Development of One-Step Single-Cell RT-PCR
for the Massively Parallel Detection of Gene Expression
by
Yuan Gong
M.S. Chemical Engineering Practice
Massachusetts Institute of Technology, 2009
B.S. Chemical Engineering and Applied and Computational Mathematics
California Institute of Technology, 2007
SUBMITTED TO THE DEPARTMENT OF CHEMICAL ENGINEERING IN PARTIAL
FULLFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY IN CHEMICAL ENGINEERING
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
JUNE 2014
© 2014 Massachusetts Institute of Technology. All rights reserved.
Signature of Author: ___________________________________________________________
Yuan Gong
Department of Chemical Engineering
May 21, 2014
Certified by: __________________________________________________________________
J. Christopher Love
Associate Professor of Chemical Engineering
Thesis Supervisor
Accepted by: _________________________________________________________________
Patrick S. Doyle
Professor of Chemical Engineering
Chairman, Committee for Graduate Students
2
Development of One-Step Single-Cell RT-PCR
for the Massively Parallel Detection of Gene Expression
by
Yuan Gong
Submitted to the Department of Chemical Engineering
on May 21, 2014 in Partial Fulfillment of the
Requirements for the degree of Doctor of Philosophy (Ph.D.) in
Chemical Engineering
ABSTRACT
The United Nations estimates that over 35 million people are afflicted with HIV/AIDS in
the world. Highly active antiretroviral treatments (HAART) that use a combination of drugs that
target the virus at different stages of its life cycle are effective at reducing the HIV plasma levels
below levels detectable by the most sensitive assays. However, upon termination of HAART,
HIV RNA transcripts are measurable in the blood after 2-3 weeks. This relapse is attributed to
the presence of a reservoir of latently infected cells, such as resting CD4+ T-cells. The latent
reservoir in resting memory CD4+ T-cells has been estimated to decay with a half-life of as long
as 44 months, thus hindering the eradication of HIV. Current knowledge of latent reservoirs
came from the isolation of possible reservoir populations by cell surface markers and querying
each population for the presence of HIV RNA. These measurements do not have single cell
resolution so the exact frequencies of latently infected cells are not known.
In this thesis, we developed and optimized a method to detect cellular transcripts of
single cells in an array of nanowells. The limit of detection of the assay was approximately 1.4
copies of DNA in a 125 pL well (18.6 fM) with a false positive rate as low as 4.6x10-5.
Combining this assay along with image-based cytometry and microengraving, we generated a
multivariate dataset on single cells to understand the relationships between cell phenotype,
transcribed genes, and secreted products. We showed that gene expression could not be a
surrogate measure for antibody secretion. We were also able to detect rare cells in a population at
a frequency as low as 1 in 10,000. We then applied the technology to samples from a patient on
HAART for more than 1.5 years. We were able to detect an infection rate of 1:3000 cells that
had low levels of HIV RNA in bulk.
Thesis supervisor: J. Christopher Love
Title: Associate Professor
3
4
Acknowledgments
I would like to thank my advisor, Professor J. Christopher Love, for accepting me in his
group. He provided me with enjoyable problems to solve and a lot of guidance and creative ideas
on new directions to overcome the many challenges in this work. I also greatly appreciate the
advice he gave me during Practice School on how to manage and direct a team of co-workers
towards a common goal. I hope I was as good a student to him as he was a mentor to me!
I would like to thank my thesis committee members, Professor K. Dane Wittrup,
Professor Darrell Irvine, and Professor Bruce Walker, for all the feedback and advice they gave
me to improve this thesis. I also want to thank my collaborators, Dr. Xu Yu and Dr. Maria J.
Buzon, for sharing their knowledge and expertise on HIV latency with me. They also patiently
waited for me as I optimized the technology to detect HIV in single cells. I am extremely grateful
to Aaron Gawlik and Alan Stockdale for designing and building the RT-PCR machine.
It has been a great pleasure to work with all current and past members of the Love Lab:
Dr. Adebola Ogunniyi, Dr. Qing Han, Dr. Jonghoon Choi, Dr. Yvonne Yamanaka, Dr. Todd
Gierahn, Dr. Rita Lucia Contento, Timothy Politano, Denis Loginov, Dr. Ayca Yalcin Ozkumur,
Dr. Qing Song, Dr. Eliseo Papa, Dr. Kerry Love, Vasiliki Panagiotou, Dr. Navin Varadarajan, Dr.
Bin Jia, Dr. Sangram Bagh, Dr. Alexis Torres, Viktor Adalsteinsson, Brittany Thomas, Lionel
Lam, Abby Hill, Sarah Schrier, Kimberly Ohn, Thomas Douce, Rachel Barry, Dr. Joe Couto, Dr.
Konstantinos Tsioris, Dr. Lilun Ho, Dr. Kartik Shah, Dr. John Ballew, Nicholas Mozdzierz,
Narmin Tahirova, Ross Zimnisky, John Clark, and Rachel Leeson. Thank you for all of your
helpful suggestions and ideas to my project and for creating a fun and enjoyable lab environment!
Finally, I would like to thank my family and friends for supporting me for the past seven
years. Your encouragement and love have kept me going throughout my graduate school
experience.
5
6
Table of Contents
List of Figures ................................................................................................................................. 9
List of Tables ................................................................................................................................ 11
Introduction ................................................................................................................................... 13
1.1. Human immunodeficiency virus ........................................................................................ 13
1.2. Existing tools to detect HIV-infected cells ........................................................................ 14
1.3. Amplification and detection techniques ............................................................................. 16
1.4. Objectives and outline of thesis ......................................................................................... 18
Chapter 2. Materials and Methods ................................................................................................ 19
2.1. Cell line culture .................................................................................................................. 19
2.2. Fabrication of array of nanowells....................................................................................... 20
2.3. Cytometry and imaging ...................................................................................................... 21
2.4. One-step reverse transcription polymerase chain reaction (RT-PCR) ............................... 22
2.4.1. Primer and TaqMan probe selection ............................................................................ 22
2.4.2. Imaging end-point RT-PCR signal .............................................................................. 23
2.4.3. Quantitative TaqMan RT-PCR .................................................................................... 25
2.4.4. Digital PCR in nanowells ............................................................................................ 25
2.5. Microengraving .................................................................................................................. 25
2.6. Surface capture of transcripts ............................................................................................. 26
2.7. Hybridization chain reaction .............................................................................................. 27
2.8. Data Analysis ..................................................................................................................... 28
Chapter 3. Establishing one-step RT-PCR in nanowells .............................................................. 31
3.1. Optimization of cell lysis ................................................................................................... 31
3.2. Optimization of RT-PCR in nanowells .............................................................................. 38
3.3. Optimization of pre-treatment of cells ............................................................................... 39
3.4. Optimization of thermocycling .......................................................................................... 41
3.4. Discussion .......................................................................................................................... 46
3.4.1. Limit of detection of transcripts .................................................................................. 46
3.4.2. Evaporation .................................................................................................................. 48
3.4.3. Limitations ................................................................................................................... 50
Chapter 4. Characterization of RT-PCR in nanowells .................................................................. 53
4.1. Sensitivity and specificity .................................................................................................. 53
7
4.2. Integration with microengraving ........................................................................................ 57
Chapter 5. Identification of target cells in large populations ........................................................ 59
5.1. Cytometry ........................................................................................................................... 59
5.2. Activation of cells .............................................................................................................. 62
5.3. Limit of detection of cells .................................................................................................. 66
5.4. HIV-positive patient sample............................................................................................... 67
5.5. Discussion .......................................................................................................................... 69
Chapter 6. Other methods to detect transcript .............................................................................. 73
6.1. Surface capture of transcripts ............................................................................................. 73
6.2. Hybridization chain reaction .............................................................................................. 78
6.3. Discussion .......................................................................................................................... 81
Chapter 7. Conclusions ................................................................................................................. 83
References ..................................................................................................................................... 87
8
List of Figures
Figure 3.1. TaqMan probe mechanism ......................................................................................... 31
Figure 3.2. Determining the RT-qPCR kit .................................................................................... 32
Figure 3.3. Comparison between 1-step Fast qScript with ROX and MGB ................................. 35
Figure 3.4. The effect of Tween-20 on the RT-qPCR reaction on 100 cells ................................ 35
Figure 3.5. The effect of NP-40 on the RT-qPCR reaction on 100 cells ...................................... 36
Figure 3.6. Direct comparison between Tween-20 and NP-40 ..................................................... 36
Figure 3.7. Efficiency of RT-qPCR with the addition of 0.05% NP-40 or 0.5% Tween-20 ........ 37
Figure 3.8. Effect of SDS on RT-qPCR on 100 cells ................................................................... 37
Figure 3.9. Temperature model for an increase in temperature from 60 °C to 95 °C after 5
seconds .......................................................................................................................................... 39
Figure 3.10. Removal of false positives by RNase, DNase-free treatment .................................. 40
Figure 3.11. Sample images of digital PCR on a serial dilution of HIVgag DNA ....................... 44
Figure 3.12. Limit of detection for PCR ....................................................................................... 44
Figure 3.13. Various cycle numbers for PCR on HIVgag DNA .................................................. 45
Figure 3.14. Detection of B2M mRNA transcripts on beads from bulk cellular mRNA extraction
....................................................................................................................................................... 47
Figure 3.15. Detection on B2M mRNA from bulk cellular mRNA extraction ............................ 47
Figure 3.16. Effect of cell lysate on RT-PCR ............................................................................... 48
Figure 4.1. Schematic of method for parallel single-cell RT-PCR reactions in nanowells .......... 55
Figure 4.2. Detection of mRNA transcripts of constitutively expressed genes in 4D20 cells ...... 56
Figure 4.3. Integrated single-cell analysis of gene expression and secreted antibodies from
human B cell hybridomas ............................................................................................................. 57
Figure 5.1. Histograms of surface marker fluorescence on PBMCs............................................. 61
Figure 5.2. Comparison between flow cytometry and microscopy for T-cell classification ........ 61
Figure 5.3. Effect of 10x activation conditions on HIVgag and cell viability in ACH2 cells ...... 62
Figure 5.4. Comparison of 1x TNFa and 10x TNFa activations on ACH2 .................................. 63
9
Figure 5.5. Comparison of 1x TNFa and 10x TNFa activations on ACH2 in nanowells ............. 65
Figure 5.6. Effect of activation time on ACH2 in nanowells ....................................................... 65
Figure 5.7. RT-qPCR on bulk cells from HIV-positive sample ................................................... 69
Figure 6.1. Schematic of mRNA capture on a glass surface ........................................................ 74
Figure 6.2. Sample scans of B2M cDNA (FAM, left) and KanR cDNA (HEX, right) ................ 75
Figure 6.3. Images of swapping dyes in two sets of probes ......................................................... 77
Figure 6.4. Captured cDNA on glass from unlabeled mutPGK1 ................................................. 77
Figure 6.5. Comparison of HCR with direct detection in 4D20 cells ........................................... 79
Figure 6.6. Combination of two sets of HCR to detect single nucleotide polymorphism ............ 79
Figure 6.7. Capture of ACTB on PDMS followed by RT-PCR in nanowells .............................. 81
10
List of Tables
Table 2.1 Primer and probe design for RT-PCR........................................................................... 22
Table 2.2 Sequences of initiator and hairpin for HCR.................................................................. 28
Table 3.1 List of tested RT-PCR lysis conditions. ....................................................................... 41
Table 5.1 Surface markers for PBMC classification .................................................................... 59
Table 5.2 Effect of activation conditions on ACH2 cell detection by RT-PCR in nanowells ...... 64
Table 5.3 Detection of serial dilutions of ACH2 cells in nanowells ............................................ 66
Table 5.4 RT-PCR on HIV-positive T-cells ................................................................................. 69
11
12
Introduction
1.1. Human immunodeficiency virus
The United Nations estimates that over 35 million people are afflicted with HIV/AIDS in
the world and almost 1% of the world’s supposedly healthy population (ages 15-49) is infected1.
With the availability of drugs and more awareness on the transmission of the disease, the number
of deaths caused by HIV and the number of new infections have dropped over the past decade1.
Highly active antiretroviral treatments (HAART) that use a combination of drugs to target the
virus at different stages of its life cycle are effective at reducing the HIV plasma levels below
levels detectable by the most sensitive clinical assays available (limit of detection of 50
copies/mL) in 3-4 weeks. While HAART is very effective, it is expensive and has known side
effects2, 3. The virus is also known to develop resistance if HAART regimen is not strictly
followed4. However, upon termination of HAART, HIV RNA transcripts are measurable in the
blood after 2-3 weeks5-8, demonstrating that HAART is not curative. This relapse is attributed to
the presence of a reservoir of latently infected cells, such as resting CD4+ T-cells9, 10, monocytes
and dendritic cells11, that are not responsive to HAART. Typically, activated CD4+ T-cells that
are infected will undergo apoptosis, but in latently infected cells, the life cycle of the virus is
interrupted by cellular factors, such as the histone deacetylation and methylation of HIV long
terminal repeat (LTR)12. The latent reservoir in resting memory CD4+ T-cells has been estimated
to decay with a half-life of as long as 44 months13, thus maintaining a long-lived latently infected
population and hindering the eradication of HIV14. Therefore, research on the identification and
eradication of these latent reservoirs has been pursued as a strategy in HIV treatment. Gene
13
therapy using Tre recombinase, which is an evolved Cre recombinase, has been shown to excise
proviral DNA from the integrated host genome15. Its effect on latently infected cells have not
been tested yet16. Recently, the discovery of a population of replication competent proviruses
that were not induced by potent reactivation conditions in vitro further complicates the
eradication of latently positive cells17.
