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.

  • Review Article
  • Published:

Decoding signalling networks by mass spectrometry-based proteomics

Nature Reviews Molecular Cell Biology volume 11, pages 427–439 (2010)Cite this article

Subjects

Key Points

  • Mass spectrometry (MS) has become a powerful and indispensable tool to study different aspects of cell signalling, such as the identification and quantification of post-translational modifications (PTMs), characterization of protein–protein interactions and analysis of changes in protein expression.

  • Dramatic advances in all aspects of the proteomic workflow, and especially in computational proteomics, have enabled the quantification of a first complete proteome.

  • Stable isotope labelling by amino acids in cell culture (SILAC) and isobaric tag for relative and absolute quantification (iTRAQ) are most widely used for MS-based quantification of proteins and PTMs. The scope for label-free quantification algorithms is likely to grow, especially in cases where expected changes in proteins or PTMs are large and accurate quantification is not essential.

  • High resolution quantitative MS has been successfully applied to quantify thousands of phosphorylation and acetylation sites. These data provide a basis for directed functional analysis on single proteins as well as for 'systems-wide' studies.

  • There are now pioneering examples of the application of high resolution quantitative MS in elucidating global signalling networks and their dynamics in response to different cellular perturbations.

  • Bioinformatic analysis of proteomic data can provide insights into the nature and evolution of signalling networks and global kinase–substrate relationships.

Abstract

Signalling networks regulate essentially all of the biology of cells and organisms in normal and disease states. Signalling is often studied using antibody-based techniques such as western blots. Large-scale 'precision proteomics' based on mass spectrometry now enables the system-wide characterization of signalling events at the levels of post-translational modifications, protein–protein interactions and changes in protein expression. This technology delivers accurate and unbiased information about the quantitative changes of thousands of proteins and their modifications in response to any perturbation. Current studies focus on phosphorylation, but acetylation, methylation, glycosylation and ubiquitylation are also becoming amenable to investigation. Large-scale proteomics-based signalling research will fundamentally change our understanding of signalling networks.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: General principles of signalling pathways.
Figure 2: Typical work flow for proteome and PTM analysis using shotgun proteomics.
Figure 3: High resolution mass spectrum of a phosphopeptide.
Figure 4: Quantitative proteomic analysis of oncogenic RTK signalling compartmentalization.

Similar content being viewed by others

References

  1. Seet, B. T., Dikic, I., Zhou, M. M. & Pawson, T. Reading protein modifications with interaction domains. Nature Rev. Mol. Cell Biol. 7, 473–483 (2006).

    CAS  Google Scholar 

  2. Hunter, T. The age of crosstalk: phosphorylation, ubiquitination, and beyond. Mol. Cell 28, 730–738 (2007).

    Article  CAS  PubMed  Google Scholar 

  3. Shevchenko, A., Tomas, H., Havlis, J., Olsen, J. V. & Mann, M. In-gel digestion for mass spectrometric characterization of proteins and proteomes. Nature Protoc. 1, 2856–2860 (2006).

    Article  CAS  Google Scholar 

  4. Link, A. J. et al. Direct analysis of protein complexes using mass spectrometry. Nature Biotech. 17, 676–682 (1999).

    Article  CAS  Google Scholar 

  5. Washburn, M. P., Wolters, D. & Yates, J. R., 3rd. Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nature Biotech. 19, 242–247 (2001).

    Article  CAS  Google Scholar 

  6. Domon, B. & Aebersold, R. Mass spectrometry and protein analysis. Science 312, 212–217 (2006).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  8. Steen, H. & Mann, M. The ABC's (and XYZ's) of peptide sequencing. Nature Rev. Mol. Cell Biol. 5, 699–711 (2004).

    Article  CAS  Google Scholar 

  9. Schroeder, M. J., Shabanowitz, J., Schwartz, J. C., Hunt, D. F. & Coon, J. J. A neutral loss activation method for improved phosphopeptide sequence analysis by quadrupole ion trap mass spectrometry. Anal. Chem. 76, 3590–3598 (2004).

    Article  CAS  PubMed  Google Scholar 

  10. Olsen, J. V. et al. Higher-energy C-trap dissociation for peptide modification analysis. Nature Methods 4, 709–712 (2007).

