From genes to cells to tissues--modelling the haematopoietic system

Mathematical Models of Pluripotent Stem Cells: At the Dawn of Predictive Regenerative Medicine

Regenerative medicine, ranging from stem cell therapy to organ regeneration, is promising to revolutionize treatments of diseases and aging. These approaches require a perfect understanding of cell reprogramming and differentiation. Predictive modeling of cellular systems has the potential to provide insights about the dynamics of cellular processes, and guide their control. Moreover in many cases, it provides alternative to experimental tests, difficult to perform for practical or ethical reasons. The variety and accuracy of biological processes represented in mathematical models grew in-line with the discovery of underlying molecular mechanisms. High-throughput data generation led to the development of models based on data analysis, as an alternative to more established modeling based on prior mechanistic knowledge. In this chapter, we give an overview of existing mathematical models of pluripotency and cell fate, to illustrate the variety of methods and questions. We conclude that current approaches are yet to overcome a number of limitations: Most of the computational models have so far focused solely on understanding the regulation of pluripotency, and the differentiation of selected cell lineages. In addition, models generally interrogate only a few biological processes. However, a better understanding of the reprogramming process leading to ESCs and iPSCs is required to improve stem-cell therapies. One also needs to understand the links between signaling, metabolism, regulation of gene expression, and the epigenetics machinery.

Transcriptional mechanisms of cell fate decisions revealed by single cell expression profiling

Transcriptional networks regulate cell fate decisions, which occur at the level of individual cells. However, much of what we know about their structure and function comes from studies averaging measurements over large populations of cells, many of which are functionally heterogeneous. Such studies conceal the variability between cells and so prevent us from determining the nature of heterogeneity at the molecular level. In recent years, many protocols and platforms have been developed that allow the high throughput analysis of gene expression in single cells, opening the door to a new era of biology. Here, we discuss the need for single cell gene expression analysis to gain deeper insights into the transcriptional control of cell fate decisions, and consider the insights it has provided so far into transcriptional regulatory networks in development.

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:j.heringa@vu.nl or ioannis.xenarios@isb-sib.ch or bg200@cam.ac.uk

Supplementary information:Supplementary data are available at Bioinformatics online.

Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysis

Cellular decision-making is mediated by a complex interplay of external stimuli with the intracellular environment, in particular transcription factor regulatory networks. Here we have determined the expression of a network of 18 key haematopoietic transcription factors in 597 single primary blood stem and progenitor cells isolated from mouse bone marrow. We demonstrate that different stem/progenitor populations are characterized by distinctive transcription factor expression states, and through comprehensive bioinformatic analysis reveal positively and negatively correlated transcription factor pairings, including previously unrecognized relationships between Gata2, Gfi1 and Gfi1b. Validation using transcriptional and transgenic assays confirmed direct regulatory interactions consistent with a regulatory triad in immature blood stem cells, where Gata2 may function to modulate cross-inhibition between Gfi1 and Gfi1b. Single-cell expression profiling therefore identifies network states and allows reconstruction of network hierarchies involved in controlling stem cell fate choices, and provides a blueprint for studying both normal development and human disease.

Stem cell heterogeneity: implications for aging and regenerative medicine

For decades, hematopoietic stem cells (HSCs) were thought to be a homogeneous population of cells with flexible behavior. Now a new picture has emerged: The HSC compartment consists of several subpopulations of HSCs each with distinct, preprogrammed differentiation and proliferation behaviors. These programs are epigenetically fixed and are stably bequeathed to all daughter HSCs on self-renewal. HSCs within each subset are remarkably similar in their self- renewal and differentiation behaviors, to the point where their life span can be predicted with mathematical certainty. Three subsets can be distinguished when HSCs are classified by their differentiation capacity: myeloid-biased, balanced, and lymphoid-biased HSCs. The relative number of the HSC subsets is developmentally regulated. Lymphoid-biased HSCs are found predominantly early in the life of an organism, whereas myeloid-biased HSCs accumulate in aged mice and humans. Thus, the discovery of distinct subpopulations of HSCs has led to a new understanding of HCS aging. This finding has implications for other aspects of HSC biology and applications in re-generative medicine. The possibility that other adult tissue stem cells show similar heterogeneity and mechanisms of aging is discussed.

