A Guide to Mass Spectrometry-Based Quantitative Proteomics

  • Bradley J. Smith
  • Daniel Martins-de-Souza
  • Mariana Fioramonte
Part of the Methods in Molecular Biology book series (MIMB, volume 1916)


Proteomics has become an attractive science in the postgenomic era, given its capacity to identify up to thousands of molecules in a single, complex sample and quantify them in an absolute and/or relative manner. The use of these techniques enables understanding of cellular and molecular mechanisms of diseases and other biological conditions, as well as identification and screening of protein biomarkers. Here we provide a straightforward, up-to-date compilation and comparison of the main quantitation techniques used in comparative proteomics such as in vitro and in vivo stable isotope labeling and label-free techniques. Additionally, this chapter includes common methods for data acquisition in proteomics and some appropriate methods for data processing. This compilation can serve as a reference for scientists who are new to, or already familiar with, quantitative proteomics.

Key words

Quantitative proteomics Label-free Mass spectrometry Stable isotope labeling 



All-ion fragmentation


Absolute quantification


Collision-activated dissociation


Collision energy


Data-dependent acquisition


Data-independent acquisition


Distributed normalized spectral abundance factor


Exponentially modified protein abundance index


Fourier transform-all reaction monitoring


High-definition MSE


Intensity-based absolute quantification


Isotope-coded protein label


Ion mobility separation


Labeled reference peptide


Multiple reaction monitoring


DIA method from Waters Co.


Multiplexed MS/MS


Mass-differential tags for relative and absolute quantitation


Normalized spectral abundance factor


Protein standard absolute quantification


Pulsed stable isotope labeling of amino acids in cell culture


Quantitative concatemers


Triple quadrupole


Standard isotope dilution


Stable isotope labeling of amino acids in mammals


Stable isotope labeling in planta


Normalized spectral index


Synchronous precursor selection MS/MS/MS


Tandem mass tags


Ultra-definition MSE


Extended data-independent acquisition


Extracted ion chromatogram



BJS, MF, and DMS would like to thank FAPESP for funding (under grant numbers2016/07948-8, 2016/18715-4, and 2013/08711-3).

Conflict of Interest

The authors declare no conflict of interest.


