Conserved Epigenetic Regulatory Logic Infers Genes Governing Cell Identity
Determining genes that orchestrate cell differentiation in development and disease remains a fundamental goal of cell biology. This study establishes a genome-wide metric based on the gene-repressive trimethylation of histone H3 at lysine 27 (H3K27me3) across hundreds of diverse cell types to identi...
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Veröffentlicht in: | Cell systems 2020-12, Vol.11 (6), p.625-639.e13 |
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Sprache: | eng |
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Zusammenfassung: | Determining genes that orchestrate cell differentiation in development and disease remains a fundamental goal of cell biology. This study establishes a genome-wide metric based on the gene-repressive trimethylation of histone H3 at lysine 27 (H3K27me3) across hundreds of diverse cell types to identify genetic regulators of cell differentiation. We introduce a computational method, TRIAGE, which uses discordance between gene-repressive tendency and expression to identify genetic drivers of cell identity. We apply TRIAGE to millions of genome-wide single-cell transcriptomes, diverse omics platforms, and eukaryotic cells and tissue types. Using a wide range of data, we validate the performance of TRIAGE in identifying cell-type-specific regulatory factors across diverse species including human, mouse, boar, bird, fish, and tunicate. Using CRISPR gene editing, we use TRIAGE to experimentally validate RNF220 as a regulator of Ciona cardiopharyngeal development and SIX3 as required for differentiation of endoderm in human pluripotent stem cells. A record of this paper’s transparent peer review process is included in the Supplemental Information.
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•Repressive H3K27me3 marks inhibit expression of genes that control other cell types•An RTS was devised using H3K27me3 broad domains and measured for all genes•TRIAGE uses RTS values to infer regulatory genes using any genome-wide data from any cell type•TRIAGE is useful for biological discovery in application to any cell type or species
Perturbing genes controlling cell decisions have major implications in development or disease. However, identifying key regulatory genes from the thousands expressed in a cell is challenging. TRIAGE is a computational method that distills patterns of epigenetic repression across diverse cell types to infer regulatory genes using input gene expression data from any cell type. Demonstrating its utility, we combine single-cell RNA-seq and TRIAGE to identify and experimentally confirm novel regulators of heart development in evolutionarily distant species. |
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ISSN: | 2405-4712 2405-4720 |
DOI: | 10.1016/j.cels.2020.11.001 |