Engineered SpCas9s and AsCas12a cleave fewer off-target genomic sites than wild-type (wt) Cas9. However, understanding their fidelity, mechanisms and cleavage outcomes requires systematic profiling across mispaired target DNAs. Here we describe NucleaSeq—nuclease digestion and deep sequencing—a massively parallel platform that measures the cleavage kinetics and time-resolved cleavage products for over 10,000 targets containing mismatches, insertions and deletions relative to the guide RNA. Combining cleavage rates and binding specificities on the same target libraries, we benchmarked five SpCas9 variants and AsCas12a. A biophysical model built from these data sets revealed mechanistic insights into off-target cleavage. Engineered Cas9s, especially Cas9-HF1, dramatically increased cleavage specificity but not binding specificity compared to wtCas9. Surprisingly, AsCas12a cleavage specificity differed little from that of wtCas9. Initial DNA cleavage sites and end trimming varied by nuclease, guide RNA and the positions of mispaired nucleotides. More broadly, NucleaSeq enables rapid, quantitative and systematic comparisons of specificity and cleavage outcomes across engineered and natural nucleases.
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A kinetic model predicts SpCas9 activity, improves off-target classification, and reveals the physical basis of targeting fidelity
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Analyzed data are available at https://github.com/finkelsteinlab/. NucleaSeq sequencing data are available through the National Center for Biotechnology Information Sequence Read Archive database (PRJNA623618). All other relevant raw data are available from the corresponding authors upon reasonable request. Source data are provided with this paper.
Custom software (CHAMP, NucleaSeq and freebarcodes repositories) used for data analysis are written in Python 2.7 and are available at https://github.com/finkelsteinlab/. Scripting for figure preparation is available from the corresponding authors upon reasonable request.
Hsu, P. D. et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. 31, 827–832 (2013).
Jinek, M. et al. A programmable dual-RNA–guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012).
Gong, S., Yu, H. H., Johnson, K. A. & Taylor, D. W. DNA unwinding is the primary determinant of CRISPR–Cas9 activity. Cell Rep. 22, 359–371 (2018).
Jiang, F. et al. Structures of a CRISPR–Cas9 R-loop complex primed for DNA cleavage. Science 351, 867–871 (2016).
Sternberg, S. H., LaFrance, B., Kaplan, M. & Doudna, J. A. Conformational control of DNA target cleavage by CRISPR–Cas9. Nature 527, 110–113 (2015).
Anderson, K. R. et al. CRISPR off-target analysis in genetically engineered rats and mice. Nat. Methods 15, 512 (2018).
Cullot, G. et al. CRISPR–Cas9 genome editing induces megabase-scale chromosomal truncations. Nat. Commun. 10, 1136 (2019).
Fu, Y. et al. High-frequency off-target mutagenesis induced by CRISPR–Cas nucleases in human cells. Nat. Biotechnol. 31, 822–826 (2013).
Amrani, N. et al. NmeCas9 is an intrinsically high-fidelity genome-editing platform. Genome Biol. 19, 214 (2018).
Chen, J. S. et al. Enhanced proofreading governs CRISPR–Cas9 targeting accuracy. Nature 550, 407–410 (2017).
Edraki, A. et al. A compact, high-accuracy Cas9 with a dinucleotide PAM for in vivo genome editing. Mol. Cell 73, 714–726 (2018).
Kim, D. et al. Genome-wide analysis reveals specificities of Cpf1 endonucleases in human cells. Nat. Biotechnol. 34, 863–868 (2016).
Kleinstiver, B. P. et al. High-fidelity CRISPR–Cas9 nucleases with no detectable genome-wide off-target effects. Nature 529, 490–495 (2016).
Lee, J. K. et al. Directed evolution of CRISPR–Cas9 to increase its specificity. Nat. Commun. 9, 3048 (2018).
Ran, F. A. et al. In vivo genome editing using Staphylococcus aureus Cas9. Nature 520, 186–191 (2015).
Shmakov, S. et al. Discovery and functional characterization of diverse class 2 CRISPR–Cas systems. Mol. Cell 60, 385–397 (2015).
