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An Overview of Mass Spectrometry-Based Methods for Functional Proteomics

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Functional Proteomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1871))

Abstract

The mechanism underlying many biological phenotypes remains unknown despite the increasing availability of whole genome and transcriptome sequencing. Direct measurement of changes in protein expression is an attractive alternative and has the potential to reveal novel processes. Mass spectrometry has become the standard method for proteomics, allowing both the confident identification and quantification of thousands of proteins from biological samples. In this review, mass spectrometry-based proteomic methods and their applications are described.

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References

  1. Wasinger VC, Cordwell SJ, Cerpa-Poljak A, Yan JX, Gooley AA, Wilkins MR, Duncan MW, Harris R, Williams KL, Humphery-Smith I (1995) Progress with gene-product mapping of the Mollicutes: mycoplasma genitalium. Electrophoresis 16(7):1090–1094

    Article  CAS  Google Scholar 

  2. Aebersold R, Mann M (2003) Mass spectrometry-based proteomics. Nature 422(6928):198–207. https://doi.org/10.1038/nature01511

    Article  CAS  PubMed  Google Scholar 

  3. de Godoy LM, Olsen JV, Cox J, Nielsen ML, Hubner NC, Fröhlich F, Walther TC, Mann M (2008) Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455(7217):1251–1254. https://doi.org/10.1038/nature07341

    Article  CAS  PubMed  Google Scholar 

  4. Consortium IHGS (2004) Finishing the euchromatic sequence of the human genome. Nature 431(7011):931–945. https://doi.org/10.1038/nature03001

    Article  CAS  Google Scholar 

  5. Legrain P, Aebersold R, Archakov A, Bairoch A, Bala K, Beretta L, Bergeron J, Borchers CH, Corthals GL, Costello CE, Deutsch EW, Domon B, Hancock W, He F, Hochstrasser D, Marko-Varga G, Salekdeh GH, Sechi S, Snyder M, Srivastava S, Uhlén M, Wu CH, Yamamoto T, Paik YK, Omenn GS (2011) The human proteome project: current state and future direction. Mol Cell Proteomics 10(7):M111.009993. https://doi.org/10.1074/mcp.M111.009993

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Omenn GS, Lane L, Lundberg EK, Overall CM, Deutsch EW (2017) Progress on the HUPO Draft Human Proteome: 2017 Metrics of the Human Proteome Project. J Proteome Res 16:4281. https://doi.org/10.1021/acs.jproteome.7b00375

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Gaudet P, Michel PA, Zahn-Zabal M, Britan A, Cusin I, Domagalski M, Duek PD, Gateau A, Gleizes A, Hinard V, Rech de Laval V, Lin J, Nikitin F, Schaeffer M, Teixeira D, Lane L, Bairoch A (2017) The neXtProt knowledgebase on human proteins: 2017 update. Nucleic Acids Res 45(D1):D177–D182. https://doi.org/10.1093/nar/gkw1062

    Article  CAS  PubMed  Google Scholar 

  8. Saiki RK, Gelfand DH, Stoffel S, Scharf SJ, Higuchi R, Horn GT, Mullis KB, Erlich HA (1988) Primer-directed enzymatic amplification of DNA with a thermostable DNA polymerase. Science 239(4839):487–491

    Article  CAS  Google Scholar 

  9. Liu L, Li Y, Li S, Hu N, He Y, Pong R, Lin D, Lu L, Law M (2012) Comparison of next-generation sequencing systems. J Biomed Biotechnol 2012:251364. https://doi.org/10.1155/2012/251364

    Article  PubMed  PubMed Central  Google Scholar 

  10. Pareek CS, Smoczynski R, Tretyn A (2011) Sequencing technologies and genome sequencing. J Appl Genet 52(4):413–435. https://doi.org/10.1007/s13353-011-0057-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Niall HD (1973) Automated Edman degradation: the protein sequenator. Methods Enzymol 27:942–1010

    Article  CAS  Google Scholar 

  12. Sperling J, Azubel M, Sperling R (2008) Structure and function of the Pre-mRNA splicing machine. Structure 16(11):1605–1615. https://doi.org/10.1016/j.str.2008.08.011

    Article  CAS  PubMed  Google Scholar 

  13. Ermini L, Olivieri C, Rizzi E, Corti G, Bonnal R, Soares P, Luciani S, Marota I, De Bellis G, Richards MB, Rollo F (2008) Complete mitochondrial genome sequence of the Tyrolean iceman. Curr Biol 18(21):1687–1693. https://doi.org/10.1016/j.cub.2008.09.028

