A pan-CRISPR analysis of mammalian cell specificity identifies ultra-compact sgRNA subsets for genome-scale experiments

Boyang Zhao (), Yiyun Rao, Scott Leighow, Edward P. O’Brien, Luke Gilbert and Justin R. Pritchard ()
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Boyang Zhao: Pennsylvania State University
Yiyun Rao: Huck Institute for the Life Sciences, Pennsylvania State University
Scott Leighow: Pennsylvania State University
Edward P. O’Brien: Pennsylvania State University
Luke Gilbert: University of California at San Francisco
Justin R. Pritchard: Pennsylvania State University

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract A genetic knockout can be lethal to one human cell type while increasing growth rate in another. This context specificity confounds genetic analysis and prevents reproducible genome engineering. Genome-wide CRISPR compendia across most common human cell lines offer the largest opportunity to understand the biology of cell specificity. The prevailing viewpoint, synthetic lethality, occurs when a genetic alteration creates a unique CRISPR dependency. Here, we use machine learning for an unbiased investigation of cell type specificity. Quantifying model accuracy, we find that most cell type specific phenotypes are predicted by the function of related genes of wild-type sequence, not synthetic lethal relationships. These models then identify unexpected sets of 100-300 genes where reduced CRISPR measurements can produce genome-scale loss-of-function predictions across >18,000 genes. Thus, it is possible to reduce in vitro CRISPR libraries by orders of magnitude—with some information loss—when we remove redundant genes and not redundant sgRNAs.

Date: 2022
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DOI: 10.1038/s41467-022-28045-w

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