Steven Woodhouse

Decoding the regulatory network of early blood development from single-cell gene expression measurements

Reconstruction of the molecular pathways controlling organ development has been hampered by a lack of methods to resolve embryonic progenitor cells. Here we describe a strategy to address this problem that combines gene expression profiling of large numbers of single cells with data analysis based on diffusion maps for dimensionality reduction and network synthesis from state transition graphs. Applying the approach to hematopoietic development in the mouse embryo, we map the progression of mesoderm toward blood using single-cell gene expression analysis of 3,934 cells with blood-forming potential captured at four time points between E7.0 and E8.5. Transitions between individual cellular states are then used as input to develop a single-cell network synthesis toolkit to generate a computationally executable transcriptional regulatory network model of blood development. Several model predictions concerning the roles of Sox and Hox factors are validated experimentally. Our results demonstrate that single-cell analysis of a developing organ coupled with computational approaches can reveal the transcriptional programs that underpin organogenesis.

Single-cell analysis of CD4+ T-cell differentiation reveals three major cell states and progressive acceleration of proliferation

Differentiation of lymphocytes is frequently accompanied by cell cycle changes, interplay that is of central importance for immunity but is still incompletely understood. Here, we interrogate and quantitatively model how proliferation is linked to differentiation in CD4+ T cells. We perform ex vivo single-cell RNA-sequencing of CD4+ T cells during a mouse model of infection that elicits a type 2 immune response and infer that the differentiated, cytokine-producing cells cycle faster than early activated precursor cells. To dissect this phenomenon quantitatively, we determine expression profiles across consecutive generations of differentiated and undifferentiated cells during Th2 polarization in vitro. We predict three discrete cell states, which we verify by single-cell quantitative PCR. Based on these three states, we extract rates of death, division and differentiation with a branching state Markov model to describe the cell population dynamics. From this multi-scale modelling, we infer a significant acceleration in proliferation from the intermediate activated cell state to the mature cytokine-secreting effector state. We confirm this acceleration both by live imaging of single Th2 cells and in an ex vivo Th1 malaria model by single-cell RNA-sequencing. The link between cytokine secretion and proliferation rate holds both in Th1 and Th2 cells in vivo and in vitro, indicating that this is likely a general phenomenon in adaptive immunity.BackgroundDifferentiation of lymphocytes is frequently accompanied by cell cycle changes, interplay that is of central importance for immunity but is still incompletely understood. Here, we interrogate and quantitatively model how proliferation is linked to differentiation in CD4+ T cells.ResultsWe perform ex vivo single-cell RNA-sequencing of CD4+ T cells during a mouse model of infection that elicits a type 2 immune response and infer that the differentiated, cytokine-producing cells cycle faster than early activated precursor cells. To dissect this phenomenon quantitatively, we determine expression profiles across consecutive generations of differentiated and undifferentiated cells during Th2 polarization in vitro. We predict three discrete cell states, which we verify by single-cell quantitative PCR. Based on these three states, we extract rates of death, division and differentiation with a branching state Markov model to describe the cell population dynamics. From this multi-scale modelling, we infer a significant acceleration in proliferation from the intermediate activated cell state to the mature cytokine-secreting effector state. We confirm this acceleration both by live imaging of single Th2 cells and in an ex vivo Th1 malaria model by single-cell RNA-sequencing.ConclusionThe link between cytokine secretion and proliferation rate holds both in Th1 and Th2 cells in vivo and in vitro, indicating that this is likely a general phenomenon in adaptive immunity.

Transcriptional hierarchies regulating early blood cell development

Hematopoiesis represents one of the paradigmatic systems for studying stem cell biology, but our understanding of how the hematopoietic system develops during embryogenesis is still incomplete. While many lessons have been learned from studying the mouse embryo, embryonic stem cells have come to the fore as an alternative and more tractable model to recapitulate hematopoietic development. Here we review what is known about the embryonic origin of blood from these complementary systems and how transcription factor networks regulate the emergence of hematopoietic tissue from the mesoderm. Furthermore, we have performed an integrated analysis of genome-wide microarray and ChIP-seq data sets from mouse embryos and embryonic stem (ES) cell lines deficient in key regulators and demonstrate how this type of analysis can be used to reconstruct regulatory hierarchies that both confirm existing regulatory linkages and suggest additional interactions.

