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Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9

Nature Biotechnology volume 34, pages 184–191 (2016)Cite this article

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Abstract

CRISPR-Cas9–based genetic screens are a powerful new tool in biology. By simply altering the sequence of the single-guide RNA (sgRNA), one can reprogram Cas9 to target different sites in the genome with relative ease, but the on-target activity and off-target effects of individual sgRNAs can vary widely. Here, we use recently devised sgRNA design rules to create human and mouse genome-wide libraries, perform positive and negative selection screens and observe that the use of these rules produced improved results. Additionally, we profile the off-target activity of thousands of sgRNAs and develop a metric to predict off-target sites. We incorporate these findings from large-scale, empirical data to improve our computational design rules and create optimized sgRNA libraries that maximize on-target activity and minimize off-target effects to enable more effective and efficient genetic screens and genome engineering.

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Figure 1: Comparative performance of the Avana library.
Figure 2: HPRT1 and NUDT5 confer 6-thioguanine resistance.
Figure 3: Tiled library screen for resistance genes.
Figure 4: Development of Rule Set 2 for prediction of sgRNA on-target activity.
Figure 5: CFD score for assessing off-target activity of sgRNAs.
Figure 6: On-target and off-target properties of the Brunello and Brie libraries.

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Acknowledgements

We thank M. Tomko, M. Greene, A. Brown, D. Alan and T. Green for software engineering support, and T. Nguyen, N. Tran and X. Yang for library production support (Broad Institute). Z.T. is funded by NIH 5K12CA087723-12, ASCO Young Investigator Award, LLS Special Fellow Award. J.G.D. is a Merkin Institute Fellow and is supported by the Next Generation Fund at the Broad Institute of MIT and Harvard.

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Author notes

  1. John G Doench, Nicolo Fusi, Meagan Sullender, Mudra Hegde, Emma W Vaimberg and Jennifer Listgarten: These authors contributed equally to this work.

Authors and Affiliations

  1. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    John G Doench, Meagan Sullender, Mudra Hegde, Emma W Vaimberg, Katherine F Donovan, Ian Smith, Zuzana Tothova & David E Root

  2. Microsoft Research New England, Cambridge, Massachusetts, USA

    Nicolo Fusi & Jennifer Listgarten

  3. Division of Hematologic Malignancies, Dana Farber Cancer Institute, Boston, Massachusetts, USA

    Zuzana Tothova

  4. Department of Pathology and Immunology, Washington University School of Medicine, Washington University School of Medicine., St. Louis, Missouri, USA

    Craig Wilen, Robert Orchard & Herbert W Virgin

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Contributions

J.G.D., M.S., E.W.V., Z.T., C.W. and R.O. designed experiments; M.S., E.W.V., K.F.D., Z.T., C.W. and R.O. performed experiments; J.G.D., M.H. and I.S. analyzed experiments; N.F. and J.L. performed the computational modeling; J.G.D., N.F., J.L. and D.E.R. wrote the manuscript with assistance from other authors; J.G.D., H.W.V. and D.E.R. supervised the research.

Corresponding authors

Correspondence to John G Doench, Nicolo Fusi, Jennifer Listgarten or David E Root.

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Competing interests

N.F. and J.L. are employed by Microsoft Research.

Supplementary information

Supplementary Figures

Supplementary Figures 1–22 (PDF 2195 kb)

Supplementary Tables 1–23

Supplementary Table 1. Rounds of selection used to design Avana and Asiago library Supplementary Table 2. sgRNAs in the six subpools of Avana library Supplementary Table 3. sgRNAs in the six subpools of Asiago library Supplementary Table 4. Screening data for vemurafenib in A375 cells for all biological replicates screened with Avana libraries (divided by subpools) as well as GeCKOv1 and GeCKOv2 libraries Supplementary Table 5. RIGER analysis of vemurafenib screens using weighted-sum option Supplementary Table 6. STARS analysis of vemurafenib screens Supplementary Table 7. List of PanCancer genes Supplementary Table 8. Screening data for selumetinib in A375 cells for all biological replicates screened with Avana library Supplementary Table 9. STARS analysis of selumetinib screens Supplementary Table 10. Negative selection screening data in HT29 and A375 cells with GeCKO libraries Supplementary Table 11. Negative selection screening data in HT29 and A375 cells with GeCKO libraries and the set of 291 core essential genes annotated by Hart and colleagues Supplementary Table 12. STARS analysis of the negative selection screening data for GeCKO and Avana libraries individually Supplementary Table 13. STARS analysis of the negative selection screening data for GeCKO and Avana libraries merged Supplementary Table 14. Screening data for 6-thioguanine screen in 293T, A375 and HT29 cells Supplementary Table 15. Screening data for interferon-gamma treatment of BV2 cells and output of STARS analysis Supplementary Table 16. Screening data for the tiling of resistance genes Supplementary Table 17. Gini importance of individual features in the gradient-boosted regression tress model, Rule Set 2 Supplementary Table 18. Screening data for off-target analysis of CD33 in MOLM13 cells Supplementary Table 19. Percent-active, delta-log-fold-change, and one-sided Welch's t-test p-value calculations for the CD33 off-target dataset that is used to calculate the CFD score Supplementary Table 20. Activity of sgRNAs designed against H2-D1 that have up to 6 mismatches to H2-K Supplementary Table 21. sgRNAs in the Brunello library Supplementary Table 22. sgRNAs in the Brie library Supplementary Table 23. sgRNA sequences and primers used for individual follow-up experiments (ZIP 125249 kb)

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Doench, J., Fusi, N., Sullender, M. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol 34, 184–191 (2016). https://doi.org/10.1038/nbt.3437

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