Increasing the specificity of CRISPR systems with engineered RNA secondary structures

Abstract

CRISPR (clustered regularly interspaced short palindromic repeat) systems have been broadly adopted for basic science, biotechnology, and gene and cell therapy. In some cases, these bacterial nucleases have demonstrated off-target activity. This creates a potential hazard for therapeutic applications and could confound results in biological research. Therefore, improving the precision of these nucleases is of broad interest. Here we show that engineering a hairpin secondary structure onto the spacer region of single guide RNAs (hp-sgRNAs) can increase specificity by several orders of magnitude when combined with various CRISPR effectors. We first demonstrate that designed hp-sgRNAs can tune the activity of a transactivator based on Cas9 from Streptococcus pyogenes (SpCas9). We then show that hp-sgRNAs increase the specificity of gene editing using five different Cas9 or Cas12a variants. Our results demonstrate that RNA secondary structure is a fundamental parameter that can tune the activity of diverse CRISPR systems.

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Fig. 1: Engineered RNA secondary structures tune the activity of dCas9-P300.
Fig. 2: Spacer secondary structure improves the performance of a kinetic model of R-loop formation.
Fig. 3: hp-sgRNAs increase the specificity of SpCas9 in human cells.
Fig. 4: hp-sgRNAs retain binding activity at off-target loci.
Fig. 5: hp-sgRNAs and -crRNAs increase the specificity of various Cas effectors.
Fig. 6: RNA secondary structure drives the specificity increases observed with hp-sgRNAs.

Data availability

Sequencing data are available through the National Center for Biotechnology Information Sequence Read Archive (SRA) database (PRJNA524383), including all deep sequencing, 5′ RACE RNA-seq and CIRCLE-seq files. All other relevant raw data are available from the corresponding author on reasonable request.

Code availability

Custom scripts used to analyze 5′ RACE experiments and conduct kinetic modeling are available upon reasonable request.

References

  1. 1.

    Barrangou, R. & Doudna, J. A. Applications of CRISPR technologies in research and beyond. Nat. Biotechnol. 34, 933–941 (2016).

    CAS  Article  Google Scholar 

  2. 2.

    Wright, A. V., Nunez, J. K. & Doudna, J. A. Biology and applications of CRISPR systems: harnessing Nature’s toolbox for genome engineering. Cell 164, 29–44 (2016).

    CAS  Article  Google Scholar 

  3. 3.

    Shmakov, S. et al. Discovery and functional characterization of diverse class 2 CRISPR–Cas systems. Mol. Cell 60, 385–397 (2015).

    CAS  Article  Google Scholar 

  4. 4.

    Burstein, D. et al. New CRISPR–Cas systems from uncultivated microbes. Nature 542, 237–241 (2017).

    CAS  Article  Google Scholar 

  5. 5.

    Yan, W. X. et al. Functionally diverse type V CRISPR–Cas systems. Science 363, 88–91 (2019).

    CAS  Article  Google Scholar 

  6. 6.

    Ran, F. A. et al. In vivo genome editing using Staphylococcus aureus Cas9. Nature 520, 186–191 (2015).

    CAS  Article  Google Scholar 

  7. 7.

    Hou, Z. et al. Efficient genome engineering in human pluripotent stem cells using Cas9 from Neisseria meningitidis. Proc. Natl Acad. Sci. USA 110, 15644–15649 (2013).

    CAS  Article  Google Scholar 

  8. 8.

    Kim, E. et al. In vivo genome editing with a small Cas9 orthologue derived from Campylobacter jejuni. Nat. Commun. 8, 14500 (2017).

    CAS  Article  Google Scholar 

  9. 9.

    Chatterjee, P., Jakimo, N. & Jacobson, J. M. Minimal PAM specificity of a highly similar SpCas9 ortholog. Sci. Adv. 4, eaau0766 (2018).

    CAS  Article  Google Scholar 

  10. 10.

    Zetsche, B. et al. Cpf1 Is a single RNA-guided endonuclease of a class 2 CRISPR–cas system. Cell 163, 759–771 (2015).

    CAS  Article  Google Scholar 

  11. 11.

    Abudayyeh, O. O. et al. RNA targeting with CRISPR–Cas13. Nature 550, 280–284 (2017).

    Article  Google Scholar 

  12. 12.