The gold standard for studying latency is the extraction of resting CD4+ T-cells from a
person on HAART. However, the frequency of these latently infected cells are 1 in a million18,
so deep mechanistic studies would not be feasible on only these cells. Latently infected primary
CD4+ T-cells can be made in a variety of methods19, 20, but these cells often require a long time
to culture19, 20. Latently infected cell lines such as ACH2 do exist and are commonly used to
model the phenomenon21. While cell lines are convenient to work with, some biologically
relevant limitations to their use exist, such as the cell line is a clonal population with the same
integration site, the integration sites are often in transcriptionally inactive regions of the
genome22 while the integration sites in resting CD4+ T-cells were in actively transcribed
regions23-25, and the cell line grows very quickly whereas latently infected T-cells are resting in
vivo18.
1.2. Existing tools to detect HIV-infected cells
The standard method to measure the size of the latent reservoir is a viral outgrowth
assay26, 27. Latent infections are identified using a population of highly purified resting T-cells28.
These cells are often taken from patients on HAART since HIV levels in their blood are below
the level of detection. The purification process removes, by flow cytometry, cells with markers
of various stages of activation such as CD69, CD25, and HLA-DR. To demonstrate that latent
14
infections exist in the population, the purified cells are stimulated with phytohemagglutinin
(PHA)29, gamma-irradiated virus-free PBMCs29, or cross-linking anti-CD3 antibodies and the
newly produced virions can be detected5. Since activation of all T-cells is highly toxic30, 31, some
recent reactivation agents that have been investigated are more specific to the reactivation of
latent proviruses. These small molecules include histone deacetylase inhibitors (HDACi) such as
valproic acid, vorinostat, givinostat, belinostat, and panobinostat32-34, disulfiram35, prostratin36-38,
and bryostatin39. Valproic acid has had inconsistent results on the reduction of latent reservoirs in
vivo40, 41.
To verify that the population contains cells with integrated HIV genome, several assays
digest the host genomic DNA with a specific restriction enzyme. Then, the digests are diluted so
that intramolecular ligation is dominant. In Alu polymerase chain reaction (PCR), one primer
binding Alu repeat elements, which are interspersed throughout the genome, and another primer
specific for HIV are used to amplify the integrated DNA18. Common integration sites can be
sequenced by using inverse PCR where the region flanking the HIV genome is amplified42, 43.
While these digestion assays only detect integrated HIV proviral DNA, they have varying
efficiencies because the viral DNA integrates at different locations, so the lengths of the
amplified sequences vary.
Although the presence of integrated HIV genome is necessary for identifying latency, it is
not sufficient. Not all integrated HIV genomes produce replication-competent virus after
activation. Deleterious mutations in the reverse transcription and integration into silenced regions
of the host genome may result in the lack of competent virus production42. Identifying integrated
HIV genome and producing competent HIV virions following stimulation cannot, however, be
15
both applied to the same population. Stimulated cells will produce virus that can infect and
integrate into uninfected cells so it is unclear if the integrated HIV was from previously infected
or newly infected cells, and the process of detecting integrated HIV genome requires killing the
cell, which would prevent their stimulation.
Monie et al. devised a method that can partially bypass the conflicting tests44. Resting Tcells were cultured in the presence of drugs that block the reverse transcription (RT) of HIV
mRNA to DNA and the integration of HIV DNA into the host genome. After stimulation, the
newly produced virions that bud from latently infected cells can infect other cells, but the drugs
prevent the integration of virus genome into the host. Finally, to avoid the varying transcript
length of Alu PCR, HIV RNA from the media can be analyzed by RT-PCR primers specific to
HIV mRNA. While this assay can detect latently infected population of cells, no assay that can
detect latency in single cells exists.
1.3. Amplification and detection techniques
The detection of transcribed genes often uses reverse transcription (RT) polymerase chain
reaction (PCR) to convert mRNA into many copies of cDNA. This reaction can amplify many
specific transcripts from single cells—usually sorted into microtiter plates by flow cytometry or
micromanipulation—to recover particular genes of interest or to quantify the amount of mRNA
present45. Traditional assays for studying genetic and proteomic responses to applied external
stimuli typically require more than 1000 cells for each analysis46,
47
. The resulting average
measures, however, obscure variations that may exist among individual cells, especially rare
cells, and can lead to misinterpretations of the biology48, 49. Using conventional plates is also
labor-intensive and costly for analyzing a statistically robust numbers of single cells.
16
Miniaturized systems have been developed that use actuated microfluidic systems50,
microdroplets of water-in-oil emulsions51-54, and arrays of microwells55-58 to define individual
PCR reactions requiring only femtoliters to nanoliters of reagents to reduce cost. These
approaches can also increase the efficiency of amplifying limited numbers of templates. On-chip
RT-PCR reactions have been demonstrated for amplifying isolated mRNA59, 60 or small numbers
of individual cells61, 62. Other techniques to amplify and detect weak signals also exist for targets
such as proteins, microRNA, mRNA, and DNA. They include fluorescent in situ hybridization6365
, hybridization chain reaction66-70, and other isothermal catalytic amplification71-75. These
assays have very good limit of detection (as low as 1 fM), but are generally still performed on a
bulk sample of cells or tissue.
To establish a single-cell methodology for detecting latent infection, RT-PCR must be
efficient in picoliter volumes. It has been demonstrated that 72 parallel PCR reactions in 450 pL
volumes on a microfluidic chip was possible50 and this number has expanded to 96 single cell
samples by the Fluidigm Dynamic Array76. Real time RT-PCR has also been done in 1241 oil
droplets with volume 70 pL containing viral RNA77. Using a modified PCR reaction, the 454
sequencing in 75 pL silicon wells has sequenced about one million transcripts on beads55, 78.
Digital PCR reactions have been shown to amplify single copies of DNA in volumes as small as
36 femtoliters using PDMS58. It has also been used as a more sensitive alternative to qPCR for
detecting HIV DNA in a bulk population54. Finally, RT-PCR has been performed directly from
single cells without purifying the mRNA in 20 microliter volumes79. No technology, however,
combines all of these techniques into one-step, high-throughput, single-cell RT-PCR in picoliter
volumes.
17
1.4. Objectives and outline of thesis
The objectives of this thesis were to establish a RT-PCR technique to detect the presence
of target genes in single cells using the array of nanowells as individual containers. By using
these nanowells, we would be able to identify which single cells were infected with HIV-1 and
interrogate its surface markers to determine its cellular lineage. Since the assay was developed in
nanowells, we could also use other processes such as microengraving to link more information
on a single cell. With this knowledge, we would be able to identify better cellular targets for
possible eradication of the disease. The specific aims of my doctoral thesis were the following:
1. Develop a new technique for the detection of mRNA transcripts from single cells using
RT-PCR reactions in nanowells for high-throughput screening.
2. Develop and optimize a multiplexed assay for detecting multiple DNA transcripts
produced by single cells.
3. Detect the production of virus in infected cells and determine the frequency and identity
of those cells.
Chapter 2 of the dissertation discusses the materials and methods used to develop and validate
the assay for detecting genetic transcripts in cells. Chapter 3 focuses on the optimization of RTPCR in nanowells. Chapter 4 uses the methods developed in Chapter 3 on single cells to
determine sensitivity and specificity and demonstrate the integration with other nanowell assays.
Chapter 5 uses RT-PCR to detect HIV in a cell line and HIV-positive patient. Chapter 6
discusses other methods that were considered for detecting rare transcripts. Finally, Chapter 7
contains a summary of the results and potential future directions for this work.
18
Chapter 2. Materials and Methods
2.1. Cell line culture
Epstein-Barr virus transformed human hybridoma 4D20 was a generous gift from James
Crowe (Vanderbilt University). The 4D20 cell line produces an IgG1 antibody against the 1918
H1N1 influenza virus. Cells were cultured as a suspension in R15 medium composed of RPMI
1640 (Mediatech) supplemented with 15% fetal bovine serum (PAA Laboratories), 2 mM Lglutamine (Mediatech), and 1x Penicillin-Streptomycin (Mediatech). The cell line was
maintained in 25 mm2 canted-neck flasks (BD Falcon) in 5% CO2 at 37 °C and was split twice a
week to 2.5x105 cells/mL.
The ACH2 cell line, a T-cell clone with one integrated copy of HIV-1, was obtained
through the NIH AIDS Reagent Program, Division of AIDS, NIAID, NIH from Dr. Thomas
Folks. These cells were cultured as a suspension in the same manner as the 4D20 cell line. ACH2
cells were split twice a week to 1x106 cells/mL.
The mutant phosphoglycerate kinase 1 (PGK1) cell line was purchased from the Coriell
Institute (GM14889). These cells were an Epstein-Barr virus transformed B-lymphocyte that
contains a nucleoside base change of A491 (normal) to T491 (mutant) at position 491 in the
PGK1 gene and produces the amino acid substitution D163V. These cells were cultured as a
suspension in the same manner as the 4D20 cell line and were split twice a week to a ratio of 1:3.
19
2.2. Fabrication of array of nanowells
Silicon masters for 50 x 50 x 50 μm3 wells were produced by photolithography (Stanford
Microfluidics Foundry or Georgia Institute of Technology). Each chip fits on a standard glass
slide (75 x 25 mm2, Corning) occupying approximately the center 60 x 22 mm2. Several types of
designs were used: the normal field of view (NFOV) array has 72 x 24 blocks of 7 x 7 wells and
the large field of view (LFOV) array has 43 x 14 blocks of 11 x 11 or 12 x 12 wells. A channel
was included to facilitate liquid removal by aspiration from the device and to act as a liquid
reservoir as water is lost into the polydimethylsiloxane (PDMS) during thermocycling. For the
NFOV, every 4 x 4 block was surrounded by channels, and for the LFOV, every block was
surrounded by channels. PDMS (Sylgard 184 Silicone Elastomer Kit, Dow Corning) or RTV615
(Momentive) was vigorously mixed at a mass ratio of 10:1 elastomer base to curing agent and
deaerated for 20 min under vacuum for Sylgard 184 or at least 1 hr for RTV615. Before the first
use, the injection mold and the silicon master were placed under vacuum with a glass vial
containing a few drops of trichloro(1H,1H,2H,2H-perfluorooctyl)silane (Sigma), and baked the
next day at 80 °C for 2 hr. Approximately 5 mL of PDMS was slowly injected into the mold so
that the final device has a thickness of 1 mm and was attached to a pre-cleaned standard glass
slide. The injection mold was then cured for 2 hr at 80 °C. The arrays were removed from the
mold while hot, and scotch tape (Staples) was applied to seal the wells so that dust did not fall
into the wells. The arrays continued to cure in this manner at room temperature until they were
used (usually more than a week). The glass backs of the arrays were further cleaned with ethanol,
hexane, or acetone before use.
20
2.3. Cytometry and imaging
For cell surface marker staining, peripheral blood mononucleated cells (PBMCs) were
thawed from -196 °C in 37 °C R15. PBMCs were then washed twice with R15, counted with a
hemacytometer, and rested in the 37 °C incubator for at least one hour. After resting, the cells
were labeled with a set of surface marker antibodies (1:1000 dilution for each antibody) for
either CD3+ or CD3– cells with a cell viability marker (1 nM calcein violet AM, Life
Technologies). The CD3+ panel contained CD4 Alexa Fluor 568 (Biolegend), CCR7 PE/Cy7
(Biolegend), CD45RA Alexa Fluor 647 (Biolegend), CD122 PerCP-eFluor 710 (eBioscience),
and CD95 Alexa Fluor 488 (Biolegend). The CD3– panel contained CD4 Alexa Fluor 568, HLADR PE/Cy7 (Biolegend), CD14 Alexa Fluor 647 (Biolegend), and CD11c Alexa Fluor 488
(Biolegend). For only staining cell viability, the cells (e.g., 4D20 and ACH2) were washed once
with PBS and stained with 1 nM calcein violet AM or 1 nM CellTracker Violet (Life
Technologies). After labeling for 30 min at 37 °C, the cells were washed once with PBS, loaded
into nanowells, and imaged on an epifluorescent microscope (Observer.Z1, Carl Zeiss GmbH) at
10x magnification (Objective EC “Plan-Neofluar” 10x/0.3, Carl Zeiss GmbH). A broad spectrum
light source was produced by a xenon lamp in a Lambda DG-4 (Sutter Instrument) and passed
through a “Pinkel” quad-band filter set (Semrock) for specific excitation bandwidths. Emissions
were filtered by a specific emission filters (Semrock) in a filter wheel (Lambda 10-3, Sutter
Instrument) just before image collection by an EM-CCD camera (C9100-13, Hamamatsu
Photonics). The entire system was controlled using the software AxioVision version 4.7 (Carl
Zeiss GmbH). The time settings were 100 ms exposure for each fluorescent channel at 100
EMCCD gain.