  1. 1.
    Prasad B, Vrana M, Mehrotra A, Johnson K, Bhatt DK (2017) The promises of quantitative proteomics in precision medicine. J Pharm Sci 106:738–744CrossRefGoogle Scholar
  2. 2.
    Achour B, Al-Majdoub ZM, Al Feteisi H, Elmorsi Y, Rostami-Hodjegan A (2015) Ten years of QconCATs: application of multiplexed quantification to small medically relevant proteomes. Int J Mass Spectrom 391:93–104CrossRefGoogle Scholar
  3. 3.
    Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A et al (2015) Tissue-based map of the human proteome. Science 347:1260419. Scholar
  4. 4.
    Dongré AR, Jones JL, Somogyi Á, Wysocki VH (1996) Influence of peptide composition, gas-phase basicity, and chemical modification on fragmentation efficiency: evidence for the Mobile proton model. J Am Chem Soc 118:8365–8374CrossRefGoogle Scholar
  5. 5.
    King R, Bonfiglio R, Fernandez-Metzler C, Miller-Stein C, Olah T (2000) Mechanistic investigation of ionization suppression in electrospray ionization. J Am Soc Mass Spectrom 11:942–950CrossRefGoogle Scholar
  6. 6.
    Hansen KC, Schmitt-Ulms G, Chalkley RJ, Hirsch J, Baldwin MA, Burlingame AL (2003) Mass spectrometric analysis of protein mixtures at low levels using cleavable 13C-isotope-coded affinity tag and multidimensional chromatography. Mol Cell Proteomics 2:299–314CrossRefGoogle Scholar
  7. 7.
    Zhang R, Sioma CS, Wang S, Regnier FE (2001) Fractionation of isotopically labeled peptides in quantitative proteomics. Anal Chem 73:5142–5149CrossRefGoogle Scholar
  8. 8.
    Griffiths J (2007) Quantitative proteomics comes of age. Anal Chem:6451–6454Google Scholar
  9. 9.
    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 17:994–999CrossRefGoogle Scholar
  10. 10.
    Yi EC, Li X-J, Cooke K, Lee H, Raught B, Page A et al (2005) Increased quantitative proteome coverage with (13)C/(12)C-based, acid-cleavable isotope-coded affinity tag reagent and modified data acquisition scheme. Proteomics 5:380–387CrossRefGoogle Scholar
  11. 11.
    Schmidt A, Kellermann J, Lottspeich F (2005) A novel strategy for quantitative proteomics using isotope-coded protein labels. Proteomics 5:4–15CrossRefGoogle Scholar
  12. 12.
    UniProt Consortium T (2018) UniProt: the universal protein knowledgebase. Nucleic Acids Res 46:2699–2699PubMedCentralCrossRefPubMedGoogle Scholar
  13. 13.
    Chakraborty A, Regnier FE (2002) Global internal standard technology for comparative proteomics. J Chromatogr A 949:173–184CrossRefGoogle Scholar
  14. 14.
    Ji J, Chakraborty A, Geng M, Zhang X, Amini A, Bina M et al (2000) Strategy for qualitative and quantitative analysis in proteomics based on signature peptides. J Chromatogr B Biomed Sci App 745:197–210CrossRefGoogle Scholar
  15. 15.
    Oda Y, Huang K, Cross FR, Cowburn D, Chait BT (1999) Accurate quantitation of protein expression and site-specific phosphorylation. Proc Natl Acad Sci U S A 96:6591–6596PubMedCentralCrossRefPubMedGoogle Scholar
  16. 16.
    Veenstra TD, Martinović S, Anderson GA, Pasa-Tolić L, Smith RD (2000) Proteome analysis using selective incorporation of isotopically labeled amino acids. J Am Soc Mass Spectrom 11:78–82CrossRefGoogle Scholar
  17. 17.
    Schnölzer M, Jedrzejewski P, Lehmann WD (1996) Protease-catalyzed incorporation of 18O into peptide fragments and its application for protein sequencing by electrospray and matrix-assisted laser desorption/ionization mass spectrometry. Electrophoresis 17:945–953CrossRefGoogle Scholar
  18. 18.
    Küster B, Mann M (1999) 18O-labeling of N-glycosylation sites to improve the identification of gel-separated glycoproteins using peptide mass mapping and database searching. Anal Chem 71:1431–1440CrossRefGoogle Scholar
  19. 19.
    DeSouza LV, Taylor AM, Li W, Minkoff MS, Romaschin AD, Colgan TJ et al (2008) Multiple reaction monitoring of mTRAQ-labeled peptides enables absolute quantification of endogenous levels of a potential cancer marker in cancerous and normal endometrial tissues. J Proteome Res 7:3525–3534CrossRefGoogle Scholar
  20. 20.
    Mertins P, Udeshi ND, Clauser KR, Mani DR, Patel J, Ong SE et al (2012) iTRAQ labeling is superior to mTRAQ for quantitative global proteomics and phosphoproteomics. Mol Cell Proteomics 11.
  21. 21.
    Thompson A, Schäfer J, Kuhn K, Kienle S, Schwarz J, Schmidt G et al (2003) Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 75:1895–1904CrossRefGoogle Scholar
  22. 22.
    Hung C-W, Tholey A (2012) Tandem mass tag protein labeling for top-down identification and quantification. Anal Chem 84:161–170CrossRefGoogle Scholar
  23. 23.
    Ross PL, Huang YN, Marchese JN, Williamson B, Parker K, Hattan S et al (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 3:1154–1169CrossRefGoogle Scholar
  24. 24.
    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 11:1582–1590CrossRefGoogle Scholar
  25. 25.
    Sandberg A, Branca RMM, Lehtiö J, Forshed J (2014) Quantitative accuracy in mass spectrometry based proteomics of complex samples: the impact of labeling and precursor interference. J Proteome 96:133–144CrossRefGoogle Scholar
  26. 26.
    Pottiez G, Wiederin J, Fox HS, Ciborowski P (2012) Comparison of 4-plex to 8-plex iTRAQ quantitative measurements of proteins in human plasma samples. J Proteome Res 11:3774–3781PubMedCentralCrossRefPubMedGoogle Scholar
  27. 27.
    Casey TM, Khan JM, Bringans SD, Koudelka T, Takle PS, Downs RA et al (2017) Analysis of reproducibility of proteome coverage and quantitation using isobaric mass tags (iTRAQ and TMT). J Proteome Res 16:384–392CrossRefGoogle Scholar
  28. 28.
    Pichler P, Köcher T, Holzmann J, Mazanek M, Taus T, Ammerer G et al (2010) Peptide Labeling with isobaric tags yields higher identification rates using iTRAQ 4-Plex compared to TMT 6-Plex and iTRAQ 8-Plex on LTQ Orbitrap. Anal Chem 82:6549–6558PubMedCentralCrossRefPubMedGoogle Scholar
  29. 29.
    McAlister GC, Huttlin EL, Haas W, Jedrychowski MP, Rogers JC, Kuhn K et al (2012) Increasing the multiplexing capacity of TMT using reporter ion isotopologues with isobaric masses. Anal Chem 84:7469–7478PubMedCentralCrossRefPubMedGoogle Scholar
  30. 30.
    Jiang H, English AM (2002) Quantitative analysis of the yeast proteome by incorporation of isotopically labeled leucine. J Proteome Res 1:345–350CrossRefGoogle Scholar
  31. 31.
    Ong S-E, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A et al (2002) Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 1:376–386CrossRefGoogle Scholar
  32. 32.
    Gu S, Pan S, Bradbury EM, Chen X (2003) Precise peptide sequencing and protein quantification in the human proteome through in vivo lysine-specific mass tagging. J Am Soc Mass Spectrom 14:1–7CrossRefGoogle Scholar
  33. 33.
    Ong S-E, Mittler G, Mann M (2004) Identifying and quantifying in vivo methylation sites by heavy methyl SILAC. Nat Methods 1:119–126CrossRefGoogle Scholar
  34. 34.
    Ong S-E, Mann M (2006) A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC). Nat Protoc 1:2650–2660CrossRefGoogle Scholar
  35. 35.
    Ibarrola N, Kalume DE, Gronborg M, Iwahori A, Pandey A (2003) A proteomic approach for quantitation of phosphorylation using stable isotope labeling in cell culture. Anal Chem 75:6043–6049CrossRefGoogle Scholar
  36. 36.
    Zhang R, Regnier FE (2002) Minimizing resolution of isotopically coded peptides in comparative proteomics. J Proteome Res 1:139–147CrossRefGoogle Scholar
  37. 37.
    Hwang S-I, Lundgren DH, Mayya V, Rezaul K, Cowan AE, Eng JK et al (2006) Systematic characterization of nuclear proteome during apoptosis: a quantitative proteomic study by differential extraction and stable isotope labeling. Mol Cell Proteomics 5:1131–1145CrossRefGoogle Scholar
  38. 38.
    Ong S-E, Kratchmarova I, Mann M (2003) Properties of 13C-substituted arginine in stable isotope labeling by amino acids in cell culture (SILAC). J Proteome Res 2:173–181CrossRefGoogle Scholar
  39. 39.
    Schmidt F, Strozynski M, Salus SS, Nilsen H, Thiede B (2007) Rapid determination of amino acid incorporation by stable isotope labeling with amino acids in cell culture (SILAC). Rapid Commun Mass Spectrom 21:3919–3926CrossRefGoogle Scholar
  40. 40.
    Hoof DV, Pinkse MWH, Oostwaard DW-V, Mummery CL, Heck AJ, Krijgsveld J (2007) An experimental correction for arginine-to-proline conversion artifacts in SILAC-based quantitative proteomics. Nat Methods 4:677–678CrossRefGoogle Scholar
  41. 41.
    Bendall SC, Hughes C, Stewart MH, Doble B, Bhatia M, Lajoie GA (2008) Prevention of amino acid conversion in SILAC experiments with embryonic stem cells. Mol Cell Proteomics 7:1587–1597PubMedCentralCrossRefPubMedGoogle Scholar
  42. 42.
    Lössner C, Warnken U, Pscherer A, Schnölzer M (2011) Preventing arginine-to-proline conversion in a cell-line-independent manner during cell cultivation under stable isotope labeling by amino acids in cell culture (SILAC) conditions. Anal Biochem 412:123–125CrossRefGoogle Scholar
  43. 43.
    Bicho CC, de Lima Alves F, Chen ZA, Rappsilber J, Sawin KE (2010) A genetic engineering solution to the “arginine conversion problem” in stable isotope labeling by amino acids in cell culture (SILAC). Mol Cell Proteomics 9:1567–1577PubMedCentralCrossRefPubMedGoogle Scholar
  44. 44.
    Schwanhäusser B, Gossen M, Dittmar G, Selbach M (2009) Global analysis of cellular protein translation by pulsed SILAC. Proteomics 9:205–209CrossRefGoogle Scholar
  45. 45.
    Kaller M, Oeljeklaus S, Warscheid B, Hermeking H (2014) Identification of microRNA targets by pulsed SILAC. Methods Mol Biol 1188:327–349CrossRefGoogle Scholar
  46. 46.
    Geiger T, Cox J, Ostasiewicz P, Wisniewski JR, Mann M (2010) Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat Methods 7:383–385CrossRefGoogle Scholar
  47. 47.
    Neubert TA, Tempst P (2010) Super-SILAC for tumors and tissues. Nat Methods 7:361–362CrossRefGoogle Scholar
  48. 48.
    Geiger T, Wisniewski JR, Cox J, Zanivan S, Kruger M, Ishihama Y et al (2011) Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics. Nat Protoc 6:147–157CrossRefGoogle Scholar
  49. 49.
    Wu CC, MacCoss MJ, Howell KE, Matthews DE, Yates JR 3rd (2004) Metabolic labeling of mammalian organisms with stable isotopes for quantitative proteomic analysis. Anal Chem 76:4951–4959CrossRefGoogle Scholar
  50. 50.
    Rauniyar N, McClatchy DB, Yates JR (2013) Stable isotope labeling of mammals (SILAM) for in vivo quantitative proteomic analysis. Methods 61:260–268CrossRefGoogle Scholar
