to homogenize your sample
before moving on to the next step. Typically, this can be done by cutting the
tissue sample up and then processing with a homogenizer, but bone or especially
fibrous tissues may need extra steps.
Fragmenting
The next step is to break the DNA into random, small pieces of roughly equal size,
typically between 200-1000 bp. This is to ensure that your downstream analysis
focuses on the DNA your protein is actively interacting with, reducing background
information. Your options for this step are sonication, which uses high-frequency
sound waves to break up the DNA, or enzymatic digestion. Sonication provides a
truly random digestion, but is harsher and requires more optimization. Enzymatic
digestion, while gentler on the sample, introduces bias due to the selective
nature of the cutting. While sonication is usually the preferred method, enzymatic
digestion can be appropriate when your protein of interest is a low-abundance
transcription factor or cofactor. In such cases, the gentler enzymatic digestion can
preserve more of the protein:DNA complexes. Once you’ve finished fragmenting
the DNA, spin down the samples to remove cell debris, which will collect in a
pellet. The supernatant will contain your fragmented, crosslinked chromatin.
Whichever method you choose, you’ll need to reserve a small aliquot, split into
two parts to (1) run a DNA gel to confirm fragment size and (2) incubate with
RNAse and Proteinase K to purify the DNA and then determine concentration.
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Protein capture
After crosslinking and sonication or enzymatic digestion, you’re ready to incubate
the sample with your antibody. Both polyclonal and monoclonal antibodies can
work well in ChIP. If you’re using an antibody for the first time, look for one that
has been validated in multiple assay types. Remember that ChIP uses native
proteins (so antibodies validated only for denatured proteins may not be a good
choice) and the amount of cross-linking can affect epitope availability.
Collect the DNA
Incubate your antibody, or antibodies, with your samples, and then use an
antibody capture method such as Protein A/G beads to collect your antibodybound samples. With a bead method, you’ll need to ensure you have a beadonly control alongside your input control. Elute the DNA after washing and then
incubate with Proteinase K to reverse the crosslinking. Finally, purify your DNA
with a PCR purification kit or with a phenol-chloroform extraction.
Okay, let’s do a quick review. At this point you have (1) crosslinked your proteins
of interest to any DNA they were bound to (2) fragmented the DNA (3)
Figure 1: Schematic overview of ChIP. Image courtesy of Shengliu via Wikicommons.
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used antibodies to select specifically for proteins of interest (4) isolated your
antibody:protein:DNA complexes and finally (5) isolated the DNA from your
antibody:protein:DNA complexes. Your DNA is now ready for downstream
analysis via any method suitable for isolated and purified DNA, such as
sequencing or qPCR. Yay!
The process
So while this method is certainly long and complex, you may be thinking, well,
these are mostly fairly standard molecular and cell biology techniques. What’s all
the fuss? Well, ChIP’s complexities come not from the techniques used, but from
the difficulty of developing an optimized protocol — it’s a bit like Goldilocks and
the Three Steps (Crosslinking, Fragmenting, Capturing), if you ask me.
Each of the three steps needs to be optimized for each specific sample type. And,
if that weren’t bad enough, optimization of one step can affect the optimization
of another. For instance, a sample may need more or less cross-linking to firmly
attach the proteins to the DNA, depending on how tightly the proteins bind
natively. But the amount of cross-linking can affect epitope availability, which
could affect antibody selection. Sonication (or enzymatic digest) will also have to
be optimized for both tissue or cell type, amount of protein, and desired fragment
length for your output assay. A longer sonication time could mean needing to
increase crosslinking to counteract the effects of increased sample agitation;
a shorter sonication time could be an opportunity to decrease crosslinking.
Homogenization for a tissue sample can also require significant optimization for
the sample type and size. It can be quite the challenge, and take a few rounds of
adjustments, to get everything just right!
Even though ChIP requires a larger upfront investment than many other antibody
applications, this investment can well be worth it once you generate your data.
However, it may be a good idea to add “patience and an extra-methodical
approach” to the top of your protocols when embarking on your ChIP journey! n
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Affinity Tags
Y
Susanna Stroik, September 2023
ou’ve designed the perfect experiment — controls, conditions,
and everything in between — now all you need are some of your
favorite proteins purified to carry out your plan. With a little
thoughtful planning, affinity tags can make protein purification a cinch. Types
of affinity tags, the proteins they work best attached to, and the species they
are compatible with are all reviewed here.
What are affinity tags?
Affinity tags are used primarily as protein purification tools (and can have
secondary functions as detection aids.) When a single protein is expressed,
with the intent of purification, it will be expressed along with all other cellular
proteins within the expression system (bacteria, yeast, mammalian, etc.).
To individually isolate a protein, there must be a special feature about that
protein that no other protein in the expression system has. This is where
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affinity tags come in — they are peptides which do not naturally exist in most
expression systems that can be added to the N or C terminus of your protein of
interest. Many of these tags also have commercially available antibodies, resins,
beads, and other purification aids readily available. Choosing the best tag for your
experiment will likely depend on:
•
•
•
The protein you are trying to purify
The expression system
If you want the tag to remain on your protein for downstream applications
Below, we describe a few of the most common tags available.
Maltose binding protein
Maltose binding protein (MBP) is a 45 kDa tag which can be purified (along with
its fusion protein) with amylose resin. The MBP tag, although large, can actually
increase expression and solubility of the tagged protein (Fox & Waugh, 2002).
The tag can also be proteolytically cleaved after purification, so as not to perturb
protein function for downstream applications. Tag cleavage must be performed
after primary purification and requires a second clean-up step to remove the
cleaved tag from the solution. MBP antibodies are available for detection and/
or purification of MBP-tagged proteins. The antibody is an alternative to amylose
resin and also provides a visual detection mechanism via applications such as
western blot and immunofluorescence.
GST
Glutathione S-Transferase (GST) is a 26 kDa tag which can be isolated with
glutathione resin. Like MBP, it can be removed via protease cleavage at sites
immediately following the tag. Unlike MBP, the GST tag can be cleaved off from
the fusion protein while it is still bound to the glutathione resin, eliminating the
need for an extra clean-up step of the tag — a handy feature. If you decide not
to cleave the GST tag after/during purification, be aware that the tag dimerizes in
solution, which can affect downstream applications. Also, purifying GST by affinity
chromatography requires proper folding of the tag, which prevents insoluble
fusion proteins from being purified by GST (maybe try MBP if you are having
solubility issues!).
Need a GST antibody? Addgene has you covered!
His
His tags, often called polyhistidine tags, typically include six consecutive His
residues (or more). Histidine bonds with immobilized metal ions (nickel,
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Figure 1: Diagram of His-tagged protein purification work flow by nickel column.
cobalt, copper, etc.), allowing His fusion proteins to be purified by single-step
metal affinity chromatography. Purification of His fusion proteins from E. coli
is generally more straightforward and of a higher purity than mammalian
purifications due to the presence of more naturally occurring histidine-rich
proteins in mammals. Mammalian purification is still possible but requires
optimization and often more stringent washing. Nonetheless, this tag can be
purified with several different resin types and is a fairly small tag that is unlikely
to perturb protein function. This makes it amenable to being left on your protein
of interest, especially if downstream antibody detection is desired … and yes, we
have an antibody for that!
Affinity tags vs. other tags
There are many different types of tags, including epitope tags (FLAG, Myc, etc.)
and fluorescent protein tags (GFP, mCherry, etc.). Fluorescent tags are primarily
used for direct visualization, while epitope tags are used for both direct and
indirect visualization. Some of these tags can also be used for protein purification,
just as affinity tags can sometimes be repurposed for visualization. However, due
to the nature of the size, structure, and other qualities of each tag class, they are
generally best suited for their primary application.
Ready to start tagging? We are here to help with vectors, protocols, antibodies,
and more! n
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References
Boguszewska, K., Szewczuk, M., Urbaniak, S., & Karwowski, B. T. (2019). Review: immunoassays in DNA damage and
instability detection. Cellular and Molecular Life Sciences, 76(23), 4689–4704. https://doi.org/10.1007/s00018-01903239-6
Jagarlamudi, K. K., Moreau, L., Westberg, S., Rönnberg, H., & Eriksson, S. (2015). A New Sandwich ELISA for Quantification
of Thymidine Kinase 1 Protein Levels in Sera from Dogs with Different Malignancies Can Aid in Disease Management.
PloS One, 10(9), e0137871. https://doi.org/10.1371/journal.pone.0137871
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To Each HIS Own
M
Meghan Rego, March 2023
uch of today’s biological research requires a close examination
of specific proteins within a system. This can be pretty
complicated given that a single cell has tens of thousands
of proteins functioning in a variety of ways. How do scientists focus on the
activity or function of a single protein? Oftentimes, they rely on protein
purification. In this strategy a single protein can be isolated from a complex
mix using affinity chromatography against an amino acid sequence, or tag,
on the protein. While a variety of methods can be used to successfully isolate
proteins, this blog will focus on one of the most common, purification based
on the polyhistidine tag.
What is the polyhistidine tag?
The polyhistidine tag is a string of six or more histidine residues expressed in
frame on the N- or C-terminus of a recombinant protein of interest.
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The tag was developed as a means to purify a protein based on the affinity of
the imidazole ring of histidine for metal ions such as Ni2+ or Co2+ (Hochuli et
al., 1988), and can elute proteins with up to 95% purity (Janknecht et al., 1991;
Hochuli et al., 1988).
In this purification approach, the metal ions are immobilized on a bead or resin
and a sample containing the protein of interest flowed through. The protein
of interest binds to the metal ions, becoming immobilized itself. Next, a series
of wash steps removes excess proteins and other contaminants. The protein
of interest is then eluted using a high imidazole buffer that outcompetes the
interaction between the protein of interest and the metal ions.
Prior to the polyhistidine tag, alternative fusion tags were used, such as S. aureus’
Protein A (Moks et al., 1987). These tags, however, tended to be very large
and often required the incorporation of a cleavage site and a post-purification
cleavage step to remove the tag in order to prevent it interfering with protein
function. In contrast, the polyhistidine tag is very small and rarely affects protein
function (Bornhorst & Falke, 2000).
Figure 1: A sample containing a polyhistidine-tagged protein of interest (purple) and several
contaminating proteins (blue, green, orange) flows through the Ni2+-containing resin. The protein of
interest (purple) binds to the Ni2+ and is immobilized along with a polyhistidine containing contaminating
protein (orange). A low imidazole wash buffer disrupts the weak interaction of the contaminating protein
but not that of the protein of interest. A high imidazole elution buffer disrupts the interaction of the
protein of interest and it is successfully isolated.
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One downside of polyhistidine tag-based purification, however, is the propensity
for nonspecific binding of untagged proteins. Despite being a relatively low
frequency amino acid, proteins containing two or more adjacent histidines are
present in most production systems and can bind and co-elute with the protein
of interest (Bornhorst & Falke, 2000). To prevent this, wash steps often include
low concentrations of imidazole. The concentration of imidazole used in the wash
buffer should be high enough to disrupt contaminant-metal ion binding without
eluting the target protein and needs to be empirically determined. In addition,
some metal ion substrates, such as those containing Co2+, have a lower affinity
for polyhistidine than more commonly used substrates such as Ni2+ and are
therefore more selective. When using systems with higher levels of histidinecontaining protein contamination, such as mammalian production systems,
consider using these more selective substrates along with stringent wash
conditions.
Where should I place the tag and how many do I need?
When designing a plasmid containing a polyhistidine tag, you’ll need to decide
whether to add the tag to the N- or C-terminus and how many histidine residues
to include.
Placement
As far as placement goes, there is no cut or dried answer. Successful purification
Figure 2: A, The polyhistidine tag is a string of histidine amino acids on the N- or C-terminus of a protein.
Neither the N- or C-terminus is preferable over the other however, tag placement can affect protein
function. B, The string of histidines in the tag is typically between six to nine residues long. The small size
of the polyhistidine tag minimizes the chance of the tag disrupting normal protein function, however
longer histidine strings can increase that risk.
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requires the tag to be accessible to the metal ions once the protein is folded.
Sometimes a tag placed on one end will be blocked during protein folding, in
which case you should try tagging the opposite end.
If neither placement works, consider purifying under denaturing conditions.
Unlike other affinity purification methods where binding relies on a specific
protein conformation, the polyhistidine-metal ion interaction is not disrupted by
denaturants (Hochuli et al., 1988). In some cases, proteins can be bound under
denaturing conditions to expose the tag and then refolded by gradual removal of
the denaturant. Not all proteins will refold once denatured, however, so take care
when using this method.
Number of residues
Deciding how many histidine residues to use is a bit easier. The more histidine
residues that are present, the more tightly the protein will bind to the metal ion
substrate. If your protein of interest is expressed at a very low level, try a longer
histidine tag to increase capture. Longer histidine tags are also recommended
if you need a very pure sample. Because binding strength is proportional to the
length of the tag, with longer tags you can use more stringent wash conditions
and remove more contaminants without losing your protein of interest. But that
doesn’t mean longer is always better — the longer the tag, the bigger it is, and the
greater the risk that it will interfere with protein function.
Additional benefits of HIS tags
In addition to providing a way to purify a protein of interest, polyhisitidine tags
can also be used as a target for antibody-based assays. Antibodies are useful
tools for studying proteins and are used for a variety of assays in the lab (for a
refresher, see Introduction to Antibodies). Unfortunately, for some proteins
there just aren’t any good antibodies available. In these cases, having a tagged
version of the protein is hugely beneficial to scientists. If their protein of interest
is fused to polyhistidine, then they can use an anti-HIS antibody, like this one
developed by Dr. James Trimmer’s lab, to perform assays that they otherwise
would not be able to do. n
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References
Hochuli, E., Bannwarth, W., Döbeli, H., Gentz, R., & Stüber, D. (1988). Genetic Approach to Facilitate Purification of
Recombinant Proteins with a Novel Metal Chelate Adsorbent. Nature Biotechnology, 6(11), 1321–1325. https://doi.
org/10.1038/nbt1188-1321
Janknecht, R., De Martynoff, G., Lou, J., Hipskind, R. A., Nordheim, A., & Stunnenberg, H. G. (1991). Rapid and efficient
purification of native histidine-tagged protein expressed by recombinant vaccinia virus. Proceedings of the National
Academy of Sciences of the United States of America, 88(20), 8972–8976. https://doi.org/10.1073/pnas.88.20.8972
Moks, T., Abrahmsén, L., Österlöf, B., Josephson, S., Östling, M., Enfors, S., Persson, I., Nilsson, B., & Uhlén, M. (1987).
