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Massively parallel Cas13 screens reveal principles for guide RNA design

Nature Biotechnology volume 38, pages 722–727 (2020)Cite this article

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Abstract

Type VI CRISPR enzymes are RNA-targeting proteins with nuclease activity that enable specific and robust target gene knockdown without altering the genome. To define rules for the design of Cas13d guide RNAs (gRNAs), we conducted massively parallel screens targeting messenger RNAs (mRNAs) of a green fluorescent protein transgene, and CD46, CD55 and CD71 cell-surface proteins in human cells. In total, we measured the activity of 24,460 gRNAs with and without mismatches relative to the target sequences. Knockdown efficacy is driven by gRNA-specific features and target site context. Single mismatches generally reduce knockdown to a modest degree, but spacer nucleotides 15–21 are largely intolerant of target site mismatches. We developed a computational model to identify optimal gRNAs and confirm their generalizability, testing 3,979 guides targeting mRNAs of 48 endogenous genes. We show that Cas13 can be used in forward transcriptomic pooled screens and, using our model, predict optimized Cas13 gRNAs for all protein-coding transcripts in the human genome.

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Fig. 1: Pooled CRISPR RfxCas13d GFP knockdown tiling screen.
Fig. 2: RfxCas13d on-target gRNA prediction model.
Fig. 3: Improvement of RfxCas13d on-target gRNA prediction model with tiling screens over endogenous transcripts.

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Data availability

Screen data have been deposited at the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) with the accession no. GSE142675. All code and software to reproduce our entire analyses are available on our gitlab repository (https://gitlab.com/sanjanalab/cas13). Moreover, we provide precomputed gRNA predictions targeting all protein-coding transcripts in the human genome on our web-based repository (https://cas13design.nygenome.org). Other data and materials that support the findings of this research are available from the corresponding author upon reasonable request.

Code availability

The predictive on-target model as well as all code for the analyses presented in the letter is available on our gitlab repository (https://gitlab.com/sanjanalab/cas13).

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Acknowledgements

We thank the entire Sanjana laboratory for support and advice. We thank R. Satija for critical feedback. We also thank D. Knowles for discussion about model building and M. Zaran for assistance with the web-tool server. N.E.S. is supported by New York University and New York Genome Center startup funds, National Institutes of Health (NIH)/National Human Genome Research Institute (grant nos. R00HG008171, DP2HG010099), NIH/National Cancer Institute (grant no. R01CA218668), Defense Advanced Research Projects Agency (grant no. D18AP00053), the Sidney Kimmel Foundation, the Melanoma Research Alliance, and the Brain and Behavior Foundation. A.M.-M. is supported by a CONACyT-Mexico Fellowship (no. 412653).

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Author notes

  1. These authors contributed equally: Hans-Hermann Wessels, Alejandro Méndez-Mancilla.

Authors and Affiliations

  1. New York Genome Center, New York, NY, USA

    Hans-Hermann Wessels, Alejandro Méndez-Mancilla, Xinyi Guo, Mateusz Legut, Zharko Daniloski & Neville E. Sanjana

  2. Department of Biology, New York University, New York, NY, USA

    Hans-Hermann Wessels, Alejandro Méndez-Mancilla, Xinyi Guo, Mateusz Legut, Zharko Daniloski & Neville E. Sanjana

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Contributions

H.H.W and N.E.S conceived the project. H.H.W., N.E.S and A.M.-M. designed the experiments. A.M.-M. and H.H.W. performed and analyzed the experiments. H.H.W. analyzed the screen data, and built the gRNA prediction software and online repository. X.G., M.L. and Z.D. helped with post-screen validation experiments. N.E.S. supervised the work. H.H.W. and N.E.S. wrote the manuscript with input from all the authors.

Corresponding author

Correspondence to Neville E. Sanjana.

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Competing interests

The New York Genome Center and New York University have applied for patents relating to the work in this article. N.E.S. is an adviser to Vertex.

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Supplementary information

Supplementary Materials

Supplementary Figs. 1–11, Tables 1–7 and Notes 1 and 2.

Reporting Summary

Supplementary Data 1

Guide RNA enrichments of sorted populations over input populations.

Supplementary Data 2

Combined on-target model input including all features.

Supplementary Data 3

Guide RNA predictions for protein-coding transcripts in GENCODE.

Supplementary Data 4

Oligonucleotide information.

Supplementary Data 5

Statistics of processed gRNA and cell numbers.

Supplementary Data 6

Sequencing read processing statistics.

Supplementary Data 7

Raw gRNA counts.

Supplementary Data 8

Final gRNA counts (after normalization; batch correction; outlier removal).

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Wessels, HH., Méndez-Mancilla, A., Guo, X. et al. Massively parallel Cas13 screens reveal principles for guide RNA design. Nat Biotechnol 38, 722–727 (2020). https://doi.org/10.1038/s41587-020-0456-9

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