Hard-wired heterogeneity in blood stem cells revealed using a dynamic regulatory network model

Motivation: Combinatorial interactions of transcription factors with cis-regulatory elements control the dynamic progression through successive cellular states and thus underpin all metazoan development. The construction of network models of cis-regulatory elements, therefore, has the potential to generate fundamental insights into cellular fate and differentiation. Haematopoiesis has long served as a model system to study mammalian differentiation, yet modelling based on experimentally informed cis-regulatory interactions has so far been restricted to pairs of interacting factors. Here, we have generated a Boolean network model based on detailed cis-regulatory functional data connecting 11 haematopoietic stem/progenitor cell (HSPC) regulator genes. Results: Despite its apparent simplicity, the model exhibits surprisingly complex behaviour that we charted using strongly connected components and shortest-path analysis in its Boolean state space. This analysis of our model predicts that HSPCs display heterogeneous expression patterns and possess many intermediate states that can act as ‘stepping stones' for the HSPC to achieve a final differentiated state. Importantly, an external perturbation or ‘trigger' is required to exit the stem cell state, with distinct triggers characterizing maturation into the various different lineages. By focusing on intermediate states occurring during erythrocyte differentiation, from our model we predicted a novel negative regulation of Fli1 by Gata1, which we confirmed experimentally thus validating our model. In conclusion, we demonstrate that an advanced mammalian regulatory network model based on experimentally validated cis-regulatory interactions has allowed us to make novel, experimentally testable hypotheses about transcriptional mechanisms that control differentiation of mammalian stem cells. Contact: [email protected] or [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

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

Reconstructing Gene Regulatory Networks That Control Hematopoietic Commitment.

Hematopoietic stem cells (HSCs) reside at the apex of the hematopoietic hierarchy, possessing the ability to self-renew and differentiate toward all mature blood lineages. Along with more specialized progenitor cells, HSCs have an essential role in maintaining a healthy blood system. Incorrect regulation of cell fate decisions in stem/progenitor cells can lead to an imbalance of mature blood cell populations-a situation seen in diseases such as leukemia. Transcription factors, acting as part of complex regulatory networks, are known to play an important role in regulating hematopoietic cell fate decisions. Yet, discovering the interactions present in these networks remains a big challenge. Here, we discuss a computational method that uses single-cell gene expression data to reconstruct Boolean gene regulatory network models and show how this technique can be applied to enhance our understanding of transcriptional regulation in hematopoiesis.Work in the author’s laboratory is supported by grants from the Wellcome, Bloodwise, Cancer Research UK, NIH-NIDDK and core support grants by the Wellcome to the Cambridge Institute for Medical Research and Wellcome & MRC Cambridge Stem Cell Institute. F.K.H. is a recipient of a Medical Research Council PhD Studentship

Towards Executable Biology

Heringa, J. [Promotor]Fokkink, W.J. [Promotor]Feenstra, K.A. [Copromotor

The Architecture And Dynamics Of Gene Regulatory Networks Directing Cell-Fate Choice During Murine Hematopoiesis