Slaymaker, I. M. et al. Rationally engineered Cas9 nucleases with improved specificity. Science 351, 84–88 (2016).
Smargon, A. A. et al. Cas13b is a type VI-B CRISPR-associated RNA-guided RNase differentially regulated by accessory proteins Csx27 and Csx28. Mol. Cell 65, 618–630 (2017).
Wu, W. Y., Lebbink, J. H. G., Kanaar, R., Geijsen, N. & van der Oost, J. Genome editing by natural and engineered CRISPR-associated nucleases. Nat. Chem. Biol. 14, 642–651 (2018).
Zetsche, B. et al. Cpf1 is a single RNA-guided endonuclease of a class 2 CRISPR–Cas system. Cell 163, 759–771 (2015).
Frock, R. L. et al. Genome-wide detection of DNA double-stranded breaks induced by engineered nucleases. Nat. Biotechnol. 33, 179–186 (2015).
Kim, D. et al. Digenome-seq: genome-wide profiling of CRISPR–Cas9 off-target effects in human cells. Nat. Methods 12, 237–243 (2015).
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).
Crosetto, N. et al. Nucleotide-resolution DNA double-strand break mapping by next-generation sequencing. Nat. Methods 10, 361–365 (2013).
Guenther, U.-P. et al. Hidden specificity in an apparently nonspecific RNA-binding protein. Nature 502, 385–388 (2013).
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).
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).
Jung, C. et al. Massively parallel biophysical analysis of CRISPR–Cas complexes on next generation sequencing chips. Cell 170, 35–47 (2017).
Nishimasu, H. et al. Engineered CRISPR–Cas9 nuclease with expanded targeting space. Science 361, 1259–1262 (2018).
Hawkins, J. A., Jones, S. K., Finkelstein, I. J. & Press, W. H.Indel-correcting DNA barcodes for high-throughput sequencing. Proc. Natl Acad. Sci. USA 115, E6217–E6226 (2018).
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).
Strohkendl, I., Saifuddin, F. A., Rybarski, J. R., Finkelstein, I. J. & Russell, R. Kinetic basis for DNA target specificity of CRISPR–Cas12a. Mol. Cell 71, 816–824 (2018).
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).
Raper, A. T., Stephenson, A. A. & Suo, Z. Functional insights revealed by the kinetic mechanism of CRISPR/Cas9. J. Am. Chem. Soc. 140, 2971–2984 (2018).
Stephenson, A. A., Raper, A. T. & Suo, Z. Bidirectional degradation of DNA cleavage products catalyzed by CRISPR/Cas9. J. Am. Chem. Soc. 140, 3743–3750 (2018).
Jiang, W., Bikard, D., Cox, D., Zhang, F. & Marraffini, L. A. RNA-guided editing of bacterial genomes using CRISPR–Cas systems. Nat. Biotechnol. 31, 233–239 (2013).
Kleinstiver, B. P. et al. Engineered CRISPR–Cas9 nucleases with altered PAM specificities. Nature 523, 481–485 (2015).
Zhang, Y. et al. Comparison of non-canonical PAMs for CRISPR/Cas9-mediated DNA cleavage in human cells. Sci. Rep. 4, 5405 (2014).
Zeng, Y. et al. The initiation, propagation and dynamics of CRISPR–SpyCas9 R-loop complex. Nucleic Acids Res. 46, 350–361 (2018).
Kimsey, I. J., Petzold, K., Sathyamoorthy, B., Stein, Z. W. & Al-Hashimi, H. M. Visualizing transient Watson–Crick-like mispairs in DNA and RNA duplexes. Nature 519, 315–320 (2015).
Sugimoto, N., Nakano, M. & Nakano, S. Thermodynamics−structure relationship of single mismatches in RNA/DNA duplexes. Biochemistry 39, 11270–11281 (2000).
Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR–Cas9. Nat. Biotechnol. 34, 184–191 (2016).
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).
Kim, S., Bae, T., Hwang, J. & Kim, J.-S. Rescue of high-specificity Cas9 variants using sgRNAs with matched 5′ nucleotides. Genome Biol. 18, 218 (2017).
