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Single-Cell Gene Expression Variation As a Cell-Type Specific Trait: a Study of Mammalian Gene Expression Using Single-Cell RNA Sequencing

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Single-Cell Gene Expression Variation As a Cell-Type Specific Trait: a Study of Mammalian Gene Expression Using Single-Cell RNA Sequencing University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2015 Single-Cell Gene Expression Variation as A Cell-Type Specific Trait: A Study of Mammalian Gene Expression Using Single-Cell RNA Sequencing Hannah R. Dueck University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Bioinformatics Commons, Biology Commons, and the Cell Biology Commons Recommended Citation Dueck, Hannah R., "Single-Cell Gene Expression Variation as A Cell-Type Specific rT ait: A Study of Mammalian Gene Expression Using Single-Cell RNA Sequencing" (2015). Publicly Accessible Penn Dissertations. 1692. https://repository.upenn.edu/edissertations/1692 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/1692 For more information, please contact [email protected]. Single-Cell Gene Expression Variation as A Cell-Type Specific rT ait: A Study of Mammalian Gene Expression Using Single-Cell RNA Sequencing Abstract In this dissertation, we used single-cell RNA sequencing data from five mammalian tissues to characterize patterns of gene expression across single cells, transcriptome-wide and in a cell-type- specific manner (Part 1). Additionally, we characterized single-cell RNA sequencing methods as a resource for experimental design and data analysis (Part 2). Part 1: Differentiation of metazoan cells requires execution of different gene expression programs but recent single cell transcriptome profiling has er vealed considerable variation within cells of seemingly identical phenotype. This brings into question the relationship between transcriptome states and cell phenotypes. We used high quality single cell RNA sequencing for 107 single cells from five mammalian tissues, along with 30 control samples, to characterize transcriptome heterogeneity across single cells. We developed methods to filter genes for reliable quantification and ot calibrate biological variation. We found evidence that ubiquitous expression across cells may be indicative of critical gene function and that, for a subset of genes, biological variability within each cell type may be regulated in order to perform dynamic functions. We also found evidence that single-cell variability of mouse pyramidal neurons was correlated with that in rats consistent with the hypothesis that levels of variation may be conserved. Part 2: Many researchers are interested in single-cell RNA sequencing for use in identification and classification of cell types, findingar r e cells, and studying single-cell expression variation; however, experimental and analytic methods for single-cell RNA sequencing are young and there is little guidance available for planning experiments and interpreting results. We characterized single-cell RNA sequencing measurements in terms of sensitivity, precision and accuracy through analysis of data generated in a collaborative control project, where known reference RNA was diluted to single-cell levels and amplified using one of three single-cell RNA sequencing protocols. All methods perform comparably overall, but individual methods demonstrate unique strengths and biases. Measurement reliability increased with expression level for all methods and we conservatively estimated measurements to be quantitative at an expression level of ~5-10 molecules. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Genomics & Computational Biology First Advisor Junhyong Kim Subject Categories Bioinformatics | Biology | Cell Biology This dissertation is available at ScholarlyCommons: https://repository.upenn.edu/edissertations/1692 SINGLE-CELL GENE EXPRESSION VARIATION AS A CELL-TYPE SPECIFIC TRAIT: A STUDY OF MAMMALIAN GENE EXPRESSION USING SINGLE-CELL RNA SEQUENCING Hannah R. Dueck A DISSERTATION in Genomics and Computational Biology Presented to the Faculties of the University of Pennsylvania in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy 2015 Supervisor of Dissertation ______ ______ ___________ Junhyong Kim Professor of Biology Graduate Group Chairperson ______ ______ ___________ Li-San Wang, Associate Professor of Pathology and Laboratory Medicine Dissertation Committee Arjun Raj, Assistant Professor of Bioengineering, University of Pennsylvania James Eberwine, Professor of Pharmacology, University of Pennsylvania Nancy Zhang, Associate Professor of Statistics, University of Pennsylvania Isidore Rigoutsos, Director of Computational Medicine Center, Thomas Jefferson University SINGLE-CELL GENE EXPRESSION VARIATION AS A CELL-TYPE SPECIFIC TRAIT: A STUDY OF MAMMALIAN GENE EXPRESSION USING