Abstract

CRISPR-based genetic screens are accelerating biological discovery, but current methods have inherent limitations. Widely used pooled screens are restricted to simple readouts including cell proliferation and sortable marker proteins. Arrayed screens allow for comprehensive molecular readouts such as transcriptome profiling, but at much lower throughput. Here we combine pooled CRISPR screening with single-cell RNA sequencing into a broadly applicable workflow, directly linking guide RNA expression to transcriptome responses in thousands of individual cells. Our method for CRISPR droplet sequencing (CROP-seq) enables pooled CRISPR screens with single-cell transcriptome resolution, which will facilitate high-throughput functional dissection of complex regulatory mechanisms and heterogeneous cell populations.

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Acknowledgements

We would like to thank J. Bigenzahn, A. Fauster, and M. Owusu (CeMM) for providing Cas9-expressing cell lines; M. Farlik for contributing to the Drop-seq setup; F. Müller and J. Menche for bioinformatic discussions; N. Winhofer for feedback on the illustrations; the Biomedical Sequencing Facility at CeMM for assistance with next-generation sequencing; and all members of the Bock lab for their help and advice. C.S. is supported by a Feodor Lynen Fellowship of the Alexander von Humboldt Foundation. C.B. is supported by a New Frontiers Group award of the Austrian Academy of Sciences and by an ERC Starting Grant (European Union's Horizon 2020 research and innovation programme, grant agreement no. 679146).

Author information

Author notes

    • André F Rendeiro
    •  & Christian Schmidl

    These authors contributed equally to this work.

Affiliations

  1. CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria.

    • Paul Datlinger
    • , André F Rendeiro
    • , Christian Schmidl
    • , Thomas Krausgruber
    • , Peter Traxler
    • , Johanna Klughammer
    • , Linda C Schuster
    • , Amelie Kuchler
    • , Donat Alpar
    •  & Christoph Bock
  2. Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria.

    • Christoph Bock
  3. Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany.

    • Christoph Bock

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Contributions

P.D., A.F.R., C.S., and C.B. conceptualized the project; P.D. designed and developed CROP-seq; P.D., C.S., P.T., and L.C.S. conducted CROP-seq experiments; D.A. optimized sequencing protocols; P.D., T.K., L.C.S., and A.K. performed the arrayed validation screen; A.F.R. and J.K. developed software; P.D., A.F.R., C.S., and T.K. analyzed data; P.D. and A.F.R. visualized data; P.D., A.F.R., C.S., and C.B. wrote the original draft; T.K., P.T., L.C.S., A.K., and D.A. reviewed the draft; and C.B. supervised the project.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Christoph Bock.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–9 and Supplementary Protocol

Excel files

  1. 1.

    Supplementary Table 1

    Oligonucleotide sequences used for developing and validating CROP-seq

  2. 2.

    Supplementary Table 2

    gRNA library used for the CROP-seq T cell receptor screen

  3. 3.

    Supplementary Table 3

    Arrayed validation screen for the CROP-seq T cell receptor screen

Text files

  1. 1.

    Supplementary Data

    CROPseq-Guide-Puro plasmid sequence

Zip files

  1. 1.

    Supplementary Software

    Source code for CROP-seq computational analyses

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DOI

https://doi.org/10.1038/nmeth.4177

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