    Article  CAS  PubMed  Google Scholar 

  11. Olsen, J. V. et al. A dual pressure linear ion trap orbitrap instrument with very high sequencing speed. Mol. Cell. Proteomics 8, 2759–2769 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zubarev, R. A. et al. Electron capture dissociation for structural characterization of multiply charged protein cations. Anal. Chem. 72, 563–573 (2000).

    Article  CAS  PubMed  Google Scholar 

  13. Syka, J. E. et al. Novel linear quadrupole ion trap/FT mass spectrometer: performance characterization and use in the comparative analysis of histone H3 post-translational modifications. J. Proteome Res. 3, 621–626 (2004).

    Article  CAS  PubMed  Google Scholar 

  14. Ong, S. E. & Mann, M. Mass spectrometry-based proteomics turns quantitative. Nature Chem. Biol. 1, 252–262 (2005).

    Article  CAS  Google Scholar 

  15. Ong, S. E. et al. 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 

  16. Mann, M. Functional and quantitative proteomics using SILAC. Nature Rev. Mol. Cell Biol. 7, 952–958 (2006).

    Article  CAS  Google Scholar 

  17. Kruger, M. et al. SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell 134, 353–364 (2008).

    Article  CAS  PubMed  Google Scholar 

  18. Geiger, T., Cox, J. Pawel, O., Wisniewski, J. R. & Mann, M. Super-SILAC mix for quantitative proteomics of human tumor tissue. Nature Methods 7, 383–385 (2010).

    Article  CAS  PubMed  Google Scholar 

  19. 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 

  20. Ow, S. Y. et al. iTRAQ underestimation in simple and complex mixtures: “the good, the bad and the ugly”. J. Proteome Res. 8, 5347–5355 (2009).

    Article  CAS  PubMed  Google Scholar 

  21. Zhang, Y. et al. A robust error model for iTRAQ quantification reveals divergent signaling between oncogenic FLT3 mutants in acute myeloid leukemia. Mol. Cell. Proteomics 17 Dec 2009 (doi: 10.1074/mcp.M900452-MCP200).

  22. Hsu, J. L., Huang, S. Y., Chow, N. H. & Chen, S. H. Stable-isotope dimethyl labeling for quantitative proteomics. Anal. Chem. 75, 6843–6852 (2003).

    Article  CAS  PubMed  Google Scholar 

  23. Boersema, P. J., Raijmakers, R., Lemeer, S., Mohammed, S. & Heck, A. J. Multiplex peptide stable isotope dimethyl labeling for quantitative proteomics. Nature Protoc. 4, 484–494 (2009).

    Article  CAS  Google Scholar 

  24. Nielsen, M. L. et al. Iodoacetamide-induced artifact mimics ubiquitination in mass spectrometry. Nature Methods 5, 459–460 (2008).

    Article  CAS  PubMed  Google Scholar 

  25. Mueller, L. N., Brusniak, M. Y., Mani, D. R. & Aebersold, R. An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data. J. Proteome Res. 7, 51–61 (2008).

    Article  CAS  PubMed  Google Scholar 

  26. Trost, M. et al. The phagosomal proteome in interferon-γ-activated macrophages. Immunity 30, 143–154 (2009).

    Article  CAS  PubMed  Google Scholar 

  27. Wong, J. W. & Cagney, G. An overview of label-free quantitation methods in proteomics by mass spectrometry. Methods Mol. Biol. 604, 273–283.

  28. Luber, C. et al. Quantitative proteomics reveals subset-specific viral recognition in dendritic cells. Immunity 32, 279–289 (2010).

    Article  CAS  PubMed  Google Scholar 

  29. de Godoy, L. M. et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 1251–1254 (2008). Haploid yeast were quantified against diploid yeast after SILAC encoding, and the differential roles of members of the pheromone pathway were revealed by this expression analysis. This is the first complete proteome analysis, as judged by comparison to previous genome-wide tagging experiments.

    Article  CAS  PubMed  Google Scholar 

  30. Malmstrom, J. et al. Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans. Nature 460, 762–765 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Rivers, J., Simpson, D. M., Robertson, D. H., Gaskell, S. J. & Beynon, R. J. Absolute multiplexed quantitative analysis of protein expression during muscle development using QconCAT. Mol. Cell. Proteomics 6, 1416–1427 (2007).

