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Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping

Nature Methods volume 10, pages 427–431 (2013)Cite this article

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Abstract

Genetic interactions influence many phenotypes and can be used as a powerful experimental tool to discover functional relationships between genes. Here we describe a robust and scalable method to systematically map genetic interactions in human cancer cells using combinatorial RNAi and high-throughput imaging. Through automated, single-cell phenotyping, we measured genetic interactions across a broad spectrum of phenotypes, including cell count, cell eccentricity and nuclear area. We mapped genetic interactions of epigenetic regulators in colon cancer cells, recovering known protein complexes. Our study also revealed the prospects and challenges of studying genetic interactions in human cells using multiparametric phenotyping.

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Figure 1: Combinatorial RNAi and multiparametric phenotyping.
Figure 2: Quality control.
Figure 3: Genetic interactions.
Figure 4: Genetic interaction profiles.

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References

  1. Hartman, J.L. IV., Garvik, B. & Hartwell, L. Principles for the buffering of genetic variation. Science 291, 1001–1004 (2001).

    Article  CAS  Google Scholar 

  2. Farmer, H. et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434, 917–921 (2005).

    Article  CAS  Google Scholar 

  3. Huang, S. et al. MED12 controls the response to multiple cancer drugs through regulation of TGF-β receptor signaling. Cell 151, 937–950 (2012).

    Article  CAS  Google Scholar 

  4. Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).

    Article  CAS  Google Scholar 

  5. Dixon, S.J., Costanzo, M., Baryshnikova, A., Andrews, B. & Boone, C. Systematic mapping of genetic interaction networks. Annu. Rev. Genet. 43, 601–625 (2009).

    Article  CAS  Google Scholar 

  6. Bandyopadhyay, S. et al. Rewiring of genetic networks in response to DNA damage. Science 330, 1385–1389 (2010).

    Article  CAS  Google Scholar 

  7. Baryshnikova, A. et al. Quantitative analysis of fitness and genetic interactions in yeast on a genome scale. Nat. Methods 7, 1017–1024 (2010).

    Article  CAS  Google Scholar 

  8. Nichols, R.J. et al. Phenotypic landscape of a bacterial cell. Cell 144, 143–156 (2011).

    Article  CAS  Google Scholar 

  9. Bernards, R. A missing link in genotype-directed cancer therapy. Cell 151, 465–468 (2012).

    Article  CAS  Google Scholar 

  10. Brough, R. et al. Functional viability profiles of breast cancer. Cancer Discov. 1, 260–273 (2011).

    Article  CAS  Google Scholar 

  11. Luo, J. et al. A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the Ras oncogene. Cell 137, 835–848 (2009).

    Article  CAS  Google Scholar 

  12. Sandmann, T. & Boutros, M. Screens, maps & networks: from genome sequences to personalized medicine. Curr. Opin. Genet. Dev. 22, 36–44 (2012).

    Article  CAS  Google Scholar 

  13. Boutros, M. & Ahringer, J. The art and design of genetic screens: RNA interference. Nat. Rev. Genet. 9, 554–566 (2008).

    Article  CAS  Google Scholar 

  14. Boehm, J.S. & Hahn, W.C. Towards systematic functional characterization of cancer genomes. Nat. Rev. Genet. 12, 487–498 (2011).

    Article  CAS  Google Scholar 

  15. Houston, S.I. et al. Catalytic function of the PR-Set7 histone H4 lysine 20 monomethyltransferase is essential for mitotic entry and genomic stability. J. Biol. Chem. 283, 19478–19488 (2008).

    Article  CAS  Google Scholar 

  16. Wu, S. et al. Dynamic regulation of the PR-Set7 histone methyltransferase is required for normal cell cycle progression. Genes Dev. 24, 2531–2542 (2010).

    Article  CAS  Google Scholar 

  17. Casey, F.P., Cagney, G., Krogan, N.J. & Shields, D.C. Optimal stepwise experimental design for pairwise functional interaction studies. Bioinformatics 24, 2733–2739 (2008).

    Article  CAS  Google Scholar 

  18. Horn, T. et al. Mapping of signaling networks through synthetic genetic interaction analysis by RNAi. Nat. Methods 8, 341–346 (2011).

    Article  CAS  Google Scholar 

  19. Mani, R., St Onge, R.P., Hartman, J.L. IV., Giaever, G. & Roth, F.P. Defining genetic interaction. Proc. Natl. Acad. Sci. USA 105, 3461–3466 (2008).

    Article  CAS  Google Scholar 

  20. Smyth, G.K. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, 3 (2004).

    Article  Google Scholar 

  21. Doyon, Y. & Cote, J. The highly conserved and multifunctional NuA4 HAT complex. Curr. Opin. Genet. Dev. 14, 147–154 (2004).

