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


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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  1. 1

    Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012).

    CAS  Article  Google Scholar 

  2. 2

    Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).

    CAS  Article  Google Scholar 

  3. 3

    Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

    CAS  Article  Google Scholar 

  4. 4

    Jinek, M. et al. RNA-programmed genome editing in human cells. eLife 2, e00471 (2013).

    Article  Google Scholar 

  5. 5

    Hartenian, E. & Doench, J.G. Genetic screens and functional genomics using CRISPR/Cas9 technology. FEBS J. 282, 1383–1393 (2015).

    CAS  Article  Google Scholar 

  6. 6

    Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).

    CAS  Article  Google Scholar 

  7. 7

    Wang, T., Wei, J.J., Sabatini, D.M. & Lander, E.S. Genetic screens in human cells using the CRISPR-Cas9 system. Science 343, 80–84 (2014).

    CAS  Article  Google Scholar 

  8. 8

    Koike-Yusa, H., Li, Y., Tan, E.-P., Velasco-Herrera, Mdel.C. & Yusa, K. Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nat. Biotechnol. 32, 267–273 (2014).

    CAS  Article  Google Scholar 

  9. 9

    Fu, Y. et al. High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells. Nat. Biotechnol. 31, 822–826 (2013).

    CAS  Article  Google Scholar 

  10. 10

    Veres, A. et al. Low incidence of off-target mutations in individual CRISPR-Cas9 and TALEN targeted human stem cell clones detected by whole-genome sequencing. Cell Stem Cell 15, 27–30 (2014).

    CAS  Article  Google Scholar 

  11. 11

    Ran, F.A. et al. Double nicking by RNA-guided CRISPR Cas9 for enhanced genome editing specificity. Cell 154, 1380–1389 (2013).

    CAS  Article  Google Scholar 

  12. 12

    Guilinger, J.P., Thompson, D.B. & Liu, D.R. Fusion of catalytically inactive Cas9 to FokI nuclease improves the specificity of genome modification. Nat. Biotechnol. 32, 577–582 (2014).

    CAS  Article  Google Scholar 

  13. 13

    Hsu, P.D. et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. 31, 827–832 (2013).

    CAS  Article  Google Scholar 

  14. 14

    Doench, J.G. et al. Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation. Nat. Biotechnol. 32, 1262–1267 (2014).

    CAS  Article  Google Scholar 

  15. 15

    Sanjana, N.E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).

    CAS  Article  Google Scholar 

  16. 16

    Whittaker, S.R. et al. A genome-scale RNA interference screen implicates NF1 loss in resistance to RAF inhibition. Cancer Discov. 3, 350–362 (2013).

    CAS  Article  Google Scholar 

  17. 17

    Bollag, G. et al. Clinical efficacy of a RAF inhibitor needs broad target blockade in BRAF-mutant melanoma. Nature 467, 596–599 (2010).

    CAS  Article  Google Scholar 

  18. 18

    Johannessen, C.M. et al. COT drives resistance to RAF inhibition through MAP kinase pathway reactivation. Nature 468, 968–972 (2010).

    CAS  Article  Google Scholar 

  19. 19

    Davies, B.R. et al. AZD6244 (ARRY-142886), a potent inhibitor of mitogen-activated protein kinase/extracellular signal-regulated kinase kinase 1/2 kinases: mechanism of action in vivo, pharmacokinetic/pharmacodynamic relationship, and potential for combination in preclinical models. Mol. Cancer Ther. 6, 2209–2219 (2007).

    CAS  Article  Google Scholar 

  20. 20

    Storey, J.D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440–9445 (2003).

    CAS  Article  Google Scholar 

  21. 21

    Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).

    Article  Google Scholar 

  22. 22

    Lawrence, M.S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014).

    CAS  Article  Google Scholar 

  23. 23

    Bae, S. et al. TRIAD1 inhibits MDM2-mediated p53 ubiquitination and degradation. FEBS Lett. 586, 3057–3063 (2012).

    CAS  Article  Google Scholar 

  24. 24

    Gamper, A.M. & Roeder, R.G. Multivalent binding of p53 to the STAGA complex mediates coactivator recruitment after UV damage. Mol. Cell. Biol. 28, 2517–2527 (2008).

    CAS  Article  Google Scholar 

  25. 25

    Hart, T., Brown, K.R., Sircoulomb, F., Rottapel, R. & Moffat, J. Measuring error rates in genomic perturbation screens: gold standards for human functional genomics. Mol. Syst. Biol. 10, 733 (2014).

    Article  Google Scholar 

  26. 26

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  Article  Google Scholar 

  27. 27

    Wang, T. et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015).

    CAS  Article  Google Scholar 

  28. 28

    Caskey, C.T. & Kruh, G.D. The HPRT locus. Cell 16, 1–9 (1979).

    CAS  Article  Google Scholar 

  29. 29

    Brinkman, E.K., Chen, T., Amendola, M. & van Steensel, B. Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Res. 42, e168 (2014).

    Article  Google Scholar 

  30. 30

    Zha, M. et al. Molecular mechanism of ADP-ribose hydrolysis by human NUDT5 from structural and kinetic studies. J. Mol. Biol. 379, 568–578 (2008).

    CAS  Article  Google Scholar 

  31. 31

    Cheok, M.H. & Evans, W.E. Acute lymphoblastic leukaemia: a model for the pharmacogenomics of cancer therapy. Nat. Rev. Cancer 6, 117–129 (2006).

    CAS  Article  Google Scholar 

  32. 32

    Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    CAS  Article  Google Scholar 

  33. 33

    Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    CAS  Article  Google Scholar 

  34. 34

    Shi, J. et al. Discovery of cancer drug targets by CRISPR-Cas9 screening of protein domains. Nat. Biotechnol. 33, 661–667 (2015).

    CAS  Article  Google Scholar 

  35. 35

    Chari, R., Mali, P., Moosburner, M. & Church, G.M. Unraveling CRISPR-Cas9 genome engineering parameters via a library-on-library approach. Nat. Methods 12, 823–826 (2015).

    CAS  Article  Google Scholar 

  36. 36

    Xu, H. et al. Sequence determinants of improved CRISPR sgRNA design. Genome Res. 25, 1147–1157 (2015).

    CAS  Article  Google Scholar 

  37. 37

    Jinek, M. et al. Structures of Cas9 endonucleases reveal RNA-mediated conformational activation. Science 343, 1247997 (2014).

    Article  Google Scholar 

  38. 38

    Sternberg, S.H., Redding, S., Jinek, M., Greene, E.C. & Doudna, J.A. DNA interrogation by the CRISPR RNA-guided endonuclease Cas9. Nature 507, 62–67 (2014).

    CAS  Article  Google Scholar 

  39. 39

    Bae, S., Kweon, J., Kim, H.S. & Kim, J.-S. Microhomology-based choice of Cas9 nuclease target sites. Nat. Methods 11, 705–706 (2014).

    CAS  Article  Google Scholar 

  40. 40

    Gilbert, L.A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).

    CAS  Article  Google Scholar 

  41. 41

    Lin, Y. et al. CRISPR/Cas9 systems have off-target activity with insertions or deletions between target DNA and guide RNA sequences. Nucleic Acids Res. 42, 7473–7485 (2014).

    CAS  Article  Google Scholar 

  42. 42

    Stemmer, M., Thumberger, T., Del Sol Keyer, M., Wittbrodt, J. & Mateo, J.L. CCTop: an intuitive, flexible and reliable CRISPR/Cas9 target prediction tool. PLoS One 10, e0124633–e11 (2015).

    Article  Google Scholar 

  43. 43

    Tsai, S.Q. et al. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases. Nat. Biotechnol. 33, 187–197 (2015).

    CAS  Article  Google Scholar 

  44. 44

    Langmead, B. & Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  Article  Google Scholar 

  45. 45

    Heigwer, F., Kerr, G. & Boutros, M. E-CRISP: fast CRISPR target site identification. Nat. Methods 11, 122–123 (2014).

    CAS  Article  Google Scholar 

  46. 46

    Bae, S., Park, J., Kim, J.S. & Kim, J.S. Cas-OFFinder: a fast and versatile algorithm that searches for potential off-target sites of Cas9 RNA-guided endonucleases. Bioinformatics 30, 1473–1475 (2014).

    CAS  Article  Google Scholar 

  47. 47

    Kampmann, M. et al. Next-generation libraries for robust RNA interference-based genome-wide screens. Proc. Natl. Acad. Sci. USA 112, E3384–E3391 (2015).

    CAS  Article  Google Scholar 

  48. 48

    Steiger, J.H. Tests for comparing elements of a correlation matrix. Psychol. Bull. 87, 245–251 (1980).

    Article  Google Scholar 

  49. 49

    Blasi, E., Radzioch, D., Durum, S.K. & Varesio, L. A murine macrophage cell line, immortalized by v-raf and v-myc oncogenes, exhibits normal macrophage functions. Eur. J. Immunol. 17, 1491–1498 (1987).

    CAS  Article  Google Scholar 

  50. 50

    Stansley, B., Post, J. & Hensley, K. A comparative review of cell culture systems for the study of microglial biology in Alzheimer's disease. J. Neuroinflammation 9, 115 (2012).

    Article  Google Scholar 

  51. 51

    Ben-Hur, A., Ong, C.S., Sonnenburg, S., Schölkopf, B. & Rätsch, G. Support vector machines and kernels for computational biology. PLoS Comput. Biol. 4, e1000173 (2008).

    Article  Google Scholar 

  52. 52

    Cock, P.J.A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009).

    CAS  Article  Google Scholar 

  53. 53

    Le Novère, N. MELTING, computing the melting temperature of nucleic acid duplex. Bioinformatics 17, 1226–1227 (2001).

    Article  Google Scholar 

  54. 54

    Steiger, J.H. Tests for comparing elements of a correlation matrix. Psychol. Bull. 87, 245–251 (1980).

    Article  Google Scholar 

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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.

Author information




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 or Nicolo Fusi or 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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