    Article  CAS  PubMed  Google Scholar 

  32. Brunner, E. et al. A high-quality catalog of the Drosophila melanogaster proteome. Nature Biotech. 25, 576–583 (2007).

    Article  CAS  Google Scholar 

  33. Hanke, S., Besir, H., Oesterhelt, D. & Mann, M. Absolute SILAC for accurate quantitation of proteins in complex mixtures down to the attomole level. J. Proteome Res. 7, 1118–1130 (2008).

    Article  CAS  PubMed  Google Scholar 

  34. Ghaemmaghami, S. et al. Global analysis of protein expression in yeast. Nature 425, 737–741 (2003).

    Article  CAS  PubMed  Google Scholar 

  35. Huh, W. K. et al. Global analysis of protein localization in budding yeast. Nature 425, 686–691 (2003).

    Article  CAS  PubMed  Google Scholar 

  36. Wisniewski, J. R., Zougman, A., Nagaraj, N. & Mann, M. Universal sample preparation method for proteome analysis. Nature Methods 6, 359–362 (2009).

    Article  CAS  PubMed  Google Scholar 

  37. Schmidt, A., Claassen, M. & Aebersold, R. Directed mass spectrometry: towards hypothesis-driven proteomics. Curr. Opin. Chem. Biol. 13, 510–517 (2009).

    Article  CAS  PubMed  Google Scholar 

  38. Wolf-Yadlin, A., Hautaniemi, S., Lauffenburger, D. A. & White, F. M. Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks. Proc. Natl Acad. Sci. USA 104, 5860–5865 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Kitteringham, N. R., Jenkins, R. E., Lane, C. S., Elliott, V. L. & Park, B. K. Multiple reaction monitoring for quantitative biomarker analysis in proteomics and metabolomics. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 877, 1229–1239 (2009).

    Article  CAS  PubMed  Google Scholar 

  40. Unwin, R. D., Griffiths, J. R. & Whetton, A. D. A sensitive mass spectrometric method for hypothesis-driven detection of peptide post-translational modifications: multiple reaction monitoring-initiated detection and sequencing (MIDAS). Nature Protoc. 4, 870–877 (2009).

    Article  CAS  Google Scholar 

  41. 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). The targeted analysis of ∼100 proteins from different yeast abundance classes, using multiple reaction monitoring (MRM), detected proteins with as few as an estimated 41 copies per cell. Expression changes in the glycolytic pathway on nutrient switch were quantified.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Kumar, C. & Mann, M. Bioinformatics analysis of mass spectrometry-based proteomics data sets. FEBS Lett. 583, 1703–1712 (2009).

    Article  CAS  PubMed  Google Scholar 

  43. Elias, J. E. & Gygi, S. P. Target-decoy search strategy for mass spectrometry-based proteomics. Methods Mol. Biol. 604, 55–71.

  44. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genet. 25, 25–29 (2000).

    Article  CAS  PubMed  Google Scholar 

  45. Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y. & Hattori, M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 32, D277–D280 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Jensen, O. N. Interpreting the protein language using proteomics. Nature Rev. Mol. Cell Biol. 7, 391–403 (2006).

    Article  CAS  Google Scholar 

  47. Witze, E. S., Old, W. M., Resing, K. A. & Ahn, N. G. Mapping protein post-translational modifications with mass spectrometry. Nature Methods 4, 798–806 (2007).

    Article  CAS  PubMed  Google Scholar 

  48. Zhao, Y. & Jensen, O. N. Modification-specific proteomics: strategies for characterization of post-translational modifications using enrichment techniques. Proteomics 9, 4632–4641 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Macek, B., Mann, M. & Olsen, J. V. Global and site-specific quantitative phosphoproteomics: principles and applications. Annu. Rev. Pharmacol. Toxicol. 49, 199–221 (2009).

    Article  CAS  PubMed  Google Scholar 

  50. Choudhary, C. et al. Lysine acetylation targets protein complexes and co-regulates major cellular functions. Science 325, 834–840 (2009).

