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CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR–Cas9 nuclease off-targets

A Corrigendum to this article was published on 27 April 2018

This article has been updated


Sensitive detection of off-target effects is important for translating CRISPR–Cas9 nucleases into human therapeutics. In vitro biochemical methods for finding off-targets offer the potential advantages of greater reproducibility and scalability while avoiding limitations associated with strategies that require the culture and manipulation of living cells. Here we describe circularization for in vitro reporting of cleavage effects by sequencing (CIRCLE-seq), a highly sensitive, sequencing-efficient in vitro screening strategy that outperforms existing cell-based or biochemical approaches for identifying CRISPR–Cas9 genome-wide off-target mutations. In contrast to previously described in vitro methods, we show that CIRCLE-seq can be practiced using widely accessible next-generation sequencing technology and does not require reference genome sequences. Importantly, CIRCLE-seq can be used to identify off-target mutations associated with cell-type-specific single-nucleotide polymorphisms, demonstrating the feasibility and importance of generating personalized specificity profiles. CIRCLE-seq provides an accessible, rapid, and comprehensive method for identifying genome-wide off-target mutations of CRISPR–Cas9.

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Figure 1: Overview of CIRCLE-seq.
Figure 2: Comparisons of CIRCLE-seq with cell-based GUIDE-seq and HTGTS methods.
Figure 3: CIRCLE-seq detects off-target sites that are cleaved in human cells.
Figure 4: Using CIRCLE-seq to assess the impacts of personalized SNPs on off-target site analysis.

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  • 20 April 2018

    In the version of this Article originally published, the wrong Protocol Exchange DOI and link (10.1038/protex.2017.047) were included in ref. 40. The URL in the reference should have been This error has been corrected in the HTML and PDF versions of the paper.


  1. 1

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

    CAS  Article  Google Scholar 

  2. 2

    Hwang, W.Y. et al. Efficient genome editing in zebrafish using a CRISPR-Cas system. Nat. Biotechnol. 31, 227–229 (2013).

    CAS  Article  Google Scholar 

  3. 3

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

    Article  Google Scholar 

  4. 4

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

    CAS  Article  Google Scholar 

  5. 5

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

    CAS  Article  Google Scholar 

  6. 6

    Doudna, J.A. & Charpentier, E. Genome editing. The new frontier of genome engineering with CRISPR-Cas9. Science 346, 1258096 (2014).

    Article  Google Scholar 

  7. 7

    Bolukbasi, M.F., Gupta, A. & Wolfe, S.A. Creating and evaluating accurate CRISPR-Cas9 scalpels for genomic surgery. Nat. Methods 13, 41–50 (2016).

    CAS  Article  Google Scholar 

  8. 8

    Mali, P., Esvelt, K.M. & Church, G.M. Cas9 as a versatile tool for engineering biology. Nat. Methods 10, 957–963 (2013).

    CAS  Article  Google Scholar 

  9. 9

    Sander, J.D. & Joung, J.K. CRISPR-Cas systems for editing, regulating and targeting genomes. Nat. Biotechnol. 32, 347–355 (2014).

    CAS  Article  Google Scholar 

  10. 10

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

    CAS  Article  Google Scholar 

  11. 11

    Lin, J. & Musunuru, K. Genome engineering tools for building cellular models of disease. FEBS J. 283, 3222–3231 (2016).

    CAS  Article  Google Scholar 

  12. 12

    Hsu, P.D., Lander, E.S. & Zhang, F. Development and applications of CRISPR-Cas9 for genome engineering. Cell 157, 1262–1278 (2014).

    CAS  Article  Google Scholar 

  13. 13

    Brandsma, I. & Gent, D.C. Pathway choice in DNA double strand break repair: observations of a balancing act. Genome Integr. 3, 9 (2012).

    CAS  Article  Google Scholar 

  14. 14

    Symington, L.S. & Gautier, J. Double-strand break end resection and repair pathway choice. Annu. Rev. Genet. 45, 247–271 (2011).

    CAS  Article  Google Scholar 

  15. 15

    Kass, E.M. & Jasin, M. Collaboration and competition between DNA double-strand break repair pathways. FEBS Lett. 584, 3703–3708 (2010).