    Article  CAS  PubMed  Google Scholar 

  14. Elguoshy A, Hirao Y, Xu B, Saito S, Quadery AF, Yamamoto K, Mitsui T, Yamamoto T, JProS CXPTo (2017) Identification and validation of human missing proteins and peptides in public proteome databases: data mining strategy. J Proteome Res 16:4403. https://doi.org/10.1021/acs.jproteome.7b00423

    Article  CAS  PubMed  Google Scholar 

  15. Thul PJ, Åkesson L, Wiking M, Mahdessian D, Geladaki A, Ait Blal H, Alm T, Asplund A, Björk L, Breckels LM, Bäckström A, Danielsson F, Fagerberg L, Fall J, Gatto L, Gnann C, Hober S, Hjelmare M, Johansson F, Lee S, Lindskog C, Mulder J, Mulvey CM, Nilsson P, Oksvold P, Rockberg J, Schutten R, Schwenk JM, Sivertsson Å, Sjöstedt E, Skogs M, Stadler C, Sullivan DP, Tegel H, Winsnes C, Zhang C, Zwahlen M, Mardinoglu A, Pontén F, von Feilitzen K, Lilley KS, Uhlén M, Lundberg E (2017) A subcellular map of the human proteome. Science 356(6340):eaal3321. https://doi.org/10.1126/science.aal3321

    Article  CAS  PubMed  Google Scholar 

  16. Espina V, Edmiston KH, Heiby M, Pierobon M, Sciro M, Merritt B, Banks S, Deng J, VanMeter AJ, Geho DH, Pastore L, Sennesh J, Petricoin EF, Liotta LA (2008) A portrait of tissue phosphoprotein stability in the clinical tissue procurement process. Mol Cell Proteomics 7(10):1998–2018. https://doi.org/10.1074/mcp.M700596-MCP200

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Aebersold R, Mann M (2016) Mass-spectrometric exploration of proteome structure and function. Nature 537(7620):347–355. https://doi.org/10.1038/nature19949

    Article  CAS  PubMed  Google Scholar 

  18. Hu Q, Noll RJ, Li H, Makarov A, Hardman M, Graham Cooks R (2005) The Orbitrap: a new mass spectrometer. J Mass Spectrom 40(4):430–443. https://doi.org/10.1002/jms.856

    Article  CAS  PubMed  Google Scholar 

  19. Collins BC, Hunter CL, Liu Y, Schilling B, Rosenberger G, Bader SL, Chan DW, Gibson BW, Gingras AC, Held JM, Hirayama-Kurogi M, Hou G, Krisp C, Larsen B, Lin L, Liu S, Molloy MP, Moritz RL, Ohtsuki S, Schlapbach R, Selevsek N, Thomas SN, Tzeng SC, Zhang H, Aebersold R (2017) Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry. Nat Commun 8(1):291. https://doi.org/10.1038/s41467-017-00249-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Michalski A, Damoc E, Lange O, Denisov E, Nolting D, Müller M, Viner R, Schwartz J, Remes P, Belford M, Dunyach JJ, Cox J, Horning S, Mann M, Makarov A (2012) Ultra high resolution linear ion trap Orbitrap mass spectrometer (Orbitrap Elite) facilitates top down LC MS/MS and versatile peptide fragmentation modes. Mol Cell Proteomics 11(3):O111.013698. https://doi.org/10.1074/mcp.O111.013698

    Article  CAS  PubMed  Google Scholar 

  21. Gillet LC, Navarro P, Tate S, Röst H, Selevsek N, Reiter L, Bonner R, Aebersold R (2012) Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics 11(6):O111.016717. https://doi.org/10.1074/mcp.O111.016717

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Souza GH, Guest PC, Martins-de-Souza D (2017) LC-MS(E), multiplex MS/MS, ion mobility, and label-free quantitation in clinical proteomics. Methods Mol Biol 1546:57–73. https://doi.org/10.1007/978-1-4939-6730-8_4

    Article  CAS  PubMed  Google Scholar 

  23. Hardman M, Makarov AA (2003) Interfacing the orbitrap mass analyzer to an electrospray ion source. Anal Chem 75(7):1699–1705

