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Hamey2017 - Blood stem cell regulatory network

ABSTRACT: Hamey2017 - Blood stem cell regulatory network This model is described in the article: Reconstructing blood stem cell regulatory network models from single-cell molecular profiles Fiona K. Hamey, Sonia Nestorowa, Sarah J. Kinston, David G. Kent, Nicola K. Wilson, and Berthold Göttgens Proceedings of the National Academy of Sciences of the United States of America Abstract: 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. This model is hosted on BioModels Database and identified by: MODEL1610060000. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

SUBMITTER: Fiona Hamey

PROVIDER: MODEL1610060000 | BioModels | 2017-06-09

REPOSITORIES: BioModels

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			Action	DRS
	MODEL1610060000?filename=MODEL1610060000-biopax2.owl	Owl
	MODEL1610060000?filename=MODEL1610060000-biopax3.owl	Owl
	MODEL1610060000?filename=MODEL1610060000.m	Other
	MODEL1610060000?filename=MODEL1610060000.png	Other
	MODEL1610060000?filename=MODEL1610060000.sci	Other

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Project description:Hamey2017 - Blood stem cell regulatory network (LMPP network) This model is described in the article: Reconstructing blood stem cell regulatory network models from single-cell molecular profiles Fiona K. Hamey, Sonia Nestorowa, Sarah J. Kinston, David G. Kent, Nicola K. Wilson, and Berthold Göttgens Proceedings of the National Academy of Sciences of the United States of America Abstract: 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. This model is hosted on BioModels Database and identified by: MODEL1610060001. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Schittler2010 - Cell fate of progenitor cells, osteoblasts or chondrocytes Mathematical model describing the mechanism of differentiation of mesenchymal stem cells to bone (osteoblasts) or cartilage (chondrocytes) cells. This model is described in the article: Cell differentiation modeled via a coupled two-switch regulatory network. Schittler D, Hasenauer J, Allgöwer F, Waldherr S. Chaos 2010 Dec; 20(4): 045121 Abstract: Mesenchymal stem cells can give rise to bone and other tissue cells, but their differentiation still escapes full control. In this paper we address this issue by mathematical modeling. We present a model for a genetic switch determining the cell fate of progenitor cells which can differentiate into osteoblasts (bone cells) or chondrocytes (cartilage cells). The model consists of two switch mechanisms and reproduces the experimentally observed three stable equilibrium states: a progenitor, an osteogenic, and a chondrogenic state. Conventionally, the loss of an intermediate (progenitor) state and the entailed attraction to one of two opposite (differentiated) states is modeled as a result of changing parameters. In our model in contrast, we achieve this by distributing the differentiation process to two functional switch parts acting in concert: one triggering differentiation and the other determining cell fate. Via stability and bifurcation analysis, we investigate the effects of biochemical stimuli associated with different system inputs. We employ our model to generate differentiation scenarios on the single cell as well as on the cell population level. The single cell scenarios allow to reconstruct the switching upon extrinsic signals, whereas the cell population scenarios provide a framework to identify the impact of intrinsic properties and the limiting factors for successful differentiation. This model is hosted on BioModels Database and identified by: BIOMD0000000493 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Human pluripotent stem cell derived models that accurately recapitulate neural development in vitro and allow for the generation of specific neuronal subtypes are of major interest to the stem cell and biomedical community. Notch signaling, particularly through the Notch effector HES5, is a major pathway critical for the onset and maintenance of neural progenitor cells (NPCs) in the embryonic and adult nervous system1-3. Use of a HES5 reporter enables the isolation distinct populations of human embryonic stem (ES) cell derived NPCs that represent building blocks of cortical development in vitro4. Here, we report the transcriptional and epigenomic analysis of six consecutive stages of human ES cell differentiation along the neural lineage aimed at modeling key cell fate decisions including specification, expansion and patterning during the ontogeny of neural stem and progenitor cells. In order to dissect the regulatory mechanisms that orchestrate the stage-specific differentiation process we developed a computational framework to infer key regulators of each cell state transition based on the progressive remodeling of the epigenetic landscape and then validated these through a pooled shRNA screen. We were also able to refine our previous observations on epigenetic priming at transcription factor binding sites and show here that they are mediated by combinations of core and stage-specific factors. Taken together, we demonstrate the utility of our reference maps and outline a general framework, not limited to the context of the neural lineage, to dissect regulatory circuits of differentiation. hESC were differentiated in vitro into 5 different NPC populations (NE, ERG, MRG, LRG and LNP) over a time period of 220 days. The hESCs and the first 3 NPC populations (NE, ERG, MRG) were subjected to ChIP-Seq for H3K4m3, H3K4me1, H3K27ac and H3K27me3 as well as RNA-Seq with two biological replicates each as well as whole genome bisulfite sequencing in singlicate. The last two populations (LRG, LNP) were both profiled by RRBS and the LRG populations also by RNA-Seq. In addition, each of the first three NPC populations were differentiated into more mature neuronal populations (NEdN, ERGdN, MRGdN). The LRG population was differentiated along the astrocyte fate (LRGdA). Each of these more mature populations was subjected to RNA-Seq in replicate. Finally, the NE and MRG populations were profiled for the transcription facctor OTX2 by ChIP-Seq.

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Hamey2017 - Blood stem cell regulatory network

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