Unknown,Transcriptomics,Genomics,Proteomics

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Mapping Epigenomic Type-Specific Differences Occurring During Hematopoiesis [RNA-Seq]

ABSTRACT: Formation of the blood from self-renewing hematopoietic stem cells to terminal lineages necessarily involves epigenomic modifications of the genome to control regulator and signature gene expression. By analysing the global expression profiles of hematopoietic stem cells (HSCs), in vivo differentiated CD4+ T cells and CD19+ B cells as well as in vitro differentiated erythrocyte precursor cells, we identified hundreds of transcripts showing type-specific expression in these cell types. To understand the epigenomic changes related to tissue-specific expression during HSC differentiation, we examined the genome-wide distribution of H3K4me1, H3K4me3, H3K27me1, H3K27me3, histone variant H2A.Z, chromatin remodeler BRG1, and RNA Polymerase II in the same four cell types, as well as embryonic stem cells. Analysis of these datasets revealed that numerous key differentiation genes are primed for expression by Brg1 and Pol II binding, as well as bivalent modifications in the HSCs prior to their expression in downstream differentiated cell types. Much of this bivalency in HSC is retained from embryonic stem cells. After differentiation, these modified regions resolve to active chromatin modification configuration in the specific lineage, while in parallel differentiated lineages the bivalent modification remains; Pol II and Brg1 are lost in closer lineages but bivalency resolves to silent monovalency in more distant lineages. Correlation of tissue-specific gene expression with the epigenomic changes predicts tens of thousands of potential common enhancers and tissue-specific enhancers, which may critically contribute to the expression patterns. We provide a valuable dataset for further understanding the regulatory mechanisms of differentiation and function of blood lineages. RNA-Seq: This submission comprises RNA-Seq profiling of in vivo differentiated human B cells and hematopoietic stem cells. Re-analyzed data for three cell types: The HSCs were previously uploaded as GSM651554 (SRX037948), but processed differently for this upload. The erythrocyte precursors and T cells have also been previously uploaded as GSM651555 (SRX037949) and GSM406414 (SRX005317), respectively. They were treated as in GSM651554, but processed as here. The processed files generated by our re-analysis are linked below as supplementary files.

ORGANISM(S): Homo sapiens

SUBMITTER: Brian Abraham

PROVIDER: E-GEOD-39537 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description: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.

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.

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Mapping Epigenomic Type-Specific Differences Occurring During Hematopoiesis [RNA-Seq]

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