    Article  CAS  Google Scholar 

  24. Krutchinsky AN, Kalkum M, Chait BT (2001) Automatic identification of proteins with a MALDI-quadrupole ion trap mass spectrometer. Anal Chem 73(21):5066–5077

    Article  CAS  Google Scholar 

  25. Johnson RS, Martin SA, Biemann K, Stults JT, Watson JT (1987) Novel fragmentation process of peptides by collision-induced decomposition in a tandem mass spectrometer: differentiation of leucine and isoleucine. Anal Chem 59(21):2621–2625

    Article  CAS  Google Scholar 

  26. Hughes C, Ma B, Lajoie GA (2010) De novo sequencing methods in proteomics. Methods Mol Biol 604:105–121. https://doi.org/10.1007/978-1-60761-444-9_8

    Article  CAS  PubMed  Google Scholar 

  27. Johnson RS, Taylor JA (2002) Searching sequence databases via de novo peptide sequencing by tandem mass spectrometry. Mol Biotechnol 22(3):301–315. https://doi.org/10.1385/MB:22:3:301

    Article  CAS  PubMed  Google Scholar 

  28. Nesvizhskii AI, Vitek O, Aebersold R (2007) Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat Methods 4(10):787–797. https://doi.org/10.1038/nmeth1088

    Article  CAS  PubMed  Google Scholar 

  29. Sadygov RG, Cociorva D, Yates JR (2004) Large-scale database searching using tandem mass spectra: looking up the answer in the back of the book. Nat Methods 1(3):195–202. https://doi.org/10.1038/nmeth725

    Article  CAS  PubMed  Google Scholar 

  30. Kusebauch U, Campbell DS, Deutsch EW, Chu CS, Spicer DA, Brusniak MY, Slagel J, Sun Z, Stevens J, Grimes B, Shteynberg D, Hoopmann MR, Blattmann P, Ratushny AV, Rinner O, Picotti P, Carapito C, Huang CY, Kapousouz M, Lam H, Tran T, Demir E, Aitchison JD, Sander C, Hood L, Aebersold R, Moritz RL (2016) Human SRMAtlas: a resource of targeted assays to quantify the complete human proteome. Cell 166(3):766–778. https://doi.org/10.1016/j.cell.2016.06.041

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Picotti P, Clément-Ziza M, Lam H, Campbell DS, Schmidt A, Deutsch EW, Röst H, Sun Z, Rinner O, Reiter L, Shen Q, Michaelson JJ, Frei A, Alberti S, Kusebauch U, Wollscheid B, Moritz RL, Beyer A, Aebersold R (2013) A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis. Nature 494(7436):266–270. https://doi.org/10.1038/nature11835

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Dasari S, Chambers MC, Martinez MA, Carpenter KL, Ham AJ, Vega-Montoto LJ, Tabb DL (2012) Pepitome: evaluating improved spectral library search for identification complementarity and quality assessment. J Proteome Res 11(3):1686–1695. https://doi.org/10.1021/pr200874e

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Lam H (2011) Building and searching tandem mass spectral libraries for peptide identification. Mol Cell Proteomics 10(12):R111.008565. https://doi.org/10.1074/mcp.R111.008565

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Jmeian Y, El Rassi Z (2009) Liquid-phase-based separation systems for depletion, prefractionation and enrichment of proteins in biological fluids for in-depth proteomics analysis. Electrophoresis 30(1):249–261. https://doi.org/10.1002/elps.200800639

    Article  CAS  PubMed  Google Scholar 

  35. Boisvert FM, Lam YW, Lamont D, Lamond AI (2010) A quantitative proteomics analysis of subcellular proteome localization and changes induced by DNA damage. Mol Cell Proteomics 9(3):457–470. https://doi.org/10.1074/mcp.M900429-MCP200

    Article  CAS  PubMed  Google Scholar 

  36. Chenau J, Michelland S, Sidibe J, Seve M (2008) Peptides OFFGEL electrophoresis: a suitable pre-analytical step for complex eukaryotic samples fractionation compatible with quantitative iTRAQ labeling. Proteome Sci 6:9. https://doi.org/10.1186/1477-5956-6-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Batth TS, Olsen JV (2016) Offline high pH reversed-phase peptide fractionation for deep phosphoproteome coverage. Methods Mol Biol 1355:179–192. https://doi.org/10.1007/978-1-4939-3049-4_12