Establishing the stem cell state: insights from regulatory network analysis of blood stem cell development

Transcription factors (TFs) have long been recognized as powerful regulators of cell-type identity and differentiation. As TFs function as constituents of regulatory networks, identification and functional characterization of key interactions within these wider networks will be required to understand how TFs exert their powerful biological functions. The formation of blood cells (hematopoiesis) represents a widely used model system for the study of cellular differentiation. Moreover, specific TFs or groups of TFs have been identified to control the various cell fate choices that must be made when blood stem cells differentiate into more than a dozen distinct mature blood lineages. Because of the relative ease of accessibility, the hematopoietic system represents an attractive experimental system for the development of regulatory network models. Here, we review the modeling efforts carried out to date, which have already provided new insights into the molecular control of blood cell development. We also explore potential areas of future study such as the need for new high-throughput technologies and a focus on studying dynamic cellular systems. Many leukemias arise as the result of mutations that cause transcriptional dysregulation, thus suggesting that a better understanding of transcriptional control mechanisms in hematopoiesis is of substantial biomedical relevance. Moreover, lessons learned from regulatory network analysis in the hematopoietic system are likely to inform research on less experimentally tractable tissues.

Transcriptional regulation of haematopoietic transcription factors

The control of differential gene expression is central to all metazoan biology. Haematopoiesis represents one of the best understood developmental systems where multipotent blood stem cells give rise to a range of phenotypically distinct mature cell types, all characterised by their own distinctive gene expression profiles. Small combinations of lineage-determining transcription factors drive the development of specific mature lineages from multipotent precursors. Given their powerful regulatory nature, it is imperative that the expression of these lineage-determining transcription factors is under tight control, a fact underlined by the observation that their misexpression commonly leads to the development of leukaemia. Here we review recent studies on the transcriptional control of key haematopoietic transcription factors, which demonstrate that gene loci contain multiple modular regulatory regions within which specific regulatory codes can be identified, that some modular elements cooperate to mediate appropriate tissue-specific expression, and that long-range approaches will be necessary to capture all relevant regulatory elements. We also explore how changes in technology will impact on this area of research in the future.

Genome-wide analysis of transcriptional reprogramming in mouse models of acute myeloid leukaemia

Acute leukaemias are commonly caused by mutations that corrupt the transcriptional circuitry of haematopoietic stem/progenitor cells. However, the mechanisms underlying large-scale transcriptional reprogramming remain largely unknown. Here we investigated transcriptional reprogramming at genome-scale in mouse retroviral transplant models of acute myeloid leukaemia (AML) using both gene-expression profiling and ChIP-sequencing. We identified several thousand candidate regulatory regions with altered levels of histone acetylation that were characterised by differential distribution of consensus motifs for key haematopoietic transcription factors including Gata2, Gfi1 and Sfpi1/Pu.1. In particular, downregulation of Gata2 expression was mirrored by abundant GATA motifs in regions of reduced histone acetylation suggesting an important role in leukaemogenic transcriptional reprogramming. Forced re-expression of Gata2 was not compatible with sustained growth of leukaemic cells thus suggesting a previously unrecognised role for Gata2 in downregulation during the development of AML. Additionally, large scale human AML datasets revealed significantly higher expression of GATA2 in CD34+ cells from healthy controls compared with AML blast cells. The integrated genome-scale analysis applied in this study represents a valuable and widely applicable approach to study the transcriptional control of both normal and aberrant haematopoiesis and to identify critical factors responsible for transcriptional reprogramming in human cancer.