    Article  CAS  Google Scholar 

  22. Morrison, A.J. & Shen, X. Chromatin remodelling beyond transcription: the INO80 and SWR1 complexes. Nat. Rev. Mol. Cell Biol. 10, 373–384 (2009).

    Article  CAS  Google Scholar 

  23. Ajuh, P. et al. Functional analysis of the human CDC5L complex and identification of its components by mass spectrometry. EMBO J. 19, 6569–6581 (2000).

    Article  CAS  Google Scholar 

  24. Rzymski, T., Grzmil, P., Meinhardt, A., Wolf, S. & Burfeind, P. PHF5A represents a bridge protein between splicing proteins and ATP-dependent helicases and is differentially expressed during mouse spermatogenesis. Cytogenet. Genome Res. 121, 232–244 (2008).

    Article  CAS  Google Scholar 

  25. Lin, Y.Y. et al. Functional dissection of lysine deacetylases reveals that HDAC1 and p300 regulate AMPK. Nature 482, 251–255 (2012).

    Article  CAS  Google Scholar 

  26. Marcotte, R. et al. Essential gene profiles in breast, pancreatic, and ovarian cancer cells. Cancer Discov. 2, 172–189 (2012).

    Article  CAS  Google Scholar 

  27. Bassik, M.C. et al. A systematic mammalian genetic interaction map reveals pathways underlying ricin susceptibility. Cell 152, 909–922 (2013).

    Article  CAS  Google Scholar 

  28. Roguev, A. et al. Quantitative genetic-interaction mapping in mammalian cells. Nat. Methods advance online publication, 10.1038/nmeth.2398 (13 February 2013).

  29. Carpenter, A.E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

    Article  Google Scholar 

  30. Fuchs, F. et al. Clustering phenotype populations by genome-wide RNAi and multiparametric imaging. Mol. Syst. Biol. 6, 370 (2010).

    Article  Google Scholar 

  31. Held, M. et al. CellCognition: time-resolved phenotype annotation in high-throughput live cell imaging. Nat. Methods 7, 747–754 (2010).

    Article  CAS  Google Scholar 

  32. Pau, G., Fuchs, F., Sklyar, O., Boutros, M. & Huber, W. EBImage—an R package for image processing with applications to cellular phenotypes. Bioinformatics 26, 979–981 (2010).

    Article  CAS  Google Scholar 

  33. Jones, T.R., Carpenter, A.E. & Golland, P. Voronoi-based segmentation of cells on image manifolds. Comput. Vis. Biomed. Image Appl. 535–543 (2005).

  34. Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A. & Vingron, M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18 (suppl. 1), S96–S104 (2002).

    Article  Google Scholar 

  35. Axelsson, E. et al. Extracting quantitative genetic interaction phenotypes from matrix combinatorial RNAi. BMC Bioinformatics 12, 342 (2011).

    Article  CAS  Google Scholar 

  36. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc., B 57, 289–300 (1995).

    Google Scholar 

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Acknowledgements

We thank T. Horn, T. Sandmann and members of the Boutros and Huber groups for helpful discussions. M. Boutros is supported by a European Research Council Advanced Grant (“Syngene”). W.H. acknowledges support by the European Union project Systems Microscopy. C.L. and B.F. were supported by the CellNetworks Cluster of Excellence of the German Research Foundation (DFG).

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Author notes

  1. Christina Laufer and Bernd Fischer: These authors contributed equally to this work.

Authors and Affiliations

  1. Division of Signalling and Functional Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Christina Laufer, Maximilian Billmann & Michael Boutros

  2. Department of Cell and Molecular Biology, Heidelberg University, Medical Faculty Mannheim, Heidelberg, Germany

    Christina Laufer, Maximilian Billmann & Michael Boutros

  3. Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany

    Bernd Fischer & Wolfgang Huber

Authors
  1. Christina Laufer
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  2. Bernd Fischer
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  3. Maximilian Billmann
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  4. Wolfgang Huber
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  5. Michael Boutros
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Contributions

C.L., B.F., W.H. and M. Boutros designed the study; C.L. performed the experiments; B.F. analyzed the data; M. Billmann contributed to experiments; C.L., B.F., W.H. and M. Boutros wrote the manuscript.

Corresponding authors

Correspondence to Wolfgang Huber or Michael Boutros.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6 (PDF 1325 kb)

Supplementary Table 1

Target genes (XLS 56 kb)

Supplementary Table 2

siRNA designs (XLS 149 kb)

Supplementary Table 3

qPCR primers (XLS 75 kb)

Supplementary Table 4

Screened siRNAs (XLS 68 kb)

Supplementary Table 5

Interaction scores (XLS 10537 kb)

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Laufer, C., Fischer, B., Billmann, M. et al. Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping. Nat Methods 10, 427–431 (2013). https://doi.org/10.1038/nmeth.2436

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