    Article  CAS  PubMed  Google Scholar 

  38. Boersema PJ, Mohammed S, Heck AJ (2008) Hydrophilic interaction liquid chromatography (HILIC) in proteomics. Anal Bioanal Chem 391(1):151–159. https://doi.org/10.1007/s00216-008-1865-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Bantscheff M, Boesche M, Eberhard D, Matthieson T, Sweetman G, Kuster B (2008) Robust and sensitive iTRAQ quantification on an LTQ Orbitrap mass spectrometer. Mol Cell Proteomics 7(9):1702–1713. https://doi.org/10.1074/mcp.M800029-MCP200

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Garbis SD, Roumeliotis TI, Tyritzis SI, Zorpas KM, Pavlakis K, Constantinides CA (2011) A novel multidimensional protein identification technology approach combining protein size exclusion prefractionation, peptide zwitterion-ion hydrophilic interaction chromatography, and nano-ultraperformance RP chromatography/nESI-MS2 for the in-depth analysis of the serum proteome and phosphoproteome: application to clinical sera derived from humans with benign prostate hyperplasia. Anal Chem 83(3):708–718. https://doi.org/10.1021/ac102075d

    Article  CAS  PubMed  Google Scholar 

  41. Michalski A, Cox J, Mann M (2011) More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS. J Proteome Res 10(4):1785–1793. https://doi.org/10.1021/pr101060v

    Article  CAS  PubMed  Google Scholar 

  42. Panchaud A, Scherl A, Shaffer S, Haller P, Kulasekara H, Miller SI, Goodlett DR (2009) Precursor acquisition independent from ion count: how to dive deeper into the proteomics ocean. Anal Chem 81:6481–6488

    Article  CAS  Google Scholar 

  43. Scherl A, Shaffer SA, Taylor GK, Kulasekara HD, Miller SI, Goodlett DR (2008) Genome-specific gas-phase fractionation strategy for improved shotgun proteomic profiling of proteotypic peptides. Anal Chem 80(4):1182–1191. https://doi.org/10.1021/ac701680f

    Article  CAS  PubMed  Google Scholar 

  44. Panchaud A, Jung S, Shaffer SA, Aitchison JD, Goodlett DR (2011) Faster, quantitative, and accurate precursor acquisition independent from ion count. Anal Chem 83(6):2250–2257. https://doi.org/10.1021/ac103079q

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Savaryn JP, Catherman AD, Thomas PM, Abecassis MM, Kelleher NL (2013) The emergence of top-down proteomics in clinical research. Genome Med 5(6):53. https://doi.org/10.1186/gm457

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Tran JC, Zamdborg L, Ahlf DR, Lee JE, Catherman AD, Durbin KR, Tipton JD, Vellaichamy A, Kellie JF, Li M, Wu C, Sweet SM, Early BP, Siuti N, LeDuc RD, Compton PD, Thomas PM, Kelleher NL (2011) Mapping intact protein isoforms in discovery mode using top-down proteomics. Nature 480(7376):254–258. https://doi.org/10.1038/nature10575

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Catherman AD, Durbin KR, Ahlf DR, Early BP, Fellers RT, Tran JC, Thomas PM, Kelleher NL (2013) Large-scale top-down proteomics of the human proteome: membrane proteins, mitochondria, and senescence. Mol Cell Proteomics 12(12):3465–3473. https://doi.org/10.1074/mcp.M113.030114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Fornelli L, Durbin KR, Fellers RT, Early BP, Greer JB, LeDuc RD, Compton PD, Kelleher NL (2017) Advancing top-down analysis of the human proteome using a Benchtop Quadrupole-Orbitrap mass spectrometer. J Proteome Res 16(2):609–618. https://doi.org/10.1021/acs.jproteome.6b00698

    Article  CAS  PubMed  Google Scholar 

  49. Fornelli L, Toby TK, Schachner LF, Doubleday PF, Srzentić K, DeHart CJ, Kelleher NL (2017) Top-down proteomics: where we are, where we are going? J Proteome 175:3. https://doi.org/10.1016/j.jprot.2017.02.002

    Article  CAS  Google Scholar 

  50. Cai W, Guner H, Gregorich ZR, Chen AJ, Ayaz-Guner S, Peng Y, Valeja SG, Liu X, Ge Y (2016) MASH suite pro: a comprehensive software tool for top-down proteomics. Mol Cell Proteomics 15(2):703–714. https://doi.org/10.1074/mcp.O115.054387