    Article  CAS  PubMed  Google Scholar 

  51. Olsen, J. V. et al. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127, 635–648 (2006). Time resolved, quantitative analysis of 6,600 phosphorylation sites provided the first dynamic and global view of the effects of growth factor stimulation on a cell.

    Article  CAS  PubMed  Google Scholar 

  52. Beausoleil, S. A., Villen, J., Gerber, S. A., Rush, J. & Gygi, S. P. A probability-based approach for high-throughput protein phosphorylation analysis and site localization. Nature Biotech. 24, 1285–1292 (2006).

    Article  CAS  Google Scholar 

  53. Gnad, F. et al. PHOSIDA (phosphorylation site database): management, structural and evolutionary investigation, and prediction of phosphosites. Genome Biol. 8, R250 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Olsen, J. V. et al. Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis. Sci. Signal. 3, ra3 (2010).

    Article  CAS  PubMed  Google Scholar 

  55. Ong, S. E., Mittler, G. & Mann, M. Identifying and quantifying in vivo methylation sites by heavy methyl SILAC. Nature Methods 1, 119–126 (2004).

    Article  CAS  PubMed  Google Scholar 

  56. Kim, S. C. et al. Substrate and functional diversity of lysine acetylation revealed by a proteomics survey. Mol. Cell 23, 607–618 (2006).

    Article  CAS  PubMed  Google Scholar 

  57. Wang, Q. et al. Acetylation of metabolic enzymes coordinates carbon source utilization and metabolic flux. Science 327, 1004–1007.

  58. Zhao, S. et al. Regulation of cellular metabolism by protein lysine acetylation. Science 327, 1000–1004. Together with reference 50, references 56–58 identify thousands of new Lys acetylation sites and functionally implicate them in the cell cycle, 14-3-3-based signalling and central metabolic pathways.

  59. Peng, J. et al. A proteomics approach to understanding protein ubiquitination. Nature Biotech. 21, 921–926 (2003).

    Article  CAS  Google Scholar 

  60. Golebiowski, F. et al. System-wide changes to SUMO modifications in response to heat shock. Sci. Signal. 2, ra24 (2009).

    Article  CAS  PubMed  Google Scholar 

  61. Pedrioli, P. G. et al. Automated identification of SUMOylation sites using mass spectrometry and SUMmOn pattern recognition software. Nature Methods 3, 533–539 (2006).

    Article  CAS  PubMed  Google Scholar 

  62. Matic, I. et al. In vivo identification of human small ubiquitin-like modifier polymerization sites by high accuracy mass spectrometry and an in vitro to in vivo strategy. Mol. Cell. Proteomics 7, 132–144 (2008).

    Article  CAS  PubMed  Google Scholar 

  63. Burlingame, A. L., Baillie, T. A. & Russell, D. H. Mass spectrometry. Anal. Chem. 64, 467R–502R (1992).

    Article  CAS  PubMed  Google Scholar 

  64. Muzio, M. et al. FLICE, a novel FADD-homologous ICE/CED-3-like protease, is recruited to the CD95 (Fas/APO-1) death-inducing signaling complex. Cell 85, 817–827 (1996).

    Article  CAS  PubMed  Google Scholar 

  65. Ficarro, S. B. et al. Phosphoproteome analysis by mass spectrometry and its application to Saccharomyces cerevisiae. Nature Biotech. 20, 301–305 (2002). The first large-scale MS-based phosphoproteome analysis, reporting the identification of several hundred yeast phosphorylation sites.

    Article  CAS  Google Scholar 

  66. Nita-Lazar, A., Saito-Benz, H. & White, F. M. Quantitative phosphoproteomics by mass spectrometry: past, present, and future. Proteomics 8, 4433–4443 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Dengjel, J., Kratchmarova, I. & Blagoev, B. Receptor tyrosine kinase signaling: a view from quantitative proteomics. Mol. Biosyst. 5, 1112–1121 (2009).

    Article  CAS  PubMed  Google Scholar 

  68. Blagoev, B., Ong, S. E., Kratchmarova, I. & Mann, M. Temporal analysis of phosphotyrosine-dependent signaling networks by quantitative proteomics. Nature Biotech. 22, 1139–1145 (2004).

