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  • Perspective
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Quantitative proteomics: challenges and opportunities in basic and applied research

Nature Protocols volume 12, pages 1289–1294 (2017)Cite this article

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Abstract

In this Perspective, we discuss developments in mass-spectrometry-based proteomic technology over the past decade from the viewpoint of our laboratory. We also reflect on existing challenges and limitations, and explore the current and future roles of quantitative proteomics in molecular systems biology, clinical research and personalized medicine.

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Figure 1: Standard mass spectrometry (MS)-based proteomics workflow and acquisition schemes.
Figure 2: A selection of methods used to explore the modular and spatial organization of the proteome.
Figure 3: Quantitative proteomics in molecular medicine.

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References

  1. Hartwell, L.H., Hopfield, J.J., Leibler, S. & Murray, A.W. From molecular to modular cell biology. Nature 402, C47–C52 (1999).

    Article  CAS  PubMed  Google Scholar 

  2. Edwards, A.M. et al. Too many roads not taken. Nature 470, 163–165 (2011).

    Article  CAS  PubMed  Google Scholar 

  3. Aebersold, R. & Mann, M. Mass spectrometry-based proteomics. Nature 422, 198–207 (2003).

    Article  CAS  PubMed  Google Scholar 

  4. Yamashita, M. & Fenn, J.B. Electrospray ion source. Another variation on the free-jet theme. J. Phys. Chem. 88, 4451–4459 (1984).

    Article  CAS  Google Scholar 

  5. Fenn, J.B., Mann, M., Meng, C.K., Wong, S.F. & Whitehouse, C.M. Electrospray ionization for mass spectrometry of large biomolecules. Science 246, 64–71 (1989).

    Article  CAS  PubMed  Google Scholar 

  6. Bantscheff, M., Schirle, M., Sweetman, G., Rick, J. & Küster, B. Quantitative mass spectrometry in proteomics: a critical review. Anal. Bioanal. Chem. 389, 1017–1031 (2007).

    Article  CAS  PubMed  Google Scholar 

  7. Bantscheff, M., Lemeer, S., Savitski, M.M. & Küster, B. Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Anal. Bioanal. Chem. 404, 939–965 (2012).

    Article  CAS  PubMed  Google Scholar 

  8. Gygi, S.P., Rochon, Y., Franza, B.R. & Aebersold, R. Correlation between protein and mRNA abundance in yeast. Mol. Cell. Biol. 19, 1720–1730 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ong, S.-E. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1, 376–386 (2002).

    Article  CAS  PubMed  Google Scholar 

  10. Thompson, A. et al. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 75, 1895–1904 (2003).

    Article  CAS  PubMed  Google Scholar 

  11. Ross, P.L. et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteomics 3, 1154–1169 (2004).

    Article  CAS  PubMed  Google Scholar 

  12. Gerber, S.A., Rush, J., Stemman, O., Kirschner, M.W. & Gygi, S.P. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc. Natl. Acad. Sci. USA 100, 6940–6945 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Tabb, D.L. et al. Reproducibility of differential proteomic technologies in CPTAC fractionated xenografts. J. Proteome Res. 15, 691–706 (2016).

    Article  CAS  PubMed  Google Scholar 

  14. Chelius, D. & Bondarenko, P.V. Quantitative profiling of proteins in complex mixtures using liquid chromatography and mass spectrometry. J. Proteome Res. 1, 317–323 (2002).

    Article  CAS  PubMed  Google Scholar 

  15. Hu, Q. et al. The Orbitrap: a new mass spectrometer. J. Mass Spectrom. 40, 430–443 (2005).

    Article  CAS  PubMed  Google Scholar 

  16. Andrews, G.L., Simons, B.L., Young, J.B., Hawkridge, A.M. & Muddiman, D.C. Performance characteristics of a new hybrid quadrupole time-of-flight tandem mass spectrometer (TripleTOF 5600). Anal. Chem. 83, 5442–5446 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    Article  CAS  PubMed  Google Scholar 

  18. Weisser, H. et al. An automated pipeline for high-throughput label-free quantitative proteomics. J. Proteome Res. 12, 1628–1644 (2013).