Large–Scale Affinity Purification of Human Insulin–Like Growth Factor I from Culture Medium of Escherichia Coli.
Nature Biotechnology, 5(4), 379–382. https://doi.org/10.1038/nbt0487-379
Bornhorst, J. A., & Falke, J. J. (2000). [16] Purification of proteins using polyhistidine affinity tags. In Methods in
enzymology on CD-ROM/Methods in enzymology (pp. 245–254). https://doi.org/10.1016/s0076-6879(00)26058-8
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Flow Cytometry
H
Meghan Rego, July 2021
ave you ever wanted to measure expression of your protein of
interest in a single cell? Or perhaps you need to analyze a specific
subset of cells in a complex population. Have you spent hours in
the biosafety cabinet with cloning rings or following labor-intensive limiting
dilution protocols? If this sounds like you, then flow cytometry can help you
overcome these challenges and make these types of experiments easier and
faster to perform.
Introduction to flow cytometry
Flow cytometry allows users to analyze single cells in a population. Single
cells are passed through the path of a laser and interrogated with various
visible and fluorescent light sources that allow users to assess a cell’s
size, granularity, and target protein composition. It is used for a variety of
applications, such as measuring target protein expression levels, assessing
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post-translational modifications, determining cell health, analyzing cell cycle
stages, and detecting specific populations of cells in a complex tissue or sample
(McKinnon, 2018). When combined with cell sorters, this technology can be
used to isolate a specific subset of cells in a population in a procedure termed
fluorescence activated cell sorting or FACS (Bonner et al., 1972). By analyzing
single cells instead of the population as a whole, scientists gain statistical power
in their observations. However, while flow cytometry provides analysis at the
cellular level, it cannot be used for subcellular analysis such as morphology
or subcellular localization studies. For these, alternative methods such as
fluorescent microscopy must be used. To learn more about the most suitable
fluorescent microscopy technique for your study, see Addgene’s blog post: Which
Fluorescence Microscopy Technique is Best for Me?
An overview of the instrumentation
A flow cytometer combines three systems to analyze single cells from a mixture
(Picot et al., 2012):
•
•
•
An optics system
A fluidics system
An electronics system
Cells are first resuspended in a pressurized buffer called sheath fluid and
transported through tubes or capillaries to a laser. As the cells move through the
fluidics system, they pass through a flow cell which restricts the size of the stream
Figure 1: Cells expressing a particular surface receptor are labeled with a fluorescent antibody against
the receptor and analyzed on a flow cytometer. As cells pass through the flow cell, the size of the sample
stream is reduced, forcing the cells to line up in a single file. Single cells pass through the path of a laser,
and the instrument collects information about the cell’s size, complexity, and fluorescence intensity.
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and forces the cells to line up in a single file in a process termed hydrodynamic
focusing. This allows each cell to pass through the path of the laser in a single file
“line” where it is interrogated by the optical system (Figure 1).
What flow cytometers detect
Cell size and complexity, measured using visible light scatter
The optical system has both visible and fluorescent light sources. Visible light
illuminates each cell as it passes through and scatters in different directions. The
degree of scatter is captured by detectors and provides useful information about
the cell. Light that continues in the same direction as it was initially traveling is
called forward scatter (FSC) and provides information about the relative size of
the cell; larger cells produce more FSC. Light that scatters sideways to the path
that it was initially traveling is called side scatter (SSC) and provides information
about the cell’s complexity. A cell with a high degree of internal complexity, such
as extensive membranes, produces a greater SSC.
Protein expression, measured using fluorescent protein fusions
In addition to visible light, flow cytometers can have a variety of fluorescent light
sources. When interrogated with a fluorescent light source, a cell expressing the
associated fluorophore or stained with the associated fluorochrome, emits a
fluorescent signal that is captured by detectors.
The strength of the emitted photons varies from cell to cell depending on
the level of fluorescence. A cell expressing high levels of the fluorophore or
fluorochrome-stained protein will emit more photons than a cell expressing low
levels of the protein. The electrical system converts the fluorescence intensity
to a voltage pulse, called an event, and assigns each event to a channel number
based on its intensity. Higher intensity events are assigned to higher channels. In
a typical assay, a user will compare the shifting intensity of events. Since intensity
positively correlates with expression, the further the shift of a cell from the
negative control, the higher the level of expression.
A routine flow cytometry experiment uses two or three different fluorescent
colors, each measuring a different target. Flow cytometers, however, are highly
versatile and some can accommodate up to 30 different colors. This lends
flow cytometry particularly well to complex experiments looking at a variety
of different targets. If you are planning on using fluorescent proteins for your
experiment and are unsure which to choose, check out Addgene’s article: Which
Fluorescent Protein Should I Use?
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However, fusion proteins tag your protein of interest with a fluorescent reporter
on the N- or C-terminus of the protein. One downside of fusion proteins is that
the tag can alter the structure of the protein. In some cases, this can lead to
changes or a complete loss of protein function. In addition, the level and timing of
expression can also be affected by the tag.
Protein expression, measured using fluorophore-conjugated antibodies
Fluorescent antibodies get around the challenges of fluorescent protein fusions
by binding to the protein in its native state. Antibody-based flow cytometry can
use both direct and indirect staining methods. In direct staining, the primary
antibody against a target is conjugated to a fluorophore. In indirect staining,
a primary antibody binds to a target and a fluorophore-conjugated secondary
antibody binds to the primary antibody. Direct staining is quicker than indirect
staining and eliminates potential non-specific staining that may arise from the
use of secondary antibodies. It’s also particularly useful for intracellular staining
where binding of large immune complexes may be hindered. Indirect staining can
be beneficial when expression levels are low, as multiple secondary antibodies
can bind to a primary antibody, thus amplifying the signal.
Intracellular protein targets, measured by fixing and permeabilizing membranes
For intracellular protein targets, users must first fix proteins within the cells
and then permeabilize the cell membrane to allow antibodies to pass through
the cell membrane. Formaldehyde fixation creates cross-links between
lysine residues, thus preserving the protein structure and keeping epitopes
intact. It does not, however, permeabilize cells and requires subsequent
permeabilization with detergents such as triton-x, saponin, or tween. Take care
when using strong detergents like triton-x and tween, as extended treatment
can lyse cells. Alcohols, like ethanol or methanol, fix and permeabilize in a
single step by denaturing proteins and dissolving lipids, including those of
the cell membrane. In some cases, this can mask epitopes such that immune
complexes will no longer recognize them. Sometimes users will combine
methods and fix with formaldehyde first to freeze everything in place, followed
by alcohol permeabilization. Users should also keep their targets in mind
when planning fixation. If staining both cell surface and intracellular proteins
in parallel, then stain the surface protein first, fix and permeabilize, and then
stain the intracellular target. When using this approach, avoid fixation-sensitive
fluorochromes such as phycoerythrin (PE) and allophycocyanin. If assaying
a secreted protein, first block the Golgi apparatus with Brefeldin A to inhibit
secretion and then follow the typical intracellular staining protocol.
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Sorting cells with FACS
In addition to the fluidics, optics, and electronics systems, FACS instruments have
specialized components that can divert a subset of cells. During FACS, the sample
stream oscillates to generate droplets that are charged as they pass through
a metal deflection plate. Each droplet contains a single cell that is assessed for
the desired parameter. A droplet containing a cell that is positive for a desired
parameter is collected in a tube or plate, while a droplet containing a negative
cell is discarded. FACS can be performed aseptically, allowing users to culture the
cells after collection. Many FACS sorters are compatible with 96-well plates as
collection vessels and can sort single cells into each well, allowing users to easily
isolate and subsequently expand monoclonal cultures.
Interpreting flow cytometry data
To interpret flow cytometry data, users will typically start by creating a dot plot of
FSC versus SSC that allows them to observe distinct cell populations and rule out
dead cells and debris. For example, a whole blood sample will contain a mix of
cells including granulocytes, lymphocytes, and monocytes. Due to differences in
size and complexity, these cell types will separate into distinct populations on a
FSC versus SSC plot (Figure 2).
A user can then “gate” around the specific population that they are interested in
and further analyze that subset of cells. An immunologist, for example, might be
interested in the lymphocytes and “gate” that population for further analysis.
Figure 2: Due to differences in their size and complexity, cells from a whole blood sample will separate
into distinct populations of monocytes, lymphocytes, and granulocytes in a plot of forward scatter (FSC)
versus side scatter (SSC). Users can gate around their desired cell population for further analysis. In this
example, the lymphocyte population is selected and analyzed further by plotting CD3 versus CD19 to
separate B cells, T cells, and natural killer (NK) cells.
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Since different populations of immune cells express unique Clusters of
Differentiation cell surface markers (CD), these markers can be targeted with
specific fluorescent antibodies during the flow cytometry experiment and will
appear as a distinct population. After initially gating the lymphocytes, one
could then create a dot plot of CD3 (T cell marker) versus CD19 (B cell marker)
to separate the B cells and T cells into distinct populations (Figure 2). An
immunologist who studies B cells, might then gate the B cell population and
create a histogram of CD19-containing cells versus total cell number to determine
the number of B cells in that sample. Alternatively, they could create additional
dot plots of various CD markers to further separate the B cell population into
progenitor B cells, immature B cells, plasma cells, or others.
Tips for flow cytometry success
Break up cell clumps
Success in a flow cytometry experiment depends on a variety of factors, including
sample preparation, staining procedures, and controls. To prevent instrument
clogs, ensure that samples are single cell suspensions. For cells that tend to
aggregate, pass the sample through a nylon mesh cell strainer to break up clumps
before running on the instrument. To sustain cell health and limit debris, keep
samples on ice at all times and use gentle pipetting instead of vortexing.
Include proper controls
To ensure accurate data analysis, include a complete set of controls. Ideally, every
flow cytometry experiment will have a negative control that does not express
the target and a positive control that does. The negative control is critical for
determining the background level of non-specific staining.
If you’re using antibodies for detection, include an unstained control with cells
that went through the staining procedure minus the antibody. An unstained
control allows you to detect any autofluorescence from your target cell type or
that has arisen from fixation. Also include an isotype control, an antibody that
does not bind to any targets but was raised in the same host species, of the
same Ig subclass, and conjugated to the same fluorochrome as your primary
antibody. The isotype control allows you to determine the level of non-specific
staining associated with the primary antibody. When following an indirect staining
protocol, you should also include a secondary antibody control in which the
primary antibody has been omitted from the staining protocol. This control allows
you to detect background staining arising from the secondary antibody.
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It is also recommended that users include a viability control as this will allow
you to omit dead or dying cells from your analysis; dead or dying cells tend to
have more autofluorescence and nonspecific staining leading to false positives.
Numerous dyes can be used for viability controls, such as DNA binding dyes
and amine reactive dyes. DNA binding dyes, such as propidium iodide, can only
enter cells with disrupted membranes, as is the case with dead or dying cells.
DNA binding dyes are not compatible with protocols that require fixation and
permeabilization, as these methods require damaging the cell membrane and
will cause all cells to stain positive. If fixation and permeabilization are required,
as in intracellular staining, consider using an amine reactive dye, such as LIVE/
DEAD stain. Like DNA binding dyes, amine reactive dyes can only pass through the
membrane of dead cells. Once in the cells, amine reactive dyes interact with free
amines in the cytoplasm, causing dead cells to fluoresce.
When running a multicolor flow cytometry experiment, users must also
prepare sets of controls to aid with compensation. Compensation is the
process of correcting spectral overlap that occurs between fluorochromes.
For example, fluorescein isothiocyanate (FITC) dye emits green, yellow, and
orange photons while PE emits yellow and orange photons. If FITC and PE were
being used together in an experiment, you would need a way to determine the
relative contribution of yellow and orange from FITC and that from PE. During
compensation, the signal arising from a specific fluorochrome is removed
from all other detectors except its dedicated detector. To ensure proper
compensation, you must include single-stained samples for each fluorochrome
in the experiment. Single-stained samples must be as bright or brighter than any
experimental sample. In addition, you should include a panel of “fluorescenceminus one” controls, samples stained with all of the fluorochromes in the
experiment except one. The fluorescence-minus-one panel will help you set up
gating during data analysis.
We hope that this section has provided you with an overview of flow cytometry
for single cell analysis and some useful background information for setting up
antibody-based flow cytometry experiments. n
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References
Bonner, W. A., Hulett, H. R., Sweet, R. G., & Herzenberg, L. A. (1972). Fluorescence activated cell sorting. Review of
Scientific Instruments Online/Review of Scientific Instruments, 43(3), 404–409. https://doi.org/10.1063/1.1685647
McKinnon, K. M. (2018). Flow Cytometry: An Overview. Current Protocols in Immunology, 120(1). https://doi.org/10.1002/
cpim.40
Picot, J., Guerin, C. L., Van Kim, C. L., & Boulanger, C. M. (2012). Flow cytometry: retrospective, fundamentals and recent
instrumentation. Cytotechnology, 64(2), 109–130. https://doi.org/10.1007/s10616-011-9415-0
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Validating Antibodies for Use
in Flow Cytometry
A
Harvinder Virk and Michael Biddle, September 2024
ntibody validation is to confirm (or refute) that the antibody is
selectively detecting the target-of-interest in your assay and
sample-of-interest. The approaches available broadly map onto
the five pillars of antibody validation (Uhlen et al., 2016). In this post, we
will describe the approaches that can be used to determine selectivity of an
antibody for flow cytometry experiments. This will include a discussion of
the Human Leucocyte Differentiation Antigens (HCDM) workshop approach,
which focuses on validating antibodies against markers of the human blood
leukocyte populations, as well as a pragmatic approach to determining
the selectivity of antibodies for other sample types and for non-surface
(intracellular) antigens.
Unfortunately, antibodies frequently do not work as assumed (Ayoubi et al.,
2023). Additionally, their performance depends on the specific protocol used
and the abundance of the epitope in the sample, relative to cross-reactive
antigens — that is, an antibody shown to perform well in one set of conditions
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may not perform as well in other conditions. For these reasons, it is essential
to test the selectivity of your antibody. But testing is not one size fits all. The
approaches used will be different depending on the sample and target type.