21
2.4. One-step reverse transcription polymerase chain reaction (RT-PCR)
2.4.1. Primer and TaqMan probe selection
Primers and dual-labeled TaqMan probes for housekeeping genes were designed using
the online software RealTimeDesign (Biosearch Technologies). Some of the design criteria were
intron spanning primers and probes to eliminate genomic DNA amplification, 55 °C melting
temperature for primers and 60 °C for probes, probes did not start with 5’ G because of
quenching, amplicon length of 90-200 base pairs, and 3’ end with G or C. The primer and probe
Table 2.1 Primer and probe design for RT-PCR.
Name
Sequence (5'->3')
Tm (°C)
B2M forward
TCCAGCGTACTCCAAAGATTCAG
56.7
B2M reverse
GAAACCCAGACACATAGCAATTCAG
56.1
B2M probe
FAM-CTCACGTCATCCAGCAGAGAATGGA-BHQ1
60.3
GAPDH forward
TTGCCCTCAACGACCACTTTG
58.1
GAPDH reverse
GAGGTCCACCACCCTGTT
57.0
GAPDH probe
FAM-TCCTGGTATGACAACGAATTTGGCTACA-BHQ1
59.8
ACTB forward
GATGCAGAAGGAGATCACTGC
55.6
ACTB reverse
GCCGATCCACACGGAGTA
56.9
ACTB probe
FAM-CAAGATCATTGCTCCTCCTGAGCGC-BHQ1
61.7
4D20 Heavy Chain for.
GGTCCTGTGCTGGTGAAAC
56.3
4D20 Heavy Chain rev.
GCTCACACCCATTCTATCATTG
53.7
4D20 Heavy Chain probe
Q670-CACAGAGACCCTCACGGTGACCT-BHQ2
62.4
HIVgag forward
CATGTTTTCAGCATTATCAGAAGGA
53.6
HIVgag reverse
TGCTTGATGTCCCCCCACT
59.0
HIVgag Q670 probe
Q670-CCACCCCACAAGATTTAAACACCATGCTAA-BHQ2
60.7
HIVgag FAM probe
FAM-CCACCCCACAAGATTTAAACACCATGCTAA-BHQ1
60.7
HIV 1LTR forward
TTAAGCCTCAATAAAGCTTGCC
53.6
HIV 1LTR reverse
GTTCGGGCGCCACTGCTAGA
62.4
HIV 1LTR probe
Q670-CCAGAGTCACACAACAGAGGGGCA-BHQ2
62.8
HIV 2LTR forward
CTAACTAGGGAACCCACTGCT
56.1
HIV 2LTR reverse
GTAGTTCTGCCAATCAGGGAAG
55.4
HIV 2LTR probe
Q670-AGCCTCAATAAAGCTTGCCTTGAGTGC-BHQ2
61.4
22
sequences (Biosearch Technologies) are shown in Table 2.1. All HIV primer and probe
sequences were given to us by our collaborators from the Xu lab in the Ragon Institute. We used
6-carboxyfluorescein (FAM) and Quasar® 670 (Q670) with their respective quenchers, Black
Hole Quencher™ 1 (BHQ1) and BHQ2, as the two channels for the probes. The primers and
probes were reconstituted in 1x TE buffer (10 mM Tris, pH 8.0, 1 mM EDTA) to a stock
concentration of 100 μM. Probes for short-term use were further diluted to 2 μM in water. To
minimize the number of freeze-thaw cycles, each aliquot of short-term use probe and 4x master
mix were for a total reaction mix of four 80 μL reactions. All primers, probes, and 4x master mix
solutions were stored at -20 °C.
2.4.2. Imaging end-point RT-PCR signal
Cells were split the day before their use in experiments. For cell labeling, the cells were
first washed once with phosphate buffered saline (PBS, Mediatech), then resuspended in 1 mL
PBS with 1 μL of the labeling dye (CellTracker Violet BMQC or calcein violet AM, Life
Technologies) according to the manufacturer’s recommended concentration. Labeling was
carried out at 37 °C for 30 min. Cells with high-viability were isolated with Ficoll-Paque Plus
(GE Healthcare Biosciences) and then treated with 30 μg/mL bovine pancreatic RNase A
(Sigma-Aldrich) at 37 °C for 30 min. The cells were then washed three times with 10% FBS in
RPMI and once with PBS before they were resuspended in 5 mL PBS. After these steps, more
than 98% of the cells remained viable as determined by the cellular exclusion of trypan blue
(Life Technologies). Each array of nanowells was cleaned by a 30 s plasma treatment (Plasma
Cleaner PDC-32G, Harrick Plasma) and blocked in 0.5% BSA in PBS for 30 min at room
23
temperature before loaded with cells by gravity such that about 50% of the wells had cells in
them.
The reaction mix used the qScript One-Step Fast qRT-PCR kit with ROX (Quanta
Biosciences). It contained 1x One-Step Fast Master Mix with ROX, 1 μM of each primer, 200
nM of each probe, 1x qScript One-Step Fast RT, 80 U of SUPERase-In RNase Inhibitor (Life
Technologies), and 0.05% NP-40 (Tergitol, Sigma) in a total volume of 40-80 μL per array. The
final NP-40 concentration was later increased to 0.5% to lyse the cells more effectively. Before
adding the reaction mix, the array was washed with 1x Tris-buffered solution (TBS, 20 mM Tris,
pH 7.5, 150 mM NaCl) and quickly rinsed with 0.5x TBS or water. The reaction mix was applied
to the nanowells and spread using a pipet tip before the device was sealed onto another glass
slide. Excess reaction mixture was removed along the sides and the entire device was placed on
an Eppendorf Mastercycler Gradient (Eppendorf) with a glass slide adaptor (in situ Adapter,
Eppendorf). Mineral oil (Sigma) was added to improve the heat conductivity between the adaptor
and the device. The thermocycle profile was initially 40 min at 50 °C, 2 min at 95 °C, 12 cycles
of 40 s at 95 °C and 1 min at 65 °C, and 38 cycles of 40 s at 95 °C and 1 min at 60 °C, with the
lid maintained at 50 °C. It was common to observe dried wells and warped wells (pincushion
distortion) around the perimeter of the array. The array also became cloudy from the penetration
of water into the PDMS. The use of RTV615 instead of Sylgard 184 as the silicone reduced the
number of dried wells almost completely (>98% usable array) and the thermocycles were later
reduced to 15 min at 50 °C, 2 min at 95 °C, 35 cycles of 40 s at 95 °C and 1 min at 60 °C.
24
2.4.3. Quantitative TaqMan RT-PCR
Quantitative TaqMan RT-PCR (RT-qPCR) was run on LightCycler 480 (Roche). The
same thermocycles were used in RT-qPCR as in the nanowell RT-PCR. Each reaction had a final
volume of 20 μL in a clear, LightCycler-specific, 96-well plate (Roche). The cycle numbers that
exceeded the threshold intensity (Ct) were calculated with the built-in analysis module. The final
PCR products were also imaged on 2% agarose gel with ethidium bromide to verify amplicon
length. The desired DNA band was cut from the gel and purified with a QIAquick gel
purification kit (Qiagen) for digital PCR in nanowells. The purified DNA concentration was
measured by forming a column with 1.5 μL of DNA on a NanoDrop 1000 spectrometer
(NanoDrop).
2.4.4. Digital PCR in nanowells
Serial dilutions of purified DNA (e.g., HIVgag PCR product) were made and 1 μL of
each dilution was added to 80 μL of reaction mix. The reaction mix contained the primer and
probe set for the desired DNA template as well as the primer and probe set for a negative gene
(e.g., ACTB) in a separate channel as a negative control. The nanowell arrays were treated as
normal (e.g., plasma cleaning and blocking) and rinsed in water for the final step before adding
the reaction mix.
2.5. Microengraving
Detailed procedures for microengraving can be found in Ogunniyi et al. Nature Protocols
(2009) vol. 4 (5) pp. 767-82. Briefly, cells were labeled for cell viability (CellTracker violet),
loaded into the nanowells, and imaged. The nanowells were then sealed with a glass slide that
25
was functionalized with anti-IgG1 antibodies at 37 °C. After 2 hr, the glass slide was separated
from the nanowells and the captured IgG1 was detected following the application of a secondary,
goat anti-human IgG1 antibody conjugated with Alexa Fluor 647 (Life Technologies).
Data from the microscopy, microengraving, and RT-PCR were collected and filtered.
Only wells that contained a single live cell initially, and had a single cell after RT-PCR (detected
by non-specific staining with the reference dye, ROX) were tabulated. Spots on the microarray
generated by microengraving that had a signal-to-noise ratio greater than 2 for more than 55% of
its pixels and a coefficient of variation less than 80 were considered positive for IgG1 secretion.
2.6. Surface capture of transcripts
One method to capture transcripts on glass slides was to use amine-epoxy linkage. Glass
slides were cleaned with 2.5 M sodium hydroxide (NaOH) in 60% ethanol (EtOH) for 2 hours
and reacted in a 0.1% (3-glycidoxypropyl)trimethoxysilane in 100% EtOH supplemented with
traces of glacial acetic acid as an acid catalyst for 2 hours at 40 °C. The slides were washed twice
with 100% EtOH and baked at 120 °C overnight to remove residual water. One surface of the
epoxy slides was then reacted with the primer mix conjugated with a 5’ amine-C9 group in 0.15
M NaOH at 80 °C for 2 hours. The free epoxy groups were washed and blocked with 0.2 M Tris
and 0.1% sodium dodecyl sulfate (SDS) at 50 °C for 4 hours before the primer-conjugated glass
were ready.
Another method to attach transcripts to surfaces (e.g., PDMS or glass slide) was to use
oligonucleotides with a 5’ amine group linked to amine groups on the surface by p-phenylene
diisothiocyanate (PDITC)80. To functionalize the PDMS with amine groups, the array of
nanowells
was
plasma treated
for 5
minutes
26
and
then
placed
in
a 10%
(3-
aminopropyl)triethoxysilane (APTES) in water and rocked for 1 hr at room temperature in a 4well polystyrene dish (Nunc). After two 30 s, manual water washes, the array was dried at 80 °C,
overnight. The next day, the dried array was soaked in acetone for 3 min to rewet the well
surfaces, and washed in dimethylformamide (DMF) before reacting in 0.2% (w/v) PDITC in
10% (v/v) pyridine/DMF for 3 hr at room temperature. Excess PDITC was removed by two
DMF washes, one methanol wash, and one 100 mM sodium bicarbonate, pH 9.0 wash. The
desired amine-conjugated oligonucleotide that was reconstituted in water was diluted to 25 μM
in 50 mM sodium borate, pH 8.5. This solution was reacted with the PDITC-conjugated array
under a lifterslip in a humidified box. The next day, the reaction was quenched with 1x TBS for
10 min, and blocked with 0.5% BSA in PBS for 1 hr at 80 °C. After three PBS washes, the array
was loaded with cells for downstream processing. Cells were loaded and lysed in MES lysis
buffer (20 mM MES pH 6.0, 500 mM NaCl, 10 mM EDTA, 0.01% NP-40, 10 mM DTT).
2.7. Hybridization chain reaction
The hairpin pairs and initiator sequences for hybridization chain reaction (HCR) were
modified from the sequences A, H1 and H2 from literature67. The modifications include moving
blocks of bases, using the complementary sequences, and adding 9 adenosines at the end of the
initiators where the oligonucleotide was attached to either an antibody (through a 3’ thio-C3
linker on the initiator) to detect a target protein or another oligonucleotide to detect a target gene.
The four sets of initiator (An) and hairpins (H2n-1 and H2n) sequences (5’ to 3’) were ordered from
Integrated DNA Technologies and purified by high performance liquid chromatography (Table
2.2).
27
All HCR oligonucleotides were reconstituted to 100 μM in 1x SPSC (0.1 M sodium
phosphate, 1 M sodium chloride). Each initiator and hairpin were denatured to 95 °C for 2 min,
immediately placed on ice for 1 min, and kept at room temperature until used. To test the
specificity of the hairpins to their initiators, each of the four initiators were diluted to 2 μM in 1x
TBE buffer (90 mM Tris-borate, 2 mM EDTA, pH 8.3) and 1 μL was spotted onto a poly-Llysine coated glass slide at different locations. A mixture of the four sets of hairpins (20 μM each)
was dispensed onto the slide under a lifter slip (Electron Microscopy Sciences) for 2 hr at room
temperature. The slide was washed with PBS/0.05% Tween-20 and PBS, and scanned using a
Genepix 4200AL (Molecular Devices). The commercial software package Genepix Pro 6.1 was
used to extract the fluorescence for each spot in each of the four channels.
Table 2.2 Sequences of initiator and hairpin for HCR.
