TRACING CO-REGULATORY NETWORK DYNAMICS IN NOISY, SINGLE-CELL TRANSCRIPTOME TRAJECTORIES

The availability of gene expression data at the single cell level makes it possible to probe the molecular underpinnings of complex biological processes such as differentiation and oncogenesis. Promising new methods have emerged for reconstructing a progression 'trajectory' from static single-cell transcriptome measurements. However, it remains unclear how to adequately model the appreciable level of noise in these data to elucidate gene regulatory network rewiring. Here, we present a framework called Single Cell Inference of MorphIng Trajectories and their Associated Regulation (SCIMITAR) that infers progressions from static single-cell transcriptomes by employing a continuous parametrization of Gaussian mixtures in high-dimensional curves. SCIMITAR yields rich models from the data that highlight genes with expression and co-expression patterns that are associated with the inferred progression. Further, SCIMITAR extracts regulatory states from the implicated trajectory-evolving co-expression networks. We benchmark the method on simulated data to show that it yields accurate cell ordering and gene network inferences. Applied to the interpretation of a single-cell human fetal neuron dataset, SCIMITAR finds progression-associated genes in cornerstone neural differentiation pathways missed by standard differential expression tests. Finally, by leveraging the rewiring of gene-gene co-expression relations across the progression, the method reveals the rise and fall of co-regulatory states and trajectory-dependent gene modules. These analyses implicate new transcription factors in neural differentiation including putative co-factors for the multi-functional NFAT pathway

Computational Stem Cell Biology: Open Questions and Guiding Principles

Computational biology is enabling an explosive growth in our understanding of stem cells and our ability to use them for disease modeling, regenerative medicine, and drug discovery. We discuss four topics that exemplify applications of computation to stem cell biology: cell typing, lineage tracing, trajectory inference, and regulatory networks. We use these examples to articulate principles that have guided computational biology broadly and call for renewed attention to these principles as computation becomes increasingly important in stem cell biology. We also discuss important challenges for this field with the hope that it will inspire more to join this exciting area

Reconstructing equations of motion for cell phenotypic transitions: integration of data science and dynamical systems theory

Dynamical systems theory has long been fruitfully applied to describe cellular processes, while a main challenge is lack of quantitative information for constraining models. Advances of quantitative approaches, especially single cell techniques, have accelerated the emergence of a new direction of reconstructing the equations of motion of a cellular system from quantitative single cell data, thus places studies under the framework of dynamical systems theories, as compared to the currently dominant statistics-based approaches. Here I review a selected number of recent studies using live- and fixed- cell data, and provide my perspective on the future development.Comment: 18 pages, 4 figure

Dissecting stem cell differentiation using single cell expression profiling.

Many assumptions about the way cells behave are based on analyses of populations. However, it is now widely recognized that even apparently pure populations can display a remarkable level of heterogeneity. This is particularly true in stem cell biology where it hinders our understanding of normal development and the development of strategies for regenerative medicine. Over the past decade technologies facilitating gene expression analysis at the single cell level have become widespread, providing access to rare cell populations and insights into population structure and function. Here we review the contributions of single cell biology to understanding stem cell differentiation so far, both as a new methodology for defining cell types and a tool for understanding the complexities of cellular decision-making.Research in the authors’ laboratories is supported by the Medical Research Council, Biotechnology and Biological Sciences Research Council, Bloodwise, the Leukemia and Lymphoma Society, a Wellcome Trust Strategic Award (Tracing Early Mammalian Lineage Decisions by Single Cell Genomics) and core support grants by the Wellcome Trust to the Cambridge Institute for Medical Research and Wellcome Trust - MRC Cambridge Stem Cell Institute.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.ceb.2016.08.00

