Development of an Enzyme-Linked Immunosorbent
Assay (ELISA) with Enhanced Sensitivity in a
Nanofluidic System
MASSACHUSETTS 'Ns
OF TECHNOLOGY
by
SEP 3 0 2009
Lih Feng Cheow
LIBRARIES
B.S. Electrical and Computer Engineering, 2005
Cornell University
Submitted to the Department of Electrical Engineering and Computer
Science in partial fulfillment of the requirements for the degree of
Masters of Science in Electrical Engineering and Computer Science
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
September 2009
ARCHIVES
© Massachusetts Institute of Technology 2009. All rights reserved.
Signature of Author:
Department of Electrical Engineering and Computer Science
June 19, 2009
Certified by:
Jongyoon Han
..
Associate Professor of Electrical Engineering and Computer Science
Thesis Supervisor
Accepted by:
Terry P. Orlando
Professor of Electrical Engineering and Computer Science
Chairman, Committee for Graduate Students
Development of an Enzyme-Linked Immunosorbent Assay
(ELISA) with Enhanced Sensitivity in a Nanofluidic
System
by
Lih Feng Cheow
Submitted to the Department of Electrical Engineering and Computer Science on
June 19, 2009, in partial fulfillment of the
requirements for the degree of
Masters of Science in Electrical Engineering and Computer Science
Abstract
Experimental studies were performed to evaluate the kinetics and
equilibrium binding constants of biomolecules in nanofluidic channels.
Binding events in the nanochannel were detected using electrical and
fluorescence methods. We concluded that antibody-antigen binding
constants in nanochannels were similar to experiments performed in
microtiter plates at low antigen concentrations; however the bound fraction
in nanochannels at high antigen concentration decreased due to steric
hindrance. Binding kinetics in nanochannels was limited by convective
transport of analytes, instead of diffusion or reaction.
We also found that enzymatic reactions in nanochannels were very
effective due to short diffusion length and high surface area to volume ratio.
A bead based ELISA was developed to exploit the rapid binding reactions in
the bulk and efficient enzymatic conversion in the nanochannels.
Additionally, electrokinetic concentrators were integrated with multiplexed
bead based ELISA to further improve the detection sensitivity of a sandwich
immunoassay.
Thesis Supervisor: Jongyoon Han
Title: Associate Professor of Electrical Engineering
Acknowledgements
First, I would first like to thank Professor Han for his guidance, support and
encouragement throughout the entire thesis process. He is a great advisor and I learnt
from him how to do good research.
I would like to thank Reto Schoch for guiding me in the initial phases of my
graduate work. Current and past lab members of the Han Lab have been very supportive
with my research, and we became good friends. They include Yong-Ak Song, Sung Jae
Kim, Pan Mao, Hansen Bow, Aniruddh Sarkar, Leon Li and Vincent Liu. I also enjoyed
the company of many exchange students who spent time in our lab. Special thanks to
Sung Hee Ko and the staff members at MTL for their help in device fabrication.
Last but not least, I will be forever grateful to my friends and family who
supported me all these years. They helped me become what I am today..
This work is possible with the funding support of Agency of Science, Technology
and Research (A*STAR) Singapore.
Contents
1.
2.
3.
4.
5.
6.
7.
8.
Introduction
Background
9
11
Equilibrium Binding of Biomolecules in Nanospace
Binding Kinetics of Biomolecules in Nanospace
Enzymatic Reactions in Nanospace
11
13
14
Device Fabrication
15
Glass Device Fabrication
Remark: Microchannel Etching
Hybrid PDMS-Glass Device Fabrication
15
17
17
Electrical Detection of Biomolecule Binding
in Nanochannels
19
Experimental Setup
19
Equilibrium Binding of Biomolecules in
Nanochannels
22
Biomolecule Immobilization in Glass Nanochannel
Equilibrium Analysis of Biomolecular Interactions in Glass
Nanochannels
Discussion
22
Binding Kinetics of Biomolecules in Nanochannels
32
Microscale Reaction Kinetics in Nanochannel
Macroscale Convection-Reaction in Nanochannel
Experimental Observation of Binding Kinetics in
Nanochannels
32
40
Enzymatic Reactions in Nanochannels
47
Trypsin Bioreactor in Nanochannel
Horseradish Peroxidase (HRP) in Nanochannel
Enzyme Reaction in Microchannel
Difficulty of Extracting Enzyme Kinetics in Nanochannel
Enzyme Kinetics in Nanochannels Formed by
Self-Assembled Beads
47
48
50
53
28
30
44
54
Enhancing microELISA Sensitivity by
Electrokinetic Accumulation
56
Bead Based ELISA in Microchannel
Limitations of Bead Based ELISA in Microchannel
57
58
9.
10.
Nanofluidic Electrokinetic Preconcentration Phenomena
Integration of Electrokinetic Concentrator with microELISA
Multiplex Microbead ELISA with Integrated Electrokinetic
Concentrator
58
59
Conclusion
Bibliography
64
66
61
Chapter 1
Introduction
Human homeostasis is the body's amazing ability to regulate its inner
environment in response to external perturbations. When a person contracts certain
diseases, the delicate equilibrium is disturbed, and the body responds by upregulating or
downregulating certain proteins to correct the imbalance. Clinically, changes in the
concentration of these proteins serve as a marker of disease progression. Hence, the
ability to reliably detect minute fluctuations in these biomarker concentrations is
extremely important for early diagnosis and treatment of many diseases.
Immunoassay is the most widely used analytical method of measuring the
concentration of a substance in a biological liquid. By using the reaction of an antibody to
its antigen, it has an extremely high specificity. The basis of Enzyme-Linked
ImmunoSorbent Assay (ELISA) is to link the detection antibody to an enzyme, which
would convert a colorless substrate into a chromogenic or fluorogenic product over time,
hence amplifying the signal. The conventional Sandwich ELISA, as its name suggests,
consists of 1) immobilizing capture antibodies on a polystyrene plate, 2) allowing
antigens in the sample to bind to the capture antibodies, 3) adding the enzyme conjugated
detection antibodies to react with the captured antigens, and 4) adding the substrate and
monitoring the product formation over time. Due to the reliability and sensitivity of
ELISA, it has been widely used for various disease diagnoses including HIV.
Despite the popularity of ELISA, there are several problems and constraints
related to its usage. Firstly, the sample volume requirement is rather large. Each ELISA
test in a 96 well plate requires approximately 100L of sample and detection antibodies.
In addition, approximately 1mL of reagent is needed to obtain the standard calibration
curve for a batch of ELISA test. This is a problem when sample volume is limited and
reagents are expensive. Secondly, the time-to-results of ELISA is typically long, ranging
from hours to days. This is due to the ineffective mass transfer of reactions on a planar
surface. Thirdly, ELISA involves troublesome liquid handling procedures, and skills are
required to obtain repeatable results. Automated assay systems used for clinical diagnoses
still require large and expensive equipment. Lastly, the sensitivity of ELISA is
insufficient to detect certain low abundance biomarkers. The lack of high affinity
antibodies for certain biomolecules compromises the sensitivity of ELISA. Improving the
binding capacity and efficiency of the ELISA system is one definite way towards better
detection.
In this thesis, we propose using a micro/nanofluidic system to perform Sandwich
ELISA with the aim of reducing sample consumption, shortening assay time, and most
importantly enhancing reaction efficiency and detection sensitivity. While integration of
ELISA with microfluidics is not new[7, 8], and has been shown to deliver many of the
above advantages, there are several phenomena unique to nanofluidics that could be
exploited.
To identify these phenomena, a thorough investigation of biomolecule
interactions in nanochannel is needed. This thesis aims to address the following three
objectives. First in the list is to compare biomolecular equilibrium binding constants in
nanochannels and in open surface. Second, is to study binding kinetics of biomolecules in
nanochannels, and evaluate the roles of convection, diffusion and reaction in determining
the binding rates. Third, we want to investigate the efficiency of enzymatic reactions in
nanochannels with a constant substrate flow rate. Experimental studies of these three
aspects will form the basis of developing a better nanofluidic ELISA.
This thesis will be organized in the following manner. In Chapter 2, we first
provide a concise background of the three objectives and review relevant literature. In
Chapter 3, we discuss the device fabrication method used in our experiments. In Chapter
4, we briefly describe the experiments and devices used to electrically detect biomolecule
binding in nanochannel. Results from these earlier experiments prompted us to study the
binding equilibrium and kinetics binding of biomolecules in nanochannels using
fluorescence microscopy, as detailed in Chapter 5 and 6. The optical experiment results
were confirmed with computer simulations and used to address Objective 1 and 2.
Chapter 7 describes experiments to extract enzyme kinetics in nanochannels and to
investigate Objective 3. Finally, in Chapter 8, we report on the integration of
electrokinetic concentrator with microELISA to enhance immunoassay sensitivity, using
concepts gleaned from experimental studies in the previous chapters. We conclude by
summarizing our results and suggest future research directions.
"
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Chapter 2
Background
With the advent of advanced nanofabrication techniques and high resolution
detection schemes, it is now possible to directly observe biomolecular interactions in
nanospace. These interactions in confined regions are very interesting from both the
scientific and engineering points of view. Biologists have come to realize that in vitro
experiments were often very different from in vivo reactions, largely due to the crowded
microenvironments in cell cytoplasm. Studies of cell-cell interactions also presented
some interesting dynamics of nanoscale biomolecular interactions, since the ligands and
receptors of neighboring cells associate within the nanogap formed between cell
membranes. From an engineering perspective, the remarkable efficiency of nanoscale
catalysts such as zeolites has captured much attention. Recently it has been demonstrated
that enzymes immobilized on nanoporous material have increased activity and stability.
A systematic characterization of biomolecular interactions in well-defined nanochannels
is valuable for understanding these findings.
Equilibrium Binding of Biomolecules in Nanospace
Recently there has been renewed interest in studying biological reactions in
nanospace. This is motivated by the discovery that the cell cytoplasm is a very crowded
microenvironment due to the presence of many proteins, biopolymers and structural
material such as actin, microtubules and intermediate filament, as shown in Figure 1. The
crowding effect resulted in various nanospaces in which protein-protein interaction
occurs. Surprisingly, the binding affinity of various proteins and their partners was
observed to increase in nanospace compared to a bulk dilute solution. A proposed
explanation for this behavior is that biomolecules prefer the bound state to maximize the
entropy of the overall system[9].
itamaent
Figure 1. The crowded state
of the cytoplasm in a
Eukaryotic cell. The square
illustrates the face of a cube of
cytoplasm with an edge
100nm in length. The sizes,
of
shapes and numbers
Protein
are
macromolecules
correct.
approximately
Adapted from DS Goodsell.
The Machinery of Life,
Actin
Springer-Verlag,
Ribosome
i t
Microtuate
s
fiament
(1992)
(1992)
New
York
c
_
1. I-
I
I
Another possibility is due to excluded volume effect, proposed by Ogston in
1970[10]. Biopolymers are sterically hindered from approaching a distance from rigid
walls comparable to their own sizes, as shown in Figure 2. In a nanochannel where gap
size is comparable to the size of these biomolecules, the effective volume of which the
molecule can occupy is significantly reduced. This reduction of available volume has an
effect of increasing the apparent concentration/activity of these molecules, leading to
higher percentage of binding.
Figure 2. Mutual exclusion
of spherical particles and
R.
rigid walls. Each dashed
curve indicates the volume
within which the center of a
sphere may not be placed.
In a microfluidic system setting, David Beebe's group has shown that the binding
affinity between Protein A and IgG increase in a nanoporous hydrogel environment[4]. In
addition to the entropic contributions in reduced space which favors complex formation
over dissociation, they argued that frequency of collision is inversely proportional to the
cube of the container size in which the molecules were confined. Therefore the proteins
in nanometer sized pores of the hydrogel collide a billion times more often than in a
micrometer sized channel, leading to higher equilibrium binding constant. This is
illustrated in Figure 3.
Figure 3. The size of the
containers reduces as the
pores become smaller. For
L
L
Smolecules,
L2\
the same number of
there are more
collisions as 'L' decreases.
On the other hand, it has been observed that ligand receptor binding between
neighboring cells are much stronger than the three dimensional case. This phenomenon
has a very strong biological relevance as it is implicated in the identification of self vs.
non-self in T cells[l , 12]. A reduction of dimensionality is believed to occur in the
nanogap formed between cell-cell adhesions. The initial reaction between the tethered
molecules fixed them in the contact area and reduces their diffusivity within the
nanogap[13]. Concentration gradient causes the laterally mobile free receptors/ligands to
diffuse into the contact area. This clustering effect is responsible for high avidity multiple
bindings within the contact area that is responsible for downstream signal amplification.
The effective dissociation rate in such two dimensional binding reactions is very low
because of the high rebinding probability within the nanogap. Cells have thus evolved to
~
utilize the high binding affinity in nanogaps to realize exquisite sensitivity and specificity
in molecular recognition.
We hypothesize that similar phenomena could occur in a microfabricated
nanofluidic channel. Our group has developed the technology to fabricate nanofluidic and
microfluidic channels in glass[14]. Briefly, conventional photolithography is used to
define an amorphous silicon mask on a Pyrex wafer. By controlling the etch time in a
Buffered Oxide Etch, nanochannels and microchannels can be fabricated and sealed via
fusion bonding to another piece of pre-drilled Pyrex wafer. This technique allows us to
define nanogaps in a rigid, transparent and nonconductive material where optical and
electrical measurements of binding events can be made reliable. Compared to gel-like
material of random pore sizes in previous studies, nanofabrication gives us precise
control over the gap size, channel design, and flow properties for a systematic study of
equilibrium binding in nanochannels.