    Article  CAS  PubMed  Google Scholar 

  19. Silva, J.C., Gorenstein, M.V., Li, G.-Z., Vissers, J.P.C. & Geromanos, S.J. Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol. Cell. Proteomics 5, 144–156 (2006).

    Article  CAS  PubMed  Google Scholar 

  20. Ahrné, E., Molzahn, L., Glatter, T. & Schmidt, A. Critical assessment of proteome-wide label-free absolute abundance estimation strategies. Proteomics 13, 2567–2578 (2013).

    Article  CAS  PubMed  Google Scholar 

  21. Ludwig, C., Claassen, M., Schmidt, A. & Aebersold, R. Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry. Mol. Cell. Proteomics 11, M111.013987 (2012).

    Article  CAS  PubMed  Google Scholar 

  22. Rosenberger, G., Ludwig, C., Röst, H.L., Aebersold, R. & Malmström, L. aLFQ: an R-package for estimating absolute protein quantities from label-free LC-MS/MS proteomics data. Bioinformatics 30, 2511–2513 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Schubert, O.T. et al. Absolute proteome composition and dynamics during dormancy and resuscitation of Mycobacterium tuberculosis. Cell Host Microbe 18, 96–108 (2015).

    Article  CAS  PubMed  Google Scholar 

  24. Tabb, D.L. et al. Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry. J. Proteome Res. 9, 761–776 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Domon, B. & Aebersold, R. Options and considerations when selecting a quantitative proteomics strategy. Nat. Biotechnol. 28, 710–721 (2010).

    Article  CAS  PubMed  Google Scholar 

  26. Gholami, A.M. et al. Global proteome analysis of the NCI-60 cell line panel. Cell Reports 4, 609–620 (2013).

    Article  CAS  PubMed  Google Scholar 

  27. Geiger, T., Cox, J., Ostasiewicz, P., Wisniewski, J.R. & Mann, M. Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat. Methods 7, 383–385 (2010).

    Article  CAS  PubMed  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Winiewski, J.R. et al. Extensive quantitative remodeling of the proteome between normal colon tissue and adenocarcinoma. Mol. Syst. Biol. 8, 611 (2012).

    Article  CAS  Google Scholar 

  30. Picotti, P., Bodenmiller, B., Mueller, L.N., Domon, B. & Aebersold, R. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell 138, 795–806 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Picotti, P. & Aebersold, R. Selected reaction monitoring–based proteomics: workflows, potential, pitfalls and future directions. Nat. Methods 9, 555–566 (2012).

    Article  CAS  PubMed  Google Scholar 

  32. Gallien, S. et al. Targeted proteomic quantification on quadrupole-orbitrap mass spectrometer. Mol. Cell. Proteomics 11, 1709–1723 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Peterson, A.C., Russell, J.D., Bailey, D.J., Westphall, M.S. & Coon, J.J. Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics. Mol. Cell. Proteomics 11, 1475–1488 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Whiteaker, J.R. et al. A targeted proteomics-based pipeline for verification of biomarkers in plasma. Nat. Biotechnol. 29, 625–634 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Chapman, J.D., Goodlett, D.R. & Masselon, C.D. Multiplexed and data-independent tandem mass spectrometry for global proteome profiling. Mass Spectrom. Rev. 33, 452–470 (2014).

    Article  CAS  PubMed  Google Scholar 

  36. Gillet, L.C., Leitner, A. & Aebersold, R. Mass spectrometry applied to bottom-up proteomics: entering the high-throughput era for hypothesis testing. Annu. Rev. Anal. Chem. (Palo Alto Calif.) 9, 449–472 (2016).

    Article  Google Scholar 

  37. Purvine, S., Eppel, J.-T., Yi, E.C. & Goodlett, D.R. Shotgun collision-induced dissociation of peptides using a time of flight mass analyzer. Proteomics 3, 847–850 (2003).

