Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

Genome-wide detection of DNA double-strand breaks by in-suspension BLISS

Nature Protocols volume 15, pages 3894–3941 (2020)Cite this article

Subjects

Abstract

sBLISS (in-suspension breaks labeling in situ and sequencing) is a versatile and widely applicable method for identification of endogenous and induced DNA double-strand breaks (DSBs) in any cell type that can be brought into suspension. sBLISS provides genome-wide profiles of the most consequential DNA lesion implicated in a variety of pathological, but also physiological, processes. In sBLISS, after in situ labeling, DSB ends are linearly amplified, followed by next-generation sequencing and DSB landscape analysis. Here, we present a step-by-step experimental protocol for sBLISS, as well as a basic computational analysis. The main advantages of sBLISS are (i) the suspension setup, which renders the protocol user-friendly and easily scalable; (ii) the possibility of adapting it to a high-throughput or single-cell workflow; and (iii) its flexibility and its applicability to virtually every cell type, including patient-derived cells, organoids, and isolated nuclei. The wet-lab protocol can be completed in 1.5 weeks and is suitable for researchers with intermediate expertise in molecular biology and genomics. For the computational analyses, basic-to-intermediate bioinformatics expertise is required.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Schematic depiction of the sBLISS workflow.
Fig. 2: Detailed overview of sBLISS library structure and read orientation.
Fig. 3: Experimental details for TK6 and enterocyte experiments.
Fig. 4: Genome browser views of mouse enterocyte sBLISS data.
Fig. 5: Analyses of mouse and human sBLISS data with the tutorial.
Fig. 6: Plotting the genome-wide density of DSB events.
Fig. 7: Integration of sBLISS data with mouse enterocyte gene expression levels.

Similar content being viewed by others

Data availability

All raw sequencing data and processed mapped files used to generate Figs. 3–7 are made available through GEO: GSE145598. The TK6 data were generated as part of a published study (ref. 48), but the mapping pipeline has been updated since the original publication.

Code availability

All code is available on the BiCroLab GitHub repository. Code for the preprocessing and mapping pipeline can be found at https://github.com/BiCroLab/blissNP, and code for the analysis tutorials can be found at https://github.com/BiCroLab/blissNPanalysis.

References

  1. McKinnon, P. J. & Caldecott, K. W. DNA strand break repair and human genetic disease. Annu. Rev. Genomics Hum. Genet. 8, 37–55 (2007).

    Article  CAS  PubMed  Google Scholar 

  2. Mills, K. D., Ferguson, D. O. & Alt, F. W. The role of DNA breaks in genomic instability and tumorigenesis. Immunol. Rev. 194, 77–95 (2003).

    Article  CAS  PubMed  Google Scholar 

  3. Roukos, V. & Misteli, T. The biogenesis of chromosome translocations. Nat. Cell Biol. 16, 293–300 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Tubbs, A. & Nussenzweig, A. Endogenous DNA damage as a source of genomic instability in cancer. Cell 168, 644–656 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Cannan, W. J. & Pederson, D. S. Mechanisms and consequences of double-strand DNA break formation in chromatin. J. Cell. Physiol. 231, 3–14 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. van Gent, D. C., Hoeijmakers, J. H. & Kanaar, R. Chromosomal stability and the DNA double-stranded break connection. Nat. Rev. Genet. 2, 196–206 (2001).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sakuma, T. & Yamamoto, T. Acceleration of cancer science with genome editing and related technologies. Cancer Sci. 109, 3679–3685 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Mirzazadeh, R., Kallas, T., Bienko, M. & Crosetto, N. Genome-wide profiling of DNA double-strand breaks by the BLESS and BLISS methods. Methods Mol. Biol. 1672, 167–194 (2018).

    Article  CAS  PubMed  Google Scholar 

  13. Marnef, A. et al. A cohesin/HUSH- and LINC-dependent pathway controls ribosomal DNA double-strand break repair. Genes Dev. 33, 1175–1190 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Iannelli, F. et al. A damaged genome’s transcriptional landscape through multilayered expression profiling around in situ-mapped DNA double-strand breaks. Nat. Commun. 8, 15656 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Shi, W. et al. Ssb1 and Ssb2 cooperate to regulate mouse hematopoietic stem and progenitor cells by resolving replicative stress. Blood 129, 2479–2492 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Clouaire, T. et al. Comprehensive mapping of histone modifications at DNA double-strand breaks deciphers repair pathway chromatin signatures. Mol. Cell 72, 250–262.e6 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Dellino, G. I. et al. Release of paused RNA polymerase II at specific loci favors DNA double-strand-break formation and promotes cancer translocations. Nat. Genet. 51, 1011–1023 (2019).

    Article  CAS  PubMed  Google Scholar 

  18. Gao, L. et al. Engineered Cpf1 variants with altered PAM specificities. Nat. Biotechnol. 35, 789–792 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ballarino R., Bouwman B. A. M. & Crosetto N. Genome-wide CRISPR off-target DNA break detection by the BLISS Method. in CRISPR Guide RNA Design (eds Fulga T. A. et al.) 261–281 (Humana, 2020).