Binding Kinetics of Biomolecules in Nanospace
The study of mass transfer and binding to solid surfaces is a crucial part of
development of sensitive on chip protein sensors such as protein arrays, surface plasmon
resonance (SPR) or evanescent wave sensors. It has been shown that the commonly used
mass transfer models developed for SPR, which assume transport from the bulk through a
mass transfer boundary layer at the surface, break down when the channel thickness
decreases to the micron size level. In these cases, surface transport of biomolecules
becomes limited by the influx of analytes (convection-limited) in the device instead of
either by diffusion or reaction rates. In thinner channels, the amount of available surface
binding sites per analyte molecule in the bulk becomes larger and incoming sample will
be depleted due to the surface binding. As more sample flows over the binding surface,
binding sites will saturate, thus allowing the free analytes to propagate further down the
channel. Thus the analyte transport occurs from the inlet to the outlet in a wave like
fashion[15].
If the same volume of sample is flown through a microchannel and a nanochannel
separately, most of the analyte will go through unreacted in the former whereas most
analyte will be captured in the latter. However, sample throughput is likely to be much
lower in the nanochannel. Taking into account the inherent tradeoff between sample
consumption and throughput, device design can be optimized for a particular application.
Besides mass transfer restrictions, other more subtle phenomena could occur in
the reduced dimensions of nanogaps. Hydrodynamic interactions between
macromolecules and nanochannel walls were known to result in hindered diffusion[16]. It
has also been postulated that the low dimensionality in a nanogap could lead to nonfickian diffusion, resulting instead in fractal diffusion relationship[17]. Brownian
dynamics simulations have shown that confinement reduces first collision rate but
increase recollision rate[ 18]. This would increase reaction with high activation energy but
decrease reaction with low activation energy.
Computer simulations can be used to supplement experiments in studying binding
reactions in nanochannel. One interesting situation to simulate is the condition of
extremely dilute solution in a nanochannel. Extremely dilute solutions may contain too
few target molecules to resemble an ensemble average at any given time. For example, a
femtomolar solution contains about one target molecule per nanoliter, the average
number of target molecule in the nanochannel is less than one. For a fraction of the time,
when the target molecule enters the nanochannel, its effective concentration is very high,
whereas for the other times, the effective concentration is zero. Such situation violates the
conventional view of concentration as an ensemble average. Monte-Carlo and a
stochastic matrix based Markov chain will be used to simulate these situations.
Enzymatic Reactions in Nanospace
Enzymes are biomolecules that catalyze chemical reactions by lowering their
activation energy. They serve a variety of important functions inside living organisms,
including signal transduction, motion generation, active transport, and metabolism. The
study of enzyme is fascinating in its own right to elucidate the mechanism of cell
regulation. In biotechnology, enzymes are indispensable. They are used everywhere from
Polymerase Chain Reaction (PCR), immunoassay, to fermentation.
It has been hypothesized that confining an enzyme molecule into a space of
comparable size could limit the surrounding three-dimensional environment available for
the enzyme to undergo unfolding, thus provide a mechanism of enzyme stabilization.
Covalently bound enzyme in nanoporous silica gel glass showed a half-life 1000 times
higher than that of a native enzyme[19]. It has been shown that functionalized
mesoporous silica offer potential electrostatic, H bond and hydrophilic interactions with
the charged amino acid residues of protein molecules[19]. Appropriately functionalized
mesoporous silica provided a much more favorable environment for the enzyme, and
exhibit high affinity to sequester proteins from solution[20].
In the field of bioreactors (e.g. for fermentation), immobilized enzyme reactors
have gradually replaced the traditional continuous stirred tank reactor. An immobilized
enzyme reactor usually consists of a solid phase column on which enzymes are attached.
Substrate is passed through one end of the column and enzymatic products are eluted
from the other end. The high enzyme binding capacity of these solid phases leads to very
efficient substrate conversion. Furthermore, enzymatic products are only present
downstream of the reactor and do not suffer dilution from the upstream volume.
These features are very attractive to implement in a nanofluidic ELISA. The
extremely short diffusion length and high surface area to volume ratios in nanochannels
can rapidly convert a significant percentage of substrate into products to be detected
downstream.
Chapter 3
Device Fabrication
In this thesis, devices containing both microchannels and nanochannels are
fabricated either in all glass substrate or in hybrid PDMS-Glass substrate. Both glass and
PDMS substrates are optically transparent and electrically insulating to enable optical and
electrical interrogation. The all glass devices are rigid and can be reused after chemical
cleaning (Nanostrip) or thermal regeneration (500'C for 5 hours). PDMS microchannels
are fabricated using micromolding technique. Polymeric nanochannels are integrated by
injecting Nafion into a groove in the PDMS substrate. Due to the fast turnaround time for
PDMS device fabrication, they are suitable for rapid prototyping; however the PDMS
devices are one-time-use only.
To study reaction equilibrium and kinetics in nanospaces, the all glass devices are
used because of their well-defined nanochannels. Hybrid PDMS-glass devices are used in
experiments that require electrokinetic concentration polarization phenomena as Nafion
nanojunctions are capable of producing higher ion permselectivity.
Glass Device Fabrication
Glass devices are fabricated at the Microsystems Technology Laboratories (MTL)
with the help of its staff members. The fabrication of these devices is modified from
those reported in [5]. The table below describes the process flow to fabricate these glass
devices.
Starting material: 6" Pyrex wafers, Red process inTRL
Parameters
Machine
Action
Step
Pattern nanochannels
1_1
1_2
1_3
Piranha cleaning
HMDS coating
Resist coating
Acid-hood
HMDS
Coater
Prebake
Exposure
Prebakeoven
EV1
5 min; hydrogen peroxide:sulfuric acid = 1:3
30 min; Prog. 5
OCG 825-34cs; 8 s, 0 krpm; 8 s, 0.75 krpm; 30
s, 2 krpm
1 4
1 5
900C, 30 min
Top side alignment, 30 pm proximity, hard
s
3.5
Exposure:
contact,
Mask /Substrate thickness: 2.3 mm /0.5 mm
1_6
17
Development
Postbake
Photo-
90 s, OCG 934 developer
wet Au
Postbakeove
1200C, 30 min
n
1 8
Wet etching
Acid-hood
19
Resist removal
Acid-hood
BOE
(7:1);
24
nm/min
- 75 s: 30 nm; 125 s: 50 nm; 175 s: 70 nm
- 100 s: 40 nm; 150 s: 60 nm; 200 s: 80 nm
5 min; hydrogen peroxide:sulfuric acid = 1:3
2_1
2_2
2_3
Pattern
microchannels
Piranha cleaning
Dehydration
Mask deposition
Acid-hood
UV-ozone
STS-CVD
2_4
2_5
HMDS coating
Resist coating
HMDS
Coater
2 6
2_7
Prebake
Exposure
Prebakeoven
EV1
2_8
2 9
Development
Postbake
2_10
Mask etching
Photowet Au
Postbake
oven
STS1
2_11
Pyrex etching
Acid-hood
2 12
Acid-hood
2_13
removal,
Resist
Piranha
Mask stripping
3 1
Bonding
Drill Wafers
3_2
3 3
3_4
cleaning,
Surface
Piranha
Surface activation
fusion
Glass-glass
bonding
5 min; hydrogen peroxide:sulfuric acid = 1:3
60 min
Chamber: oxygen clean CF 4 + 02 with
oxystrip.dot, argon clean with arclean.dot
Predeposition: asilicon.dot for 15 s
Wafer: asilicon.dot for 1 min 40 s (50 nm)
(deposition rate 5 A/s)
30 min; Prog. 5
OCG 825-34cs; 16 s, 0 krpm; 8 s, 0.75 krpm;
30 s, 2 krpm
90 0C, 30 min
Top side alignment, 30 pm proximity, hard
s
3.5
Exposure:
contact,
Mask / Substrate thickness: 2.3 mm / 0.5 mm
75 s, OCG 934 developer
120 0C, 30 min
SF6_14; RF power (coil) 300; RF power (platen)
120; 10 s
Water:hydrofluoric acid:ammonium hydroxide =
650:200:150; 0.8 pm/min; -15 min. Check with
profiler to obtain etch depth of 10 pm
10 min; hydrogen peroxide:sulfuric acid = 1:3
STS1
SF6_14; RF power (coil) 300; RF power (platen)
120; 15 s
CNC Mill
(Sherline
Model 2000)
Acid-hood
Manually drill 500pm thick cover glass wafer to
make fluidic connections. Instructions in
http://www.openwetware.orq/wiki/Modrilla
10 min; hydrogen peroxide:sulfuric acid = 1:3
Acid-hood
Box Furnace
EML
30 min, heated ammonium hydroxide
550C, overnight
An example of the all-glass device is shown in the Figure 4. Microchannel depth
was 850nm and platinum electrodes were integrated into the microchannels to perform
electrical interrogation of biomolecule binding in the nanochannel. Holes 1, 6, 7 and 12
provided electrical connections to external measurement units while outlets 3, 4, 9 and 10
provided fluidic access. A drop of epoxy was placed in outlets 2, 5, 8 and 11 to prevent
fluid from entering the electrical connection pads.
Devices used for optical detection of binding events have microchannel depth of
10 Lm to increase sample throughput and facilitate fluid replacement during washing
steps in the experiments. Apart from not having integrated electrodes, the device design
was essentially the same as previously described.
(b)
Figure 4. Design of the device, consisting of two microchannels joined by
nanochannels. Adapted with permission from [5]. Copyright 2007 American
Chemical Society.
Remark: Microchannel Etching
Deep wet etching (-10 pm) of microchannels presented some difficulty initially.
The poor adhesion of amorphous silicon mask on the Pyrex substrate resulted in serious
undercutting during HF etching, and sometimes led to the amorphous silicon mask lifting
off from the Pyrex surface. We found that extensive cleaning of the wafer (using Piranha)
and the mask deposition chamber (Argon cleaning) is needed to alleviate this problem. In
addition, >1 hour of UV ozone treatment immediately before mask deposition step
significantly improved the adhesion strength between the amorphous silicon mask and
Pyrex wafer.
DRIE etching of Pyrex was investigated as an alternative to wet etching. We tried
several glass etching recipes using the STS etcher in MTL. Unfortunately, the etch rate
was too low to be useful due to insufficient RF power. We note that a better process flow
can be realized if Pyrex dry etching capabilities are available in future since is more
resistant to mask undercutting and produces vertical sidewalls instead of isotropic etch
profile seen in wet etching.
Finally, we observed a lateral: vertical etch ratio of -1-1.2 during isotropic deep
wet etching of glass. This should be taken into account during the mask design by
allowing room for channel expansion due to undercutting.
Hybrid PDMS-Glass Device Fabrication
The fabrication details of these devices are detailed elsewhere[21]. Briefly, silicon
masters are dry etched to form molds for PDMS molding process. The following is a
process flow for fabrication of the silicon masters.
1
2
Process Step
Piranha clean
Photoresist Deposition
Machine
Wet hood
Coater
3
4
5
6
Exposure
Develop
DRIE etch
Strip photoresist
EVI
PhotoWet Au
STS 2
Asher
The silicon master is silanized for 1 hour under vacuum to facilitate mold
detachment. PDMS is prepared by mixing 10:1 w/w base to curing agent and degassing
under vacuum for at least 1 hour. The degassed mixture is poured onto the silicon master
and cured in a 65'C over overnight. Cured PDMS is peeled from the master and holes are
punched using 1.5mm round Biopsy to form fluidic connection.
A self sealing vertical Nafion junction is formed in the PDMS device using a
previously reported method[3]. In this method, a thin cut is made into the PDMS piece
using a razor blade. The PDMS piece is prebaked at 95oC for 5 minutes followed by
injection of 1 p.L 5% Nafion into the groove using a pipette. After another 5 minutes
prebake step at 95C on a hotplate, excess cured Nafion is removed from the PDMS
surface using Scotch Tape. This method is illustrated in Figure 5.
To bond PDMS to glass slide, they are both treated in oxygen plasma for 1 minute
and placed together[21]. A strong bond forms instantaneously. Annealing on a hotplate at
95°C for >1 hour significantly increase the bond strength. Before performing experiments
on hybrid PDMS-glass devices, the dry microchannels are hydrophilized by passing
corona discharge through wires inserted into the reservoirs[22]. This step is necessary to
ease channel filling, reduce bubble formation, and increase electroosmotic flow in the
device.
(a)
(b)
PDMS mcrochannels
Cure at 95C and remove
resdual on PDMS suface
Mechanical cutting
across mnrcchannes
Send and drop a nanoporous
material soluhon on the dge
Plasma bonding
Figure 5. Schematic of fabrication process of self-sealing vertical Nafion junction in
PDMS. Adapted with permission from [3]. Copyright 2008 American Chemical
Society.
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Chapter 4
Electrical Detection of Biomolecule Binding in
Nanochannels
In this chapter we will briefly discuss describe the experiments and devices used
to electrically detect biomolecule binding in nanochannel. Further details of the
experiments and results are described in [5]. We have observed some interesting
phenomena from these earlier experiments prompted us to study the kinetics and
equilibrium binding of biomolecules in nanochannels using fluorescence microscopy.
Experimental Setup
The Pyrex device described in chapter 3 is used to perform these experiments.
For electrical detection of immobilized proteins in nanochannels, streptavidin-biotin was
chosen as the model ligand-receptor pair. To perform such bindings in nanochannels,
surfaces were precoated with commercially available PLL-g-PEG-biotin[23]. After
flushing out the excess polymer, streptavidin is injected into the nanochannel for binding.
Impedance spectroscopy is performed across the nanochannel to detect the change in
conductance during the experiment.
(b) PLL-g-PEGIPEGbiotin
(a) Nanochannel
h
(c) Streptavidin
.
50 nm
..
50
Figure 6. Sequential surface modifications for streptavidin sensing. Impedance
spectroscopy is performed across the nanochannel. Adapted with permission from
[5]. Copyright 2007 American Chemical Society.
Streptavidin has a net charge of -2e at pH 7.4, therefore streptavidin binding in a
nanochannel results in more negative surface charge. At low ionic strength (10mM
HEPES buffer), the nanochannel conductance is dominated by surface charge density.
