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CRISPR-SURF: discovering regulatory elements by deconvolution of CRISPR tiling screen data

Nature Methods volume 15, pages 992–993 (2018)Cite this article

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To the Editor – Tiling screens that use CRISPR–Cas technologies provide a powerful approach for the mapping of regulatory elements to phenotypes of interest1,2,3,4,5,6. Here we present CRISPR screening uncharacterized region function (CRISPR-SURF), a deconvolution framework that can be used to identify functional regulatory regions in the genome from data generated by CRISPR–Cas nuclease, CRISPR interference (CRISPRi), or CRISPR activation (CRISPRa) tiling screens. CRISPR-SURF can be run as a stand-alone command line utility (https://github.com/pinellolab/CRISPR-SURF) or as a web application (http://crisprsurf.pinellolab.org/) (Supplementary Note 1).

The methodology underlying the CRISPR-SURF framework leverages the concept that single guide RNAs (sgRNAs) represent a functional readout for base pairs within the perturbation range. This range depends on the CRISPR screening approach used: CRISPR–Cas nucleases introduce insertion and deletion (indel) mutations of varying lengths (typically <30 bp, although potentially varying with cell type), whereas CRISPRi and CRISPRa strategies may remodel chromatin structure across hundreds of nucleotides. Importantly, each CRISPR technology offers its own advantage: CRISPRi and CRISPRa strategies increase the likelihood of detecting regulatory elements, given their larger perturbation ranges, whereas CRISPR–Cas nucleases provide higher resolution on the boundaries of regulatory elements, given their sharper perturbation windows. Because each sgRNA perturbs variable-size regions around its target site, the sgRNA data from CRISPR tiling screens can be seen as imprecise measurements of an underlying genomic regulatory signal. To address this variable, we model these imprecise measurements by means of a convolution operation that accounts for the perturbation profiles associated with different CRISPR technologies.

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Fig. 1: CRISPR-SURF deconvolution framework.

Data availability

The data are available at SRA under project number PRJNA494935.

References

  1. Canver, M. C. et al. Nature 527, 192–197 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  2. Fulco, C. P. et al. Science 354, 769–773 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  3. Simeonov, D. R. et al. Nature 549, 111–115 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  4. Korkmaz, G. et al. Nat. Biotechnol. 34, 192–198 (2016).

    Article  CAS  PubMed  Google Scholar 

  5. Sanjana, N. E. et al. Science 353, 1545–1549 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Klann, T. S. et al. Nat. Biotechnol. 35, 561–568 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. Bauer, D. E. et al. Science 342, 253–257 (2013).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  8. Vierstra, J. et al. Nat. Methods 12, 927–930 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgements

We thank R. Kurita and Y. Nakamura (RIKEN BioResource Center, Tsukuba, Japan) for sharing HUDEP-2 cells. L.P. is supported by NHGRI Career Development Award R00 HG008399 and CEGS RM1HG009490. D.E.B. is support by NIH R03 DK109232, NIH DP2 OD022716, NIH P01 HL032262, the Burroughs Wellcome Fund, and the Doris Duke Charitable Foundation. J.K.J. is supported by NIH R35 GM118158, NIH RM1 HG009490, and the Desmond and Ann Heathwood MGH Research Scholar Award. M.C.C. is supported by NIDDK Award F30-DK103359. J.M.E. is supported by NIH NHGRI 1K99HG009917-01 and the Harvard Society of Fellows.

Author information

Authors and Affiliations

  1. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Jonathan Y. Hsu

  2. Molecular Pathology Unit, Center for Cancer Research, Center for Computational and Integrative Biology, Massachusetts General Hospital, Charlestown, MA, USA

    Jonathan Y. Hsu, Matthew C. Canver, Rick Farouni, Kendell Clement, Jimmy A. Guo, J. Keith Joung & Luca Pinello

  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Charles P. Fulco, Rick Farouni, Kendell Clement, Jesse M. Engreitz, Eric S. Lander & Luca Pinello

  4. Department of Systems Biology, Harvard Medical School, Boston, MA, USA

    Charles P. Fulco & Eric S. Lander

  5. Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA, USA

    Mitchel A. Cole, Matthew C. Canver, Falak Sher, Stuart H. Orkin & Daniel E. Bauer

  6. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Mitchel A. Cole, Matthew C. Canver, Falak Sher, Stuart H. Orkin & Daniel E. Bauer

  7. Harvard Stem Cell Institute, Department of Pediatrics, Harvard Medical School, Boston, MA, USA

    Mitchel A. Cole, Matthew C. Canver, Falak Sher, Stuart H. Orkin & Daniel E. Bauer

