Rhonda Bacher

Design and computational analysis of single-cell RNA-sequencing experiments

Single-cell RNA-sequencing (scRNA-seq) has emerged as a revolutionary tool that allows us to address scientific questions that eluded examination just a few years ago. With the advantages of scRNA-seq come computational challenges that are just beginning to be addressed. In this article, we highlight the computational methods available for the design and analysis of scRNA-seq experiments, their advantages and disadvantages in various settings, the open questions for which novel methods are needed, and expected future developments in this exciting area.

SCnorm: robust normalization of single-cell RNA-seq data

The normalization of RNA-seq data is essential for accurate downstream inference, but the assumptions upon which most normalization methods are based are not applicable in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of single-cell RNA-seq data.

Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress

From bacteria to humans, individual cells within isogenic populations can show significant variation in stress tolerance, but the nature of this heterogeneity is not clear. To investigate this, we used single-cell RNA sequencing to quantify transcript heterogeneity in single Saccharomyces cerevisiae cells treated with and without salt stress to explore population variation and identify cellular covariates that influence the stress-responsive transcriptome. Leveraging the extensive knowledge of yeast transcriptional regulation, we uncovered significant regulatory variation in individual yeast cells, both before and after stress. We also discovered that a subset of cells appears to decouple expression of ribosomal protein genes from the environmental stress response in a manner partly correlated with the cell cycle but unrelated to the yeast ultradian metabolic cycle. Live-cell imaging of cells expressing pairs of fluorescent regulators, including the transcription factor Msn2 with Dot6, Sfp1, or MAP kinase Hog1, revealed both coordinated and decoupled nucleocytoplasmic shuttling. Together with transcriptomic analysis, our results suggest that cells maintain a cellular filter against decoupled bursts of transcription factor activation but mount a stress response upon coordinated regulation, even in a subset of unstressed cells.

Species-specific developmental timing is maintained by pluripotent stem cells ex utero

How species-specific developmental timing is controlled is largely unknown. By following human embryonic stem (ES) cell and mouse epiblast stem (EpiS) cell differentiation through detailed RNA-sequencing time courses, here we show that pluripotent stem cells closely retain in vivo species-specific developmental timing in vitro. In identical neural differentiation conditions in vitro, gene expression profiles are accelerated in mouse EpiS cells compared to human ES cells with relative rates of differentiation closely reflecting the rates of progression through the Carnegie stages in utero. Dynamic Time Warping analysis identified 3389 genes that were regulated more quickly in mouse EpiS cells and identified none that were regulated more quickly in human ES cells. Interestingly, we also find that human ES cells differentiated in teratomas maintain the same rate of differentiation observed in vitro in spite of being grown in a mouse host. These results suggest the existence of a cell autonomous, species-specific developmental clock that pluripotent stem cells maintain even out of context of an intact embryo.

SCnorm: A quantile-regression based approach for robust normalization of single-cell RNA-seq data

Normalization of RNA-sequencing data is essential for accurate downstream inference, but the assumptions upon which most methods are based do not hold in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of scRNA-seq data.

Genetic Drivers of Pancreatic Islet Function

The majority of gene loci that have been associated with type 2 diabetes play a role in pancreatic islet function. To evaluate the role of islet gene expression in the etiology of diabetes, we sensitized a genetically diverse mouse population with a Western diet high in fat (45% kcal) and sucrose (34%) and carried out genome-wide association mapping of diabetes-related phenotypes. We quantified mRNA abundance in the islets and identified 18,820 expression QTL. We applied mediation analysis to identify candidate causal driver genes at loci that affect the abundance of numerous transcripts. These include two genes previously associated with monogenic diabetes (PDX1 and HNF4A), as well as three genes with nominal association with diabetes-related traits in humans (FAM83E, IL6ST, and SAT2). We grouped transcripts into gene modules and mapped regulatory loci for modules enriched with transcripts specific for α-cells, and another specific for δ-cells. However, no single module enriched for β-cell-specific transcripts, suggesting heterogeneity of gene expression patterns within the β-cell population. A module enriched in transcripts associated with branched-chain amino acid metabolism was the most strongly correlated with physiological traits that reflect insulin resistance. Although the mice in this study were not overtly diabetic, the analysis of pancreatic islet gene expression under dietary-induced stress enabled us to identify correlated variation in groups of genes that are functionally linked to diabetes-associated physiological traits. Our analysis suggests an expected degree of concordance between diabetes-associated loci in the mouse and those found in human populations, and demonstrates how the mouse can provide evidence to support nominal associations found in human genome-wide association mapping.

Spatial patterns of gene expression are unveiled in the chick primitive streak by ordering single-cell transcriptomes

During vertebrate development, progenitor cells give rise to tissues and organs through a complex choreography that commences at gastrulation. A hallmark event of gastrulation is the formation of the primitive streak, a linear assembly of cells along the anterior-posterior (AP) axis of the developing organism. To examine the primitive streak at a single-cell resolution, we measured the transcriptomes of individual chick cells from the streak or the surrounding tissue (the rest of the area pellucida) in Hamburger-Hamilton stage 4 embryos. The single-cell transcriptomes were then ordered by the statistical method Wave-Crest to deduce both the relative position along the AP axis and the prospective lineage of single cells. The ordered transcriptomes reveal intricate patterns of gene expression along the primitive streak.

Trendy: segmented regression analysis of expression dynamics in high-throughput ordered profiling experiments

BackgroundHigh-throughput expression profiling experiments with ordered conditions (e.g. time-course or spatial-course) are becoming more common for studying detailed differentiation processes or spatial patterns. Identifying dynamic changes at both the individual gene and whole transcriptome level can provide important insights about genes, pathways, and critical time points.ResultsWe present an R package, Trendy, which utilizes segmented regression models to simultaneously characterize each gene’s expression pattern and summarize overall dynamic activity in ordered condition experiments. For each gene, Trendy finds the optimal segmented regression model and provides the location and direction of dynamic changes in expression. We demonstrate the utility of Trendy to provide biologically relevant results on both microarray and RNA-sequencing (RNA-seq) datasets.ConclusionsTrendy is a flexible R package which characterizes gene-specific expression patterns and summarizes changes of global dynamics over ordered conditions. Trendy is freely available on Bioconductor with a full vignette at https://bioconductor.org/packages/release/bioc/html/Trendy.html.

Statistical Methods for Latent Class Quantitative Trait Loci Mapping

Identifying the genetic basis of complex traits is an important problem with the potential to impact a broad range of biological endeavors. A number of effective statistical methods are available for quantitative trait loci (QTL) mapping that allow for the efficient identification of multiple, potentially interacting, loci under a variety of experimental conditions. Although proven useful in hundreds of studies, the majority of these methods assumes a single model common to each subject, which may reduce power and accuracy when genetically distinct subclasses exist. To address this, we have developed an approach to enable latent class QTL mapping. The approach combines latent class regression with stepwise variable selection and traditional QTL mapping to estimate the number of subclasses in a population, and to identify the genetic model that best describes each subclass. Simulations demonstrate good performance of the method when latent classes are present as well as when they are not, with accurate estimation of QTL. Application of the method to case studies of obesity and diabetes in mouse gives insight into the genetic basis of related complex traits.