Processing, visualising and reconstructing network models from single-cell data.

New single‐cell technologies readily permit gene expression profiling of thousands of cells at single‐cell resolution. In this review, we will discuss methods for visualisation and interpretation of single‐cell gene expression data, and the computational analysis needed to go from raw data to predictive executable models of gene regulatory network function. We will focus primarily on single‐cell real‐time quantitative PCR and RNA‐sequencing data, but much of what we cover will also be relevant to other platforms, such as the mass cytometry technology for high‐dimensional single‐cell proteomics.

Single-cell analyses of regulatory network perturbations using enhancer-targeting TALEs suggest novel roles for PU.1 during haematopoietic specification

Transcription factors (TFs) act within wider regulatory networks to control cell identity and fate. Numerous TFs, including Scl (Tal1) and PU.1 (Spi1), are known regulators of developmental and adult haematopoiesis, but how they act within wider TF networks is still poorly understood. Transcription activator-like effectors (TALEs) are a novel class of genetic tool based on the modular DNA-binding domains of Xanthomonas TAL proteins, which enable DNA sequence-specific targeting and the manipulation of endogenous gene expression. Here, we report TALEs engineered to target the PU.1-14kb and Scl+40kb transcriptional enhancers as efficient new tools to perturb the expression of these key haematopoietic TFs. We confirmed the efficiency of these TALEs at the single-cell level using high-throughput RT-qPCR, which also allowed us to assess the consequences of both PU.1 activation and repression on wider TF networks during developmental haematopoiesis. Combined with comprehensive cellular assays, these experiments uncovered novel roles for PU.1 during early haematopoietic specification. Finally, transgenic mouse studies confirmed that the PU.1-14kb element is active at sites of definitive haematopoiesis in vivo and PU.1 is detectable in haemogenic endothelium and early committing blood cells. We therefore establish TALEs as powerful new tools to study the functionality of transcriptional networks that control developmental processes such as early haematopoiesis.

BTR: training asynchronous Boolean models using single-cell expression data.

BackgroundRapid technological innovation for the generation of single-cell genomics data presents new challenges and opportunities for bioinformatics analysis. One such area lies in the development of new ways to train gene regulatory networks. The use of single-cell expression profiling technique allows the profiling of the expression states of hundreds of cells, but these expression states are typically noisier due to the presence of technical artefacts such as drop-outs. While many algorithms exist to infer a gene regulatory network, very few of them are able to harness the extra expression states present in single-cell expression data without getting adversely affected by the substantial technical noise present.ResultsHere we introduce BTR, an algorithm for training asynchronous Boolean models with single-cell expression data using a novel Boolean state space scoring function. BTR is capable of refining existing Boolean models and reconstructing new Boolean models by improving the match between model prediction and expression data. We demonstrate that the Boolean scoring function performed favourably against the BIC scoring function for Bayesian networks. In addition, we show that BTR outperforms many other network inference algorithms in both bulk and single-cell synthetic expression data. Lastly, we introduce two case studies, in which we use BTR to improve published Boolean models in order to generate potentially new biological insights.ConclusionsBTR provides a novel way to refine or reconstruct Boolean models using single-cell expression data. Boolean model is particularly useful for network reconstruction using single-cell data because it is more robust to the effect of drop-outs. In addition, BTR does not assume any relationship in the expression states among cells, it is useful for reconstructing a gene regulatory network with as few assumptions as possible. Given the simplicity of Boolean models and the rapid adoption of single-cell genomics by biologists, BTR has the potential to make an impact across many fields of biomedical research.

Synthesising Executable Gene Regulatory Networks from Single-Cell Gene Expression Data

Recent experimental advances in biology allow researchers to obtain gene expression profiles at single-cell resolution over hundreds, or even thousands of cells at once. These single-cell measurements provide snapshots of the states of the cells that make up a tissue, instead of the population-level averages provided by conventional high-throughput experiments. This new data therefore provides an exciting opportunity for computational modelling. In this paper we introduce the idea of viewing single-cell gene expression profiles as states of an asynchronous Boolean network, and frame model inference as the problem of reconstructing a Boolean network from its state space. We then give a scalable algorithm to solve this synthesis problem. We apply our technique to both simulated and real data. We first apply our technique to data simulated from a well established model of common myeloid progenitor differentiation. We show that our technique is able to recover the original Boolean network rules. We then apply our technique to a large dataset taken during embryonic development containing thousands of cell measurements. Our technique synthesises matching Boolean networks, and analysis of these models yields new predictions about blood development which our experimental collaborators were able to verify.

