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Rational design of highly active sgRNAs for CRISPR-Cas9–mediated gene inactivation

Abstract

Components of the prokaryotic clustered, regularly interspaced, short palindromic repeats (CRISPR) loci have recently been repurposed for use in mammalian cells1,2,3,4,5,6. The CRISPR-associated (Cas)9 can be programmed with a single guide RNA (sgRNA) to generate site-specific DNA breaks, but there are few known rules governing on-target efficacy of this system7,8. We created a pool of sgRNAs, tiling across all possible target sites of a panel of six endogenous mouse and three endogenous human genes and quantitatively assessed their ability to produce null alleles of their target gene by antibody staining and flow cytometry. We discovered sequence features that improved activity, including a further optimization of the protospacer-adjacent motif (PAM) of Streptococcus pyogenes Cas9. The results from 1,841 sgRNAs were used to construct a predictive model of sgRNA activity to improve sgRNA design for gene editing and genetic screens. We provide an online tool for the design of highly active sgRNAs for any gene of interest.

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Figure 1: sgRNA activity screens in mouse and human cells.
Figure 2: Features of sgRNA activity.
Figure 3: Model of sgRNA activity.

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Sequence Read Archive

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Acknowledgements

We thank M. Waring (Ragon Institute, Cambridge, MA) for expert assistance with flow cytometry; S. Rosenbluh (Broad Institute) for sharing the pLX_311-Cas9 vector; T. Mason (Broad Institute) for Illumina sequencing advice and execution; D. Alan, A. Brown, M. Tomko, M. Greene and T. Green (Broad Institute) for assistance in building the sgRNA design website; J. Listgarten and N. Fuso (Microsoft Research) for a critical analysis of the sgRNA activity prediction model.

Author information

Authors and Affiliations

Authors

Contributions

E.H., D.B.G., Z.T. and J.G.D. designed experiments; E.H., M.S., D.B.G. and Z.T. ran experiments; E.H. and J.G.D. analyzed experimental results; M.H. and J.G.D. analyzed sequencing data and developed analysis tools; I.S. developed the sgRNA scoring model; E.H., D.E.R. and J.G.D. wrote the manuscript with help from other authors; B.L.E., R.J.X. and D.E.R. supervised the research. J.G.D. is a Merkin Institute Fellow. Z.T. is supported by US National Institutes of Health 5T32CA009172-39.

Corresponding authors

Correspondence to John G Doench or David E Root.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–10 and Supplementary Tables 4–6 and 9 (PDF 4229 kb)

Supplementary Table 1

List of 6500 synthesized oligonucleotides. A small number of oligonucleotides appear more than once. (XLSX 123 kb)

Supplementary Table 2

Full dataset of Illumina sequencing reads from the mouse pool. Each sample was tagged with several different sample barcodes and thus appears in multiple columns of data. See Methods for additional details on data analysis. (XLSX 2375 kb)

Supplementary Table 3

Full dataset of Illumina sequencing reads from the human pool. (XLSX 547 kb)

Supplementary Table 7

List of 1,841 CDS-targeting sgRNAs. On-target activity is expressed as within-gene percent-rank in the marker-negative population relative to the unsorted population. The predicted sgRNA score from the final model is also given. See Methods for additional details on data analysis. (XLSX 204 kb)

Supplementary Table 8

For each single and dinucleotide feature, number of sgRNA examples, frequency of scoring in the top quintile percent-rank for that target gene, and statistical significance for over- or under-representation of sgRNAs with that feature in the most-active sgRNA quintile. (XLSX 102 kb)

Supplementary Table 10

1,278 sgRNAs targeting 414 essential genes in A375 cells. sgRNA activity is expressed as log2 fold change in abundance during two weeks of growth. See Methods for additional details on data analysis. (XLSX 252 kb)

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Doench, J., Hartenian, E., Graham, D. et al. Rational design of highly active sgRNAs for CRISPR-Cas9–mediated gene inactivation. Nat Biotechnol 32, 1262–1267 (2014). https://doi.org/10.1038/nbt.3026

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  • DOI: https://doi.org/10.1038/nbt.3026

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