Compact and highly active next-generation libraries for CRISPR-mediated gene repression and activation.

Max A Horlbeck,Britt Adamson,Ryan A Pak,Jacqueline E Villalta,Martin Kampmann,Jacob E Corn,Jonathan S Weissman,Chong Yon Park,Yuwen Chen,Luke A Gilbert,Alexander P Fields

doi:10.7554/elife.19760

Max A Horlbeck, Britt Adamson + Show 9 more

Open Access

https://doi.org/10.7554/elife.19760

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Abstract

We recently found that nucleosomes directly block access of CRISPR/Cas9 to DNA (Horlbeck et al., 2016). Here, we build on this observation with a comprehensive algorithm that incorporates chromatin, position, and sequence features to accurately predict highly effective single guide RNAs (sgRNAs) for targeting nuclease-dead Cas9-mediated transcriptional repression (CRISPRi) and activation (CRISPRa). We use this algorithm to design next-generation genome-scale CRISPRi and CRISPRa libraries targeting human and mouse genomes. A CRISPRi screen for essential genes in K562 cells demonstrates that the large majority of sgRNAs are highly active. We also find CRISPRi does not exhibit any detectable non-specific toxicity recently observed with CRISPR nuclease approaches. Precision-recall analysis shows that we detect over 90% of essential genes with minimal false positives using a compact 5 sgRNA/gene library. Our results establish CRISPRi and CRISPRa as premier tools for loss- or gain-of-function studies and provide a general strategy for identifying Cas9 target sites.

Highlights

Multiplexed pooled genetic screening methodologies have emerged as powerful and broadly accessible tools for systematically profiling gene function at the scale of mammalian genomes (Paddison et al, 2004)
We sought to improve upon our first generation CRISPRi and CRISPRa libraries by taking a comprehensive approach that incorporated nucleosome positioning, sequence features, refinement of our original single-guide RNAs (sgRNAs) design rules, and other potentially informative factors
In order to quantitatively model the contribution of these features to CRISPRi activity, we turned to our recently described CRISPRi activity dataset (Horlbeck et al, 2016) in which we integrated data from 30 CRISPRi screens to select 1,539 high-confidence hit genes, and normalized the phenotypes for sgRNAs targeting each gene to the strongest sgRNAs for that gene, resulting in ’activity scores’ for 18,380 sgRNAs

Summary

Introduction

Multiplexed pooled genetic screening methodologies have emerged as powerful and broadly accessible tools for systematically profiling gene function at the scale of mammalian genomes (Paddison et al, 2004). Computational and Systems Biology Genes and Chromosomes expression at genome scale (Gilbert et al, 2014; Konermann et al, 2015). Together, these screening platforms represent a powerful toolkit for unbiased forward gain-of-function and loss-of-function genetic screens in mammalian cells

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