    Article  CAS  Google Scholar 

  69. Schmelzle, K., Kane, S., Gridley, S., Lienhard, G. E. & White, F. M. Temporal dynamics of tyrosine phosphorylation in insulin signaling. Diabetes 55, 2171–2179 (2006).

    Article  CAS  PubMed  Google Scholar 

  70. Kruger, M. et al. Dissection of the insulin signaling pathway via quantitative phosphoproteomics. Proc. Natl Acad. Sci. USA 105, 2451–2456 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Gruhler, A. et al. Quantitative phosphoproteomics applied to the yeast pheromone signaling pathway. Mol. Cell. Proteomics 4, 310–327 (2005).

    Article  CAS  PubMed  Google Scholar 

  72. Mayya, V. et al. Quantitative phosphoproteomic analysis of T cell receptor signaling reveals system-wide modulation of protein-protein interactions. Sci. Signal. 2, ra46 (2009).

    Article  PubMed  Google Scholar 

  73. Jorgensen, C. et al. Cell-specific information processing in segregating populations of Eph receptor ephrin-expressing cells. Science 326, 1502–1509 (2009).

    Article  CAS  PubMed  Google Scholar 

  74. Kratchmarova, I., Blagoev, B., Haack-Sorensen, M., Kassem, M. & Mann, M. Mechanism of divergent growth factor effects in mesenchymal stem cell differentiation. Science 308, 1472–1477 (2005). Differential quantification of Tyr phosphorylation in response to EGF or PDGF, which elucidated the mechanism responsible for the proliferation or differentiation of mesenchymal stem cells.

    Article  CAS  PubMed  Google Scholar 

  75. Graumann, J. et al. Stable isotope labeling by amino acids in cell culture (SILAC) and proteome quantitation of mouse embryonic stem cells to a depth of 5,111 proteins. Mol. Cell. Proteomics 7, 672–683 (2008).

    Article  CAS  PubMed  Google Scholar 

  76. Prokhorova, T. A. et al. Stable isotope labeling by amino acids in cell culture (SILAC) and quantitative comparison of the membrane proteomes of self-renewing and differentiating human embryonic stem cells. Mol. Cell. Proteomics 8, 959–970 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Van Hoof, D. et al. Phosphorylation dynamics during early differentiation of human embryonic stem cells. Cell Stem Cell 5, 214–226 (2009).

    Article  CAS  PubMed  Google Scholar 

  78. Brill, L. M. et al. Phosphoproteomic analysis of human embryonic stem cells. Cell Stem Cell 5, 204–213 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Swaney, D. L., Wenger, C. D., Thomson, J. A. & Coon, J. J. Human embryonic stem cell phosphoproteome revealed by electron transfer dissociation tandem mass spectrometry. Proc. Natl Acad. Sci. USA 106, 995–1000 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Dephoure, N. et al. A quantitative atlas of mitotic phosphorylation. Proc. Natl Acad. Sci. USA 105, 10762–10767 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Steen, J. A. et al. Different phosphorylation states of the anaphase promoting complex in response to antimitotic drugs: a quantitative proteomic analysis. Proc. Natl Acad. Sci. USA 105, 6069–6074 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Matsuoka, S. et al. ATM and ATR substrate analysis reveals extensive protein networks responsive to DNA damage. Science 316, 1160–1166 (2007). Used a panel of antibodies recognizing phosphorylation sites of the ATM and ATR consensus motif (Ser/Thr-Gln) to enrich substrates of these kinases and their regulation on DNA damage.

    Article  CAS  PubMed  Google Scholar 

  83. Stokes, M. P. et al. Profiling of UV-induced ATM/ATR signaling pathways. Proc. Natl Acad. Sci. USA 104, 19855–19860 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Daub, H. et al. Kinase-selective enrichment enables quantitative phosphoproteomics of the kinome across the cell cycle. Mol. Cell 31, 438–448 (2008).

    Article  CAS  PubMed  Google Scholar 

  85. Malik, R. et al. Quantitative analysis of the human spindle phosphoproteome at distinct mitotic stages. J. Proteome Res. 8, 4553–4563 (2009).

    Article  CAS  PubMed  Google Scholar 

  86. Blume-Jensen, P. & Hunter, T. Oncogenic kinase signalling. Nature 411, 355–365 (2001).

    Article  CAS  PubMed  Google Scholar 

  87. Qiao, Y., Molina, H., Pandey, A., Zhang, J. & Cole, P. A. Chemical rescue of a mutant enzyme in living cells. Science 311, 1293–1297 (2006).