    CAS  Article  Google Scholar 

  16. 16

    Wyman, C. & Kanaar, R. DNA double-strand break repair: all's well that ends well. Annu. Rev. Genet. 40, 363–383 (2006).

    CAS  Article  Google Scholar 

  17. 17

    Rouet, P., Smih, F. & Jasin, M. Introduction of double-strand breaks into the genome of mouse cells by expression of a rare-cutting endonuclease. Mol. Cell. Biol. 14, 8096–8106 (1994).

    CAS  Article  Google Scholar 

  18. 18

    Sternberg, S.H., LaFrance, B., Kaplan, M. & Doudna, J.A. Conformational control of DNA target cleavage by CRISPR-Cas9. Nature 527, 110–113 (2015).

    CAS  Article  Google Scholar 

  19. 19

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

    CAS  Article  Google Scholar 

  20. 20

    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 

  21. 21

    Fu, Y., Sander, J.D., Reyon, D., Cascio, V.M. & Joung, J.K. Improving CRISPR-Cas nuclease specificity using truncated guide RNAs. Nat. Biotechnol. 32, 279–284 (2014).

    CAS  Article  Google Scholar 

  22. 22

    Anders, C., Niewoehner, O., Duerst, A. & Jinek, M. Structural basis of PAM-dependent target DNA recognition by the Cas9 endonuclease. Nature 513, 569–573 (2014).

    CAS  Article  Google Scholar 

  23. 23

    Shah, S.A., Erdmann, S., Mojica, F.J.M. & Garrett, R.A. Protospacer recognition motifs: mixed identities and functional diversity. RNA Biol. 10, 891–899 (2013).

    CAS  Article  Google Scholar 

  24. 24

    Tsai, S.Q. & Joung, J.K. Defining and improving the genome-wide specificities of CRISPR–Cas9 nucleases. Nat. Rev. Genet. 17, 300–312 (2016).

    CAS  Article  Google Scholar 

  25. 25

    Gori, J.L. et al. Delivery and specificity of CRISPR-Cas9 genome editing technologies for human gene therapy. Hum. Gene Ther. 26, 443–451 (2015).

    CAS  Article  Google Scholar 

  26. 26

    Cox, D.B.T., Platt, R.J. & Zhang, F. Therapeutic genome editing: prospects and challenges. Nat. Med. 21, 121–131 (2015).

    CAS  Article  Google Scholar 

  27. 27

    Gabriel, R. et al. An unbiased genome-wide analysis of zinc-finger nuclease specificity. Nat. Biotechnol. 29, 816–823 (2011).

    CAS  Article  Google Scholar 

  28. 28

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

    CAS  Article  Google Scholar 

  29. 29

    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 

  30. 30

    Frock, R.L. et al. Genome-wide detection of DNA double-stranded breaks induced by engineered nucleases. Nat. Biotechnol. 33, 179–186 (2015).

    CAS  Article  Google Scholar 

  31. 31

    Crosetto, N. et al. Nucleotide-resolution DNA double-strand break mapping by next-generation sequencing. Nat. Methods 10, 361–365 (2013).

    CAS  Article  Google Scholar 

  32. 32

    Pattanayak, V. et al. High-throughput profiling of off-target DNA cleavage reveals RNA-programmed Cas9 nuclease specificity. Nat. Biotechnol. 31, 839–843 (2013).

    CAS  Article  Google Scholar 

  33. 33

    Kim, D. et al. Digenome-seq: genome-wide profiling of CRISPR-Cas9 off-target effects in human cells. Nat. Methods 12, 237–243, 1 p following 243 (2015).

    CAS  Article  Google Scholar 

  34. 34

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

    CAS  Article  Google Scholar 

  35. 35

    Yang, L. et al. Targeted and genome-wide sequencing reveal single nucleotide variations impacting specificity of Cas9 in human stem cells. Nat. Commun. 5, 5507 (2014).

    CAS  Article  Google Scholar 

  36. 36

    1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  37. 37

    Sherry, S.T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001).

    CAS  Article  Google Scholar 

  38. 38

    ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  39. 39

    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 

  40. 40

    Tsai, S. et al. Circularization for in vitro reporting of cleavage effects (CIRCLEseq). Protoc. Exch. (2017).