Statistical methods for the integrative analysis of single-cell multi-omics data

Single-cell profiling techniques have provided an unprecedented opportunity to study cellular heterogeneity at the molecular level. This represents a remarkable advance over traditional bulk sequencing methods, particularly to study lineage diversification and cell fate commitment events in heterogeneous biological processes. While the large majority of single-cell studies are focused on quantifying RNA expression, transcriptomic readouts provide only a single dimension of cellular heterogeneity. Recently, technological advances have enabled multiple biological layers to be probed in parallel one cell at a time, unveiling a powerful approach for investigating multiple dimensions of cellular heterogeneity. However, the increasing availability of multi-modal data sets needs to be accompanied by the development of suitable integrative strategies to fully exploit the data generated. In this thesis I worked in collaboration with different research groups to introduce innovative experimental and computational strategies for the integrative study of multi-omics at single-cell resolution. The first contribution is the development of scNMT-seq, a protocol for the simultaneous profiling of RNA expression, DNA methylation and chromatin accessibility in single cells. I demonstrate how this assay provides a powerful approach for investigating regulatory relationships between the epigenome and the transcriptome within individual cells. The second contribution is Multi-Omics Factor Analysis (MOFA), a statistical framework for the unsupervised integration of multi-omics data sets. MOFA is a Bayesian latent variable model that can be viewed as a statistically rigorous generalization of Principal Component Analysis to multi-omics data. The method provides a principled approach to retrieve, in an unsupervised manner, the underlying sources of sample heterogeneity while at the same time disentangling which axes of heterogeneity are shared across multiple modalities and which are specific to individual data modalities. The third contribution is the generation of a comprehensive molecular roadmap of mouse gastrulation at single-cell resolution. We employed scNMT-seq to simultaneously profile RNA expression, DNA methylation and chromatin accessibility for hundreds of cells, spanning multiple time points from the exit from pluripotency to primary germ layer specification. Using MOFA, and other tools, I performed an integrative analysis of the multi-modal measurements, revealing novel insights into the role of the epigenome in regulating this key developmental process. The fourth contribution is an extended formulation of the MOFA model tailored to the analysis of large-scale single-cell data with complex experimental designs. I extended the model to incorporate a flexible regularisation that enables the joint analysis of multiple omics as well as multiple sample groups (batches and/or experimental conditions). In addition, I implemented a GPU-accelerated stochastic variational inference framework, thus enabling the scalable analysis of potentially millions of samples

The Cellular and Physiological Basis for Lung Repair and Regeneration: Past, Present, and Future.

The respiratory system, which includes the trachea, airways, and distal alveoli, is a complex multi-cellular organ that intimately links with the cardiovascular system to accomplish gas exchange. In this review and as members of the NIH/NHLBI-supported Progenitor Cell Translational Consortium, we discuss key aspects of lung repair and regeneration. We focus on the cellular compositions within functional niches, cell-cell signaling in homeostatic health, the responses to injury, and new methods to study lung repair and regeneration. We also provide future directions for an improved understanding of the cell biology of the respiratory system, as well as new therapeutic avenues

Understanding Gene Regulation In Development And Differentiation Using Single Cell Multi-Omics

Transcriptional regulation is a major determinant of tissue-specific gene expression during development. My thesis research leverages powerful single-cell approaches to address this fundamental question in two developmental systems, C. elegans embryogenesis and mouse embryonic hematopoiesis. I have also developed much-needed computational algorithms for single-cell data analysis and exploration. C. elegans is an animal with few cells, but a striking diversity of cell types. In this thesis, I characterize the molecular basis for their specification by analyzing the transcriptomes of 86,024 single embryonic cells. I identified 502 terminal and pre-terminal cell types, mapping most single cell transcriptomes to their exact position in C. elegans’ invariant lineage. Using these annotations, I find that: 1) the correlation between a cell’s lineage and its transcriptome increases from mid to late gastrulation, then falls dramatically as cells in the nervous system and pharynx adopt their terminal fates; 2) multilineage priming contributes to the differentiation of sister cells at dozens of lineage branches; and 3) most distinct lineages that produce the same anatomical cell type converge to a homogenous transcriptomic state. Next, I studied the development of hematopoietic stem cells (HSCs). All HSCs come from a specialized type of endothelial cells in the major arteries of the embryo called hemogenic endothelium (HE). To examine the cellular and molecular transitions underlying the formation of HSCs, we profiled nearly 40,000 rare single cells from the caudal arteries of embryonic day 9.5 (E9.5) to E11.5 mouse embryos using single-cell RNA-Seq and single-cell ATAC-Seq. I identified a continuous developmental trajectory from endothelial cells to early precursors of HSCs, and several critical transitional cell types during this process. The intermediate stage most proximal to HE, which we termed pre-HE, is characterized by increased accessibility of chromatin enriched for SOX, FOX, GATA, and SMAD binding motifs. I also identified a developmental bottleneck separates pre-HE from HE, and RUNX1 dosage regulates the efficiency of the pre-HE to HE transition. A distal enhancer of Runx1 shows high accessibility in pre-HE cells at the bottleneck, but loses accessibility thereafter. Once cells pass the bottleneck, they follow distinct developmental trajectories leading to an initial wave of lympho-myeloid-biased progenitors, followed by precursors of HSCs. During the course of both projects, I have developed novel computational methods for analyzing single-cell multi-omics data, including VERSE, PIVOT and VisCello. Together, these tools constitute a comprehensive single cell data analysis suite that facilitates the discovery of novel biological mechanisms