  8. Gene Therapy Program, Harvard Medical School, Boston, MA, USA

    Danilo Pellin & Luca Biasco

  9. Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Boston, MA, USA

    Danilo Pellin & Luca Biasco

  10. Department of Pathology, Harvard Medical School, Boston, MA, USA

    Rick Farouni, Kendell Clement, J. Keith Joung & Luca Pinello

  11. Howard Hughes Medical Institute, Boston, MA, USA

    Stuart H. Orkin

  12. Harvard Society of Fellows, Harvard University, Cambridge, MA, USA

    Jesse M. Engreitz

  13. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Eric S. Lander

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  1. Jonathan Y. Hsu
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Contributions

J.Y.H. and L.P. conceived of and developed the CRISPR-SURF framework. M.A.C., M.C.C., and F.S. performed the experiments. C.P.F., D.P., R.F., K.C., J.A.G., L.B., S.H.O., J.M.E., and E.S.L. provided statistical and experimental expertise. J.K.J., L.P., and D.E.B. oversaw the project and offered feedback and guidance. J.Y.H., L.P., D.E.B., and J.K.J. wrote the manuscript with input from all other authors.

Corresponding authors

Correspondence to Daniel E. Bauer or Luca Pinello.

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Competing interests

J.K.J. has financial interests in Beam Therapeutics, Editas Medicine, Endcadia, Monitor Biotechnologies (formerly known as Beacon Genomics), Pairwise Plants, Poseida Therapeutics, and Transposagen Biopharmaceuticals. J.K.J. holds equity in EpiLogic Therapeutics. 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. J.K.J. is a member of the Board of Directors of the American Society of Gene and Cell Therapy, and an inventor on patents and patent applications covering CRISPR-based nucleases and gene regulatory proteins. E.S.L. serves on the Board of Directors for Codiak BioSciences and Neon Therapeutics, and on the Scientific Advisory Board of F-Prime Capital Partners and Third Rock Ventures; he is also affiliated with several nonprofit organizations, including through his service on the Board of Directors of the Innocence Project and Biden Cancer Initiative and the Board of Trustees for the Parker Institute for Cancer Immunotherapy. He has served and continues to serve on various federal advisory committees. The Broad Institute, which E.S.L. directs, holds patents and has filed patent applications on technologies related to other aspects of CRISPR.

Integrated supplementary information

Supplementary Figure 1 Reanalysis of a CRISPR–Cas9 tiling screen from Canver et al.1.

(a) An overview of the BCL11A CRISPR–Cas9 enhancer dissection tiling screen. (b) Zoom-in panels of DHS +55, +58, +62, and BCL11A exon 2 to highlight critical regions identified by CRISPR-SURF. All significant regions identified with FDR < 0.05. All panels are shown at same scale.

Supplementary Figure 2 Reanalysis of a CRISPRi tiling screen from Fulco et al.2.

(a) An overview of the MYC CRISPRi enhancer discovery tiling screen. (b) Zoom-in panels of MYC TSS, e1–e7, and r1 (regions identified in ref. 2) along with newly identified regions by CRISPR-SURF (SURF1 and SURF2). All significant regions identified with FDR < 0.05. All panels are shown at same scale.

Supplementary Figure 3 Reanalysis of a CRISPRa tiling screen from Simeonov et al.3.

(a) An overview of the IL2RA CRISPRa enhancer discovery tiling screen. (b) Zoom-in panels of IL2RA TSS and CaREs 1–6 (regions identified in ref. 3) along with regions newly identified by CRISPR-SURF (SURF3 and SURF4). All significant regions identified with FDR < 0.05. All panels are shown at same scale.

Supplementary Figure 4 CRISPR-SURF analysis of parallel CRISPRi and CRISPR–Cas9 DHS tiling screens targeted to the BCL11A locus.

(a) An overview of the BCL11A CRISPRi and CRISPR–Cas9 DHS tiling screens. (b) Shown are zoom-in panels of BCL11A exon 2 and common significant regions (FDR < 0.05) between the CRISPRi and CRISPR–Cas9 tiling screens as determined by CRISPR-SURF.

Supplementary Information

Supplementary Text and Figures

Supplementary Figures 1–4 and Supplementary Notes 1–7

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Hsu, J.Y., Fulco, C.P., Cole, M.A. et al. CRISPR-SURF: discovering regulatory elements by deconvolution of CRISPR tiling screen data. Nat Methods 15, 992–993 (2018). https://doi.org/10.1038/s41592-018-0225-6

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