ID Sequence (5'->3')
A1
GCA CGT CCA CGG TGT CGC TTG AAT AAA AAA AAA
H1
FAM-ATT CAA GCG ACA CCG TGG ACG TGC ACC CAC GCA CGT CCA CGG TGT CGC ACC
H2
FAM-GTT GCA CGT CCA CGG TGT CGC TTG AAT GCG ACA CCG TGG ACG TGC GTG GGT
A2
GCA GCC GTA GAC TAG TGC GCG AAT AAA AAA AAA
H3
TYE563-ATT CGC GCA CTA GTC TAC GGC TGC ACG ACC GCA GCC GTA GAC TAG TGC CAC
H4
TYE563-GTT GCA GCC GTA GAC TAG TGC GCG AAT GCA CTA GTC TAC GGC TGC GGT CGT
A3
CGT CGG CAT CTG ATC ACG CGC TTA AAA AAA AAA
H5
TYE665-TAA GCG CGT GAT CAG ATG CCG ACG TGC TGG CGT CGG CAT CTG ATC ACG GTG
H6
TYE665-CAA CGT CGG CAT CTG ATC ACG CGC TTA CGT GAT CAG ATG CCG ACG CCA GCA
A4
CGT GCA GGT GCC ACA GCG AAC TTA AAA AAA AAA
H7
TEX615-TAA GTT CGC TGT GGC ACC TGC ACG TGG GTG CGT GCA GGT GCC ACA GCG CTG
H8
TEX615-CAA CGT GCA GGT GCC ACA GCG AAC TTA CGC TGT GGC ACC TGC ACG CAC CCA
2.8. Data Analysis
Images generated by automated microscopy were analyzed using custom software
(Enumerator, mabanalyze, and CellProfiler). The location, the number of cells, and the
28
fluorescence intensity of each channel were tabulated in a text file. This information was filtered
and plotted using MATLAB (MathWorks). The data were filtered to remove wells with more
than four cells because too many cells gave inaccurate measures of the well intensity. Wells with
large variation in the reference channel (greater than two standard deviations from the mean
reference signal) were also removed to eliminate wells with no liquid and wells with a high
degree of covariance in fluorescence. This filter was important because dividing by a low
reference signal would give a relative intensity that was too high and artificially positive. This
artificial positive signal was especially problematic for the FAM channel. For each block of
wells, the mean gene-specific fluorescence intensity of empty wells (Iempty) was calculated and
used to determine the relative fluorescence of every well (Iwell/Iempty). A histogram was plotted to
bin the relative fluorescence intensities. The histogram peak for Iwell/Iempty of empty wells was fit
to a Gaussian curve to compute estimated values for the mean and standard deviation of negative
reactions. The threshold value on the relative fluorescence for positive reactions was set to be
three standard deviations above the mean. From this value (e.g., Iwell/Iempty = 1.4), the sensitivity,
specificity, and positive predictive value were determined for each gene. For the analysis of
Q670 fluorescence data from HIV-infected cells, a threshold of 1.5 times the mean empty
fluorescence, which corresponded to approximately 8-12 standard deviations from the mean, was
used. Such a high cutoff was possible because the signal intensity from the Q670 dye was much
brighter than that of the FAM dye.
29
30
Chapter 3. Establishing one-step RT-PCR in nanowells
3.1. Optimization of cell lysis
To test the RT-PCR efficiency in the nanowells, we used beta-2-microglobulin (B2M) as
the target gene because B2M is constitutively expressed in the 4D20 cell line. B2M primers and
TaqMan probes were designed to reverse transcribe bases 122 to 211 from the mature mRNA
(GENBANK NM_004048). No additional steps or reagents were used to remove genomic DNA
from the reaction. The signal we used to determine a positive reaction came from the digestion of
a quenched TaqMan probe. If the target gene were present, the intact probe would bind to the
Figure 3.1. TaqMan probe mechanism. When the probe is intact, the emitted light by FAM is
quenched by the BHQ-1. As the Taq polymerase extends the primer, its exonuclease will cut
the probe, thus freeing FAM. FAM is no longer within the proper distance from BHQ-1 for
quenching, so it can be detected.
31
desired gene by complementary base pairing. As the PCR progressed, the Taq enzyme would
cleave the probe because it has 5’ to 3’ exonuclease capabilities (Figure 3.1). Thus, the
fluorophore (e.g., FAM or Quasar 670) would no longer be at a fixed distance from the quencher
and the detection of its fluorescence would be possible by epifluorescence microscopy.
Several commercial kits were tested in tubes to identify the kit with the best efficiency
for use in subsequent RT-PCR reactions. Two of kits were the qScript 1-Step Fast RT-PCR with
ROX (Quanta Biosciences) and QuantiFast SYBR Green RT-PCR (Qiagen). We found that
using a template HeLa mRNA, the qScript 1-Step Fast RT-PCR with ROX had the lower Ct
0.6
Relative Fluorescence
0.5
0.4
QuantiFast SYBR 10 ng
QuantiFast SYBR 0.1 ng
0.3
QuantiFast SYBR 10 pg
QuantiFast SYBR 1 pg
qScript ROX 10 ng
0.2
qScript ROX 0.1 ng
qScript ROX 10 pg
0.1
qScript ROX 1 pg
0
0
5
10
15
20
25
30
35
40
45
50
Cycle number
Figure 3.2. Determining the RT-qPCR kit. The 1-step Fast qScript with ROX kit was
significantly better than the QuantiFast SYBR Green kit. The Ct value for qScript at
10 pg mRNA was approximately the same as the Ct value for the 0.1 ng mRNA for
QuantiFast.
value (a measurement of reaction effectiveness), so it was the best at the reverse transcription of
mRNA from single cells (Figure 3.2). In fact, the fluorescence profile of the 10 pg of mRNA
32
with qScript was similar to that of the 100 pg of mRNA with QuantiFast, which suggested that
qScript was about 10 times more sensitive to mRNA than QuantiFast. We also tested two types
of kits from Quanta Biosciences (qScript with ROX and qScript MGB). From serial dilutions of
mRNA, we observed that both kits were similarly sensitive, but the qScript kit with ROX had a
higher relative fluorescence than the MGB kit for the 10 pg mRNA sample (Figure 3.3). Since
our target genes will be rare cellular mRNA, we chose the more sensitive detection and higher
relative fluorescence kit, 1-step Fast qScript with ROX, for all future studies.
To adapt a typical multistep RT-qPCR in a PCR tube to a one-step reaction in nanowells,
we added a detergent to help lyse the cells. We chose to test two non-ionic detergents, Tergitol
type NP-40 (NP-40) and Tween-20, and one ionic detergent, sodium dodecyl sulfate (SDS), for
their ability to help lyse cells during RT-PCR55. These detergents were commonly added in the
RT-PCR kits to help stabilize the enzymes in the reaction mix. It was also important to ensure
that the addition of even more detergents did not significantly hinder the reaction itself.
We tested an initial range of final concentrations (0.75%, 0.5%, 0.25%, 0.1%, 0.05%,
0.005% and 0%) for Tween-20 and NP-40 on reactions with 100 4D20 cells and observed that
the optimal concentration for Tween-20 was 0.5% (Figure 3.4) while the optimal NP-40
concentration was approximately 0.75% (Figure 3.5). The NP-40 concentration was less clear
because the two no detergent controls were not consistent. A direct comparison of 0.5%, 0.25%,
and 0.05% Tween-20 to 0.75%, 0.5% and 0.05% NP-40 showed that 0.5% and 0.05% NP-40 and
0.5% Tween-20 were the best conditions (Figure 3.6). At cycle 30, 0.5% NP-40 was slightly
better than the other conditions, but 0.05% NP-40 was chosen for initial tests to minimize the
interference with the kit. To determine the efficiency of the RT-qPCR reaction, the logarithm
33
(base 10) of the cell number was plotted against the Ct value at that cell number and regressed to
a linear line to determine the slope. The efficiency of the reaction, which is (10-1/slope – 1), was
approximately 1.00 for 0.05% NP-40 and 0.39 for 0.5% Tween-20 (Figure 3.7). This result
suggested that Tween-20 was interfering with the RT-qPCR reaction, so subsequent reactions
only used NP-40 as the detergent.
These serial dilutions of detergent also revealed that amplification of genomic DNA did
occur in bulk reactions (no RT controls) despite using intron-spanning primers and probes, but it
required 10 more PCR cycles than the amplification of cDNA (Figure 3.6). This result further
suggested that the removal of genomic DNA was unnecessary given an appropriate number of
cycles.
RT-qPCR experiments with the ionic detergent SDS showed strong inhibition of the
reaction at SDS concentrations greater than 0.01% (Figure 3.8). At concentrations of 0.01% and
0.005%, the SDS had a Ct value that was lower than 0.05% NP-40. These results were expected
since SDS is often used to denature proteins in SDS-PAGE, so in addition to breaking up the cell
more effectively than NP-40, SDS could also be denaturing the RT and Taq enzymes, thus
inhibiting the reaction completely.
34
Figure 3.3. Comparison between 1-step Fast qScript with ROX and MGB.
Both kits performed equally for sensitivity to mRNA. The ROX kit had a
higher relative fluorescence than the MGB kit.
0.6
0.75% Tween20
Relative Fluorescence
0.5
0.5% Tween20
0.4
0.25% Tween20
0.1% Tween20
0.3
0.05% Tween20
0.2
0.005% Tween20
0% Tween20
0.1
0% Tween20
0
0
10
20
30
40
50
60
Cycle Number
Figure 3.4. The effect of Tween-20 on the RT-qPCR reaction on 100 cells. The
optimal concentration of Tween-20 was 0.5%.
35
Figure 3.5. The effect of NP-40 on the RT-qPCR reaction on 100 cells. The
optimal concentration for NP-40 was approximately 0.75%, but it was not
clearly determined since the no detergent controls were not consistent.
0.45
Relative Fluorescence
0.4
0.75% NP40
0.35
0.5% NP40
0.3
0.05% NP40
0.25
0.2
0.5% Tween20
0.15
0.25% Tween20
0.1
0.05% Tween20
No Detergent
0.05
No RT, No Detergent
0
0
10
20
30
40
50
60
Cycle Number
Figure 3.6. Direct comparison between Tween-20 and NP-40. Both 0.5% and
0.05% NP-40 were similar to 0.5% Tween-20.
36
45
40
y = -6.9353x + 39.031
R² = 0.9212
35
Ct number
30
25
20
y = -3.3175x + 30.576
R² = 0.9934
0.05% NP-40
0.5% Tween-20
Linear (0.05% NP-40)
Linear (0.5% Tween-20)
15
10
5
0
0
0.2
0.6
0.4
0.8
1
1.2
1.4
1.6
1.8
Log(Cell number)
Figure 3.7. Efficiency of RT-qPCR with the addition of 0.05% NP-40 or 0.5%
Tween-20. RT-qPCR efficiency was 1.00 for NP-40 and 0.39 for Tween-20.
0.9
Relative Fluorescence
0.8
0.7
0.6
100, 0.005% SDS
0.5
100, 0.01% SDS
100, 0.05% SDS
0.4
100, 0.1% SDS
100, 0.5% SDS
0.3
100, 1% SDS
Ct Threshold
0.2
0.1
0
0
10
20
30
40
50
60
Cycle Number
Figure 3.8. Effect of SDS on RT-qPCR on 100 cells. SDS strongly inhibited
the reaction at concentrations greater than 0.01%.
37
3.2. Optimization of RT-PCR in nanowells
When translating the reaction from a typical 20 µL tube to an array of 125 pL reactors,
several additional design considerations were addressed. One extra consideration was heat
conduction. The PCR machine was designed for PCR tubes with 0.2 mm thickness that fit
perfectly in each well. This fit allowed direct contact of the wall of the tube with the metal heat
conductor. For RT-PCR in nanowells, a metal adaptor for glass slides was fitted to the 96-well
format. Also, the glass slide was approximately 1 mm thick, compared to the 0.19 mm thickness
of the PCR tube. To ensure good thermal contact, 20-30 µL of mineral oil was added between
the glass and the metal adaptor. The thermal conductivity of water, glass, mineral oil, and PDMS
are on the order of 0.1 to 1 W/mK. A COMSOL Multiphysics model for heat conduction through
the glass slide from 60 °C to 95 °C showed that by 5 s, the glass slide and liquid in the nanowells
would reach the desired temperature (Figure 3.9). To be more conservative, an additional 10
seconds were added to every step of the thermocycle to allow the temperature to equilibrate.
Although the kit could be run at “fast” cycling profile with shorter denaturing and extension
steps, we opted for the normal length.
Another concern for the RT-PCR in nanowells was that the size of the wells would
increase the concentration of cellular RNase as well as the total surface of the array. Due to the
small reactor volume, RNase concentrations increased dramatically compared to large tube
reactions, so SUPERase In, an RNase inhibitor, was added to the reaction mix. To reduce the
non-specific adhesion of enzymes to the walls of the nanowells, the PDMS and the glass slide
were blocked with bovine serum albumin (BSA). Initial tests also included 0.5% BSA in the
reaction mixture to further reduce non-specific binding, but this additive was not necessary.
