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

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.S.W is supported by a Microsoft Research PhD Scholarship.This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/icb.2015.10

Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data.

Many genes are expressed in bursts, which can contribute to cell-to-cell heterogeneity. It is now possible to measure this heterogeneity with high throughput single cell gene expression assays (single cell qPCR and RNA-seq). These experimental approaches generate gene expression distributions which can be used to estimate the kinetic parameters of gene expression bursting, namely the rate that genes turn on, the rate that genes turn off, and the rate of transcription. We construct a complete pipeline for the analysis of single cell qPCR data that uses the mathematics behind bursty expression to develop more accurate and robust algorithms for analyzing the origin of heterogeneity in experimental samples, specifically an algorithm for clustering cells by their bursting behavior (Simulated Annealing for Bursty Expression Clustering, SABEC) and a statistical tool for comparing the kinetic parameters of bursty expression across populations of cells (Estimation of Parameter changes in Kinetics, EPiK). We applied these methods to hematopoiesis, including a new single cell dataset in which transcription factors (TFs) involved in the earliest branchpoint of blood differentiation were individually up- and down-regulated. We could identify two unique sub-populations within a seemingly homogenous group of hematopoietic stem cells. In addition, we could predict regulatory mechanisms controlling the expression levels of eighteen key hematopoietic transcription factors throughout differentiation. Detailed information about gene regulatory mechanisms can therefore be obtained simply from high throughput single cell gene expression data, which should be widely applicable given the rapid expansion of single cell genomics.This work was supported by: Royal Society Research Fellowship, Marshall Scholarship, Medical Research Council, the Leukemia and Lymphoma Society and core support grants from the Wellcome Trust to the Cambridge Institute for Medical Research and the Wellcome Trust and MRC Cambridge Stem Cell Institute.This is the final version of the article. It first appeared from the Public Library of Science via http://dx.doi.org/10.1371/journal.pcbi.100507

BNC1 regulates cell heterogeneity in human pluripotent stem cell derived-epicardium

The murine developing epicardium heterogeneously expresses the transcription factors TCF21 and WT1. Here, we show that this cell heterogeneity is conserved in human epicardium, regulated by BNC1 and associated with cell fate and function. Single cell RNAseq of epicardium derived from human pluripotent stem cells (hPSC-epi) revealed that distinct epicardial sub-populations are defined by high levels of expression for the transcription factors BNC1 or TCF21. WT1+ cells are included in the BNC1+ population, which was confirmed in human foetal hearts. THY1 emerged as a membrane marker of the TCF21 population. We show that THY1+ cells can differentiate into cardiac fibroblast (CF) and smooth muscle cells (SMC), while THY1-cells were predominantly restricted to SMC. Knocking down BNC1 during the establishment of the epicardial populations resulted in a homogeneous, predominantly, TCF21high population. Network inference methods using transcriptomic data from the different cell lineages derived from the hPSC-epi, delivered a core transcriptional network organized around WT1, TCF21 and BNC1. This study is a step towards engineering sub-populations of epicardial cells with selective biological activities and unveils a list of epicardial regulators.This work was supported by the British Heart Foundation Oxbridge Centre for Regenerative Medicine RM/13/3/30159 and RM/17/2/33380 (LG, SS) and BHF grants FS/14/59/31282 (SAM), FS/13/29/30024 and FS/18/46/33663 (SS). SS was also supported by the British Heart Foundation Centre for Cardiovascular Research Excellence. Core support was provided by the Wellcome-MRC Cambridge Stem Cell Institute (203151/Z/16/Z) and the Cambridge Hospitals National Institute for Health Research Biomedical Research Centre funding (SS). VM was supported by a Wellcome PhD studentship as part of the Stem Cell Institute PhD programme. Research in the Gottgens group is supported by programmatic funding from Wellcome, CRUK and Bloodwise. Single cell experiments were supported through an MRC Clinical Research Infrastructure award. NL was supported by the Biotechnology and Biological Sciences Research Council (Institute Strategic Programmes BBS/E/B/000C0419 and BBS/E/B/000C0434). DS was supported by an ERASMUS+ internship. WGB was supported from the Stroke Association (TSA 2016/02 PP11_Sinha)

Generation of multipotent foregut stem cells from human pluripotent stem cells

Human pluripotent stem cells (hPSCs) could provide an infinite source of clinically relevant cells with potential applications in regenerative medicine. However, hPSC lines vary in their capacity to generate specialized cells, and the development of universal protocols for the production of tissue-specific cells remains a major challenge. Here, we have addressed this limitation for the endodermal lineage by developing a defined culture system to expand and differentiate human foregut stem cells (hFSCs) derived from hPSCs. hFSCs can self-renew while maintaining their capacity to differentiate into pancreatic and hepatic cells. Furthermore, near-homogenous populations of hFSCs can be obtained from hPSC lines which are normally refractory to endodermal differentiation. Therefore, hFSCs provide a unique approach to bypass variability between pluripotent lines in order to obtain a sustainable source of multipotent endoderm stem cells for basic studies and to produce a diversity of endodermal derivatives with a clinical value

An experimentally validated network of nine haematopoietic transcription factors reveals mechanisms of cell state stability.

Transcription factor (TF) networks determine cell-type identity by establishing and maintaining lineage-specific expression profiles, yet reconstruction of mammalian regulatory network models has been hampered by a lack of comprehensive functional validation of regulatory interactions. Here, we report comprehensive ChIP-Seq, transgenic and reporter gene experimental data that have allowed us to construct an experimentally validated regulatory network model for haematopoietic stem/progenitor cells (HSPCs). Model simulation coupled with subsequent experimental validation using single cell expression profiling revealed potential mechanisms for cell state stabilisation, and also how a leukaemogenic TF fusion protein perturbs key HSPC regulators. The approach presented here should help to improve our understanding of both normal physiological and disease processes.Research in the authors’ laboratories was supported by Bloodwise, The Wellcome Trust, Cancer Research UK, the Biotechnology and Biological Sciences Research Council, the National Institute of Health Research, the Medical Research Council, the MRC Molecular Haematology Unit (Oxford) core award, a Weizmann-UK “Making Connections” grant (Oxford) and core support grants by the Wellcome Trust to the Cambridge Institute for Medical Research (100140) and Wellcome Trust–MRC Cambridge Stem Cell Institute (097922).This is the final version of the article. It first appeared from eLife via http://dx.doi.org/10.7554/eLife.1146

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.We thank J. Downing (St. Jude Children's Research Hospital, Memphis, TN, USA) for the Runx1-ires-GFP mouse. Research in the authors' laboratory is supported by the Medical Research Council, Biotechnology and Biological Sciences Research Council, Leukaemia and Lymphoma Research, the Leukemia and Lymphoma Society, Microsoft Research and core support grants by the Wellcome Trust to the Cambridge Institute for Medical Research and Wellcome Trust - MRC Cambridge Stem Cell Institute. V.M. is supported by a Medical Research Council Studentship and Centenary Award and S.W. by a Microsoft Research PhD Scholarship.This is the accepted manuscript for a paper published in Nature Biotechnology 33, 269–276 (2015) doi:10.1038/nbt.315

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.Research in the authors’ laboratories was supported by Leukaemia and Lymphoma Research, The Wellcome Trust, Cancer Research UK, the Biotechnology and Biological Sciences Research Council, the National Institute of Health Research, the Medical Research Council and core support grants by the Wellcome Trust to the Cambridge Institute for Medical Research and Wellcome Trust–MRC Cambridge Stem Cell Institute. V.K.S.K. was supported by a Japan Society for the Promotion of Science (JSPS) Research Fellowship for Young Scientists.This is the final version. It was first published by the Company of Biologists http://dev.biologists.org/content/141/20/4018.long

CHD7 and Runx1 interaction provides a braking mechanism for hematopoietic differentiation.

Hematopoietic stem and progenitor cell (HSPC) formation and lineage differentiation involve gene expression programs orchestrated by transcription factors and epigenetic regulators. Genetic disruption of the chromatin remodeler chromodomain-helicase-DNA-binding protein 7 (CHD7) expanded phenotypic HSPCs, erythroid, and myeloid lineages in zebrafish and mouse embryos. CHD7 acts to suppress hematopoietic differentiation. Binding motifs for RUNX and other hematopoietic transcription factors are enriched at sites occupied by CHD7, and decreased RUNX1 occupancy correlated with loss of CHD7 localization. CHD7 physically interacts with RUNX1 and suppresses RUNX1-induced expansion of HSPCs during development through modulation of RUNX1 activity. Consequently, the RUNX1:CHD7 axis provides proper timing and function of HSPCs as they emerge during hematopoietic development or mature in adults, representing a distinct and evolutionarily conserved control mechanism to ensure accurate hematopoietic lineage differentiation.Bloodwise, CRUK, MRC, Wellcome Trust, NIH, Leukemia and Lymphoma Societ

GFI1 proteins regulate stem cell formation in the AGM

In vertebrates, the first haematopoietic stem cells (HSCs) with multi-lineage and long-term repopulating potential arise in the AGM (aorta-gonad-mesonephros) region. These HSCs are generated from a rare and transient subset of endothelial cells, called haemogenic endothelium (HE), through an endothelial-to-haematopoietic transition (EHT). Here, we establish the absolute requirement of the transcriptional repressors GFI1 and GFI1B (growth factor independence 1 and 1B) in this unique trans-differentiation process. We first demonstrate that Gfi1 expression specifically defines the rare population of HE that generates emerging HSCs. We further establish that in the absence of GFI1 proteins, HSCs and haematopoietic progenitor cells are not produced in the AGM, revealing the critical requirement for GFI1 proteins in intra-embryonic EHT. Finally, we demonstrate that GFI1 proteins recruit the chromatin-modifying protein LSD1, a member of the CoREST repressive complex, to epigenetically silence the endothelial program in HE and allow the emergence of blood cells.We thank the staff at the Advanced Imaging, animal facility, Molecular Biology Core facilities and Flow Cytometry of CRUK Manchester Institute for technical support and Michael Lie-A-Ling and Elli Marinopoulou for initiating the DamID-PIP bioinformatics project. We thank members of the Stem Cell Biology group, the Stem Cell Haematopoiesis groups and Martin Gering for valuable advice and critical reading of the manuscript. Work in our laboratory is supported by the Leukaemia and Lymphoma Research Foundation (LLR), Cancer Research UK (CRUK) and the Biotechnology and Biological Sciences Research Council (BBSRC). SC is the recipient of an MRC senior fellowship (MR/J009202/1).This is the author accepted manuscript. The final version is available from NPG via http://dx.doi.org/10.1038/ncb327