Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Interlaboratory reproducibility of large-scale human protein-complex analysis by standardized AP-MS

Nature Methods volume 10, pages 307–314 (2013)Cite this article

Subjects

Abstract

The characterization of all protein complexes of human cells under defined physiological conditions using affinity purification–mass spectrometry (AP-MS) is a highly desirable step in the quest to understand the phenotypic effects of genomic information. However, such a challenging goal has not yet been achieved, as it requires reproducibility of the experimental workflow and high data consistency across different studies and laboratories. We systematically investigated the reproducibility of a standardized AP-MS workflow by performing a rigorous interlaboratory comparative analysis of the interactomes of 32 human kinases. We show that it is possible to achieve high interlaboratory reproducibility of this standardized workflow despite differences in mass spectrometry configurations and subtle sample preparation–related variations and that combination of independent data sets improves the approach sensitivity, resulting in even more-detailed networks. Our analysis demonstrates the feasibility of obtaining a high-quality map of the human protein interactome with a multilaboratory project.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Experimental design.
Figure 2: Cross-laboratory comparison of weighted spectral counts filtering.
Figure 3: Intra- and interlaboratory AP-MS reproducibility.
Figure 4: The kinase interactome.
Figure 5: Statistics for the number of kinase interactors.

References

  1. Krogan, N.J. et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440, 637–643 (2006).

    Article  CAS  Google Scholar 

  2. Zhao, R. et al. Navigating the chaperone network: an integrative map of physical and genetic interactions mediated by the hsp90 chaperone. Cell 120, 715–727 (2005).

    Article  CAS  Google Scholar 

  3. Glatter, T. et al. Modularity and hormone sensitivity of the Drosophila melanogaster insulin receptor/target of rapamycin interaction proteome. Mol. Syst. Biol. 7, 547 (2011).

    Article  Google Scholar 

  4. Butland, G. et al. Interaction network containing conserved and essential protein complexes in Escherichia coli. Nature 433, 531–537 (2005).

    Article  CAS  Google Scholar 

  5. Ho, Y. et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415, 180–183 (2002).

    Article  CAS  Google Scholar 

  6. Gavin, A.C. et al. Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636 (2006).

    Article  CAS  Google Scholar 

  7. Guruharsha, K.G. et al. A protein complex network of Drosophila melanogaster. Cell 147, 690–703 (2011).

    Article  CAS  Google Scholar 

  8. Rigaut, G. et al. A generic protein purification method for protein complex characterization and proteome exploration. Nat. Biotechnol. 17, 1030–1032 (1999).

    Article  CAS  Google Scholar 

  9. Sowa, M.E., Bennett, E.J., Gygi, S.P. & Harper, J.W. Defining the human deubiquitinating enzyme interaction landscape. Cell 138, 389–403 (2009).

    Article  CAS  Google Scholar 

  10. Sardiu, M.E. et al. Probabilistic assembly of human protein interaction networks from label-free quantitative proteomics. Proc. Natl. Acad. Sci. USA 105, 1454–1459 (2008).

    Article  CAS  Google Scholar 

  11. Stukalov, A., Superti-Furga, G. & Colinge, J. Deconvolution of targeted protein-protein interaction maps. J. Proteome Res. 11, 4102–4109 (2012).

    Article  CAS  Google Scholar 

  12. Bürckstümmer, T. et al. An efficient tandem affinity purification procedure for interaction proteomics in mammalian cells. Nat. Methods 3, 1013–1019 (2006).

    Article  Google Scholar 

  13. Glatter, T., Wepf, A., Aebersold, R. & Gstaiger, M. An integrated workflow for charting the human interaction proteome: insights into the PP2A system. Mol. Syst. Biol. 5, 237 (2009).

    Article  Google Scholar 

  14. Scigelova, M., Hornshaw, M., Giannakopulos, A. & Makarov, A. Fourier transform mass spectrometry. Mol. Cell. Proteomics 10, M111.009431 (2011).

  15. Choi, H. et al. SAINT: probabilistic scoring of affinity purification-mass spectrometry data. Nat. Methods 8, 70–73 (2011).

    Article  CAS  Google Scholar 

  16. Lavallée-Adam, M., Cloutier, P., Coulombe, B. & Blanchette, M. Modeling contaminants in AP-MS/MS experiments. J. Proteome Res. 10, 886–895 (2011).

    Article  Google Scholar 

  17. Brehme, M. et al. Charting the molecular network of the drug target Bcr-Abl. Proc. Natl. Acad. Sci. USA 106, 7414–7419 (2009).

    Article  CAS  Google Scholar 

  18. Bouwmeester, T. et al. A physical and functional map of the human TNF-α/NF-κB signal transduction pathway. Nat. Cell Biol. 6, 97–105 (2004).

    Article  CAS  Google Scholar 

  19. Fang, L. et al. Characterization of the human COP9 signalosome complex using affinity purification and mass spectrometry. J. Proteome Res. 7, 4914–4925 (2008).