To start the validation process, one must first confirm that a candidate antibody
can detect the antigen of interest in the given protocol. This requires use of
one of the five pillars of antibody validation, and it is recommended to screen
multiple candidate antibodies when possible. If cost is prohibitive, consider
an approach that prioritizes more reproducible/renewable reagents, such as
recombinant or hybridoma-derived monoclonal antibodies. It is also important
to seek out supportive data on candidate antibodies from the manufacturer and/
or in the published literature. Note that the antibody being published doesn’t
always indicate the presence of supportive data. To be supportive, the data must
show that the antigen being detected in the intended application — here, flow
cytometry — is the desired target, using one or more of the approaches
outlined below.
Knockout cell line approach
Genetic knockout-based validation can conclusively prove the ability of an
antibody to detect the antigen of interest when expressed at endogenous levels
(Laflamme et al., 2019). CRISPR-Cas9 approaches are commonly used to produce
knockout (KO) cell lines, some of which are commercially available. Various
proteomic and transcriptomic datasets can be used to select a candidate cell line
(e.g. DepMap) — you’ll need to ensure that the parental cell lines of your chosen
KO line are likely to express the target at a robust level, similar to that in samples
of interest. If you are working with cells that are amenable to genetic knockout,
this represents a very robust approach to antibody validation. If your protein of
interest is essential to the cell’s function, it likely is not amenable to knockout
approaches. In these cases, we would recommend a knockdown
approach instead.
In this approach, you’re looking for an antibody that detects the target in the
parental cell line, but not in the iso-genic knockout control. This can involve
cell surface labeling and/or intracellular labeling (or both). It is important to
test for the protocol you intend to use in your experiment. An antibody that
demonstrates selectivity for cell surface labeling may become less selective if
used in intracellular labeling because the intracellular compartment may contain
cross-reacting antigens not present on the cell surface. The presentation of
antigens can also be affected by cell fixation and permeabilization techniques —
and so it may be necessary to try multiple techniques.
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Figure 1: HCT 116 wildtype (WT) and SYT1 KO cells were labeled with a green or violet, fluorescent dye,
respectively. WT and KO cells were mixed in a 1:1 ratio and fixed in 4% PFA and permeabilized in 0.1%
saponin. 400,000 cells were stained with the indicated Synaptotagmin-1 antibodies and corresponding
Multi-rAb CoraLite® Plus 647 secondary antibodies. Antibody staining was quantified using the Attune
NxT Flow Cytometer with representative images showing the staining intensity in the KO population (pink
histogram) compared to the WT cells (green histogram). The unfilled histograms show cells that have only
been stained with secondary antibodies, showing the level of background staining. All antibodies were
diluted to 1 µg/mL except for 14558, which was used at 0.35 µg/mL (1/100). * = monoclonal antibody, **
= recombinant antibody. Figure adapted from Biddle et al., 2024.
For example, we have found that some antibodies perform well in a PFA-saponin
fixation and permeabilization protocol, but very poorly in a methanol fixationpermeabilization (fix/perm) protocol. Although the expected location of the target
is an important factor in deciding which protocol is likely to work, we have found
that it is often best to determine this by testing them. For intracellular targets, we
usually test three different fix/perms (methanol, PFA-saponin, PFA-triton). The fix/
perm solutions can make a significant difference in the efficacy of your antibody.
When compared, 4% PFA and 0.1% saponin; 4% PFA and 0.1% Triton X-100; and
methanol, we found that the optimal perm/fix solution depended on the specific
antibody:target combination of interest (Figure 1). It can therefore be helpful to
try all three when looking to label an intracellular target.
Once an antibody is found that shows separation between the wildtype and
the KO population (Figure 1), further optimization can be performed to both
maximize the separation and reduce any background staining. This can include
using different blocking reagents; titration of antibody concentration (this can also
save money!); and optimizing fixation/perm reagents for intracellular targets.
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Since knockout experiments can be time-consuming and expensive, it may be
helpful to find an antibody that has already been validated for flow cytometry
through this approach. If you’re looking for a flow antibody, the YCharOS
(Antibody Characterization through Open Science) initiative has now started
producing flow cytometry characterization data for selected antibodies, using the
KO approach. You can see an example of their work on their Synaptotagmin-1
page. The data produced is available on the F1000 gateway and
Zenodo community.
Knockdown approach
For cell types where knockout studies are not feasible, an alternative approach
is RNA interference (RNAi). The most common method is transfection of short
interfering RNA (siRNA). Knockdown caused by siRNA is also transient, and as
such, the optimal knockdown of the protein target will depend on the protein
turnover rate. If you need a more permanent knockdown, consider using
shRNA vectors instead. With both siRNA and shRNA, it can be difficult to achieve
sufficient knockdown to enable robust assessment of antibody selectivity.
Additionally, it can be a real challenge to troubleshoot RNAi failures. Potential
explanations for RNAi not reducing the antibody signal include:
•
•
•
•
The antibody is not selective
The knockdown is not efficient at the RNA level
The knockdown is not efficient at the protein level at the timepoint studied
Off-target activities of the RNAi
Correctly interpreting your data therefore necessitates careful design of RNAi
sequences, confirmation of RNA knockdown by RT-qPCR, and potentially the need
to study protein expression at multiple timepoints. When the target of interest is
critical to cell survival or proliferation, an additional challenge is that only partial
or ephemeral knockdown will be possible, and the small difference in protein
expression makes confirmation of antibody selectivity even more challenging.
Correlation with RNA or proteomic data from multiple cell lines/ types with
different expression
Flow cytometry is often used with mixed populations of cells, like a blood sample
containing many different leukocytes. Where data is available from antibodyindependent methods, like RNAseq or proteomics, it is possible to compare
antibody labeling of different cell types within a sample to expected labeling
(Figure 2). The panel of antibodies used in this approach should also allow for the
clear identification of the cell types of interest. By correlating your data sets, you
can increase confidence that the labeling observed represents the expression
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Figure 2: Schematic showing the use of an orthogonal approach to understand the relationship between
antibody staining intensity by flow cytometry, and mRNA expression.
level of the target of interest. This approach maps onto the orthogonal approach
within the five pillars and is frequently used in combination with
other approaches.
If several cell lines express the target at different levels, it is possible to design
experiments where the performance of the antibody can be assessed through
its ability to separate the cell lines based on expression of the target. In this case,
you could compare the antibody’s labeling of the cell lines to the -omics data.
The cell lines with higher expression of the target protein, as determined by the
-omics data, should have more antibodies labeling them; the cell lines with lower
expression should have less antibodies labeling them. We find that cell tracker
dyes are helpful in designing such panels, so that pre-stained cell lines can be
mixed together and then labeled with the antibody in the same tube.
If your protein of interest is expressed at similar levels in your cell lines, you can
consider a cell treatment approach. If there is a cell treatment (e.g. PMA or a
specific cytokine) known to either induce or suppress the expression of a target of
interest, you can add an additional control containing treated cells. The expected
change in expression should then be reflected in antibody labeling. This approach
does allow you to determine expression levels in the same cell lines/background.
However, the cell treatment will frequently induce/suppress the expression of
many related proteins, not just your protein of interest, which can affect your
experimental readouts.
Be aware that in any version of this approach, you are relying on correlations
between actual antibody labeling and expected labeling. Correlation cannot prove
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antibody selectivity, and for that reason, it is best practice to combine correlation
with other approaches when possible.
Finally, correlation of -omics data can also be used in conjunction with an
assessment of labeling pattern between multiple antibody clones targeting
the same protein (independent antibodies). If two antibodies, that recognize
different epitopes of the same protein, show the same pattern of labeling, this is
supportive of their specificity for the protein. A challenge to this approach is that
the identity of the epitope is often not available to confirm that they recognize
different epitopes.
Detection of overexpressed (tagged or untagged) protein
When expression plasmids are available, or the necessary expertise to clone
them, it can be useful to produce cell lines that overexpress the target of interest.
This approach often involves the transient transfection of expression plasmids in
a given cell line. For flow cytometry, it is useful to produce a sample with mixed
expression, with the expression measurable through an epitope tag such as
FLAG, His, or GFP. Fluorescent protein tags, which correlate closely with target
expression, are often preferred for this approach.
A highly selective antibody should be able to detect even in cells transfected
with low efficacy. But since there can be large differences between endogenous
expression and transfected overexpression, this approach cannot confirm the
antibody’s ability to detect endogenous levels of the target protein. It may only
be able to detect the target protein at the high levels caused by overexpression.
Additionally, if the cell line used has endogenous levels of expression, this can
make the results more difficult to interpret, and a knockout/ or knockdown
approach should be preferred. The overexpression approach is therefore best
suited to cell lines without any expression of the target of interest, which can be
difficult to find for many proteins.
In practice, it was necessary for us to combine overexpression with the use of
orthogonal approaches, cell treatments, and independent antibody approaches
in different assay systems to validate flow antibodies (Virk et al., 2019, 2021).
In those studies, we used this combination to find suitable antibodies for a
target with very low expression, due to induced toxicity caused by the protein of
interest.
HLDA workshop approved antibody clones
The antibody validation process represents a high level of effort and time, which
may not be available to every scientist looking to do a flow cytometry experiment.
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But validation is critical to ensuring accurate interpretation of your results.
Therefore, taking advantage of antibody validation efforts and information
provided by external organizations like the Human Cell Differentiation Molecules
(HCDM) can be quite useful.
HCDM, among other things, works to test flow cytometry antibodies through their
Human Leucocyte Differentiation Antigens (HLDA) workshops. Let’s use TIM-1
(Single pass type-1 membrane protein), which has been designated CD365 in the
most recent HLDA workshop (HLDA10), as an example. For CD365, they examined
two different antibody clones produced by different vendors. The specific epitope
recognized by either antibody was not shared. Tables describe that the antibodies
both recognized the target when transiently overexpressed in CHO cells and
displayed similar labeling patterns on different primary blood leukocytes. The file
shared also contains information about the labeling pattern of several cell lines
using these two antibodies. This is the most usual level of evidence supplied to
support the suitability of an antibody for its target.
Figure 3: Our recommendations for an approach that may be used to determine whether an antibody
is suitable for your flow experiment. We find it useful to start with a screening procedure in easily
genetically modifiable cell types (top). The specific approach will depend on the characteristics of the
target and whether suitable cell types are available. Further evidence is then needed to confirm that the
staining represents the target in your sample of interest (bottom). The approach maps onto the
five pillars, with some specific recommendations for how to use these in practice.
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HCDM is focused on characterizing cell surface molecules expressed on human
blood leukocytes, including antibodies against these markers. The 371 CD
markers characterized so far, and the additional subtypes, are available on
their website, and include information on tested antibodies against the target.
Helpfully, this also includes clone names, which can allow you to search different
providers for the same clone. This can be particularly useful when designing
larger multicolor panels, where different manufacturers may have the same clone
available with different conjugates.
Summary
Flow cytometry is a powerful tool to delineate cell populations and to study
relative protein expression. Its reliability will be entirely dependent on the
selectivity of the antibody used in the specific sample and protocol. This can be
a challenge to confirm. As such, the approach used may need to vary according
to the target, sample type, and available resources. The approach we suggest
is summarized in Figure 3. The principle is to confirm that the antibody can
detect the antigen of interest in the protocol of interest, ideally at endogenously
expressed levels, and then adjust the approach to the specific end-use of an
individual experiment. n
References
Ayoubi, R., Ryan, J., Biddle, M. S., Alshafie, W., Fotouhi, M., Bolivar, S. G., Ruiz Moleon, V., Eckmann, P., Worrall, D.,
McDowell, I., Southern, K., Reintsch, W., Durcan, T. M., Brown, C., Bandrowski, A., Virk, H., Edwards, A. M., McPherson,
P., & Laflamme, C. (2023). Scaling of an antibody validation procedure enables quantification of antibody performance
in major research applications. eLife, 12, RP91645. https://doi.org/10.7554/eLife.91645
Biddle, M. S., Alende, C., Fotouhi, M., Jones, C., Ayoubi, R., Southern, K., Laflamme, C., Virk, H., Group, N. collaborative,
& Consortium, A. (2024). A guide to selecting high-performing antibodies for Synaptotagmin-1 (Uniprot ID P21579)
for use in western blot, immunoprecipitation, immunofluorescence and flow cytometry (No. 13:817). F1000Research.
https://doi.org/10.12688/f1000research.154034.1
Laflamme, C., McKeever, P. M., Kumar, R., Schwartz, J., Kolahdouzan, M., Chen, C. X., You, Z., Benaliouad, F., Gileadi, O.,
McBride, H. M., Durcan, T. M., Edwards, A. M., Healy, L. M., Robertson, J., & McPherson, P. S. (2019). Implementation of
an antibody characterization procedure and application to the major ALS/FTD disease gene C9ORF72. eLife, 8, e48363.
https://doi.org/10.7554/eLife.48363
Uhlen, M., Bandrowski, A., Carr, S., Edwards, A., Ellenberg, J., Lundberg, E., Rimm, D. L., Rodriguez, H., Hiltke, T., Snyder,
M., & Yamamoto, T. (2016). A proposal for validation of antibodies. Nature Methods, 13(10), 823–827. https://doi.
org/10.1038/nmeth.3995
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Reading a Flow Plot
I
Rachel Leeson, February 2024
f you’re interested in studying immunology or subpopulations of cells,
you’ll soon find yourself encountering flow data in the literature. Data
reported from flow cytometry experiments can be a little challenging
to understand if you’ve never done any flow. But in order to plan a flow
experiment, you’ll need to first read papers with flow data ... and to read them
it’s helpful to have done flow first … which can send you in an endless loop.
Let’s break out of the loop with all you need to know on how to read a flow
plot.
Dot plots
Flow cytometry is a method that allows users to analyze single cells in a
population. It can be used to measure protein expression, identify rare
cells, or even sort out single cells through FACS. This work is done in flow
cytometers, and output is generated through measurement of events.
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An event is something that passes through the machine’s lasers and gives off a
signal read by the machine’s sensors. (Usually this something is a cell, but it could
be debris or unbound antibodies), Each event read by the machine is plotted
as a dot on an X and Y axis to generate a dot plot (Figure 1). Clustered events
are called a population. The goal of flow cytometry is to use increasingly more
stringent markers to identify your specific cell population of interest.
