Processing of Mass Spectrometry Data in Clinical Applications

  • Dario Di Silvestre
  • Pietro Brunetti
  • Pier Luigi Mauri
Part of the Translational Bioinformatics book series (TRBIO, volume 3)


Mass spectrometry-based proteomics has become the leading approach for analyzing complex biological samples at a large-scale level. Its importance for clinical applications is more and more increasing, thanks to the development of high-performing instruments which allow the discovery of disease-specific biomarkers and an automated and rapid protein profiling of the analyzed samples. In this scenario, the large-scale production of proteomic data has driven the development of specific bioinformatic tools to assist researchers during the discovery processes. Here, we discuss the main methods, algorithms, and procedures to identify and use biomarkers for clinical and research purposes. In particular, we have been focused on quantitative approaches, the identification of proteotypic peptides, and the classification of samples, using proteomic data. Finally, this chapter is concluded by reporting the integration of experimental data with network datasets, as valuable instrument for identifying alterations that underline the emergence of specific phenotypes. Based on our experience, we show some examples taking into consideration experimental data obtained by multidimensional protein identification technology (MudPIT) approach.


Mass spectrometry-based proteomics Disease-specific biomarkers Bioinformatic tools Algorithms Integration Multidimensional protein identification technology 



This study was supported by the Italian Ministry of Economy and Finance to the CNR for the Project “FaReBio di Qualita,” by Italian Ministry of University and Research for the Project FAR and by Fondazione Cariplo (2010-0653).


  1. Abu-Asab MS, Chaouchi M, Alesci S, Galli S, Laassri M, Cheema AK, Atouf F, VanMeter J, Amri H. Biomarkers in the age of omics: time for a systems biology approach. OMICS. 2011;15:105–12.PubMedCrossRefGoogle Scholar
  2. Alfarano C, Andrade CE, Anthony K, Bahroos N, Bajec M, Bantoft K, Betel D, Bobechko B, Boutilier K, Burgess E, Buzadzija K, Cavero R, D’Abreo C, Donaldson I, Dorairajoo D, Dumontier MJ, Dumontier MR, Earles V, Farrall R, Feldman H, Garderman E, Gong Y, Gonzaga R, Grytsan V, Gryz E, Gu V, Haldorsen E, Halupa A, Haw R, Hrvojic A, et al. The biomolecular interaction network database and related tools 2005 update. Nucleic Acids Res. 2005;33:D418–24.PubMedCrossRefGoogle Scholar
  3. Anderson NL, Anderson NG, Haines LR, Hardie DB, Olafson RW, Pearson TW. Mass spectrometric quantitation of peptides and proteins using stable isotope standards and capture by anti-peptide antibodies (SISCAPA). J Proteome Res. 2004;3:235–44.PubMedCrossRefGoogle Scholar
  4. Arneberg R, Rajalahti T, Flikka K, Berven FS, Kroksveen AC, Berle M, Myhr K-M, Vedeler CA, Ulvik RJ, Kvalheim OM. Pretreatment of mass spectral profiles: application to proteomic data. Anal Chem. 2007;79:7014–26.PubMedCrossRefGoogle Scholar
  5. Arrell DK, Zlatkovic Lindor J, Yamada S, Terzic A. K(ATP) channel-dependent metaboproteome decoded: systems approaches to heart failure prediction, diagnosis, and therapy. Cardiovasc Res. 2011;90:258–66.PubMedCrossRefGoogle Scholar
  6. Avila-Campillo I, Drew K, Lin J, Reiss DJ, Bonneau R. BioNetBuilder: automatic integration of biological networks. Bioinformatics. 2007;23:392–3.PubMedCrossRefGoogle Scholar
  7. Bader GD, Hogue CWV. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 2003;4:2.PubMedCrossRefGoogle Scholar
  8. Barabási A-L, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004;5:101–13.PubMedCrossRefGoogle Scholar
  9. Barla A, Jurman G, Riccadonna S, Merler S, Chierici M, Furlanello C. Machine learning methods for predictive proteomics. Brief Bioinform. 2008;9:119–28.PubMedCrossRefGoogle Scholar
  10. Barnidge DR, Hall GD, Stocker JL, Muddiman DC. Evaluation of a cleavable stable isotope labeled synthetic peptide for absolute protein quantification using LC-MS/MS. J Proteome Res. 2004;3:658–61.PubMedCrossRefGoogle Scholar
  11. Barrett CL, Kim TY, Kim HU, Palsson BØ, Lee SY. Systems biology as a foundation for genome-scale synthetic biology. Curr Opin Biotechnol. 2006;17:488–92.PubMedCrossRefGoogle Scholar
  12. Bergamini G, Di Silvestre D, Mauri P, Cigana C, Bragonzi A, De Palma A, Benazzi L, Döring G, Assael BM, Melotti P, Sorio C. MudPIT analysis of released proteins in Pseudomonas aeruginosa laboratory and clinical strains in relation to pro-inflammatory effects. Integr Biol (Camb). 2012;4:270–9.CrossRefGoogle Scholar
  13. Bowers PM, Pellegrini M, Thompson MJ, Fierro J, Yeates TO, Eisenberg D. Prolinks: a database of protein functional linkages derived from coevolution. Genome Biol. 2004;5:R35.PubMedCrossRefGoogle Scholar