38
Figure 3.9. Temperature model for an increase in temperature from 60 °C to
95 °C after 5 seconds. This model showed that in 5 seconds, the temperature
profile of the glass slide and thin water (e.g., liquid in the nanowells) had
raised to 94 °C.
3.3. Optimization of pre-treatment of cells
Methanol, water and thermal treatments were tested for the most efficient method to lyse
cells in nanowells. Methanol was chosen as a protocol to fix the cells and help reduce the activity
of RNase and DNase. Water was effective at lysing cells since it is hypotonic compared to the
cellular cytoplasm. Initial data showed that treating cells with pre-cooled methanol (-20 °C) for
10 min after the cells were loaded, quickly using water as a hypotonic lysis before adding the
reaction mixture, and adding a heat lysis step at 50 °C for 30 min with reverse transcription
resulted in 80% positive reactions (Table 3.1). However, 40% of empty wells were also bright.
These false positives were postulated to be from mRNA contamination from the supernatant of
39
the cell suspension. To test this hypothesis, the cell suspension was treated with RNase A for 30
min at 37 °C and washed 3 times to remove the RNase A before the cells were loaded onto the
array. Using RNase A that was DNase-free, we saw near complete elimination of the false
positive signal (Figure 3.10). Therefore, all future reactions had a pretreatment step for the cell
suspension to digest any free mRNA in the supernatant. Interestingly, using RNase that was not
Figure 3.10. Removal of false positives by RNase, DNase-free treatment. The
histogram on the right show that most wells with no cells had a FAM/ROX
less than 2 (cutoff for positive signal), while wells with cells did have specific
positive signal.
labeled as DNase-free had a FAM/ROX ratio of approximately 3 in every well (data not shown).
This minor difference showed that DNase was not inhibited during the reaction and that the
maximum signal from FAM was approximately 3. Adding DNase to the master mix also verified
these results. Other pretreatment steps that were required were the quick wash with water before
applying the reaction mixture to help burst the cells open. The methanol treatment of the cells,
however, did not improve the percent of true positives in wells with cells.
40
Table 3.1 List of tested RT-PCR lysis conditions.
Lysis conditions
Parameter
Fraction of bright wells
Methanol
Detergent
Water lysis
RT
# of cycles
Empty
Occupied
Yes
Yes
Yes
Yes
50
0.3853
0.8131
Yes
No
Yes
Yes
50
0.3832
0.8098
Yes
Yes
Yes
No
50
0.0123
0.0492
Yes
No
Yes
No
50
0.0102
0.0375
Positive control
Yes
Yes
Yes
Yes
50
0.4536
0.8194
No detergent
Yes
No
Yes
Yes
50
0.4062
0.6758
No probe
Yes
Yes
Yes
Yes
50
0.0001
0
No primer
Yes
Yes
Yes
Yes
50
0.0003
0.0008
Yes
Yes
Yes
Yes
40
0.1509
0.421
Yes
No
Yes
Yes
40
0.1188
0.3899
Yes
Yes
Yes
No
40
0.0077
0.0335
Yes
No
Yes
No
40
0.0103
0.037
No
Yes
Yes
Yes
40
0.4355
0.7955
No
No
Yes
Yes
40
0.5229
0.8088
No
Yes
Yes
No
40
0.0043
0.0745
No
No
Yes
No
40
0.0253
0.1876
Yes
Yes
Yes
Yes
50
0.4724
0.7876
Yes
No
Yes
Yes
50
0.2811
0.5679
Yes
Yes
No
Yes
50
0.0001
0
Yes
No
No
Yes
50
0.0019
0.0004
Base case
40 Cycle
No methanol
Quick water rinse
41
3.4. Optimization of thermocycling
One of the concerns with running too many thermocycles was that the bulk RT-qPCR on
cells showed positive fluorescence at 10 cycles later in the sample with no reverse transcriptase
added than in samples with the RT enzyme. This result indicated that genomic DNA would be
amplified given enough cycles. To determine how many cycles were needed, we first ran digital
PCR with HIVgag PCR product as the template on the array and started with 50 or 70 cycles.
These experiments used a serial dilution of the HIVgag DNA ranging from an average input
number of DNA of 8 copies/well to 0.125 copies/well (Figure 3.11). Even though 70 cycles were
run, the fluorescence intensity did not increase significantly compared to the 50 cycles. This lack
of increase in the signal further showed that we could attain a maximum signal in the nanowells
at an optimal cycle number. The variability in the positive signal did not disappear with more
cycles, so it suggested that the variability was innate to the system. One cause for the fluctuation
in positive signal could be the distribution of probe in each well.
Several assumptions were made to estimate the fraction of wells that should be positive.
Considering that the nanowells were initially filled with water and assuming that the limit of
detection for DNA was 1 copy per well, the estimated fraction of bright wells in the array was
diluted by a factor of approximately 0.14 or a positive fraction of 1.12 (i.e., 1) to 0.0175. Plotting
this estimated fraction with the actual fraction of bright wells, the limit of detection was
determined to be approximately 1.4 copies instead of 1 copy per well (Figure 3.12). This number
of transcripts in a 125 pL volume corresponded to a concentration of 18.6 fM.
To determine the optimal cycle number, we added enough HIVgag DNA (100 fold more
than 0.4 estimated fraction) to the array so that every well would be bright. We also included the
42
ACTB or GAPDH primers and probe as a negative control. Plotting the histogram of the relative
intensities showed that at 15 cycles, the fluorescence was just barely above the negative peak. By
cycle 20, the fluorescence was twice the negative peak, and by 30 cycles, the fluorescence was
approximately 3 times the negative peak (Figure 3.13). Interestingly, the no DNA control at 35
cycles had a small positive peak, but the mean of this peak was not higher than that of the 30
cycles. This false positive showed that there was some HIVgag contamination in the negative
control and that 30 cycles were enough for a maximum fluorescence signal of 3 times the
negative peak. Note that theoretical calculations on the perfectly efficient amplification of 1
molecule of DNA showed that 24 cycles were needed to exceed the concentration of 200 nM
probe. Since we added enough DNA for approximately 40-80 copies per well, the 30 cycles
would correspond to about 35-37 cycles for the detection of single copy of DNA. Therefore, all
future experiments used 35 cycles.
43
Figure 3.11. Sample images of digital PCR on a serial dilution of HIVgag
DNA. From the top to bottom, then left to right, the average input DNA
number per well was 8, 4, 2, 1, 0.5, and 0 copies. These input values
corresponded to a fraction of bright wells of 1.12 (i.e., 1), 0.56, 0.28, 0.14,
0.07, and 0.
0.6
y = 0.7164x + 0.0009
R² = 0.9667
Actual fraction
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
Estimate fraction
Figure 3.12. Limit of detection for PCR. The limit of detection was 1/slope or
approximately 1.4 copies of DNA.
44
RTPCR HIVgag 45e-4ng
RTPCR ACTB (neg)
0.4
0.25
Preimage
20 cycles
10 cycles
15 cycles
0.35
0.2
Normalized frequency
Normalized frequency
0.3
Preimage
20 cycles
10 cycles
15 cycles
0.25
0.2
0.15
0.15
0.1
0.1
0.05
0.05
0
0
0.2
0.4
0.6
1.4
1.2
1
0.8
ROX normalized intensity
1.6
1.8
0
2
0
0.2
0.4
0.6
PCR HIVgag 18e-4ng
1.6
1.8
2
PCR ACTB (neg)
0.45
0.35
Preimage, no DNA
5 cycles
30 cycles
20 cycles
35 cycles, no DNA
0.4
0.35
Preimage, no DNA
5 cycles
30 cycles
20 cycles
35 cycles, no DNA
0.3
0.25
0.3
Normalized frequency
Normalized frequency
1.4
1.2
1
0.8
ROX normalized intensity
0.25
0.2
0.15
0.2
0.15
0.1
0.1
0.05
0.05
0
0
0.2
0.4
0.6
0.8
1
1.2
1.4
ROX normalized intensity
1.6
1.8
2
0
0
0.2
0.4
0.6
1
1.2
1.4
0.8
ROX normalized intensity
1.6
1.8
2
Figure 3.13. Various cycle numbers for PCR on HIVgag DNA. The progression of
signal can be seen over the number of cycles. After 20 cycles, the fluorescence
intensity was approximately twice that of the preimage (0 cycle), and after 30 cycles, it
was approximately 3 times that of the preimage. Note that 35 cycles, no DNA had
some contaminating DNA in it, but its fluorescence was not greater than that of the 30
cycles.
45
3.4. Discussion
3.4.1. Limit of detection of transcripts
Although we had shown that the assay could detect a few copies of DNA, it was not
clear what the limit of detection of mRNA was. In a control experiment with diluted standards of
cell-free mRNA bound to oligo-dT beads, we were able to detect positive reactions from the
beads (Figure 3.14). However, the number of copies of the mRNA on the beads and in bulk
could not be measured accurately. Similar to digital PCR, mRNA could be detected in nanowells
(Figure 3.15), but the exact number of B2M mRNA could not be determined. Although this
result cannot be compared directly to conditions in which residual components of the lysed cell
remain, it would suggest that nanowell-based RT-PCR allows for detection of small quantities of
mRNA.
To more accurately determine the limit of detection, cells were added to the digital
PCR. This addition simulated the effect of cellular lysate on the detection of DNA in
nanowells. RT-PCR mixture was spiked with HIVgag DNA to have approximately 20%
positive wells, and 4D20 cells (HIV negative) were loaded in the nanowells at high density
so that the cell occupancy was from 0 to 4 or more cells (Figure 3.16). Wells with no cells
had the expected 20% of bright wells, but if a well had cells, the fraction of bright wells
dropped. From 0 to 1 cell, the drop was 4-fold, with another 4-fold decrease from 1 cell to 4
or more cells. Since the DNA was randomly distributed onto the array, the expectation was
that the fraction of bright nanowells would be equal no matter how many cells were in them.
The drop in the fraction of bright nanowells showed that the cell lysate was interfering with
the PCR, making the reaction less sensitive to small number of DNA copies. The limit of
46
detection of DNA with cell lysate in the nanowell would be 6 to 23 copies of DNA.
Figure 3.14. Detection of B2M mRNA transcripts on beads from bulk cellular mRNA
extraction. 4D20 mRNA was extracted with oligo-dT beads. The beads were settled into
nanowells and RT-PCR was run. A positive fluorescence signal for reactions with RT
enzyme was present around 3 and was absent in the no RT enzyme control.
Figure 3.15. Detection on B2M mRNA from bulk cellular mRNA extraction. 10 ng or 100
pg of bulk cellular mRNA was added to the reaction mix and applied to the array. The exact
number of mRNA copies in each well was unknown.
47
RTPCR 18.4e-6ng gag
0.25
Fraction positive for HIV gag
0.2
0.15
0.1
0.05
0
0
1
2
Number of cells
3
4
Figure 3.16. Effect of cell lysate on RT-PCR. HIVgag DNA was spiked into an RT-PCR
reaction with 4D20 cells. Having just one cell in a nanowell reduced the fraction of positive
fluorescence by 4-fold from that of wells with no cells.
3.4.2. Evaporation
Evaporation caused by detachment of the array from the sealing glass slide and
permeation of water from the nanowells into the PDMS were observed after the thermocycles81.
The loss of water also caused the nanowells to pucker and shrink into a pincushion distortion. In
case of extreme loss of water, the entire nanowells would detach from the glass slide and had no
fluorescence signal in them. This evaporation was especially prevalent in the few rows and
columns of entire blocks bordering the edges of the array. Also, wells that bordered evaporated
blocks had brighter signal in all fluorescence channels. The increased fluorescence often required
additional wells to be removed, thus reducing the percentage of usable wells in the array.
48
Changing the PDMS from Sylgard 184 to RTV615 significantly decreased the number of
evaporation events to less than 10% of the array and often to less than 5%. One explanation is
that the RTV615 was softer than Sylgard 184, so it could more easily mold to a flat glass slide if
there were surface unevenness on the array. The RTV615 was also more stuck to the glass slide
than Sylgard 184 as more force was needed to pry the glass slide off the array after
thermocycling.
Another important feature of the array was the presence of microfluidic channels. To
maximize the loading of cells in wells, arrays without channels were often used. However, for
RT-PCR, arrays with no channels had evaporation in greater than 20% of the array. This result
suggested that the channels acted as a liquid reservoir in the array. The importance of a reservoir
had been observed to prevent evaporation in femtoliter digital PCR in PDMS58. To increase the
reservoir volume further, we added channels between every block of nanowells, up from the
original design of every four blocks. In the original four block format, wells bordering a channel
were removed because they had brighter fluorescence than the wells that did not border channels.
Interestingly, if there were channels between every block, the outer wells did not have a
significantly brighter fluorescence. This phenomenon was observed in the array of 12 x 12
nanowells as well as the 11 x 11 design, where the wells were closer to the channels, so it was
not proximity to the channel that caused the brighter fluorescence. One possible explanation
could be that while the overall loss of liquid could be the same, the fraction lost in all the
channels dropped since the size of the reservoir increased by 4-fold.