Gating strategies
The series of markers you use to define your population is called your “gating
strategy.” This series of markers are set up sequentially, from broadest to most
specific marker, and are represented as a series of dot plots in the same order.
However, in an actual flow cytometry run, all markers are read in parallel.
Let’s imagine we’re looking for cells in a sample that are negative for marker A
and positive for marker B, or A-B+ cells. If you look at the dot plot in Figure 2, you
might assume that all cell populations are positive for both markers, because we
usually assume (0,0) to be at the intersection of X,Y. For flow plots, however, that
is generally not true. Because of the variations in fluorescent signals, and how
the machine reads/interprets the signals, the axis is instead divided into positive
and negative signal at a point on the axis determined by the researcher, based on
their controls. You can usually tell where this point is by looking at the different
populations. In the case of Figure 2, one population is B- and another two are B+.
Our A-B+ cells are in the lower right corner.
Figure 1: While the plot generated often looks like the graph on the left, it might make more sense once
you realize events are plotted on an invisible Cartesian plane (right.) Each event is plotted by the amount
of signal read from Marker A and Marker B. Created with BioRender.com.
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Figure 2: This flow plot has two populations that are B+ and one population that is B-. Created with
BioRender.com.
If it’s hard to conceptualize where the populations are, try drawing a quadrant
over the plot that divides it into four, using the location of the cell populations as
a guide (Figure 3). You can see that in our dot plot, we have A-B- cells, A-B+ cells,
and A+B+ cells, but no cells that are A+B-.
Of course, this is quite easy to do on a plot generated in BioRender! While a
helpful visualization tool, a quadrant will often be too clean for real-world data.
Most plots will have a shape, like a box, ellipse, or circle, drawn around clusters
to define populations. This shape is called a ‘gate’; it tells the machine that
everything inside the gate is one population and everything outside the gate is a
different population.
Figure 3: It’s often easier to identify different populations by imagining (or drawing) a quadrant over the
dot plot. Created with BioRender.com.
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Gating and plot orders
Pro tip!
As the gates progress from broadest markers to most specific, the first step is to
separate cells from debris and roughly group the cells by size. This is done using
side scatter (SSC) and forward scatter (FSC), which measure light that “scatters”
past the cell, instead of fluorescent markers. There
are various types of SSC and FSC that can be used
for this purpose.
The forward scatter is a proxy for
diameter and volume of the cell, thus
allowing the exclusion of doublets
and debris. The side scatter roughly
measures granularity, a proxy for nucleic
complexity of the cell.
The sample will have been labeled using a panel
of markers, which is comprised of antibodies
conjugated to fluorescent markers and/or cellular
dyes and stains. These will be used in a series of
one- or two-marker plots, like in Figure 3, until
the population of interest has been successfully
identified and isolated.
Following a gating strategy
Figure 4 shows a gating strategy from a published experiment (Barlow-Anacker et
al., 2017). This gating strategy is used to identify two types of dendritic cells (DCs):
mDCs and pDCs. Let’s follow it from left to right.
First, SSC and FSC are measured to differentiate cells from debris. Cells are gated,
and in the second plot, two types of FSC are used to separate single cells from
doublets, ignoring anything not within the gate of the first plot. Notice that the
doublets and single cell populations are very close to each other.
In the third plot, anti-CD14 and anti-HLA-DR antibodies conjugated to fluorescent
markers are used to identify monocytes (upper right) and a mixed population of B
cells and dendritic cells (DCs) in the lower middle right. DCs and B cells are HLADR+ and CD14-.
Figure 4: Gating strategy used in Barlow-Anacker et al. (2017) to identify mDCs and pDCs. Used under
Creative Commons license.
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In the final plot, the mixed population of B cells and dendritic cells is further
separated into three different populations using conjugated antibodies against
CD11c and CD123. You can see that mDCs are CD11c+ CD123- and pDCs are
CD123+ CD11c-. Finally, B cells are CD11c- and CD123-, forming a population in
the lower left(ish) corner.
Pro tip!
Subtypes of immune cells are often
defined by a subset of the markers used
to identify them, such as “CD123+CD11cpDCs.” Some cells are defined not by the
presence or absence of a marker, but by
the amount of marker that is present,
which would be annotated like so:
CD14high or CD14low.
If it’s a bit difficult to see these populations, I
suggest using the imaginary quadrant to help
(Figure 5). Even though this real-world data is far
messier than BioRender plots, quadrants can still
help me as a reader if I’m struggling to see the
different populations on a plot.
Don’t be intimidated if you’re struggling to
understand how the researchers decided to
place their gates on the plots! Flow data is messy,
so learning how to gate takes a lot longer than
learning how to read a gating strategy.
Other information
FACS plots often contain other data. Some will have numbers by gated
populations; this indicates the percentage of total events contained in the gated
population. Heat maps are often used to indicate the relative density of cells. You
can see both of these features in the gating strategy shown in Figure 6.
Contour plots
FACS data can also be presented as contour plots. Contour lines, which you may
Figure 5: The gating strategy from Barlow-Anacker et al. (2017) with quadrants to help visualize the
different populations. Used under Creative Commons license.
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Figure 6: This flow data, from Boix et al. (2018), uses quadrants to gate and has numbers representing the
percentage of cells in each gate. Used under Creative Commons license.
have seen on elevation maps, are used to show frequency of events, instead of
relying on clustered dots. In a contour plot, each line contains the same number
of events. Thus, contour lines that are close together show a high density of cells,
while lines that are spread apart show a low density of cells (Figure 7).
Histograms
For analysis of a single marker, a histogram showing frequency of events on the Y
axis and strength of signal read on the X axis may be presented. A horizontal
Figure 7: Contour lines in flow data from Jhunjhunwala et al. (2015). Used under Creative Commons
license.
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Figure 8: A series of histograms from Alyamani et al. (2018) showing number of events within a single
signal in a flow experiment. Used under Creative Commons license.
line across the peak will indicate the cut-off value for a positive signal (Figure
8). Note that the signal is actually on the X axis; what the horizontal line does is
define a point where the peak is sufficiently narrow to count as a “true” signal.
Of course, there are many more things to learn about when diving into flow
cytometry data. There’s different ways to use FSC and SSC, as briefly alluded to
above. There’s many variations in how one can gate, select, and analyze data.
But now that you can read a FACS plot and follow a gating strategy, you can start
to ask why the researchers made the decisions they did in their experiments
and determine if a similar approach would be good for your work. Hopefully this
section helps you with that… and happy flowing! n
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References
Alyamani, A., Kalamegam, G., Ahmed, F., Abbas, M., Sait, K., Anfinan, N., Al-Wasiyah, Mohammad Khalid, Huwait, E., Gari,
M., & Al-Qahtani, M. (2018). Evaluation of in vitro chondrocytic differentiation: A stem cell research initiative at the King
Abdulaziz University, Kingdom of Saudi Arabia. Bioinformation, 14(02), 53–59. https://doi.org/10.6026/97320630014053
Barlow-Anacker, A., Bochkov, Y., Gern, J., & Seroogy, C. (2017). Neonatal immune response to rhinovirus A16 has
diminished dendritic cell function and increased B cell activation. PLoS One, 12(10), e0180664. https://doi.org/10.1371/
journal.pone.0180664
Boix, F., Llorente, S., Eguía, J., Gonzalez-Martinez, G., Alfaro, R., Galián Megías, J. A., Campillo, J., Moya-Quiles, M., Minguela
Puras, A., Pons, J. A., & Muro, M. (2018). In vitro intracellular IFNγ, IL-17 and IL-10 producing T cells correlates with the
occurrence of post-transplant opportunistic infection in liver and kidney recipients. World Journal of Transplantation,
8(1), 23–37. https://doi.org/10.5500/wjt.v8.i1.23
Jhunjhunwala, S., Aresta-Dasilva, S., Tang, K., Alvarez, D., Webber, M., Tang, B., Lavin, D., Veiseh, O., Doloff, J., Bose, S.,
Vegas, A., Ma, M., Sahay, G., Chiu, A., Bader, A., Langan, E., Siebert, S., Li, J., Greiner, D., & Anderson, D. (2015). Neutrophil
Responses to Sterile Implant Materials. PloS One, 10(9), e0137550. https://doi.org/10.1371/journal.pone.0137550
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Introduction to Gating
W
Paul Heisig, April 2024
hen using flow cytometry to analyze your samples, it is
necessary to set up a sequence of gates to be able to
select and precisely measure your cells of interest. In many
experiments you’ll be working with a heterogeneous cell population, for
example from a processed piece of tissue, where cells are present that
vary in cell type, size, and marker proteins. But even if you are analyzing a
homogenous cell population in culture, it is normal to observe diversity in cell
size, marker expression, and viability. Gates are the parameters the machine
uses to differentiate between variations in those factors.
To understand the principles of gating, you’ll first need to learn a little bit of
theoretical background on the different parameters that your gating strategy
will be based upon. We’ll try to keep it short though, promise!
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FSC and SSC: scattering
To digitally extract your cells based on their size, the cytometer provides you with
two measurements: forward scatter (FSC) and side scatter (SSC). To acquire these
measurements, the cytometer illuminates the passing cell with a laser and detects
the light scattered by that cell at a low angle (FSC) or large angle (SSC) (Figure 1).
FSC values depend on the cell’s size, while SSC values depend on the structural
complexity inside the cell or on its surface. Bear in mind, the voltage setting
of the laser impacts the magnitude of FSC, SSC, and all fluorescent labels. The
cytometer’s software will allow you to adjust this setting as needed. To figure out
the appropriate voltages to observe your cells on a flow plot, you might have to
do some testing or ask experienced colleagues.
Height, width, and area
Within FSC and SSC, there are three parameters that are being measured: height
(H), area (A), and width (W). The different measures are represented as FSC-A/
SSC-A, FSC-H/SSC-H, or FSC-W/SSC-W, respectively. H, A, and W describe the
shape of a histogram, which graphically represents the time and intensity of the
cell’s illumination (see Figure 2). H describes the maximum signal strength, while
W results from the time that the cell spent passing through the laser beam. A
is simply the area under the resulting curve. By default, a sample on the flow
cytometer (before you set the first gate) will be represented through FSC-A vs.
SSC-A, so most people use that setting.
Figure 1: The laser pulse illuminates the cell and is scattered forward at a low angle (FSC) or to the side at
a large angle (SSC). FSC and SSC depend on the cell’s size, morphology, and structural complexity.
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Figure 2: As the cell passes through the laser beam and causes it to scatter (FSC/SSC), the detected
photons can be represented through a graph of photocurrent vs. time. The resulting histograms is
described through maximum current (H), the time the cell needed to pass through the laser (W) and the
area under the curve (A).
While flow cytometry can be used for virtually all kinds of experiments and
research fields, for any experiment, your first three gates will likely be: the FSC/
SSC gate to identify cells, the single cell gate to exclude duplicate events, and the
Live/Dead gate to focus your analysis on what’s biologically active. After that, the
next gates fully depend on your individual experiment.
Practical example: lymphocytes in a mouse tumor sample
You made it! So much about the theoretical background. Let’s dive into the praxis
of gating strategies. As an example, we’ll use one of my previous experiments
where I harvested a melanoma tumor from a C57BL/6 mouse and analyzed the
tumor-infiltrating lymphocytes of that tumor.
In Figure 3, you can see how I gated out my lymphocyte population from a tumor
sample. Many cell types can be found in the tumor microenvironment (tumor
cells, immune cells, blood cells, and more), each coming with a different size and
structural complexity. After several steps of tissue processing prior to running my
flow analysis, I can however clearly identify my lymphocyte population (10.7% of
all recorded events) based on their expected size. The strong signals close to the
graph’s origin mainly stem from cell debris and other small fragments.
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Figure 3: (A) With the acquisition voltage (not shown) used in my flow analysis, I can spot my lymphocyte
population distinct from cellular debris and other larger cells. Note, we don’t mind the warning message
at the top, as our cells of interest do not lie beyond the dimensions displayed in the plot. (B) A more
restrictive gate results in fewer cells being considered for the analysis. (C) A more liberal gate allows more
cells to be considered for the analysis. Depending on the following gates, cells that are not of interest can
still be gated out later.
Where exactly a given cell population will show up on an FSC/SSC plot, largely
depends on cell type and laser voltage. The voltage describes an electric potential
that can be applied to the photomultiplier inside the cytometer to increase the
electric current and thereby the signal strength. So, we need to adjust the voltage
in the cytometer’s software in order to generate signals in a reasonable range. If
colleagues in your group already have experience with using flow cytometry, or if
you have access to a professional flow core facility, it’s always good to ask around
for advice before running your analysis. In this case, I and other lab members had
worked with lymphocytes before, so I knew where to look for them on the FSC-A/
SSC-A plot.
Pro tip!
If you only get very few events at the
end of your gating pipeline, it might be
worth going back and loosening the FSC/
SSC gate.
Based on personal preference, some people like
to make stricter gates while some like to make
looser ones (Figure 3). A more “liberal” gate in the
beginning should not have a great impact on your
analysis, and I often choose a looser gate here. I
am not worried about including irrelevant events
in my analysis, as these will get excluded through
further, more specific gates (single cells, live cells,
my marker labels of interest). Let’s double-click
inside our gate and proceed to the next plot!
Single-cell gating
Once you have identified and gated on your cells of interest, you will likely need
to exclude all duplicate events (two cells stuck together) from your single cells, as
duplicates can’t be reliably analyzed on their labeling signals. For this, you’ll
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Figure 4: (A) Using the proportional relationship between FSC-H and FSC-A, we can gate on single cells and
exclude any events where multiple cells were clumped together. (B) Instead of FSC, one is free to use SSC
for a single cell gate. However, make sure to compare H and A (or W and A).
need to gate based on the proportion between H and A (or W and A) within the
same type of scatter. Here I employed an FSC-H/FSC-A gate, but an SSC-H/SSC-A
gate would work just as fine (Figure 4). As H and A scale in proportion (while H
and W do not; see Figure 2), single cell events will show up in a straight line on the
plot, while duplicates deviate from that pattern.