  14. Braisted JC, Kuntumalla S, Vogel C, Marcotte EM, Rodrigues AR, Wang R, Huang S-T, Ferlanti ES, Saeed AI, Fleischmann RD, Peterson SN, Pieper R. The APEX quantitative proteomics tool: generating protein quantitation estimates from LC-MS/MS proteomics results. BMC Bioinformatics. 2008;9:529.PubMedCrossRefGoogle Scholar
  15. Brambilla F, Lavatelli F, Di Silvestre D, Valentini V, Rossi R, Palladini G, Obici L, Verga L, Mauri P, Merlini G. Reliable typing of systemic amyloidoses through proteomic analysis of subcutaneous adipose tissue. Blood. 2012;119:1844–7.PubMedCrossRefGoogle Scholar
  16. Breiman L. Random forests. Mach Learn. 2001;45:5–32.CrossRefGoogle Scholar
  17. Breitkreutz B-J, Stark C, Tyers M. Osprey: a network visualization system. Genome Biol. 2003;4:R22.PubMedCrossRefGoogle Scholar
  18. Bridges SM, Magee GB, Wang N, Williams WP, Burgess SC, Nanduri B. ProtQuant: a tool for the label-free quantification of MudPIT proteomics data. BMC Bioinformatics. 2007;8 Suppl 7:S24.PubMedCrossRefGoogle Scholar
  19. Brusniak M-Y, Bodenmiller B, Campbell D, Cooke K, Eddes J, Garbutt A, Lau H, Letarte S, Mueller LN, Sharma V, Vitek O, Zhang N, Aebersold R, Watts JD. Corra: computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics. BMC Bioinformatics. 2008;9:542.PubMedCrossRefGoogle Scholar
  20. Carvalho PC, Fischer JSG, Chen EI, Yates 3rd JR, Barbosa VC. PatternLab for proteomics: a tool for differential shotgun proteomics. BMC Bioinformatics. 2008;9:316.PubMedCrossRefGoogle Scholar
  21. Cerami EG, Gross BE, Demir E, Rodchenkov I, Babur O, Anwar N, Schultz N, Bader GD, Sander C. Pathway commons, a web resource for biological pathway data. Nucleic Acids Res. 2011;39:D685–90.PubMedCrossRefGoogle Scholar
  22. Craig R, Beavis RC. TANDEM: matching proteins with tandem mass spectra. Bioinformatics. 2004;20:1466–7.PubMedCrossRefGoogle Scholar
  23. Craig R, Cortens JP, Beavis RC. Open source system for analyzing, validating, and storing protein identification data. J Proteome Res. 2004;3:1234–42.PubMedCrossRefGoogle Scholar
  24. Craig R, Cortens JP, Beavis RC. The use of proteotypic peptide libraries for protein identification. Rapid Commun Mass Spectrom. 2005;19:1844–50.PubMedCrossRefGoogle Scholar
  25. Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods. Cambridge: Cambridge University Press; 2000.CrossRefGoogle Scholar
  26. Dakna M, He Z, Yu WC, Mischak H, Kolch W. Technical, bioinformatical and statistical aspects of liquid chromatography-mass spectrometry (LC-MS) and capillary electrophoresis-mass spectrometry (CE-MS) based clinical proteomics: a critical assessment. J Chromatogr B Analyt Technol Biomed Life Sci. 2009;877:1250–8.PubMedCrossRefGoogle Scholar
  27. Demir E, Babur O, Dogrusoz U, Gursoy A, Nisanci G, Cetin-Atalay R, Ozturk M. PATIKA: an integrated visual environment for collaborative construction and analysis of cellular pathways. Bioinformatics. 2002;18:996–1003.PubMedCrossRefGoogle Scholar
  28. Desiere F. The PeptideAtlas project. Nucleic Acids Res. 2006;34:D655–8.PubMedCrossRefGoogle Scholar
  29. Deutsch EW, Mendoza L, Shteynberg D, Farrah T, Lam H, Tasman N, Sun Z, Nilsson E, Pratt B, Prazen B, Eng JK, Martin DB, Nesvizhskii AI, Aebersold R. A guided tour of the trans-proteomic pipeline. Proteomics. 2010;10:1150–9.PubMedCrossRefGoogle Scholar
  30. Di Silvestre D, Daminelli S, Brunetti P, Mauri P. Bioinformatics tools for mass spectrometry-based proteomics analysis. In: Li P, editor. Reviews in pharmaceutical and biomedical analysis. Bussum: Bentham Science Publishers; 2011. p. 30–51.Google Scholar
  31. Domon B, Aebersold R. Options and considerations when selecting a quantitative proteomics strategy. Nat Biotechnol. 2010;28:710–21.PubMedCrossRefGoogle Scholar
  32. Falkner JA, Andrews PC. P6-T Tranche: secure decentralized data storage for the proteomics community. J Biomol Technol. 2007;18:3.Google Scholar
  33. Florens L, Washburn MP, Raine JD, Anthony RM, Grainger M, Haynes JD, Moch JK, Muster N, Sacci JB, Tabb DL, Witney AA, Wolters D, Wu Y, Gardner MJ, Holder AA, Sinden RE, Yates JR, Carucci DJ. A proteomic view of the Plasmodium falciparum life cycle. Nature. 2002;419:520–6.PubMedCrossRefGoogle Scholar
  34. Fusaro VA, Mani DR, Mesirov JP, Carr SA. Prediction of high-responding peptides for targeted protein assays by mass spectrometry. Nat Biotechnol. 2009;27:190–8.PubMedCrossRefGoogle Scholar
  35. Gao J, Opiteck GJ, Friedrichs MS, Dongre AR, Hefta SA. Changes in the protein expression of yeast as a function of carbon source. J Proteome Res. 2003;2:643–9.PubMedCrossRefGoogle Scholar
  36. Gerber SA, Rush J, Stemman O, Kirschner MW, Gygi SP. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc Natl Acad Sci U S A. 2003;100:6940–5.PubMedCrossRefGoogle Scholar
  37. Gstaiger M, Aebersold R. Applying mass spectrometry-based proteomics to genetics, genomics and network biology. Nat Rev Genet. 2009;10:617–27.PubMedCrossRefGoogle Scholar