    Article  CAS  PubMed  Google Scholar 

  38. Venable, J.D., Dong, M.-Q., Wohlschlegel, J., Dillin, A. & Yates, J.R. III. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat. Methods 1, 39–45 (2004).

    Article  CAS  PubMed  Google Scholar 

  39. Silva, J.C. et al. Quantitative proteomic analysis by accurate mass retention time pairs. Anal. Chem. 77, 2187–2200 (2005).

    Article  CAS  PubMed  Google Scholar 

  40. Gillet, L.C. et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteomics 11, O111.016717 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Schubert, O.T. et al. Building high-quality assay libraries for targeted analysis of SWATH MS data. Nat. Protoc. 10, 426–441 (2015).

    Article  CAS  PubMed  Google Scholar 

  42. Röst, H.L. et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotechnol. 32, 219–223 (2014).

    Article  CAS  PubMed  Google Scholar 

  43. Geromanos, S.J., Hughes, C., Ciavarini, S., Vissers, J.P.C. & Langridge, J.I. Using ion purity scores for enhancing quantitative accuracy and precision in complex proteomics samples. Anal. Bioanal. Chem. 404, 1127–1139 (2012).

    Article  CAS  PubMed  Google Scholar 

  44. Egertson, J.D. et al. Multiplexed MS/MS for improved data-independent acquisition. Nat. Methods 10, 744–746 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Tsou, C.-C. et al. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat. Methods 12, 258–264 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wang, J. et al. MSPLIT-DIA: sensitive peptide identification for data-independent acquisition. Nat. Methods 12, 1106–1108 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Okada, H., Ebhardt, H.A., Vonesch, S.C., Aebersold, R. & Hafen, E. Proteome-wide association studies identify biochemical modules associated with a wing-size phenotype in Drosophila melanogaster. Nat. Commun. 7, 12649 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Bruderer, R. et al. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol. Cell. Proteomics 14, 1400–1410 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Selevsek, N. et al. Reproducible and consistent quantification of the Saccharomyces cerevisiae proteome by SWATH-mass spectrometry. Mol. Cell. Proteomics 14, 739–749 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Williams, E.G. et al. Systems proteomics of liver mitochondria function. Science 352, aad0189 (2016).

    Article  CAS  PubMed  Google Scholar 

  51. Röst, H.L., Malmström, L. & Aebersold, R. Reproducible quantitative proteotype data matrices for systems biology. Mol. Biol. Cell 26, 3926–3931 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Rudnick, P.A. et al. Performance metrics for liquid chromatography-tandem mass spectrometry systems in proteomics analyses. Mol. Cell. Proteomics 9, 225–241 (2010).

    Article  CAS  PubMed  Google Scholar 

  53. Addona, T.A. et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring–based measurements of proteins in plasma. Nat. Biotechnol. 27, 633–641 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Collins, B.C. et al. Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry. bioRxiv. Preprint at http://biorxiv.org/content/early/2016/09/14/074567 (2016).

  55. Navarro, P. et al. A multicenter study benchmarks software tools for label-free proteome quantification. Nat. Biotechnol. 34, 1130–1136 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Picotti, P. et al. A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis. Nature 494, 266–270 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Wu, Y. et al. Multilayered genetic and omics dissection of mitochondrial activity in a mouse reference population. Cell 158, 1415–1430 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Alberts, B. The cell as a collection of protein machines: preparing the next generation of molecular biologists. Cell 92, 291–294 (1998).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  60. Gingras, A.-C., Gstaiger, M., Raught, B. & Aebersold, R. Analysis of protein complexes using mass spectrometry. Nat. Rev. Mol. Cell Biol. 8, 645–654 (2007).

    Article  CAS  PubMed  Google Scholar 

  61. Collins, B.C. et al. Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system. Nat. Methods 10, 1246–1253 (2013).

    Article  CAS  PubMed  Google Scholar 

  62. Huttlin, E.L. et al. The BioPlex network: a systematic exploration of the human interactome. Cell 162, 425–440 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Foster, L.J. et al. A mammalian organelle map by protein correlation profiling. Cell 125, 187–199 (2006).

