Project description:Background: Recent advances in single-cell techniques have provided the opportunity to finely dissect cellular heterogeneity within populations previously defined by âbulkâ assays and to uncover rare cell types. In human hematopoiesis, megakaryocytes and erythroid cells differentiate from a shared precursor, the megakaryocyte-erythroid progenitor (MEP), which remains poorly defined.Results: To clarify the cellular pathway in erythro-megakaryocyte differentiation, we correlated the surface immunophenotype, transcriptional profile and differentiation potential of individual MEP cells. Highly purified, single MEP cells (n=681) were analyzed using index fluorescence-activated cell sorting with parallel targeted transcriptional profiling of the same cells performed using a specifically designed panel of 87 genes. Differentiation potential was tested in novel, single-cell differentiation assays. Our results demonstrated that immunophenotypic MEP in fact comprise three distinct subpopulations: (1) âPre-MEPâ, enriched for erythroid/megakaryocyte progenitors but with residual myeloid differentiation capacity (2) âE-MEPâ, strongly biased towards erythroid differentiation, and (3) âMK-MEPâ, a previously undescribed, rare population of cells that are bipotent but primarily generate megakaryocytic progeny. Therefore, conventionally-defined MEP are in fact a mixed population: a minority give rise to mixed-lineage colonies while the majority of cells are transcriptionally-primed to generate exclusively single-lineage output. Conclusions: Our study clarifies the cellular hierarchy in human megakaryocyte/erythroid lineage commitment and highlights the importance of using a combination of single-cell approaches to dissect cellular heterogeneity and identify rare cell types within a population. We present a novel immunophenotyping strategy that enables the prospective identification of specific intermediate progenitor populations in erythro-megakaryopoiesis, allowing for in-depth study of disorders including inherited cytopenias, myeloproliferative disorders and erythromegakaryocytic leukemias. Multiplex RT-PCR gene expression profiling of 807 human megakaryocyte-erythroid progenitor cells (MEP) isolated from three healthy donors by apheresis following G-CSF treatment. Cells were excluded if more than 70 assays did not result in amplification or displayed Ct higer than 13 for B2M or higher than 15 for GAPDH. Furthermore cells with a mean non-dropout Ct value greater than 20 were removed. This resulted in a dataset of 681 cells, which were subsequently normalised to the mean of B2M and GAPDH expression.

Project description:Background: Recent advances in single-cell techniques have provided the opportunity to finely dissect cellular heterogeneity within populations previously defined by “bulk” assays and to uncover rare cell types. In human hematopoiesis, megakaryocytes and erythroid cells differentiate from a shared precursor, the megakaryocyte-erythroid progenitor (MEP), which remains poorly defined.Results: To clarify the cellular pathway in erythro-megakaryocyte differentiation, we correlated the surface immunophenotype, transcriptional profile and differentiation potential of individual MEP cells. Highly purified, single MEP cells (n=681) were analyzed using index fluorescence-activated cell sorting with parallel targeted transcriptional profiling of the same cells performed using a specifically designed panel of 87 genes. Differentiation potential was tested in novel, single-cell differentiation assays. Our results demonstrated that immunophenotypic MEP in fact comprise three distinct subpopulations: (1) “Pre-MEP”, enriched for erythroid/megakaryocyte progenitors but with residual myeloid differentiation capacity (2) “E-MEP”, strongly biased towards erythroid differentiation, and (3) “MK-MEP”, a previously undescribed, rare population of cells that are bipotent but primarily generate megakaryocytic progeny. Therefore, conventionally-defined MEP are in fact a mixed population: a minority give rise to mixed-lineage colonies while the majority of cells are transcriptionally-primed to generate exclusively single-lineage output. Conclusions: Our study clarifies the cellular hierarchy in human megakaryocyte/erythroid lineage commitment and highlights the importance of using a combination of single-cell approaches to dissect cellular heterogeneity and identify rare cell types within a population. We present a novel immunophenotyping strategy that enables the prospective identification of specific intermediate progenitor populations in erythro-megakaryopoiesis, allowing for in-depth study of disorders including inherited cytopenias, myeloproliferative disorders and erythromegakaryocytic leukemias.

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: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.

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Homo sapiens

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