    Article  CAS  PubMed  Google Scholar 

  88. Guo, A. et al. Signaling networks assembled by oncogenic EGFR and c-Met. Proc. Natl Acad. Sci. USA 105, 692–697 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Amanchy, R. et al. Identification of c-Src tyrosine kinase substrates using mass spectrometry and peptide microarrays. J. Proteome Res. 7, 3900–3910 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Rikova, K. et al. Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer. Cell 131, 1190–1203 (2007).

    Article  CAS  PubMed  Google Scholar 

  91. Zanivan, S. et al. Solid tumor proteome and phosphoproteome analysis by high resolution mass spectrometry. J. Proteome Res. 7, 5314–5326 (2008).

    Article  CAS  PubMed  Google Scholar 

  92. Old, W. M. et al. Functional proteomics identifies targets of phosphorylation by B-Raf signaling in melanoma. Mol. Cell 34, 115–131 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Sorkin, A. & von Zastrow, M. Endocytosis and signalling: intertwining molecular networks. Nature Rev. Mol. Cell Biol. 10, 609–622 (2009).

    Article  CAS  Google Scholar 

  94. Choudhary, C. et al. Mislocalized activation of oncogenic RTKs switches downstream signaling outcomes. Mol. Cell 36, 326–339 (2009). Used chemical inhibitors, genetic manipulation and SILAC-based MS to analyse the role of cellular compartmentalization in oncogenic signalling on a global scale. The oncogenic FLT3 receptor is found to signal inappropriately during the biosynthetic route before reaching the plasma membrane.

    Article  CAS  PubMed  Google Scholar 

  95. Bantscheff, M. et al. Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors. Nature Biotech. 25, 1035–1044 (2007).

    Article  CAS  Google Scholar 

  96. Karaman, M. W. et al. A quantitative analysis of kinase inhibitor selectivity. Nature Biotech. 26, 127–132 (2008).

    Article  CAS  Google Scholar 

  97. Sharma, K. et al. Proteomics strategy for quantitative protein interaction profiling in cell extracts. Nature Methods 6, 741–744 (2009). References 95–97 developed and applied chemical proteomics tools to identify in vivo targets of kinase inhibitors, to profile their kinase specificity and binding affinity.

    Article  CAS  PubMed  Google Scholar 

  98. Pan, C., Olsen, J. V., Daub, H. & Mann, M. Global effects of kinase inhibitors on signaling networks revealed by quantitative phosphoproteomics. Mol. Cell. Proteomics 8, 2796–2808 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Huber, A. et al. Characterization of the rapamycin-sensitive phosphoproteome reveals that Sch9 is a central coordinator of protein synthesis. Genes Dev. 23, 1929–1943 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Mertins, P. et al. Investigation of protein-tyrosine phosphatase 1B function by quantitative proteomics. Mol. Cell. Proteomics 7, 1763–77 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Hilger, M., Bonaldi, T., Gnad, F. & Mann, M. Systems-wide analysis of a phosphatase knock-down by quantitative proteomics and phosphoproteomics. Mol. Cell. Proteomics 8, 1908–1920 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Smolka, M. B., Albuquerque, C. P., Chen, S. H. & Zhou, H. Proteome-wide identification of in vivo targets of DNA damage checkpoint kinases. Proc. Natl Acad. Sci. USA 104, 10364–10369 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Holt, L. J. et al. Global analysis of Cdk1 substrate phosphorylation sites provides insights into evolution. Science 325, 1682–1686 (2009). Used an analogue sensitive mutant of Cdk1, a mutant-specific inhibitor and quantitative MS to reveal new substrates of this kinase.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Kumar, N., Wolf-Yadlin, A., White, F. M. & Lauffenburger, D. A. Modeling HER2 effects on cell behavior from mass spectrometry phosphotyrosine data. PLoS Comput. Biol. 3, e4 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Linding, R. et al. Systematic discovery of in vivo phosphorylation networks. Cell 129, 1415–1426 (2007). Developed a novel software algorithm, NetworKIN, to predict likely kinase–substrate relationships from large-scale phosphoproteomic data sets.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Miller, M. L. et al. Linear motif atlas for phosphorylation-dependent signaling. Sci. Signal. 1, ra2 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Tan, C. S. et al. Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases. Sci. Signal. 2, ra39 (2009).