Mammals produce hundreds of billions of new blood cells every day througha process known as hematopoiesis. Hematopoiesis starts with stem cells that develop into all the different types of cells found in blood by changing their genome-wide gene expression. The remodeling of genome-wide gene expression can be primarily attributed to a special class of proteins called transcription factors (TFs) that can activate or repress other genes, including genes encoding TFs. TFs and their targets therefore form recurrent networks called gene regulatory networks (GRNs). GRNs are crucial during physiological developmental processes, such as hematopoiesis, while abnormalities in the regulatory interactions of GRNs can be detrimental to the organisms. To this day we do not know all the key compo-nents that comprise hematopoietic GRNs or the complete set of their regulatory interactions. Inference of GRNs directly from genetic experiments is low throughput and labor intensive, while computational inference of comprehensive GRNs is challenging due to high processing times. This dissertation focuses on deriving the architecture and the dynamics of hematopoietic GRNs from genome-wide gene expression data obtained from high-resolution time-series experiments. The dissertation also aims to address the technical challenge of speeding up the process of GRN inference. Here GRNs are inferred and modeled using gene circuits, a data-driven method based on Ordinary Differential Equations (ODEs). In gene circuits, the rate of change of a gene product depends on regulatory influences from other genes encoded as a set of parameters that are inferred from time-series data. A twelve-gene GRN comprising genes encoding key TFs and cytokine receptors involved in erythrocyte-neutrophil differentiation was inferred from a high-resolution time-series dataset of the in vitro differentiation of a multipotential cell line. The inferred GRN architecture agreed with prior empirical evidence and pre- dicted novel regulatory interactions. The inferred GRN model was also able to predict the outcome of perturbation experiments, suggesting an accurate inference of GRN architecture. The dynamics of the inferred GRN suggested an alternative explanation to the currently accepted sequence of regulatory events during neutrophil differentiation. The analysis of the model implied that two TFs, C/EBPα and Gfi1, initiate cell-fate choice in the neutrophil lineage, while PU.1, believed to be a master regulator of all white-blood cells, is activated only later. This inference was confirmed in a single-cell RNA-Seq dataset from mouse bone marrow, in which PU.1 upregulation was preceded by C/EBPα and Gfi1 upregulation. This dissertation also presents an analysis of a high-temporal resolution genome-wide gene expression dataset of in vitro macrophage-neutrophil differentiation. Analysis of these data reveal that genome-wide gene expression during differentiation is highly dynamic and complex. A large-scale transition is observed around 8h and shown to be related to wide-spread physiological remodeling of the cells. The genes associated by myeloid differentiation mainly change during the first 4 hours, implying that the cell-fate decision takes place in the first four hours of differentiation. The dissertation also presents a new classification-based model-training technique that addresses the challenge of the high computational cost of inferring GRNs. This method, called Fast Inference of Gene Regulation (FIGR), is demonstrated to be two orders magnitude faster than global non-linear optimization techniques and its computational complexity scales much better with GRN size. This work has demonstrated the feasibility of simulating relatively large realistic GRNs using a dynamical and mechanistically accurate model coupled to high-resolution time series data and that such models can yield novel biological insight. Taken together with the macrophage-neutrophil dataset and the computationally efficient GRN inference methodology, this work should open up new avenues for modeling more comprehensive GRNs in hematopoiesis and the broader field of developmental biology

Reconstructing blood stem cell regulatory network models from single-cell molecular profiles.

Adult blood contains a mixture of mature cell types, each with specialized functions. Single hematopoietic stem cells (HSCs) have been functionally shown to generate all mature cell types for the lifetime of the organism. Differentiation of HSCs toward alternative lineages must be balanced at the population level by the fate decisions made by individual cells. Transcription factors play a key role in regulating these decisions and operate within organized regulatory programs that can be modeled as transcriptional regulatory networks. As dysregulation of single HSC fate decisions is linked to fatal malignancies such as leukemia, it is important to understand how these decisions are controlled on a cell-by-cell basis. Here we developed and applied a network inference method, exploiting the ability to infer dynamic information from single-cell snapshot expression data based on expression profiles of 48 genes in 2,167 blood stem and progenitor cells. This approach allowed us to infer transcriptional regulatory network models that recapitulated differentiation of HSCs into progenitor cell types, focusing on trajectories toward megakaryocyte-erythrocyte progenitors and lymphoid-primed multipotent progenitors. By comparing these two models, we identified and subsequently experimentally validated a difference in the regulation of nuclear factor, erythroid 2 (Nfe2) and core-binding factor, runt domain, alpha subunit 2, translocated to, 3 homolog (Cbfa2t3h) by the transcription factor Gata2. Our approach confirms known aspects of hematopoiesis, provides hypotheses about regulation of HSC differentiation, and is widely applicable to other hierarchical biological systems to uncover regulatory relationships.Work in the author’s laboratory is supported by grants from Bloodwise, Cancer Research UK, Biotechnology and Biological Sciences Research Council, Leukemia Lymphoma Society, 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. S.N. and F.K.H. are recipients of Medical Research Council PhD Studentships. D.G.K. is supported by a Bloodwise Bennett Fellowship (15008) and a European Hematology Association Non-Clinical Advanced Research Fellowship