  20. Figueroa-González, G. & Pérez-Plasencia, C. Strategies for the evaluation of DNA damage and repair mechanisms in cancer. Oncol. Lett. 13, 3982–3988 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Banerjee, U. & Soutoglou, E. Finding DNA ends within a haystack of chromatin. Mol. Cell 63, 726–728 (2016).

    Article  CAS  PubMed  Google Scholar 

  22. Bouwman, B. A. M. & Crosetto, N. Endogenous DNA double-strand breaks during DNA Transactions: emerging insights and methods for genome-wide profiling. Genes 9, (2018).

  23. Martin, F., Sánchez-Hernández, S., Gutiérrez-Guerrero, A., Pinedo-Gomez, J. & Benabdellah, K. Biased and unbiased methods for the detection of off-target cleavage by CRISPR/Cas9: an overview. Int. J. Mol. Sci. 17, 1507 (2016).

  24. Klein, I. A. et al. Translocation-capture sequencing reveals the extent and nature of chromosomal rearrangements in B lymphocytes. Cell 147, 95–106 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Chiarle, R. et al. Genome-wide translocation sequencing reveals mechanisms of chromosome breaks and rearrangements in B cells. Cell 147, 107–119 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  27. 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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  29. Breton, C., Clark, P. M., Wang, L., Greig, J. A. & Wilson, J. M. ITR-Seq, a next-generation sequencing assay, identifies genome-wide DNA editing sites in vivo following adeno-associated viral vector-mediated genome editing. BMC Genomics 21, 239 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Hanlon, K. S. et al. High levels of AAV vector integration into CRISPR-induced DNA breaks. Nat. Commun. 10, 4439 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Lensing, S. V. et al. DSBCapture: in situ capture and sequencing of DNA breaks. Nat. Methods 13, 855–857 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Shastri, N. et al. Genome-wide Identification of structure-forming repeats as principal sites of fork collapse upon ATR inhibition. Mol. Cell 72, 222–238.e11 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Canela, A. et al. DNA breaks and end resection measured genome-wide by end sequencing. Mol. Cell 63, 898–911 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Biernacka, A. et al. i-BLESS is an ultra-sensitive method for detection of DNA double-strand breaks. Commun. Biol. 1, 181 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Baranello, L. et al. Mapping DNA breaks by next-generation sequencing. Methods Mol. Biol. 1672, 155–166 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Leduc, F. et al. Genome-wide mapping of DNA strand breaks. PloS One 6, e17353 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Grégoire, M.-C. et al. Quantification and genome-wide mapping of DNA double-strand breaks. DNA Repair 48, 63–68 (2016).

    Article  PubMed  CAS  Google Scholar 

  38. Hoffman, E. A., McCulley, A., Haarer, B., Arnak, R. & Feng, W. Break-seq reveals hydroxyurea-induced chromosome fragility as a result of unscheduled conflict between DNA replication and transcription. Genome Res. 25, 402–412 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Canela, A. et al. Topoisomerase II-induced chromosome breakage and translocation is determined by chromosome architecture and transcriptional activity. Mol. Cell 75, 252–266.e8 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Gittens, W. H. et al. A nucleotide resolution map of Top2-linked DNA breaks in the yeast and human genome. Nat. Commun. 10, 4846 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Dorsett, Y. et al. HCoDES reveals chromosomal DNA end structures with single-nucleotide resolution. Mol. Cell 56, 808–818 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Szlachta, K., Raimer, H. M., Comeau, L. D. & Wang, Y.-H. CNCC: an analysis tool to determine genome-wide DNA break end structure at single-nucleotide resolution. BMC Genomics 21, 25 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Zhu, Y. et al. qDSB-Seq is a general method for genome-wide quantification of DNA double-strand breaks using sequencing. Nat. Commun. 10, 2313 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  45. Kim, D. & Kim, J.-S. DIG-seq: a genome-wide CRISPR off-target profiling method using chromatin DNA. Genome Res. 28, 1894–1900 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Cameron, P. et al. Mapping the genomic landscape of CRISPR-Cas9 cleavage. Nat. Methods 14, 600–606 (2017).

    Article  CAS  PubMed  Google Scholar 

  47. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Gothe, H. J. et al. Spatial chromosome folding and active transcription drive DNA fragility and formation of oncogenic MLL translocations. Mol. Cell 75, 267–283.e12 (2019).

    Article  CAS  PubMed  Google Scholar 

  49. Dziubańska-Kusibab, P. J. et al. Colibactin DNA-damage signature indicates mutational impact in colorectal cancer. Nat. Med. 26, 1063–1069 (2020).

    Article  PubMed  CAS  Google Scholar 

  50. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Belaghzal, H., Dekker, J. & Gibcus, J. H. Hi-C 2.0: an optimized Hi-C procedure for high-resolution genome-wide mapping of chromosome conformation. Methods 123, 56–65 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Kordon, M. M. et al. STRIDE—a fluorescence method for direct, specific in situ detection of individual single- or double-strand DNA breaks in fixed cells. Nucleic Acids Res. 48, e14 (2020).