Live/dead gating
Having gated my single lymphocyte cells, I now want to exclude all dead cells
from my analysis. For this measurement, I used a fluorescent dye that can only
enter dead cells, while living cells can protect themselves from it. So here, I
actually want to gate on my negative, unlabeled population, which I can clearly
differentiate from the positive one (Figure 5). Note that in many cases a label will
not result in clearly distinct positive and negative populations. Oftentimes, you’ll
be presented with a trend of two populations with basically a smear in between.
Figure 5: (A) A gate to select the negative live population from my live-dead staining. (B) Changing the
y-axis from SSC to FSC can alter the positioning and shape of the populations displayed. As the separation
pattern largely stays the same and does not impact my ability to draw a proper gate, I am free to choose
either one to use.
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Again, it can depend on the label whether a tighter or looser gate makes sense.
Here, luckily, the vast majority of my cells of interest lies within a clear
negative population.
When you’re interested in measuring a population based on a fluorescent label,
you might be wondering which second measurement to choose for graphically
displaying your events. You are free to choose any size-related parameter, such as
FSC-H, FSC-A, SSC-H, SSC-A, etc. You’ll notice that while the height of your events
changes, the separation pattern of positive vs. negative signal remains the same
(Figure 5). With our first three gates out of the way, it’s time to move on to my
experimentally-specific gates.
Two-dimensional labeling (quadrant gating)
For our last example plot, I will show you a combination of two labels, in this case
through conjugated antibodies targeting the cell surface markers CD11 (antimouse CD11b-BV510), which is present on dendritic cells but not T cells, and CD8
(anti-mouse CD8b-PerCP-Cy5.5), which is present on cytotoxic T killer cells but not
dendritic cells.
Using a graph displaying both labels, I can quickly separate T killer cells (CD8+)
from dendritic cells (CD11b+) using a quadrant gate. Within my “lymphocyte +
single cell + live” population, acquired from the previous gating pipeline, about
53% of my cells are dendritic cells and ~13% are CD8+ T cells. Around 34% of
the cells are neither of the two cell types (likely B cells and others). Almost no
cells are positively labeled with both markers. When comparing two markers
simultaneously, we commonly describe the resulting populations as double
Figure 6: (A) A flow plot showing two labels in relation to each other. Using a quadrant gate, I can divide
my cell population into DN, DP, and SP (for each label, respectively). (B) Instead of a quadrant gate, I am
free to use multiple individual gates. Note that in this case, square gates were used.
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positive (DP, Q2), single positive (SP, Q1/Q3), or double negative (DN, Q1). If I wish
to employ a more refined gating strategy, I can, of course, also draw multiple
individual gates around the populations of interest (Figure 6).
Congratulations! You’ve just learned the basic principles of gating in flow
cytometry. Let’s briefly recapitulate what we’ve covered in this section. First,
you learned about the types of scattering that occur when a cell is pulsed by
the laser. Within each type of scattering, you also discovered that there are
three parameters — H, W, and A — describing the intensity and duration of the
detected signal. Using a practical example of lymphocytes from a mouse tumor,
you’ve learned how to read an FSC/SSC plot and how to use H vs. A to distinguish
single cells from duplicates. Lastly, you learned which gates should always be
included in your analysis, regardless of your experiment, and how you can
combine two colors (labels) to visualize four populations at once. n
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Flow Cytometry Controls
W
Ashlyn Lemmen, June 2024
hen you are running flow cytometry, you’ll need various
controls to help you set up and analyze your samples.
While you are probably familiar with the basics of controls
in experimental design, you’ll need a few controls specific to flow as an
application. These controls will allow you to distinguish real results from
background noise or nonspecific binding.
Flow specific controls
The three types of flow cytometry controls we’ll discuss are single color,
fluorescence minus one (FMO), and isotype. You should always have single
color controls in your experiment, so we’ll go over those first. Depending on
the markers you are looking at and the antibodies you are using, you may
need to include additional controls like FMO or isotype controls, which we’ll
describe a bit later.
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Single colors
Single color controls contain, as the name suggests, a single color from your
antibody panel. They are made up of compensation beads and a conjugated
antibody. Compensation beads are a mixture between synthetic beads that
can bind to a conjugated antibody and beads that are unable to bind to them:
like cells but with far more predictability. The beads create an artificial positive
(bound) and negative (unbound) population. The benefit of beads is that they
do not require cells from your experiment, and they are nonspecific. The
compensation beads can bind to any primary antibody you add.
Single color controls are used to help you set up your voltages and can also
be helpful for compensation. You should have a single color control for every
fluorophore you intend to use in your panel.
Using single color controls
On a flow cytometer, you generally want your positive cells to read between 10^4
and 10^5 in order to have enough room on the plot to see all of your positive and
negative cells. This can be achieved by adjusting your voltage using your single
color controls (Figure 1A), instead of using your actual samples. They may also be
used as a starting point for compensation (Fig. 1B–C). However, it’s important to
note that because you’re using beads and not experimental samples, the voltages
and compensation you set from your single color controls may not be accurate
for your samples. The single color controls should be used as a starting point, to
preserve your sample, and you should then confirm or adjust the settings with
experimental samples.
Figure 1: Example flow plots for a single color control. A) A flow plot showing a single color control for
the APC channel. B) and C) A flow plot showing an APC single color control prior to compensation (B) and
after compensation (C) with APC-Cy7.
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FMOs
FMO stands for fluorescence minus one. It is used as a negative control for a
specific marker in your experiment. To make an FMO sample, you take a subset
of your experimental samples, combine it into one well or tube, and stain it
with every marker you are using in the experiment except for one marker of
interest. This allows you to more easily and accurately gate populations that may
otherwise be difficult to separate out. In Figure 2, you can see that, without the
FMO, it would be difficult to differentiate the Ly6C negative population from the
Ly6C positive population.
Pro tip!
Early gates, like your live/dead markers
or commonly used markers that define
broader cell populations (e.g., CD4,
CD8b, CD11b, CD19), are less likely to
require an FMO. Markers that define
more specific cell populations, often
used in the later gates, are more likely to
require FMOs.
Using FMO controls
In an experiment, you should have a FMO sample
for any marker without a clear positive and
negative population. If you’re unsure what your
population will look like for a particular marker,
you can use the reference plots provided by the
antibody companies or check the literature.
Isotypes
Isotype controls are negative controls that allow
you to determine what level of nonspecific binding
you have in your sample. The isotype antibody will be virtually identical to the
antibody for your marker of interest. It will be from the same host species, have
the same Ig subclass, and be in the same fluorophore (e.g., if you are staining
for anti-mouse Ly6C in APC with the IgG2c subclass, you will use the APC IgG2c
Figure 2: An FMO gating example. The FMO sample (A) can be used while setting up your gating schematic
to easily determine where your cells of interest are (B).
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isotype control). The only difference is that the isotype antibody will not target
your marker of interest.
Using isotype controls
An isotype sample is prepared very similarly to a FMO sample: you make a sample
from a combined subset of your experimental samples, stain it with every marker
you are using in the experiment except for your marker of interest, and then add
in your isotype control for that particular marker. This enables you to identify
any nonspecific binding to know that your positive cells are truly positive for your
marker of interest (Figure 3).
Pro tip!
Many companies will have appropriate
isotype controls linked on their
commercial antibody pages.
There is some debate in the literature as
to when you need isotype controls. Our lab
prefers to use them to gate our populations,
but others do not. Some journals require
isotype controls, while others don’t. However,
the general consensus seems to be that if
you are looking for a rare population or are
unsure what amount of antibody to use, you
should consider using an isotype control.
Flow cytometry can be a complicated and daunting process. Controls like single
color controls, FMOs, and isotypes can help you more easily gate your target
population. In addition, the controls will help you save on sample volume, so you
can use most of your samples for flow analysis rather than for setting up your
initial voltages and gates. n
Figure 3: Isotype flow plot example. The isotype sample (A) can be used to confirm that your positive
population is specific for your target antibody and may assist in gating your target population (B).
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Designing Your First Flow
Panel
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Paul Heisig, May 2024
hen analyzing your cells using flow cytometry, you are
typically measuring the presence or absence of certain
markers on the surface or the inside of your cells. While
proteins themselves can emit intrinsic fluorescence when excited by
ultraviolet (UV) light, they do so via aromatic amino acids found in all proteins,
so you can’t distinguish the different proteins from each other. To distinguish
between different target proteins in your flow analysis, you’ll need to use
fluorophores that attach to the target. When you design a flow experiment,
you’ll need to pick a fluorophore for each target and ensure the fluorophores,
together, are able to give you the appropriate readout.
A common method of labeling proteins is to fuse them with a fluorescent
reporter, such as GFP or mCherry, through genetic modification. As your
protein of interest will only occur in combination with that reporter, you can
infer its presence through detecting the reporter’s fluorescence. Another
way to label proteins is through treating cells with fluorophore-conjugated
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antibodies that attach to a specific target. By using a mix of different antibodyfluorophore combinations for different markers of interest, you can separately
detect these markers in your analysis. Lastly, there are fixable dye stains that
react with free amines on and inside the cells and are thus typically used for
distinguishing live from dead cells. The combination of antibody-fluorophores,
fluorescent reporters, and/or dye stains used in your flow cytometry experiment
are known as your “panel”. In this blog post, we’ll discuss the principles and
process of designing a flow panel.
Principles of excitation and emission
Before we design your first panel, we will introduce some background on the
color spectrum and the principles of excitation and emission. The visible color
spectrum is located in a range of wavelengths of about 380–700 nm. Photons of
lower wavelengths are higher in energy, while photons of higher wavelengths are
lower in energy. As such, wavelengths below 380 nm lie within the UV spectrum,
while wavelengths over 700 nm make up the infrared (IR) spectrum.
When photons are absorbed by matter, they promote electrons within atoms to
a higher energy state. This process is called excitation. After a short period, the
electrons revert to a lower energy state, whereby a photon is emitted. This step
is called emission. During excitation, some of the photon’s energy is lost, so the
energy of an emitted photon is lower than the energy of the absorbed photon.
Correspondingly, wavelengths of emitted photons are higher than those of
absorbed photons.
When photons excite electrons of a fluorophore, they can do so at a range of
wavelengths, rather than at just one defined wavelength. Likewise, the emitted
photons also appear within a range of wavelengths. For that reason, we speak of
excitation and emission spectra.
Both spectra possess clear maxima where excitation and emission are the
most efficient (i.e., happen most of the time). Figure 1 shows a graph from the
BD® Spectrum Viewer, outlining a few commonly used fluorophores and their
emission spectra. Some fluorophores carry their emission peaks within their
names, for example, BV421 and RB545.
Fluorophores can also come in combination of two conjugated single
fluorophores, like APC-Cy7. Conjugating Cy7 to APC results in a higher-wavelength
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Figure 1: Emission spectra and respective colors of a few commonly used fluorophores. Each fluorophore
has a defined emission maximum. Note, you can also use the tool to display excitation spectra, which
I excluded here for better visibility. Source: https://www.bdbiosciences.com/en-us/resources/bdspectrum-viewer.
emission spectrum compared to APC. This happens due to a process called
Fluorescence Resonance Energy Transfer (FRET).
Brightness
Apart from the excitation and emission wavelengths, it is also important to know
the brightness of individual fluorophores. Table 1 describes the brightness of a
few commonly used fluorophores. A fluorophore’s brightness refers to the signal
strength of the emitted photons detected by the cytometer. Brighter fluorophores
have a stronger signal.
Many factors can influence the level of brightness beyond the fluorophore
itself, like laser type and power, acquisition voltage, the concentration of the
fluorophore, and whether that fluorophore appears alone or in conjugation with
another fluorophore. The brighter a fluorophore, the better the signal-to-noise
ratio, and the better the detection of the target marker.
Looking at Table 1, we can recognize a couple of fluorophores from Figure 1. For
example, the chart tells us that PE and PE-Cy7 are “very bright” when they are
excited by the yellow/green laser but only “bright” for the blue laser.
To understand why that is, let’s look at Figure 2. The blue laser excites at a
wavelength of 488 nm (Table 1), which is near a local maximum of PE’s excitation
spectrum (dotted line, Figure 2). The yellow/green laser, however, excites at 561
nm, which is close to the global maximum of PE’s excitation spectrum where the
excitation efficiency is much greater. As a result, more photons are absorbed and
then emitted, which creates a relatively stronger — or brighter — signal.
Another example
APC is a “bright” fluorophore when excited by the red laser. When conjugated to
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Cy7, however, (APC-Cy7) the brightness changes to “dim” for the same laser type.
Knowing that the red laser excites at 640 nm (Table 1), while comparing the two
fluorophore’s excitation spectra around 640 nm (Figure 3), we see that APC-Cy7 is
excited at a lower efficiency than APC. While APC’s global excitation maximum lies
around 650 nm, APC-Cy7’s global excitation maximum is about 750 nm. If we were
to use a laser that excites at 750 nm, APC-Cy7 would show a much brighter signal,
while APC would show no signal at all, as it cannot be excited at this wavelength.
But what is the real-life advantage of a brighter fluorophore?
Table 1: Degrees of brightness for a few commonly used fluorophores in
relation to the laser type. Table adapted from: https://emea.bd.com/paneldesign/en/flow-cytometry-fluochrome-brightness-spillover
Excitation
Laser Color
Very Bright
Ultraviolet
(355 nm)
BD Horizon™
BUV737
Violet
(405 nm)
BD Horizon™
BV421
BD Horizon™
BV650
BD Horizon™
BV711
BD Horizon™
BV605
BD Horizon™
BV786
BD Horizon™
BV510
BD Horizon™
V450
BD Horizon™
V500
BD Horizon™
BB515
BD Horizon™ PECF594
Pe-CyBD™5
PE
PE-Cy7
FITC
Alexa Fluor 488
PerCP-CY5.5
PerCP
Blue
(488 nm)
Yellow/Green
(561 nm)
Red
(640 nm)
Bright
Moderate
Dim
BD Horizon™
BUV395
PE
BD Horizon™ PECF594
PE-Cy5
PE-Cy7
APC
Alexa Fluor 647
Alexa Fluor 700
APC-H7
APC-Cy7
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Figure 2: Excitation and emission spectra for PE. The excitation spectrum shows two maxima, one local
maximum at just under 500 nm and a global maximum at around 560 nm, leading to different levels of
brightness, depending on the laser light’s wavelength. Source: https://www.bdbiosciences.com/en-us/
resources/bd-spectrum-viewer.