Liu, M.-S. et al. Basis for discrimination by engineered CRISPR/Cas9 enzymes. Preprint at https://doi.org/10.1101/630509 (2019).
Hu, J. H. et al. Evolved Cas9 variants with broad PAM compatibility and high DNA specificity. Nature 556, 57–63 (2018).
Walton, R. T., Christie, K. A., Whittaker, M. N. & Kleinstiver, B. P. Unconstrained genome targeting with near-PAMless engineered CRISPR–Cas9 variants. Science 368, 290–296 (2020).
Tycko, J., Myer, V. E. & Hsu, P. D. Methods for optimizing CRISPR–Cas9 genome editing specificity. Mol. Cell 63, 355–370 (2016).
Gao, P., Yang, H., Rajashankar, K. R., Huang, Z. & Patel, D. J. Type V CRISPR–Cas Cpf1 endonuclease employs a unique mechanism for crRNA-mediated target DNA recognition. Cell Res. 26, 901–913 (2016).
Stella, S. et al. Conformational activation promotes CRISPR–Cas12a catalysis and resetting of the endonuclease activity. Cell 175, 1856–1871 (2018).
Yamano, T. et al. Crystal structure of Cpf1 in complex with guide RNA and target DNA. Cell 165, 949–962 (2016).
Chen, J. S. et al. CRISPR–Cas12a target binding unleashes indiscriminate single-stranded DNase activity. Science 360, 436–439 (2018).
Li, S.-Y. et al. CRISPR–Cas12a has both cis- and trans-cleavage activities on single-stranded DNA. Cell Res. 28, 491 (2018).
Murugan, K., Seetharam, A. S., Severin, A. J. & Sashital, D. G. CRISPR–Cas12a has widespread off-target and dsDNA-nicking effects. J. Biol. Chem. 295, 5538–5553 (2020).
Swarts, D. C. & Jinek, M. Mechanistic insights into the cis- and trans-acting DNase activities of Cas12a. Mol. Cell 73, 589–600 (2018).
Doench, J. G. et al. Rational design of highly active sgRNAs for CRISPR–Cas9-mediated gene inactivation. Nat. Biotechnol. 32, 1262–1267 (2014).
Moreno-Mateos, M. A. et al. CRISPRscan: designing highly efficient sgRNAs for CRISPR–Cas9 targeting in vivo. Nat. Methods 12, 982–988 (2015).
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).
Xu, X., Duan, D. & Chen, S.-J. CRISPR–Cas9 cleavage efficiency correlates strongly with target-sgRNA folding stability: from physical mechanism to off-target assessment. Sci. Rep. 7, 143 (2017).
Abadi, S., Yan, W. X., Amar, D. & Mayrose, I. A machine learning approach for predicting CRISPR–Cas9 cleavage efficiencies and patterns underlying its mechanism of action. PLoS Comput. Biol. 13, e1005807 (2017).
Lin, J. & Wong, K.-C. Off-target predictions in CRISPR–Cas9 gene editing using deep learning. Bioinformatics 34, i656–i663 (2018).
Listgarten, J. et al. Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs. Nat. Biomed. Eng. 2, 38–47 (2018).
Stormo, G. D. & Zhao, Y. Determining the specificity of protein–DNA interactions. Nat. Rev. Genet. 11, 751–760 (2010).
Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723 (1974).
Sugimoto, N., Yasumatsu, I. & Fujimoto, M. Stabilities of internal rU-dG and rG-dT pairs in RNA/DNA hybrids. Nucleic Acids Symp. Ser. 199–200 (1997).
Fu, B. X. H., St. Onge, R. P., Fire, A. Z. & Smith, J. D. Distinct patterns of Cas9 mismatch tolerance in vitro and in vivo. Nucleic Acids Res. 44, 5365–5377 (2016).
Kim, H. K. et al. In vivo high-throughput profiling of CRISPR-Cpf1 activity. Nat. Methods 14, 153–159 (2017).
Kleinstiver, B. P. et al. Genome-wide specificities of CRISPR–Cas Cpf1 nucleases in human cells. Nat. Biotechnol. 34, 869–874 (2016).