  51. 51.
    Huttlin EL, Chen X, Barrett-Wilt GA, Hegeman AD, Halberg RB, Harms AC et al (2009) Discovery and validation of colonic tumor-associated proteins via metabolic labeling and stable isotopic dilution. Proc Natl Acad Sci U S A 106:17235–17240PubMedCentralCrossRefPubMedGoogle Scholar
  52. 52.
    Rose JC, Epperson LE, Carey HV, Martin SL (2011) Seasonal liver protein differences in a hibernator revealed by quantitative proteomics using whole animal isotopic labeling. Comp Biochem Physiol Part D Genomics Proteomics 6:163–170PubMedCentralCrossRefPubMedGoogle Scholar
  53. 53.
    McClatchy DB, Yates JR (2014) Stable isotope labeling in mammals (SILAM). Methods Mol Biol 1156:133–146CrossRefGoogle Scholar
  54. 54.
    Schaff JE, Mbeunkui F, Blackburn K, Bird DM, Goshe MB (2008) SILIP: a novel stable isotope labeling method for in planta quantitative proteomic analysis. Plant J 56:840–854CrossRefGoogle Scholar
  55. 55.
    Gerber SA, Rush J, Stemman O, Kirschner MW, Gygi SP (2003) Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc Natl Acad Sci U S A 100:6940–6945PubMedCentralCrossRefPubMedGoogle Scholar
  56. 56.
    Kirkpatrick DS, Gerber SA, Gygi SP (2005) The absolute quantification strategy: a general procedure for the quantification of proteins and post-translational modifications. Methods 35:265–273CrossRefGoogle Scholar
  57. 57.
    Kettenbach AN, Rush J, Gerber SA (2011) Absolute quantification of protein and post-translational modification abundance with stable isotope-labeled synthetic peptides. Nat Protoc 6:175–186PubMedCentralCrossRefPubMedGoogle Scholar
  58. 58.
    Zhang H, Liu Q, Zimmerman LJ, Ham AJ, Slebos RJ, Rahman J et al (2011) Methods for peptide and protein quantitation by liquid chromatography-multiple reaction monitoring mass spectrometry. Mol Cell Proteomics 10.
  59. 59.
    Al-Majdoub ZM, Carroll KM, Gaskell SJ, Barber J (2014) Quantification of the proteins of the bacterial ribosome using QconCAT technology. J Proteome Res 13:1211–1222CrossRefGoogle Scholar
  60. 60.
    Pratt JM, Simpson DM, Doherty MK, Rivers J, Gaskell SJ, Beynon RJ (2006) Multiplexed absolute quantification for proteomics using concatenated signature peptides encoded by QconCAT genes. Nat Protoc 1:1029–1043CrossRefGoogle Scholar
  61. 61.
    Scott KB, Turko IV, Phinney KW (2016) Chapter eleven—QconCAT: internal standard for protein quantification. Methods Enzymol 566:289–303CrossRefGoogle Scholar
  62. 62.
    Brownridge PJ, Harman VM, Simpson DM, Beynon RJ (2012) Absolute multiplexed protein quantification using QconCAT technology. Methods Mol Biol 893:267–293CrossRefGoogle Scholar
  63. 63.
    Dupuis A, Hennekinne J-A, Garin J, Brun V (2008) Protein standard absolute quantification (PSAQ) for improved investigation of staphylococcal food poisoning outbreaks. Proteomics 8:4633–4636CrossRefGoogle Scholar
  64. 64.
    Adrait A, Lebert D, Trauchessec M, Dupuis A, Louwagie M, Masselon C et al (2012) Development of a protein standard absolute quantification (PSAQTM) assay for the quantification of Staphylococcus aureus enterotoxin a in serum. J Proteome 75:3041–3049CrossRefGoogle Scholar
  65. 65.
    Kaiser SE, Riley BE, Shaler TA, Trevino RS, Becker CH, Schulman H et al (2011) Protein standard absolute quantification (PSAQ) method for the measurement of cellular ubiquitin pools. Nat Methods 8:691–696PubMedCentralCrossRefPubMedGoogle Scholar
  66. 66.
    Liebler DC, Zimmerman LJ (2013) Targeted quantitation of proteins by mass spectrometry. Biochemistry 52:3797–3806PubMedCentralCrossRefPubMedGoogle Scholar
  67. 67.
    Lange V, Picotti P, Domon B, Aebersold R (2008) Selected reaction monitoring for quantitative proteomics: a tutorial. Mol Syst Biol 4:222. Scholar
  68. 68.
    Stahl-Zeng J, Lange V, Ossola R, Eckhardt K, Krek W, Aebersold R et al (2007) High sensitivity detection of plasma proteins by multiple reaction monitoring of N-glycosites. Mol Cell Proteomics 6:1809–1817CrossRefGoogle Scholar
  69. 69.
    Keshishian H, Addona T, Burgess M, Kuhn E, Carr SA (2007) Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope dilution. Mol Cell Proteomics 6:2212–2229PubMedCentralCrossRefPubMedGoogle Scholar
  70. 70.
    Kitteringham NR, Jenkins RE, Lane CS, Elliott VL, Park BK (2009) Multiple reaction monitoring for quantitative biomarker analysis in proteomics and metabolomics. J Chromatogr B 877:1229–1239CrossRefGoogle Scholar
  71. 71.
    MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B et al (2010) Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26:966–968PubMedCentralCrossRefPubMedGoogle Scholar
  72. 72.
    Colangelo CM, Chung L, Bruce C, Cheung K-H (2013) Review of software tools for design and analysis of large scale MRM proteomic datasets. Methods 61:287–298PubMedCentralCrossRefPubMedGoogle Scholar
  73. 73.
    Peterson AC, Russell JD, Bailey DJ, Westphall MS, Coon JJ (2012) Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics. Mol Cell Proteomics 11:1475–1488PubMedCentralCrossRefPubMedGoogle Scholar
  74. 74.
    Rauniyar N (2015) Parallel reaction monitoring: a targeted experiment performed using high resolution and high mass accuracy mass spectrometry. Int J Mol Sci 16:28566–28581PubMedCentralCrossRefPubMedGoogle Scholar