Strong brightness makes it easier to distinguish a signal from noise; therefore, it is
recommended to use brighter fluorophores for markers that are relatively lower
in abundance. In contrast, when you expect a marker to be highly abundant,
it is fine to use a dimmer fluorophore. However, these aren’t strict rules and
in practice the differences in brightness levels are often not that great. When
thinking of terms like “very bright” vs. “dim” you might get the impression of a
candlelight vs. a floodlight. For fluorophore brightness levels, these differences
are much more subtle (think of brightness levels on your smartphone or
computer screen), and you should be able to properly label many markers with
any fluorophore.
Bleeding
Before we start building your first panel, we’ll talk about one last principle of color
emission.
As you can see in Figure 1, emission spectra of neighboring colors can and do
overlap. For example, APC overlaps noticeably with both BV605 and Alexa Fluor
700. It also overlaps with a few other fluorophores, although to a much lower
Figure 3: Excitation and emission spectra for APC and APC-Cy7. While the excitation spectra of both
fluorophores show efficient absorption at 650 nm, APC-Cy7’s optimal excitation happens at higher
wavelengths of about 750 nm. Source: https://www.bdbiosciences.com/en-us/resources/bd-spectrumviewer.
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Figure 4: Excitation and emission spectra for eGFP and FITC. As these fluorophores’ spectra virtually
overlap, you cannot use them together in the same panel. Source: https://www.bdbiosciences.com/enus/resources/bd-spectrum-viewer.
degree. The phenomenon of spectral overlap in flow cytometry is commonly
referred to as bleeding. When two fluorophores bleed into each other, it means
that the cytometer could detect a certain photon to be emitted by either of the
two. As a result, inaccuracies in the detection of fluorophores (and the markers
they label) can occur.
As I mentioned previously, you can also use fluorescent reporters, such as GFP,
and fluorophore-conjugated antibodies together in the same color panel. In this
case, make sure to compare the excitation/emission spectra of your reporter
with the ones from your fluorophore labels and see whether and to what extent
they overlap. Figure 4 compares the spectra of enhanced GFP (eGFP) and FITC,
the latter being a commonly used fluorophore for antibody-conjugated labeling.
Even though the two compounds are completely different in nature (eGFP being a
protein and FITC an organic molecule), their bleed is so strong that these spectra
almost overlap. Hence, they should not be used within the same panel.
To make life easier, always try to use fluorophores with distant excitation/
emission spectra to avoid bleeding as much as possible. However, when you need
to label multiple markers, some bleeding is often unavoidable. But don’t worry,
there are strategies to resolve this issue. Make sure to check out our section on
color compensation to learn more about them. Alright, let’s build your first panel!
Designing a panel
In this example, I will describe a basic panel (Table 2, Figure 5) for a hypothetical
experiment in the lab. Let’s say I am culturing HEK 293T cells in vitro. I transduced
the cultured cells with a virus that encodes a transgenic protein for the HEK 293T
cells to express: CD45. Upon expression, CD45 is incorporated into the plasma
membrane and can be detected through a cell surface label. The viral vector also
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encodes for eGFP, which serves as a transduction reporter. Note that eGFP is not
fused to CD45, but is separately expressed. (The reason for having a separately
expressed transduction reporter is that not every transduced cell will necessarily
manage to express CD45. Thus, if transduction efficiency is much higher than
CD45 expression, this could, for example, point at issues with the doubleexpression vector.) In our flow experiment, we’ll want to identify CD45+ cells
(CD45 expression) and assess our transduction efficiency (eGFP expression).
The first item in Table 2 is a standard and should always be included — the
live/dead stain. This stain is in fact not created through the actual APC-Cy7
fluorophore but displays an emission spectrum that overlaps with APC-Cy7.
Therefore, you can detect it through the APC-Cy7 parameter in the cytometer
software. It also means we should exclude APC-Cy7 from the rest of our panel.
Besides some background cell death constantly happening in cell culture, the
transduction process itself also leads to some cell death. As a result, there is a
high abundance of positively stained dead cells present and it is fine to use a dim
color, like APC-Cy7.
Next comes our transduction reporter, eGFP. As eGFP emits fluorescence by itself
and does not need to be bound through a separate fluorophore, we have to tell
the cytometer which channel to use to detect eGFP. As you saw in Figure 4, eGFP
strongly overlaps with FITC, hence reading out the FITC channel in the cytometer’s
software (without using the actual FITC fluorophore in our panel) will allow us to
detect the eGFP signal. We will now exclude FITC from the rest of our panel.
Lastly, we want to detect the expression of our protein of interest: CD45. I
am using a conjugated antibody to detect CD45, and I’ll need to choose the
Table 2: A simple flow panel to detect live, transduced, and CD45-expressing
cells.
Marker
Fluorophore detection
Live
APC-Cy7 channel
eGFP
FITC channel
CD45
BV421
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Figure 5: Emission spectra of the fluorophores used in our panel (Table 2). Source: https://www.
bdbiosciences.com/en-us/resources/bd-spectrum-viewer.
fluorophore my antibody is conjugated to. Since CD45 is a common marker,
finding an antibody already conjugated to my fluorophore of choice will not be
difficult.
I chose BV421 for the following two reasons: 1) The emission spectrum of BV421
is far enough from the other fluorophores to avoid bleeding and 2) BV421 is a
very bright fluorophore, which makes it easier to detect the transgenic protein.
We don’t know yet how abundantly CD45 will be expressed on the transduced
cells, so it’s better to use a brighter fluorophore in case the CD45 expression turns
out to be weak.
Conclusion
You made it! You learned about the principles behind fluorophores and their
colors for use in flow cytometry. Let’s briefly go through each section of this
article again. In the beginning, we talked about the relationship between a
photon’s wavelength and energy, how photons can be absorbed and emitted by
matter, and how this process creates visible colors. Next, you learned about how
fluorophores can be classified into different levels of brightness and their relation
to the fluorophore’s excitation efficiency, and when it might be better to use a
brighter fluorophore. Then, you discovered the concept of bleeding, or spectral
overlap, and why you should try to avoid or reduce it whenever possible. Finally,
we designed a simple panel to analyze transduced HEK 293T cells. Here, we
looked at both the transduction efficiency and the expression of the transduced
protein. I hope that helps you as you begin to design your first flow panels! n
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Flow Compensation
I
Ashlyn Lemmen, May 2024
n flow cytometry, compensation is the process of correcting spillover
from one fluorescent channel to another. When you label your samples
with multiple antibodies, the fluorescent probes on the antibodies
may have similar emission spectra, meaning they will emit fluorescent light
at similar wavelengths (see this handy emission spectra chart to find your
antibody’s spectra). Your cytometer will therefore record the fluorescence
for both at similar levels, meaning that your populations will ‘look’ similar
on your flow plot. This will make it difficult to properly gate your desired cell
population. By compensating between fluorescent channels, you can correct
for this spillover and more easily separate out your target population.
How do I know if I will need compensation?
To know if you will need to compensate in your flow panel, you will need to
know both the fluorescent markers you intend to use and your cytometer’s
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laser configuration. Your flow core or an online manual for your cytometer
should be able to tell you the laser configuration. In general, if two fluorescent
markers are read by the same laser, they will need to be compensated for one
another. This overlap in spectra is often called spillover. For example, if your flow
cytometer reads out the fluorophores FITC and PE on the same laser, you will
need to compensate between the two of them because they emit at a similar
wavelength. However, if your flow cytometer reads them out on different lasers,
you should not need to compensate between them.
Pro tip!
Some antibodies go by different names
depending on your flow cytometer’s
setup. For instance, BV510 and AmCyan
have almost identical emission spectra
but may be called BV510 on one
cytometer and AmCyan on another.
Another rule of thumb is if the names of the
fluorescent markers are similar, you will need
to compensate between them (although there
are exceptions). For example, on most flow
cytometers you will need compensation between
APC and APC-Cy7; PE and PE-Cy7; APC-Cy7 and
PE-Cy7; or BV421 and BV51. It is a good idea to
look up new fluorescent markers before deciding
to use them, using a tool like BioLegend’s
Fluorophore Guide Chart.
What does spillover look like and how do I fix it?
When plotting two channels against each other, you should be able to draw
perpendicular lines that intersect your populations. If you see a diagonal
population, this is a sign of spillover and indicates that it is necessary to
apply a compensation value between the two channels (Figure 1A). Applying
compensation means you are telling your cytometer to adjust the fluorescent
signals by a specific value between the two channels. It is something your
computer will do for you to better analyze your results; it does NOT change
anything in your samples. This means that, unlike voltages, you can adjust
compensation values later in your analysis, unlike voltages.
Figure 1A shows a single-color APC control, with APC and APC-Cy7 plotted against
each other. Since it is a single-color control, you know the beads should only
be positive for APC, yet it seems as if the beads are also positive for APC-Cy7.
You therefore would say that APC is bleeding, or spilling over, into the APC-Cy7
channel and needs to be compensated out of APC-Cy7. After increasing the
compensation values for APC against APC-Cy7, the populations are in more of a
straight line and are only positive for APC (Figure 1B).
For single-color samples, you can only change the compensation values for
whichever fluorophore is present in the sample (APC in the case of Figure 1). In
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Figure 1: Compensation example with single-color control. Flow plots showing an APC single-color control
sample before (A) and after (B) compensation with APC-Cy7. The positive APC population is circled in red
in both panels.
Figure 1A, the population needs to be shifted towards the x-axis, so you would
increase the compensation value for APC. If the population needed to be shifted
away from the x-axis, you would decrease the compensation value.
Pro tip!
To determine which way your
population needs to be shifted, consider
your perpendicular lines. In this case,
the population on the left is a horizontal
line, so our circled population on the
right needs to shift towards the X axis to
become a perpendicular vertical line.
In general, most spillover between samples can
be fixed with fairly low compensation values
(0–50). Inputting compensation values differs
between machines or the program you’re using,
but usually you can click on the correct box
and either manually change the compensation
values or adjust the values with up/down arrow
buttons.
For anyone just starting to use flow cytometry,
it’s generally recommended to start
compensating with single-color controls before
moving onto your experimental samples. With
time and practice, you may be able to do your compensation setup on your
experimental samples without needing to use your single-color controls as a
starting point.
Applying compensation to your experimental samples
In order to do compensation on your experimental samples, you will first need to
gate out your main population and singlets. Then, you can start to compensate
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Figure 2: Compensation example based on experimental sample. Flow plots showing a sample before (A)
and after (B) compensation between APC and APC-Cy7, with compensation panels below each plot.
on your experimental samples. Let’s look at an experimental example in Figure 2.
Figure 2A shows the sample prior to compensation. APC and APC-Cy7 are both
bleeding into each other, as is evident by the two diagonal populations in the plot.
A compensation value of five was applied to APC-Cy7 against APC to correct for
this bleed. A compensation value of 3.7 for APC into APC-Cy7 applied next, which
fixed the bleed in this population. Note that in the corrected flow plot in 2A, the
populations are not perfectly straight, but the center of each population is in a
line with the other populations.
The general rules of compensation apply to your experimental samples as
they did with your single control samples: if the population needs to be shifted
towards an axis, increase the compensation value, whereas if the population
needs to be shifted away from an axis, decrease the compensation value.
If neither of these helps shift your population into the correct spot, you may need
to look at the other channels you’re using first. A different fluorophore channel
may be affecting your populations and may need to be comped first before you
apply compensation values to your current channels.
A notable exception
If you have a sample you are trying to compensate in the top right corner of a flow
plot, meaning that it should be double-positive for the two markers on your plot,
and the population does not move even after applying very large compensation
values (100+), consider what two markers your fluorescent markers are labeling
for. If a cell is positive for both markers, it will show up as a diagonal line in
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Figure 3: Flow plot for a double-positive sample. The cell population in the top right corner of the plot is
positive for both markers, so it shows up on a bit of a diagonal line in the plot. However, because we can
see straight lines in the populations in the other corners of the dot plot, compensation is not required.
the top right corner of the plot and large compensation values will not move it
(Figure 3). If the population is not a double-positive population and is instead
due to bleeding, you will see the populations move, as in Figure 2, following
compensation.
In Figure 3, we also know that on most cytometers, FITC and APC should not
require compensation between each other. We can therefore conclude that this
population is double-positive for both fluorophores and that there is no issue
with compensation here.
Conclusion
Compensating between your fluorescent markers can be challenging! But we
hope this guide can help you get started. Remember, you can always change
your compensation values during the analysis portion of your experiment. So,
as long as you are able to distinguish your populations from each other on your
flow cytometer, you should be able to run your experiment and finalize your
compensation values later. This can be helpful as you’re learning! n
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Yes, No, and Everything in
Between
N
Priyamvada Prathima, August 2024
ow that you know how to read flow plots and have designed your
first flow panel, you’ll load your samples into the cytometer and
see one of two results for your antibody of interest: two clear
populations or a huge smear across your FSH vs reporter plot. In this section,
I’ll walk you through how to interpret the antibody readout on a flow plot.
Note that for the flow plots discussed in this section, voltage has been
adjusted appropriately and compensation performed. Before you read
out your plots, you’ll want to make sure you’ve also adjusted voltage and
compensated your colors.
Here are some good practice tips on adjusting voltage: During data
acquisition, make sure that the signal for the positive population is no higher
than 105 signal intensity. Signals any higher than this may not be clearly
picked up by the sensors. You can adjust this by changing the voltage for
the particular fluorophore prior to recording data. Increasing the voltage will
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shift the population to the right while decreasing the voltage will shift it to the
left. If your signal is too high, lower the voltage to have your positive population
between 103.5 and 104.5 for a cleaner readout.
A yes-no plot
If you are using an antibody to mark the presence or absence of a protein, a yesno plot is the best case result. For example, you have introduced a GFP protein
into your cells (visualized via the FITC channel) and you are trying to see what
percentage of total live cells are GFP+. In this plot, with adjusted voltage, cells
that did not receive the GFP protein are most likely to be clustered at signals
below 103, while the GFP+ population cluster would be shifted to the right in
comparison, say somewhere between 104 and 105 signal intensity.
Pro tip!
If your samples and control underwent
different treatment processes, you may
see a shift in the negative population, so
it no longer overlaps with the control.