Building an ENCODE-style data compendium on a shoestring

One perhaps unintended consequence of the unquestionable success of the human genome project has been a shift in the biomedical research funding landscape towards large scale programs, commonly involving several hundred scientists and budgets of hundreds of millions of dollars. This emphasis on large-scale projects however is sometimes questioned, as illustrated by recent debates following last year’s publications from the ENCODE project 1,2. Here we have explored an alternative approach. Rather than making advanced decisions about the datasets that should be generated for a given research community, as large scale projects have to do, we have instead compiled all datasets produced by that community, as soon as they are deposited in public databases. We demonstrate that such real-time curation can exceed large consortia efforts, which constitutes a highly topical contribution to the ongoing ‘small vs big science’ debate. We created HAEMCODE, a repository for transcription factor (TF) binding maps in mouse blood cells, generated by chromatin immunoprecipitation sequencing (ChIP-seq) . Using a standardized analysis pipeline we manually curated more than 300 TF ChIP-Seq studies from a wide range of primary mouse haematopoietic cells and major cell line models. As of September 2013, the HAEMCODE compendium covered 84 TFs across 24 major blood cell types. Haemopoiesis is also a major focus of ENCODE, yet the currently available mouse ENCODE data covers less than half of HAEMCODE (36 TFs, May 2013), with only 9 ENCODE TFs not available elsewhere. We next developed a web interface (http://haemcode.stemcells.cam.ac.uk) to provide data access as well as a range of online analysis tools, designed to be useful to both experimentalist and computational biologists. The classical use case consists of selecting experiments within HAEMCODE before being directed to a workspace, which offers pre-computed options to inspect and/or download selected ChIP-Seq datasets. Additional online tools can compute global similarity between selected experiments, investigate overrepresentation of a user-submitted gene list in any subset of ChIP-Seq experiments3, inspect pre-computed results from de-novo motif discovery, and output all ChIP-Seq experiments with binding peaks for a user-supplied gene locus. Integration of publicly available data represents a powerful approach to make novel discoveries across diseases, species and platforms that would be impossible to achieve from single projects4. Successful completion of the HAEMCODE project on a small budget highlights this approach as a potentially widely applicable complement to multi-million dollar research initiatives.

Program Synthesis Meets Deep Learning for Decoding Regulatory Networks

Abstract With ever growing data sets spanning DNA sequencing all the way to single-cell transcriptomics, we are now facing the question of how can we turn this vast amount of information into knowledge. How do we integrate these large data sets into a coherent whole to help understand biological programs? The last few years have seen a growing interest in machine learning methods to analyse patterns in high-throughput data sets and an increasing interest in using program synthesis techniques to reconstruct and analyse executable models of gene regulatory networks. In this review, we discuss the synergies between the two methods and share our views on how they can be combined to reconstruct executable mechanistic programs directly from large-scale genomic data.

SCNS: a graphical tool for reconstructing executable regulatory networks from single-cell genomic data.

BackgroundReconstruction of executable mechanistic models from single-cell gene expression data represents a powerful approach to understanding developmental and disease processes. New ambitious efforts like the Human Cell Atlas will soon lead to an explosion of data with potential for uncovering and understanding the regulatory networks which underlie the behaviour of all human cells. In order to take advantage of this data, however, there is a need for general-purpose, user-friendly and efficient computational tools that can be readily used by biologists who do not have specialist computer science knowledge.ResultsThe Single Cell Network Synthesis toolkit (SCNS) is a general-purpose computational tool for the reconstruction and analysis of executable models from single-cell gene expression data. Through a graphical user interface, SCNS takes single-cell qPCR or RNA-sequencing data taken across a time course, and searches for logical rules that drive transitions from early cell states towards late cell states. Because the resulting reconstructed models are executable, they can be used to make predictions about the effect of specific gene perturbations on the generation of specific lineages.ConclusionsSCNS should be of broad interest to the growing number of researchers working in single-cell genomics and will help further facilitate the generation of valuable mechanistic insights into developmental, homeostatic and disease processes.