    Article  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  109. Vermeulen, M., Hubner, N. C. & Mann, M. High confidence determination of specific protein-protein interactions using quantitative mass spectrometry. Curr. Opin. Biotechnol. 19, 331–337 (2008).

    Article  CAS  PubMed  Google Scholar 

  110. 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  CAS  PubMed  PubMed Central  Google Scholar 

  111. 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  PubMed  PubMed Central  Google Scholar 

  112. von Kriegsheim, A. et al. Cell fate decisions are specified by the dynamic ERK interactome. Nature Cell Biol. 11, 1458–1464 (2009).

    Article  CAS  PubMed  Google Scholar 

  113. Blagoev, B. et al. A proteomics strategy to elucidate functional protein-protein interactions applied to EGF signaling. Nature Biotech. 21, 315–318 (2003).

    Article  CAS  Google Scholar 

  114. Schulze, W. X., Deng, L. & Mann, M. Phosphotyrosine interactome of the ErbB-receptor kinase family. Mol. Syst. Biol. 1, 2005.0008 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Hanke, S. & Mann, M. The phosphotyrosine interactome of the insulin receptor family and its substrates IRS-1 and IRS-2. Mol. Cell. Proteomics 8, 519–534 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Selbach, M. et al. Host cell interactome of tyrosine-phosphorylated bacterial proteins. Cell Host Microbe 5, 397–403 (2009).

    Article  CAS  PubMed  Google Scholar 

  117. Trinkle-Mulcahy, L. & Lamond, A. I. Toward a high-resolution view of nuclear dynamics. Science 318, 1402–1407 (2007).

    Article  CAS  PubMed  Google Scholar 

  118. Waanders, L. F. et al. Quantitative proteomic analysis of single pancreatic islets. Proc. Natl Acad. Sci. USA 106, 18902–18907 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  119. Mallick, P. et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nature Biotech. 25, 125–131 (2007).

    Article  CAS  Google Scholar 

  120. Makarov, A. et al. Performance evaluation of a hybrid linear ion trap/orbitrap mass spectrometer. Anal. Chem. 78, 2113–2120 (2006).

    Article  CAS  PubMed  Google Scholar 

  121. Olsen, J. V. et al. Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap. Mol. Cell. Proteomics 4, 2010–2021 (2005).

    Article  CAS  PubMed  Google Scholar 

  122. Zubarev, R. & Mann, M. On the proper use of mass accuracy in proteomics. Mol. Cell. Proteomics 6, 377–381 (2007).

    Article  CAS  PubMed  Google Scholar 

  123. Mann, M. & Kelleher, N. L. Precision proteomics: the case for high resolution and high mass accuracy. Proc. Natl Acad. Sci. USA 105, 18132–18138 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  124. Wang, Z. et al. Extensive crosstalk between O-GlcNAcylation and phosphorylation regulates cytokinesis. Sci. Signal. 3, ra2 (2010).

    PubMed  PubMed Central  Google Scholar 

  125. Soufi, B. et al. Global analysis of the yeast osmotic stress response by quantitative proteomics. Mol. Biosyst. 5, 1337–1346 (2009).

    Article  CAS  PubMed  Google Scholar 

  126. Pan, C., Gnad, F., Olsen, J. V. & Mann, M. Quantitative phosphoproteome analysis of a mouse liver cell line reveals specificity of phosphatase inhibitors. Proteomics 8, 4534–4546 (2008).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank our co-workers for fruitful discussions, especially J. Olsen for providing figure 3. The Protein Research Center (CPR) is supported by a generous donation from the Novo Nordisk Foundation. This project was supported by the European Commission's 7th Framework Program PROteomics SPECification in Time and Space (PROSPECTS, HEALTH-F4-2008-021,648).