Gfi1 expression is controlled by five distinct regulatory regions spread over 100 kilobases, with Scl/Tal1, Gata2, PU.1, Erg, Meis1, and Runx1 acting as upstream regulators in early hematopoietic cells

The growth factor independence 1 (Gfi1) gene was originally discovered in the hematopoietic system, where it functions as a key regulator of stem cell homeostasis, as well as neutrophil and T-cell development. Outside the blood system, Gfi1 is essential for inner-ear hair and intestinal secretory cell differentiation. To understand the regulatory hierarchies within which Gfi1 operates to control these diverse biological functions, we used a combination of comparative genomics, locus-wide chromatin immunoprecipitation assays, functional validation in cell lines, and extensive transgenic mouse assays to identify and characterize the complete ensemble of Gfi1 regulatory elements. This concerted effort identified five distinct regulatory elements spread over 100kb each driving expression in transgenic mice to a subdomain of endogenous Gfi1. Detailed characterization of an enhancer 35 kb upstream of Gfi1 demonstrated activity in the dorsal aorta region and fetal liver in transgenic mice, which was bound by key stem cell transcription factors Scl/Tal1, PU.1/Sfpi1, Runx1, Erg, Meis1, and Gata2. Taken together, our results reveal the regulatory regions responsible for Gfi1 expression and importantly establish that Gfi1 expression at the sites of hematopoietic stem cell (HSC) emergence is controlled by key HSC regulators, thus integrating Gfi1 into the wider HSC regulatory networks.

Modeling reveals bistability and low-pass filtering in the network module determining blood stem cell fate

Combinatorial regulation of gene expression is ubiquitous in eukaryotes with multiple inputs converging on regulatory control elements. The dynamic properties of these elements determine the functionality of genetic networks regulating differentiation and development. Here we propose a method to quantitatively characterize the regulatory output of distant enhancers with a biophysical approach that recursively determines free energies of protein-protein and protein-DNA interactions from experimental analysis of transcriptional reporter libraries. We apply this method to model the Scl-Gata2-Fli1 triad—a network module important for cell fate specification of hematopoietic stem cells. We show that this triad module is inherently bistable with irreversible transitions in response to physiologically relevant signals such as Notch, Bmp4 and Gata1 and we use the model to predict the sensitivity of the network to mutations. We also show that the triad acts as a low-pass filter by switching between steady states only in response to signals that persist for longer than a minimum duration threshold. We have found that the auto-regulation loops connecting the slow-degrading Scl to Gata2 and Fli1 are crucial for this low-pass filtering property. Taken together our analysis not only reveals new insights into hematopoietic stem cell regulatory network functionality but also provides a novel and widely applicable strategy to incorporate experimental measurements into dynamical network models.

Hematopoiesis—blood cell development—has long served as a model for study of cellular differentiation and its control by underlying gene regulatory networks. The Scl-Gata2-Fli1 triad is a network module essential for the development of hematopoietic stem cells but its mechanistic role is not well understood. The transcription factors Scl, Gata2 and Fli1 act in combination to upregulate transcription of each other via distal enhancer site binding. Similar network architectures are essential in other multipotent cell lines. We propose a method that uses experimental results to circumvent the difficulties of mathematically modeling the combinatorial regulation of this triad module. Using this dynamical model we show that the triad exhibits robust bistable behavior. Environmental signals can irreversibly switch the triad between stable states in a manner that reflects the unidirectional switching in the formation and subsequent differentiation of hematopoietic stem cells. We also show that the triad makes reliable decisions in noisy environments by only switching in response to transient signals that persist longer than the threshold duration. These results suggest that the Scl-Gata2-Fli1 module possibly functions as a control switch for hematopoietic stem cell development. The proposed method can be extended for quantitative characterization of other combinatorial gene regulatory modules.

Transcriptome study for early hematopoiesis--achievement, challenge and new opportunity

Hematopoietic stem progenitor cells are the source for the entire hematopoietic system. Studying gene expression in hematopoietic stem progenitor cells will provide information to understand the genetic programs controlling early hematopoiesis, and to identify the gene targets to interfere hematopoietic disorders. Extensive efforts using cell biology, molecular biology, and genomics approaches have generated rich knowledge for the genes and functional pathways involving in early hematopoiesis. Challenges remain, however, including the rarity of the hematopoietic stem progenitor cells that set physical limitation for the study, the difficulty for reaching comprehensive transcriptome detection under the conventional genomics technologies, and the difficulty for using conventional biological methods to identify the key genes among large number of expressed genes controlling stem cell self-renewal and differentiation. The newly developed single-cell transcriptome method and the next-generation DNA sequencing technology provide new opportunities for transcriptome study for early hematopoietic. Using systems biology approach may reveal the insight of the genetic mechanisms controlling early hematopoiesis.