Abstract

Pooled CRISPR screens are a powerful tool for assessments of gene function. However, conventional analysis is based exclusively on the relative abundance of integrated single guide RNAs (sgRNAs) between populations, which does not discern distinct phenotypes and editing outcomes generated by identical sgRNAs. Here we present CRISPR-UMI, a single-cell lineage-tracing methodology for pooled screening to account for cell heterogeneity. We generated complex sgRNA libraries with unique molecular identifiers (UMIs) that allowed for screening of clonally expanded, individually tagged cells. A proof-of-principle CRISPR-UMI negative-selection screen provided increased sensitivity and robustness compared with conventional analysis by accounting for underlying cellular and editing-outcome heterogeneity and detection of outlier clones. Furthermore, a CRISPR-UMI positive-selection screen uncovered new roadblocks in reprogramming mouse embryonic fibroblasts as pluripotent stem cells, distinguishing reprogramming frequency and speed (i.e., effect size and probability). CRISPR-UMI boosts the predictive power, sensitivity, and information content of pooled CRISPR screens.

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Acknowledgements

We acknowledge everybody involved in the generation of data and of the manuscript. We thank J. Zuber (Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna, Austria) for the retroviral backbone used to generate the guide library, and K. Hochedlinger (Department of Molecular Biology, Cancer Center and Center for Regenerative Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Stem Cell and Regenerative Biology and Harvard Stem Cell Institute, Cambridge, Massachusetts, USA; Howard Hughes Medical Institute, Chevy Chase, Maryland, USA) for provision of the Dox-inducible reprogramming system. We thank A. Stark for important suggestions and critical reading of the manuscript, J. Jude for critical discussion and sharing of protocols, and P. Svoboda for discussion and advice. We are grateful to all IMBA/IMP services, in particular bioinformatics, biooptics, molecular biology, media kitchen, and graphics for technical support, as well as to J. Brennecke and A. Andersen (Life Science Editors) for critical reading and editing of the manuscript. We thank the VBCF NGS facility. This work was supported by IMBA, the Austrian Academy of Sciences (OEAW), Novartis Institute of Biomedical Research, and AstraZeneca.

Author information

Affiliations

  1. Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna Biocenter (VBC), Vienna, Austria.

    • Georg Michlits
    • , Maria Hubmann
    • , Szu-Hsien Wu
    • , Gintautas Vainorius
    • , Elena Budusan
    • , Sergei Zhuk
    • , Thomas R Burkard
    • , Maria Novatchkova
    •  & Ulrich Elling
  2. Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC),Vienna, Austria.

    • Thomas R Burkard
    • , Maria Novatchkova
    •  & Martin Aichinger
  3. Center for Systems Biology, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada.

    • Yiqing Lu
    •  & Daniel Schramek
  4. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.

    • Yiqing Lu
    •  & Daniel Schramek
  5. Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, USA.

    • John Reece-Hoyes
  6. Discovery Sciences RAD, AstraZeneca R&D, Gothenburg, Sweden.

    • Roberto Nitsch
  7. Novartis Institutes for BioMedical Research, Basel, Switzerland.

    • Dominic Hoepfner

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Contributions

G.M. and U.E. conceived the study. G.M. cloned the library and performed the etoposide screens and bioinformatic studies. M.H. generated the Cas9 inducible ESC line and supported follow-up experiments. E.B. and U.E. performed the iPSC screen, and S.-H.W., G.V., and U.E. validated it. T.R.B. and M.N. performed bioinformatic analyses. S.Z., Y.L., and D.S. supported experiments. M.A. generated the vector backbone for the sgRNA library. J.R.-H., R.N., and D.H. supported the design and generation of sgRNA libraries. U.E. wrote the manuscript with support from G.M., D.S., D.H., and all other co-authors.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Ulrich Elling.

Integrated supplementary information

Supplementary information

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    Supplementary Text and Figures

    Supplementary Figures 1–10

  2. 2.

    Life Sciences Reporting Summary

Excel files

  1. 1.

    Supplementary Table 1

    sgRNA library content.

  2. 2.

    Supplementary Table 2

    Individual oligonucleotides used for this study.

  3. 3.

    Supplementary Table 3

    Experimental indices used in NGS experiments.

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DOI

https://doi.org/10.1038/nmeth.4466

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