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Development of One-Step Single-Cell RT-PCR for the Massively Parallel Detection of Gene Expression by Yuan Gong M.S

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Development of One-Step Single-Cell RT-PCR for the Massively Parallel Detection of Gene Expression by Yuan Gong M.S 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
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