    Konermann, S. et al. Transcriptome engineering with RNA-targeting type VI-D CRISPR effectors. Cell 173, 665–676.e14 (2018).

    CAS  Article  Google Scholar 

  13. 13.

    Kleinstiver, B. P. et al. Broadening the targeting range of Staphylococcus aureus CRISPR–Cas9 by modifying PAM recognition. Nat. Biotechnol. 33, 1293–1298 (2015).

    CAS  Article  Google Scholar 

  14. 14.

    Kleinstiver, B. P. et al. Genome-wide specificities of CRISPR–Cas Cpf1 nucleases in human cells. Nat. Biotechnol. 34, 869–874 (2016).

    CAS  Article  Google Scholar 

  15. 15.

    Kim, D. et al. Genome-wide analysis reveals specificities of Cpf1 endonucleases in human cells. Nat. Biotechnol. 34, 863–868 (2016).

    CAS  Article  Google Scholar 

  16. 16.

    Maeder, M. L. & Gersbach, C. A. Genome editing technologies for gene and cell therapy. Mol. Ther. 24, 430–446 (2016).

    CAS  Article  Google Scholar 

  17. 17.

    Tsai, S. Q. et al. Dimeric CRISPR RNA-guided FokI nucleases for highly specific genome editing. Nat. Biotechnol. 32, 569–576 (2014).

    CAS  Article  Google Scholar 

  18. 18.

    Shen, B. et al. Efficient genome modification by CRISPR–Cas9 nickase with minimal off-target effects. Nat. Methods 11, 399–402 (2014).

    CAS  Article  Google Scholar 

  19. 19.

    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 

  20. 20.

    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 

  21. 21.

    Slaymaker, I. M. et al. Rationally engineered Cas9 nucleases with improved specificity. Science 351, 84–88 (2016).

    CAS  Article  Google Scholar 

  22. 22.

    Kleinstiver, B. P. et al. High-fidelity CRISPR–Cas9 nucleases with no detectable genome-wide off-target effects. Nature 529, 490–495 (2016).

    CAS  Article  Google Scholar 

  23. 23.

    Fu, Y. et al. Improving CRISPR–Cas nuclease specificity using truncated guide RNAs. Nat. Biotechnol. 32, 279–284 (2014).

    CAS  Article  Google Scholar 

  24. 24.

    Chen, J. S. et al. Enhanced proofreading governs CRISPR–Cas9 targeting accuracy. Nature 550, 407–410 (2017).

    CAS  Article  Google Scholar 

  25. 25.

    Bolukbasi, M. F. et al. DNA-binding-domain fusions enhance the targeting range and precision of Cas9. Nat. Methods 12, 1150–1156 (2015).

    CAS  Article  Google Scholar 

  26. 26.

    Casini, A. et al. A highly specific SpCas9 variant is identified by in vivo screening in yeast. Nat. Biotechnol. 36, 265–271 (2018).

    CAS  Article  Google Scholar 

  27. 27.

    Lee, J. K. et al. Directed evolution of CRISPR–Cas9 to increase its specificity. Nat. Commun. 9, 3048 (2018).

    Article  Google Scholar 

  28. 28.

    Vakulskas, C. A. et al. A high-fidelity Cas9 mutant delivered as a ribonucleoprotein complex enables efficient gene editing in human hematopoietic stem and progenitor cells. Nat. Med. 24, 1216–1224 (2018).

    CAS  Article  Google Scholar 

  29. 29.

    Josephs, E. A. et al. Structure and specificity of the RNA-guided endonuclease Cas9 during DNA interrogation, target binding and cleavage. Nucleic Acids Res. 43, 8924–8941 (2015).

    CAS  Article  Google Scholar 

  30. 30.

    Sternberg, S. H. et al. Conformational control of DNA target cleavage by CRISPR–Cas9. Nature 527, 110–113 (2015).

    CAS  Article  Google Scholar 

  31. 31.

    Bevilacqua, P. C. & Blose, J. M. Structures, kinetics, thermodynamics, and biological functions of RNA hairpins. Annu. Rev. Phys. Chem. 59, 79–103 (2008).

    CAS  Article  Google Scholar 

  32. 32.

    Klosterman, P. S. et al. Three-dimensional motifs from the SCOR, structural classification of RNA database: extruded strands, base triples, tetraloops and U-turns. Nucleic Acids Res. 32, 2342–2352 (2004).

