Conserved Epigenetic Regulatory Logic Infers Genes Governing Cell Identity
Name
nihms-1653552.pdf
Description
Accepted version
Size
3.33 MB
Format
Unknown
Checksum (MD5)
3cb642156780bf946406c3de94e989d2
Author(s)
Shim, Woo Jun
Sinniah, Enakshi
Xu, Jun
Vitrinel, Burcu
Alexanian, Michael
Andreoletti, Gaia
Shen, Sophie
Sun, Yuliangzi
Balderson, Brad
Boix, Carles
Date Issued
2020
Journal
Cell Systems
Publisher
Elsevier BV
Version
Author's final manuscript
Abstract
© 2020 The Authors 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. 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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.cels.2020.11.001
