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High-throughput analysis of the activities of xCas9, SpCas9-NG and SpCas9 at matched and mismatched target sequences in human cells

Nature Biomedical Engineering volume 4, pages 111–124 (2020)Cite this article

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Abstract

The applications of clustered regularly interspaced short palindromic repeats (CRISPR)-based genome editing can be limited by a lack of compatible protospacer adjacent motifs (PAMs), insufficient on-target activity and off-target effects. Here, we report an extensive comparison of the PAM-sequence compatibilities and the on-target and off-target activities of Cas9 from Streptococcus pyogenes (SpCas9) and the SpCas9 variants xCas9 and SpCas9-NG (which are known to have broader PAM compatibility than SpCas9) at 26,478 lentivirally integrated target sequences and 78 endogenous target sites in human cells. We found that xCas9 has the lowest tolerance for mismatched target sequences and that SpCas9-NG has the broadest PAM compatibility. We also show, on the basis of newly identified non-NGG PAM sequences, that SpCas9-NG and SpCas9 can edit six previously unedited endogenous sites associated with genetic diseases. Moreover, we provide deep-learning models that predict the activities of xCas9 and SpCas9-NG at the target sequences. The resulting deeper understanding of the activities of xCas9, SpCas9-NG and SpCas9 in human cells should facilitate their use.

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Fig. 1: High-throughput evaluation of the xCas9, SpCas9-NG and SpCas9 activities.
Fig. 2: Effects of sgRNA expression formats on the activities of xCas9, SpCas9-NG and SpCas9.
Fig. 3: PAM sequence determination using fixed protospacers for xCas9, SpCas9-NG and SpCas9 in human cells.
Fig. 4: PAM sequence determination for xCas9, SpCas9-NG and SpCas9 using a wider range of protospacers and PAM sequences.
Fig. 5: Activities of xCas9, SpCas9-NG and SpCas9 at mismatched target sequences.
Fig. 6: Development and evaluation of the computational models DeepxCas9 and DeepSpCas9-NG, which predict the activity of xCas9 and SpCas9-NG, respectively.

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Data availability

The authors declare that all data supporting the results in this study are available within the paper and its Supplementary Information. The deep-sequencing data from this study are available at the NCBI Sequence Read Archive under the accession number SRP158724.

Code availability

The source code for DeepxCas9, DeepSpCas9-NG and the custom Python scripts used for the indel-frequency calculations are available on Github at https://github.com/MyungjaeSong/Paired-Library and https://github.com/CRISPRJWCHOI/IndelSearcher.

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Acknowledgements

We thank O. Nureki and H. Nishimasu at the University of Tokyo for sharing a plasmid encoding SpCas9-NG. We thank S. Park and C. Lee at Yonsei University for their assistance with the data analysis. We also thank Y. Kim and S. Park at Yonsei University for their assistance with experiments. We thank S. Miller at Harvard University for critical reading of the manuscript. This work was supported in part by the National Research Foundation of Korea (grant nos 2017R1A2B3004198, 2017M3A9B4062403 and 2018R1A5A2025079 to H.H.K.), Brain Korea 21 Plus Project (Yonsei University College of Medicine), Institute for Basic Science (IBS; grant no. IBS-R026-D1), Yonsei University Future-leading Research Initiative of 2015 (grant no. RMS2 2015-22-0092; Challenge Grant), Korean Health Technology R&D Project, Ministry of Health and Welfare of the Republic of Korea (grant nos HI17C0676 and HI16C1012 to H.H.K.), US NIH (grant nos RM1 HG009490, R01 EB022376 and R35 GM118062 to D.R.L.) and HHMI (D.R.L.).

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Authors and Affiliations

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

    Hui Kwon Kim, Sungtae Lee, Younggwang Kim, Jinman Park, Jae Woo Choi & Hyongbum Henry Kim

  2. Brain Korea 21 Plus Project for Medical Sciences, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hui Kwon Kim, Younggwang Kim, Jinman Park & Hyongbum Henry Kim

  3. Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea

    Hui Kwon Kim & Hyongbum Henry Kim

  4. Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea

    Hui Kwon Kim & Hyongbum Henry Kim

  5. Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea

    Seonwoo Min & Sungroh Yoon

  6. Severance Biomedical Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea

    Jae Woo Choi & Hyongbum Henry Kim

  7. Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Tony P. Huang & David R. Liu

  8. Howard Hughes Medical Institute, Harvard University, Cambridge, MA, USA

    Tony P. Huang & David R. Liu

  9. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

    Tony P. Huang & David R. Liu

  10. Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea

    Sungroh Yoon

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Contributions

H.K.K. performed experiments to build high-throughput datasets of xCas9- and SpCas9-induced indel frequencies. Y.K. contributed to the generation of the high-throughput datasets. S.L., S.M. and S.Y. developed the framework and carried out the model training and computational validation. T.P.H. provided the computational identification of the endogenous disease-relevant sites. H.K.K. evaluated the activities of the Cas9 variants at the endogenous sites. J.P. and J.W.C. contributed significantly to the bioinformatics analyses. H.K.K. and H.H.K. conceived and designed the study and analysed the data. H.K.K., D.R.L. and H.H.K. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Hyongbum Henry Kim.

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Competing interests

The authors declare that Yonsei University has filed a patent based on this work, in which H.K.K. and H.H.K. are the co-inventors (patent no. PCT/KR2019/011166). D.R.L. is a consultant and co-founder of Beam Therapeutics, Prime Medicine, Editas Medicine and Pairwise Plants, which are companies that use genome editing.

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Supplementary information

Supplementary Information

Supplementary Text, Supplementary Figs. and Supplementary Tables.

Reporting Summary

Supplementary Dataset 1

Design and indel frequencies from library A.

Supplementary Dataset 2

Design and indel frequencies from library B.

Supplementary Dataset 3

Datasets obtained from endogenous target sites.

Supplementary Dataset 4

Average indel frequencies in the target sequences, grouped by different potential PAM sequences for xCas9 and SpCas9 on the basis of fixed protospacers.

Supplementary Dataset 5

Average indel frequencies at target sequences, grouped by five-nucleotide PAM sequences.

Supplementary Dataset 6

Average indel frequencies at target sequences, grouped by four-nucleotide PAM sequences.

Supplementary Dataset 7

Primer sequences.

Supplementary Dataset 8

Model selection for DeepxCas9 and DeepSpCas9-NG.

Supplementary Dataset 9

P values and sample sizes for the data in Fig. 5.

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Kim, H.K., Lee, S., Kim, Y. et al. High-throughput analysis of the activities of xCas9, SpCas9-NG and SpCas9 at matched and mismatched target sequences in human cells. Nat Biomed Eng 4, 111–124 (2020). https://doi.org/10.1038/s41551-019-0505-1

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