  38. Guyon I, Gunn S, Nikravesh M, Zadeh LA. Feature extraction: foundations and applications. Berlin: Springer; 2006.CrossRefGoogle Scholar
  39. Hermjakob H, Apweiler R. The Proteomics Identifications Database (PRIDE) and the ProteomExchange Consortium: making proteomics data accessible. Expert Rev Proteomics. 2006;3:1–3.PubMedCrossRefGoogle Scholar
  40. Hill JA, Smith BE, Papoulias PG, Andrews PC. collaborative annotation and project management resource integrated with the Tranche repository. J Proteome Res. 2010;9:2809–11.PubMedCrossRefGoogle Scholar
  41. Hu Z, Mellor J, Wu J, Yamada T, Holloway D, Delisi C. VisANT: data-integrating visual framework for biological networks and modules. Nucleic Acids Res. 2005;33:W352–7.PubMedCrossRefGoogle Scholar
  42. Iragne F, Nikolski M, Mathieu B, Auber D, Sherman D. ProViz: protein interaction visualization and exploration. Bioinformatics. 2005;21:272–4.PubMedCrossRefGoogle Scholar
  43. Ishihama Y, Oda Y, Tabata T, Sato T, Nagasu T, Rappsilber J, Mann M. Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein. Mol Cell Proteomics. 2005;4:1265–72.PubMedCrossRefGoogle Scholar
  44. Isserlin R, Merico D, Alikhani-Koupaei R, Gramolini A, Bader GD, Emili A. Pathway analysis of dilated cardiomyopathy using global proteomic profiling and enrichment maps. Proteomics. 2010;10:1316–27.PubMedCrossRefGoogle Scholar
  45. Jianu R, Yu K, Cao L, Nguyen V, Salomon AR, Laidlaw DH. Visual integration of quantitative proteomic data, pathways, and protein interactions. IEEE Trans Vis Comput Graph. 2010;16:609–20.PubMedCrossRefGoogle Scholar
  46. Joshi-Tope G, Gillespie M, Vastrik I, D’Eustachio P, Schmidt E, de Bono B, Jassal B, Gopinath GR, Wu GR, Matthews L, Lewis S, Birney E, Stein L. Reactome: a knowledgebase of biological pathways. Nucleic Acids Res. 2005;33:D428–32.PubMedCrossRefGoogle Scholar
  47. Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004;32:D277–80.PubMedCrossRefGoogle Scholar
  48. Kerrien S, Alam-Faruque Y, Aranda B, Bancarz I, Bridge A, Derow C, Dimmer E, Feuermann M, Friedrichsen A, Huntley R, Kohler C, Khadake J, Leroy C, Liban A, Lieftink C, Montecchi-Palazzi L, Orchard S, Risse J, Robbe K, Roechert B, Thorneycroft D, Zhang Y, Apweiler R, Hermjakob H. IntAct–open source resource for molecular interaction data. Nucleic Acids Res. 2007;35:D561–5.PubMedCrossRefGoogle Scholar
  49. Kim HU, Sohn SB, Lee SY. Metabolic network modeling and simulation for drug targeting and discovery. Biotechnol J. 2012;7:330–42.PubMedCrossRefGoogle Scholar
  50. Kline KG, Sussman MR. Protein quantitation using isotope-assisted mass spectrometry. Annu Rev Biophys. 2010;39:291–308.PubMedCrossRefGoogle Scholar
  51. Kuster B, Schirle M, Mallick P, Aebersold R. Scoring proteomes with proteotypic peptide probes. Nat Rev Mol Cell Biol. 2005;6:577–83.PubMedCrossRefGoogle Scholar
  52. Lange V, Picotti P, Domon B, Aebersold R. Selected reaction monitoring for quantitative proteomics: a tutorial. Mol Syst Biol. 2008;4:222.PubMedCrossRefGoogle Scholar
  53. Levner I. Feature selection and nearest centroid classification for protein mass spectrometry. BMC Bioinformatics. 2005;6:68.PubMedCrossRefGoogle Scholar
  54. Li J, Zimmerman LJ, Park B-H, Tabb DL, Liebler DC, Zhang B. Network-assisted protein identification and data interpretation in shotgun proteomics. Mol Syst Biol. 2009;5:303.PubMedCrossRefGoogle Scholar
  55. Liu H, Sadygov RG, Yates 3rd JR. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem. 2004;76:4193–201.PubMedCrossRefGoogle Scholar
  56. Lu Y, Bottari P, Aebersold R, Turecek F, Gelb MH. Absolute quantification of specific proteins in complex mixtures using visible isotope-coded affinity tags. Methods Mol Biol. 2007;359:159–76.PubMedCrossRefGoogle Scholar
  57. Maere S, Heymans K, Kuiper M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005;21:3448–9.PubMedCrossRefGoogle Scholar
  58. Mallick P, Schirle M, Chen SS, Flory MR, Lee H, Martin D, Ranish J, Raught B, Schmitt R, Werner T, Kuster B, Aebersold R. Computational prediction of proteotypic peptides for quantitative proteomics. Nat Biotechnol. 2007;25:125–31.PubMedCrossRefGoogle Scholar
  59. Mann B, Madera M, Sheng Q, Tang H, Mechref Y, Novotny MV. ProteinQuant suite: a bundle of automated software tools for label-free quantitative proteomics. Rapid Commun Mass Spectrom. 2008;22:3823–34.PubMedCrossRefGoogle Scholar
  60. Martens L, Hermjakob H, Jones P, Adamski M, Taylor C, States D, Gevaert K, Vandekerckhove J, Apweiler R. PRIDE: the proteomics identifications database. Proteomics. 2005;5:3537–45.PubMedCrossRefGoogle Scholar
  61. Marzolf B, Deutsch EW, Moss P, Campbell D, Johnson MH, Galitski T. SBEAMS-microarray: database software supporting genomic expression analyses for systems biology. BMC Bioinformatics. 2006;7:286.PubMedCrossRefGoogle Scholar
  62. Mauri P, Dehò G. A proteomic approach to the analysis of RNA degradosome composition in Escherichia coli. Methods Enzymol. 2008;447:99–117.PubMedCrossRefGoogle Scholar
