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

Modeling Collective Cell Behavior in Cancer: Perspectives From an Interdisciplinary Conversation

Collective cell behavior contributes to all stages of cancer progression. Understanding how collective behavior emerges through cell-cell interactions and decision-making will advance our understanding of cancer biology and provide new therapeutic approaches. Here, we summarize an interdisciplinary discussion on multicellular behavior in cancer, draw lessons from other scientific disciplines, and identify future directions

PIVOT: platform for interactive analysis and visualization of transcriptomics data

Abstract Background Many R packages have been developed for transcriptome analysis but their use often requires familiarity with R and integrating results of different packages requires scripts to wrangle the datatypes. Furthermore, exploratory data analyses often generate multiple derived datasets such as data subsets or data transformations, which can be difficult to track. Results Here we present PIVOT, an R-based platform that wraps open source transcriptome analysis packages with a uniform user interface and graphical data management that allows non-programmers to interactively explore transcriptomics data. PIVOT supports more than 40 popular open source packages for transcriptome analysis and provides an extensive set of tools for statistical data manipulations. A graph-based visual interface is used to represent the links between derived datasets, allowing easy tracking of data versions. PIVOT further supports automatic report generation, publication-quality plots, and program/data state saving, such that all analysis can be saved, shared and reproduced. Conclusions PIVOT will allow researchers with broad background to easily access sophisticated transcriptome analysis tools and interactively explore transcriptome datasets

Additional file 1: Table S1. of PIVOT: platform for interactive analysis and visualization of transcriptomics data

Comparison of tools integrated/implemented in PIVOT to other similar applications. (DOCX 80 kb

Deep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation

Background: Differentiation of metazoan cells requires execution of different gene expression programs but recent single-cell transcriptome profiling has revealed considerable variation within cells of seeming identical phenotype. This brings into question the relationship between transcriptome states and cell phenotypes. Additionally, single-cell transcriptomics presents unique analysis challenges that need to be addressed to answer this question. Results: We present high quality deep read-depth single-cell RNA sequencing for 91 cells from five mouse tissues and 18 cells from two rat tissues, along with 30 control samples of bulk RNA diluted to single-cell levels. We find that transcriptomes differ globally across tissues with regard to the number of genes expressed, the average expression patterns, and within-cell-type variation patterns. We develop methods to filter genes for reliable quantification and to calibrate biological variation. All cell types include genes with high variability in expression, in a tissue-specific manner. We also find evidence that single-cell variability of neuronal genes in mice is correlated with that in rats consistent with the hypothesis that levels of variation may be conserved. Conclusions: Single-cell RNA-sequencing data provide a unique view of transcriptome function; however, careful analysis is required in order to use single-cell RNA-sequencing measurements for this purpose. Technical variation must be considered in single-cell RNA-sequencing studies of expression variation. For a subset of genes, biological variability within each cell type appears to be regulated in order to perform dynamic functions, rather than solely molecular noise. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0683-4) contains supplementary material, which is available to authorized users