    CAS  Article  Google Scholar 

  33. 33.

    Zalatan, J. G. et al. Engineering complex synthetic transcriptional programs with CRISPR RNA scaffolds. Cell 160, 339–350 (2015).

    CAS  Article  Google Scholar 

  34. 34.

    Perez-Pinera, P. et al. RNA-guided gene activation by CRISPR–Cas9-based transcription factors. Nat. Methods 10, 973–976 (2013).

    CAS  Article  Google Scholar 

  35. 35.

    Hilton, I. B. et al. Epigenome editing by a CRISPR–Cas9-based acetyltransferase activates genes from promoters and enhancers. Nat. Biotechnol. 33, 510–517 (2015).

    CAS  Article  Google Scholar 

  36. 36.

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

    CAS  Article  Google Scholar 

  37. 37.

    Kim, D. et al. Genome-wide target specificities of CRISPR–Cas9 nucleases revealed by multiplex Digenome-seq. Genome Res. 26, 406–415 (2016).

    CAS  Article  Google Scholar 

  38. 38.

    Dahlman, J. E. et al. Orthogonal gene knockout and activation with a catalytically active Cas9 nuclease. Nat. Biotechnol. 33, 1159–1161 (2015).

    CAS  Article  Google Scholar 

  39. 39.

    Kiani, S. et al. Cas9 gRNA engineering for genome editing, activation and repression. Nat. Methods 12, 1051–1054 (2015).

    CAS  Article  Google Scholar 

  40. 40.

    Wu, X. et al. Genome-wide binding of the CRISPR endonuclease Cas9 in mammalian cells. Nat. Biotechnol. 32, 670–674 (2014).

    CAS  Article  Google Scholar 

  41. 41.

    Kuscu, C. et al. Genome-wide analysis reveals characteristics of off-target sites bound by the Cas9 endonuclease. Nat. Biotechnol. 32, 677–683 (2014).

    CAS  Article  Google Scholar 

  42. 42.

    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 

  43. 43.

    Tsai, S. Q. et al. CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR–Cas9 nuclease off-targets. Nat. Methods 14, 607–614 (2017).

    CAS  Article  Google Scholar 

  44. 44.

    Nelson, C. E. et al. In vivo genome editing improves muscle function in a mouse model of Duchenne muscular dystrophy. Science 351, 403–407 (2016).

    CAS  Article  Google Scholar 

  45. 45.

    Nishimasu, H. et al. Crystal Structure of Staphylococcus aureus Cas9. Cell 162, 1113–1126 (2015).

    CAS  Article  Google Scholar 

  46. 46.

    Yamano, T. et al. Crystal structure of Cpf1 in complex with guide RNA and target DNA. Cell 165, 949–962 (2016).

    CAS  Article  Google Scholar 

  47. 47.

    Fonfara, I. et al. The CRISPR-associated DNA-cleaving enzyme Cpf1 also processes precursor CRISPR RNA. Nature 532, 517–521 (2016).

    CAS  Article  Google Scholar 

  48. 48.

    Yan, W. X. et al. BLISS is a versatile and quantitative method for genome-wide profiling of DNA double-strand breaks. Nat. Commun. 8, 15058 (2017).

    CAS  Article  Google Scholar 

  49. 49.

    Thyme, S. B. et al. Internal guide RNA interactions interfere with Cas9-mediated cleavage. Nat. Commun. 7, 11750 (2016).

    CAS  Article  Google Scholar 

  50. 50.

    Boyle, E. A. et al. High-throughput biochemical profiling reveals sequence determinants of dCas9 off-target binding and unbinding. Proc. Natl Acad. Sci. USA 114, 5461–5466 (2017).

    CAS  Article  Google Scholar 

  51. 51.

    Jung, C. et al. Massively parallel biophysical analysis of CRISPR–Cas complexes on next generation sequencing chips. Cell 170, 35–47.e13 (2017).

    CAS  Article  Google Scholar 

  52. 52.

    Harrington, L. B. et al. Programmed DNA destruction by miniature CRISPR–Cas14 enzymes. Science 362, 839–842 (2018).

    CAS  Article  Google Scholar 

  53. 53.

    Liu, J. J. et al. CasX enzymes comprise a distinct family of RNA-guided genome editors. Nature 566, 218–223 (2019).

    CAS  Article  Google Scholar 

  54. 54.