Finally, dropping the total number of thermocycles from 50 to 35 further alleviated the
loss of water issues to just the bordering few rows and columns of the outer blocks. Not only did
the reduction in thermocycles increase the percentage of usable wells in the array, it also
49
separated the positive and negative fluorescence signal. While cycles more than 35 did not
increase the mean positive fluorescence signal, they did increase the mean negative fluorescence
signal.
3.4.3. Limitations
One of the limitations of this method is that it was not quantitative. Unlike RT-qPCR in
tubes, RT-PCR in nanowells was an endpoint measurement of the fluorescence from TaqMan
probes, so only a presence or absence of a target gene could be obtained. The fluorescence was
not measured after every cycle. Questions such as how much transcript was in the cell could not
be answered by this platform. While we could detect the fluorescence increase in 5 cycle
increments, as shown in the thermocycle optimization experiments, this required a different array
for each cycle that was measured. When the same array was reimaged multiple times, the entire
array was photo-bleached and lost all fluorescence signals after 3 to 4 reimages. In fact, a drop of
ROX signal was observed in the last quarter of the array even before the entire array was imaged
for the first time. These observations could be attributed to the high transmittance of light of
PDMS82. As one block of nanowells was imaged, some of the light also hit other blocks, so when
the last block of the array was imaged, some light could have already hit the last block more than
600 times.
A second limitation to this assay was that it could not consistently detect nuclear
transcripts such as 1LTR circles in HIV-infected cells. In bulk RT-qPCR experiments, the no RT
control still had a positive fluorescence profile, but at a later cycle number than the sample with
RT enzyme. Bulk qPCR of ACH2 cell lysates showed detectable levels of 1LTR DNA circles
and these circles were exclusively located in the nucleus83. However, when RT-PCR was run on
50
ACH2 cells, the 1LTR signal was not consistent: most wells with ACH2 cells were not positive
for 1LTR. Since the limit of detection for PCR was approximately 10 copies of DNA, this lack
of signal showed that the nucleus was not fully lysed by the reaction mixture.
51
52
Chapter 4. Characterization of RT-PCR in nanowells
4.1. Sensitivity and specificity
To establish the feasibility for in situ lysis and detection of an expressed gene of interest
in wells containing cells, we used a human B cell hybridoma (4D20) that produces an antibody
(IgG1) against the 1918 influenza virus84. Lysis of the cells and subsequent reverse transcription
of a constitutively expressed gene (beta-2-microglobulin, B2M) was achieved in the closed
reactors at 50 °C for 40 min. Then, the array was subjected to 50 rounds of thermocycling to
amplify the transcribed cDNA and hydrolyze the quenched fluorophore from the labeled probes
(Figure 4.1a). The array was imaged to detect the fluorescent signals evolved from the digested
probes (Figure 4.1b). The images were analyzed using a custom program to determine the
location of each well, the number of cells per well, and the fluorescence intensities of both the
released probe and reference dye. These data were then filtered to discard wells with more than
four cells and wells with a large coefficient of variation in the soluble reference signal (ROX).
To normalize for regional variations of the measured intensities, we calculated the relative
fluorescence as the ratio of the gene-specific signal (Iwell) to the mean of the gene-specific signal
of nearby empty wells (Iempty) (Figure 4.1c, top).
To determine the threshold value for a positive RT-PCR reaction, we fit the relative
fluorescence of the wells containing no cells to a single Gaussian distribution to obtain estimates
for the mean and standard deviation of the peak representing negative reactions (0.96±0.12). We
defined positive reactions as those wells containing cells with a ratio greater than three standard
deviations above the mean ratio determined for empty wells. The percentage of positive events
53
scored in control experiments in which either the primers, probe, or reverse transcriptase were
excluded was less than 0.01% (Figure 4.1c). The lack of positive events scored upon omission of
reverse transcriptase from the reaction indicates that the genomic DNA was not amplified, and
implies that it is not necessary to remove residual genomic DNA from the reaction when using
intron-spanning primers. Digestion of the gene-specific probe with DNase I in the reaction
mixture prior to application to an array without cells yielded a measured ratio of 2.65±0.08 (data
not shown). This experiment, in combination with the cell-based experiments, suggested that the
maximum relative fluorescence for a positive reaction is about 2.7, and that 50 rounds of
thermocycling were sufficient to obtain this endpoint.
54
Figure 4.1 (a) Schematic of method for parallel single-cell RT-PCR reactions in nanowells.
Cells are deposited in nanowells, filled with a solution of components for RT-PCR, and then
sealed to a glass slide. The thermal lysis, first strand synthesis, and amplification of cDNA
are conducted on a thermocycler. The fluorescence intensity of cleaved probes is detected by
epifluorescent microscopy. (b) Fluorescent micrographs of gene-specific (B2M) and a
reference signal (ROX) confined in individual, sealed nanowells. (c) Histogram of the
relative fluorescence of wells that contain cells. Positive reactions have a relative
fluorescence greater than 1.4.
Next, we determined the sensitivity and specificity of the method using three genes that
are commonly employed as standards for RT-qPCR (B2M, glyceraldehyde 3-phosphate
dehydrogenase (GAPDH), beta-actin (ACTB)), as well as the heavy chain of the antibody
produced by the 4D20 hybridomas (HC) (Figure 4.2). The threshold values for positive reactions
were determined for all four genes. Based on the maximum threshold of 1.4, the sensitivity and
specificity of the assay were greater than 84% and 98%, respectively. The positive predictive
55
value, which indicates the confidence in the assignments, was greater than 95%. It is expected
that the sensitivity will be lower for one-step, single-cell RT-PCR than for RT-PCR using bulk
purified mRNA since the single reaction does not individually optimize the release of mRNA
Figure 4.2. Detection of mRNA transcripts of constitutively expressed genes in 4D20 cells.
Boxplot of Iwell/Iempty for four genes (ACTB, GAPDH, B2M, and HC). The boxplot follows
Tukey’s convention. The median is marked with a red line, and the upper and lower edges
of the box indicate the values of the upper and lower quartiles. Notches on the box adjacent
to the median value represent its 5% significance level. Whiskers extending from each end
of the box represent extreme values within 1.5 times the interquartile range. The numbers of
wells included in each box are indicated below each one. The red dashed line indicates the
minimum value for positive reactions used for all four genes.
from cells, or the subsequent RT and PCR steps. We also note that the apparent sensitivity could
be lower than determined: it is possible that a small fraction of the cells were not expressing the
target gene at the time of the assay.
56
4.2. Integration with microengraving
For integrated single-cell analysis of both gene expression and secretory phenotypes,
the method described here can also be combined with other nanowell-based techniques such
as imaging cytometry and microengraving—a technique for quantifying the frequencies and
rates of secretion of proteins for populations of single cells85, 86. We sought to determine if
the detection of transcripts for HC in the hybridomas correlated with antibody secretion in
the period of time immediately beforehand. To examine this relationship between transcribed
genes and secreted proteins, 4D20 cells were labeled with a live cell marker, loaded into
nanowells, and imaged to quantify the number of cells in each well. The array containing
cells was then sealed with a functionalized glass slide to capture secreted antibodies by
Figure 4.3. Integrated single-cell analysis of gene expression and secreted antibodies from
human B cell hybridomas. 4D20 cells were labeled with a live cell stain (Celltracker Violet)
and interrogated for IgG1 secretion and heavy chain mRNA. Sample images of correlated
data for representative phenotypes are shown (left). The relative fluorescence of the RT-PCR
is false colored from red-orange (no reaction) to green (positive reaction). Positive IgG1
secretion is false colored red. The graphic profile (right) shows the distribution of
phenotypes measured. The area of each circle is proportional to the number of each
phenotype enumerated.
57
microengraving85. After two hours, the glass slide was removed and probed for captured
antibodies, while the cells in the nanowells were then subjected to on-chip RT-PCR to detect
HC mRNA (Figure 4.3). Out of 6,086 wells with single cells, 5,392 cells (88.6%) expressed
the heavy chain mRNA, but only 1,795 cells (29.5%) secreted IgG1 during the preceding
period of time. Most of the cells secreting IgG1 also had detectable transcripts (90%).
These data provide direct evidence that analyzing transcribed genes alone does not
necessarily provide a suitable surrogate for complex functional activities such as secretion.
58
Chapter 5. Identification of target cells in large populations
5.1. Cytometry
To investigate what types of cells were infected by HIV, we used imaging cytometry to
determine the surface markers on PBMCs. The markers that we were interested in identifying
were CD4, CCR7, CD45RA, CD122, CD95, CD14, HLA-DR, and CD11c (Table 5.1). The
markers were split into two panels, one for CD3+ cells and one for CD3– cells, to reduce
crosstalk between the fluorescence channels when imaging on the microscope. After imaging,
the normalized fluorescence intensities from the surface markers were plotted on histograms,
similar to the RT-PCR data (Figure 5.1). The histograms from the microscopy data showed
similar results as a flow cytometer. We observed that some markers were more bimodal (e.g.,
CD4) and other markers had a more continuous spectrum for the fluorescence signal (CCR7).
Using the threshold of the mean plus three standard deviations of the negative peak as the
Table 5.1 Surface markers for PBMC classification
Cell type
Effector memory T
Central memory T
Naïve T
Stem cell like memory T
Cytotoxic T
Monocytes
Dendritic cells
Other APC
Fluorescent
dye:
CD3+ panel:
CD3– panel:
CD3+
CD3+
CD3+
CD3+
CD3+
CD3–
CD3–
CD3–
Alexa
Fluor
488
CD95
CD11c
–
+
Alexa
Fluor
568
CD4
CD4
+
+
+
+
–
Alexa
Fluor
647
CD45RA
CD14
–
–
+
+
PE/Cy7
CCR7
HLA-DR
–
+
+
+
+
+
+
+
59
PerCPeFluor
710
CD122
–
+
threshold for a positive signal, we obtained very similar results for classifying the CD3+
populations of T-cells as the flow cytometer (Figure 5.2). The CD95 histogram was unusually
broad with a mean of 0.03, so this marker was not considered for identifying the stem cell like
memory T-cells; only CD122 was used. The thresholds for the other markers were bunched
closely together around 0.03 to 0.04, and if we used these cutoffs for CD95, the percentage of
CD122+CD95+ cells were slightly lower (~10%) than just CD122+. To match the flow
percentages with the microscopy percentages, our threshold for CCR7 should be slightly higher
and CD45RA slightly lower.
60
6000
4000
CD11c
CD4
CD14
HLA-DR
5000
CD95
CD4
CD45RA
CCR7
CD122
3500
3000
Frequency
4000
2500
3000
2000
1500
2000
1000
1000
500
0
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0
-0.1
0.3
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
Relative fluorescence intensity
Figure 5.1. Histograms of surface marker fluorescence on PBMCs. The mean plus 3 standard
deviations from the negative peak were used to determine the threshold between positive and
negative in each marker. This cutoff corresponds to a statistical false positive rate of 0.13%.
Figure 5.2. Comparison between flow cytometry and microscopy for T-cell classification. The
flow cytometry plots were plotted for CCR7 vs. CD45RA and CD95 vs. CD122. The purple
lines indicated the thresholds and the purple numbers represented the fraction of cells in the
quadrant. Black numbers near the purple numbers were the results from microscopy.
61
5.2. Activation of cells
To detect copies of mRNA in a cell, an activation protocol was used to boost the
production of virus so that the mRNA would be more likely detected by RT-PCR. The goal of
the activation was to increase mRNA copies within the cells without killing the cells since dead
cells often released their mRNAs into the supernatant. We also wanted the protocol for activation
to be as short as possible. We stimulated ACH2 cells with phorbol 12-myristate 13-acetate with
ionomycin (PMA/iono), tumor necrosis factor alpha (TNFa), and suberoyl anilide hydroxamic
acid (SAHA, vorinostat) at two concentrations (1x and 10x) for a series of times (6 hr, 24 hr and
48 hr). The 1x concentrations of the activators were 1 ng/mL TNFa, 1 μM SAHA, and 1 ng/mL
PMA with 1 μM iono, and the 10x concentrations were used as a potential way to decrease the
time needed for activation. For the 10x activation condition, TNFa and PMA/ionomycin
20
18
16
14
12
10
8
6
4
2
0
Cell viability
HIVgag (fold difference)
treatments at 24 hr were the best activation conditions to induce HIVgag mRNA production in
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Activation conditions (10x concentration)
Activation conditions (10x concentration)
Figure 5.3. Effect of 10x activation conditions on HIVgag and cell viability in ACH2 cells. RTqPCR was used to determine the relative fold differences in HIVgag levels (left). All results
were relative to the ACH2 cells that were not activated. Cell viability was measured by trypan
blue staining (right). TNFa was the best stimulus for maintaining cell viability over 48 hr.
