Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs.
IF: 68.164
Cited by: 89


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.


Gene Expression

MeSH terms

CRISPR-Cas Systems
Computational Biology
Deep Learning
Gene Editing
Gene Expression
Genomic Library
HeLa Cells
K562 Cells
RNA, Guide, CRISPR-Cas Systems
Sequence Analysis, RNA
Single-Cell Analysis


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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