    Briner, A. E. et al. Guide RNA functional modules direct Cas9 activity and orthogonality. Mol. Cell 56, 333–339 (2014).

    CAS  Article  Google Scholar 

  55. 55.

    Yin, H. et al. Partial DNA-guided Cas9 enables genome editing with reduced off-target activity. Nat. Chem. Biol. 14, 311–316 (2018).

    CAS  Article  Google Scholar 

  56. 56.

    Kartje, Z. J. et al. Chimeric guides probe and enhance Cas9 biochemical activity. Biochemistry 57, 3027–3031 (2018).

    CAS  Article  Google Scholar 

  57. 57.

    Gruber, A. R. et al. The vienna RNA websuite. Nucleic Acids Res. 36, W70–W74 (2008).

    CAS  Article  Google Scholar 

  58. 58.

    Guschin, D. Y. et al. A rapid and general assay for monitoring endogenous gene modification. Methods Mol. Biol. 649, 247–256 (2010).

    CAS  Article  Google Scholar 

  59. 59.

    Pinello, L. et al. Analyzing CRISPR genome-editing experiments with CRISPResso. Nat. Biotechnol. 34, 695–697 (2016).

    CAS  Article  Google Scholar 

  60. 60.

    Lazzarotto, C. R. et al. Defining CRISPR–Cas9 genome-wide nuclease activities with CIRCLE-seq. Nat. Protoc. 13, 2615–2642 (2018).

    CAS  Article  Google Scholar 

  61. 61.

    SantaLucia, J., Allawi, H. T. & Seneviratne, P. A. Improved nearest-neighbor parameters for predicting DNA duplex stability. Biochemistry 35, 3555–3562 (1996).

    CAS  Article  Google Scholar 

  62. 62.

    Sugimoto, N. et al. Thermodynamic parameters to predict stability of RNA/DNA hybrid duplexes. Biochemistry 34, 11211–11216 (1995).

    CAS  Article  Google Scholar 

  63. 63.

    Wuchty, S. et al. Complete suboptimal folding of RNA and the stability of secondary structures. Biopolymers 49, 145–165 (1999).

    CAS  Article  Google Scholar 

  64. 64.

    Mathews, D. H. et al. Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Mol. Biol. 288, 911–940 (1999).

    CAS  Article  Google Scholar 

  65. 65.

    Colquhoun, D. H. & Hawkes, A. G. A Q-matrix cookbook. in Single-Channel Recording (eds Sakmann B. & Neher E.) 589–633 (Springer, 2009).

  66. 66.

    Dagdas, Y. S. et al. A conformational checkpoint between DNA binding and cleavage by CRISPR–Cas9. Sci. Adv. 3, eaao0027 (2017).

    Article  Google Scholar 

  67. 67.

    Shlyakhtenko, L. S. et al. Silatrane-based surface chemistry for immobilization of DNA, protein–DNA complexes and other biological materials. Ultramicroscopy 97, 279–287 (2003).

    CAS  Article  Google Scholar 

  68. 68.

    Yang, Y. et al. Determination of protein-DNA binding constants and specificities from statistical analyses of single molecules: MutS-DNA interactions. Nucleic Acids Res. 33, 4322–4334 (2005).

    CAS  Article  Google Scholar 

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Acknowledgements

We thank C. E. Nelson and T. S. Klann for useful discussions related to experimental design and execution. This work was supported by an Allen Distinguished Investigator Award from the Paul G. Allen Frontiers Group; a US National Institutes of Health (NIH) Director’s New Innovator Award (no. DP2OD008586); NIH grant nos. R01DA036865, R01AR069085 and P30AR066527; and National Science Foundation grant nos. DMR-1709527 and EFMA-1830957.

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Contributions

D.D.K., E.A.J. and C.A.G. designed the experiments. D.D.K., E.A.J., V.B., S.S.A. and J.B.K. performed the experiments. D.D.K., E.A.J. and C.A.G. analyzed the data. D.D.K., E.A.J. and C.A.G. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Charles A. Gersbach.

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Competing interests

D.D.K., E.A.J. and C.A.G. have filed for a patent related to this work. C.A.G. is an advisor for Sarepta Therapeutics and a cofounder of and advisor for Element Genomics and Locus Biosciences.

