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Functional genomics platform for pooled screening and generation of mammalian genetic interaction maps

Nature Protocols volume 9, pages 18251847 (2014) | Download Citation

  • A Corrigendum to this article was published on 26 March 2015

This article has been updated

Abstract

Systematic genetic interaction maps in microorganisms are powerful tools for identifying functional relationships between genes and for defining the function of uncharacterized genes. We have recently implemented this strategy in mammalian cells as a two-stage approach. First, genes of interest are robustly identified in a pooled genome-wide screen using complex shRNA libraries. Second, phenotypes for all pairwise combinations of 'hit' genes are measured in a double-shRNA screen and used to construct a genetic interaction map. Our protocol allows for rapid pooled screening under various conditions without a requirement for robotics, in contrast to arrayed approaches. Each round of screening can be implemented in 2 weeks, with additional time for analysis and generation of reagents. We discuss considerations for screen design, and we present complete experimental procedures, as well as a full computational analysis suite for the identification of hits in pooled screens and generation of genetic interaction maps. Although the protocol outlined here was developed for our original shRNA-based approach, it can be applied more generally, including to CRISPR-based approaches.

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Change history

  • 16 January 2015

     In the version of this article initially published, a sentence in Step 49 read "Order the top and bottom oligonucleotides corresponding to hit shRNA sequences formatted as in the example below for the target site TTTCTTACTCACCCTAAGAACT". The sentence has been corrected to replace 'target site' with 'guide sequence'. The error has been corrected in the HTML and PDF versions of the article.

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Acknowledgements

We thank E. LeProust and S. Chen of Agilent Technologies for oligonucleotide libraries; Y. Chen and S. Wang for their technical assistance and feedback on the protocol; M. Horlbeck for testing pMK1200; Y. Chen, M. Horlbeck and L. Gilbert for sharing gel images; and S. Collins for helpful discussions on data analysis. M.K. was funded by fellowships from the Jane Coffin Childs Memorial Fund for Medical Research and the University of California, San Francisco Program for Breakthrough Biomedical Research; M.C.B. was funded by a fellowship from the Leukemia and Lymphoma Society; J.S.W. was funded by the Howard Hughes Medical Institute, National Institutes of Health Grant no. 1U01CA168370-01, and a Howard Hughes Collaborative Initiative Award.

Author information

Author notes

    • Michael C Bassik

    Present address: Department of Genetics, Stanford University, Stanford, California, USA.

    • Martin Kampmann
    •  & Michael C Bassik

    These authors contributed equally to this work.

Affiliations

  1. Department of Cellular and Molecular Pharmacology, California Institute for Quantitative Biomedical Research, University of California, San Francisco, San Francisco, California, USA.

    • Martin Kampmann
    • , Michael C Bassik
    •  & Jonathan S Weissman
  2. Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, California, USA.

    • Martin Kampmann
    • , Michael C Bassik
    •  & Jonathan S Weissman

Authors

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Contributions

M.K., M.C.B. and J.S.W. designed the study; M.K. and M.C.B. developed the experimental methods; M.K. developed the quantitative framework and scripts for computational analysis; M.K., M.C.B. and J.S.W. wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Martin Kampmann or Michael C Bassik.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Figure 1

    Graphical output of the script analyze_primary_screen.py.

  2. 2.

    Supplementary Figure 2

    Graphical output of the script analyze_primary_screen.py.

  3. 3.

    Supplementary Figure 3

    Graphical output of the script double_shRNA_phenotypes.py.

  4. 4.

    Supplementary Figure 4

    Graphical output of the script double_shRNA_phenotypes.py.

  5. 5.

    Supplementary Figure 5

    Graphical output of the script calculate_GIs.py.

  6. 6.

    Supplementary Figure 6

    Graphical output of the script calculate_GIs.py.

  7. 7.

    Supplementary Figure 7

    Graphical output of the script compare_GIs.py.

  8. 8.

    Supplementary Figure 8

    Graphical output of the script filter_GIs.py.

  9. 9.

    Supplementary Table 1

    Oligonucleotide sequences. Illumina index sequences are shown in red.

  10. 10.

    Supplementary Data 1

    Sequence of pMK1047, the backbone used for primary shRNA screens9,10.

  11. 11.

    Supplementary Data 2

    Sequence of pMK1200, the backbone used for cloning and barcoding of individual shRNAs9.

About this article

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DOI

https://doi.org/10.1038/nprot.2014.103

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