Titrating gene expression with allelic series of CRISPR guide RNAs

Cellular phenotypes arise from the degrees to which different genes are expressed, yet the ability to monitor these phenotypes is limited by a lack of tools to precisely control gene expression. We describe the development of allelic series of systematically compromised sgRNAs to titrate expression of human genes with CRISPR interference. Using large-scale measurements of compromised sgRNA activities, we both identify empirically validated intermediate-activity sgRNAs and derive the factors governing sgRNA activity using deep learning, enabling construction of a compact sgRNA library to titrate expression of ~2,400 essential genes and a genome-wide in silico library. Staging cells along a continuum of essential gene expression levels using sgRNA series combined with rich single-cell RNA-seq readout reveals expression threshold-specific responses and gene-specific expression-to-phenotype relationships. Our work provides a general tool to control gene expression with applications ranging from tuning of biochemical pathways to identification of suppressors for diseases of dysregulated gene expression. Overall design: Pooled CRISPR screening experiment with intermediate-activity sgRNAs conducted via Perturb-seq