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Integrated supplementary information

Supplementary Figure 1 Engineered RNA secondary structures tune the activity of dCas9-P300.

a,b, Gene activation of HBG1 and IL1B using hp-sgRNAs with varying stem lengths, measured by qRT–PCR. Gene activation is plotted as a function of the predicted folding energy of engineered secondary structure for each hp-sgRNA. WT-sgRNA is shown in black, ns-sgRNAs are shown in gray and hairpin sgRNAs are shown in blue. Data are shown as fold increase relative to the control sample. The control sample was transfected with dCas9-P300 only. The mean is the measure of center and error bars represent s.e.m. for n = 3. The sequences of all sgRNAs are listed in Supplementary Table 1.

Supplementary Figure 2 Expression levels and in vivo processing of hp- and ns-sgRNAs.

a, Schema depicting experimental work-flow. Cells were transfected with plasmids encoding dCas9-P300 and sgRNA variants. RNA collected from cells was used to measure gene activation of IL1RN, sgRNA expression levels and spacer sequence identities. b, Schema depicting 5′ RACE applied specifically to sequence sgRNAs. Template switching of the reverse transcriptase ensures accurate profiling of the 5′ ends of sgRNA variants. c, Gene activation induced by each sgRNA variant. This experiment was performed as shown in Fig. 1, except that extraction of total RNA, rather than only mRNA, was performed. d, Replotting the mean of each group in c as a function of the predicted folding energy of each hp-sgRNA’s engineered secondary structure. e, Expression level of each sgRNA variant as measured by RT–qPCR. f, Replotting the mean of each hp-sgRNA’s activity in c against its mean expression level as shown in e. g, The distribution of spacer lengths in cells treated with various sgRNA variants, as determined by 5′ RACE followed by deep sequencing. The number next to the sgRNA alias indicates the number of nucleotides added to the 5′ end of the spacer (for example, hp17 and ns17 have 17 nucleotides (nt) added, and a total spacer length of 37 nt). The expected unprocessed length of an sgRNA variant is highlighted in an orange box. h, Percentage of processing to 20 nt for each sgRNA variant, as measured by RNA-seq. i, Replotting the mean of each hp-sgRNA’s activity in c against its mean degree of processing as shown in i. The mean is the measure of center and error bars represent s.e.m. for n = 3. The sequences of all sgRNAs are listed in Supplementary Table 1.

Supplementary Figure 3 Optimizing hairpin structures for VEGFA spacer 1.

a, On-target and off-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins utilize an external loop and stems form on the 5′ end of the spacer. b, Predicted structures of sgRNA variants. c, On-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins utilize an internal loop and stems form near the 5′ end of the spacer. d, Predicted structures of sgRNA variants. e, On-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins utilize a truncated spacer, an external loop and a stem that forms near the 5′ end of the spacer. f, Predicted structures of sgRNA variants. Representative gels are shown from optimizations that were performed between one and three times. Optimized structures were further investigated with deep-sequencing, as shown in Fig. 3 and Supplementary Fig. 6. The sequences of all sgRNAs are listed in Supplementary Table 1.

Supplementary Figure 4 Optimizing hairpin structures for EMX1 spacer 1.

a, On-target and off-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins utilize an external loop and stems form on the 5′ end of the spacer. b, On-target and off-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins utilize an internal loop and stems form on the 3′ end of the spacer. c, Predicted structures of sgRNA variants. Representative gels are shown from optimizations that were performed between one and three times. Optimized structures were further investigated with deep-sequencing, as shown in Fig. 3 and Supplementary Fig. 6. The sequences of all sgRNAs used are listed in Supplementary Table 1.

Supplementary Figure 5 Optimizing hairpin structures for VEGFA spacer 2.

a, On-target and off-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins utilize an external loop and stems form on the 5′ end of the spacer. b, On-target and off-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins utilize an internal loop and stems form near the 5′ end of the spacer. c, On-target and off-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins an external loop, non-canonical base pairing, and stems form near the 3′ end of the spacer. d,e, Predicted structures of sgRNA variants. Representative gels are shown from optimizations that were performed between one and three times. Optimized structures were further investigated with deep-sequencing, as shown in Fig. 3 and Supplementary Fig. 6. The sequences of all sgRNAs used are listed in Supplementary Table 1.