Lineage decision-making within normal haematopoietic and leukemic stem cells

This article belongs to the Special Issue Cellular and Molecular Mechanisms of Hematopoiesis.To produce the wide range of blood and immune cell types, haematopoietic stem cells can “choose” directly from the entire spectrum of blood cell fate-options. Affiliation to a single cell lineage can occur at the level of the haematopoietic stem cell and these cells are therefore a mixture of some pluripotent cells and many cells with lineage signatures. Even so, haematopoietic stem cells and their progeny that have chosen a particular fate can still “change their mind” and adopt a different developmental pathway. Many of the leukaemias arise in haematopoietic stem cells with the bulk of the often partially differentiated leukaemia cells belonging to just one cell type. We argue that the reason for this is that an oncogenic insult to the genome “hard wires” leukaemia stem cells, either through development or at some stage, to one cell lineage. Unlike normal haematopoietic stem cells, oncogene-transformed leukaemia stem cells and their progeny are unable to adopt an alternative pathway.This project received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 315902. Research in ISG group is partially supported by SAF2015-64420-R MINECO/FEDER,UE, RTI2018-093314-B-I00 MCIU/AEI/FEDER,UE, by Junta de Castilla y León (UIC-017, CSI001U16, and CSI234P18), by the German Carreras Foundation (DJCLS 02R-2016; DJCLS 07R/2019) and by the German Federal Office for Radiation Protection (BfS)-Germany (FKZ: 3618S32274), and by the Fundacion Unoentrecienmil (CUNINA project).Peer reviewe

Mammalian Transcription Factor Networks: Recent Advances in Interrogating Biological Complexity

Transcription factor (TF) networks are a key determinant of cell fate decisions in mammalian development and adult tissue homeostasis and are frequently corrupted in disease. However, our inability to experimentally resolve and interrogate the complexity of mammalian TF networks has hampered the progress in this field. Recent technological advances, in particular large-scale genome-wide approaches, single-cell methodologies, live-cell imaging, and genome editing, are emerging as important technologies in TF network biology. Several recent studies even suggest a need to re-evaluate established models of mammalian TF networks. Here, we provide a brief overview of current and emerging methods to define mammalian TF networks. We also discuss how these emerging technologies facilitate new ways to interrogate complex TF networks, consider the current open questions in the field, and comment on potential future directions and biomedical applications.ACW is funded by a Bloodwise Visiting Fellowship. HN is funded by the Japan Science and Technology Agency, the California Institute of Regenerative Medicine and Ludwig Foundation. BG is funded by Bloodwise, Cancer Research UK, the Wellcome Trust, the MRC, NIH-NIDDK and core funding from the Wellcome Trust to the Cambridge Stem Cell Institute

Fitting structure to function in gene regulatory networks

Cascades of transcriptional regulation are the common source of the forward drive in all developmental systems. Increases in complexity and specificity of gene expression at successive stages are based on the collaboration of varied combinations of transcription factors already expressed in the cells to turn on new genes, and the logical relationships between the transcription factors acting and becoming newly expressed from stage to stage are best visualized as gene regulatory networks. However, gene regulatory networks used in different developmental contexts underlie processes that actually operate through different sets of rules, which affect the kinetics, synchronicity, and logical properties of individual network nodes. Contrasting early embryonic development in flies and sea urchins with adult mammalian hematopoietic development from stem cells, major differences are seen in transcription factor dosage dependence, the silencing or damping impacts of repression, and the impact of cellular regulatory history on the parts of the genome that are accessible to transcription factor action in a given cell type. These different features not only affect the kinds of models that can illuminate developmental mechanisms in the respective biological systems, but also reflect the evolutionary needs of these biological systems to optimize different aspects of development

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