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A glycan-based approach to cell characterization and isolation (scRNA-Seq)

ABSTRACT: We leverage the extraordinary molecular diversity of modified heparan sulfate (HS) glycans 8 to establish cellular glycotypes, defined by binding patterns of a panel of flow-cytometry compatible single-chain variable fragment antibodies (scFvs) specific for differentially modified HS. We find distinct glycotypes between closely related hematopoietic progenitors and lineages. The glycotypes of murine and human hematopoietic stem and progenitor cells (HSPCs) reveal dynamic yet similar HS modification patterns in vivo and in vitro, including along megakaryocyte and erythrocyte differentiation. Prospective HS scFv-based sorting identifies new cellular subtypes from both immunophenotypic megakaryocyte-erythrocyte progenitors and heterogeneous pools of HSPCs, thus offering additional discriminative power beyond conventional CD markers. Mechanistically, single-cell RNAseq revealed that a heptad of HS-related genes participate in megakaryocyte-erythrocyte fate determination and are reflective of the HS epitope recognized by specific HS scFvs. In summary, HS glycotyping establishes a role for HS modification patterns in hematopoietic lineage differentiation in mouse and human, and provides an orthogonal approach to define and isolate viable cell types across different cell lineages and species at unprecedented resolution.

ORGANISM(S): Mus musculus

PROVIDER: GSE206671 | GEO | 2022/08/24

REPOSITORIES: GEO

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Project description:Hematopoiesis is a well-established model system to study molecular mechanisms of lineage-specific differentiation. Key transcription factors (TFs), such as PU.1, NF-E2 and GATA1 are implicated in crucial aspects of distinct hematopoietic lineages. How TFs collaborate with histone modificatons and how they affect the chromatin status remains to be elucidated. Chromatin-Immunoprecipitation followed by next-generation sequencing (ChIP-seq) has been proven to be an excellent tool to study chromatin modifications genome-wide. In this study, ChIP-seq was used to investigate the H3K4me2 landscape at enhancers during hematopoietic differentiation, starting from the hematopoietic stem cell (HSC) up to the fully differentiated erythrocyte, megakaryocyte or granulocyte. Although cell morphology and gene expression profiles differ extensively in committed erythrocytes, megakaryocytes and granulocytes, the genomic landscape of H3K4me2 is surprisingly alike across the three cell types. Granulocytes, in particular, seem to display an ‘erythocyte-like’ chromatin pattern. Similar results were observed for another chromatin mark, H3K27ac. Unexpectedly, the common progenitors of erythrocytes and granulocytes do not display a similar chromatin pattern, excluding the idea that the H3K4me2 mark is placed early on during differentiation. These results also suggest that the chromatin state is probably not the determining factor for lineage differentiation, but that rather lineage specific TF binding is important. In agreement with this, mature megakaryocytes loose the H3K4me2 mark surrounding erythrocyte-specific genes. This might be explained because megakaryocytes and erythrocytes both express the lineage specific TFs GATA1 and NF-E2. Altogether, we show that committed granulocytes and erythrocytes display similar H3K4me2 and H3K27ac patterns, and that those marks alone cannot predict which genes will be expressed eventually. Genomic ChIP-Seq on key transcription factors and histone modification marks in hematopoiesis

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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A glycan-based approach to cell characterization and isolation (scRNA-Seq)

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