  75. 75.
    Tsuchiya H, Tanaka K, Saeki Y (2013) The parallel reaction monitoring method contributes to a highly sensitive polyubiquitin chain quantification. Biochem Biophys Res Commun 436:223–229CrossRefGoogle Scholar
  76. 76.
    Liu H, Sadygov RG, Yates JR (2004) A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem 76:4193–4201CrossRefGoogle Scholar
  77. 77.
    Bantscheff M, Schirle M, Sweetman G, Rick J, Kuster B (2007) Quantitative mass spectrometry in proteomics: a critical review. Anal Bioanal Chem 389:1017–1031CrossRefGoogle Scholar
  78. 78.
    Old WM, Meyer-Arendt K, Aveline-Wolf L, Pierce KG, Mendoza A, Sevinsky JR et al (2005) Comparison of label-free methods for quantifying human proteins by shotgun proteomics. Mol Cell Proteomics 4:1487–1502CrossRefGoogle Scholar
  79. 79.
    Braisted JC, Kuntumalla S, Vogel C, Marcotte EM, Rodrigues AR, Wang R et al (2008) The APEX quantitative proteomics tool: generating protein quantitation estimates from LC-MS/MS proteomics results. BMC Bioinformatics 9:529. Scholar
  80. 80.
    Park CY, Käll L, Klammer AA, MacCoss MJ, Noble WS (2008) Rapid and accurate peptide identification from tandem mass spectra. J Proteome Res 7:3022–3027PubMedCentralCrossRefPubMedGoogle Scholar
  81. 81.
    Shinoda K, Tomita M, Ishihama Y (2010) emPAI Calc—for the estimation of protein abundance from large-scale identification data by liquid chromatography-tandem mass spectrometry. Bioinformatics 26:576–577CrossRefGoogle Scholar
  82. 82.
    Heinecke NL, Pratt BS, Vaisar T, Becker L (2010) PepC: proteomics software for identifying differentially expressed proteins based on spectral counting. Bioinformatics 26:1574–1575PubMedCentralCrossRefPubMedGoogle Scholar
  83. 83.
    Choi H, Fermin D, Nesvizhskii AI (2008) Significance analysis of spectral count data in label-free shotgun proteomics. Mol Cell Proteomics 7:2373–2385PubMedCentralCrossRefPubMedGoogle Scholar
  84. 84.
    Choi H, Kim S, Fermin D, Tsou CC, Nesvizhskii AI (2015) QPROT: statistical method for testing differential expression using protein-level intensity data in label-free quantitative proteomics. J Proteome 129:121–126CrossRefGoogle Scholar
  85. 85.
    Fu X, Gharib SA, Green PS, Aitken ML, Frazer DA, Park DR et al (2008) Spectral index for assessment of differential protein expression in shotgun proteomics. J Proteome Res 7:845–854CrossRefGoogle Scholar
  86. 86.
    Griffin NM, Yu J, Long F, Oh P, Shore S, Li Y et al (2010) Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis. Nat Biotechnol 28:83–89CrossRefGoogle Scholar
  87. 87.
    Ishihama Y, Oda Y, Tabata T, Sato T, Nagasu T, Rappsilber J et al (2005) 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 4:1265–1272CrossRefGoogle Scholar
  88. 88.
    Paoletti AC, Parmely TJ, Tomomori-Sato C, Sato S, Zhu D, Conaway RC et al (2006) Quantitative proteomic analysis of distinct mammalian mediator complexes using normalized spectral abundance factors. Proc Natl Acad Sci U S A 103:18928–18933PubMedCentralCrossRefPubMedGoogle Scholar
  89. 89.
    Zhang Y, Wen Z, Washburn MP, Florens L (2010) Refinements to label free proteome quantitation: how to deal with peptides shared by multiple proteins. Anal Chem 82:2272–2281CrossRefGoogle Scholar
  90. 90.
    Bondarenko PV, Chelius D, Shaler TA (2002) Identification and relative quantitation of protein mixtures by enzymatic digestion followed by capillary reversed-phase liquid chromatography-tandem mass spectrometry. Anal Chem 74:4741–4749CrossRefGoogle Scholar
  91. 91.
    Milac TI, Randolph TW, Wang P (2012) Analyzing LC-MS/MS data by spectral count and ion abundance: two case studies. Stat Interface 5:75–87PubMedCentralCrossRefPubMedGoogle Scholar
  92. 92.
    Zybailov B, Coleman MK, Florens L, Washburn MP (2005) Correlation of relative abundance ratios derived from peptide ion chromatograms and spectrum counting for quantitative proteomic analysis using stable isotope labeling. Anal Chem 77:6218–6224CrossRefGoogle Scholar
  93. 93.
    Leptos KC, Sarracino DA, Jaffe JD, Krastins B, Church GM (2006) MapQuant: open-source software for large-scale protein quantification. Proteomics 6:1770–1782CrossRefGoogle Scholar
  94. 94.
    Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26:1367–1372CrossRefGoogle Scholar
  95. 95.
    Sturm M, Bertsch A, Gröpl C, Hildebrandt A, Hussong R, Lange E et al (2008) OpenMS - an open-source software framework for mass spectrometry. BMC Bioinformatics 9:163. Scholar
  96. 96.
    Zhang J, Xin L, Shan B, Chen W, Xie M, Yuen D et al (2012) PEAKS DB: de novo sequencing assisted database search for sensitive and accurate peptide identification. Mol Cell Proteomics 11.
  97. 97.
    Häkkinen J, Vincic G, Månsson O, Wårell K, Levander F (2009) The proteios software environment: an extensible multiuser platform for management and analysis of proteomics data. J Proteome Res 8:3037–3043CrossRefGoogle Scholar
  98. 98.
    Li X, Yi EC, Kemp CJ, Zhang H, Aebersold R (2005) A software suite for the generation and comparison of peptide arrays from sets of data collected by liquid chromatography-mass spectrometry. Mol Cell Proteomics 4:1328–1340CrossRefGoogle Scholar