  63. Mauri P, Scigelova M. Multidimensional protein identification technology for clinical proteomic analysis. Clin Chem Lab Med. 2009;47:636–46.PubMedCrossRefGoogle Scholar
  64. Mauri P, Scarpa A, Nascimbeni AC, Benazzi L, Parmagnani E, Mafficini A, Peruta MD, Bassi C, Miyazaki K, Sorio C. Identification of proteins released by pancreatic cancer cells by multidimensional protein identification technology: a strategy for identification of novel cancer markers. FASEB J. 2005;19:1125–7.PubMedGoogle Scholar
  65. Mirzaei H, McBee JK, Watts J, Aebersold R. Comparative evaluation of current peptide production platforms used in absolute quantification in proteomics. Mol Cell Proteomics. 2008;7:813–23.PubMedGoogle Scholar
  66. Mishra GR, Suresh M, Kumaran K, Kannabiran N, Suresh S, Bala P, Shivakumar K, Anuradha N, Reddy R, Raghavan TM, Menon S, Hanumanthu G, Gupta M, Upendran S, Gupta S, Mahesh M, Jacob B, Mathew P, Chatterjee P, Arun KS, Sharma S, Chandrika KN, Deshpande N, Palvankar K, Raghavnath R, Krishnakanth R, Karathia H, Rekha B, Nayak R, Vishnupriya G, et al. Human protein reference database–2006 update. Nucleic Acids Res. 2006;34:D411–14.PubMedCrossRefGoogle Scholar
  67. Mortensen P, Gouw JW, Olsen JV, Ong S-E, Rigbolt KTG, Bunkenborg J, Cox J, Foster LJ, Heck AJR, Blagoev B, Andersen JS, Mann M. MSQuant, an open source platform for mass spectrometry-based quantitative proteomics. J Proteome Res. 2010;9:393–403.PubMedCrossRefGoogle Scholar
  68. Nesvizhskii AI, Aebersold R. Interpretation of shotgun proteomic data the protein inference problem. Mol Cell Proteomics. 2005;4:1419–40.PubMedCrossRefGoogle Scholar
  69. Nikitin A, Egorov S, Daraselia N, Mazo I. Pathway studio–the analysis and navigation of molecular networks. Bioinformatics. 2003;19:2155–7.PubMedCrossRefGoogle Scholar
  70. Nilsson T, Mann M, Aebersold R, Yates 3rd JR, Bairoch A, Bergeron JJM. Mass spectrometry in high-throughput proteomics: ready for the big time. Nat Methods. 2010;7:681–5.PubMedCrossRefGoogle Scholar
  71. Orchard S, Albar J-P, Deutsch EW, Eisenacher M, Binz P-A, Hermjakob H. Implementing data standards: a report on the HUPOPSI workshop September 2009, Toronto, Canada. Proteomics. 2010;10:1895–8.PubMedCrossRefGoogle Scholar
  72. Pagel P, Kovac S, Oesterheld M, Brauner B, Dunger-Kaltenbach I, Frishman G, Montrone C, Mark P, Stümpflen V, Mewes H-W, Ruepp A, Frishman D. The MIPS mammalian protein-protein interaction database. Bioinformatics. 2005;21:832–4.PubMedCrossRefGoogle Scholar
  73. Palmblad M, Tiss A, Cramer R. Mass spectrometry in clinical proteomics – from the present to the future. Proteomics Clin Appl. 2009;3:6–17.PubMedCrossRefGoogle Scholar
  74. Park SK, Venable JD, Xu T, Yates 3rd JR. A quantitative analysis software tool for mass spectrometry-based proteomics. Nat Methods. 2008;5:319–22.PubMedGoogle Scholar
  75. Pflieger D, Gonnet F, de la Fuente van Bentem S, Hirt H, de la Fuente A. Linking the proteins – -elucidation of proteome-scale networks using mass spectrometry. Mass Spectrom Rev. 2011;30:268–97.PubMedCrossRefGoogle Scholar
  76. Pico AR, Kelder T, van Iersel MP, Hanspers K, Conklin BR, Evelo C. WikiPathways: pathway editing for the people. PLoS Biol. 2008;6:e184.PubMedCrossRefGoogle Scholar
  77. Pluskal T, Castillo S, Villar-Briones A, Oresic M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics. 2010;11:395.PubMedCrossRefGoogle Scholar
  78. Regonesi ME, Del Favero M, Basilico F, Briani F, Benazzi L, Tortora P, Mauri P, Dehò G. Analysis of the Escherichia coli RNA degradosome composition by a proteomic approach. Biochimie. 2006a;88:151–61.PubMedCrossRefGoogle Scholar
  79. Ressom HW, Varghese RS, Zhang Z, Xuan J, Clarke R. Classification algorithms for phenotype prediction in genomics and proteomics. Front Biosci. 2008;13:691–708.PubMedCrossRefGoogle Scholar
  80. Rho S, You S, Kim Y, Hwang D. From proteomics toward systems biology: integration of different types of proteomics data into network models. BMB Rep. 2008;41:184–93.PubMedCrossRefGoogle Scholar
  81. Riedmiller M, Braun H. A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: IEEE international conference on neural networks, 1993, vol. 1. Piscataway: IEEE Service Center; 1993. p. 586–91.CrossRefGoogle Scholar
  82. Sampson DL, Parker TJ, Upton Z, Hurst CP. A comparison of methods for classifying clinical samples based on proteomics data: a case study for statistical and machine learning approaches. PLoS One. 2011;6:e24973.PubMedCrossRefGoogle Scholar
  83. Sanders WS, Bridges SM, McCarthy FM, Nanduri B, Burgess SC. Prediction of peptides observable by mass spectrometry applied at the experimental set level. BMC Bioinformatics. 2007;8 Suppl 7:S23.PubMedCrossRefGoogle Scholar
  84. Scardoni G, Petterlini M, Laudanna C. Analyzing biological network parameters with CentiScaPe. Bioinformatics. 2009;25:2857–9.PubMedCrossRefGoogle Scholar