    Article  CAS  PubMed  Google Scholar 

  51. Desiere F, Deutsch EW, King NL, Nesvizhskii AI, Mallick P, Eng J, Chen S, Eddes J, Loevenich SN, Aebersold R (2006) The PeptideAtlas project. Nucleic Acids Res 34(Database issue):D655–D658. https://doi.org/10.1093/nar/gkj040

    Article  CAS  PubMed  Google Scholar 

  52. Zolg DP, Wilhelm M, Schnatbaum K, Zerweck J, Knaute T, Delanghe B, Bailey DJ, Gessulat S, Ehrlich HC, Weininger M, Yu P, Schlegl J, Kramer K, Schmidt T, Kusebauch U, Deutsch EW, Aebersold R, Moritz RL, Wenschuh H, Moehring T, Aiche S, Huhmer A, Reimer U, Kuster B (2017) Building ProteomeTools based on a complete synthetic human proteome. Nat Methods 14(3):259–262. https://doi.org/10.1038/nmeth.4153

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Mallick P, Schirle M, Chen SS, Flory MR, Lee H, Martin D, Ranish J, Raught B, Schmitt R, Werner T, Kuster B, Aebersold R (2007) Computational prediction of proteotypic peptides for quantitative proteomics. Nat Biotechnol 25(1):125–131. https://doi.org/10.1038/nbt1275

    Article  CAS  PubMed  Google Scholar 

  54. Ebhardt HA, Root A, Sander C, Aebersold R (2015) Applications of targeted proteomics in systems biology and translational medicine. Proteomics 15(18):3193–3208. https://doi.org/10.1002/pmic.201500004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Navarro P, Kuharev J, Gillet LC, Bernhardt OM, MacLean B, Röst HL, Tate SA, Tsou CC, Reiter L, Distler U, Rosenberger G, Perez-Riverol Y, Nesvizhskii AI, Aebersold R, Tenzer S (2016) A multicenter study benchmarks software tools for label-free proteome quantification. Nat Biotechnol 34(11):1130–1136. https://doi.org/10.1038/nbt.3685

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Röst HL, Aebersold R, Schubert OT (2017) Automated SWATH data analysis using targeted extraction of ion chromatograms. Methods Mol Biol 1550:289–307. https://doi.org/10.1007/978-1-4939-6747-6_20

    Article  CAS  PubMed  Google Scholar 

  57. Geiger T, Velic A, Macek B, Lundberg E, Kampf C, Nagaraj N, Uhlen M, Cox J, Mann M (2013) Initial quantitative proteomic map of 28 mouse tissues using the SILAC mouse. Mol Cell Proteomics 12(6):1709–1722. https://doi.org/10.1074/mcp.M112.024919

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A, Olsson I, Edlund K, Lundberg E, Navani S, Szigyarto CA, Odeberg J, Djureinovic D, Takanen JO, Hober S, Alm T, Edqvist PH, Berling H, Tegel H, Mulder J, Rockberg J, Nilsson P, Schwenk JM, Hamsten M, von Feilitzen K, Forsberg M, Persson L, Johansson F, Zwahlen M, von Heijne G, Nielsen J, Pontén F (2015) Proteomics. Tissue-based map of the human proteome. Science 347(6220):1260419. https://doi.org/10.1126/science.1260419

    Article  CAS  PubMed  Google Scholar 

  59. Görg A, Weiss W, Dunn MJ (2004) Current two-dimensional electrophoresis technology for proteomics. Proteomics 4(12):3665–3685. https://doi.org/10.1002/pmic.200401031

    Article  CAS  PubMed  Google Scholar 

  60. Granier F, de Vienne D (1986) Silver staining of proteins: standardized procedure for two-dimensional gels bound to polyester sheets. Anal Biochem 155(1):45–50

    Article  CAS  Google Scholar 

  61. Dowsey AW, Dunn MJ, Yang GZ (2003) The role of bioinformatics in two-dimensional gel electrophoresis. Proteomics 3(8):1567–1596. https://doi.org/10.1002/pmic.200300459

    Article  CAS  PubMed  Google Scholar 

  62. Altelaar AF, Frese CK, Preisinger C, Hennrich ML, Schram AW, Timmers HT, Heck AJ, Mohammed S (2013) Benchmarking stable isotope labeling based quantitative proteomics. J Proteome 88:14–26. https://doi.org/10.1016/j.jprot.2012.10.009

    Article  CAS  Google Scholar 

  63. Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, Mann M (2002) Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 1(5):376–386