Thus one would expect higher nanochannel conductance after streptavidin binding.
~i--
5
j
Indeed, experimental results have shown that nanochannel conductance increased when it
is incubated with streptavidin solution. The response time decreased when the flow
velocity through the nanochannel increase. Without convection, streptavidin
concentrations below 0.4jiM cannot be measured because binding equilibrium has not
been reached after 1 hour of incubation. However, at high flow velocities InM of
streptavidin achieved binding equilibrium in the nanochannel and can be detected
electrically after 1 hour.
flow
pressure.drivenR
22.9ms
--presw e-driven fow
3.1rmwns,
04 mnVs.pressre-en flow
- 0.4
O mm/s. eleto-r noc flow
-0... Control (resure). 3.1 mms
.
-+-
0.12
0, 5
c
0,25
0.200.08-
-
0.
0.10
11
-6
1E-10
0
1E-5
2
4
10
6
12
Figure 8. Reaction kinetics i0.02
o.oo
IE-11
IE-9
IE-10
1E-8
IE-7
15
0
1E-5
2
4
5
a
10
12
i nM streptavidn flow time (h)
Streptpvidln concentration (M)
Figure 7. a) Normalized conductance change in the nanochannel as a function of
streptavidin concentration after 1 hour of incubation, b) Normalized conductance change in
nanochannel as a function of flow time at InM streptavidin concentration. Control
measurement was made with InM streptavidin solution and a protein resistant nanochannel
coating. Adapted with permission from [5]. Copyright 2007 American Chemical Society.
1
Figure 8. Reaction kinetics in
0.15Streptavidin flow time (h)flow,
measured by normalized
M0.20
it is obvious
First,versus
in these results.
noticing
are worth
featureswhen
interesting
Several
change
is iconductance
convection
is enhanced
kinetics
that binding
concentrations. The
reaction fronstreptavidin
flow velocity of 3.1mm/s is equal
I iPM
0.05--for all measurements. Adapted
InM
/
(9
0_
0
2
_
_
_
4
16
a
Streptavidin flow time (h)
_
10
12
with permission from [5].
Copyright 2007 American
Chemical Society.
Several interesting features are worth noticing in these results. First, it is obvious
that binding kinetics is enhanced when convection is introduced across the nanochannel.
Theoretical studies have predicted that binding kinetics is convection limited[15] and the
reaction front proceeds as a linear wave-like propagation. However, the electrical
measurement cannot tell us the spatial binding pattern within the nanochannel.
Fluorescence detection of labeled streptavidin binding in the nanochannel can give us
detailed information about the position and the intensity of the binding front, thus
allowing us to better understand the binding kinetics in nanochannel with an induced
flow.
Secondly, experimental results showed that the saturation signal changes with
analyte concentration and equilibrium is reached after equal times of -2 hours. This is
unexpected from a kinetics point of view: saturation should have occurred much faster at
higher analyte concentration. Furthermore, the final amount of streptavidin bound at
equilibrium should be very similar, since the concentrations used are much higher than
the dissociation constant of biotin-streptavidin bonds (40fM). We have previously
attributed this to shear induced bond cleavage under strong flow[5]. A detailed
investigation fluorescence measurement should provide us with a better understanding of
binding equilibrium in the nanochannel.
Chapter 5
Equilibrium Binding
Nanochannels
of
Biomolecules
in
Results from the Chapter 4 have shown that binding events in the nanochannel
can be measured electrically, and that convective flow significantly alters the binding
kinetics of biomolecules in the nanochannel. Despite the advantage of being label free,
there are several shortcomings in using electrical methods to measure binding
equilibrium and kinetics in nanochannel. First is the lack of calibration and linearity. The
change in nanochannel conductance is due to the increase in surface charge density as the
negatively charged streptavidin binds. However, the percentage conductance change is
very sensitive to the initial native surface charge density and the ionic environment in the
nanochannel, and these factors are very difficult to measure and control during an
experiment. The conductance change in nanochannel can be used as a qualitative measure
of biomolecular binding, but a quantitative relationship has not been established.
Secondly, electrical measurement cannot provide any spatial resolution of biomolecular
binding in the nanochannel. We do not know whether binding events happen at the
nanochannel entrance, or uniformly within the entire nanochannel. Finally, electrical
measurements have limited sensitivity. The limit of electrical detection of streptavidinbiotin binding is InM, almost 5 orders of magnitude above the dissociation constant of
this complex. Furthermore, electrical measurement cannot distinguish between
streptavidin binding and nonspecific binding of other molecules in the nanochannel.
These problems can be solved by adopting an optical measurement scheme. The
transparent glass devices allow measurement of fluorescent biomolecules in the
nanochannel. Fluorescence intensity is linear with respect to the amount of tagged
biomolecules. The intensity distribution of fluorescence signal indicates the spatial
binding patterns in the device. As we will show later, sensitive optical detection allow us
to detect equilibrium binding at concentrations lower than the dissociation constants of
antibody-antigen complex. This is essential in order to obtain an equilibrium binding
curve within the nanochannel.
Biomolecule Immobilization in Glass Nanochannel
In order to study binding reactions in nanochannel, the capture molecules must
first be immobilized in the glass nanochannel. In this section, we describe several
immobilization schemes that we developed to achieve this purpose.
The starting point is using electrostatic adsorption of PLL-g-PEG-Biotin to
immobilize biotin in the nanochannels, as described in the previous chapter. We first
deposited PLL-g-PEG-Biotin throughout the device, rinse with buffer solution, and then
OC~
--
~~-
~a
I
I
I
flowed in AlexaFluor 488 tagged streptavidin molecules from one side of the
microchannel, similar to the procedure described in the Chapter 4. In this experiment, we
observed significant fluorescence intensity in the inlet microchannel, much more than the
nanochannel. Based on this observation, we realized that many streptavidin molecules
bind to biotin in the microchannel, even before they reach the nanochannel. At low
concentrations of target molecules, they could be significantly depleted in the
microchannel, leading to the low detection sensitivity shown in the Chapter 4.
We developed an immobilization scheme to saturate the nanochannel with capture
molecules but eliminate them in the inlet microchannel. This scheme utilizes a flow
patterning method as shown in Figure 9. We flow the capture molecules into the top
microchannel at 3g±L/min and flow a passivation molecules into the bottom microchannel
at 2L/min. Due to the higher flow rate at the top compared to the bottom microchannel,
capture molecules flow through the nanochannel and is electrostatically adsorbed. Only a
very small amount of the capture molecules enters the bottom microchannel from the
nanochannel, but they are prevented from adsorption to the surface by the passivation
molecules. Moreover, the tiny amount of capture molecules quickly diffuses into a large
volume in the bottom channel, further reducing their ability to adsorb to glass surface.
After rinsing both channels with buffer solution, the target molecule is introduced from
the bottom channel. Since capture molecules were only deposited in the nanochannel and
top channel, target molecules were not depleted in the bottom channel and only bind in
the nanochannel.
3 ul/min
Capture
molecule
(b)
Nanochannel
Binint
Passivation
molecule
molecule
Figure 9. Flow patterning scheme to prevent target molecule depletion in inlet
microchannel. a) Capture molecule is immobilized in nanochannel and top
microchannel, b) target molecules bind to capture molecules in nanochannel.
We tested this approach using PLL-g-PEG-biotin as the capture molecule, PLL-gPEG as the passivation molecule, and AlexaFluor 488 labeled streptavidin as the target
molecule. Figure 10 shows that the target molecules only bind in the nanochannel.
AlexaFluor
Streptavidin
PLL-g-PEG++++++
+
+
+++
Biotin
Figure 10. a) Fluorescent streptavidin binding to PLL-g-PEG-biotin in nanochannel, b)
schematic of immobilization scheme
~ ~
_--
--
---
II
-
Although this scheme works, it is not ideal for studying binding kinetics and
equilibrium for the following reasons: First, the dissociation constant for biotinstreptavidin binding is in the femtomolar regime. If we want to study binding reaction for
target concentration below the dissociation constant, it will take excessively long time for
sufficient amount of target molecules to be transported across the nanochannel. Secondly,
streptavidin is a tetramer with four binding sites for biotin. It can be captured by multiple
biotin molecules leading to high avidity and masking the intrinsic binding constants.
Lastly, this scheme lacks generality because it only works for biotin-streptavidin binding
and cannot be extended to other molecules that we might be interested to study.
In order to extend this system to study antibody-antigen binding, we tried out the
following system in the nanochannel. PLL-g-PEG-Biotin is first electrostatically
adsorbed in the nanochannel, and is used to capture streptavidin molecules. Following
that, the streptavidin molecules are used to capture biotinylated anti-R-Phycoerythrin
(RPE). Using this scheme, we hoped to study the binding reaction between RPE and its
antibody in the nanochannel. Figure 11 below illustrates this concept.
RPE
iotin-antiRPE
.trep
jtptLL-g-PEG
Biotin
Figure 11. Schematic of the first
immobilization scheme used to
study antibody-antigen binding in
nanochannel.
+++++++++++++
When we performed the actual experiments, we did not observe binding of any
fluorescent RPE molecules in the nanochannel. This could be due to two reasons: First,
the biotinylated anti-RPE did not bind to streptavidin. Second, anti-RPE bound to
streptavidin but RPE did not bind to anti-RPE in the nanochannel. To investigate the first
possibility we captured FITC-biotin molecules using the (PLL-g-PEG-biotin)-(TRITC
avidin) stack in a 96 well plate. The result of this experiment is illustrated in Figure 12.
800(b)
(a) 700
(b)
TRITC avidin
600
500-
FITC biotin
STRITC Filter
m FITC Filter
300
200
+++++++++++++
100
m TRITC Filter
m FITC Filter
nonspecific
PEG
PPB10
PPB50
246
112
334
662
189
165
480
676
Figure 12. a) Comparison of binding efficiency with different immobilization scheme
in a 96 well plate. Blue and red bars indicate binding efficiency of TRITC-avidin
FITC-biotin respectively, b) Schematic of immobilization scheme.
---
I
--
-
I
I
We tried two formulations of 50% PLL-g-PEG-biotin (PPB50) and 10% PLL-gPEG-biotin (PPB10) in this experiment. Results indicate that both PPB10 and PPB50
showed stronger specific binding than the control experiment. On the other hand, PLL-gPEG effectively prevents nonspecific binding. PPB50 has a higher biotin density than
PPB10, thus it is able to capture more avidin molecules creating more binding sites for
FITC-biotin. By monitoring the relative density of TRITC-Avidin and FITC-Biotin
molecules in the nanochannel, we observed that less FITC-Biotin molecule binds per
avidin molecule when PPB50 was used. This is attributed to the biotin in PPB50
competing with FITC-Biotin for the same binding avidin binding sites. The reason for no
RPE binding in the nanochannel is probably due to steric hindrance. A simple calculation
revealed that the combined size of the complex in the nanochannel is -58nm, which
exceeded the nanochannel height of 50nm.
To reduce steric hindrance and eliminate competition effects of long-armed biotin
linkers in PLL-g-PEG-Biotin, we replaced the first layer with the smaller biotinylatedBSA, as shown in Figure 13.
(a)
1400-
00
1200
1000 800
600
400
200 0-
t Series 1
a Series2
no treatment
biotin bsa
PPB10
PPB50
control
1320
1266
1294
1275
788
786
717
653
296
313
0.5%
(b)
(c)
4500
4500
4000
4000
3500
3500
3000
3000-
2500
2so
2000-
S2000
1500
1500
1000
1000
500.
5Wo
0i
0
1.00E-09
1.00E-08 1.00E-07 1.00E-06 1.00E-05 1,00E-04 1.00E-03
RPE Concentration (g/mL)
1.00E-09 1.00E-08 1.00E-07 1.00E-06 1.00E-05 1.00E-04 1.00E-03
RPE Concentration (g/mL)
Figure 13. a) Comparison of binding efficiency using different molecules as the
first layer in a 96 well plate, no treatment means neutravidin is directly adsorbed as
the first layer, b) dose response curve of RPE binding using biotin-BSA as the first
layer, c) dose response curve of RPE binding using neutravidin as the first layer
Q-
The (biotin-BSA)-(neutravidin)-(biotin-antiRPE) stack is able to capture more
RPE molecules compared to PPB10 an PPB50, probably because the biotin on spherical
BSA lack flexibility and cannot compete with biotin-antiRPE for binding sites on
neutravidin. Surprisingly, good RPE binding can be obtained when neutravidin is directly
adsorbed onto 96 well plate surfaces. The dose response curve for RPE binding
equilibrium in 96 well plates is similar whether or not biotin-BSA is immobilized as the
first layer.
When we used (biotin-BSA)-(Neutravidin)-(biotin-antiRPE) to capture RPE in the
nanochannels, we again observed no binding reaction. Binding reaction was observed
when (biotin-BSA)-(TRITC-Avidin) is used to capture FITC-Biotin, as shown in the
figure below. This observation led us to conclude that despite replacing PLL-g-PEGBiotin with biotin-BSA, steric hindrance is still preventing the binding of the large biotinantiRPE and RPE molecules in the nanochannel.
(a)
ITC Biotin
RITC Avidin
Biotin BSA
Figure 14. a) Immobilization scheme in the nanochannel, b) binding of TRITC-avidin
in nanochannel, c) subsequent binding of FITC-biotin in nanochannel.
In the previous experiment, we have observed that a (neutravidin)-(antiRPE)
stack can effectively capture RPE molecules in a 96 well plate. When we tried this
system in the nanochannel, we still did not observe any RPE binding although the
complex size is -40nm, less than the nanochannel height. RPE was observed to pass
through the nanochannel into the opposite microchannel without binding. Our conclusion
for this series of experiments is that binding events require extra space to maneuver in the
nanochannel, and steric hindrance presented a major difficulty in studying binding events
in confined spaces.
