Concerted bioinformatic analysis of the genome-scale blood transcription factor compendium reveals new control mechanisms.

Transcription factors play a key role in the development of a disease. ChIP-sequencing has become a preferred technique to investigate genome-wide binding patterns of transcription factors in vivo. Although this technology has led to many important discoveries, the rapidly increasing number of publicly available ChIP-sequencing datasets still remains a largely unexplored resource. Using a compendium of 144 publicly available murine ChIP-sequencing datasets in blood, we show that systematic bioinformatic analysis can unravel diverse aspects of transcription regulation; from genome-wide binding preferences, finding regulatory partners and assembling regulatory complexes, to identifying novel functions of transcription factors and investigating transcription dynamics during development.This is the final published version as published by the Royal Society of Chemistry in Molecular Biosystems here: http://pubs.rsc.org/en/Content/ArticleLanding/2014/MB/C4MB00354C#divAbstract

TRES predicts transcription control in embryonic stem cells.

SUMMARY: Unraveling transcriptional circuits controlling embryonic stem cell maintenance and fate has great potential for improving our understanding of normal development as well as disease. To facilitate this, we have developed a novel web tool called 'TRES' that predicts the likely upstream regulators for a given gene list. This is achieved by integrating transcription factor (TF) binding events from 187 ChIP-sequencing and ChIP-on-chip datasets in murine and human embryonic stem (ES) cells with over 1000 mammalian TF sequence motifs. Using 114 TF perturbation gene sets, as well as 115 co-expression clusters in ES cells, we validate the utility of this approach. AVAILABILITY AND IMPLEMENTATION: TRES is freely available at http://www.tres.roslin.ed.ac.uk. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.This work was supported by a University of Edinburgh Chancellors Fellowship awarded to AJ and strategic funding from the BBSRC. CP was funded by the Scottish Government through the Strategic Partnership for Animal Science Excellence (SPASE). The Gottgens’ lab is supported by LLR, the MRC, BBSRC, Cancer Research UK, and Wellcome Trust core support to the Cambridge Institute for Medical Research and Wellcome Trust–MRC Cambridge Stem Cell Institute.This version is the author accepted manuscript. The published advanced access version can be viewed on the journals website at: http://bioinformatics.oxfordjournals.org/content/early/2014/06/23/bioinformatics.btu399.full.pdf+htm

Improved arteriogenesis with simultaneous skeletal muscle repair in ischemic tissue by SCL plus multipotent adult progenitor cell clones from peripheral blood

Background: The CD34- murine stem cell line RM26 cloned from peripheral blood mononuclear cells has been shown to generate hematopoietic progeny in lethally irradiated animals. The peripheral blood-derived cell clones expresses a variety of mesodermal and erythroid/myeloid transcription factors suggesting a multipotent differentiation potential like the bone marrow-derived `multipotent adult progenitor cells' (MAP-C). Methods: SCL+ CD34- RM26 cells were transfused intravenously into mice suffering from chronic hind-limb ischemia, evaluating the effect of stem cells on collateral artery growth and simultaneous skeletal muscle repair. Results: RM26 cells are capable of differentiating in vitro into endothelial cells when cultured on the appropriate collagen matrix. Activation of the SCL stem cell enhancer (SCL+) is mediated through the binding to two Ets and one GATA site and cells start to express milieu- and growth condition-dependent levels of the endothelial markers CD31 (PECAM) and Flt-1 (VEGF-R1). Intravenously infused RM26 cells significantly improved the collateral blood flow (arteriogenesis) and neo-angiogenesis formation in a murine hind-limb ischemia transplant model. Although transplanted RM26 cells did not integrate into the growing collateral arteries, cells were found adjacent to local arteriogenesis, but instead integrated into the ischemic skeletal muscle exclusively in the affected limb for simultaneous tissue repair. Conclusion: These data suggest that molecularly primed hem-/mesangioblast-type adult progenitor cells can circulate in the peripheral blood improving perfusion of tissues with chronic ischemia and extending beyond the vascular compartment. Copyright (C) 2004 S. Karger AG, Basel

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

Background Reconstruction 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. Results The 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. Conclusions SCNS 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.Research in the Gottgens lab is supported by infrastructure support funding from the Wellcome Trust to the Wellcome Trust and MRC Cambridge Stem Cell Institute. Steven Woodhouse is a postdoctoral researcher supported by Microsoft Researc

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)

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

Abstract Background Rapid 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. Results Here 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. Conclusions BTR 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

Improved arteriogenesis with simultaneous skeletal muscle repair in ischemic tissue by SCL plus multipotent adult progenitor cell clones from peripheral blood