    Article  PubMed  CAS  Google Scholar 

  53. Orlitsky, A., Suresh, A. T. & Wu, Y. Optimal prediction of the number of unseen species. Proc. Natl Acad. Sci. 113, 13283–13288 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Dsouza, M., Larsen, N. & Overbeek, R. Searching for patterns in genomic data. Trends Genet. 13, 497–498 (1997).

    Article  CAS  PubMed  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  56. Ballinger, T. J. et al. Modeling double strand break susceptibility to interrogate structural variation in cancer. Genome Biol. 20, 28 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Hoa, N. N. et al. Mre11 Is essential for the removal of lethal topoisomerase 2 covalent cleavage complexes. Mol. Cell 64, 580–592 (2016).

    Article  CAS  PubMed  Google Scholar 

  58. Moor, A. E. et al. Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis. Cell 175, 1156–1167.e15 (2018).

    Article  CAS  PubMed  Google Scholar 

  59. Anaconda, Inc. Anaconda Software Distribution https://docs.conda.io/en/latest/miniconda.html (2017).

  60. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

  62. Tange, O. GNU Parallel 2018 https://doi.org/10.5281/zenodo.1146014 (2018).

  63. R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2014).

  64. Wang, X. et al. Unbiased detection of off-target cleavage by CRISPR-Cas9 and TALENs using integrase-defective lentiviral vectors. Nat. Biotechnol. 33, 175–178 (2015).

    Article  CAS  PubMed  Google Scholar 

  65. Baranello, L. et al. DNA break mapping reveals topoisomerase II activity genome-wide. Int. J. Mol. Sci. 15, 13111–13122 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Morgan, M. & Shepherd, L. AnnotationHub: Client to Access AnnotationHub Resources (Bioconductor, 2020).

  68. Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Lawrence, M., Gentleman, R. & Carey, V. rtracklayer: an R package for interfacing with genome browsers. Bioinformatics 25, 1841–1842 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Xie Y. knitr: A General-Purpose Package for Dynamic Report Generation in R. https://rdrr.io/cran/knitr/ (2020).

  71. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

  72. Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).

    Article  CAS  PubMed  Google Scholar 

  73. Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by a Rubicon postdoctoral scholarship from the Dutch Research Council (NWO) to B.A.M.B.; by funding from the Ragnar Söderberg Foundation (Fellows in Medicine 2016) to M.B.; by funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)–Project-ID 393547839–SFB 1361 and Project-ID 402733153-SPP 2202 to V.R.; and by funding from the Swedish Research Council (2018-02950), the Swedish Cancer Research Foundation (CAN 2018/728), the Ragnar Söderberg Foundation (Fellows in Medicine 2016), and the Strategic Research Programme in Cancer (StratCan) at Karolinska Institutet to N.C.

Author information

Authors and Affiliations

  1. Science for Life Laboratory, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden

    Britta A. M. Bouwman, Federico Agostini, Silvano Garnerone, Magda Bienko & Nicola Crosetto

  2. Institute of Molecular Biology (IMB), Mainz, Germany

    Giuseppe Petrosino, Henrike J. Gothe, Sergi Sayols & Vassilis Roukos

  3. Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland

    Andreas E. Moor

  4. Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel

    Shalev Itzkovitz

Authors
  1. Britta A. M. Bouwman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Federico Agostini
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Silvano Garnerone
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Giuseppe Petrosino
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Henrike J. Gothe
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sergi Sayols
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Andreas E. Moor
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Shalev Itzkovitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Magda Bienko
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Vassilis Roukos
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Nicola Crosetto
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: B.A.M.B., N.C.; data curation: B.A.M.B., F.A., S.G., H.J.G.; formal analysis: F.A., S.G.; funding acquisition: B.A.M.B., S.I., M.B., V.R., N.C.; investigation: B.A.M.B., H.J.G., A.E.M.; methodology: B.A.M.B., N.C.; project administration: N.C.; software: F.A., S.G., G.P., S.S.; supervision: N.C.; visualization: B.A.M.B., F.A.; writing: B.A.M.B. with input from all the other authors.

Corresponding authors

Correspondence to Britta A. M. Bouwman or Nicola Crosetto.

Ethics declarations

Competing interests

N.C. is a co-inventor in a US patent describing applications of BLISS for CRISPR off-target detection. The other authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Toni Cathomen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key references using this protocol

Gothe, H. J. et al. Mol. Cell 75, 267–283.e12 (2019): https://doi.org/10.1016/j.molcel.2019.05.015

Dellino, G. I. et al. Nat. Genet. 51, 1011–1023 (2019): https://doi.org/10.1038/s41588-019-0421-z

Dziubańska-Kusibab, P. J. et al. Nat. Med. 26, 1063–1069 (2020): https://doi.org/10.1038/s41591-020-0908-2

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bouwman, B.A.M., Agostini, F., Garnerone, S. et al. Genome-wide detection of DNA double-strand breaks by in-suspension BLISS. Nat Protoc 15, 3894–3941 (2020). https://doi.org/10.1038/s41596-020-0397-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-020-0397-2

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing