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Titrating gene expression with series of systematically compromised CRISPR guide RNAs

By Marco Jost, Daniel A Santos, Reuben A. Saunders, Max A. Horlbeck, John S. Hawkins, Sonia M Scaria, Thomas M Norman, Jeffrey A Hussmann, Christina R. Liem, Carol A. Gross, Jonathan S. Weissman

Posted 28 Jul 2019
bioRxiv DOI: 10.1101/717389 (published DOI: 10.1038/s41587-019-0387-5)

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.

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