Background: The CD34- murine stem cell line RM26 cloned from peripheral blood mononuclear cells has been shown to generate hematopoietic progeny in lethally irradiated animals. The peripheral blood-derived cell clones expresses a variety of mesodermal and erythroid/myeloid transcription factors suggesting a multipotent differentiation potential like the bone marrow-derived `multipotent adult progenitor cells' (MAP-C). Methods: SCL+ CD34- RM26 cells were transfused intravenously into mice suffering from chronic hind-limb ischemia, evaluating the effect of stem cells on collateral artery growth and simultaneous skeletal muscle repair. Results: RM26 cells are capable of differentiating in vitro into endothelial cells when cultured on the appropriate collagen matrix. Activation of the SCL stem cell enhancer (SCL+) is mediated through the binding to two Ets and one GATA site and cells start to express milieu- and growth condition-dependent levels of the endothelial markers CD31 (PECAM) and Flt-1 (VEGF-R1). Intravenously infused RM26 cells significantly improved the collateral blood flow (arteriogenesis) and neo-angiogenesis formation in a murine hind-limb ischemia transplant model. Although transplanted RM26 cells did not integrate into the growing collateral arteries, cells were found adjacent to local arteriogenesis, but instead integrated into the ischemic skeletal muscle exclusively in the affected limb for simultaneous tissue repair. Conclusion: These data suggest that molecularly primed hem-/mesangioblast-type adult progenitor cells can circulate in the peripheral blood improving perfusion of tissues with chronic ischemia and extending beyond the vascular compartment. Copyright (C) 2004 S. Karger AG, Basel

Index sorting resolves heterogeneous murine hematopoietic stem cell populations.

Recent advances in the cellular and molecular biology of single stem cells have uncovered significant heterogeneity in the functional properties of stem cell populations. This has prompted the development of approaches to study single cells in isolation, often performed using multiparameter flow cytometry. However, many stem cell populations are too rare to test all possible cell surface marker combinations, and virtually nothing is known about functional differences associated with varying intensities of such markers. Here we describe the use of index sorting for further resolution of the flow cytometric isolation of single murine hematopoietic stem cells (HSCs). Specifically, we associate single-cell functional assay outcomes with distinct cell surface marker expression intensities. High levels of both CD150 and EPCR associate with delayed kinetics of cell division and low levels of differentiation. Moreover, cells that do not form single HSC-derived clones appear in the 7AAD(dim) fraction, suggesting that even low levels of 7AAD staining are indicative of less healthy cell populations. These data indicate that when used in combination with single-cell functional assays, index sorting is a powerful tool for refining cell isolation strategies. This approach can be broadly applied to other single-cell systems, both to improve isolation and to acquire additional cell surface marker information.This work was supported by grants from Leukaemia and Lymphoma Research, the Medical Research Council, the National Institute for Health Research Cambridge Biomedical Research Centre, and core support grants by the Wellcome Trust to the Cambridge Institute for Medical Research and Wellcome Trust–MRC Cambridge Stem Cell Institute. DGK is the recipient of a Canadian Institutes of Health Research Postdoctoral Fellowship and a European Hematology Association non-clinical advanced research fellowship. The authors declare that they have no conflict of interest.This is the author accepted manuscript. The final version will be available from Elsevier at http://dx.doi.org/10.1016/j.exphem.2015.05.006

The stem/progenitor landscape is reshaped in a mouse model of essential thrombocythaemia and causes excess megakaryocyte production

Frameshift mutations in CALR (calreticulin) are associated with essential thrombocythaemia (ET), but the stages at and mechanisms by which mutant CALR drives transformation remain incompletely defined. Here, we use single-cell approaches to examine the haematopoietic stem/progenitor cell (HSPC) landscape in a mouse model of mutant CALR-driven ET. We identify a trajectory linking HSCs with megakaryocytes and prospectively identify a novel intermediate population that is overrepresented in the disease state. We also show that mutant CALR drives transformation primarily from the earliest stem cell compartment, with some contribution from megakaryocyte progenitors. Finally, relative to wild-type HSCs, mutant CALR HSCs show increases in JAK-STAT signalling, the unfolded protein response, cell cycle, and a previously undescribed upregulation of cholesterol biosynthesis. Overall, we have identified a novel megakaryocyte-biased cell population that is increased in a mouse model of ET and described transcriptomic changes linking CALR mutations to increased HSC proliferation and megakaryopoiesis.Work in the Göttgens lab is supported by the Medical Research Council (MR/M008975/1), Wellcome (206328/Z/17/Z), Blood Cancer UK (18002), and Cancer Research UK (RG83389, jointly with A.R.G). Work in the Green lab is supported by Wellcome (RG74909), WBH Foundation (RG91681), and Cancer Research UK (RG83389, jointly with B.G.)

Drug target optimization in chronic myeloid leukemia using innovative computational platform.

Chronic Myeloid Leukemia (CML) represents a paradigm for the wider cancer field. Despite the fact that tyrosine kinase inhibitors have established targeted molecular therapy in CML, patients often face the risk of developing drug resistance, caused by mutations and/or activation of alternative cellular pathways. To optimize drug development, one needs to systematically test all possible combinations of drug targets within the genetic network that regulates the disease. The BioModelAnalyzer (BMA) is a user-friendly computational tool that allows us to do exactly that. We used BMA to build a CML network-model composed of 54 nodes linked by 104 interactions that encapsulates experimental data collected from 160 publications. While previous studies were limited by their focus on a single pathway or cellular process, our executable model allowed us to probe dynamic interactions between multiple pathways and cellular outcomes, suggest new combinatorial therapeutic targets, and highlight previously unexplored sensitivities to Interleukin-3.We would like to thank the members of the Fisher laboratory, in particular to Gavin Smyth and Caroline Dahl for their help with the BMA development, and Alex Hajnal for valuable comments on the manuscript and insightful discussions. Research in BG laboratory is supported by the Medical 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.This is the final published version. It was originally published in Scientific Reports 5: 8190. DOI: 10.1038/srep08190