  85. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.PubMedCrossRefGoogle Scholar
  86. Shipkova P, Drexler DM, Langish R, Smalley J, Salyan ME, Sanders M. Application of ion trap technology to liquid chromatography/mass spectrometry quantitation of large peptides. Rapid Commun Mass Spectrom. 2008;22:1359–66.PubMedCrossRefGoogle Scholar
  87. Simioniuc A, Campan M, Lionetti V, Marinelli M, Aquaro GD, Cavallini C, Valente S, Di Silvestre D, Cantoni S, Bernini F, Simi C, Pardini S, Mauri P, Neglia D, Ventura C, Pasquinelli G, Recchia FA. Placental stem cells pre-treated with a hyaluronan mixed ester of butyric and retinoic acid to cure infarcted pig hearts: a multimodal study. Cardiovasc Res. 2011;90:546–56.PubMedCrossRefGoogle Scholar
  88. Simpson KL, Whetton AD, Dive C. Quantitative mass spectrometry-based techniques for clinical use: biomarker identification and quantification. J Chromatogr B Analyt Technol Biomed Life Sci. 2009;877:1240–9.PubMedCrossRefGoogle Scholar
  89. Sodek KL, Evangelou AI, Ignatchenko A, Agochiya M, Brown TJ, Ringuette MJ, Jurisica I, Kislinger T. Identification of pathways associated with invasive behavior by ovarian cancer cells using multidimensional protein identification technology (MudPIT). Mol Biosyst. 2008;4:762–73.PubMedCrossRefGoogle Scholar
  90. Stark C, Breitkreutz B-J, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006;34:D535–9.PubMedCrossRefGoogle Scholar
  91. Suderman M, Hallett M. Tools for visually exploring biological networks. Bioinformatics. 2007;23:2651–9.PubMedCrossRefGoogle Scholar
  92. Tang H, Arnold RJ, Alves P, Xun Z, Clemmer DE, Novotny MV, Reilly JP, Radivojac P. A computational approach toward label-free protein quantification using predicted peptide detectability. Bioinformatics. 2006;22:e481–8.PubMedCrossRefGoogle Scholar
  93. Vapnik V. The nature of statistical learning theory. New York: Springer; 1999.Google Scholar
  94. von Mering C, Jensen LJ, Kuhn M, Chaffron S, Doerks T, Krüger B, Snel B, Bork P. STRING 7–recent developments in the integration and prediction of protein interactions. Nucleic Acids Res. 2007;35:D358–62.CrossRefGoogle Scholar
  95. Wang W, Zhou H, Lin H, Roy S, Shaler TA, Hill LR, Norton S, Kumar P, Anderle M, Becker CH. Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. Anal Chem. 2003;75:4818–26.PubMedCrossRefGoogle Scholar
  96. Webb-Robertson B-JM. Support vector machines for improved peptide identification from tandem mass spectrometry database search. Methods Mol Biol. 2009;492:453–60.PubMedCrossRefGoogle Scholar
  97. Wheelock CE, Wheelock AM, Kawashima S, Diez D, Kanehisa M, van Erk M, Kleemann R, Haeggström JZ, Goto S. Systems biology approaches and pathway tools for investigating cardiovascular disease. Mol Biosyst. 2009;5:588–602.PubMedCrossRefGoogle Scholar
  98. Xenarios I, Rice DW, Salwinski L, Baron MK, Marcotte EM, Eisenberg D. DIP: the database of interacting proteins. Nucleic Acids Res. 2000;28:289–91.PubMedCrossRefGoogle Scholar
  99. Yang X, Lazar IM. MRM screening/biomarker discovery with linear ion trap MS: a library of human cancer-specific peptides. BMC Cancer. 2009;9:96.PubMedCrossRefGoogle Scholar
  100. Yates JR, Ruse CI, Nakorchevsky A. Proteomics by mass spectrometry: approaches, advances, and applications. Annu Rev Biomed Eng. 2009;11:49–79.PubMedCrossRefGoogle Scholar
  101. Yu W, Li X, Liu J, Wu B, Williams KR, Zhao H. Multiple peak alignment in sequential data analysis: a scale-space-based approach. IEEE/ACM Trans Comput Biol Bioinform. 2006;3:208–19.PubMedCrossRefGoogle Scholar
  102. Zanzoni A, Montecchi-Palazzi L, Quondam M, Ausiello G, Helmer-Citterich M, Cesareni G. MINT: a Molecular INTeraction database. FEBS Lett. 2002;513:135–40.PubMedCrossRefGoogle Scholar
  103. Zhang B, VerBerkmoes NC, Langston MA, Uberbacher E, Hettich RL, Samatova NF. Detecting differential and correlated protein expression in label-free shotgun proteomics. J Proteome Res. 2006;5:2909–18.PubMedCrossRefGoogle Scholar
  104. Zhu W, Smith JW, Huang C-M. Mass spectrometry-based label-free quantitative proteomics. J Biomed Biotechnol. 2010;2010:840518.PubMedGoogle Scholar
  105. Zybailov B, Mosley AL, Sardiu ME, Coleman MK, Florens L, Washburn MP. Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae. J Proteome Res. 2006;5:2339–47.PubMedCrossRefGoogle Scholar

Supplementary Information References

  1. Ahn HS, Shin YS, Park PJ, Kang KN, Kim Y, Lee H-J, Yang H-K, Kim CW. Serum biomarker panels for the diagnosis of gastric adenocarcinoma. Br J Cancer. 2012;106:733–9.PubMedCrossRefGoogle Scholar
  2. Alvarez-Buylla A, Culebras E, Picazo JJ. Identification of Acinetobacter species: is Bruker biotyper MALDI-TOF mass spectrometry a good alternative to molecular techniques? Infect Genet Evol. 2012;12:345–9.PubMedCrossRefGoogle Scholar