Supplementary Figure 6 hp-sgRNAs increase the specificity of SpCas9 in human cells.

ac, On-target and off-target mutation rates for sgRNA variants targeting the EMX1 and VEGFA genes, measured by deep-sequencing. ‘Percent modified’ indicates percentage of reads mutated when compared with the wild-type loci. Significant differences in mutational activity were found at all off-target sites when comparing WT-sgRNA (‘WT’) with control samples, except for VEGFA spacer 2 at OT10 (P < 0.01, FDR). At all measured off-target sites, hp-sgRNAs show significant decreases in activity compared with that of WT-sgRNA (P < 0.05, FDR). Hypothesis testing using a one-sided Fisher exact test with pooled read counts, adjusting for multiple comparisons using the Benjamini–Hochberg method. The sequences of all sgRNAs used are listed in Supplementary Table 1. d, On-target editing rates for sgRNA variants plotted on a linear scale. e, Specificity metrics for sgRNA variants. Specificity metric defined as on-target indel rate divided by the sum of all off-target indel rates. The mean is the measure of center and error bars represent s.e.m. for n = 3. Hairpins A, B and C refer to the respective hp-sgRNAs characterized in panels ac and are color matched. For example, hairpins A, B and C for spacer VEGFA.1 are hairpins 4, 5 and 6. The sequences of all sgRNAs used are listed in Supplementary Table 1. FDR, false discovery rate.

Supplementary Figure 7 Unbiased genome-wide detection of off-target activity using CIRCLE-seq.

ad, Read counts for two replicates were plotted to demonstrate reproducibility of the assay. Activity at the on-target site shown in green. The experiment was performed with EMX1 spacer 1 and hp-sgRNA 2. Data points clustered along an axis have corresponding read counts of zero but were given a pseudocount for display purposes.

Supplementary Figure 8 Comparative genome-wide activity of sgRNA variants applied with SpCas9.

ac, Plotting CIRCLE-seq read counts of WT-sgRNA against tru-, hp- and ns-sgRNAs, respectively. Only those off-target sites present in both replicates (Supplementary Fig. 7) were used for this analysis. The on-target sites are shown in green. Read counts represent the sum of two replicate experiments. The experiment was performed with EMX1 spacer 1 and hp-sgRNA 2. Data points clustered along an axis have a corresponding read count of zero but were given a pseudocount for display purposes. d, Venn diagram representing the overlap of all identified CIRCLE-seq cleavage sites. For each condition, only sites identified in both replicates were used. e, An UpSetR plot showing the number of sites that overlap between various intersections of sample groups. Sample group intersections are indicated by the matrix below.

Supplementary Figure 9 Off-target sites identified by CIRCLE-seq for SpCas9 using EMX1 spacer 1.

ad, Sequence identity of off-target sites detected using CIRCLE-seq. WT- and tru-sgRNAs have truncated listings that are continued in Supplementary Fig. 11. Brackets indicate the same off-target site that has two same-scoring alignments to the on-target site. WT and Tru off-target lists were truncated due to space limitations.

Supplementary Figure 10 Optimizing hairpin structures for SaCas9-KKH spacers.

ac, On-target and off-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins utilize an external loop and stems form on the 5′ end of the spacer. df, Predicted structures of sgRNA variants. Representative gels are shown from optimizations that were performed between one and three times. Optimized structures were further investigated with deep-sequencing, as shown in Fig. 6. The sequences of all sgRNAs used are listed in Supplementary Table 1.

Supplementary Figure 11 Optimizing hairpin structures for Cas12a spacers.

Optimizing hairpin structures for LbCas12a. ac, On-target and off-target activity of hairpin variants in human cells, as determined by the Surveyor assay. Hairpins utilize an external loop and stems form on the 5′ end of the spacer. df, Predicted structures of sgRNA variants. Representative gels are shown from optimizations that were performed between one and three times. Optimized structures were further investigated with deep-sequencing, as shown in Fig. 6. The sequences of all sgRNAs used are listed in Supplementary Table 1.

