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CRISPR off-target detection with DISCOVER-seq

Nature Protocols volume 15, pages 1775–1799 (2020)Cite this article

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Abstract

DISCOVER-seq (discovery of in situ Cas off-targets and verification by sequencing) is a broadly applicable approach for unbiased CRISPR–Cas off-target identification in cells and tissues. It leverages the recruitment of DNA repair factors to double-strand breaks (DSBs) after genome editing with CRISPR nucleases. Here, we describe a detailed experimental protocol and analysis pipeline with which to perform DISCOVER-seq. The principle of this method is to track the precise recruitment of MRE11 to DSBs by chromatin immunoprecipitation followed by next-generation sequencing. A customized open-source bioinformatics pipeline, BLENDER (blunt end finder), then identifies off-target sequences genome wide. DISCOVER-seq is capable of finding and measuring off-targets in primary cells and in situ. The two main advantages of DISCOVER-seq are (i) low false-positive rates because DNA repair enzyme binding is required for genome edits to occur and (ii) its applicability to a wide variety of systems, including patient-derived cells and animal models. The whole protocol, including the analysis, can be completed within 2 weeks.

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Fig. 1: Overview of DISCOVER-seq workflow and quality controls.
Fig. 2: Comparison of MRE11 antibodies.
Fig. 3: Quality controls of a DISCOVER-seq experiment.
Fig. 4: Example output of a successful DISCOVER-seq experiment.

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Data availability

Data that support the findings of this study have been deposited in SRA BioProject accession no. PRJNA509652.

Code availability

Our open-source analysis pipeline, called BLENDER (https://github.com/staciawyman/blender), is freely available under the GNU Affero General Public License. The code in this paper has been peer reviewed.

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Acknowledgements

J.E.C. and C.D.Y. were supported by the NOMIS Foundation, the Lotte and Adolf Hotz-Sprenger Stiftung, the Swiss National Science Foundation (310030_188858), the Fanconi Anemia Research Foundation, and the Bill and Melinda Gates Foundation. B.W. was supported by an NHMRC Early Career Fellowship. B.R.C and B.W. received support from the Gladstone Institutes and NIH grants R01-EY028249, R01-HL130533 and R01-HL13535801. S.K.W. was supported by the Li Ka Shing Foundation. The authors used the Vincent J. Coates Genomics Sequencing Laboratory at UC Berkeley, which is supported by NIH Instrumentation grant S10 OD018174.

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Authors and Affiliations

  1. Innovative Genomics Institute, University of California, Berkeley, Berkeley, California, USA

    Beeke Wienert, Stacia K. Wyman & Bruce R. Conklin

  2. Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, California, USA

    Beeke Wienert & Bruce R. Conklin

  3. Department of Biology, ETH Zurich, Zurich, Switzerland

    Charles D. Yeh & Jacob E. Corn

  4. Departments of Medicine, Ophthalmology & Pharmacology, University of California, San Francisco, San Francisco, California, USA

    Bruce R. Conklin

Authors
  1. Beeke Wienert
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  2. Stacia K. Wyman
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  3. Charles D. Yeh
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  4. Bruce R. Conklin
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  5. Jacob E. Corn
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Contributions

B.W., S.K.W. and J.E.C. wrote the manuscript with input from all authors. B.W. and S.K.W. developed the original experimental protocol in the J.E.C. lab. C.D.Y. performed ChIP-qPCR time-course experiments. B.W. performed all other experiments. S.K.W. analyzed ChIP-seq data and developed BLENDER software. B.R.C. provided reagents and expertise.

Corresponding authors

Correspondence to Beeke Wienert or Jacob E. Corn.

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Competing interests

J.E.C. is a cofounder of Spotlight Therapeutics. J.E.C. has received sponsored research support from AstraZeneca and Pfizer. B.R.C. is a founder of Tenaya Therapeutics.

Additional information

Peer review information Nature Protocols thanks Britta Bouwman, Nicola Crosetto 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.

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Key references using this protocol

Wienert, B. et al. Science 364, 286–289 (2019): https://science.sciencemag.org/content/364/6437/286

Richardson, C. D. et al. Nat. Genet. 50, 1132–1139 (2018): https://www.nature.com/articles/s41588-018-0174-0

Integrated supplementary information

Supplementary Figure 1 Uncropped western blot images.

(a) Whole blot images corresponding to Fig. 2a. Cross-reactivity between human and mouse of five different commercially available MRE11 antibodies as determined by Western Blot. Whole cell extracts (30 μg) from human K562 cells and murine B16-F10 cells were used for the analysis. (b) Whole blot images corresponding to Fig. 2c. Increasing amounts of antibody lead to more MRE11 depletion from cell lysate, as determined by Western Blot. However, increasing pulldown efficiency with larger amounts of antibody is not directly reflected in the levels of eluted MRE11, possibly due to non-linearity of Western blots. FT – flow through, IP - immunoprecipitation.

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Wienert, B., Wyman, S.K., Yeh, C.D. et al. CRISPR off-target detection with DISCOVER-seq. Nat Protoc 15, 1775–1799 (2020). https://doi.org/10.1038/s41596-020-0309-5

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