The other alternative to study antibody-antigen binding in nanochannel is to
directly immobilize antibodies on glass. This method has not been considered as a first
I
choice as direct immobilization often reduces the activity of antibody. Furthermore, a
high concentration of antibody is needed for direct immobilization, resulting in high
reagent cost. Four protein pairs with high dissociation constants were investigated. They
are illustrated in Figure 15 below.
++++++-
RPE
FITC-IgG
FITC-Biotin
Cy2-antiBiotin
Anti-RPE
Protein A
Captavidin
PLL-PEG-Biotin
D
C
B
A
Figure 15. Four model protein pairs to study direct immobilization of capture
molecule on glass nanochannel and subsequent binding of target molecules.
A. Cy2-antiBiotin binding to PLL-g-PEG-Biotin
antiBiotin has a much higher
dissociation constant (nM) compared
to Streptavidin or Avidin (fM).
Therefore, it is possible to study
binding equilibrium in nanochannel
within a reasonable amount of time.
However, antiBiotin is bivalent. Each
antiBiotin molecule can bind two
biotin molecules, and the antigenantibody bonds can be broken only if
both binding regions dissociate. This
avidity effect could mask the true
binding constants.
B. FITC-Biotin binding to Captavidin
Captavidin is modified Avidin that has
lower binding at higher pH. However,
FITC-Biotin fluorescent signal is
quenched significantly when it binds to
Captavidin. Furthermore, increasing
the dissociation constant by increasing
pH lowers the FITC fluorescence
dramatically.
II
I
L
C. FITC-IgG binding to Protein A
Protein A is a bacterial surface protein that
binds the Fc region of immunoglobulin to
help the bacteria evade immune system
detection. This is an example of receptorligand interaction. Experiments in the
nanochannel yielded good results.
D. RPE binding to physically adsorbed antiRPE
RPE is a natural red fluorescent protein
that can be detected without any labeling
steps. Anti-RPE is physically adsorbed
at high
onto glass nanochannel
concentrations. Direct adsorption can
cause antibodies to loose some activity
because they are not oriented properly.
Nevertheless, detection sensitivity is
sufficient to obtain an equilibrium
binding curve of this antibody-antigen
reaction in the nanochannel.
Equilibrium Analysis of Biomolecular Interactions in Glass
Nanochannel
Based on our previous findings, we decided to study the interaction of two protein
pairs in the nanochannel. The first pair is FITC-IgG binding to Protein A, representative
of receptor ligand interactions. The second pair is RPE binding to anti-RPE,
representative of antibody-antigen interactions.
Equilibrium Analysis of FITC-IgG binding to Protein A in Nanochannel
We directly adsorbed Protein A in the nanochannel using the method described in
the Biomolecule Immobilization section. lmg/mL Protein A coating solution was applied
for 30 minutes, followed by flushing with lxPBS to remove excess Protein A. FITC-IgG
..
. .....
.......................
was applied from the other inlet channel at a flow rate of 5gL/min. This corresponds to
approximately 10flJs flow rate in the nanochannel. Figure 16 shows the intensity versus
time plot as different concentrations of FITC-IgG were injected into the channel. One
advantage of optical detection in nanochannel is that fluorescence of surface bound
molecules overwhelms fluorescence in the bulk solution, due to the high surface to
volume ratio. We confirmed this by observing that fluorescence intensity in the
nanochannel changed very little before and after washing with buffer solution. For all
intents and purposes, fluorescence intensity in the nanochannel represents real time
binding reaction.
1000
900
800
A
-
-
700
-4nM
S600
40nM
S500
400nM
-4uM
S400
300
-
200
100
1 7 13 19 25 31 37 43 49 55 61 67 73 79 85
Time (10s)
Figure 16. Intensity versus time plot when different concentrations of FITC-IgG were
injected into the Protein A functionalized nanochannels.
Figure 17 shows a plot of the dose response curve for FITC-IgG binding to
Protein A in nanochannels. Data is normalized and fitted to a Hill Equation which
describes equilibrium binding. From the best fit curve, we calculated an equilibrium
binding constant of -200nM. This is consistent with values published in other literature.
Unlike antibodies which can have different affinity from lot to lot, we expect the affinity
of Protein A to the Fc (constant region) of IgG (receptor-ligand pair) to be comparable
across experiments. This justifies the comparison with reported binding constants.
,
Qi
1
10
100
1000
mRA;lgG cncereation (n
10000
100000
Figure 17. Dose
response curve of
FITC-IgG binding
to Protein A in
nanochannels.
Data is fitted to a
Hill Equation. The
calculated binding
constant is 200nM
Equilibrium Analysis of RPE binding to anti-RPE in Nanochannel
To study antibody-antigen binding interaction in nanochannel, we immobilized
anti-RPE in nanochannel through physical adsorption. Fluorescent RPE molecules were
then injected into the channel to monitor binding reactions. We experimentally found that
RPE is prone to photobleaching even when illuminated for short intervals using an
electronic shutter. This prevents us from continuously monitoring the binding process.
Instead, we measured the fluorescence intensity in the nanochannel at a fixed time, after
equilibrium has been achieved. Due to lot-to-lot variation in antibody affinity, we
compared the normalized dose response curve for RPE binding in nanochannel with
results in 96 well plates using the same antibody reagent. The results are shown in Figure
18.
1.2
1
O.6
O.2
0
-Q2
FME cron eataon (ngfml)
Figure 18. Dose response curve of RPE binding to anti-RPE in nanochannel (blue
curve) and in 96 well plates (red curve). The binding curves are fitted to Hill Equation to
yield dissociation constant value of 500ng/mL for reaction in nanochannel and 2ug/mL
for reaction in 96 well plates.
The dose response curves are fitted using the Hill Equation assuming no
cooperativity. From the curve fitting parameters, we obtained a KD value of 500ng/mL
for reaction in nanochannels and 2ug/mL for reaction in 96 well plates.
Discussion
Experimental results showed that dissociation constant values for antiRPE-RPE
binding are lowered by a factor of 4 in the nanochannels compared to open surface.
However, there is no measurable change in dissociation constant value for Protein A-IgG
binding under similar experimental conditions.
i
Il~bJI
-
One possible explanation for this observation is that Protein A + IgG complex
(-50kDa + 150kDa) is much smaller than the antiRPE + RPE complex (-160kDa +
240kDa). Therefore, crowding effect in the first case is less significant. The phenomena
of lowered dissociation constant in confined environment have been observed in polymer
solution and hydrogel. This is the first time such phenomenon is directly observed in
well-defined nanochannels.
We considered an alternative hypothesis to explain the observed decrease in
dissociation constant. As we have described earlier, steric hindrance prevents
biomolecular binding interactions when the complex size approaches the nanochannel
height. It might be possible that large complex formation in nanochannels is prevented
when RPE concentration is high, thus lowering the maximum fluorescence signals
intensity in the nanochannel. This would artificially lower the dissociation constant,
defined as the concentration where total binding is at half the maximum. Another
possibility is shear forces in the nanochannel at high flow rates might induce bond
breaking, leading to inaccurate determination of the intrinsic binding constant[24, 25].
Currently, we still have not been able to determine whether the dissociation constant of
RPE in nanochannel is decreased due to crowding effects or steric hindrance/shear effects.
Finally, we consider the possibility of an increase in effective concentration due
to excluded volume effect. Anti-RPE is a -150kDa molecule with dimensions of -15nm
while RPE has dimensions of -10nm. Due to steric hindrance the effective volume that
the target molecule can occupy is decreased, leading to an increase in apparent
concentration as shown in Figure 19. The effective volume decreased 5-folds in a 50nm
nanochannel, which implies a 5-folds increase in effective RPE concentration in the
nanochannel. This is consistent with the experimental observation that the dissociation
constant decreased 4 times in a nanochannel compared with bulk measurement.
151un'
50lun
,5un
RPE
-ti-RPE
Figure 19. Schematic showing excluded
volume effect on immunobinding in
7
\
nanochannel. Due to the large size of the
A
antigens, they are excluded from a 20nm
......
..
I u Sllspace adjacent to nanochannel walls.
0
RPE
Solvent molecules, on the other hand,
can occupy the entire volume in the
Theoretical calculations
Ai -Rnanochannel.
RPE
the
effective
that
_show
concentration is 5 times higher in the
nanochannel.
Chapter 6
Binding Kinetics of Biomolecules in Nanochannels
In this chapter we will study the kinetics of biomolecule interaction in
nanochannels. Kinetics study of low concentration biomolecular interactions in
nanochannel is interesting because there are only a few reactants present in the
nanochannel at any time. For example, at InM concentration, only 1 molecule is present
in a 1 tm x 1ltm x 1ilm volume. Under these circumstances, the ensemble average
assumption breaks down. To study reaction kinetics under confinement, we need to study
discrete and stochastic molecular events.
We investigate two hypotheses about microscale reaction kinetics in
nanochannels. First, reaction rate in nanochannel is higher because there the free ligands
make more collisions with the wall bound receptors. More collisions may lead to higher
association rate, but have no effect on the dissociation rate. The first hypothesis is similar
to that proposed in [4] where they claimed that number of collisions increases as the
'container' size decreases. Second, we will investigate the feasibility of 'Coulter
Counting' mode in the nanochannel. In the Coulter Counting mode, the average number
of molecules within the nanochannel is less than one at any one time. However, the
instantaneous concentration within the nanochannel is very high during the brief duration
that a molecule passes through the nanochannel, due to the small volume. We are
interested in investigating whether such discrete events can modify reaction kinetics.
Monte-Carlo and stochastic Markov chain matrix simulations were performed to verify
the above hypotheses.
Next, we will proceed to study how macroscale mass transport affects the spatial
distribution of binding reactions within a nanochannel. Recently it has been proposed that
bulk concentration front propagates in a wave-like fashion throughout a thin microfluidic
channel [15]. Numerical analysis and experiments will be performed to investigate the
effect of convective transport on kinetics of surface reactions in nanochannel.
Microscale Reaction Kinetics in Nanochannel
Hypothesis 1: Reaction Rate in nanochannel is higher due to more
frequent collisions between free ligands and surface bound receptors
A. Monte Carlo Simulation
In the collision theory of reaction rates, two particles must collide for a reaction to
occur. Therefore, reaction rate is proportional to collision frequency. As device
dimensions shrinks, there are more collision between a free ligand molecule and the wallbound receptor. This high collision frequency may lead to higher association rate but
have no effect on the dissociation rate. Therefore, both reaction rate and equilibrium
binding constant could increase in the nanochannel.
We performed Monte-Carlo simulation to obtain the number of times that a
molecule entering a nanochannel collides with the wall. Figure 20 provides an example of
one such instance. It is clear that a ligand in a nanochannel collides with receptors on the
walls much more frequently than a ligand in a microchannel.
50,
30
90
300 -
0
0
0
1
2
3
4
5
6
7
8
9
1
2
3
10
4
5
6
7
8
9
10
x 105
Figure 20. Monte Carlo simulation of the trajectory of a target molecule going through
a) a nanochannel, b) a microchannel. The target molecule collides with receptors on the
walls much more frequently in the nanochannel than in the microchannel.
However, the above simulation does not take into account there are more ligand
molecules in the microchannel for the same concentration. To compare the total number
of collisions in both cases for the same concentration, we simulate the following
conditions:
Nanochannel Height = 100 unit
Case 1:
Number of ligands = 1
Ligand initial position = 50
Time step = 107
Total number of collisions = 51662
Case 2:
Nanochannel Height = 1000 unit
Number of ligands = 10
Time step = 107
Ligand initial position
50
150
250
350
450
550
Number of collisions
4515
5429
4288
6255
4239
5366
650
750
870
950
Total number of collisions
3126
7771
1443
4990
= 47422
As can be seen from the results, the total number of collisions is the same within
margins of errors regardless of the channel height.
In doing the above simulation I have used the following assumptions:
ID Diffusion <x2>=2Dt
Set Diffusion Coefficient D=6 2 /z= 1/2
Time to diffuse through nanochannel tdiff=<X2 >
When h=100, tdiff104
When h=1000, tdiff=106
I have used 107 time steps to ensure that steady state has been achieved.
The diffusion coefficient, step size, time step and nanochannel height in the above
example has arbitrary units. In order to put in some physiologically relevant numbers, we
consider the following:
For a 15kDa protein such as Lysozyme, the mass is m=2.3X10 -23 g
The value of kT at 300K is 4.14X10 -14 g/cm 2/s2
The measured diffusion coefficient of this molecule D is 10-6 cm 2/s
From Thermodynamics, <v2> 1/2=(kT/m)1/2=1 .3X10 3 cm/s
v= 6/= 103 cm/s
D= 82/U= 10-6 cm2/S
The step size (mean free path) 6=2D/v=10-9 cm
Time step z = 6/v=10 - 12 S
Therefore the above simulation exactly describes ID diffusion of protein in
channel heights of h= 1006 (Inm) and h= 10008 (10nm) for duration of t=10 7T (10 ts)
Monte Carlo simulations make use of a random walk generated by the program.
Each simulation represents one possible realization of the actual experiment. To obtain an
accurate binding kinetics, many such realizations needed to be performed and averaged.
Furthermore, Monte Carlo simulations are time consuming if we need to track more than
one particle, as is always the case in real experiments. For this, we introduce a stochastic
matrix based Markov Chain simulation.
B. Markov Chain Simulation
First we will introduce the model without any surface reaction. Based on a 1D
random walk model, a molecule at position k has equal probability (0.5) of going to
position k-6 or k+6 in a time step i. At the wall boundaries (k=O and k=h) the molecule is
reflected with probability 1. This is illustrated in the Figure 21.