Supplementary Figure 12 RNA secondary structure drives the specificity increases observed with hp-sgRNAs used with SpCas9.

ac, Nuclease activity of hp-sgRNAs and corresponding non-structured (n.s.) controls in human cells; sgRNA variants were tested with SpCas9, as characterized in Supplementary Fig. 5. Deep sequencing was used to measure gene editing activity. Significant differences in mutational activity were found at all off-target sites when comparing WT-sgRNA with control samples (P < 0.01 × 10−129). At all examined off-target sites, hp-sgRNAs significantly reduced gene editing activity when compared with WT-sgRNA (P < 0.05 × 10−14). At all examined off-target (OT) sites, hp-sgRNAs significantly reduced editing activity when compared with the corresponding ns-sgRNA, except for VEGFA spacer 1 at OT3 and VEGFA spacer 2 at OT1 and OT2 (P < 0.05 × 10−9). Hypothesis testing was carried out using a one-sided Fisher exact test with pooled read counts, adjusting for multiple comparisons using the Benjamini–Hochberg method. The mean is the measure of center and error bars represent s.e.m. for n = 3. The sequences of all sgRNAs used are listed in Supplementary Table 1.

Supplementary Figure 13 CRISPR off-target activity as a function of mismatch number and mismatch position.

a, The normalized indel activity for off-targets, grouped by the number of mismatches present, as measured by deep sequencing. Activity was normalized by the on-target activity of the corresponding WT- or hp-sgRNA. Each data point represents a different off-target site and is the mean value of editing from three biological replicates. Boxes represent quartiles, the median is the measure of center and n for each group is shown in the panel. Individual points with a normalized indel rate of zero are not shown. b, The positional dependence of mismatches in off-targets identified via CIRCLE-seq, as determined by off-targets shown in Supplementary Fig. 9.

Supplementary Figure 14 In vitro nuclease activity of Cas9 and Cas12 effectors with engineered sgRNA variants.

ac, In vitro digests demonstrating the on-target and off-target activity of sgRNA variants. PCR amplicons containing various target sites were incubated with the purified Cas effector complexed with chemically synthesized sgRNAs. EMX1 spacer 1 was applied with SpCas9 using hp-sgRNA 2. EMX1 spacer 2 was applied with SaCas9 using hp-sgRNA 4. DNMT1 spacer 1 was applied with AsCas12a using hp-sgRNA 4. Each experiment was performed once. The sequences of all sgRNAs used are listed in Supplementary Table 1.

Supplementary Figure 15 Single-molecule imaging reveals the effect of sgRNA spacer secondary structure on DNA-binding profiles of CRISPR effectors in vitro.

a, Example AFM image of SpCas9 bound to different points on a single, streptavidin-labeled DNA molecule. The DNA molecule has a target site and two known off-target sites distributed along its length. OT1 is a designed off-target with seven consecutive PAM-distal mismatches. OT2 is an off-target that was validated in human cells by deep-sequencing; it is the off-target with the highest mutational rate for the given spacer. (below) Aligned and averaged structures of CRISPR effectors by AFM. At least three preparations for each experimental condition were imaged and analyzed. bd, Normalized binding profiles of (b) SpCas9 (apparent dissociation constants (Kd) at the on-target site for sgRNA: 8.8 ± 0.9 nM (s.e.m.); hp-sgRNA: 10.0 ± 0.9 nM; ns-sgRNA: 2.1 ± 0.1 nM), (c) SaCas9 (sgRNA: 5.1 ± 0.4 nM (s.e.m.); hp-sgRNA: 8.7 ± 1.1 nM; ns-sgRNA: 9.0 ± 0.9 nM) and (d) AsCas12a (sgRNA: 5.7 ± 1.2 nM; hp-sgRNA: 16.9 ± 3.8 nM; ns-sgRNA: 8.9 ± 1.6 nM). e, A specificity metric for the binding of each sgRNA variant. The metric is defined as the frequency of observed DNA molecules bound by Cas9/Cas12a proteins at the on-target site divided by the frequency of observed DNA molecules bound by Cas9/Cas12a with no Cas9/Cas12a proteins at the on-target site. The mean is the measure of center. EMX1 spacer 1 was applied with SpCas9 using hp-sgRNA 2. EMX1 spacer 2 was applied with SaCas9 using hp-sgRNA 4. DNMT1 spacer 1 was applied with AsCas12a using hp-sgRNA 4. The sequences of all sgRNAs used are listed in Supplementary Table 1.

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Supplementary Figs. 1–15 and Supplementary Tables 1 and 2

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Kocak, D.D., Josephs, E.A., Bhandarkar, V. et al. Increasing the specificity of CRISPR systems with engineered RNA secondary structures. Nat Biotechnol 37, 657–666 (2019). https://doi.org/10.1038/s41587-019-0095-1

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