Author information

Authors and Affiliations

  1. The Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3, Copenhagen, 2200, Denmark

    Chunaram Choudhary & Matthias Mann

  2. Department of Proteomics and Signal Transduction, Max Planck Institute for Biochemistry, Martinsried, Germany

    Matthias Mann

Authors
  1. Chunaram Choudhary
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthias Mann
    View author publications

    You can also search for this author in PubMed Google Scholar

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

Related links

FURTHER INFORMATION

Matthias Mann's homepage

NNF Center for Protein Research

Glossary

SH2 domain

(SRC homology 2 domain). An ∼100 amino acid domain that recognizes phosphoTyr residues in a specific sequence context.

PTB domain

(PhosphoTyr-binding domain). Like the SH2 domain, the PTB domain binds to phosphoTyr, but usually binding specificity is determined by the sequence N-terminal to the phosphorylation site.

Liquid chromatography

In high performance liquid chromatography (HPLC), the peptide mixture is separated in liquid phase based on hydrophobic interactions with the C18 stationary phase of the chromatography column (C18 is the length of the alky chains decorating the chromatographic beads). In proteomics, columns are typically very small (75 m inner diameter) and flow rates very low (200 nl min−1).

Electrospray ionization

An ionization method developed by J. Fenn, for which he shared the 2002 Nobel Prize in chemistry. A liquid is passed through a charged needle, producing electrosprayed droplets that contain the peptides. On evaporation of the solvent, intact and protonated peptides (or other analyte molecules) are left in the gas phase.

Mass analyser

A part of a mass spectrometer that measures mass to charge (m/z) ratios of ions (for example, ionized peptides). Multiplying the m/z value by the charge and subtracting the weight of the charging entity (typically two protons) yields the mass of the peptide. A mass spectrometer can contain several mass analysers of the same or different types, and ions can be moved between these analysers at will.

Fourier transformation

A mathematical operation that transforms one complex-valued function of a real variable (typically a frequency spectrum) into another domain. In Fourier transformation MS, the frequencies associated with ions moving in a trap are mass dependent and this signal is transformed by Fourier transformation into a mass spectrum.

MS resolution

This value is defined as the width of the peak at half height divided by the mass of the peak and is therefore a dimensionless number. High resolution distinguishes co-eluting peptides with similar mass, a prerquisite for unambiguous identification and quantification of peptides.

Chemical derivatization

A chemical method used to transform one chemical compound into a derivative. In proteomics, side chains of amino acids can be chemically modified, which can be used for enriching these peptides from complex mixtures or for quantification of the modified peptide in MS.

False discovery rate

(FDR). A statistical method used in multiple hypotheses testing to correct for multiple comparisons. In a list of positive calls, FDR controls the expected proportion of false positives. In proteomic data analysis, a 1% FDR is currently customary, which means that, at most, 1% of the identified proteins should be false positives.

Metal affinity complexation

The coordinated binding between immobilized metal ions and charged peptides. Immobilized metals such as Fe3+ or Ga3+, or metal oxides such as TiO2 or ZrO2, are commonly used to enrich phosphorylated peptides from non-phosphorylated peptides.

F-actin cup

(Filamentous actin cup). A polymer of globular actin (G-actin) subunits, which accumulates underneath phagocytic cups — cup-shaped extensions of the plasma membrane that encircle foreign particles during early processes of phagocytosis.

Endocytic route

The trafficking of cell surface receptors from the plasma membrane to intracellular compartments by receptor endocytosis, which generally involves early and late endosomes, multivesicular bodies and lysosomes. It is a major pathway that regulates the amplitude and duration of receptor signalling at the plasma membrane.

Biosynthetic route

The trafficking of receptors from their intracellular biosynthesis compartments to the surface, involving protein synthesis at the ER, relocation to the Golgi and, finally, transport to the cell surface.

FLT3-ITD

The FLT3 receptor containing an in-frame insertion of amino acid sequence (internal tandem duplication (ITD); of ∼3–100 amino acids in length) in the intracellular juxtamembrane region, which results in ligand-independent receptor activation. These oncogenic mutations are found in about 20% of human acute myeloid leukaemia and are associated with oncogenic transformation.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Choudhary, C., Mann, M. Decoding signalling networks by mass spectrometry-based proteomics. Nat Rev Mol Cell Biol 11, 427–439 (2010). https://doi.org/10.1038/nrm2900

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrm2900

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research