PT  - JOURNAL ARTICLE
AU  - Jost, Marco
AU  - Santos, Daniel A.
AU  - Saunders, Reuben A.
AU  - Horlbeck, Max A.
AU  - Hawkins, John S.
AU  - Scaria, Sonia M.
AU  - Norman, Thomas M.
AU  - Hussmann, Jeffrey A.
AU  - Liem, Christina R.
AU  - Gross, Carol A.
AU  - Weissman, Jonathan S.
TI  - Titrating gene expression with series of systematically compromised CRISPR guide RNAs
AID  - 10.1101/717389
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 717389
4099  - http://biorxiv.org/content/early/2019/07/28/717389.short
4100  - http://biorxiv.org/content/early/2019/07/28/717389.full
AB  - Biological phenotypes arise from the degrees to which genes are expressed, but the lack of tools to precisely control gene expression limits our ability to evaluate the underlying expression-phenotype relationships. Here, we describe a readily implementable approach to titrate expression of human genes using series of systematically compromised sgRNAs and CRISPR interference. We empirically characterize the activities of compromised sgRNAs using large-scale measurements across multiple cell models and derive the rules governing sgRNA activity using deep learning, enabling construction of a compact sgRNA library to titrate expression of ∼2,400 genes involved in central cell biology and a genome-wide in silico library. Staging cells along a continuum of gene expression levels combined with rich single-cell RNA-seq readout reveals gene-specific expression-phenotype relationships with expression level-specific responses. 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.