    Article  CAS  Google Scholar 

  20. Behrends, C., Sowa, M.E., Gygi, S.P. & Harper, J.W. Network organization of the human autophagy system. Nature 466, 68–76 (2010).

    Article  CAS  Google Scholar 

  21. Stumpf, M.P. et al. Estimating the size of the human interactome. Proc. Natl. Acad. Sci. USA 105, 6959–6964 (2008).

    Article  CAS  Google Scholar 

  22. Fortney, K. & Jurisica, I. Integrative computational biology for cancer research. Hum. Genet. 130, 465–481 (2011).

    Article  CAS  Google Scholar 

  23. Venkatesan, K. et al. An empirical framework for binary interactome mapping. Nat. Methods 6, 83–90 (2009).

    Article  CAS  Google Scholar 

  24. Braun, P. et al. An experimentally derived confidence score for binary protein-protein interactions. Nat. Methods 6, 91–97 (2009).

    Article  CAS  Google Scholar 

  25. Cusick, M.E. et al. Literature-curated protein interaction datasets. Nat. Methods 6, 39–46 (2009).

    Article  CAS  Google Scholar 

  26. Bell, A.W. et al. A HUPO test sample study reveals common problems in mass spectrometry-based proteomics. Nat. Methods 6, 423–430 (2009).

    Article  CAS  Google Scholar 

  27. Burkard, T.R. et al. Initial characterization of the human central proteome. BMC Syst. Biol. 5, 17 (2011).

    Article  CAS  Google Scholar 

  28. Geiger, T., Wehner, A., Schaab, C., Cox, J. & Mann, M. Comparative proteomic analysis of eleven common cell lines reveals ubiquitous but varying expression of most proteins. Mol. Cell Proteomics 11 M111.014050 (2012).

  29. Beck, M. et al. The quantitative proteome of a human cell line. Mol. Syst. Biol. 7, 549 (2011).

    Article  Google Scholar 

  30. Wu, Z., Moghaddas Gholami, A. & Kuster, B. Systematic identification of the HSP90 candidate regulated proteome. Mol. Cell Proteomics 11 M111.016675 (2012).

  31. Goudreault, M. et al. A PP2A phosphatase high density interaction network identifies a novel striatin-interacting phosphatase and kinase complex linked to the cerebral cavernous malformation 3 (CCM3) protein. Mol. Cell. Proteomics 8, 157–171 (2009).

    Article  CAS  Google Scholar 

  32. Kean, M.J. et al. Structure-function analysis of core STRIPAK proteins: a signaling complex implicated in Golgi polarization. J. Biol. Chem. 286, 25065–25075 (2011).

    Article  CAS  Google Scholar 

  33. Herzog, F. et al. Structural probing of a protein phosphatase 2A network by chemical cross-linking and mass spectrometry. Science 337, 1348–1352 (2012).

    Article  CAS  Google Scholar 

  34. Draviam, V.M. et al. A functional genomic screen identifies a role for TAO1 kinase in spindle-checkpoint signalling. Nat. Cell Biol. 9, 556–564 (2007).

    Article  CAS  Google Scholar 

  35. Fidalgo, M. et al. The adaptor protein cerebral cavernous malformation 3 (CCM3) mediates phosphorylation of the cytoskeletal proteins ezrin/radixin/moesin by mammalian Ste20-4 to protect cells from oxidative stress. J. Biol. Chem. 287, 11556–11565 (2012).

    Article  CAS  Google Scholar 

  36. Preisinger, C. et al. YSK1 is activated by the Golgi matrix protein GM130 and plays a role in cell migration through its substrate 14-3-3ζ. J. Cell Biol. 164, 1009–1020 (2004).

    Article  CAS  Google Scholar 

  37. Hannigan, G.E. et al. Regulation of cell adhesion and anchorage-dependent growth by a new β1-integrin-linked protein kinase. Nature 379, 91–96 (1996).

    Article  CAS  Google Scholar 

  38. Tu, Y., Huang, Y., Zhang, Y., Hua, Y. & Wu, C. A new focal adhesion protein that interacts with integrin-linked kinase and regulates cell adhesion and spreading. J. Cell Biol. 153, 585–598 (2001).

    Article  CAS  Google Scholar 

  39. Dougherty, G.W., Jose, C., Gimona, M. & Cutler, M.L. The Rsu-1-PINCH1-ILK complex is regulated by Ras activation in tumor cells. Eur. J. Cell Biol. 87, 721–734 (2008).

    Article  CAS  Google Scholar 

  40. Masuelli, L. & Cutler, M.L. Increased expression of the Ras suppressor Rsu-1 enhances Erk-2 activation and inhibits Jun kinase activation. Mol. Cell Biol. 16, 5466–5476 (1996).