  3. Balog CIA, Alexandrov T, Derks RJ, Hensbergen PJ, van Dam GJ, Tukahebwa EM, Kabatereine NB, Thiele H, Vennervald BJ, Mayboroda OA, Deelder AM. The feasibility of MS and advanced data processing for monitoring Schistosoma mansoni infection. Proteomics Clin Appl. 2010;4:499–510.PubMedGoogle Scholar
  4. Bergamini G, Di Silvestre D, Mauri P, Cigana C, Bragonzi A, De Palma A, Benazzi L, Döring G, Assael BM, Melotti P, Sorio C. MudPIT analysis of released proteins in Pseudomonas aeruginosa laboratory and clinical strains in relation to pro-inflammatory effects. Integr Biol (Camb). 2012;4:270–9.CrossRefGoogle Scholar
  5. Brusniak M-Y, Bodenmiller B, Campbell D, Cooke K, Eddes J, Garbutt A, Lau H, Letarte S, Mueller LN, Sharma V, Vitek O, Zhang N, Aebersold R, Watts JD. Corra: computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics. BMC Bioinformatics. 2008;9:542.PubMedCrossRefGoogle Scholar
  6. Brusniak M-YK, Kwok S-T, Christiansen M, Campbell D, Reiter L, Picotti P, Kusebauch U, Ramos H, Deutsch EW, Chen J, Moritz RL, Aebersold R. ATAQS: a computational software tool for high throughput transition optimization and validation for selected reaction monitoring mass spectrometry. BMC Bioinformatics. 2011;12:78.PubMedCrossRefGoogle Scholar
  7. Camaggi CM, Zavatto E, Gramantieri L, Camaggi V, Strocchi E, Righini R, Merina L, Chieco P, Bolondi L. Serum albumin-bound proteomic signature for early detection and staging of hepatocarcinoma: sample variability and data classification. Clin Chem Lab Med. 2010;48:1319–26.PubMedCrossRefGoogle Scholar
  8. Choe L, D’Ascenzo M, Relkin NR, Pappin D, Ross P, Williamson B, Guertin S, Pribil P, Lee KH. 8-plex quantitation of changes in cerebrospinal fluid protein expression in subjects undergoing intravenous immunoglobulin treatment for Alzheimer’s disease. Proteomics. 2007;7:3651–60.PubMedCrossRefGoogle Scholar
  9. Courcelles M, Lemieux S, Voisin L, Meloche S, Thibault P. ProteoConnections: a bioinformatics platform to facilitate proteome and phosphoproteome analyses. Proteomics. 2011;11:2654–71.PubMedCrossRefGoogle Scholar
  10. Dawson J, Walters M, Delles C, Mischak H, Mullen W. Urinary proteomics to support diagnosis of stroke. PLoS One. 2012;7:e35879.PubMedCrossRefGoogle Scholar
  11. Deutsch EW, Shteynberg D, Lam H, Sun Z, Eng JK, Carapito C, von Haller PD, Tasman N, Mendoza L, Farrah T, Aebersold R. Trans-proteomic pipeline supports and improves analysis of electron transfer dissociation data sets. Proteomics. 2010;10:1190–5.PubMedCrossRefGoogle Scholar
  12. Djidja M-C, Claude E, Snel MF, Francese S, Scriven P, Carolan V, Clench MR. Novel molecular tumour classification using MALDI-mass spectrometry imaging of tissue micro-array. Anal Bioanal Chem. 2010;397:587–601.PubMedCrossRefGoogle Scholar
  13. Dyrlund TF, Poulsen ET, Scavenius C, Sanggaard KW, Enghild JJ. MS Data Miner: a web-based software tool to analyze, compare and share mass spectrometry protein identifications. Proteomics. 2012;12(18):2792–6.PubMedCrossRefGoogle Scholar
  14. Fan Y, Wang J, Yang Y, Liu Q, Fan Y, Yu J, Zheng S, Li M, Wang J. Detection and identification of potential biomarkers of breast cancer. J Cancer Res Clin Oncol. 2010;136:1243–54.PubMedCrossRefGoogle Scholar
  15. Fan N-J, Gao C-F, Zhao G, Wang X-L, Liu Q-Y. Serum peptidome patterns of breast cancer based on magnetic bead separation and mass spectrometry analysis. Diagn Pathol. 2012;7:45.PubMedCrossRefGoogle Scholar
  16. Fassbender A, Waelkens E, Verbeeck N, Kyama CM, Bokor A, Vodolazkaia A, Van de Plas R, Meuleman C, Peeraer K, Tomassetti C, Gevaert O, Ojeda F, De Moor B, D’Hooghe T. Proteomics analysis of plasma for early diagnosis of endometriosis. Obstet Gynecol. 2012;119:276–85.PubMedCrossRefGoogle Scholar
  17. Frenzel J, Gessner C, Sandvoss T, Hammerschmidt S, Schellenberger W, Sack U, Eschrich K, Wirtz H. Outcome prediction in pneumonia induced ALI/ARDS by clinical features and peptide patterns of BALF determined by mass spectrometry. PLoS One. 2011;6:e25544.PubMedCrossRefGoogle Scholar
  18. Gao B-X, Li M-X, Liu X-J, Cai J-F, Fan X-H, Yang X-L, Li X-M, Li X-W. Analyzing urinary proteome patterns of metabolic syndrome patients with early renal injury by magnet bead separation and matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Zhongguo Yi Xue Ke Xue Yuan Xue Bao. 2011;33:511–16.PubMedGoogle Scholar
  19. Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol. 1999;17:994–9.PubMedCrossRefGoogle Scholar
  20. Han H. Nonnegative principal component analysis for mass spectral serum profiles and biomarker discovery. BMC Bioinformatics. 2010;11 Suppl 1:S1.PubMedCrossRefGoogle Scholar
  21. Ishigami N, Tokuda T, Ikegawa M, Komori M, Kasai T, Kondo T, Matsuyama Y, Nirasawa T, Thiele H, Tashiro K, Nakagawa M. Cerebrospinal fluid proteomic patterns discriminate Parkinson’s disease and multiple system atrophy. Mov Disord. 2012;27:851–7.PubMedCrossRefGoogle Scholar
  22. Izbicka E, Streeper RT, Michalek JE, Louden CL, Diaz 3rd A, Campos DR. Plasma biomarkers distinguish non-small cell lung cancer from asthma and differ in men and women. Cancer Genomics Proteomics. 2012;9:27–35.PubMedGoogle Scholar
  23. Kallio MA, Tuimala JT, Hupponen T, Klemelä P, Gentile M, Scheinin I, Koski M, Käki J, Korpelainen EI. Chipster: user-friendly analysis software for microarray and other high-throughput data. BMC Genomics. 2011;12:507.PubMedCrossRefGoogle Scholar
  24. Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004;32:D277–80.PubMedCrossRefGoogle Scholar
  25. Kessner D, Chambers M, Burke R, Agus D, Mallick P. ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics. 2008;24:2534–6.PubMedCrossRefGoogle Scholar
  26. Kim HU, Sohn SB, Lee SY. Metabolic network modeling and simulation for drug targeting and discovery. Biotechnol J. 2012;7:330–42.PubMedCrossRefGoogle Scholar
  27. Komori M, Matsuyama Y, Nirasawa T, Thiele H, Becker M, Alexandrov T, Saida T, Tanaka M, Matsuo H, Tomimoto H, Takahashi R, Tashiro K, Ikegawa M, Kondo T. Proteomic pattern analysis discriminates among multiple sclerosis-related disorders. Ann Neurol. 2012;71:614–23.PubMedCrossRefGoogle Scholar
  28. Krause B, Seifert S, Panne U, Kneipp J, Weidner SM. Matrix-assisted laser desorption/ionization mass spectrometric investigation of pollen and their classification by multivariate statistics. Rapid Commun Mass Spectrom. 2012;26:1032–8.PubMedCrossRefGoogle Scholar
  29. Kyama CM, Mihalyi A, Gevaert O, Waelkens E, Simsa P, Van de Plas R, Meuleman C, De Moor B, D’Hooghe TM. Evaluation of endometrial biomarkers for semi-invasive diagnosis of endometriosis. Fertil Steril. 2011;95:1338–43.e1–3.PubMedCrossRefGoogle Scholar
  30. Lasch P, Drevinek M, Nattermann H, Grunow R, Stämmler M, Dieckmann R, Schwecke T, Naumann D. Characterization of Yersinia using MALDI-TOF mass spectrometry and chemometrics. Anal Chem. 2010;82:8464–75.PubMedCrossRefGoogle Scholar
  31. Lazova R, Seeley EH, Keenan M, Gueorguieva R, Caprioli RM. Imaging mass spectrometry – a new and promising method to differentiate Spitz nevi from Spitzoid malignant melanomas. Am J Dermatopathol. 2012;34:82–90.PubMedCrossRefGoogle Scholar
  32. Le Faouder J, Laouirem S, Chapelle M, Albuquerque M, Belghiti J, Degos F, Paradis V, Camadro J-M, Bedossa P. Imaging mass spectrometry provides fingerprints for distinguishing hepatocellular carcinoma from cirrhosis. J Proteome Res. 2011;10:3755–65.PubMedCrossRefGoogle Scholar
  33. Liao CCL, Ward N, Marsh S, Arulampalam T, Norton JD. Mass spectrometry protein expression profiles in colorectal cancer tissue associated with clinico-pathological features of disease. BMC Cancer. 2010;10:410.PubMedCrossRefGoogle Scholar
  34. Lin Q, Peng Q, Yao F, Pan X-F, Xiong L-W, Wang Y, Geng J-F, Feng J-X, Han B-H, Bao G-L, Yang Y, Wang X, Jin L, Guo W, Wang J-C. A classification method based on principal components of SELDI spectra to diagnose of lung adenocarcinoma. PLoS One. 2012;7:e34457.PubMedCrossRefGoogle Scholar
  35. Liu Q, Chen X, Hu C, Zhang R, Yue J, Wu G, Li X, Wu Y, Wen F. Serum protein profiling of smear-positive and smear-negative pulmonary tuberculosis using SELDI-TOF mass spectrometry. Lung. 2010;188:15–23.PubMedCrossRefGoogle Scholar
  36. M’Koma AE, Seeley EH, Washington MK, Schwartz DA, Muldoon RL, Herline AJ, Wise PE, Caprioli RM. Proteomic profiling of mucosal and submucosal colonic tissues yields protein signatures that differentiate the inflammatory colitides. Inflamm Bowel Dis. 2011;17:875–83.PubMedCrossRefGoogle Scholar
  37. Mauri P, Dehò G. A proteomic approach to the analysis of RNA degradosome composition in Escherichia coli. Methods Enzymol. 2008;447:99–117.PubMedCrossRefGoogle Scholar
  38. Mauri P, Scarpa A, Nascimbeni AC, Benazzi L, Parmagnani E, Mafficini A, Peruta MD, Bassi C, Miyazaki K, Sorio C. Identification of proteins released by pancreatic cancer cells by multidimensional protein identification technology: a strategy for identification of novel cancer markers. FASEB J. 2005;19:1125–7.PubMedGoogle Scholar
  39. Meding S, Nitsche U, Balluff B, Elsner M, Rauser S, Schöne C, Nipp M, Maak M, Feith M, Ebert MP, Friess H, Langer R, Höfler H, Zitzelsberger H, Rosenberg R, Walch A. Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging. J Proteome Res. 2012;11:1996–2003.PubMedCrossRefGoogle Scholar
  40. Oda Y, Huang K, Cross FR, Cowburn D, Chait BT. Accurate quantitation of protein expression and site-specific phosphorylation. Proc Natl Acad Sci U S A. 1999;96:6591–6.PubMedCrossRefGoogle Scholar
  41. Ong S-E, Mann M. Mass spectrometry-based proteomics turns quantitative. Nat Chem Biol. 2005;1:252–62.PubMedCrossRefGoogle Scholar
  42. Ong S-E, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, Mann M. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics. 2002;1:376–86.PubMedCrossRefGoogle Scholar