  41. 41

    Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).

    Article  Google Scholar 

  42. 42

    Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).

    CAS  Article  Google Scholar 

  43. 43

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

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This work was supported by a National Institutes of Health (NIH) Director's Pioneer Award (DP1 GM105378), NIH R35 GM118158, and NIH R01 GM107427 (to J.K.J.); and the Jim and Ann Orr Research Scholar Award (to J.K.J.).

Author information




S.Q.T. and J.K.J. conceived of and designed experiments. S.Q.T. and N.T.N. performed all experiments. S.Q.T., J.M.-L., V.V.T., and M.J.A. wrote the CIRCLE-seq analysis pipeline and analyzed CIRCLE-seq data. S.Q.T. and J.K.J. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Shengdar Q Tsai or J Keith Joung.

Ethics declarations

Competing interests

J.K.J. is a consultant for Horizon Discovery. J.K.J. has financial interests in Beacon Genomics, Editas Medicine, Poseida Therapeutics, and Transposagen Biopharmaceuticals. J.K.J.'s interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies. S.Q.T., M.J.A., and J.K.J. are scientific cofounders of Beacon Genomics.

Integrated supplementary information

Supplementary Figure 1 Detailed schematic overview of linear stem-loop method

Genomic DNA is randomly sheared to an average of ~300 bp, end-repaired, A-tailed, and ligated to uracil-containing stem loop adapter 1. Covalently closed DNA molecules with stem-loop adapters ligated to both ends are selected for by treatment with a mixture of Lambda exonuclease and E. coli Exonuclease I, and then treated with Cas9-sgRNA complex. Cleaved molecules will have a newly available end for subsequent ligation of stem-loop adapter 2. Ligation of both stem loop adapters provides required 5’ and 3’ sequences for PCR and high-throughput sequencing.

Supplementary Figure 2 Detailed schematic overview of CIRCLE-seq method.

Genomic DNA is randomly sheared to an average of ~300 bp, end-repaired, A-tailed, and ligated to uracil-containing stem-loop adapters. Covalently closed DNA molecules with stem-loop adapters ligated to both ends are selected for by treatment with a mixture of Lambda exonuclease and E. coli Exonuclease I. 4 bp overhangs are released with a mixture of USER enzyme and T4 PNK, and DNA molecules are circularized at low concentrations favoring intramolecular ligation. Unwanted linear DNA is degraded with Plasmid-Safe ATP-dependent DNase. Circular DNA is treated with Cas9–sgRNA complex and cleaved, linearized DNA is ligated to sequencing adapters and amplified for high-throughput sequencing.

Supplementary Figure 3 Optimization of in vitro circularization conditions with uracil-containing stem loop adapters and a PCR amplicon.

Qiaxcel capillary electrophoretic traces of intramolecular ligation, exonuclease treatment, and restriction enzyme digestion. The observed electrophoretic mobility shift is consistent with circularization. An exonuclease-resistant population of circular DNA molecules is observed after Plasmid-Safe treatment. Digestion with BamHI restriction enzyme linearizes the circularized DNA and results in the expected shift in mobility.

Supplementary Figure 4 Comparison of CIRCLE-seq and covalently closed linear stem-loop strategies for identifying nuclease-induced off-target effects.

(a) Scatterplot of read counts for linear stem-loop and circular (CIRCLE-seq) methods for detecting Cas9 nuclease-induced off-target sites for a sgRNA targeted against VEGFA site 1. (b) Venn diagram showing overlap of sites detected by CIRCLE-seq and alternative linear stem-loop method.

Supplementary Figure 5 CIRCLE-seq read counts are highly reproducible.

Scatterplots of CIRCLE-seq read counts between two independent CIRCLE-seq libraries prepared from the same source of genomic DNA (human U2OS cells) for sgRNAs targeted against EMX1 and VEGFA site 1.

Supplementary Figure 6 Comparison of CIRCLE-seq with Digenome-seq.

