Abstract

CRISPR from Prevotella and Francisella 1 (Cpf1) is an effector endonuclease of the class 2 CRISPR–Cas (clustered regularly interspaced short palindromic repeats–CRISPR-associated proteins) gene editing system. We developed a method for evaluating Cpf1 activity, based on target sequence composition in mammalian cells, in a high-throughput manner. A library of >11,000 target sequence and guide RNA pairs was delivered into human cells using lentiviral vectors. Subsequent delivery of Cpf1 into this cell library induced insertions and deletions (indels) at the integrated synthetic target sequences, which allowed en masse evaluation of Cpf1 activity by using deep sequencing. With this approach, we determined protospacer-adjacent motif sequences of two Cpf1 nucleases, one from Acidaminococcus sp. BV3L6 (hereafter referred to as AsCpf1) and the other from Lachnospiraceae bacterium ND2006 (hereafter referred to as LbCpf1). We also defined target-sequence-dependent activity profiles of AsCpf1, which enabled the development of a web tool that predicts the indel frequencies for given target sequences (http://big.hanyang.ac.kr/cindel). Both the Cpf1 characterization profile and the in vivo high-throughput evaluation method will greatly facilitate Cpf1-based genome editing.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Accessions

Primary accessions

Sequence Read Archive

Referenced accessions

Protein Data Bank

References

  1. 1.

    & A guide to genome engineering with programmable nucleases. Nat. Rev. Genet. 15, 321–334 (2014).

  2. 2.

    , & Development and applications of CRISPR–Cas9 for genome engineering. Cell 157, 1262–1278 (2014).

  3. 3.

    et al. Cpf1 is a single-RNA-guided endonuclease of a class 2 CRISPR–Cas system. Cell 163, 759–771 (2015).

  4. 4.

    et al. Surrogate reporter-based enrichment of cells containing RNA-guided Cas9 nuclease-induced mutations. Nat. Commun. 5, 3378 (2014).

  5. 5.

    et al. Surrogate reporters for enrichment of cells with nuclease-induced mutations. Nat. Methods 8, 941–943 (2011).

  6. 6.

    et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).

  7. 7.

    , , & Unraveling CRISPR–Cas9 genome-engineering parameters via a library-on-library approach. Nat. Methods 12, 823–826 (2015).

  8. 8.

    et al. HIV-1 integration in the human genome favors active genes and local hotspots. Cell 110, 521–529 (2002).

  9. 9.

    et al. Retroviral DNA integration: ASLV, HIV, and MLV show distinct target site preferences. PLoS Biol. 2, E234 (2004).

  10. 10.

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

  11. 11.

    et al. Genome-scale CRISPR–Cas9 knockout screening in human cells. Science 343, 84–87 (2014).

  12. 12.

    , , & Genetic screens in human cells using the CRISPR–Cas9 system. Science 343, 80–84 (2014).

  13. 13.

    et al. Crystal structure of Cpf1 in complex with guide RNA and target DNA. Cell 165, 949–962 (2016).

  14. 14.

    et al. Rational design of highly active sgRNAs for CRISPR–Cas9-mediated gene inactivation. Nat. Biotechnol. 32, 1262–1267 (2014).

  15. 15.

    et al. Genome-wide specificities of CRISPR–Cas Cpf1 nucleases in human cells. Nat. Biotechnol. 34, 869–874 (2016).

  16. 16.

    et al. Gene disruption by cell-penetrating peptide-mediated delivery of Cas9 protein and guide RNA. Genome Res. 24, 1020–1027 (2014).

  17. 17.

    et al. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR–Cas nucleases. Nat. Biotechnol. 33, 187–197 (2015).

  18. 18.

    et al. Genome-wide analysis reveals specificities of Cpf1 endonucleases in human cells. Nat. Biotechnol. 34, 863–868 (2016).

  19. 19.

    , , , & Improving CRISPR–Cas nuclease specificity using truncated guide RNAs. Nat. Biotechnol. 32, 279–284 (2014).

  20. 20.

    et al. A high-throughput optomechanical retrieval method for sequence-verified clonal DNA from the NGS platform. Nat. Commun. 6, 6073 (2015).

  21. 21.

    , & Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).

  22. 22.

    , & Quantification bias caused by plasmid DNA conformation in quantitative real-time PCR assay. PLoS One 6, e29101 (2011).

  23. 23.

    , , , & Analysis of the cytogenetic stability of the human embryonal kidney cell line 293 by cytogenetic and STR profiling approaches. Cytogenet. Genome Res. 106, 28–32 (2004).

  24. 24.

    et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR–Cas9. Nat. Biotechnol. 34, 184–191 (2016).

  25. 25.