If your samples and controls were
processed the same way, the GFPpopulation should overlap with the
negative control population (Figure 1).
Based on where the GFP- population
ends, you can draw a gate which selects
anything to the right of it, marking it as
GFP+. Quantification for such graphs
could simply be the percentage of the
total cells present in the gate.
Figure 1: Example of yes-no flow plot of the (negative) control (left) or with the sample (right). Negative
and positive samples are clearly distinguishable as two separate populations. Created with
BioRender.com.
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Figure 2: (A) a smeary dot plot showing a range of antibody readouts. (B) A gate on the smeary dot plot
shows the population of interest. Created with BioRender.com.
In tricky experiments, these populations may overlap slightly, or the distinct
populations may not be as obvious in a dot plot. If that is the case for you,
switching to a contour plot should indicate where the two populations diverge.
The smear plot!
The dreaded smear — fear not! When you are looking to study the level of protein
expression, instead of its presence or absence, the plots can look quite different.
You might have high expression, low expression, and everything in between
(Figure 2). These smears tend to occur when you are looking at proteins that are
expressed regularly in cells and might be upregulated or downregulated based on
your experiment. You can see from Figure 2 that determining where to place your
gate based on the dot plot would be complicated.
In such cases, instead of trying to quantify based on percentage positive or
negative in your dot plot, looking at a histogram vs antibody plot would be
Figure 3: An example of a histogram plot for a yes-no flow cytometry readout. Created with
BioRender.com.
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Figure 4: A histogram plot for a smeary antibody readout. The orange peak is the baseline expression of
the protein of interest. Created with BioRender.com.
better. The histogram shows peaks of high signal densities, so signal shifts due
to upregulation or downregulation are easier to identify by comparing peak
positions to the base expression. When compared to the control — which
should be base expression — a peak to the right indicates a higher signal, or
upregulation, and a peak to the left indicates a lower signal, or downregulation.
Figure 3 shows a histogram for a yes-no dot plot, like the one shown in Figure 1.
Using histogram plots to quantify smeary plots
For smeary expressions, you can quantify shifts in signal using the mean
fluorescence intensity (MFI) of the different populations. MFI is the average
brightness from all cells that are positive for the marker of interest. MFI would be
higher for a signal peak that shifts to the right on a histogram plot and lower for
a signal peak that shifts to the left. When using MFI, you can still continue gating
populations of interest with normal gates, just based on a histogram plot instead
of a dot plot. However, the histogram plots look a bit different than they do for
yes-no readouts.
The plot in Figure 4 is for a smeary expression and is quite different from the
yes-no histogram plot shown previously. Here, the orange peak is a baseline
expression of a protein while the green (top) has an upregulated expression (shift
to the right). The blue (third from top) could be a downregulation of signal, though
it’s hard to call from this plot. The red (bottom) peak overlaps with the baseline
suggesting this sample has a comparable expression to the baseline/negative
control.
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You can see how it is easier to visualize these four populations in the histogram
plot in Figure 4 compared to the dot plot in Figure 2. Quantification using MFI
makes it easier to identify and study these subtle shifts. Additionally, you can gate
off the histograms above, allowing you to visualize your data as a dot plot.
To summarize, if you have clear positive and negative populations for your
marker of interest, a dot plot would serve you well. Percentage of cells in a parent
gate (all live cells for example) that are positive for your marker would be a good
statistic to compare between samples. In cases where there is no clear distinction
between the presence/absence of your marker but there are subtle shifts in
expression, using a histogram plot and MFI values to characterize your samples
may allow for better interpretation. n
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Beyond Surface Labeling
W
Paul Heisig, June 2024
hen it comes to labeling cells for flow cytometric analysis,
the most common method is a cell surface label, where
fluorophore-conjugated antibodies directly bind to epitopes
of interest that are found in the extracellular space. The targeted epitopes
can be motifs within transmembrane proteins, such as receptors, or posttranslational modifications on those proteins, like glycosylation patterns.
Surface labeling is particularly useful for classifying and sorting cells by
identifying lineage markers, like many of the cluster of differentiation (CD)
proteins found on immune cells.
However, there are more techniques and strategies one can employ beyond
simply incubating cells with single-fluorophore-conjugated antibodies. These
methods can include labeling markers with multiple proteins/antibodies,
targeting markers found within the cytosol/nucleus, or visualizing DNA instead
of proteins. Some stains can diffuse through the cell membrane freely,
while others require a permeabilized membrane to enter cells. Depending
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on the research question and downstream application, one or several of those
techniques can be used alongside standard surface labels. Here, I will introduce
you to a few useful labeling and staining techniques beyond standard
surface labeling.
Indirect labeling
Pro tip!
While antibodies used for target marker
detection are usually conjugated to a
fluorophore, not every marker will have
a corresponding fluorophore-conjugated
antibody commercially available. In other
cases, fluorophore-conjugated antibodies
may exist, but only with a limited selection of
fluorophores. If the available colors are already
reserved for other markers in your panel, and
thus cannot be used, you may want to look at other methods. In the above cases,
a good alternative is using a two-protein detection system, as is commonly used
in western blotting or ELISAs.
Many people use ‘labeling’ and
‘staining’ interchangeably to refer to
any technique that combines a marker
with a signal. For clarity, Addgene uses
‘labeling’ to refer to antibodies and
‘staining’ to refer to small molecule dyes.
A popular method for indirect labeling employs a primary epitope-targeting
antibody that is conjugated to a biotin tag but not a fluorophore. In a
separate step, cells are incubated with a fluorophore-conjugated streptavidin
protein, which binds the primary antibody through the biotin-streptavidin
interaction (Figure 1a). This interaction brings the fluorophore in proximity to
Figure 1: Indirect detection of target markers can be achieved through two-protein labeling by utilizing
a) biotin-streptavidin binding and b) antibody host species interactions. Created using BioRender.com.
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the target marker, allowing for its detection. The advantage of this system is
that streptavidin conjugates are commercially available for a wide range of
fluorophores, allowing for great flexibility when it comes to fluorophore selection
for panel design.
Another way of indirect labeling is through antibody host species reactivity. Just
like for western blotting, a primary epitope-targeting antibody can originate from
a range of species; for example, rabbit or mouse. In a second step, a fluorophoreconjugated secondary antibody is added that binds the conserved, host speciesspecific (“anti-rabbit”) region of the primary antibody (Figure 1b). As with
streptavidin, the advantage of this system is increased flexibility for panel design.
Another perk is that one primary antibody can be bound by several secondary
antibodies, increasing the fluorescence signal intensity. However, you need to be
careful when selecting the primary antibody host species, as it has to be different
from the other antibodies in your panel. Otherwise, your secondary antibody will
bind to multiple primary antibodies, yielding a false positive signal.
Dump gating
When you are working with heterogeneous cell mixes, like bulk lympho-/
splenocytes, you might only be interested in one of the various cell populations;
for example, CD8+ T (“killer T”) cells. Typically, you would label the CD8 coreceptor protein on the surface of the CD8+ T cells and gate on the positive
events for your flow analysis. However, certain downstream applications, like an
in vivo adoptive transfer of those CD8+ T cells, require the cells to be minimally
altered, so you would not be able to use antibodies that block surface proteins. In
that case, you would want to look at negative selection: labeling markers that your
cells of interest do not have, and selecting for the cells that are not labeled.
An easy and quick method of negatively selecting your cells of interest is magnetic
bead-activated cell sorting, or MACS. But MACS usually only allows for sorting
based on a single (lineage) marker, while fluorescence-activated cell sorting (FACS)
enables you to include further (non-lineage) markers alongside, e.g., for cellular
activation. In addition, FACS achieves greater purity of your sorted cell sample
than MACS.
An effective strategy to sort out your cells of interest via FACS using negative
selection is called dump gating. Let’s say we want to sort out all CD8+ T cells from
a mix of CD8+ T cells, CD4+ T cells, B cells, and dendritic cells. In this case, we
simply need to label the lineage markers of all cell types except CD8+ T cells. We
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Figure 2: Dump gating labels all unwanted cell lineages with the same fluorophore to easily gate on the
unlabeled cells of interest while the rest can be excluded, or “dumped”. From left to right: B cell, CD4+ T
cell, dendritic cell, CD8+ T cell. Created using BioRender.com.
might choose the markers CD4, CD19, and CD11c, which would label everything
but the CD8+ T cells (Figure 2). Even better, we can use the same fluorophore for
the three lineage markers we have chosen. As a result, all cells positively labeled
with the selected color will not be CD8+ T cells and we can simply “dump” them by
gating on the negative population.
Intracellular labeling
Oftentimes, a marker of interest is not located on the cell membrane but inside
the cell. Due to their chemical nature, antibodies cannot penetrate the cell
membrane; hence, intracellular labeling with antibodies requires chemically pretreating the cells to allow for entry of the antibodies. Here, a two-step process is
employed: the cells are first fixed and then permeabilized, using two different
buffer solutions. Fixing causes protein cross-linking, killing the cell but preserving
the cellular state (think of mummification). Permeabilization, as the name
suggests, perforates the cell membrane to allow for antibodies to enter the cell
(Figure 3). A useful side effect of the fixing/permeabilization (fix/perm) treatment
is that the cells are now stable for much longer, even at room temperature,
allowing for a flow analysis to be shifted to a later day. Note that intracellular
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labeling must be performed only after viability staining and surface labeling are
already done.
Intracellular labeling can be subdivided into two categories, depending on
the location of the targeted markers. Some commercially available fix/perm
kits allow for labeling of cytosolic proteins, as well as proteins of the secretory
pathway. Those can include cytokines and chemokines; enzymes like kinases,
phosphatases, and ubiquitin ligases; and many more. This process, however,
leaves the nucleus intact. If markers of interest are located inside the nucleus,
a different fix/perm kit is required (to be precise, the fixing solution is different
while the permeabilization solution can be the same). Examples of such targets
include transcription factors, histones, and DNA repair enzymes. Note that when
using the nuclear fix/perm buffer to label intranuclear proteins of interest, you
can label cytosolic proteins alongside the intranuclear ones. You do not need to
use a separate fix/perm cytosolic procedure in that case.
Figure 3: For intracellular labeling cells need to be fixed and permeabilized prior to incubation with the
labeling antibodies. Only then can the antibodies bind to their targets in the cytosol and nucleus. Created
using BioRender.com.
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Dye stains
Dye stains are non-antibody-based stains that bind DNA or free amines of
proteins. Depending on the application, they can be membrane-permeant or
membrane-impermeant, and the cells of interest can be the ones that are positive
or negative for the dye. The main applications of dye stains in flow cytometry are
cell proliferation and cell viability. Unlike intracellular labeling, dye staining can be
done on live cells.
For cellular proliferation, esterified dyes that can enter cells freely are typically
used. Inside the cell, the ester group is cleaved off by enzymes, turning the
resulting molecule fluorescent and membrane-impermeant. With each cell
division, the (invariant) amount of dye is reduced per cell (Figure 4a), yielding
a stepwise decrease in fluorescence intensity in a (histogram) flow plot (Figure
4b). Through this method, differences in proliferation between cell populations
can be visualized, achieved through, for example, a previously induced gene
knockout. Besides amine- and DNA-binding dyes, nucleoside analogs like
bromodeoxyuridine (BrdU) can be used. These dyes are incorporated into the
DNA during DNA synthesis and then diluted with subsequent rounds of DNA
synthesis (cell divisions).
Cell viability dyes are membrane-impermeant and can only enter cells with
compromised plasma membranes — i.e., dead cells — while live cells are
Figure 4: Proliferation dyes can visualize proliferative capacity and speed of cells. a) With each division
half of the dye molecules are lost and the fluorescence signal weakens. b) The heterogenicity of
proliferative behavior in a cell population can be seen as a stepwise decrease in signal strength in a flow
plot. d = days post treatment. Created using BioRender.com.
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protected. Amine-binding viability dyes can still bind free amines on the cell
surface, which yields a much weaker signal on live cells compared to dead cells.
As a result, the largely negative live cells can still be easily gated on during
the analysis.
Conclusion
Congratulations! You made it through this section and hopefully learned a few
new methods of target visualization beyond the simple surface labeling. Let’s
briefly walk through the topics we covered in this section.
Besides direct labeling, indirect labeling can be used when suitable fluorophoreconjugated antibodies are not available for your marker of interest. Indirect
labeling is achieved through protein-tag or protein-protein interactions, like
biotin-streptavidin binding and antibody host species reactivity. When sorting cells
with a high resolution for specific downstream applications, while maintaining
a clean cell surface, dump gating is a useful way to get rid of undesired cell
populations. Dump gating uses the same color for different markers, making it
easy to gate on the negative cell population. To detect non-surface markers, a
separate intracellular labeling protocol is necessary, requiring chemical treatment
of the cells after surface labeling. Intracellular labeling can be performed on
cytosolic and/or nuclear markers. Lastly, stains, based not on antibodies but
on small molecule dyes, are useful, most notably when it comes to viability and
proliferation staining. Dye stains are membrane-permeant or -impermeant and
bind DNA or free amines, depending on the application. n
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Conventional vs Spectral Flow
Cytometry
F
Lila Witt, August 2023
low cytometry is one of the most powerful tools available to
immunologists, allowing for the rapid analysis of cell populations
within a heterogeneous tissue type, such as peripheral blood
mononuclear cells (PBMCs) or tumors, and moreso than other methods, the
identification and isolation of rare cell types. In recent years, full spectrum
flow cytometry (“spectral flow”) has emerged as a particularly useful tool for
those wishing to run larger panels: five laser spectral flow machines have 64
detectors, allowing for the use of up to 64 markers in a single panel! Although
conventional flow cytometry and spectral flow cytometry are more alike than
they are different, there are a few aspects in which spectral flow cytometry
requires special considerations.
Conventional flow cytometry
During conventional flow cytometry, light emitted by a fluorophore is guided
to a single detector using a series of mirrors and filters. Importantly,
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conventional flow cytometry uses one detector to detect one fluorophore
and therefore one cellular marker. However, emission spectra of certain
fluorophores can “spill over” into detectors that are not the primary detector for
that fluorophore. For example, in Figure 1, the FITC signal is spilling over into
the primary detector for PE. In order to correct for this, compensation has to be
applied to “subtract” this spillover and correctly distinguish between markers.