  43. Parikh JR, Askenazi M, Ficarro SB, Cashorali T, Webber JT, Blank NC, Zhang Y, Marto JA. Multiplierz: an extensible API based desktop environment for proteomics data analysis. BMC Bioinformatics. 2009;10:364.PubMedCrossRefGoogle Scholar
  44. Pecks U, Schütt A, Röwer C, Reimer T, Schmidt M, Preschany S, Stepan H, Rath W, Glocker MO. A mass spectrometric multicenter study supports classification of preeclampsia as heterogeneous disorder. Hypertens Pregnancy. 2012;31:278–91.PubMedCrossRefGoogle Scholar
  45. Polpitiya AD, Qian W-J, Jaitly N, Petyuk VA, Adkins JN, Camp 2nd DG, Anderson GA, Smith RD. DAnTE: a statistical tool for quantitative analysis of -omics data. Bioinformatics. 2008;24:1556–8.PubMedCrossRefGoogle Scholar
  46. Qian W-J, Petritis BO, Nicora CD, Smith RD. Trypsin-catalyzed oxygen-18 labeling for quantitative proteomics. Methods Mol Biol. 2011;753:43–54.PubMedCrossRefGoogle Scholar
  47. Regonesi ME, Del Favero M, Basilico F, Briani F, Benazzi L, Tortora P, Mauri P, Dehò G. Analysis of the Escherichia coli RNA degradosome composition by a proteomic approach. Biochimie. 2006;88:151–61.PubMedCrossRefGoogle Scholar
  48. Ross PL, Huang YN, Marchese JN, Williamson B, Parker K, Hattan S, Khainovski N, Pillai S, Dey S, Daniels S, Purkayastha S, Juhasz P, Martin S, Bartlet-Jones M, He F, Jacobson A, Pappin DJ. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics. 2004;3:1154–69.PubMedCrossRefGoogle Scholar
  49. Schmidt A, Kellermann J, Lottspeich F. A novel strategy for quantitative proteomics using isotope-coded protein labels. Proteomics. 2005;5:4–15.PubMedCrossRefGoogle Scholar
  50. Simioniuc A, Campan M, Lionetti V, Marinelli M, Aquaro GD, Cavallini C, Valente S, Di Silvestre D, Cantoni S, Bernini F, Simi C, Pardini S, Mauri P, Neglia D, Ventura C, Pasquinelli G, Recchia FA. Placental stem cells pre-treated with a hyaluronan mixed ester of butyric and retinoic acid to cure infarcted pig hearts: a multimodal study. Cardiovasc Res. 2011;90:546–56.PubMedCrossRefGoogle Scholar
  51. Song Z, Dong R, Fan Y, Zheng S. Identification of serum protein biomarkers in biliary atresia by mass spectrometry and ELISA. J Pediatr Gastroenterol Nutr. 2012;55(4):370–5.Google Scholar
  52. Specht M, Kuhlgert S, Fufezan C, Hippler M. Proteomics to go: proteomatic enables the user-friendly creation of versatile MS/MS data evaluation workflows. Bioinformatics. 2011;27:1183–4.PubMedCrossRefGoogle Scholar
  53. Sui W, Dai Y, Zhang Y, Chen J, Liu H, Huang H. Proteomic profiling of uremia in serum using magnetic bead-based sample fractionation and MALDI-TOF MS. Ren Fail. 2010;32:1153–9.PubMedCrossRefGoogle Scholar
  54. Tang K-L, Li T-H, Xiong W-W, Chen K. Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data. BMC Bioinformatics. 2010;11:109.PubMedCrossRefGoogle Scholar
  55. Thompson D, Pepys MB, Wood SP. The physiological structure of human C-reactive protein and its complex with phosphocholine. Structure. 1999;7:169–77.PubMedCrossRefGoogle Scholar
  56. Timms JF, Menon U, Devetyarov D, Tiss A, Camuzeaux S, McCurrie K, Nouretdinov I, Burford B, Smith C, Gentry-Maharaj A, Hallett R, Ford J, Luo Z, Vovk V, Gammerman A, Cramer R, Jacobs I. Early detection of ovarian cancer in samples pre-diagnosis using CA125 and MALDI-MS peaks. Cancer Genomics Proteomics. 2011;8:289–305.PubMedGoogle Scholar
  57. Trudgian DC, Thomas B, McGowan SJ, Kessler BM, Salek M, Acuto O. CPFP: a central proteomics facilities pipeline. Bioinformatics. 2010;26:1131–2.PubMedCrossRefGoogle Scholar
  58. Van Gorp T, Cadron I, Daemen A, De Moor B, Waelkens E, Vergote I. Proteomic biomarkers predicting lymph node involvement in serum of cervical cancer patients. Limitations of SELDI-TOF MS. Proteome Sci. 2012;10:41.PubMedCrossRefGoogle Scholar
  59. Waloszczyk P, Janus T, Alchimowicz J, Grodzki T, Borowiak K. Proteomic patterns analysis with multivariate calculations as a promising tool for prompt differentiation of early stage lung tissue with cancer and unchanged tissue material. Diagn Pathol. 2011;6:22.PubMedCrossRefGoogle Scholar
  60. Wang L, Zheng W, Ding X, Yu J, Jiang W, Zhang S. Identification biomarkers of eutopic endometrium in endometriosis using artificial neural networks and protein fingerprinting. Fertil Steril. 2010;93:2460–2.PubMedCrossRefGoogle Scholar
  61. Wong JWH, Schwahn AB, Downard KM. FluTyper-an algorithm for automated typing and subtyping of the influenza virus from high resolution mass spectral data. BMC Bioinformatics. 2010;11:266.PubMedCrossRefGoogle Scholar
  62. Zhu Y-W, Wang Y-D, Ye Z-Y, Hu X, Yu J-K. Application of serum protein fingerprint in diagnosis of pancreatic cancer. Zhejiang Da Xue Xue Bao Yi Xue Ban. 2012;41:289–97.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Dario Di Silvestre
    • 1
  • Pietro Brunetti
    • 1
  • Pier Luigi Mauri
    • 1
  1. 1.Proteomics and Metabolomics LaboratoryInstitute for Biomedical Technologies – National Research CouncilSegrate, MilanItaly

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