Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs.

PMID:31932729

IF: 68.164

Cited by: 89

Abstract

A lack of tools to precisely control gene expression has limited our ability to evaluate relationships between expression levels and phenotypes. Here, we describe an approach to titrate expression of human genes using CRISPR interference and series of single-guide RNAs (sgRNAs) with systematically modulated activities. We used large-scale measurements across multiple cell models to characterize activities of sgRNAs containing mismatches to their target sites and derived rules governing mismatched sgRNA activity using deep learning. These rules enabled us to synthesize a compact sgRNA library to titrate expression of ~2,400 genes essential for robust cell growth and to construct an in silico sgRNA library spanning the human genome. Staging cells along a continuum of gene expression levels combined with single-cell RNA-seq readout revealed sharp transitions in cellular behaviors at gene-specific expression thresholds. Our work provides a general tool to control gene expression, with applications ranging from tuning biochemical pathways to identifying suppressors for diseases of dysregulated gene expression.

Keywords

Gene Expression

MeSH terms

CRISPR-Cas Systems

Computational Biology

Deep Learning

Gene Editing

Gene Expression

Genomic Library

HeLa Cells

Humans

K562 Cells

Phenotype

RNA, Guide, CRISPR-Cas Systems

Sequence Analysis, RNA

Single-Cell Analysis

Authors

Jost, Marco

Santos, Daniel A

Saunders, Reuben A

Horlbeck, Max A

Hawkins, John S

Scaria, Sonia M

Norman, Thomas M

Hussmann, Jeffrey A

Liem, Christina R

Gross, Carol A

Weissman, Jonathan S

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