62
12
1
10
0.9
8
0.8
6
0.7
Cell viability
HIVgag (fold difference)
cells (Figure 5.3). While PMA/iono was similar to TNFa in activating HIV production,
4
2
0
0.6
0.5
0.4
0.3
0.2
0.1
0
Unactivated
ACH2
TNFa 6hr,
10 ng/mL
TNFa 12 hr,
10 ng/mL
TNFa 6 hr,
1 ng/mL
TNFa 12 hr,
1 ng/mL
Figure 5.4. Comparison of 1x TNFa and 10x TNFa activations on ACH2. RT-qPCR was used to
determine the relative fold differences in HIVgag levels (left). All results were relative to the
resting ACH2 cells. No significant difference was detected between the two concentrations. The
1x may even be better than the 10x. Cell viability was measured by trypan blue staining (right).
Cell viability did not drop significantly over 12 hr.
PMA/iono activated cells were dying more than TNFa activated cells over time. Further
experiments with TNFa showed that the HIVgag signal activated with 1x TNFa was similar to
that of the 10x TNFa and cell viability did not decrease much from the cells that were not
activated (Figure 5.4). These experiments also showed that after three washes, the virus in the
supernatant of the cell suspension was at a low enough concentration that it was not detectable
by RT-qPCR.
After determining that the best activation condition by bulk RT-qPCR measurements was
1 ng/mL TNFa, we wanted to verify that it was still the best condition for nanowells by singlecell RT-PCR. For a direct comparison, we activated ACH2 cells in the nanowells for 6 hr with
10x TNFa or 24 hr with 1x TNFa (Figure 5.5). HIV-negative PBMCs were added to simulate a
patient sample. We observed significantly more positive reactions in wells with cells that were
activated with 1x TNFa for 24 hr than with 10x TNFa for 6 hr. While both cases had positive
63
reactions in wells with no cells, the 10x TNFa activation had a large portion of wells with no
Table 5.2 Effect of activation conditions on ACH2 cell detection by RT-PCR in nanowells
Activation condition
of ACH2
Positive wells
with ACH2 (%)
Positive wells
with no ACH2 (%)
1 ng/mL, 24 hr
38.3
2.36
Chi-squared
p-value
<0.0001
10 ng/mL, 6 hr
14.3
6.45
<0.0001
Negative (PBMC only)
0.0474
1 ng/mL, 24 hr
21.9
1.42
<0.0001
1 ng/mL, 18 hr
18.7
0.418
<0.0001
0 ng/mL, 6 hr
2.80
0.423
<0.0001
Negative (PBMC only)
0.0375
cells that were positive. This result suggested that the mRNA that was induced by the activation
was not contained within the cell. The exact source of the mRNA was not known, but possible
sources include budded virus particles or dead cells. The negative control (PBMC) had a false
positive rate of 0.047% (40 positive wells on the entire array).
The 1 ng/mL TNFa activation for 24 hr still had a considerable number of positive
signals in wells with no cells. We experimented with shorter activation times to see if we could
reduce the number of positive signals in wells with no cells (Table 5.2). We observed that there
was an approximately 3-fold drop in the fraction of wells with no cells that had a positive signal
in the 18 hr and resting ACH2 cells. The percent of cells that were activated also dropped in the
18 hr activation compared to the 24 hr activation, and this drop was 3 percentage points (15%
decrease). We also noticed that there was a drop between the experiments on different days for
the 24 hr activation (38.3% to 21.9%), and this 43% decrease could be from the degradation of
TNFa by the extra freeze thaw in the later experiment or day-to-day variability. In all cases, the
detected positive signal was from mRNA in a cell since the Chi-squared contingency tests all had
p-values less than 0.0001.
64
0.35
1 ng/mL TNFa, 24 hr,
1 ng/mL TNFa, 24 hr,
10 ng/mL TNFa, 6 hr,
10 ng/mL TNFa, 6 hr,
PBMC, No cell
PBMC, Cell
0.3
Normalized frequency
0.25
No cell
Cell
No cell
Cell
0.2
0.15
0.1
0.05
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
ROX normalized intensity
0.8
0.9
1
Figure 5.5. Comparison of 1x TNFa and 10x TNFa activations on ACH2 in nanowells.
ACH2 cells were activated for either 24 hr at 1 ng/mL TNFa or 6 hr at 10 ng/mL in
nanowells. PBMCs were added after the activation to act as a negative control.
Figure 5.6. Effect of activation time on ACH2 in nanowells. ACH2 cells were activated with
1 ng/mL TNFa for 24 hr, 18 hr, and 0 hr (no activation). Only the histograms of the wells
with cells were plotted.
65
Table 5.3 Detection of serial dilutions of ACH2 cells in nanowells
Serial dilution of
activated ACH2 cells
Wells with
ACH2
Positive wells
with ACH2
Positive wells
with ACH2 (%)
Positive wells
with no ACH2 (%)
Dilution 1:100, 24 hr
2662
1149
43.16
8.93
Dilution 1:500, 24 hr
427
113
26.46
1.12
Dilution 1:5000, 24 hr
45
17
37.78
0.0901
Negative (PBMC only)
0.0291
Dilution 1:200, 18 hr
1666
751
45.1
10.85
Dilution 1:2000, 18 hr
208
78
37.5
0.82
Dilution 1:10000, 18 hr
25
5
20.0
0.47
Negative (no PBMC)
0.0677
Neg. PBMC HIVGag FAM
0.37
Neg. PBMC 1LTR Q670
0.62
Neg. PBMC FAM+Q670
0.0046
5.3. Limit of detection of cells
To determine the limit of detection of HIV infected cells, we activated a serial dilution of
ACH2 cells (1:100, 1:200, 1:500, 1:2000, 1:5000 and 1:10000) in nanowells. After the 18 hr or
24 hr activation, HIV-negative PBMCs were added to the array before running RT-PCR. The
negative controls (PBMCs only) had 23 and 47 bright wells in the arrays and these numbers
correspond to a false positive rate of 2.91x10-4 and 6.77x10-4, respectively (Table 5.3). Since the
false positive rates were so low, we would need at least 1400 positive wells with a cell to expect
that one of those wells was a false positive well. Therefore, if the number of events in the array
were less than 1400, we would only count the wells with cells in them. Again, the p-values from
Chi-squared contingency tests were all less than 0.0001, and showed that the positive signals in
wells with cells were from cells that had internal HIV rather than a well with cell and
contaminating HIV.
66
As a method to further reduce the number of false positives, we tested the use of two
probes in different fluorescence channels, specifically 1LTR Q670 and gag FAM as the two
probes to detect HIV. We found that the false positive rate for gag FAM and 1LTR Q670
individually were approximately 0.37% and 0.62%, respectively, but the false positive rate for
wells that were positive in both channel was 0.0046%. The false positive rate for wells that were
double positive was approximately 100 fold improvement over the individual rates.
The number of cells that were in wells followed the expected serial dilutions, but the
number of cells that were detected by RT-PCR varied. The percentage of wells with ACH2 cells
that were positive ranged from 20 to 45% of all wells with ACH2 cells. Since values were all
greater than the percentage of positive wells in the entire chip, the positive wells with ACH2
cells were not from random distribution of positive signal on the array. Interestingly, the overall
positive percentages fit the serial dilutions better than the positive wells with ACH2 cells. This
observation could be because RT-PCR detected the virus particles produced by the ACH2 cells
during activation that went into a random well. The serial dilution experiment also showed that
we could detect very few positive wells on the array with RT-PCR (1 in 10000).
5.4. HIV-positive patient sample
Since we showed that we could detect less than 10 positive wells with cells, we
proceeded with a preliminary study on T-cells from an HIV-positive patient. The patient had
been treated with HAART for more than 1.5 years. A side-by-side comparison between RTqPCR on a bulk sample of 1 million cells and RT-PCR in nanowells was performed. Comparing
the relative number of viral RNA in the bulk sample, we observed that the infected T-cells after
67
activation with PMA/ionomycin for 18 hr had more RNA than infected T-cells that were not
activated (Figure 5.7).
For RT-PCR in nanowells, the T-cells were run in a staggered manner so that the unused
cells were resting in normal R15 media. One set of arrays were run after 18 hr activation, 18 hr
activation plus 4 hr rest, and 18 hr activation plus 7 hour rest (Table 5.4). To further reduce the
probability of a false positive result, we used both FAM and Q670 probes for HIVgag and
required that a well had to be double positive in both channels to be considered a well with a
positive signal. With this extra criterion, we determined that the HIV-infected population of Tcells had a frequency of 1:2000 to 1:13000, depending on the condition. The additional 4 to 7 hr
rest times appeared to increase the frequency of positive events. While the exact reason for this
increased frequency was unknown, it could be explained by the sticking of virus to the T-cell or
endocytosis of the virus by the T-cell. This basal level of virus had been observed after
immediate washing of the T-cells87. There were also fewer events in the PMA/ionomycin
activated cells, even though this activation was much more potent than panobinostat. This
discrepancy could be a sampling issue since only 1 array was used for PMA/ionomycin in each
set whereas panobinostat had 3 arrays.
68
Figure 5.7. RT-qPCR on bulk cells from HIV-positive sample. RT-qPCR was performed on
1x106 cells to determine the relative number of viral RNA in each sample.
Table 5.4 RT-PCR on HIV-positive T-cells
Total
cells
Double
positive,
cell
Double
positive,
no cell
Corrected
ratio of HIV
infected
cells
Condition
Total
wells
Wells
with
cells
18 hr act, pano, HIV T-cell
86578
13103
13637
4
0
1:3505
18 hr act, pano, HIV T-cell
86600
11873
12318
7
5
1:6143
18 hr act, pano, HIV T-cell
86528
12638
13295
3
11
1:13024
18 hr act, PMA/iono, HIV T-cell
86309
16430
17862
0
4
18 hr act + 4 hr rest, pano, HIV T-cell
85332
15650
16348
14
35
18 hr act + 4 hr rest, pano, HIV T-cell
86499
15259
15995
0
3
18 hr act + 4 hr rest, pano, HIV T-cell
82912
15512
16304
12
17
1:2067
18 hr act + 4 hr rest, PMA/iono, HIV T-cell
84483
22165
24454
3
0
1:8151
18 hr act + 7 hr rest, pano, HIV T-cell
77631
16498
17596
12
15
1:2290
18 hr act + 7 hr rest, PMA/iono, HIV T-cell
79249
37128
46374
14
1
1:3595
25 hr rest, no act, HIV T-cell
57895
39087
53097
26
3
1:3028
23 hr act, TNFa, ACH2
51303
1967
1983
81
1214
1:24
1:2824
5.5. Discussion
We had shown that imaging cytometry by microscopy was possible and comparable to
flow cytometry. Each of the two panels of surface markers gave reasonable percentages for the
classifications of PBMCs. In flow cytometry, we drew gates depending on the background
69
fluorescence of a negative sample before the actual sample was run, and in microscopy, the
thresholds were derived from the mean intensity of the negative sample plus three standard
deviations after measuring the values of the sample. The three standard deviations correspond to
a false positive rate of 0.13%. In spite of not having a powerful laser for the microscope, this
threshold worked well even for markers (e.g., CCR7) that had a continuous level of display on
the cell surface.
In the patient sample, the expected frequency of HIV-infected T-cells was 1:1000088. The
observed frequencies by RT-PCR in nanowells were as much as 5 times higher than expectation.
This observation could be explained by the production of new virus particles. HIV life cycle
models have shown that the time from integration of HIV into the genome to new production of
virus particles was 7-17 hr89, 90. Therefore, during the 18 hr activation period, new virus particles
could have been produced and released into the supernatant to attach to uninfected cells. More
viral production would occur during the 4 and 7 hr resting period between the sets of RT-PCR.
Detection of these virus particles by RT-PCR in nanowells with cells would result in a higher
frequency of infection. The production of virus could also explain the correlation of overall
positive signal in the serial dilution of ACH2 cells to the dilution amount.
The resting HIV-infected T-cells were our negative control since it had such a low
number of RNA in the bulk RT-qPCR. But with RT-PCR in nanowells, we could detect a similar
frequency in this resting sample as in the activated samples. This result was not entirely
surprising since all of the cells were from one blood draw, so the frequencies of all the conditions
should be similar with a sensitive assay. While we did not have a true negative control (i.e., HIV-
70
negative PBMCs) run with the patient sample, our previous negative controls showed that the
false positive rate was less than 4.6x10-5 using two probes.
These preliminary results also showed that we could detect some cells that were infected
in the resting sample. These resting cells could have been producing virus at low levels (e.g., not
latently infected) or could have spontaneously reactivated (may have been latently infected) and
reflect the residual viremia in patients even on HAART6,
91
. The precise case could not be
determined by the assay. After activation, a portion of the latently infected cells would reactivate
so that the HIV mRNA would be detectable. Our one experiment showed that there was an
increase in the frequency of infection when the cells were activated by panobinostat, but further
experiments would be needed to draw any clear conclusions.
71
72
Chapter 6. Other methods to detect transcript
6.1. Surface capture of transcripts
To overcome the limitations of RT-PCR in nanowells, we wanted to capture the
information from single cells onto a glass slide in a stable manner. The format would be cDNA
since it is more stable than mRNA and the attachment of cDNA to the glass would be through a
covalent bond so that potential downstream applications such as PCR would not disrupt the bond.