    Article  CAS  PubMed  Google Scholar 

  64. Kristensen, A.R., Gsponer, J. & Foster, L.J. A high-throughput approach for measuring temporal changes in the interactome. Nat. Methods 9, 907–909 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Havugimana, P.C. et al. A census of human soluble protein complexes. Cell 150, 1068–1081 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Young, M.M. et al. High throughput protein fold identification by using experimental constraints derived from intramolecular cross-links and mass spectrometry. Proc. Natl. Acad. Sci. USA 97, 5802–5806 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Rappsilber, J., Siniossoglou, S., Hurt, E.C. & Mann, M. A generic strategy to analyze the spatial organization of multi-protein complexes by cross-linking and mass spectrometry. Anal. Chem. 72, 267–275 (2000).

    Article  CAS  PubMed  Google Scholar 

  68. Leitner, A., Walzthoeni, T. & Aebersold, R. Lysine-specific chemical cross-linking of protein complexes and identification of cross-linking sites using LC-MS/MS and the xQuest/xProphet software pipeline. Nat. Protoc. 9, 120–137 (2014).

    Article  CAS  PubMed  Google Scholar 

  69. Joachimiak, L.A., Walzthoeni, T., Liu, C.W., Aebersold, R. & Frydman, J. The structural basis of substrate recognition by the eukaryotic chaperonin TRiC/CCT. Cell 159, 1042–1055 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Walzthoeni, T. et al. xTract: software for characterizing conformational changes of protein complexes by quantitative cross-linking mass spectrometry. Nat. Methods 12, 1185–1190 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Roux, K.J., Kim, D.I., Raida, M. & Burke, B. A promiscuous biotin ligase fusion protein identifies proximal and interacting proteins in mammalian cells. J. Cell Biol. 196, 801–810 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Mertins, P. et al. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 534, 55–62 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Zhang, H. et al. Integrated proteogenomic characterization of human high-grade serous ovarian cancer. Cell 166, 755–765 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Liu, Y. et al. Quantitative variability of 342 plasma proteins in a human twin population. Mol. Syst. Biol. 11, 786 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Topol, E.J. Individualized medicine from prewomb to tomb. Cell 157, 241–253 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Rifai, N., Gillette, M.A. & Carr, S.A. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat. Biotechnol. 24, 971–983 (2006).

    Article  CAS  PubMed  Google Scholar 

  77. Cima, I. et al. Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer. Proc. Natl. Acad. Sci. USA 108, 3342–3347 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Surinova, S. et al. Non-invasive prognostic protein biomarker signatures associated with colorectal cancer. EMBO Mol. Med. 7, 1153–1165 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by the Human Frontier Science Program (grant LT000737/2016 to O.T.S.), the Swiss National Science Foundation (R.A.; grant P2EZP3_165280 to O.T.S.; grant P2EZP3_162268 to H.L.R.; Ambizione grant PZ00P3_161435 to B.C.C.), EMBO (grant ALTF_854-2015 to H.L.R.), the Swiss Initiative for Systems Biology (SystemsX.ch; to R.A.) and the European Union via ERC (grant AdG-670821 to R.A.).

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Author notes

  1. Hannes L Röst, Ben C Collins and George Rosenberger: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Human Genetics, University of California, Los Angeles, Los Angeles, California, USA.,

    Olga T Schubert

  2. Department of Genetics, Stanford University, Stanford, California, USA

    Hannes L Röst

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

    Ben C Collins, George Rosenberger & Ruedi Aebersold

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

    Ruedi Aebersold

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  1. Olga T Schubert
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O.T.S., H.L.R., B.C.C., G.R. and R.A. prepared the manuscript.

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Correspondence to Ruedi Aebersold.

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R.A. holds shares of Biognosys AG, which operates in the field covered by this Perspective.

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Schubert, O., Röst, H., Collins, B. et al. Quantitative proteomics: challenges and opportunities in basic and applied research. Nat Protoc 12, 1289–1294 (2017). https://doi.org/10.1038/nprot.2017.040

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