- ---
I --
I -
I
1
L L
0. 5
0.5
0. 5
0.5
0. 5
0.5
0. 5
0.5
0. 5
0.5
0. 5
0.5
1
I
--
Figure 21. Markov chain model of 1D
random walk, with perfect reflection
boundary conditions (no surface
reactions)
|
We can describe this situation with the following transition matrix A and initial
position vo, assuming uniform initial distribution:
0
0
0
0
0
0
0
0
0
0
0.5 0
0
0
0
0.5 0
0
0
0
0.5 o
0.5 o
0
0.5 o
0.5 o
0
0.5 o
0.5 o
0
0
o
o
0
0
0
0.5 0
1
o
o
0.5 o
o
o
o
Io
To find the particle distribution after T time steps, we multiply the transition
matrix A to the power of T with the initial vector vo: VT= A Tv. Matrix manipulation is
extremely efficient in programs such as Matlab, thus we can simulate for more complex
situations. For a perfectly reflecting wall with no surface reaction, the final concentration
distribution is uniform if initial concentration is uniform regardless of device height. The
lack of concentration gradient in the final concentration indicates that there is no increase
in collision frequency in nanochannels.
0o
1
0
0
o
o
0.5
0
0.5
0
0
0
The matrix manipulation method allows us to add finite reaction rate at the
nanochannel surface. We simulated the case where the top surface is perfectly reflecting
and bottom surface has a finite reaction rate k,,o and koff. The transition matrix becomes:
0
0
0
0
0o 0.5 o
0.5 0
0
0
0
0
1
0
0
0
0
0.5 o0 0.5 0
0
0
0
0.5 0
0.5 0
0
0
0
0.5 o
0.5 o
0
0
0
0
0.5 o0 0.5
0
0
0
0
0.5 0.5(1-kon ) koff
0
0
0
0
0
o
0
0
0
0
0
0.5kon
1-koff
- re
I
In the above matrix, column (i) represents the reflecting boundary, (ii) represents
the 1D random walk, (iii) represents the binding to bottom surface receptors, and (iv)
represents dissociation from bottom surface receptors.
By performing the matrix multiplication vT= ATvo we can track the particle
distribution function in the presence of finite surface reaction.
We performed the Markov chain simulations for different heights and different on
and off rates. The reaction shown in Figure 22 is for incubation without any convection.
(1
II
03
*..
...
.
...........
..-.
...
.....
-10
(17
=1K (H=10
..........
..........
= 1 (H=10)
G7
G4
c5
(3
....
(12
(12
0
2D
400
800800
1O0D
=
1400
1600 lD
0
10"
10
101
10o
10'
Figure 22. Markov chain simulation of a) Binding kinetics, and b) equilibrium
binding in channels of different heights. Ligand depletion effect is observed in thinner
nanochannels.
The three sets curves shown represent the binding kinetics of three different
ligand concentrations at different heights. The blue, red and green curve shows binding
kinetics of ligand concentrations corresponding to 10KD, IKD and 0.1KD respectively.
Solid lines and dotted lines are for reactions in nanochannel heights of 100 and 10 units.
As seen in the incubation case, the nanochannel height affects the equilibrium number of
ligands captured. This is due to ligand depletion phenomena. At small nanochannel
heights, the bulk ligand concentration is significantly depleted leading to lower number
of captured ligands. At high ligand concentration, this phenomenon is alleviated. The
dose response curve on the right shows that ligand depletion effects in nanochannels have
an artificial effect of increasing the apparent dissociation constant, defined as the ligand
concentration where 50% of it is bound.
Binding Kinetics with Forced Convection
We can simulate the condition where pressure differences across the nanochannel
continuously replenish ligand molecules in the nanochannel. Previous studies have shown
that a forced convection across the nanochannel improves mass transport and increase the
binding kinetics by a factor of -54 compared to diffusion limited transport[5]. In this
section we study this situation using both Monte Carlo and Markov chain simulation. One
particularly interesting case is when there is on average less than one ligand molecule in
the nanochannel at any one time. This corresponds to the "Coulter Counting" case where
the effective concentration in the nanochannel is higher than the bulk at some intervals
and zero at other intervals. Simulations will show the effects of this phenomenon on the
overall binding kinetics.
The Monte Carlo simulation can be extended to track multiple particles and
include surface reactions. Figure 23 shows results obtained with association rate ka=l and
dissociation rate kd=0.001. A convection flow brings ligand molecules from left to right
of the channel.
(b)
(a)
(i)
5
L5
........
......
......
...........
30o 400
100 20
5oo 600 700 an
900 1000
0
l
0
oo 0 an o
10
200
a
400
ooaoo
700 aoo 9ooaoo0
40
00
700a
0
an
0
4,0* 1ai
(iii)10
ON
__
1
_
--
-11
--
-.. .
.0
......
,4
4W aMPA
20D
o
(ii)
(ii) o0
a
04
01.*
0
1i
1..
30D
600
5M
400
0
00ao
i1
200
500
400
ta
an
6
00
700
00
1000
(iii)
0
6 44 a8
2
1
1
1.4
82
108
2
0
1
0123456789
10
)x
0
0
1W
100
2n
*Z
4W
406
a
Z!
5M
7
6a
8M
800
1
9
90MO 100D
2
3
4
5
6
7
8
9
Figure 23. Monte Carlo simulation of surface
reaction in nanochannels and microchannels.
The dissociation constant KD in all simulations
is 0.001. Target molecule concentrations are a)
10 KD, b) KD, and c) 0.1 KD respectively. The
0
2
4
6
8
10
12
subplots are (i) binding patterns in 100 unit
high channels, (ii) binding patterns in 10 unit
high channels, (iii) molecules bound vs time in
H= 100 channel (blue) and H= 10 channel (red).
x 104
Subplots (i) and (ii) show the binding patterns in nanochannels of different height
after they have reached equilibrium. The blue markers show ligands that are bound to
surface receptors, while red markers show free ligands. In subplot (iii), the red curves
show the binding kinetics in a nanochannel while blue curves show binding kinetics in a
microchannel. The results show that binding in both microchannel and nanochannel
reaches the same equilibrium, as opposed to the case when no flow is induced. This is
because continuous replenishment of ligands prevents its depletion. However, the binding
kinetics in the nanochannel is several times slower than in the microchannel. This is
attributed to less efficient mass transport in the nanochannel.
We can also plot the ligand binding patterns before equilibrium is achieved.
Figure 24 shows the detailed binding kinetics when ligand concentration is above the
dissociation constant. In the nanochannel, binding kinetics follows a wave-like pattern the nanochannel inlet receptors are saturated while downstream ligand concentration is
depleted. This is attributed to the small diffusion length in the nanochannel - free ligands
quickly encounter surface bound receptors as soon as they enter the nanochannel and are
captured. On the other hand, in the microchannel, binding occurs almost simultaneously
at every part of the nanochannel. This is because ligands far away from the nanochannel
walls are able to diffuse downstream without being captured. From Figure 24, we also
observed that the binding density is higher in the nanochannel. For example, in the
nanochannel (H=10), 93 ligands were bound to the first 100 unit lengths of the channel,
whereas 290 ligands were bound to 1000 unit lengths in the microchannel. Although
binding efficiency is better in nanochannel (no molecules pass unreacted), ligand flux
through a nanochannel is much less than a microchannel, thus leading to convection
limited kinetics.
Figure
o
0o
achieved.
-5_1
0
t=100,
ooo,
t=10,
-
_
5oo
1000
U=1,
gas bard = 93
100,, Ugarnc
bourd = 290
_
0o
24.
Ligand
binding
patterns before equilibrium is
*
wavelike
_
_5
oo
(a),(b)
shows
propagation
the
of
lc00 reaction front in nanochannel
(H=10). (c),(d) shows much
more uniform binding across
the entire surface receptor patch
in microchannel (H=100).
t=5000, H=10, Ugacs bond = 476
t=00, H=100, Ugands bound = 1589
The Monte Carlo simulation is verified using Markov chain simulation. Figure
25a shows binding kinetics in microchannel and nanochannel when ligand concentration
is equals to KD. Binding kinetics is nanochannel (H=10) is slower than in microchannel
(H=100), for the same reason stated above. Whereas both top and bottom surfaces
contain receptors in the Monte Carlo simulation, only the bottom surface contains
receptors in the Markov chain simulation. Simulation results show that the binding
kinetics is faster when only one surface contains receptors. This is because the top
surface can now be considered a symmetric boundary condition, so the binding kinetics is
equivalent to a nanochannel of height 20 with both surfaces containing receptors. Figure
25b shows the binding kinetics at individual lateral positions in the microchannel (solid
lines) or nanochannel (dashed lines). Consistent with the previous results, mass transport
limitations in the nanochannel results in a slower binding kinetics and retardation of the
surface binding velocity.
~~~
8D
Tr
10
iTn
1200 1400
16
1800 200
Figure 25. Markov chain simulation of a) binding kinetics in nanochannel (H=10
red curve) and microchannel (H=100 blue curve), b) binding kinetics at different xpositions in the microchannel (solid lines) and nanochannel (dashed lines).
Based on simulation results, we reject the hypothesis that reaction rates in
nanochannel is higher than in microchannel due to more frequent collisions between free
ligands and surface bound receptors. The overall reaction kinetics can be represented by
the following formula: reaction rate=ligandflux x probability of reaction. Although
each free ligand in the nanochannel has a better chance of reacting with surface bound
receptor, the ligand flux in the microchannel is even higher. Overall, the reaction rate in
microchannel is higher because mass transport limitations severely reduce the reaction
rate in nanochannels.
Hypothesis 2: Coulter Counting (Stochastic Sensing) Mode Modifies
Reaction Kinetics in Nanochannels
Finally, we simulate the "Coulter Counter" case where there is less than one free
ligand in the entire nanochannel at any one time. The nanochannel has a height of 5 and
length of 50. We compare the following two cases: 1) one ligand is injected into the
nanochannel every 500 time steps, 2) 1/500 ligand is injected into the nanochannel every
time step. The first case is the actual stochastic binding events, while the second case is
the ensemble average approximation. As can be seen in Figure 26a, the ensemble average
curve is a time average version of the stochastic binding curve. Fluctuations in the
stochastic binding curve indicate the discrete molecular binding and dissociation events
within the nanochannel, which is not captured in the ensemble average model. This
means that binding kinetics in a nanochannel at very low ligand concentrations is
inherently very noisy. In a 50nmx5umx50pjm nanochannel used in our experiment, the
Coulter Counting limit occurs at ligand concentration of 0.InM. Quantification below
this concentration is prone to large errors. The Markov chain simulation results is verified
using Monte Carlo simulation (H=6, L=50, 1 ligand injected every 500 time steps). As
shown in the Figure 26b, there is large fluctuation in the number of bound molecules.
3
-
Stocastic Bindrng
--
Enserrtie Aerage
1.8
1.6
-8
i1.4
QL8
0
500
100
150
2000
2500
Tirne
3000
3500
0
4000
5000
0
O
500
100D
1500
200
2nD
3500
4000
4500
5000
Time
Figure 26. a) Markov chain simulation of stochastic binding (red curve) compared to
the ensemble average approximation (blue curve) in the single molecule limit, b)
Monte Carlo simulation of Coulter Counting mode in nanochannel.
Finally, we would like to note one caveat about these simulation results. Due to
computational constraints, the ligand concentrations (1 ligand/250 unit volume) and
dissociation constants (0.1) used in our simulations are different from experimental
conditions. Experiments need to be done to verify these phenomena.
Macroscale Convection-Reaction in Nanochannel
Recently there were several papers studying the effect of mass transport on
reaction in nanochannel. Karnik et. al.[26] investigated the case where ligands diffuse
into a nanochannel and react with surface bound receptors. They have found that the time
to saturate a finite length d of the nanochannel with ligands is proportional to d2 . Schoch
et. al.[5] investigated the case where ligands were injected into a nanochannel with a
finite velocity, and found that the time scale to saturate the nanochannel is proportional to
d. By applying convection, the mass transport within nanochannel is greatly enhanced,
leading to a reported -54X increase in binding kinetics.
An excellent review on convection, diffusion and surface reaction in microfluidics
is given by Squires et. al.[27] In this review they have characterized receptor-ligand
binding kinetics under convection and diffusion. However, a detailed comparison
between binding kinetics in microchannel and nanochannel is still lacking. Our aim is to
obtain an analytical solution to the steady state binding reaction in microchannel and
nanochannel under convection. Based on these results, we can optimize the channel
dimensions and receptor patch to achieve fastest binding kinetics given experimental
constraints.
First we state the two major assumptions used in this analysis: 1) The system is
assumed to be mass transport limited, the time scale for reaction to occur is much shorter
than the time scale for ligands replenishment; 2) we assume Poisseuille flow profile in
both microchannel and nanochannels.
The figure of merit used in the following analysis is the dimensionless ligand flux
to the surface receptor, F.A large Ymeans faster binding kinetics, since we assumed that
all ligands that reach the surface receptors bind instantaneously. 7 is a
nondimensionalized ligand collection rate according to the following formula:
JD = DcWF
Next, we consider whether it takes longer for a target molecule to diffuse across
the channel or to be swept downstream that same distance. The ratio of these two time
scales is the channel Peclet Number:
diffusive time
H2 D
Q
H-I
Pe H
convective time
H 2W /Q
WD
D
For PeH << 1, the diffusion time of ligands to the channel walls is much faster
than the time for them to be convected downstream. Therefore, complete reaction occurs
at the entrance of the channel, ligands do not travel downstream until the upstream
receptors are saturated. This corresponds to the wave-like binding pattern observed in
nanochannels that have low Peclet number. Since reaction probability is very high in
nanochannel, as shown in our previous microscale simulation, the ligand collection rate is
100%. Therefore, we can calculate the dimensionless flux Y7as follows:
JD = DcWF = cQ
F=
Q
WD
PeH
At low Peclet number, YFis equals to the channel Peclet number.
For PeH >> 1, convection is able to bring ligand molecules further downstream.
The Poisseuille flow profile is given by:
u=
6Q
WH
z(H - z)
At small z, velocity near the walls can be linearized as
6Q
WH
A target molecule that flows 6s distance above a binding patch of length L
requires time c-L/(y8s) to convect past the binding patch. At the same time, this
molecule will take time td
6
s2/D to diffuse towards the surface bound receptors. Equating
these two time scales give us the distance 6, where molecules within this distance from
the channel walls can react with the surface receptors.
LL
6s2
S2
Y6 s
D
1/3
W0
L
s
The sensor Peclet Number Pes relates the diffusion length with the channel
geometry, binding patch dimensions and flow profile. It is related to the channel Peclet
number PeH as follows:
Pes
DS
6Q L2
H2W D
( L) 2 Q = 6 2PeH where
WD
-H
= L
H
Now, ligands in the bulk need to diffuse across the diffusion distance 6s to react
with the surface bound receptors. Therefore, the ligand collection flux JD is determined
by the diffusion flux in the following manner:
Jo = WLDVc = WLD
--
= DcWF
9S
L = Pe 1/3
s
As an example, let us compare the flux Fin two different channel heights, H and
10H,in the high channel Peclet number regime. For this comparison, we keep the binding
patch length L and the average flow velocity constant. Substituting the values H and 10H,
we obtain PeloH=IOPeHand AIOH=AH/10.
FIOH
F
1/3
6o2
FJ 6OH Pe1
62
,2 PeH
1
1/3
= 0.4642
10)
Therefore, the dimensionless flux 9is halved for each decade of increase in H, for
the same average flow velocity. The flux decrease when channel height increase is not
intuitive. A graphical illustration of the Poisseuille flow profile might better explain this
phenomenon.
Figure
-2
1.....
1.4
thinner channel.
1.5
2
Poisseuille
flow
velocity. Near the channel wall,
local flow velocity is higher in the
"
1.2-
27.
in channels of different
height, with the same average
1.6
.profile
25
I
-
-~
-
-
As shown in Figure 27, near the channel walls the local flow velocity is higher in
the thinner channel for the same average velocity. Because of this there is better mass
transfer towards the surface receptors in the thin channel. This contributes to the higher
dimensionless flux in the thin channels at high Peclet number.
The exact solution for the dimensionless flux has been calculated.
The Newman solution, for high X,is F = 0.8 Pes1/ 3 + 0.71Pes
/6
- 0.2Pes- 1/ 3
2
The Ackerberg solution, for low X,is F = r(ln(4/ Pes / ) + 1.06
Using the above two solutions, as well as r--PeH at low PeH, we can plot the
dimensionless flux vs channel height for a given ligand-receptor pair and flow velocity.
For this example we simulate streptavidin-biotin binding in a channel with pressure
driven flow. The diffusion coefficient of the streptavidin ligand is D=6X10-" m2/s. We
also set the average flow velocity to be 600[tm/s. Under these conditions, we can plot the
dimensionless flux vs channel height, as shown in Figure 28.
----
LW-lpq
E L---oCrn
111
l2
I11
ill
111
111
I
10H
Chamwriel He3t H (pn)
Figure 28. Dimensionless flux Yversus channel height for different binding patch sizes.
The average flow velocity is 6001jm/s in all cases.
At very low channel heights and long binding patches, the ligands are 100%
captured. Therefore the flux is proportional to the flow rate through the channel. Since
the flow rate is also proportional to channel height, the flux increases linearly with
channel height. At very high channel heights, the ligands far away from the channel walls
can pass by unreacted, the local flow profile near the channel walls becomes important in
determining the binding kinetics. We have shown graphically that the local flow velocity
near the walls is greater in thinner channels. Thus, thin channels have better mass transfer
I*
efficiency in this regime, and dimensionless flux decreases with channel height. In
between these two regimes, the dimensionless flux F is the largest. An optimal design
should exploit the fast reaction kinetics in this intermediate regime. From the graph above,
we observed that for a typical flow velocity of 600jtm/s and a binding patch of length
10ptm, the optimal channel height is 1 tm (green curve). Binding kinetics is optimal in a
nanochannel only when the binding patch is short (blue curve). With current efforts to
create high density, multiplexed detection array, confinement of the reaction in
nanospace might bring about kinetics benefits.
Experimental
Nanochannels
Observation
of
Binding
Kinetics
in
To investigate some of the phenomena described earlier in the chapter, we
perform binding kinetics experiments in nanochannels. PLL-g-PEG-biotin is
electrostatically immobilized in the nanochannels using the flow patterning method
described in Chapter 5. Alexa Fluor 488 labeled streptavidin is chosen as the target ligand.
We chose this ligand-receptor pair because PLL-g-PEG-biotin can be rapidly patterned at
high density in the nanochannel. Since they are strongly electrostatically adsorbed to the
glass nanochannel surface, it is highly unlikely that they desorb from the surface during
experiments. The biotin-streptavidin bond is among the strongest among biomolecule
interactions, they are less likely to be affected by mechanical shear stress due to the high
flow velocity in the nanochannel. Finally, Alexa Fluor 488 label is very phostable
compared to the other fluorescent molecules such as RPE and FITC-IgG, negligible
photobleaching was observed throughout the experiments which could last for an hour.
T=25
T=500
T=60
T=700
Figure 29. Wave-like propagation of streptavidin-biotin reaction front in the
nanochannel at different times.
ill
I
c
---
--
We performed the experiment on nanochannels pre-patterned with PLL-g-PEGbiotin. 50nM of fluorescent streptavidin molecules is introduced into one side of the
microchannel using a syringe pump. The other three reservoirs were left floating and
exposed to atmosphere. Figure 29 show binding patterns of the streptavidin molecules in
the nanochannel at different times. The experimental results clearly show the wave-like
propagation of ligand-receptor binding in the nanochannels. The nanochannel height is
-50nm and the calculated average velocity in the nanochannel in this case is 600rm/s.
Under these conditions the ligands are 100% captured and binding kinetics is severely
mass transport limited as simulated previously.
We investigated the effect of flow rate on the binding kinetics of streptavidin in
nanochannels. Initially, the streptavidin flow rate in the microchannel is 2R]L/min. After
300s, the flow rate is changed to 4[IL/min, after another 300s the flow rate is changed to
8gL/min. Figure 30 shows the total fluorescence intensity in the nanochannel vs time. As
expected, the slope of the increase in fluorescence over time is approximately linear with
flow velocity. Deviation from linearity is probably due to two factors: 1) the syringe and
tubing might expand due to the high pressure needed to obtain high flow rate in the
device, this mechanical relaxation time could cause a delay between switching the flow
rate on the syringe pump and achieving the desired flow rate in the device, 2) there are
multiple nanochannels connecting two sides of the microchannels. The flow rate
distributions in the individual nanochannels are not uniform. Binding kinetics studies in
nanochannel is eventually limited by experimental constraints. We can theoretically
arbitrarily increase binding kinetics in nanochannel by increasing flow velocity, but
eventually excessive pressure causes the fluidic interconnections to fail at a flow rate of
50iL/min.
9.E+06 &8E+06
,
7.E+06
S6.E+06
y
y = 62519x+
S5.E+06
106901x-1E6
4.E+06
3.E+06
y718x+2E6
g=
E
1.E+06 0.E+O0
0
200
400
600
800
1000
Time (s)
Figure 30. Kinetics of streptavidin-biotin binding in the nanochannel. Initial
streptavidin flow rate is 2jL/min. It is increased to 4pL/min and 8pl/min at t=300s and
t=600s respectively.
In conclusion, simulation as well as experimental results both show that binding
kinetics is slower in a nanochannel compared to a microchannel. Although individual
ligands have a higher chance of reacting with the surface bound receptors in a
nanochannel, their numbers are low. The probability of each ligand reacting with surface
receptors might be lower in a microchannel, but due to the sheer number the reaction
kinetics proceeds more rapidly. We conclude that if the figure of merit is number of
target molecules reacted, reaction in a nanochannel is not efficient due to mass transport
limitations. However, if the figure of merit is percentage/fraction of target molecules
reacted instead, reaction in nanochannels are very effective. We will explore this concept
in the context of enzymatic reaction in the next chapter.
Chapter 7
Enzymatic Reactions in Nanochannels
In Chapter 6, we have seen that a high percentage of target molecules reacted in a
nanochannel. However, because the nanochannel throughput is very low, even this high
reaction efficiency translates into low total reactions. Therefore, we have concluded that
reaction in a nanochannel is not optimum if the figure of merit is the total number of
reactions.
However, there are circumstances where high reaction efficiency is more
important than total number of reactions. One of these is enzymatic reactions. We cite an
example to illustrate this.
In this example, enzymes are immobilized in a bioreactor to convert a raw
material into useful product. In the first case, the enzymes are immobilized on the walls
of a thick channel, raw material is flown in from one end and the product is collected
from the other. Due to the large channel height, most of the raw material passed through
without encountering the channel walls, and therefore remain unreacted. The product
concentration collected downstream is very low. Increasing the flow rate through the
reactor decreases the final product concentration. In the second case, the enzymes are
immobilized on the walls of a thin nanochannel of the same length, and the experiment is
repeated. As the individual raw material molecules frequently collide with the enzyme
molecules on the channel walls, the conversion fraction approaches unity. The product
concentration collected downstream is maximal. Increasing flow rate through the
nanochannel reactor does not decrease the final product concentration, but increase the
throughput. Similarly, by parallelizing the nanochannel reactors, we can increase
throughput without sacrificing product concentration.
Therefore, reaction in a nanochannel is advantageous when a high product
concentration is desired. This is the situation for enzyme bioreactors used to ferment or
digest raw material, or in a detection scheme where enzymes convert initially invisible
target molecules into highly detectable products.
From another point of view, in the binding reaction studies conducted in the
previous chapters we were interested in target molecules that were bound to the solid
phase, whereas in enzyme reaction studies we are interested in target molecules in the
mobile phase. This calls for a different figure of merit in which reaction in nanochannels
is superior.
Trypsin Bioreactor in Nanochannel
Trypsin is a serine protease found in the digestive tract of many vertebrates,
where it digests proteins into peptides. It is also commonly used in proteomics research to
digest proteins into peptides for mass spectrometry analysis. For this purpose, high
digestion efficiency is necessary to provide a high peptide concentration for mass
spectrometry analysis.
We attempted to study the efficacy of Trypsin digestion when the enzyme is
immobilized in a nanochannel. The assay design is as follows: Trypsin molecules are
adsorbed onto glass nanochannel surface using the flow patterning method described in
Chapter 3. After flushing off excess enzyme, Bodipy-Casein is injected from one side of
the nanochannel. The fluorescence of Bodipy-Casein is quenched in its native form, but it
is unquenched when Casein is digested by Trypsin. Thus, an increase in fluorescence
indicates Trypsin activity.
When we performed the experiment, we observed no increase in fluorescence
intensity when the substrate (Bodipy-Casein) passes through the enzyme functionalized
nanochannel. This may be due to several factors. First, Trypsin activity might be lost
when they adsorb on glass surface, the active site might be altered in such a way that it
cannot react with substrate molecules. Second, Trypsin can undergo autohydrolysis at the
high concentrations that were used during immobilization steps. In such cases, Trypsin
can digest other Trypsin molecules and denature them.
We tried other ways such as biotinylating Trypsin molecules and binding them on
immobilized streptavidin molecules. Even in this case enzyme activity was not detected.
In the end, due to instability of immobilized Trypsin, we chose to investigate another
enzyme instead.
Horseradish Peroxidase (HRP) in Nanochannel
We chose Horseradish Peroxidase (HRP) as the next substrate to investigate. HRP
is used extensively in molecular biology applications for its ability to convert various
invisible substrates into highly fluorescent or colored products. The substrate molecule
that we used in HRP experiments is Amplex Red from Invitrogen. In the presence of
hydrogen peroxide, the nonfluorescent Amplex Red is oxidized by HRP to yield highly
fluorescent Resorufin.
H,0,
NO
Amplex Fd
HA
O
Fsorufin
Figure 31. Conversion of the nonfluorescent Amplex Red substrate into highly
fluorescent Resorufin in the presence of HRP and H 20 2.
--
re - -
-- ----P
I
I
To study substrate conversion of HRP in nanochannels, we first pattern PLL-gPEG-biotin in the nanochannel. Subsequently, HRP conjugated avidin is injected into the
device and binds to the biotin in the nanochannel. Excess enzymes were flushed away
before starting the experiment. 20tM Amplex Red and 10tM hydrogen peroxide in
lxPBS is injected from one side of the nanochannel using a syringe pump.
In Figure 32, substrate molecules are injected from the bottom microchannel at
various flow rates. As they pass through the enzyme functionalized nanochannels, they
are converted into highly fluorescent product molecules at the top microchannel.
Figure 32. Substrate molecules are injected from the bottom microchannel. When they
pass through the enzyme functionalized nanochannel, highly fluorescent product
molecules are observed at the top channel.
Figure 33 shows the change in fluorescent intensity in the top channel when
different substrate flow rate is applied in the device. We can see that the rate of
fluorescence increase is dependent on the flow rate. At low flow rates (2-6RtL/min) the
substrate conversion rate (slope) increases with flow rate. At high flow rates (8-10L/min)
the substrate conversion efficiency decreases with flow rate, because some substrates are
able to pass through the nanochannels unreacted at such high velocities. As there is little
mixing, the fluorescence intensity at the top channel does not approach steady state.
Figure 33. Fluorescence intensity in top channel increases at slow flow rate and
saturates at high flow rate. No tangential flow is applied to top channel.
I c
-3
I-
r
_I------
1
Figure 34 shows results from a different experiment. Instead of stopped flow, a
small flow is applied at the top microchannel. Fluorescence intensity is taken at some
distance downstream of the top microchannel, where the product molecules were well
mixed. We are able to observed steady state product concentrations in this case.
60
50
40
30
20
10
01
51
101
151
201
251
301
351
401
451
501
551
time
Figure 34. Steady state product intensity at top microchannel when substrate flow rate
is varied. A finite tangential flow is applied at the top microchannel to promote mixing
At these low flow rates, substrate conversion is 100%. However, the throughput is
higher with increasing flow rate; hence the final product concentration (product
throughput from nanochannel / microchannel volume) increases with flow rate.
Enzyme Reaction in Microchannel
We performed HRP enzymatic reactions in microchannels to compare with
enzymatic reaction in nanochannel. The experimental protocols are detailed as follows.
First, a 50jim wide avidin-HRP patch is patterned on a glass slide using a reversibly
bonded microfluidic channel. After that, an array of 7 parallel PDMS microfluidic
channels is bonded over the enzyme patch. We fill 6 side channels with different
concentrations of the HRP product molecules to serve as calibration. Substrate is injected
into the center channel at a certain flow rate using a syringe pump. Initially the substrate
is nonfluorescent, but when it passes through the enzyme patch, it is converted into a
highly fluorescent product. The downstream fluorescent product in the center channel is
compared with the standard calibration curve to obtain the actual product concentration.
Figure 35 below illustrates the experimental setup.
1
35. Experimental
Figure
setup to study enzyme kinetics
in microchannel. Channels
1,2,3,5,6,7
contain
luM,
500nM,
250nM,
125nM,
62.5nM and 31.75nM product
respectively.
concentration
Channel 4 contains substrate
reacting with immobilized
enzyme.
Figure 36 shows the downstream fluorescence intensity as a function of substrate
concentration and flow rate in the above experiment.
700
600
500oo
-IuMH202
2uMH202
400
0
i
tSuM H202
-1OuMH202
sooe~ 1.5minInmin
200
1
21
41
61
81
101
121
141
161
181
201
Figure 36. Product concentration downstream of the enzyme patch at different
substrate flow rates in a microchannel
For enzyme reaction in microchannel, we observed that the product concentration
increases as flow rate decreases. At low flow rates, the residence time of substrate
molecules above the enzyme patch is high, therefore the substrate conversion efficiency
is inversely proportional to flow rate. This is a stark contrast with the nanochannel
enzyme reaction case where substrate conversion efficiency is almost 100%.
Enzyme kinetics parameters in the microchannel can be extracted using the
method described in[28]. The product intensity after enzymatic conversion can be
converted into actual product concentration by referring to a standard calibration curve.
Using this value, we plotted the product concentration versus flow rate for different
substrate concentrations in Figure 37a. Figure 37b shows the product concentration
versus substrate concentrations at different flow rates. As expected, the product
concentration is proportional to initial substrate concentration and inversely proportional
to substrate flow rate.
~,
Il
-r
-
1.60E-06
1-.40E-06
1.20E-06
1.OOE-06
-2I
m2uM
8.00E-07
o
S6.00E-07
5uM
4.00E-07
8uM
C 2.00E-07
0.OOE+00
0.6
0.4
0.2
0
Flow rate (uUmin)
2.E-06
* 0.5 uuin
S1.E-06
O
1.E-06
a 0A ulknin
S1.E-06
8 8.E-07
0.3 uin
S6.E-07
0.2 ul/min
4.E-07
-'
A 0.1
. 2.E-07
ulnin
O.E+O0
0.OOE+00 2.00E-06 4.00E-06 6.00E-06 8.00E-06 1.00E-05
Substrate Concentration (M)
Figure 37. a) Product concentration versus substrate flow rate, b) product
concentration versus substrate concentration for enzyme reaction in a microchannel
Enzyme kinetics in immobilized enzyme reactor systems is often described by the
Lily-Hornby equation[29]:
f[A] = CIQ + KM(AP) In(1 - f)
Here, f is the conversion fraction, Ao is the substrate concentration, C is the
reactor capacity, Q is the flow rate and Kapp) is the apparent Michaelis constant. By
plottingf[Ao] vs - In(1 -f), the negative slope gives us the apparent Michaelis Constant.
1.4E-6
1.4E-O
1.2E-6
1.0E-6
-+-o.s
uain
.
8.OE-7
-e-OA
ulkin
0.3
6.OE-7
4.OE-7
2.OE-7
O.OE+O
0
0.2
-kIn1-f)
0.4
0.6
Figure 38. Plot of
f[Ao]
vs -In(J-f).
The flow rates range
from 0.1 to 0.5
UL/min
-
-
--
--
1.40E-05
-
1.20E-05
1.00E-05
--
8.00E-06
6.E-06
,
Michaelis Constant
vs flow rate Q.
2
i
Figure 39. Plot of
Km(app), the apparent
= 4.97E-08x - 8.68E-07x + 4.70E-06
4.00E-06 2.00E-06
O.OOE+O0
0
2
4
6
8
10
12
1/Q (min/uL)
As shown in Figure 38, the experimental data fits the Lily-Hornby equation well
at substrate concentrations lower than the calculated Michaelis Constant (4.7pM), a good
fit is also obtained when the flow velocity is low (<0.4pgUmin). These data seem to
suggest that the Lily-Hornby equation is valid in the mass transport limited regime. In the
reaction limited regime (high substrate concentration and high velocity), the Lily-Hornby
model seem to breakdown. From Figure 39, the extrapolated Michaelis Constant from our
experiment is 4.7p1M, which is comparable to the 1.55pM value reported in literature[28].
The elevated value of the Michaelis Constant might be due to loss of activity upon
enzyme adsorption to solid phase, or alteration of enzyme activity upon conjugation to
Avidin molecule.
Difficulty of Extracting Enzyme Kinetics in Nanochannel
So far we have been able to obtain surface bound enzyme kinetics in a
microchannel, however we ran into considerable difficulty to do so in a nanochannel.
Below are some of the reasons.
First, although the substrate conversion efficiency is expected to be very high in
the nanochannel, we have not been able to measure product fluorescence in the
nanochannel. This is due to the short optical path length in the nanochannel, which causes
the fluorescence intensity within the nanochannel to be very weak. To obtain product
concentration, we have resorted to measuring the fluorescence intensity in the
microchannel at the nanochannel exit. There are several problems to this method. First,
fluorescent product molecules from the nanochannels diffuse slowly into the
microchannel. Due to the lack of mixing, the fluorescent product concentration would
take a long time to reach steady state. Second, the height ratio between the microchannel
(10um) and the nanochannel (50nm) is 200:1. Fluorescent product molecules are
immediately diluted 200 times when they exit the nanochannel and enter the
microchannel. This leads to very weak fluorescent signal and necessitate high excitation
intensity/long exposure time for image acquisition.
Second, the HRP substrate molecule Amplex Red is not stable under strong
fluorescence excitation and long exposure time. They were found to spontaneously
generate fluorescence under these conditions. This can be a major source of error for
~
quantitative extraction of enzyme kinetics parameters. Unfortunately, due to the weak
fluorescence signal we could not detect the product molecules otherwise. This
phenomenon is also reported in a recent paper[l].
4000-
15
Figure 40. Photooxidation
of 100pM Amplex Red
(without HRP and H 20 2 ) at
-w 30 s
30s
S3000
3
300 s
different excitation time
intervals. Adapted from [1].
C
S2000-
o 100000
-
I
200
400
600
Time (s)
Finally, we could obtain a steady state product concentration downstream of the
receiving microchannel by applying a small flow. However, flow control must be very
accurate because it affects the dilution factor of the product molecules. Based on the
height ratios of 200:1 between microchannel and nanochannel, we must be able to
accurately control flow rate in the microchannel to be at least 1:200 times the flow rate in
the nanochannel. The commercial syringe pumps are not equipped with this capability.
In view of these problems, we look for alternative ways to measure enzyme
kinetics in nanochannels. One way is to use form nanochannels using self-assembled
beads. This is described in the next section.
Enzyme Kinetics in Nanochannels Formed by Self-Assembled Beads
Based on the Lily-Hornby equation, f[Ao] =C/Q + KM(app) ln(l - J) , enzyme
conversion efficiency of the packed bead reactor system increases when C, the reaction
capacity increases. This can be achieved by utilizing small beads with large surface to
area ratio. Thus, we can expect good conversion efficiency in these bead structures even
when they are functionalized with low enzyme concentrations.
With reference to a recent paper [28], we trapped enzyme functionalized beads in
a microfluidic weir structure. The device used is the same as described in [30]. Briefly,
low concentration of biotinylated HRP is incubated with 6-8um streptavidin coated
microbeads. The large beads are injected into the microchannel and trapped in front of
the weir structure (depth = 5.5 itm). The space between the microbeads (-10% of the
bead diameter) forms the nanochannels.
Surprisingly, we are able to detect conversion of the nonfluorescent substrate to a
fluorescent product at enzyme concentrations of 50fM. The limit of detection of biotin
using conventional ELISA has been reported to be 4pM. By packing enzyme conjugated
beads to form nanochannels, the limit of detection has been dramatically reduced 80X.
Figure 41 below shows the experimental results obtained using streptavidin beads
incubated with 50fM of biotin-HRP.
Figure 41. Enzymatic conversion of nonfluorescent substrate to fluorescent product
using closely packed beads that are incubated with 50fM biotin-HRP
450
4 00
- - -
- --
-....
- ........--........................
---------
350
300
250
0.25ulI
200
150
100
0.5ul/min
3ul/min
0 I
lul/min
.......
closely packed
Figure 42. Product intensity at different substrate flow rates across closely packed
beads that are incubated with 50fM biotin-HRP
As can be seen from Figure 42, the product fluorescent intensity increases when
the flow rate decreases. Thus we conclude that 100% conversion efficiency is not
possible when such low enzyme concentrations are used. Nevertheless, the high
sensitivity of nanochannel enzyme assay inspired us to develop an ultrasensitive
nanofluidic ELISA system, described in the next chapter.
Chapter 8
microELISA
Enhancing
Electrokinetic Accumulation
Sensitivity
by
We have investigated experimentally and theoretically the binding equilibrium,
kinetics as well as enzymatic reaction in a nanochannel. By now, we can make full use
of the conclusions gained from our previous studies to solve technological problems. As
stated in the Introduction chapter, the aim of this thesis is to develop a nanofluidic ELISA
with better sensitivity, low sample consumption, and short assay time. Before going
further, we would like to recap the conclusions of our studies and discuss the implications
for a nanofluidic ELISA system.
Equilibrium Binding in Nanochannel
We learnt that different surface functionalization schemes have huge impacts on
equilibrium binding in nanochannel. In some cases, target molecules are forced to
compete with other surface bound molecules for the same binding sites. In other cases,
steric hindrance prevented target molecules from binding to the surface bound receptors.
Antigens can be captured by antibodies that are directly adsorbed in the 50nm
high nanochannels. Directly adsorbed antibodies are randomly placed and might not have
the correct orientation to bind antigens. We attempted to achieve proper orientation by
binding biotinylated antibodies to a streptavidin monolayer on the surface. However,
these antibodies were not able to capture antigen molecules afterwards because there is
too much steric hindrance. It is also highly likely that steric hindrance will prevent
binding of an enzyme-coupled secondary antibody to captured antigens in the
nanochannel. Therefore, a sandwich ELISA cannot be performed in a nanochannel.
Compared to experiments in open surface, Protein A- IgG displayed similar KD
value in nanochannel while AntiRPE - RPE displayed 4 times lower KD value in
nanochannel. The effect of confinement on biomolecule binding is equivocal - it seemed
to affect certain binding pairs but not the others. In any case, the effect is not substantial.
Binding Kinetics in Nanochannel
Binding kinetics in nanochannel is found to be severely mass transfer limited.
Simulations and experiments have shown that binding occurs in a wave-like fashion
within the nanochannel, indicating convection-limited regime. As a result, overall
binding kinetics is much slower in the nanochannel compared to microchannel or open
surfaces. This is a drawback in nanofluidic ELISA because it increases assay time.
Through simulations, we observed that there is an optimal channel height for
maximum binding kinetics. For a typical binding patch of 10tm length, the optimal
channel height is 1l m. This means that primary binding reaction in ELISA is suboptimal
if performed in a nanochannel.
Enzyme Reactions in Nanochannel
Enzyme reactions in nanochannels are very effective due to the short diffusion
length. In 50nm nanochannels, conversion efficiency is 100% even at very high linear
velocities. In contrast, conversion efficiency in microchannel is only 20% at the lowest
flow rate.
In 500nm nanochannels formed by bead packing, the high conversion efficiency
allows us to detect as little as 50fM of enzyme. The enzymatic amplification step in
ELISA can get a sensitivity boost when performed in nanochannels.
Bead Based ELISA in Microchannel
We have seen that binding reactions are not effective in nanochannels, but
enzyme reactions are very effective in nanochannels. Therefore, the ideal solution is to
conduct binding reactions in the bulk, and allow enzymatic reaction to occur in the
nanochannel. This can be achieved by first incubating antibody coated beads with antigen
and enzyme coupled secondary antibodies in a centrifuge tube, and then packing the
functionalized beads into the microchannel. After that, the substrate solution is flown past
the bead packs, and efficient enzymatic conversion will occur in the nanogaps formed
between the beads.
Sato et. al.[7] has published on obtaining high ELISA sensitivity when performed
on microbeads trapped in a microfluidic device. We took a similar approach but
performed the primary and secondary binding step in the bulk, since we have found that
binding is mass transport limited in nanochannels. Binding reaction kinetics is fastest
when performed in a well-mixed, homogeneous format. We expect binding rate between
the antibody-coupled microbeads with target antigens in the solution to approach
homogeneous kinetics, since the microbeads are small and freely diffusible in solution.
One other advantage of performing the binding reaction outside the microfluidic
device is reduction of nonspecific binding. Since the microfluidic device has high surface
area, the ELISA antigens and antibodies can bind nonspecifically. Care has to be taken to
passivate the surface to eliminate nonspecific binding. However, this problem is not
present if binding is performed in a centrifuge tube because solution exchange and
multiple bead washing can be done before loading them into the channel.
The incubation step for primary binding in ELISA is usually the most time
consuming, by performing it outside the microchannel we can parallelize this process. In
contrast, performing binding reaction in the microfluidic device is considerably more
difficult as a constant flow rate needs to be maintained using syringe pumps.