    Article  CAS  Google Scholar 

  41. Delcommenne, M. et al. Phosphoinositide-3-OH kinase–dependent regulation of glycogen synthase kinase 3 and protein kinase B/AKT by the integrin-linked kinase. Proc. Natl. Acad. Sci. USA 95, 11211–11216 (1998).

    Article  CAS  Google Scholar 

  42. Li, Z., Gourguechon, S. & Wang, C.C. Tousled-like kinase in a microbial eukaryote regulates spindle assembly and S-phase progression by interacting with Aurora kinase and chromatin assembly factors. J. Cell Sci. 120, 3883–3894 (2007).

    Article  CAS  Google Scholar 

  43. Mochida, S., Maslen, S.L., Skehel, M. & Hunt, T. Greatwall phosphorylates an inhibitor of protein phosphatase 2A that is essential for mitosis. Science 330, 1670–1673 (2010).

    Article  CAS  Google Scholar 

  44. Filimonenko, M. et al. The selective macroautophagic degradation of aggregated proteins requires the PI3P-binding protein Alfy. Mol. Cell 38, 265–279 (2010).

    Article  CAS  Google Scholar 

  45. Hanson, D. et al. Exome sequencing identifies CCDC8 mutations in 3-M syndrome, suggesting that CCDC8 contributes in a pathway with CUL7 and OBSL1 to control human growth. Am. J. Hum. Genet. 89, 148–153 (2011).

    Article  CAS  Google Scholar 

  46. Hart, G.T., Ramani, A.K. & Marcotte, E.M. How complete are current yeast and human protein-interaction networks? Genome Biol. 7, 120 (2006).

    Article  Google Scholar 

  47. Hart, G.T., Lee, I. & Marcotte, E.R. A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality. BMC Bioinformatics 8, 236 (2007).

    Article  Google Scholar 

  48. Varjosalo, M. et al. Application of active and kinase-deficient kinome collection for identification of kinases regulating hedgehog signaling. Cell 133, 537–548 (2008).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank A. Nebreda for expert discussion on the biological implications of interactions found, F. Breitwieser and K. Parapatics for support and all laboratory members in Zurich and Vienna for useful discussions. At ETHZ this work was supported by the European Union 7th Framework project PROSPECTS (Proteomics Specification in Space and Time, grant HEALTH-F4-2008-201648), the SystemsX project PhosphonetX and European Research Council advanced grant 'Proteomics v3.0' (grant no. 233226) of the European Union to R.A. and European Union 7th Framework Marie Curie Actions Intra-European Fellowships grant 'Cancer Kinome' (grant no. 236839) to M.V. At CeMM the work was supported by the Austrian Academy of Sciences and Austrian Federal Ministry for Science and Research (Genome Research in Austria projects Austrian Proteomics Platform III (grant no. 820965) to G.S.-F. and Bioinformatics Integration Network III (grant no. 820962) to J.C.).

Author information

Author notes

  1. Markku Varjosalo, Roberto Sacco and Alexey Stukalov: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.,

    Markku Varjosalo, Audrey van Drogen, Simon Hauri, Ruedi Aebersold & Matthias Gstaiger

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

    Roberto Sacco, Alexey Stukalov, Melanie Planyavsky, Keiryn L Bennett, Jacques Colinge & Giulio Superti-Furga

  3. Faculty of Science, University of Zurich, Zurich, Switzerland

    Ruedi Aebersold

Authors
  1. Markku Varjosalo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roberto Sacco
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexey Stukalov
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Audrey van Drogen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Melanie Planyavsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Simon Hauri
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ruedi Aebersold
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Keiryn L Bennett
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jacques Colinge
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Matthias Gstaiger
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Giulio Superti-Furga
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.V., R.S., A.v.D., M.P. and S.H. performed experiments, M.G. and G.S.-F. conceived the study and M.V., R.S., K.L.B., J.C., M.G. and G.S.-F. designed experiments. A.S. and J.C. did bioinformatic and statistical data analysis. M.V., R.S., A.S., R.A., K.L.B., J.C., M.G. and G.S.-F. contributed to the discussion of results and participated in manuscript preparation. M.V., R.S., A.S., J.C., M.G. and G.S.-F. wrote the manuscript.

Corresponding authors

Correspondence to Matthias Gstaiger or Giulio Superti-Furga.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1 and 2, Supplementary Tables 2, 3 and 5 and Supplementary Notes 1 and 2 (PDF 561 kb)

Supplementary Data

Kinase interaction networks: Cytoscape file containing filtered and unfiltered interactomes obtained in the study (ZIP 1008 kb)

Supplementary Table 1

Properties of the 32 kinases selected for the study (XLSX 16 kb)

Supplementary Table 4

Table of full unfiltered AP-MS data (XLSX 1688 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Varjosalo, M., Sacco, R., Stukalov, A. et al. Interlaboratory reproducibility of large-scale human protein-complex analysis by standardized AP-MS. Nat Methods 10, 307–314 (2013). https://doi.org/10.1038/nmeth.2400

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.2400

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing