Abstract
The evolution of mass spectrometry–based proteomic technologies has advanced our understanding of the complex and dynamic nature of proteomes while concurrently revealing that no 'one-size-fits-all' proteomic strategy can be used to address all biological questions. Whereas some techniques, such as those for analyzing protein complexes, have matured and are broadly applied with great success, others, such as global quantitative protein expression profiling for biomarker discovery, are still confined to a few expert laboratories. In this Perspective, we attempt to distill the wide array of conceivable proteomic approaches into a compact canon of techniques suited to asking and answering specific types of biological questions. By discussing the relationship between the complexity of a biological sample and the difficulty of implementing the appropriate analysis approach, we contrast areas of proteomics broadly usable today with those that require significant technical and conceptual development. We hope to provide nonexperts with a guide for calibrating expectations of what can realistically be learned from a proteomics experiment and for gauging the planning and execution effort. We further provide a detailed supplement explaining the most common techniques in proteomics.
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References
Wasinger, V.C. et al. Progress with gene-product mapping of the Mollicutes: Mycoplasma genitalium. Electrophoresis 16, 1090–1094 (1995).
Ducret, A., Van Oostveen, I., Eng, J.K., Yates, J.R. III & Aebersold, R. High throughput protein characterization by automated reverse-phase chromatography/electrospray tandem mass spectrometry. Protein Sci. 7, 706–719 (1998).
Washburn, M.P., Wolters, D. & Yates, J.R. III. Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 19, 242–247 (2001).
Wilm, M. et al. Femtomole sequencing of proteins from polyacrylamide gels by nano-electrospray mass spectrometry. Nature 379, 466–469 (1996).
Aebersold, R. Constellations in a cellular universe. Nature 422, 115–116 (2003).
Keshishian, H. et al. Quantification of cardiovascular biomarkers in patient plasma by targeted mass spectrometry and stable isotope dilution. Mol. Cell Proteomics 8, 2339–2349 (2009).
Omenn, G.S. et al. Overview of the HUPO Plasma Proteome Project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database. Proteomics 5, 3226–3245 (2005).
de Godoy, L.M. et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 1251–1254 (2008).
Rush, J. et al. Immunoaffinity profiling of tyrosine phosphorylation in cancer cells. Nat. Biotechnol. 23, 94–101 (2005).
Olsen, J.V. et al. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127, 635–648 (2006).
Bouwmeester, T. et al. A physical and functional map of the human TNF-alpha/NF-κB signal transduction pathway. Nat. Cell Biol. 6, 97–105 (2004).
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).
Heck, A.J. Native mass spectrometry: a bridge between interactomics and structural biology. Nat. Methods 5, 927–933 (2008).
Sharon, M. & Robinson, C.V. The role of mass spectrometry in structure elucidation of dynamic protein complexes. Annu. Rev. Biochem. 76, 167–193 (2007).
Mann, M. & Jensen, O.N. Proteomic analysis of post-translational modifications. Nat. Biotechnol. 21, 255–261 (2003).
Ong, S.E., Mittler, G. & Mann, M. Identifying and quantifying in vivo methylation sites by heavy methyl SILAC. Nat. Methods 1, 119–126 (2004).
Denison, C., Kirkpatrick, D.S. & Gygi, S.P. Proteomic insights into ubiquitin and ubiquitin-like proteins. Curr. Opin. Chem. Biol. 9, 69–75 (2005).
Zaia, J. Mass spectrometry of oligosaccharides. Mass Spectrom. Rev. 23, 161–227 (2004).
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).
Steen, H., Jebanathirajah, J.A., Springer, M. & Kirschner, M.W. Stable isotope-free relative and absolute quantitation of protein phosphorylation stoichiometry by MS. Proc. Natl. Acad. Sci. USA 102, 3948–3953 (2005).
Zhang, X., Jin, Q.K., Carr, S.A. & Annan, R.S. N-terminal peptide labeling strategy for incorporation of isotopic tags: a method for the determination of site-specific absolute phosphorylation stoichiometry. Rapid Commun. Mass Spectrom. 16, 2325–2332 (2002).
Kirkpatrick, D.S., Gerber, S.A. & Gygi, S.P. The absolute quantification strategy: a general procedure for the quantification of proteins and post-translational modifications. Methods 35, 265–273 (2005).
Gerber, S.A., Kettenbach, A.N., Rush, J. & Gygi, S.P. The absolute quantification strategy: application to phosphorylation profiling of human separase serine 1126. Methods Mol. Biol. 359, 71–86 (2007).
Rudd, P.M. et al. The glycosylation of the complement regulatory protein, human erythrocyte CD59. J. Biol. Chem. 272, 7229–7244 (1997).
Phanstiel, D. et al. Mass spectrometry identifies and quantifies 74 unique histone H4 isoforms in differentiating human embryonic stem cells. Proc. Natl. Acad. Sci. USA 105, 4093–4098 (2008).
Siuti, N. & Kelleher, N.L. Decoding protein modifications using top-down mass spectrometry. Nat. Methods 4, 817–821 (2007).
Mayya, V., Rezual, K., Wu, L., Fong, M.B. & Han, D.K. Absolute quantification of multisite phosphorylation by selective reaction monitoring mass spectrometry: determination of inhibitory phosphorylation status of cyclin-dependent kinases. Mol. Cell. Proteomics 5, 1146–1157 (2006).
Desiere, F. et al. Integration with the human genome of peptide sequences obtained by high-throughput mass spectrometry. Genome Biol. 6, R9 (2005).
Margulies, M. et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376–380 (2005).
Alberts, B. The cell as a collection of protein machines: preparing the next generation of molecular biologists. Cell 92, 291–294 (1998).
Neubauer, G. et al. Identification of the proteins of the yeast U1 small nuclear ribonucleoprotein complex by mass spectrometry. Proc. Natl. Acad. Sci. USA 94, 385–390 (1997).
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).
Blagoev, B. et al. A proteomics strategy to elucidate functional protein-protein interactions applied to EGF signaling. Nat. Biotechnol. 21, 315–318 (2003).
Rual, J.F. et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature 437, 1173–1178 (2005).
Uetz, P. et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 403, 623–627 (2000).
Bauer, A. & Kuster, B. Affinity purification-mass spectrometry. Powerful tools for the characterization of protein complexes. Eur. J. Biochem. 270, 570–578 (2003).
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).
Poser, I. et al. BAC TransgeneOmics: a high-throughput method for exploration of protein function in mammals. Nat. Methods 5, 409–415 (2008).
Rigaut, G. et al. A generic protein purification method for protein complex characterization and proteome exploration. Nat. Biotechnol. 17, 1030–1032 (1999).
Schmitt-Ulms, G. et al. Time-controlled transcardiac perfusion cross-linking for the study of protein interactions in complex tissues. Nat. Biotechnol. 22, 724–731 (2004).
Andersen, J.S. et al. Proteomic characterization of the human centrosome by protein correlation profiling. Nature 426, 570–574 (2003).
Pflieger, D. et al. Quantitative proteomic analysis of protein complexes: concurrent identification of interactors and their state of phosphorylation. Mol. Cell. Proteomics 7, 326–346 (2008).
Andersen, J.S. et al. Nucleolar proteome dynamics. Nature 433, 77–83 (2005).
Alber, F. et al. Determining the architectures of macromolecular assemblies. Nature 450, 683–694 (2007).
Alber, F. et al. The molecular architecture of the nuclear pore complex. Nature 450, 695–701 (2007).
Hochleitner, E.O., Sondermann, P. & Lottspeich, F. Determination of the stoichiometry of protein complexes using liquid chromatography with fluorescence and mass spectrometric detection of fluorescently labeled proteolytic peptides. Proteomics 4, 669–676 (2004).
Menetret, J.F. et al. Single copies of Sec61 and TRAP associate with a nontranslating mammalian ribosome. Structure 16, 1126–1137 (2008).
Nanavati, D., Gucek, M., Milne, J.L., Subramaniam, S. & Markey, S.P. Stoichiometry and absolute quantification of proteins with mass spectrometry using fluorescent and isotope-labeled concatenated peptide standards. Mol. Cell. Proteomics 7, 442–447 (2008).
Hernandez, H. & Robinson, C.V. Determining the stoichiometry and interactions of macromolecular assemblies from mass spectrometry. Nat. Protoc. 2, 715–726 (2007).
Lorenzen, K., Olia, A.S., Uetrecht, C., Cingolani, G. & Heck, A.J. Determination of stoichiometry and conformational changes in the first step of the P22 tail assembly. J. Mol. Biol. 379, 385–396 (2008).
Gavin, A.C. et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415, 141–147 (2002).
Maiolica, A. et al. Structural analysis of multiprotein complexes by cross-linking, mass spectrometry, and database searching. Mol. Cell. Proteomics 6, 2200–2211 (2007).
Sinz, A. Chemical cross-linking and mass spectrometry to map three-dimensional protein structures and protein-protein interactions. Mass Spectrom. Rev. 25, 663–682 (2006).
Ewing, R.M. et al. Large-scale mapping of human protein-protein interactions by mass spectrometry. Mol. Syst. Biol. 3, 89 (2007).
Gavin, A.C. et al. Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636 (2006).
Krogan, N.J. et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440, 637–643 (2006).
Ho, Y. et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415, 180–183 (2002).
Kolch, W. Meaningful relationships: the regulation of the Ras/Raf/MEK/ERK pathway by protein interactions. Biochem. J. 351, 289–305 (2000).
Schubert, P., Hoffman, M.D., Sniatynski, M.J. & Kast, J. Advances in the analysis of dynamic protein complexes by proteomics and data processing. Anal. Bioanal. Chem. 386, 482–493 (2006).
White, F.M. Quantitative phosphoproteomic analysis of signaling network dynamics. Curr. Opin. Biotechnol. 19, 404–409 (2008).
Kung, L.A. & Snyder, M. Proteome chips for whole-organism assays. Nat. Rev. Mol. Cell Biol. 7, 617–622 (2006).
Paweletz, C.P. et al. Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front. Oncogene 20, 1981–1989 (2001).
Speer, R. et al. Molecular network analysis using reverse phase protein microarrays for patient tailored therapy. Adv. Exp. Med. Biol. 610, 177–186 (2008).
Zhu, H. et al. Global analysis of protein activities using proteome chips. Science 293, 2101–2105 (2001).
Huang, P.H. & White, F.M. Phosphoproteomics: unraveling the signaling web. Mol. Cell 31, 777–781 (2008).
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).
Van, P.T. et al. Halobacterium salinarum NRC-1 PeptideAtlas: toward strategies for targeted proteomics and improved proteome coverage. J. Proteome Res. 7, 3755–3764 (2008).
King, N.L. et al. Analysis of the Saccharomyces cerevisiae proteome with PeptideAtlas. Genome Biol. 7, R106 (2006).
Chen, E.I., Hewel, J., Felding-Habermann, B. & Yates, J.R. III. Large scale protein profiling by combination of protein fractionation and multidimensional protein identification technology (MudPIT). Mol. Cell. Proteomics 5, 53–56 (2006).
Malmstrom, J. et al. Optimized peptide separation and identification for mass spectrometry based proteomics via free-flow electrophoresis. J. Proteome Res. 5, 2241–2249 (2006).
Hubner, N.C., Ren, S. & Mann, M. Peptide separation with immobilized pI strips is an attractive alternative to in-gel protein digestion for proteome analysis. Proteomics 8, 4862–4872 (2008).
Chen, E.I., McClatchy, D., Park, S.K. & Yates, J.R. III. Comparisons of mass spectrometry compatible surfactants for global analysis of the mammalian brain proteome. Anal. Chem. 80, 8694–8701 (2008).
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).
Schirle, M., Heurtier, M.A. & Kuster, B. Profiling core proteomes of human cell lines by one-dimensional PAGE and liquid chromatography-tandem mass spectrometry. Mol. Cell. Proteomics 2, 1297–1305 (2003).
Kuster, B., Schirle, M., Mallick, P. & Aebersold, R. Scoring proteomes with proteotypic peptide probes. Nat. Rev. Mol. Cell Biol. 6, 577–583 (2005).
Mallick, P. et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nat. Biotechnol. 25, 125–131 (2007).
Tabb, D.L. et al. Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry. J. Proteome Res. 9, 761–776 (2009).
Swaney, D.L., Wenger, C.D. & Coon, J.J. Value of using multiple proteases for large-scale mass spectrometry-based proteomics. J. Proteome Res. 9, 1323–1329 (2010).
Gruhler, A. et al. Quantitative phosphoproteomics applied to the yeast pheromone signaling pathway. Mol. Cell. Proteomics 4, 310–327 (2005).
Pinkse, M.W. et al. Highly robust, automated, and sensitive online TiO2-based phosphoproteomics applied to study endogenous phosphorylation in Drosophila melanogaster. J. Proteome Res. 7, 687–697 (2008).
Reiland, S. et al. Large-scale Arabidopsis phosphoproteome profiling reveals novel chloroplast kinase substrates and phosphorylation networks. Plant Physiol. 150, 889–903 (2009).
Villen, J., Beausoleil, S.A., Gerber, S.A. & Gygi, S.P. Large-scale phosphorylation analysis of mouse liver. Proc. Natl. Acad. Sci. USA 104, 1488–1493 (2007).
Zielinska, D.F., Gnad, F., Jedrusik-Bode, M., Wisniewski, J.R. & Mann, M. Caenorhabditis elegans has a phosphoproteome atypical for metazoans that is enriched in developmental and sex determination proteins. J. Proteome Res. 8, 4039–4049 (2009).
Lemeer, S. et al. Endogenous phosphotyrosine signaling in zebrafish embryos. Mol. Cell. Proteomics 6, 2088–2099 (2007).
Zhang, Y. et al. Time-resolved mass spectrometry of tyrosine phosphorylation sites in the epidermal growth factor receptor signaling network reveals dynamic modules. Mol. Cell. Proteomics 4, 1240–1250 (2005).
Au, C.E. et al. Organellar proteomics to create the cell map. Curr. Opin. Cell Biol. 19, 376–385 (2007).
Lilley, K.S. & Dupree, P. Plant organelle proteomics. Curr. Opin. Plant Biol. 10, 594–599 (2007).
Dunkley, T.P., Watson, R., Griffin, J.L., Dupree, P. & Lilley, K.S. Localization of organelle proteins by isotope tagging (LOPIT). Mol. Cell. Proteomics 3, 1128–1134 (2004).
Jang, J.H. & Hanash, S. Profiling of the cell surface proteome. Proteomics 3, 1947–1954 (2003).
Wollscheid, B. et al. Mass-spectrometric identification and relative quantification of N-linked cell surface glycoproteins. Nat. Biotechnol. 27, 378–386 (2009).
Zhang, H., Li, X.J., Martin, D.B. & Aebersold, R. Identification and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. Nat. Biotechnol. 21, 660–666 (2003).
Bantscheff, M., Schirle, M., Sweetman, G., Rick, J. & Kuster, B. Quantitative mass spectrometry in proteomics: a critical review. Anal. Bioanal. Chem. 389, 1017–1031 (2007).
Griffin, N.M. et al. Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis. Nat. Biotechnol. 28, 83–89 (2009).
Saito, A. et al. AYUMS: an algorithm for completely automatic quantitation based on LC-MS/MS proteome data and its application to the analysis of signal transduction. BMC Bioinformatics 8, 15 (2007).
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).
Ono, M. et al. Label-free quantitative proteomics using large peptide data sets generated by nanoflow liquid chromatography and mass spectrometry. Mol. Cell. Proteomics 5, 1338–1347 (2006).
Parish, R. Comparison of linear regression methods when both variables contain error: relation to clinical studies. Ann. Pharmacother. 23, 891–898 (1989).
Mueller, L.N. et al. SuperHirn - a novel tool for high resolution LC-MS-based peptide/protein profiling. Proteomics 7, 3470–3480 (2007).
Cox, J. et al. A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics. Nat. Protoc. 4, 698–705 (2009).
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).
Han, D.K., Eng, J., Zhou, H. & Aebersold, R. Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometry. Nat. Biotechnol. 19, 946–951 (2001).
Faca, V. et al. Quantitative analysis of acrylamide labeled serum proteins by LC-MS/MS. J. Proteome Res. 5, 2009–2018 (2006).
Rauch, A. et al. Computational Proteomics Analysis System (CPAS): an extensible, open-source analytic system for evaluating and publishing proteomic data and high throughput biological experiments. J. Proteome Res. 5, 112–121 (2006).
Jaffe, J.D. et al. PEPPeR, a platform for experimental proteomic pattern recognition. Mol. Cell. Proteomics 5, 1927–1941 (2006).
Park, S.K., Venable, J.D., Xu, T. & Yates, J.R. III. A quantitative analysis software tool for mass spectrometry-based proteomics. Nat. Methods 5, 319–322 (2008).
Du, X. et al. A computational strategy to analyze label-free temporal bottom-up proteomics data. J. Proteome Res. 7, 2595–2604 (2008).
Domon, B. & Aebersold, R. Three strategies for quantitative proteomics and their use. Nat. Biotechnol. 28, 710–721 (2010).
Zhang, R. & Regnier, F.E. Minimizing resolution of isotopically coded peptides in comparative proteomics. J. Proteome Res. 1, 139–147 (2002).
Zhang, R., Sioma, C.S., Wang, S. & Regnier, F.E. Fractionation of isotopically labeled peptides in quantitative proteomics. Anal. Chem. 73, 5142–5149 (2001).
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 7, 780–790 (2009).
Faca, V. et al. Contribution of protein fractionation to depth of analysis of the serum and plasma proteomes. J. Proteome Res. 6, 3558–3565 (2007).
Liu, Y., Patricelli, M.P. & Cravatt, B.F. Activity-based protein profiling: the serine hydrolases. Proc. Natl. Acad. Sci. USA 96, 14694–14699 (1999).
Bantscheff, M. et al. Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors. Nat. Biotechnol. 25, 1035–1044 (2007).
Cravatt, B.F., Wright, A.T. & Kozarich, J.W. Activity-based protein profiling: from enzyme chemistry to proteomic chemistry. Annu. Rev. Biochem. 77, 383–414 (2008).
Rikova, K. et al. Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer. Cell 131, 1190–1203 (2007).
Blethrow, J.D., Glavy, J.S., Morgan, D.O. & Shokat, K.M. Covalent capture of kinase-specific phosphopeptides reveals Cdk1-cyclin B substrates. Proc. Natl. Acad. Sci. USA 105, 1442–1447 (2008).
Emmert-Buck, M.R. et al. Laser capture microdissection. Science 274, 998–1001 (1996).
Lu, Q. et al. Analysis of mouse brain microvascular endothelium using immuno-laser capture microdissection coupled to a hybrid linear ion trap with Fourier transform-mass spectrometry proteomics platform. Electrophoresis 29, 2689–2695 (2008).
Johann, D.J. et al. Approaching solid tumor heterogeneity on a cellular basis by tissue proteomics using laser capture microdissection and biological mass spectrometry. J. Proteome Res. 8, 2310–2318 (2009).
Reimel, B.A. et al. Proteomics on fixed tissue specimens - a review. Curr. Proteomics 6, 63–69 (2009).
Faca, V.M. et al. A mouse to human search for plasma proteome changes associated with pancreatic tumor development. PLoS Med. 5, e123 (2008).
Harsha, H.C. et al. A compendium of potential biomarkers of pancreatic cancer. PLoS Med. 6, e1000046 (2009).
Bandhakavi, S., Stone, M.D., Onsongo, G., Van Riper, S.K. & Griffin, T.J. A dynamic range compression and three-dimensional peptide fractionation analysis platform expands proteome coverage and the diagnostic potential of whole saliva. J. Proteome Res. 8, 5590–5600 (2009).
Righetti, P.G., Boschetti, E., Lomas, L. & Citterio, A. Protein equalizer technology: the quest for a “democratic proteome”. Proteomics 6, 3980–3992 (2006).
Brand, J., Haslberger, T., Zolg, W., Pestlin, G. & Palme, S. Depletion efficiency and recovery of trace markers from a multiparameter immunodepletion column. Proteomics 6, 3236–3242 (2006).
Seam, N. et al. Quality control of serum albumin depletion for proteomic analysis. Clin. Chem. 53, 1915–1920 (2007).
Anderson, N.L. et al. Mass spectrometric quantitation of peptides and proteins using Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA). J. Proteome Res. 3, 235–244 (2004).
Kuhn, E. et al. Developing multiplexed assays for troponin I and interleukin-33 in plasma by peptide immunoaffinity enrichment and targeted mass spectrometry. Clin. Chem. 55, 1108–1117 (2009).
Pitteri, S.J. et al. Integrated proteomic analysis of human cancer cells and plasma from tumor bearing mice for ovarian cancer biomarker discovery. PLoS ONE 4, e7916 (2009).
Katayama, H. et al. Application of serum proteomics to the Women's Health Initiative conjugated equine estrogens trial reveals a multitude of effects relevant to clinical findings. Genome Med. 1, 47 (2009).
Faça, V., Wang, H. & Hanash, S. Proteomic global profiling for cancer biomarker discovery. Methods Mol. Biol. 492, 309–320 (2009).
Faça, V.M. & Hanash, S.M. In-depth proteomics to define the cell surface and secretome of ovarian cancer cells and processes of protein shedding. Cancer Res. 69, 728–730 (2009).
Faça, V.M. et al. Proteomic analysis of ovarian cancer cells reveals dynamic processes of protein secretion and shedding of extra-cellular domains. PLoS ONE 3, e2425 (2008).
Hanash, S.M., Pitteri, S.J. & Faça, V.M. Mining the plasma proteome for cancer biomarkers. Nature 452, 571–579 (2008).
Caprioli, R.M., Farmer, T.B. & Gile, J. Molecular imaging of biological samples: localization of peptides and proteins using MALDI-TOF MS. Anal. Chem. 69, 4751–4760 (1997).
Cornett, D.S., Reyzer, M.L., Chaurand, P. & Caprioli, R.M. MALDI imaging mass spectrometry: molecular snapshots of biochemical systems. Nat. Methods 4, 828–833 (2007).
Hsieh, Y., Chen, J. & Korfmacher, W.A. Mapping pharmaceuticals in tissues using MALDI imaging mass spectrometry. J. Pharmacol. Toxicol. Methods 55, 193–200 (2007).
Woods, A.S. & Jackson, S.N. Brain tissue lipidomics: direct probing using matrix-assisted laser desorption/ionization mass spectrometry. AAPS J. 8, E391–E395 (2006).
Taguchi, F. et al. Mass spectrometry to classify non-small-cell lung cancer patients for clinical outcome after treatment with epidermal growth factor receptor tyrosine kinase inhibitors: a multicohort cross-institutional study. J. Natl. Cancer Inst. 99, 838–846 (2007).
Deutsch, E.W. et al. Human Plasma PeptideAtlas. Proteomics 5, 3497–3500 (2005).
Deutsch, E.W., Lam, H. & Aebersold, R. PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep. 9, 429–434 (2008).
Zhang, Q. et al. A mouse plasma peptide atlas as a resource for disease proteomics. Genome Biol. 9, R93 (2008).
Castellana, N.E. et al. Discovery and revision of Arabidopsis genes by proteogenomics. Proc. Natl. Acad. Sci. USA 105, 21034–21038 (2008).
Gupta, N. et al. Comparative proteogenomics: combining mass spectrometry and comparative genomics to analyze multiple genomes. Genome Res. 18, 1133–1142 (2008).
Gupta, N. et al. Whole proteome analysis of post-translational modifications: applications of mass-spectrometry for proteogenomic annotation. Genome Res. 17, 1362–1377 (2007).
Tanner, S. et al. InsPecT: identification of posttranslationally modified peptides from tandem mass spectra. Anal. Chem. 77, 4626–4639 (2005).
Gerszten, R.E., Carr, S.A. & Sabatine, M. Integration of proteomic-based tools for improved biomarkers of myocardial injury. Clin. Chem. 56, 194–201 (2010).
Kentsis, A. et al. Discovery and validation of urine markers of acute pediatric appendicitis using high-accuracy mass spectrometry. Ann. Emerg. Med. 55, 62–70 (2010).
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).
Acknowledgements
The authors thank M. Bergman, C. Flinders, K. Kramer and S. Mumenthaler for comments on the manuscript. The work of P.M. was supported by NCI-1U54CA143907 and NCI-U54CA119367.
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Mallick, P., Kuster, B. Proteomics: a pragmatic perspective. Nat Biotechnol 28, 695–709 (2010). https://doi.org/10.1038/nbt.1658
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DOI: https://doi.org/10.1038/nbt.1658
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