Kaden M. Southard, Rico C. Ardy, Anran Tang, Deirdre D. O’Sullivan, Eli Metzner, Karthik Guruvayurappan, Thomas M. Norman
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引用次数: 0
Abstract
Cell atlas projects have revealed that common cell types often comprise distinct, recurrent transcriptional states, but the function and regulation of these states remain poorly understood. Here, we show that systematic activation of transcription factors can recreate such states in vitro, providing tractable models for mechanistic and functional studies. Using a scalable CRISPR activation (CRISPRa) Perturb-seq platform, we activated 1,836 transcription factors in two cell types. CRISPRa induced gene expression within physiological ranges, with chromatin features predicting responsiveness. Comparisons with atlas datasets showed that transcription factor perturbations recapitulated key fibroblast states and identified their regulators, including KLF2 and KLF4 for a universal state present in many tissues, and PLAGL1 for a disease-associated inflammatory state. Inducing the universal state suppressed the inflammatory state, suggesting therapeutic potential. These findings position CRISPRa as a nuanced tool for perturbing differentiated cells and establish a general strategy for studying clinically relevant transcriptional states ex vivo. CRISPR activation of 1,836 human transcription factors recapitulates fibroblast transcriptional states observed in vivo and identifies regulators that can revert inflammatory states.
期刊介绍:
Nature Genetics publishes the very highest quality research in genetics. It encompasses genetic and functional genomic studies on human and plant traits and on other model organisms. Current emphasis is on the genetic basis for common and complex diseases and on the functional mechanism, architecture and evolution of gene networks, studied by experimental perturbation.
Integrative genetic topics comprise, but are not limited to:
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-Molecular analysis of simple and complex genetic traits
-Cancer genetics
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-Developmental genetics
-Regulatory variation in gene expression
-Strategies and technologies for extracting function from genomic data
-Pharmacological genomics
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