Pattanayak, V. et al. High-throughput profiling of off-target DNA cleavage reveals RNA-programmed Cas9 nuclease specificity. Nat. Biotechnol. 31, 839–843 (2013).
Eslami-Mossallam, B. et al. A mechanistic model improves off-target predictions and reveals the physical basis of SpCas9 fidelity. Preprint at https://doi.org/10.1101/2020.05.21.108613 (2020).
Aach, J., Mali, P. & Church, G. M. CasFinder: flexible algorithm for identifying specific Cas9 targets in genomes. Preprint at https://doi.org/10.1101/005074 (2014).
Bae, S., Park, J. & 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).
Concordet, J.-P. & Haeussler, M. CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens. Nucleic Acids Res. 46, W242–W245 (2018).
Heigwer, F., Kerr, G. & Boutros, M. E-CRISP: fast CRISPR target site identification. Nat. Methods 11, 122–123 (2014).
Montague, T. G., Cruz, J. M., Gagnon, J. A., Church, G. M. & Valen, E. CHOPCHOP: a CRISPR/Cas9 and TALEN web tool for genome editing. Nucleic Acids Res. 42, W401–W407 (2014).
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 (2015).
Wang, A. S. et al. The histone chaperone FACT induces Cas9 multi-turnover behavior and modifies genome manipulation in human cells. Mol. Cell https://doi.org/10.1016/j.molcel.2020.06.014 (2019).
Babu, K. et al. Bridge helix of Cas9 modulates target DNA cleavage and mismatch tolerance. Biochemistry 58, 1905–1917 (2019).
Nishimasu, H. et al. Crystal structure of Cas9 in complex with guide RNA and target DNA. Cell 156, 935–949 (2014).
Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).
Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A. & Liu, D. R. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533, 420–424 (2016).
Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).
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).
Creutzburg, S. C. A. et al. Good guide, bad guide: spacer sequence-dependent cleavage efficiency of Cas12a. Nucleic Acids Res. 48, 3228–3243 (2020).
Hinz, J. M., Laughery, M. F. & Wyrick, J. J. Nucleosomes inhibit Cas9 endonuclease activity in vitro. Biochemistry 54, 7063–7066 (2015).
Horlbeck, M. A. et al. Nucleosomes impede Cas9 access to DNA in vivo and in vitro. eLife 5, e12677 (2016).
Isaac, R. S. et al. Nucleosome breathing and remodeling constrain CRISPR–Cas9 function. eLife 5, e13450 (2016).
Liu, X. et al. Sequence features associated with the cleavage efficiency of CRISPR/Cas9 system. Sci. Rep. 6, 1–9 (2016).
Thyme, S. B., Akhmetova, L., Montague, T. G., Valen, E. & Schier, A. F. Internal guide RNA interactions interfere with Cas9-mediated cleavage. Nat. Commun. 7, 1–7 (2016).
Chang, H. H. Y. et al. Different DNA end configurations dictate which NHEJ components are most important for joining efficiency. J. Biol. Chem. 291, 24377–24389 (2016).
Daley, J. M. & Wilson, T. E. Rejoining of DNA double-strand breaks as a function of overhang length. Mol. Cell. Biol. 25, 896–906 (2005).
Liang, Z., Sunder, S., Nallasivam, S. & Wilson, T. E. Overhang polarity of chromosomal double-strand breaks impacts kinetics and fidelity of yeast non-homologous end joining. Nucleic Acids Res. 44, 2769–2781 (2016).
Allen, F. et al. Predicting the mutations generated by repair of Cas9-induced double-strand breaks. Nat. Biotechnol. 37, 64–72 (2019).
Lemos, B. R. et al. CRISPR/Cas9 cleavages in budding yeast reveal templated insertions and strand-specific insertion/deletion profiles. Proc. Natl Acad. Sci. USA 115, E2040–E2047 (2018).
van Overbeek, M. et al. DNA repair profiling reveals nonrandom outcomes at Cas9-mediated breaks. Mol. Cell 63, 633–646 (2016).
Cock, P. J. A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009).
Cieślik, M., Pederson, B. & Arindrarto, W. Align: polite, proper sequence alignment. https://github.com/brentp/align (2016).
Efron, B. & Tibshirani, R. J. An Introduction to the Bootstrap (Chapman and Hall/CRC, 1993).
Hoerl, A. E. & Kennard, R. W. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12, 55–67 (1970).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Edelstein, A. D. et al. Advanced methods of microscope control using μManager software. J. Biol. Methods 1, 10 (2014).
We thank I. Strohkendl, R. Russell and members of the University of Texas at Austin Genomic Sequencing and Analysis Facility staff for valuable insights. We are grateful to members of the Finkelstein laboratory for carefully reading the manuscript and for additional contributions by K. Dillard, F. Saifuddin, G. Nguyen and J. Kula. This work was supported by a College of Natural Sciences Catalyst award, the Welch Foundation (F-1808 to I.J.F.) and the National Institutes of Health (R01GM124141 to I.J.F. and F32 AG053051 to S.K.J.).
The authors declare competing financial interests. The authors have filed patent applications on the CHAMP platform. The Regents of the University of California have patents issued and pending for CRISPR technologies on which J.A.D. is an inventor. J.A.D. is a co-founder of Caribou Biosciences, Editas Medicine, Intellia Therapeutics, Scribe Therapeutics and Mammoth Biosciences. J.A.D. is a scientific advisory board member of Caribou Biosciences, Intellia Therapeutics, eFFECTOR Therapeutics, Scribe Therapeutics, Synthego, Mammoth Biosciences and Inari. J.A.D. is a member of the board of directors at Driver and Johnson & Johnson and has sponsored research projects by Roche Biopharma and Biogen. J.A.C. is a co-founder of Mammoth Biosciences. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare no competing non-financial interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figs. 1–9, Supplementary Tables 1–3 and Supplementary File 1–3 descriptions
Complete DNA sequences as synthesized for DNA libraries used with CHAMP and NucleaSeq methods. From 5′ to 3′: primer-binding region for library amplification, first barcode for identifying a cleaved DNA fragment, constant region for uniform target sequence context, 5′ Cas12a PAM, target sequence, 3′ Cas9 PAM, constant region for uniform target sequence context and length, second barcode for identifying a cleaved DNA fragment, primer-binding region for library amplification (Supplementary Fig. 1b and Online Methods).
Cleavage rates and normalized changes in apparent binding affinity (ΔABA) for all measured DNAs with the indicated RNPs. Sequence: The DNA sequence related to the PAM and guide RNA for the tested RNP. Descriptor: Relationship between the tested DNA sequence and the intended DNA target matching the guide RNA and PAM. ndABA: The change in apparent binding affinity (as measured using CHAMP) for the indicated DNA sequence compared to that of a matched DNA, normalized to the matched DNA (0) and an unmatched negative control DNA (1). ndABA_unc: s.d. of the normalized change in apparent binding affinity as measured by bootstrap analysis. cleavage_rate_log: The log-transformed cleavage rate as measured by NucleaSeq for the indicated DNA sequence. cleavage_rate_log_unc: s.d. of the log-transformed cleavage rate as measured by bootstrap analysis.
Cleavage rates and average cleavage sites for all measured DNAs treated with the indicated RNPs. Sequence: The DNA sequence related to the PAM and guide RNA for the tested RNP. Descriptor: Relationship between the tested DNA sequence and the intended DNA target matching the guide RNA and PAM. cleavage_rate_log: The log-transformed cleavage rate as measured by NucleaSeq for the indicated DNA sequence. cleavage_rate_log_unc: s.d. of the log-transformed cleavage rate as measured by bootstrap analysis. L_## or R_##: Average left and right side (5′→3′NTS) cleavage sites for the indicated DNA sequence with the indicated RNP at the indicated time. None: Less than 33% of DNAs with the indicated sequence were cleaved in the overall reaction; no average cleavage site available.
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Jones, S.K., Hawkins, J.A., Johnson, N.V. et al. Massively parallel kinetic profiling of natural and engineered CRISPR nucleases. Nat Biotechnol 39, 84–93 (2021). https://doi.org/10.1038/s41587-020-0646-5
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