One covalent bond that was easily introduced and well-studied for microarrays was the epoxyamine bond. Our goal was to detect the transcripts expressed in the cells of each well by probing
with fluorescently labelled DNA.
First, we established the theoretical feasibility of this approach with some estimates. A
typical mammalian cell contains 10-30 pg of total RNA of which 1-5% is mRNA, so one cell has
approximately 0.6 pg of mRNA (20 pg RNA, 3% mRNA) (Invitrogen oligo-dT bead product
insert). The average number of nucleotides in mRNA is approximately 2,200 bases plus 250
adenosines in the poly-A tail, so the average molecular weight of mRNA is 788,000 g/mole.
Using these values, one cell has on the order of 500,000 strands of mRNA (maximum of ~1
million strands). The footprint of the nanowell on the glass slide is 50 μm by 50 μm, or 2500 μm2.
The maximum density of amine groups on a glass surface is about 4 amine/nm2, which is
approximately 109 amine per well92, and a more typical oligomers surface count is 2.5x108 per
well93. Therefore, more than 200 fold excess of capture probes are on the 2500 μm2 area for
every strand of mRNA in a single cell.
73
Figure 6.1. Schematic of mRNA capture on a glass surface. Cleaned glass slides were first
conjugated with epoxy groups that react with amine-oligo-dT primers before they were
sealed to an array of nanowells containing cells with a reaction mixture for RT. The RT
reaction was run at 50 °C for 4 hours to lyse the cell and synthesize the first-strand cDNA.
These cDNA can be detected by sequential probing with fluorescence probes.
With feasibility shown, the experiment was designed as follows. First, a batch of epoxymodified slides was manufactured (Figure 6.1). The primer mix was composed of 100 µM oligodT30 to capture mRNA and 1 µM kanamycin resistance (KanR) primer as a uniformity control.
Similar to RT-PCR in nanowells, 4D20 cells were dispersed onto the array, Superscript III RT
solution containing NP-40, dNTPs, and RNase inhibitor were applied onto the array, and the
74
Figure 6.2. Sample scans of B2M cDNA (FAM, left) and KanR cDNA (HEX, right). The
numbers at the upper left corner of each spot indicates the number of cells in the well. Every
bright spot had at least one cell in the corresponding well.
primer-conjugated glass was pressed onto the array. The device was sealed shut in a
hybridization clamp and incubated at 50 °C for 4 hours. After the RT reaction, the glass was
probed for B2M and poly-T or KanR.
We observed that every spot with a brighter B2M signal had a cell, but interestingly, not
every well with a cell was bright (Figure 6.2, left). These results suggested that the signal we
detected was not entirely from nonspecific binding of probes to cellular debris and that reverse
transcription was occurring at the surface of the glass. While wells with more cells generally had
stronger signal, this rule was not universally applicable. It may be that the surface capture
efficiency was not uniform throughout the slide, the cells actually had less mRNA, or the RT
efficiency was just lower in that well. The uniformity control of the fluorescence signal (KanR)
showed that across the array, the primers were distributed evenly (Figure 6.2, right). Wells with
cells, however, still had a brighter signal than wells without cells. Curiously, we observed that
75
wells with stronger B2M signal also had stronger KanR signal, suggesting that bleed-through
from the FAM channels may account for the variation. Similar results were observed when polyT was the target.
76
Figure 6.3. Images of swapping dyes in two sets of probes. MutPGK1 cells were labelled
with Celltracker Red and deposited onto a chip and sealed with a capture slide for cell lysis
and RT. The captured cDNA on the glass was probed with either HEX labelled T491 (left)
or 665 labelled T491 (right) and imaged in the HEX channel.
Figure 6.4. Captured cDNA on glass from unlabeled mutPGK1. The glass was probed with
either HEX-T491 and 665-A491 (left) or HEX-A491 and 665-T491 (right). The images
show the ratio of the HEX (green) and 665 (red) signals. The signal from the captured cDNA
is much weaker when the cells are not labelled with Celltracker Red, but the signal is
specific for the correct labelled probe.
To further characterize the specificity of the assay, we targeted the PGK1 gene in a
normal cell line (4D20) and a mutated cell line (mutPGK1). The mutation is homozygous in
mutPGK1 where the 491st nucleotide is substituted with T (T491) instead of the normal A491.
77
To identify the cells in the wells and determine well occupancy, mutPGK1 cells were labelled
with Celltracker Red, and imaged before the lysis and RT steps. After the RT step, the slide was
probed with either HEX-labelled T491 or 665-labelled T491 (Figure 6.3). We observed that the
HEX channel was always brighter, independent of the fluorophore used. One reason could be
that the Celltracker Red in the cell debris was also stuck on the glass and this signal was stronger
than the fluorophores on the probes. To test this hypothesis, cells not labelled with Celltracker
were used and indeed the signal dropped (Figure 6.4). This result confirmed that cell debris that
was labelled by the Celltracker dyes were sticking to the glass surface and masking the specific
signal from the probes. This could also explain why the spots with cells also had higher KanR
and oligo-dT signal since these probes all had the same fluorescent dye. We did observe that the
weak signal was correct for identifying the single nucleotide polymorphism. Since the highly
transcribed gene PGK1 had such a low signal, this method would not be feasible to detect low
copies of transcript that may be in latently infected cells without some amplification step.
6.2. Hybridization chain reaction
One possible amplification step was to use hybridization chain reaction (HCR). In this
method, an initiator sequence was attached to the probe instead of a fluorescent dye. This
initiator sequence would trigger the opening and attachment of a pair of hairpins that have been
fluorescently labeled. Thus, a large number of fluorescent molecules could be attached to a
single binding event, allowing for more fluorescence signal to be detected. In addition, multiple
hairpin and initiator sets have been designed to have specific amplification of signal to different
targets with minimal signal bleedthrough68.
78
Figure 6.5. Comparison of HCR with direct detection in 4D20 cells. We observed strong
signal over background when using HCR to amplify the signal (left). Detection of B2M
mRNA with 10 fluorescent probes had high background and less than half of the array was
useable (right).
Figure 6.6. Combination of two sets of HCR to detect single nucleotide polymorphism. In
both figures, the normal phenotype A491 was conjugated with a TYE563 dye (false colored
green) and the mutant phenotype T491 was conjugated with TYE665 dye (false colored red).
(Left) Normal 4D20 cells had more T491 signal than A491, and (right) mutant PGK1 cells
had more A491 signal than T491. Both sets had a lot of background noise.
To test the feasibility of HCR as an amplification strategy, we compared the background
and signal quality over a direct detection of 10 probes. This experiment showed that HCR
generated a strong signal above the background noise (Figure 6.5). The image quality was
79
observed throughout the array in HCR whereas less than half the array was useable when
detecting with a set of 10 probes.
We proceeded to use HCR to detect the single nucleotide polymorphism in PGK1
between the normal 4D20 cells and mutant PGK1 cells. Here, each array was loaded with either
4D20 cells or mutant PGK1 cells and both sets of hairpins and initiator conjugated probes were
used. We found, oddly, that the normal 4D20 cells were brighter in the mutant signal and the
mutant PGK1 cells were bright in both (Figure 6.6). We also observed that both arrays had
noisier background than just the single set of hairpins and probe. These higher background
intensities and swapped signal suggested that there was a lot of random sticking to the surface of
the glass by the HCR hairpins and/or probes. Since HCR produced such a strong signal, any
random sticking or insufficient washing would give false positive signal. We were also uncertain
if the epoxy on the surface was completely pacified to prevent nonspecific sticking.
80
Figure 6.7. Capture of ACTB on PDMS followed by RT-PCR in nanowells. The pre-image
(left) showing the location of the cells was matched to the RT-PCR results for ACTB (right).
The wells that matched properly are boxed in green. Wells with cells but no signal are boxed
in orange (left) and wells with no cells but with signal are boxed in orange (right).
Since the capture of transcripts was possible, we tried a second approach that covalently
attached oligo-dT to the surface of the PDMS so that cells could be lysed with a more powerful
buffer before running RT-PCR. We found that after adding DNase and proteinase K to the MES
lysis buffer, a lot of the nonspecific signal could be removed. However, there were still
unmatched cells and wells with positive signal in the RT-PCR (Figure 6.7).
6.3. Discussion
The goal of surface capture was to allow for a two-step procedure to improve the
sensitivity and specificity of RT-PCR. By capturing the transcripts onto a surface, we would be
able to remove cellular debris and other contaminations from the reaction and recover the 1.4
transcript sensitivity of the reaction in nanowells. This method could also allow us to capture
81
transcripts in the nucleus that we were previously unable to access because of weaker lysing
conditions.
We have shown that surface capture of mRNA onto a glass slide is possible as well as the
reverse transcription of the transcript to cDNA. These bound transcripts could then be detected
with fluorescent probes and the fluorescent signal amplified with HCR. However, the signal was
also convoluted by the presence of cellular debris such as proteins and genomic DNA. If the
debris were also fluorescently labeled by Celltracker, the debris signal was overwhelming the
probe signal on bound transcripts. Removing these labels did allow the detection of specific
single nucleotide polymorphisms on the glass, but the signal was very weak. It was not clear if
the low signal was from insufficient capture of the target transcript. We modified the surface
chemistry to link the oligo-dT to the PDMS walls so that a larger surface area (i.e., 5-fold) was
available to capture the mRNA. The results from the modified surface were not better than
running RT-PCR in the nanowells directly on the cells, even for the detection of highly
transcribed genes. One possibility for the additional false positive and false negative signals was
that the transcripts were not completely bound to the surface after the cells were lysed and the
cover glass was removed. This would allow the mRNA to freely diffuse to other wells from a
well previously containing cells. More work would be needed to understand the cause of these
issues.
82
Chapter 7. Conclusions
In this thesis, we have developed and optimized a simple RT-PCR assay to detect the
gene expression in tens of thousands of individual cells in parallel with high sensitivity and
specificity in an array of nanowells. By confining these reactions to small 125 pL wells, we
showed that the limit of detection was approximately 1.4 copies of DNA in nanowells and that
the false positive rate was as low as 0.000046 or 1 in 22000 events. By combining with
microengraving and image-based cytometry on a cell line that produces antibodies, we showed
that most cells had the requisite transcripts for their antibody, but only a subset of those cells
secreted the antibody.
We have also applied the technology to detect HIV-infected cells in peripheral blood
mononuclear cells. By using our technology, we are better suited to attain the infection rate in an
HIV-infected sample. Previous studies on infection rate used bulk measurements with either RTqPCR or digital PCR to attain a measure on how much RNA or DNA was in the sample. These
measurement, however, do not accurately represent the number of infected cells in the sample
because the measured number of transcripts lacked single cell resolution. Our technology is
capable of detecting single cell events by isolating the cells in individual nanowells. A second
advantage of our technology is that it can be easily integrated with other processes that can be
performed in nanowells such as image-based cytometry and microengraving. The cells can be
first imaged by cytometry to determine the identity of the cells in each well and then measured
for interesting functional phenotypes such as secretion by microengraving before performing
RT-PCR on them. This combination provides a multivariate and direct measure of the
83
relationships between the presence of transcribed genes, the cell surface markers, and functional
cellular activities for many individual cells. To acquire this variety of data without our
technology, we would need to first isolate the cells by the desired surface markers, assess the
secretions of each population, and then determine the RNA content. All of these measurements
lack single cell resolution and it would not be possible to link secretion with cellular transcripts
in one cell.
Despite the advantages of RT-PCR in nanowells, there are still areas for improvements.
Some disadvantages of the current approach are that the measures of gene expression are not
quantitative, the cell nucleus cannot be efficiently accessed, and the number of fluorescent labels
that can be distinguished distinctly (~4-6 for most fluorescent microscopes) will limit the number
of transcripts detected per cell. Capture of DNA and RNA transcripts on the walls or a glass slide
could also potentially solutions to the first two challenges. These captured transcripts could be
attained with more stringent lysis conditions that would obliterate the cell nuclei and be detected
by fluorescent in situ hybridization for counting of positive spots. We have begun examining
these possibilities and observed that significant blocking of nonspecific binding was needed to
use surface capture and amplification of signal. By using advanced detection methods such as
time-of-flight (TOF) mass spectrometry, the limitations of fluorescent labels could be addressed.
Combinations of TOF mass spectrometry and amplification techniques could potentially be used
to detect small amounts of transcripts in a cell.
In summary, the one-step RT-PCR assay described in this thesis provides a powerful
method to determine the presence and absence of transcripts. The technology can be used to
identify HIV-infected cells and their surface markers when linked with cell cytometry. This
84
combination of multiple assays on one array of nanowells allows for a simple and efficient
method to better understand the cells associated with HIV latency. When combined with
microengraving, our approach can evaluate relationships between the transcription of genes and
the secretion of the translated products—a useful intersection to evaluate the suitability of
surrogate markers for monitoring clonal production in biomanufacturing or clinical factors in
diagnostics.
85
86
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