  99. 99.
    Mueller LN, Rinner O, Schmidt A, Letarte S, Bodenmiller B, Brusniak MY et al (2007) SuperHirn—a novel tool for high resolution LC-MS-based peptide/protein profiling. Proteomics 7:3470–3480CrossRefGoogle Scholar
  100. 100.
    Välikangas T, Suomi T, Elo LL (2017) A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation. Brief Bioinform.
  101. 101.
    Silva JC, Gorenstein MV, Li G-Z, Vissers JP, Geromanos SJ (2006) Absolute quantification of proteins by LCMSE a virtue of parallel ms acquisition. Mol Cell Proteomics 5:144–156CrossRefGoogle Scholar
  102. 102.
    Schwanhäusser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J et al (2011) Global quantification of mammalian gene expression control. Nature 473:337–342CrossRefGoogle Scholar
  103. 103.
    Krey JF, Wilmarth PA, Shin J-B, Klimek J, Sherman NE, Jeffery ED et al (2014) Accurate label-free protein quantitation with high- and low-resolution mass spectrometers. J Proteome Res 13:1034–1044CrossRefGoogle Scholar
  104. 104.
    Gerster S, Kwon T, Ludwig C, Matondo M, Vogel C, Marcotte EM et al (2014) Statistical approach to protein quantification. Mol Cell Proteomics 13:666–677CrossRefGoogle Scholar
  105. 105.
    McIlwain S, Mathews M, Bereman MS, Rubel EW, MacCoss MJ, Noble WS et al (2012) Estimating relative abundances of proteins from shotgun proteomics data. BMC Bioinformatics 13:308. Scholar
  106. 106.
    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 10:1785–1793CrossRefGoogle Scholar
  107. 107.
    Kuster B, Schirle M, Mallick P, Aebersold R (2005) Scoring proteomes with proteotypic peptide probes. Nat Rev Mol Cell Biol 6:577–583CrossRefGoogle Scholar
  108. 108.
    Venable JD, Dong M-Q, Wohlschlegel J et al (2004) Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat Methods 1:39–45CrossRefGoogle Scholar
  109. 109.
    Purvine S, Eppel JT, Yi EC, Goodlett DR (2003) Shotgun collision-induced dissociation of peptides using a time of flight mass analyzer. Proteomics 3:847–850CrossRefGoogle Scholar
  110. 110.
    Aivett B, Emmanuel V, Jeremy L, Strambio-De-Castillia C, Hopfgartner G et al (2015) Processing strategies and software solutions for data-independent acquisition in mass spectrometry. Proteomics 15:964–980CrossRefGoogle Scholar
  111. 111.
    Geiger T, Cox J, Mann M (2010) Proteomics on an Orbitrap benchtop mass spectrometer using all-ion fragmentation. Mol Cell Proteomics 9:2252–2261PubMedCentralCrossRefPubMedGoogle Scholar
  112. 112.
    Silva JC, Denny R, Dorschel CA, Gorenstein M, Kass IJ, Li GZ et al (2005) Quantitative proteomic analysis by accurate mass retention time pairs. Anal Chem 77:2187–2200CrossRefGoogle Scholar
  113. 113.
    Silva JC, Denny R, Dorschel C, Gorenstein MV, Li GZ, Richardson K et al (2006) Simultaneous qualitative and quantitative analysis of the Escherichia coli proteome a sweet tale. Mol Cell Proteomics 5:589–607CrossRefGoogle Scholar
  114. 114.
    Distler U, Kuharev J, Navarro P, Levin Y, Schild H, Tenzer S (2014) Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics. Nat Methods 11:167–170CrossRefGoogle Scholar
  115. 115.
    Distler U, Kuharev J, Navarro P, Tenzer S (2016) Label-free quantification in ion mobility-enhanced data-independent acquisition proteomics. Nat Protoc 11:795–812CrossRefGoogle Scholar
  116. 116.
    Bond NJ, Shliaha PV, Lilley KS, Gatto L (2013) Improving qualitative and quantitative performance for MSE-based label-free proteomics. J Proteome Res 12:2340–2353CrossRefGoogle Scholar
  117. 117.
    Shliaha PV, Bond NJ, Gatto L, Lilley KS (2013) Effects of traveling wave ion mobility separation on data independent acquisition in proteomics studies. J Proteome Res 12:2323–2339CrossRefGoogle Scholar
  118. 118.
    Carvalho PC, Han X, Xu T, Carvalho Mda G, Barbosa VC, Yates JR 3rd (2010) XDIA: improving on the label-free data-independent analysis. Bioinformatics 26:847–848PubMedCentralCrossRefPubMedGoogle Scholar
  119. 119.
    Panchaud A, Jung S, Shaffer SA, Aitchison JD, Goodlett DR (2011) Faster, quantitative, and accurate precursor acquisition independent from ion count. Anal Chem 83:2250–2257PubMedCentralCrossRefPubMedGoogle Scholar
  120. 120.
    Weisbrod CR, Eng JK, Hoopmann MR, Baker T, Bruce JE (2012) Accurate peptide fragment mass analysis: multiplexed peptide identification and quantification. J Proteome Res 11:1621–1632PubMedCentralCrossRefPubMedGoogle Scholar
  121. 121.
    Gillet LC, Navarro P, Tate S, Röst H, Selevsek N, Reiter L et al (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.
  122. 122.
    Vowinckel J, Capuano F, Campbell K, Deery MJ, Lilley KS, Ralser M (2014) The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics. F1000Res 2:272. Scholar
  123. 123.
    Egertson JD, MacLean B, Johnson R, Xuan Y, MacCoss MJ (2015) Multiplexed peptide analysis using data-independent acquisition and skyline. Nat Protoc 10:887–903PubMedCentralCrossRefPubMedGoogle Scholar
  124. 124.
    Egertson JD, Kuehn A, Merrihew GE, Bateman NW, MacLean BX, Ting YS et al (2013) Multiplexed MS/MS for improved data-independent acquisition. Nat Methods 10:744–746PubMedCentralCrossRefPubMedGoogle Scholar