Limitations of Bead Based ELISA in Microchannel
Although bead based ELISA integrated into a microchip has been shown to
provide lower detection limit and require shorter assay time, the ELISA product
concentration quickly approaches a steady state that is inversely proportional to flow rate.
If the substrate flow rate is high, its residence time in the bead pack is short, which leads
to low steady state product concentration. On the other hand, the substrate residence time
in the bead pack is long at low flow rate resulting in high steady state product
concentration, but substrate depletion or product inhibition might occur under these
circumstances.
In conventional ELISA, the signal intensity increases with time as the substrates
are continuously converted into products. However, in microfluidic bead based ELISA
the maximum sensitivity is limited by the lowest flow rate that one could accurately
control. If we can devise a method to accumulate the converted product molecules in a
small volume, the sensitivity of microfluidic ELISA can be significantly enhanced.
Nanofluidic Electrokinetic Preconcentration Phenomena
Our group has developed a highly efficient microfluidic sample preconcentration
device by utilizing the electrokinetic trapping mechanism enabled by nanochannels[31].
The electrokinetic trapping and collection can be maintained for several hours, and
concentration factors as high as 106 -108 have been demonstrated. This method can be
used to accumulate the charged product molecules; thereby achieving the goal of
enhancing the sensitivity of microfluidic bead based ELISA.
Cel
nnowm
PChanne
(a)
--
(c)
+ 4,
-t
G
EnE
RT)
Depletion zo ne
eonE
- buffer on
antained
I
Figure 43. Mechanism of pre-concentration: (a) ion-selective property of the
nanochannel under small En; (b) concentration polarization under diffusion-limited
condition; (c) with proper ET and En, the trapping region and depletion region will be
formed as indicated; therefore, samples will be collected in front of the virtual barrier
driven by nonlinear electrokinetic flow. Adapted with permission from [6]. Copyright
2008 American Chemical Society.
We briefly describe the mechanism of preconcentration illustrated in Figure 43.
The negatively charged nanochannel acts as an ion exchange membrane that allows the
selective transfer of positive ions. When an electric field is applied across the
nanochannel, more positive charge than negative charge migrates across it. To maintain
charge electroneutrality in the vicinity of the nanochannel after selective positive charge
transfer, both positive and negative ions in the anodic side of the nanochannel will
decrease. By balancing this depletion force with external flow, charged molecules can be
efficiently concentrated.
Integration of Electrokinetic Concentrator with microELISA
In this section we discuss the experimental procedure and results for integration of
electrokinetic concentrator with microELISA. We use hybrid PDMS-Glass devices for all
experiments. Details of device fabrication are provided in Chapter 3. The device consists
of 3 parallel microchannels as shown in Figure 44b. 5[tm gap size pillar structures were
designed in the center channels to trap ELISA beads. Downstream of the pillar structures,
we fabricated a vertical self-sealed Nafion nanojunction as described in Chapter 3,
connecting the center channel to the two side channels. For this demonstration, we chose
the carbohydrate antigen CA19-9 as the target molecule for enhanced ELISA detection.
CA 19-9 is a tumor marker that is overexpressed in patients with pancreatic and gastrointestinal cancer. Early detection of this biomarker can lead to better treatment and higher
chance of recovery.
The experimental procedure is shown in the Figure 44 below. The sandwich
immunoassay consisting of antibody coated beads, carbohydrate antigen CA19-9 in
serum and HRP-coupled secondary antibody is conducted in a microcentrifuge tube.
After extensive washing, the functionalized beads are physically trapped in front of pillar
structures within the center microchannel. Downstream of the self-assembled bead array
is the nanochannel as described above. When voltages are applied at the reservoirs,
electroosmosis induces flow of the substrate solution (20tM Amplex Red + 10 tM H202
in O.O1XPBS) across the beads. Fluorescent product is continuously generated as the
enzymes in the bead array catalyze the reaction between the substrate molecules. When
there is low number of enzymes on the beads, the fluorescent product is undetectable.
However, at the vicinity of the nanochannel, electrokinetic trapping lead to accumulation
of the charged fluorescent product molecules and clear increase in signal intensity.
Measurements are initially done by repeatedly loading and unloading different
concentration beads into the center channel. Results for these experiments are shown in
Figure 45. When the antigen concentration is 0 U/mL, the intensity ratio between the
product and fluorescein tracer is 1:3. This ratio becomes 1:2 and 2:1 when the antigen
concentration is increased to 0.05 U/mL and 0.5 U/mL respectively. Without
preconcentrating product molecules, the detection limit of bead based micro ELISA is 1
U/mL. Therefore, we are able to detect at least 20X less enzyme molecules in the first
unoptimized experiment.
..............................
............
microcentrifuge tube b)
Pipette
C) ......
d)
V
...................
Fluorescent detection
Ion depletion
coupled
tinyla
mary
antibody
tibody
tibodY
Strept vidin
zone
v
v
secondary
Wa
ftE
OV
Bead
Bead
trap
Trapped
product
molecules
OV
coated bead
Figure 44. Experimental procedure: a) Streptavidin coated beads are incubated with biotinylated
primary antibody and antigen for 1 hour, b) After washing, the beads are incubated with HRPconjugated secondary antibody for another hour, c) Washed beads are trapped in the center
microchannel by pillar structures, a nanojunction is fabricated downstream of the bead array, d)
When voltages are applied, the fluorescent product molecules are trapped by ion depletion forces,
leading to accumulation.
d)
120
30
2,
A
°o
-o
Position
1.5
etection limit without
w
30
-30
0_
Position
1 U/ink5L1
01
2
03
04
05
06
AMig-eno.erarn(l)
Figure 45. Experimental results: Fluorescence
intensity of product molecules (red) and tracer
12090
(blue) for CA 19-9 concentration a) 0 U/mL, b)
o60
30
o
-30
preconcentration is
0
201
0.05 U/mL, c) 0.5 U/mL, and d) relationship
etween intensity ratio and antigen concentration
Position
We would like to comment on several aspects of this novel method to enhance
ELISA. One problem in the field of immunoassay is the existence of multiple standards
which makes it difficult to compare results across different assays. ELISA is often used
as the golden standard to which all these detection schemes must refer to, due to its
reliability and the fact that it is widely used in clinical studies. Our method is not another
new way of performing immunoassay. Rather, it can readily take the output of standard
ELISA and provide more sensitive readout without using expensive reagents or
unfamiliar equipments. This could be attractive to clinical practitioners who are already
familiar and possess equipments to ELISA but wished to achieve better assay sensitivity.
~--
-1
-
I
I
-LII
Since the primary and secondary binding reactions are performed outside of the
microchannel, we are free of the problems that are usually encountered in microfluidics,
such as high nonspecific binding, mass transport limitations, and cumbersome solution
exchange/washing.
However, the above single-channel device still has certain shortcomings
compared to conventional ELISA done on 96-well plates. Multiple parallel reactions can
be performed simultaneously in the 96 well-plate ELISA, while samples can only be
assayed serially in this device. It becomes very time consuming to obtain a calibration
curve using standards of known antigen concentration, because beads incubated in
different standards have to be packed and unpacked into the device serially. Furthermore,
the channels suffer from debris contamination during these mechanical steps which
lowers detection sensitivity. Finally, the integrity of the Nafion nanojunction might be
compromised during these steps, which leads to device failure. To solve these problems,
we have developed a multiplexed electrokinetic concentrator integrated with bead based
ELISA, as described in the next section.
Multiplex Microbead
Concentrator
ELISA
with
Integrated
Electrokinetic
We have fabricated multichannel devices to perform multiplex ELISA with
integrated electrokinetic concentrators. Briefly, five input channels are connected to one
output channel as shown in Figure 46. Each input channel contains pillar structures to
trap ELISA beads. Downstream from the pillar structure, a Nafion nanojunction is
fabricated to connect all the channels to a separate buffer channel.
Figure 46. Schematic of
based
bead
multiplexed
with
integrated
ELISA
electrokinetic concentrators.
grour,
We have optimized the devices to perform multiplex ELISA with greater
sensitivity. Each of the five channels contains beads functionalized with different
concentrations of antigens and enzymes. This allows simultaneous accumulation of
enzymatic products and provide for internal calibration. Figure 47 shows simultaneous
accumulation of ELISA products for beads incubated with serial dilution of CA19-9 in
Is
serum. The concentrated tracer plugs have similar intensity, indicating similar
accumulation efficiency in all channels. However, intensities of the concentrated product
plugs have different intensities depending on the antigen concentration.
Figure 47. CA 19-9 immunoassay results: Fluorescence intensity of tracer (left) and
product molecules (right) in the multichannel device Channels contain 6.25, 1.25, 0.25,
0.05, and 0.01 U/mL CA 19-9 in serum (from top to bottom). Concentrated tracer plugs
have similar intensities while the product plug intensities are proportional to antigen
concentration .Mean and standard deviation of blank samples is obtained by measuring
intensities upstream of the enzyme functionalized beads.
Electrokinetic accumulation of ELISA products improves the detection sensitivity
by increasing the signal to noise ratio. We were able to fit the dose response curve to a
Hill Equation when there is no product accumulation and to a Power Law equation when
there is product accumulation. Obtaining the dose response curve when there is product
accumulation is more complicated because of the nonlinear concentration profiles near
the nanojunction. Furthermore, the microscope might display nonlinear response for such
steep concentration profiles. Nevertheless, the values fit well to a Power Law equation,
from which we can estimate the LOD.
0.01
0.1
1
10
CA 19-9 Concenbation (UYmL)
Figure 48. CA 19-9 ELISA in human serum. Blue curve represents intensities at the
concentrated plug (with accumulation) while red curve represents intensities between
the beads and the concentrated plug (without accumulation). The limit of detection is
0.0016 U/mL with accumulation and 0.57U/mL without accumulation. Sensitivity
enhancement due to product accumulation is -360 fold.
I
1
I
I
I
~I
CF~
C~-- --
The limit of detection (LOD) is estimated as the mean of blank sample plus three
standard deviations. From the best fit curve as determined from the method above, the
LOD of CA19-9 is found to be 0.00016 U/mL with product accumulation compared to
0.57 U/mL without product accumulation, representing 360-fold sensitivity enhancement
as shown in Figure 48.
To show the generality of this method, we repeated the experiment using Prostate
Specific Antigen (PSA) sandwich ELISA in serum. PSA is a well known biomarker that
is elevated in Prostate Cancer patients. It is also an important marker in monitoring
patient response to treatment. The LOD of CA19-9 is 0.004 ng/mL with product
accumulation compared to 0.37ng/mL without product accumulation, representing 90fold sensitivity enhancement as shown in Figure 49.
0.0001
0.001
0.01
0.1
1
10
PSA onoentrUtion (ng/rL)
Figure 49. Dose response curve of PSA ELISA in human serum. Blue curve represents
intensities at the concentrated plug (with accumulation) while red curve represents intensities
between the beads and the concentrated plug (without accumulation). The limit of detection is
0.004 ng/mL with accumulation and 0.37 ng/mL without accumulation. Sensitivity
enhancement due to product accumulation is -90X.
We presented a novel device that provides 2 orders of magnitude sensitivity
enhancement for ELISA readout by concentrating product molecules in a confined plug.
This method capitalizes on the standard techniques and reagents of conventional ELISA
and is able to operate with complex samples. We have demonstrated results with two
tumor markers CA19-9 and PSA and are confident that it can be readily extended to
using other enzyme and substrate molecules in ELISA, for both fluorescent and
colorimetric detection. This concept can be integrated with existing techniques to serve as
a novel platform for early detection of low abundance biomarkers.
'CI
Chapter 9
Conclusion
Our initial goal in conducting these studies was to enhance the sensitivity of
ELISA using nanofluidics phenomena. Using experiments and simulations, we have
studied 1) binding equilibrium in nanochannels, 2) binding kinetics in nanochannels, and
3) enzymatic reaction in nanochannels.
We found that biomolecular binding equilibrium in nanochannels is hindered by
steric hindrance, due to the confining effect of nanochannel walls and crowding effect of
other macromolecules. Very few target molecules bind react in the nanochannel when
steric hindrance is high. When steric hindrance is low, the equilibrium binding constant
in a nanochannel is not significantly different from that in an open environment.
Biomolecular binding kinetics in nanochannels is very slow due to mass transport
limitations. We observed convection limited reaction in a nanochannel, where the
reaction front proceeds as a linear wave-like propagation. Simulations have shown that
although each ligand molecule in the nanochannel has a high probability of reaction, the
total number of reactions is low because there is much lesser ligand molecules contained
within the volume of the nanochannel. We have also shown that binding events in the
single-molecule limit (Coulter-Counting mode) is noisy, but no binding kinetics
enhancement is obtained by operating in this regime.
Enzyme reactions in nanochannels are very effective due to the short diffusion
length. In 50nm nanochannels, conversion efficiency is 100% even at very high linear
velocities. In contrast, conversion efficiency in microchannel is only 20% at the lowest
flow rate.
Based on these findings, we developed a bead-based ELISA integrated with
electrokinetic concentrator to enhance assay sensitivity. Primary and secondary binding
steps are performed outside of the microfluidic device to maximize binding kinetics and
minimize steric hindrance effects on binding equilibrium. Enzymatic conversion is
performed after the beads are closely packed in the microchannel. The high substrate
conversion efficiency obtained while flowing past nanogaps between the beads are fully
exploited to increase sensitivity. Furthermore, by integrating an electrokinetic
concentrator downstream of the beads, we can accumulate charged product molecules,
thus obtaining better signal to noise ratios over time.
Finally, we designed and tested a multiplexed bead-based ELISA integrated with
concentrator. We obtained a 360x and 90 x improvements in limit of detection for two
important cancer markers CA 19-9 (Pancreatic and Gastro-Intestinal Cancer) and PSA
(Prostate Cancer). In future, we will optimize device design to obtain better performance,
and use it to study biomarkers that are currently low abundant to be detected by
conventional methods.
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