    , & WU–CRISPR: characteristics of functional guide RNAs for the CRISPR–Cas9 system. Genome Biol. 16, 218 (2015).

  26. 26.

    , & Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

  27. 27.

    et al. Sequence determinants of improved CRISPR sgRNA design. Genome Res. 25, 1147–1157 (2015).

  28. 28.

    , , & ROCR: visualizing classifier performance in R. Bioinformatics 21, 3940–3941 (2005).

Download references

Acknowledgements

The authors would like to thank D.-S. Jang (Medical Illustrator, Medical Research Support Section, Yonsei University College of Medicine) for his help with the illustrations, D. Kim for developing Python programs, M. K. Song (Biostatistics Collaboration Unit, Yonsei University College of Medicine) and Y. Kim for the assistance with statistical analysis, E.-S. Lee for proofreading, R. Gopalappa for assistance with sample preparation, Severance Biomedical Science Institute for bioinformatics analysis, and B. Kleinstiver and K. Joung (both at Massachusetts General Hospital) for sharing raw data for Supplementary Figure 9 and Figure 3d. This work was supported in part by the National Research Foundation of Korea (grants 2014R1A1A1A05006189 (H.K.), 2013M3A9B4076544 (H.K.), 2014M3C9A3063541 (J.-W.N.), and 2015R1A2A1A15052668 (H.K.)), the Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (grants HI14C2019 (Medistar program) (H.K.), HI16C1012 (H.K.), and HI15C1578 (J.-W.N.)), and a faculty research grant of Yonsei University College of Medicine for 2015 (6-2015-0086 (H.K.)).

Author information

Author notes

    • Hui K Kim
    •  & Myungjae Song

    These authors contributed equally to this work.

Affiliations

  1. Department of Pharmacology, Yonsei University College of Medicine, Seoul, South Korea.

    • Hui K Kim
    • , Myungjae Song
    • , Soobin Jung
    •  & Hyongbum Kim
  2. Brain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine, Seoul, South Korea.

    • Hui K Kim
    • , Soobin Jung
    •  & Hyongbum Kim
  3. Graduate School of Biomedical Science and Engineering, Hanyang University, Seoul, South Korea.

    • Myungjae Song
    •  & Hyun C Koh
  4. College of Pharmacy, Yonsei Institute of Pharmaceutical Sciences, Yonsei University, Incheon, South Korea.

    • Jinu Lee
  5. Department of Life Science, College of Natural Sciences, Hanyang University, Seoul, South Korea.

    • A Vipin Menon
    • , Young-Mook Kang
    •  & Jin-Wu Nam
  6. Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, South Korea.

    • Jae W Choi
    •  & Hyongbum Kim
  7. Drug Target Structure Research Center, Korea Institute of Bioscience and Biotechnology, Daejeon, South Korea.

    • Euijeon Woo
  8. Department of Pharmacology, College of Medicine, Hanyang University, Seoul, South Korea.

    • Hyun C Koh
  9. Graduate Program of Nano Science and Technology, Yonsei University, Seoul, South Korea.

    • Hyongbum Kim

Authors

  1. Search for Hui K Kim in:

  2. Search for Myungjae Song in:

  3. Search for Jinu Lee in:

  4. Search for A Vipin Menon in:

  5. Search for Soobin Jung in:

  6. Search for Young-Mook Kang in:

  7. Search for Jae W Choi in:

  8. Search for Euijeon Woo in:

  9. Search for Hyun C Koh in:

  10. Search for Jin-Wu Nam in:

  11. Search for Hyongbum Kim in:

Contributions

H.K.K. and M.S. performed most of the experiments (including library preparation, lentivirus generation, cell culture, and deep-sequencing experiments), analyzed the data, and contributed to the writing of the manuscript; J.L. generated lentivirus, determined MOI after transduction, and contributed to the writing of the manuscript; A.V.M., Y.-M.K., and J.-W.N. developed the computation model for AsCpf1 indel frequency prediction and contributed to the writing of the manuscript; S.J. significantly contributed to the performance of the experiments including cell culture, lentivirus generation, and deep-sequencing; J.W.C. calculated chromatin accessibility scores and performed related analyses; E.W. contributed to the analysis of target sequence nucleotide preferences based on the structure of AsCpf1 and to the writing of the manuscript; H.C.K. contributed to the writing of the manuscript; and H.K. conceived and supervised the study, analyzed the data, and wrote the manuscript.

Competing interests

Yonsei University has filed a patent based on this work, in which H.K.K., M.S., and H.K. are co-inventors.

Corresponding author

Correspondence to Hyongbum Kim.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–15, Supplementary Tables 1–4, Supplementary Results and Supplementary Notes 1–6.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nmeth.4104

Further reading