  125. 125.
    Zhang Y, Bilbao A, Bruderer T, Luban J, Strambio-De-Castillia C, Lisacek F et al (2015) The use of variable Q1 isolation windows improves selectivity in LC–SWATH–MS acquisition. J Proteome Res 14:4359–4371CrossRefGoogle Scholar
  126. 126.
    Tsou C-C, Avtonomov D, Larsen B, Tucholska M, Choi H, Gingras AC et al (2015) DIA-umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat Methods 12:258–264PubMedCentralCrossRefPubMedGoogle Scholar
  127. 127.
    Li Y, Zhong C-Q, Xu X, Cai S, Wu X, Zhang Y et al (2015) Group-DIA: analyzing multiple data-independent acquisition mass spectrometry data files. Nat Methods 12:1105–1106CrossRefGoogle Scholar
  128. 128.
    Wang J, Tucholska M, Knight JDR, Lambert JP, Tate S, Larsen B et al (2015) MSPLIT-DIA: sensitive peptide identification for data independent acquisition. Nat Methods 12:1106–1108PubMedCentralCrossRefPubMedGoogle Scholar
  129. 129.
    Ting YS, Egertson JD, Bollinger JG, Searle BC, Payne SH, Noble WS et al (2017) PECAN: library-free peptide detection for data-independent acquisition tandem mass spectrometry data. Nat Methods 14:903–908PubMedCentralCrossRefPubMedGoogle Scholar
  130. 130.
    Kryuchkov F, Verano-Braga T, Hansen TA, Sprenger RR, Kjeldsen F (2013) Deconvolution of mixture spectra and increased throughput of peptide identification by utilization of intensified complementary ions formed in tandem mass spectrometry. J Proteome Res 12:3362–3371CrossRefGoogle Scholar
  131. 131.
    McAlister GC, Nusinow DP, Jedrychowski MP, Wühr M, Huttlin EL, Erickson BK et al (2014) MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal Chem 86:7150–7158PubMedCentralCrossRefPubMedGoogle Scholar
  132. 132.
    Ting L, Rad R, Gygi SP, Haas W (2011) MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat Methods 8:937–940PubMedCentralCrossRefPubMedGoogle Scholar
  133. 133.
    Wiśniewski JR, Hein MY, Cox J, Mann M (2014) A “proteomic ruler” for protein copy number and concentration estimation without spike-in standards. Mol Cell Proteomics 13:3497–3506PubMedCentralCrossRefPubMedGoogle Scholar
  134. 134.
    van Holde KE (1989) The proteins of chromatin. I. Histones. In: Chromatin, 1st edn. Springer-Verlag, New York, p 70 ISBN 978-1-4612-3490-6CrossRefGoogle Scholar
  135. 135.
    Rigaut G, Shevchenko A, Rutz B, Wilm M, Mann M, Séraphin B (1999) A generic protein purification method for protein complex characterization and proteome exploration. Nat Biotechnol 17:1030–1032CrossRefGoogle Scholar
  136. 136.
    Puig O, Caspary F, Rigaut G, Rutz B, Bouveret E, Bragado-Nilsson E et al (2001) The tandem affinity purification (TAP) method: a general procedure of protein complex purification. Methods 24:218–229CrossRefGoogle Scholar
  137. 137.
    Dunham WH, Mullin M, Gingras A-C (2012) Affinity-purification coupled to mass spectrometry: basic principles and strategies. Proteomics 12:1576–1590CrossRefGoogle Scholar
  138. 138.
    Smith BJ, Cassoli JS, Guest PC, Martins-de-Souza D (2017) Co-immunoprecipitation for deciphering protein Interactomes. Adv Exp Med Biol 974:229–236CrossRefGoogle Scholar
  139. 139.
    Bantscheff M, Eberhard D, Abraham Y, Bastuck S, Boesche M, Hobson S et al (2007) Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors. Nat Biotechnol 25:1035–1044CrossRefGoogle Scholar
  140. 140.
    Kool J, Jonker N, Irth H, Niessen WMA (2011) Studying protein-protein affinity and immobilized ligand-protein affinity interactions using MS-based methods. Anal Bioanal Chem 401:1109–1112PubMedCentralCrossRefPubMedGoogle Scholar
  141. 141.
    Raida M (2011) Drug target deconvolution by chemical proteomics. Curr Opin Chem Biol 15:570–575CrossRefGoogle Scholar
  142. 142.
    Müller J, Hemphill A (2011) Identification of a host cell target for the thiazolide class of broad-spectrum anti-parasitic drugs. Exp Parasitol 128:145–150CrossRefGoogle Scholar
  143. 143.
    Jansen G, Wu C, Schade B, Thomas DY, Whiteway M (2005) Drag&Drop cloning in yeast. Gene 344:43–51CrossRefGoogle Scholar
  144. 144.
    Slobodin B, Gerst JE (2011) RaPID: an aptamer-based mRNA affinity purification technique for the identification of RNA and protein factors present in ribonucleoprotein complexes. Methods Mol Biol 714:387–406CrossRefGoogle Scholar
  145. 145.
    Tsai BP, Wang X, Huang L, Waterman ML (2011) Quantitative profiling of in vivo-assembled RNA-protein complexes using a novel integrated proteomic approach. Mol Cell Proteomics 10.
  146. 146.
    Wei X, Herbst A, Ma D, Aiken J, Li L (2011) A quantitative proteomic approach to prion disease biomarker research: delving into the glycoproteome. J Proteome Res 10:2687–2702PubMedCentralCrossRefPubMedGoogle Scholar
  147. 147.
    Schulze WX, Mann M (2004) A novel proteomic screen for peptide-protein interactions. J Biol Chem 279:10756–10764CrossRefGoogle Scholar
  148. 148.
    Lambert J-P, Pawson T, Gingras A-C (2012) Mapping physical interactions within chromatin by proteomic approaches. Proteomics 12:1609–1622CrossRefGoogle Scholar
  149. 149.
    Oeffinger M (2012) Two steps forward—one step back: advances in affinity purification mass spectrometry of macromolecular complexes. Proteomics 12:1591–1608CrossRefGoogle Scholar