Additionally, since the emission spectra of the fluorescent molecules can be quite
broad, detectors will usually only pick up a small portion of that spectra to use for
compensation.
Spectral flow cytometry
Full spectrum flow cytometry, or “spectral flow” cytometry, on the other hand,
utilizes the full spectrum of light to distinguish one fluorophore from another.
Instead of using a single (primary) detector to identify a fluorophore, spectral
flow cytometers use all detectors for all fluorophores. This allows for the creation
of much larger panels, as fluorophores that cannot be distinguished using
conventional flow can (sometimes) be distinguished using spectral flow since the
machine uses the full spectral signature, not just a narrow band, to discern one
marker from another.
Instead of compensation, which relies on user adjustment, spectral flow
cytometry relies on “unmixing.” Unmixing uses a complex mathematical
algorithm and single stained reference controls to distinguish one fluorophore
from another within a fully stained sample.
Figure 2 is an example of the emission spectra for two fluorophores excited
by the violet laser, BV421 and BV711. These “rainbow box” plots are unique to
spectral flow and can be thought of as a series of histograms flipped onto its side
and compressed into a heat map. BV421 peaks in V1, with secondary peaks in V3
Figure 1: The emission spectra and filters for FITC and PE on a conventional flow cytometer.
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Figure 2: The full emission spectra for BV421 and BV711 on a 3-laser spectral flow cytometer
and V4, whereas BV711 peaks in the V13 channel, with secondary peaks in the R3
and R4 channels. These unique peaks in the spectra can be used to distinguish
these two fluorophores from one another during spectral unmixing.
Using spectral flow
When deciding between weather to use conventional or spectral flow cytometry
for a given experiment, it is important to consider how many parameters you
want to use in a given experiment. For very large panels (upwards of 30–40
colors), spectral flow cytometry might be the better choice. Of note, there is no
minimum number of markers that needs to be used with spectral flow — you
can run a three color panel or a 40 color panel! When preparing a spectral flow
experiment and panel, there are a few critical factors to keep in mind in order to
get the best data possible.
Spectral flow allows for autofluorescence extraction
Cells produce a low level of fluorescence called autofluorescence (you can see this
in your unstained flow controls). Spectral flow cytometers include a tool called
“autofluorescence extraction” which allows the user to use the autofluorescence
in a sample as a fluorescent marker. Given that the level of autofluorescence
across various tissue types varies, it is critical to include an unstained sample
for each tissue type you are running in your panel if you wish to use the
autofluorescence extraction feature. Although autofluorescence extraction is an
optional feature of spectral flow cytometry, it can greatly improve data quality, as
it allows for better resolution of positive and negative populations.
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Careful panel design
In both conventional and spectral flow cytometry, it is important to consider the
expression levels of the markers you are including in your panel. Highly expressed
markers should be paired with dimmer dyes so they don’t “wash out” the rest of
the panel and create spreading error. Conversely, markers with low expression
should be paired with brighter dyes, so they can more easily be detected. Cytek’s
website has a convenient tool, called the similarity index, which allows the user
to compare the emission spectra between all of the markers in the panel. This
complexity index ranges from 0 to 1, with a value of “0” indicating those markers
are completely distinct from one another, and a value of “1” indicating those
markers are identical and completely indistinguishable. This tool is of particular
value when designing a panel.
Level of co-expression among cellular markers
You’ll also need to know if markers are co-expressed in your cells when designing
your panel. If you have two highly overlapping cellular markers, it is important to
pair them with two fluorophores that have distinct spectral signatures and a low
similarity index. In Figure 3, we can see that PerCP and PE/Cy5 have a similarity
index of 0.87; therefore, two overlapping cellular markers should not be paired
with these dyes, as this would create difficulty when trying to resolve those
Figure 3: Similarity index across 12 different colors on a three-laser spectral flow cytometer.
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populations. PE/Cy7 and BB515, on the other hand, have a similarity index of 0,
which would be a better choice for overlapping markers.
Compensation/reference controls
Compensation or “reference” controls are samples stained with a single
fluorophore. They’re required for every fluorophore you use in your panel,
whether it’s conventional or spectral flow. In spectral flow, reference controls tell
the software to look for that exact signature within your fully stained samples.
If you are using conjugated dyes, it is critical that your controls and your sample
antibodies are from the same batch, to avoid unmixing issues from batch-tobatch variation. Tandem dyes (two fluorescent molecules conjugated together)
can be especially sensitive to batch effect. For example, when a PE molecule
is conjugated with a Cy7 molecule, there is no telling exactly how many Cy7
molecules are added during that particular batch. This can present particular
difficulty in unmixing if your sample is stained with a PE/Cy7 antibody from a
batch that is not the same as your single color control.
Reference controls: beads or cells?
Compensation beads are a convenient tool to obtain a strong positive signal
for a given fluorophore, as they contain a mixture of beads that will either bind
any antibody (positive population) or won’t bind anything (negative population).
It is strongly recommended that when you first run a new panel on a spectral
cytometer, you prepare reference controls of both beads and cells for a single
fluorophore. Although compensation beads are convenient in that they provide a
strong positive signal, even for rare cellular markers, beads can sometimes distort
the fluorescent signature upon antibody binding, leading to discrepancies during
unmixing. Additionally, it is critical to include unstained beads or cells within your
reference group to ensure proper unmixing. This is because both compensation
beads and cells will have a level of background autofluorescence that needs to be
accounted for in order to distinguish the spectral signature of a fluorophore itself
from the autofluorescence of the beads or cells.
Overall, spectral flow cytometry has the ability to generate incredible amounts
of data from a single experiment. Although there are a few aspects of spectral
flow that some users may find more difficult compared to conventional flow
cytometry, there are many additional resources that can help ensure that your
first spectral flow cytometry experiment is a successful one! n
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Making Antibodies
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Producing Recombinant
Antibodies
W
Kate Harten DeMaio, August 2024
hile monoclonal and polyclonal antibodies are readily
available from several sources, fewer sources of recombinant
antibodies (rAbs) exist (though Addgene has a great collection
of ready-to-use rAbs and rAb plasmids!). Since recombinant antibodies
conveniently allow for unlimited production, reliable expression, and easy
distribution as DNA (Trimmer, 2020), you may be interested in using them
in your own experiments. It is possible to produce your own recombinant
antibodies with some molecular biology and cell culture experience. Let’s go
over the basics of making rAbs.
Production
Recombinant antibody production can take place in bacterial, yeast, plant,
or mammalian cells. Each of these systems has their advantages and
disadvantages. Briefly, bacterial and yeast cells are more cost-effective and
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faster growing than mammalian cells, but mammalian cells can perform humanlike post-translational modifications that bacterial and most yeast cells cannot.
Your choice of production system may depend on what downstream applications
you are planning, cost, and the amount of protein you need to produce.
The most common yeast strain used for recombinant antibody production is
Komagataella phaffii (a.k.a Pichia pastoris) because it secretes fewer of its own
proteins, making the purification process simpler. Due to its high yields, E. coli
is often used for the production of Fab fragments where variable chains are
secreted into the periplasmic space to be oxidized and form disulfide bonds
(Frenzel et al., 2013). If you’re using a mammalian production system, cell lines
typically used include Human Embryonic Kidney (HEK) cells and Chinese Hamster
Ovary (CHO) cells. (L’Abbé et al., 2018).
Recombinant antibodies can be produced through a single or dual plasmid
transfection, depending on whether the heavy and light chain genes are
contained to one plasmid or if they’ve been separated onto two. You’ll first
transfect your cells with your plasmid(s), and then wait 7–14 days (in mammalian
cells) to collect the cell culture supernatant for further processing. It may be
helpful to incorporate regular feeds during this incubation time to maintain cell
viability and increase protein production (Schwarz et. al., 2020).
Harvest and purification
At its core, harvesting recombinant antibodies consists of separating antibodies
from media, cells, and debris. Affinity chromatography is a popular choice for
this. Chromatography columns containing appropriate immobilized ligands are
incubated with cell culture supernatant containing antibodies. The antibodies
bind the immobilized ligands and can be eluted once the other materials have
been washed away (Figure 1).
Another method of harvesting uses magnetic beads. In this method, beads are
incubated with the cell suspension. Antibodies bind ligands on the beads and a
magnet is used to separate the beads from the media (Brechmann et al., 2021)
while the media is aspirated. The beads are then washed, followed by antibody
elution.
Whatever method you choose, you may not be able to elute the antibodies
directly into your preferred buffer. If that’s the case, a buffer exchange can be
performed using a desalting column or an ultrafiltration concentrator. As the
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Figure 1: The steps of rAb production. Created with BioRender.com.
name suggests, you may also concentrate your prep to the desired concentration
using these columns. If you are conjugating your antibody in a downstream step,
you’ll want to ensure that your buffer components are compatible with your
conjugate and conjugation chemistry.
Quantification
Once you’ve collected your antibody in the appropriate buffer, it’s time to quantify
it. There are several options to choose from depending on the time, reagents,
and equipment available to you. Whatever you choose, you’ll want an accurate
concentration to give your downstream applications the best chance at being
successful.
Using a spectrophotometer is among the faster methods of quantification. Many
spectrophotometers have an IgG protein setting. If yours does not, be sure to
set the absorbance to 280 nm (Pace et al., 1995). You’ll also need to know the
extinction coefficient to calculate the concentration. At 280 nm, the extinction
coefficient of IgG is around 1.35, though this can vary depending on the specific
amino acid sequence (Maity et al., 2015). If your antibody is not IgG, make sure to
look up the setting for your isotype.
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Antibody concentration may also be measured through a protein stain, such
as Coomassie blue. Though more time-consuming, this assay will also measure
the purity of your prep. In this method, antibodies are separated by size on an
SDS-PAGE gel. Once stained, the heavy and light chains become visible alongside
your ladder and a protein standard. The density of the antibody bands can be
measured against the protein standard and the concentration may be calculated
using imaging software. Any bands on the gel beside the heavy and light chains
can also be measured. Contaminating bands lower the purity of your antibody
prep. If you have multiple contaminating bands or a few strong ones, consider
increasing the number of washes performed during your harvest.
Validation
Recombinant antibodies should be validated to ensure they bind their
antigens specifically. Possible applications for validation include western blot,
immunohistochemistry, and immunocytochemistry. It’s important to note that
some antibodies are better suited for certain applications. An antibody that
performs well in immunohistochemistry may not perform well in a western blot.
This can be due to conformational changes in the antigen or lower detection
sensitivity of the assay. Where possible, it can be useful to compare recombinant
antibodies to their parental hybridomas.
Aggregation and storage
Aggregation of antibodies may impact their quality and efficacy (Wang et al.,
2018). Aggregation can arise from a shift in temperature, pH, or salt concentration
(Amin et al., 2014). Antibodies may even aggregate just from dropping the
tube they are contained in (Randolph et al., 2018). At Addgene, we ensure the
efficacy of our antibodies through stress testing. An aliquot of our recombinant
antibodies is tested by storing at 37 °C for a period of two weeks. After this time,
quality control assays showed that the majority of antibodies were still equally
effective compared to those aliquots frozen immediately post-harvest. Stresstested antibodies that are not equally effective are shipped to customers on ice,
preventing any temperature shifts. Of course, we are also cautious during the
aliquoting and storage process to prevent dropped tubes.
Now that your antibody has been produced, harvested, purified, quantified, and
validated, it’s ready for use! n
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References
Trimmer, J. S. (2020b). Recombinant antibodies in basic neuroscience research. Current Protocols in Neuroscience,
94(1). https://doi.org/10.1002/cpns.106
Frenzel, A., Hust, M., & Schirrmann, T. (2013). Expression of recombinant antibodies. Frontiers in Immunology, 4.
https://doi.org/10.3389/fimmu.2013.00217
L’Abbé, D., Bisson, L., Gervais, C., Grazzini, E., & Durocher, Y. (2018). Transient gene expression in suspension HEK293EBNA1 cells. Methods in Molecular Biology, 1–16. https://doi.org/10.1007/978-1-4939-8730-6_1
Schwarz, H., Zhang, Y., Zhan, C., Malm, M., Field, R., Turner, R., Sellick, C., Varley, P., Rockberg, J., & Chotteau, V.
(2020). Small-scale bioreactor supports high density HEK293 cell perfusion culture for the production of recombinant
Erythropoietin. Journal of Biotechnology, 309, 44–52. https://doi.org/10.1016/j.jbiotec.2019.12.017
Brechmann, N. A., Schwarz, H., Eriksson, P., Eriksson, K., Shokri, A., & Chotteau, V. (2021). Antibody capture process
based on magnetic beads from very high cell density suspension. Biotechnology and Bioengineering, 118(9), 3499–
3510. https://doi.org/10.1002/bit.27776
Pace, C. N., Vajdos, F., Fee, L., Grimsley, G., & Gray, T. (1995). How to measure and predict the molar absorption
coefficient of a protein. Protein Science, 4(11), 2411–2423. https://doi.org/10.1002/pro.5560041120
Maity, H., Wei, A., Chen, E., Haidar, J. N., Srivastava, A., & Goldstein, J. (2015). Comparison of predicted extinction
coefficients of monoclonal antibodies with experimental values as measured by the Edelhoch method. International
Journal of Biological Macromolecules, 77, 260–265. https://doi.org/10.1016/j.ijbiomac.2015.03.027
Wang, W., & Roberts, C. J. (2018). Protein aggregation – Mechanisms, detection, and control. International Journal of
Pharmaceutics, 550(1–2), 251–268. https://doi.org/10.1016/j.ijpharm.2018.08.043
Amin, S., Barnett, G. V., Pathak, J. A., Roberts, C. J., & Sarangapani, P. S. (2014). Protein aggregation, particle formation,
characterization & rheology. Current Opinion in Colloid & Interface Science, 19(5), 438–449. https://doi.org/10.1016/j.
cocis.2014.10.002
Randolph, T. W., Schiltz, E., Sederstrom, D., Steinmann, D., Mozziconacci, O., Schöneich, C., Freund, E., Ricci, M. S.,
Carpenter, J. F., & Lengsfeld, C. S. (2015). Do not drop: Mechanical shock in vials causes cavitation, protein aggregation,
and particle formation. Journal of Pharmaceutical Sciences, 104(2), 602–611. https://doi.org/10.1002/jps.24259
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