Evaluation and Design of Genome-Wide CRISPR/SpCas9 Knockout Screens

Abstract

The adaptation of CRISPR/SpCas9 technology to mammalian cell lines is transforming the study of human functional genomics. Pooled libraries of CRISPR guide RNAs (gRNAs) targeting human protein-coding genes and encoded in viral vectors have been used to systematically create gene knockouts in a variety of human cancer and immortalized cell lines, in an effort to identify whether these knockouts cause cellular fitness defects. Previous work has shown that CRISPR screens are more sensitive and specific than pooled-library shRNA screens in similar assays, but currently there exists significant variability across CRISPR library designs and experimental protocols. In this study, we reanalyze 17 genome-scale knockout screens in human cell lines from three research groups, using three different genome-scale gRNA libraries. Using the Bayesian Analysis of Gene Essentiality algorithm to identify essential genes, we refine and expand our previously defined set of human core essential genes from 360 to 684 genes. We use this expanded set of reference core essential genes, CEG2, plus empirical data from six CRISPR knockout screens to guide the design of a sequence-optimized gRNA library, the Toronto KnockOut version 3.0 (TKOv3) library. We then demonstrate the high effectiveness of the library relative to reference sets of essential and nonessential genes, as well as other screens using similar approaches. The optimized TKOv3 library, combined with the CEG2 reference set, provide an efficient, highly optimized platform for performing and assessing gene knockout screens in human cell lines.

Interrogation of Mammalian Protein Complex Structure, Function, and Membership Using Genome-Scale Fitness Screens

“…We identified features of protein complexes that underpin differences in recall between the two genetic perturbation datasets. Protein complexes recalled only in the CRISPR dataset were significantly depleted of core essential genes (Hart et al, 2015; Hart et al, 2017b), lower median gene expression levels, and had lower sequence conservation (Wilcoxon rank sum test, p < 1e-3) (Figure 2E, Table S3). …”

“…Core essential genes were defined as the union of the CEG1 (Hart et al, 2015) and CEG2 (Hart et al, 2017b) datasets. Gene expression data was taken from the CCLE RNA-seq data for these cell lines (Barretina et al, 2012), and human-mouse dN/dS was obtained from BioMart (Smedley et al, 2015).…”

High-Throughput Approaches to Pinpoint Function within the Noncoding Genome

“…Negative controls are essential for library design to evaluate experimental noise and the effects of the delivery of CRISPR reagents. Published libraries have often included non-targeting controls as 1%–5% of the total number of gRNAs in the library (Sanjana et al, 2014), but recent studies have suggested that gRNAs targeting non-essential genes can also serve this purpose (Hart et al, 2017). Alternative negative controls include targeting safe-harbor genomic regions (e.g., AAVS1 ) (Rosenbluh et al, 2017; Wang et al, 2014) as well as targeting GFP or luciferase in GFP- and luciferase-negative cells, respectively.…”

“…However, a number of gene-targeted studies have sought to identify essential genes in different cell types. These screens have, in general, identified similar sets of genes, although they also have found a smaller number of genes unique to a particular study and different relative essentiality ranks among genes identified in multiple studies (Hart et al, 2017; Rauscher et al, 2017a). It remains unclear whether these discrepancies are due to cell-type-specific effects, experimental artifacts, or differences in computational analysis of screen data.…”

“…A final consideration concerns the use of non-targeting gRNAs as negative controls. It has been suggested that gRNAs that target nonessential genes are a superior negative control for pooled screens because they better account for Cas9 toxicity and DSB induction (Hart et al, 2017; Morgens et al, 2017). …”