  150. 150.
    Larsen MR, Thingholm TE, Jensen ON, Roepstorff P, Jørgensen TJ (2005) Highly selective enrichment of phosphorylated peptides from peptide mixtures using titanium dioxide microcolumns. Mol Cell Proteomics 4:873–886CrossRefGoogle Scholar
  151. 151.
    Zhou H, Watts JD, Aebersold R (2001) A systematic approach to the analysis of protein phosphorylation. Nat Biotechnol 19:375–378CrossRefGoogle Scholar
  152. 152.
    Cheng Z, Tang Y, Chen Y, Roschitzki B, Schlapbach R, Greber UF et al (2009) Molecular characterization of propionyllysines in non-histone proteins. Mol Cell Proteomics 8:45–52PubMedCentralCrossRefPubMedGoogle Scholar
  153. 153.
    Zhang K, Chen Y, Zhang Z, Zhao Y (2009) Identification and verification of lysine propionylation and butyrylation in yeast core histones using PTMap software. J Proteome Res 8:900–906PubMedCentralCrossRefPubMedGoogle Scholar
  154. 154.
    Ong S-E, Mann M (2005) Mass spectrometry–based proteomics turns quantitative. Nat Chem Biol 1:252–262CrossRefGoogle Scholar
  155. 155.
    Hanke S, Besir H, Oesterhelt D, Mann M (2008) Absolute SILAC for accurate quantitation of proteins in complex mixtures down to the Attomole level. J Proteome Res 7:1118–1130CrossRefGoogle Scholar
  156. 156.
    Trinh HV, Grossmann J, Gehrig P, Roschitzki B, Schlapbach R, Greber UF et al (2013) iTRAQ-based and label-free proteomics approaches for studies of human adenovirus infections. Int J Proteomics 2013:16. Scholar
  157. 157.
    Wang H, Alvarez S, Hicks LM (2012) Comprehensive comparison of iTRAQ and label-free LC-based quantitative proteomics approaches using two Chlamydomonas reinhardtii strains of interest for biofuels engineering. J Proteome Res 11:487–501CrossRefGoogle Scholar
  158. 158.
    Latosinska A, Vougas K, Makridakis M, Klein J, Mullen W, Abbas M et al (2015) Comparative analysis of label-free and 8-Plex iTRAQ approach for quantitative tissue proteomic analysis. PLoS One 10:e0137048. Scholar
  159. 159.
    Megger DA, Pott LL, Ahrens M, Padden J, Bracht T, Kuhlmann K et al (2014) Comparison of label-free and label-based strategies for proteome analysis of hepatoma cell lines. Biochim Biophys Acta 1844:967–976CrossRefGoogle Scholar
  160. 160.
    Al Feteisi H, Achour B, Barber J, Rostami-Hodjegan A (2015) Choice of LC-MS methods for the absolute quantification of drug-metabolizing enzymes and transporters in human tissue: a comparative cost analysis. AAPS J 17:438–446PubMedCentralCrossRefPubMedGoogle Scholar
  161. 161.
    Patel VJ, Thalassinos K, Slade SE, Connolly JB, Crombie A, Murrell JC et al (2009) A comparison of labeling and label-free mass spectrometry-based proteomics approaches. J Proteome Res 8:3752–3759CrossRefGoogle Scholar
  162. 162.
    Bubis JA, Levitsky LI, Ivanov MV, Tarasova IA, Gorshkov MV (2017) Comparative evaluation of label-free quantification methods for shotgun proteomics. Rapid Commun Mass Spectrom 31:606–612CrossRefGoogle Scholar
  163. 163.
    Hu A, Noble WS, Wolf-Yadlin A (2016) Technical advances in proteomics: new developments in data-independent acquisition. F1000Res 5. F1000 Faculty Rev-419.
  164. 164.
    Ronsein GE, Pamir N, von Haller PD, Kim DS, Oda MN, Jarvik GP et al (2015) Parallel reaction monitoring (PRM) and selected reaction monitoring (SRM) exhibit comparable linearity, dynamic range and precision for targeted quantitative HDL proteomics. J Proteome 113:388–399CrossRefGoogle Scholar
  165. 165.
    Wolf-Yadlin A, Hautaniemi S, Lauffenburger DA, White FM (2007) Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks. Proc Natl Acad Sci U S A 104:5860–5865PubMedCentralCrossRefPubMedGoogle Scholar
  166. 166.
    Sheng Z, Zhang S, Bustos D, Kleinheinz T, Le Pichon CE, Dominguez SL et al (2012) Ser1292 autophosphorylation is an indicator of LRRK2 kinase activity and contributes to the cellular effects of PD mutations. Sci Transl Med 4:164ra161. Scholar
  167. 167.
    Phu L, Izrael-Tomasevic A, Matsumoto ML, Bustos D, Dynek JN, Fedorova AV et al (2011) Improved quantitative mass spectrometry methods for characterizing complex ubiquitin signals. Mol Cell Proteomics 10.
  168. 168.
    Kirkpatrick DS, Hathaway NA, Hanna J, Elsasser S, Rush J, Finley D, King RW et al (2006) Quantitative analysis of in vitro ubiquitinated cyclin B1 reveals complex chain topology. Nat Cell Biol 8:700–710CrossRefGoogle Scholar
  169. 169.
    Tabb DL, Vega-Montoto L, Rudnick PA, Variyath AM, Ham AJ, Bunk DM et al (2010) Repeatability and reproducibility in proteomic identifications by liquid chromatography−tandem mass spectrometry. J Proteome Res 9:761–776PubMedCentralCrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Bradley J. Smith
    • 1
  • Daniel Martins-de-Souza
    • 1
    • 2
    • 3
  • Mariana Fioramonte
    • 1
  1. 1.Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of BiologyUniversity of Campinas (UNICAMP)CampinasBrazil
  2. 2.Center for Neurobiology, University of Campinas (UNICAMP)CampinasBrazil
  3. 3.Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION), Conselho Nacional de Desenvolvimento Cientifico e TecnologicoSao PauloBrazil

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