SINGLE-CELL RNA SEQUENCING COPYRIGHT 2015 Hannah Ruth Dueck This work is licensed under the Creative Commons Attribution- NonCommercial-ShareAlike 3.0 License To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/3.0/us/ Dedication For Ryan, whose encouragement helped me take the first step, and the next one. And for Mom and Dad, Allan and Laura Sue Dueck, whose lives demonstrate the courage of persistence and the joy of creativity. iii ACKNOWLEDGMENTS Many people have contributed to my graduate school education and research, and I greatly appreciate each one. Junhyong Kim, my advisor, has provided invaluable instruction, stimulating conversations, and much needed encouragement throughout. I particularly value his thoughtful approach to research, conceptual creativity, attention to experimental and analytic detail, educational lab meetings, and supportive mentorship. I have learned a lot from discussions with all of the members of the Kim lab, and have enjoyed our shared commiserations and laughter. Particular thanks to Mugdha Khaladkar, Youngji Na, Sarah Middleton and Stephen Fisher, who have been great sounding boards regarding computational analyses, and to Chantal Francis and Hoa Giang, who have provided insight into the workings of a wet lab. My committee has offered thoughtful suggestions, advice and support: Arjun Raj, Nancy Zhang, Rigoutsos. I am particularly indebted to James Eberwine, who has guided and collaborated with me in all of the research contained in this dissertation. Much of the work in this dissertation is rooted in collaborative research. Chapter 1 includes material prepared in collaboration with Junhyong Kim and James Eberwine that has been submitted for publication. Work in this Chapter 2 was performed in collaboration with Mugdha Khaladkar, Tae Kyung Kim, Jennifer M. Spaethling, Chantal Francis, Sangita Suresh, Stephen A. Fisher, Patrick Seale, Sheryl G Beck, Tamas Bartfai, Bernhard Kuhn, James Eberwine, and Junhyong Kim and was published in Genome Biology (Dueck et al., 2015a). Material from this publication is presented in the dissertation abstract and in Chapter 2 with limited modification. The research in Chapter 3 was performed in collaboration with Rizi Ai, Ray Dominguez, Oleg Evgrafov, Jian-Bing Fan, Stephen Fisher, Chantal Francis, Jennifer Hernstein, Tai Kyung Kim, Hugo Kim, Sonia Lin, Rui Liu, Bill Mack, Neeraj Salathia, Jennifer Spaethling, Tade Souaiaia, Jai-Yoon Sul, Andre Wilberg, Robert Chow, James Eberwine, James Knowles, Kun Zhang, and Junhyoung Kim, and a manuscript describing this work is in preparation. iv Dissertation research was funded in part by a National Science Foundation Graduate Research Fellowship, and by a fellowship under the NIH Integrated Interdisciplinary Training Program in Computational Neuroscience at the University of Pennsylvania (T90 DA022763-05). And thank you, especially, to my family – for sending encouragement when it was needed, celebrating milestones, and sharing life with me. And to Ryan, my gratitude is beyond words. v ABSTRACT SINGLE-CELL GENE EXPRESSION VARIATION AS A CELL-TYPE SPECIFIC TRAIT: A STUDY OF MAMMALIAN GENE EXPRESSION USING SINGLE-CELL RNA SEQUENCING Hannah R. Dueck Junhyong Kim In this dissertation, we used single-cell RNA sequencing data from five mammalian tissues to characterize patterns of gene expression across single cells, transcriptome-wide and in a cell- type-specific manner (Part 1). Additionally, we characterized single-cell RNA sequencing methods as a resource for experimental design and data analysis (Part 2). Part 1: Differentiation of metazoan cells requires execution of different gene expression programs but recent single cell transcriptome profiling has revealed considerable variation within cells of seemingly identical phenotype. This brings into question the relationship between transcriptome states and cell phenotypes. We used high quality single cell RNA sequencing for 107 single cells from five mammalian tissues, along with 30 control samples, to characterize transcriptome heterogeneity across single cells. We developed methods to filter genes for reliable quantification and to calibrate biological variation. We found evidence that ubiquitous expression across cells may be indicative of critical gene function and that, for a subset of genes, biological variability within each cell type may be regulated in order to perform dynamic functions. We also found evidence that single-cell variability of mouse pyramidal neurons was correlated with that in rats consistent with the hypothesis that levels of variation may be conserved. Part 2: Many researchers are interested in single-cell RNA sequencing for use in identification and classification of cell types, finding rare cells, and studying single-cell expression variation; however, experimental and analytic methods
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