(a) Venn diagram showing intersections of off-target sites of Cas9 and a sgRNA targeted against the HBB gene detected by CIRCLE-seq (blue) and Digenome-seq (clear). (b) CIRCLE-seq reads observed at 3 sites that are called by Digenome-seq but not CIRCLE-seq. Integrated Genome Viewer (IGV) plots showing supporting CIRCLE-seq read alignments mapped to human reference genome (GrCh37). Reads mapping to the reverse strand are colored in blue, reads mapping to the forward strand are colored in red. (c) Barplot of Digenome-seq start mapping read counts at off-target cleavage positions identified by CIRCLE-seq but not called by Digenome-seq for nuclease-treated (red) and control (blue) HAP1 genomic DNA. (d) Plots comparing mapping of sequencing reads for CIRCLE-seq and Digenome-seq at the on-target site of a sgRNA targeted to the HBB locus. Both nuclease-treated and control samples are shown. A thin grey line indicates expected cleavage site position; read coverage for forward reads is colored in red, and reverse reads in blue. (e) CIRCLE-seq start mapping position plot at the on-target site for the HBB sgRNA used in (c). (+) strand mapping reads are colored in blue, (-) strand mapping reads are colored in green.

Supplementary Figure 7 Histogram of number of mismatches for CIRCLE-seq off-target sites.

Number of mismatches in CIRCLE-seq detected off-target sites relative to the intended target site of sgRNAs targeted against standard sites in HEK293 & U2OS cells, repetitive sites in HEK293 & U2OS cells, and sites in K562 cells.

Supplementary Figure 8 Venn diagrams showing intersection of CIRCLE-seq and GUIDE-seq detected genomic off-target cleavage sites.

CIRCLE-seq sites are indicated in blue and GUIDE-seq sites with clear circles. The top six comparisons are for sgRNAs targeted against standard genomic sites, and the bottom four comparison are targeted against more repetitive sites.

Supplementary Figure 9 Venn diagrams showing overlap between sets of off-target cleavage sites detected between CIRCLE-seq, GUIDE-seq, and HTGTS.

CIRCLE-seq (solid blue) detects virtually all off-target cleavage sites detected by both GUIDE-seq (hatched blue) and HTGTS (clear).

Supplementary Figure 10 CIRCLE-seq read count percentile vs. GUIDE-seq read count.

Normalized GUIDE-seq read counts plotted against normalized CIRCLE-seq reads grouped by mismatch numbers between 0 and 6.

Supplementary Figure 11 CIRCLE-seq sites detected by reference-free site discovery algorithm.

Percentage of unique cleavage sites that can be found using a reference-independent site discovery algorithm, for CIRCLE-seq experiments performed with sgRNAs targeting non-repetitive sites in HEK293 (red), K562 (green), and U2OS genomic DNA (blue).

Supplementary Figure 12 Effects of titrating Cas9 protein concentration on in vitro cleavage efficiency and number of CIRCLE-seq sites detected.

(a) Barplot of percent in vitro cleavage of a targetsite containing PCR amplicon by Cas9 at different concentrations. (b) Number of sites detected by CIRCLE-seq at different concentrations of Cas9:sgRNA complex. Validated sites include both those detected by GUIDE-seq and by confirmatory targeted tag sequencing.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12, Supplementary Notes 1–2 and Supplementary Table 1. (PDF 5708 kb)

Supplementary Table 2

List of all CIRCLE-seq detected off-target sites. (XLSX 640 kb)

Supplementary Table 3

List of CIRCLE-seq read counts and HTGTS scores for off-target sites detected for Cas9 and gRNAs targeted against EMX1 and VEGFA site 1. (XLSX 66 kb)

Supplementary Table 4

Deep sequencing read counts for targeted tag integration sequencing of off-target cleavage sites of Cas9 and gRNAs targeted against EMX1 and VEGFA site 1. (XLSX 53 kb)

Supplementary Table 5

Listing of cell-type specific SNPs in protospacer or PAM of off-target cleavage sites detected by CIRCLE-seq. (XLSX 52 kb)

Supplementary Table 6

Primers used in target tag integration sequencing. (XLSX 18 kb)

Supplementary Protocol

Supplementary Protocol: CIRCLE-seq Library Preparation. (PDF 581 kb)

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

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