Single-cell gene expression variation as a cell-type specific trait: A study of mammalian gene expression using single-cell RNA sequencing

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 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 This dissertation is available at ScholarlyCommons: http://repository.upenn. edu/edissertations/1692 Subject Categories Bioinformatics | Biology | Cell Biology This dissertation is available at ScholarlyCommons: http://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 AttributionNonCommercial-ShareAlike 3.0 License To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/3.0/us/