    Article  CAS  Google Scholar 

  64. Krüger M, Moser M, Ussar S, Thievessen I, Luber CA, Forner F, Schmidt S, Zanivan S, Fässler R, Mann M (2008) SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell 134(2):353–364. https://doi.org/10.1016/j.cell.2008.05.033

    Article  CAS  PubMed  Google Scholar 

  65. Sury MD, Chen JX, Selbach M (2010) The SILAC fly allows for accurate protein quantification in vivo. Mol Cell Proteomics 9(10):2173–2183. https://doi.org/10.1074/mcp.M110.000323

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Larance M, Bailly AP, Pourkarimi E, Hay RT, Buchanan G, Coulthurst S, Xirodimas DP, Gartner A, Lamond AI (2011) Stable-isotope labeling with amino acids in nematodes. Nat Methods 8(10):849–851. https://doi.org/10.1038/nmeth.1679

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Geiger T, Cox J, Ostasiewicz P, Wisniewski JR, Mann M (2010) Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat Methods 7(5):383–385. https://doi.org/10.1038/nmeth.1446

    Article  CAS  PubMed  Google Scholar 

  68. Geiger T, Wehner A, Schaab C, Cox J, Mann M (2012) Comparative proteomic analysis of eleven common cell lines reveals ubiquitous but varying expression of most proteins. Mol Cell Proteomics 11(3):M111.014050. https://doi.org/10.1074/mcp.M111.014050

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R (1999) Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol 17(10):994–999. https://doi.org/10.1038/13690

    Article  CAS  PubMed  Google Scholar 

  70. Zhou H, Ranish JA, Watts JD, Aebersold R (2002) Quantitative proteome analysis by solid-phase isotope tagging and mass spectrometry. Nat Biotechnol 20(5):512–515. https://doi.org/10.1038/nbt0502-512

    Article  CAS  PubMed  Google Scholar 

  71. Zhang R, Sioma CS, Wang S, Regnier FE (2001) Fractionation of isotopically labeled peptides in quantitative proteomics. Anal Chem 73(21):5142–5149

    Article  CAS  Google Scholar 

  72. Antonov VK, Ginodman LM, Rumsh LD, Kapitannikov YV, Barshevskaya TN, Yavashev LP, Gurova AG, Volkova LI (1981) Studies on the mechanisms of action of proteolytic enzymes using heavy oxygen exchange. Eur J Biochem 117(1):195–200

    Article  CAS  Google Scholar 

  73. Hsu JL, Huang SY, Chow NH, Chen SH (2003) Stable-isotope dimethyl labeling for quantitative proteomics. Anal Chem 75(24):6843–6852. https://doi.org/10.1021/ac0348625

    Article  CAS  PubMed  Google Scholar 

  74. 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 (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 3(12):1154–1169. https://doi.org/10.1074/mcp.M400129-MCP200

    Article  CAS  PubMed  Google Scholar 

  75. Thompson A, Schäfer J, Kuhn K, Kienle S, Schwarz J, Schmidt G, Neumann T, Johnstone R, Mohammed AK, Hamon C (2003) Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 75(8):1895–1904

    Article  CAS  Google Scholar 

  76. Bouchal P, Roumeliotis T, Hrstka R, Nenutil R, Vojtesek B, Garbis SD (2009) Biomarker discovery in low-grade breast cancer using isobaric stable isotope tags and two-dimensional liquid chromatography-tandem mass spectrometry (iTRAQ-2DLC-MS/MS) based quantitative proteomic analysis. J Proteome Res 8(1):362–373. https://doi.org/10.1021/pr800622b

    Article  CAS  PubMed  Google Scholar 

  77. Luk VN, Wheeler AR (2009) A digital microfluidic approach to proteomic sample processing. Anal Chem 81(11):4524–4530. https://doi.org/10.1021/ac900522a

    Article  CAS  PubMed  Google Scholar 

  78. Rai AJ, Gelfand CA, Haywood BC, Warunek DJ, Yi J, Schuchard MD, Mehigh RJ, Cockrill SL, Scott GB, Tammen H, Schulz-Knappe P, Speicher DW, Vitzthum F, Haab BB, Siest G, Chan DW (2005) HUPO Plasma Proteome Project specimen collection and handling: towards the standardization of parameters for plasma proteome samples. Proteomics 5(13):3262–3277. https://doi.org/10.1002/pmic.200401245

    Article  CAS  PubMed  Google Scholar 

  79. Lundgren DH, Hwang SI, Wu L, Han DK (2010) Role of spectral counting in quantitative proteomics. Expert Rev Proteomics 7(1):39–53. https://doi.org/10.1586/epr.09.69

    Article  CAS  PubMed  Google Scholar 

  80. Carvalho PC, Hewel J, Barbosa VC, Yates JR (2008) Identifying differences in protein expression levels by spectral counting and feature selection. Genet Mol Res 7(2):342–356

    Article  CAS  Google Scholar 

  81. Griffin NM, Yu J, Long F, Oh P, Shore S, Li Y, Koziol JA, Schnitzer JE (2010) Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis. Nat Biotechnol 28(1):83–89. https://doi.org/10.1038/nbt.1592

    Article  CAS  PubMed  Google Scholar 

  82. Li Z, Adams RM, Chourey K, Hurst GB, Hettich RL, Pan C (2012) Systematic comparison of label-free, metabolic labeling, and isobaric chemical labeling for quantitative proteomics on LTQ Orbitrap Velos. J Proteome Res 11(3):1582–1590. https://doi.org/10.1021/pr200748h

    Article  CAS  PubMed  Google Scholar 

  83. Chelius D, Bondarenko PV (2002) Quantitative profiling of proteins in complex mixtures using liquid chromatography and mass spectrometry. J Proteome Res 1(4):317–323

    Article  CAS  Google Scholar 

  84. Listgarten J, Emili A (2005) Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry. Mol Cell Proteomics 4(4):419–434. https://doi.org/10.1074/mcp.R500005-MCP200

    Article  CAS  PubMed  Google Scholar 

  85. Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144(5):646–674. https://doi.org/10.1016/j.cell.2011.02.013

    Article  CAS  PubMed  Google Scholar 

  86. Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P, Varela I, Phillimore B, Begum S, McDonald NQ, Butler A, Jones D, Raine K, Latimer C, Santos CR, Nohadani M, Eklund AC, Spencer-Dene B, Clark G, Pickering L, Stamp G, Gore M, Szallasi Z, Downward J, Futreal PA, Swanton C (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366(10):883–892. https://doi.org/10.1056/NEJMoa1113205

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Caprioli RM, Farmer TB, Gile J (1997) Molecular imaging of biological samples: localization of peptides and proteins using MALDI-TOF MS. Anal Chem 69(23):4751–4760

    Article  CAS  Google Scholar 

  88. Aichler M, Walch A (2015) MALDI Imaging mass spectrometry: current frontiers and perspectives in pathology research and practice. Lab Investig 95(4):422–431. https://doi.org/10.1038/labinvest.2014.156

    Article  CAS  PubMed  Google Scholar 

  89. Minerva L, Clerens S, Baggerman G, Arckens L (2008) Direct profiling and identification of peptide expression differences in the pancreas of control and ob/ob mice by imaging mass spectrometry. Proteomics 8(18):3763–3774. https://doi.org/10.1002/pmic.200800237

    Article  CAS  PubMed  Google Scholar 

  90. Balluff B, Schöne C, Höfler H, Walch A (2011) MALDI imaging mass spectrometry for direct tissue analysis: technological advancements and recent applications. Histochem Cell Biol 136(3):227–244. https://doi.org/10.1007/s00418-011-0843-x

    Article  CAS  PubMed  Google Scholar 

  91. Perkins DN, Pappin DJ, Creasy DM, Cottrell JS (1999) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20(18):3551–3567. https://doi.org/10.1002/(SICI)1522-2683(19991201)20:18<3551::AID-ELPS3551>3.0.CO;2-2

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, UK

    J. Robert O’Neill

  2. Department of Clinical Surgery, Royal Infirmary of Edinburgh, Edinburgh, UK

    J. Robert O’Neill

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  1. J. Robert O’Neill
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Correspondence to J. Robert O’Neill .

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Editors and Affiliations

  1. Array Bridge Inc., St. Louis, MO, USA

    Xing Wang

  2. Biotech Support Group LLC, Monmouth Junction, NJ, USA

    Matthew Kuruc

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O’Neill, J.R. (2019). An Overview of Mass Spectrometry-Based Methods for Functional Proteomics. In: Wang, X., Kuruc, M. (eds) Functional Proteomics. Methods in Molecular Biology, vol 1871. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8814-3_13

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  • DOI: https://doi.org/10.1007/978-1-4939-8814-3_13

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