1932

Abstract

Mass spectrometry–based quantitative proteomics is a powerful tool for gaining insights into function and dynamics of biological systems. However, peptides with different sequences have different ionization efficiencies, and their intensities in a mass spectrum are not correlated with their abundances. Therefore, various label-free or stable isotope label–based quantitation methods have emerged to assist mass spectrometry to perform comparative proteomic experiments, thus enabling nonbiased identification of thousands of proteins differentially expressed in healthy versus diseased cells. Here, we discuss the most widely used label-free and metabolic-, enzymatic-, and chemical labeling–based proteomic strategies for relative and absolute quantitation. We summarize the specific strengths and weaknesses of each technique in terms of quantification accuracy, proteome coverage, multiplexing capability, and robustness. Applications of each strategy for solving specific biological complexities are also presented.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-anchem-061516-045357
2018-06-12
2024-04-18
Loading full text...

Full text loading...

/deliver/fulltext/11/1/annurev-anchem-061516-045357.html?itemId=/content/journals/10.1146/annurev-anchem-061516-045357&mimeType=html&fmt=ahah

Literature Cited

  1. 1.  Oda Y, Huang K, Cross F, Cowburn D, Chait B 1999. Accurate quantitation of protein expression and site-specific phosphorylation. PNAS 96:6591–96
    [Google Scholar]
  2. 2.  Conrads TP, Alving K, Veenstra TD, Belov ME, Anderson GA et al. 2001. Quantitative analysis of bacterial and mammalian proteomes using a combination of cysteine affinity tags and 15N-metabolic labeling. Anal. Chem. 73:2132–39
    [Google Scholar]
  3. 3.  Chen X, Smith LM, Bradbury EM 2000. Site-specific mass tagging with stable isotopes in proteins for accurate and efficient protein identification. Anal. Chem. 72:1134–43
    [Google Scholar]
  4. 4.  Zhu H, Pan S, Gu S, Bradbury EM, Chen X 2002. Amino acid residue specific stable isotope labeling for quantitative proteomics. Rapid Commun. Mass Spectrom. 16:2115–23
    [Google Scholar]
  5. 5.  Ong S-E, Blagoev B, Kratchmarova I, Kristensen DB, Steen H et al. 2002. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteom. 1:376–86
    [Google Scholar]
  6. 6.  Krüger M, Moser M, Ussar S, Thievessen I, Luber CA et al. 2008. SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell 134:353–64
    [Google Scholar]
  7. 7.  Lewandowska D, ten Have S, Hodge K, Tillemans V, Lamond AI, Brown JW 2013. Plant SILAC: stable-isotope labelling with amino acids of Arabidopsis seedlings for quantitative proteomics. PLOS ONE 8:e72207
    [Google Scholar]
  8. 8.  Sury MD, Chen JX, Selbach M 2010. The SILAC fly allows for accurate protein quantification in vivo. . Mol. Cell. Proteom. 9:2173–83
    [Google Scholar]
  9. 9.  Westman-Brinkmalm A, Abramsson A, Pannee J, Gang C, Gustavsson MK et al. 2011. SILAC zebrafish for quantitative analysis of protein turnover and tissue regeneration. J. Proteom. 75:425–34
    [Google Scholar]
  10. 10.  Geiger T, Cox J, Ostasiewicz P, Wisniewski JR, Mann M 2010. Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat. Methods 7:383–85
    [Google Scholar]
  11. 11.  Wang T, Gu S, Ronni T, Du YC, Chen X 2005. In vivo dual-tagging proteomic approach in studying signaling pathways in immune response. J. Proteome Res. 4:941–49
    [Google Scholar]
  12. 12.  Wang T, Chuang TH, Ronni T, Gu S, Du YC et al. 2006. Flightless I homolog negatively modulates the TLR pathway. J. Immunol. 176:1355–62
    [Google Scholar]
  13. 13.  Dai P, Jeong SY, Yu Y, Leng T, Wu W et al. 2009. Modulation of TLR signaling by multiple MyD88-interacting partners including leucine-rich repeat Fli-I-interacting proteins. J. Immunol. 182:3450–60
    [Google Scholar]
  14. 14.  Gunawardena HP, Huang Y, Kenjale R, Wang H, Xie L, Chen X 2011. Unambiguous characterization of site-specific phosphorylation of leucine-rich repeat Fli-I-interacting protein 2 (LRRFIP2) in Toll-like receptor 4 (TLR4)-mediated signaling. J. Biol. Chem. 286:10897–910
    [Google Scholar]
  15. 15.  Du YC, Gu S, Zhou J, Wang T, Cai H et al. 2006. The dynamic alterations of H2AX complex during DNA repair detected by a proteomic approach reveal the critical roles of Ca2+/calmodulin in the ionizing radiation-induced cell cycle arrest. Mol. Cell. Proteom. 5:1033–44
    [Google Scholar]
  16. 16.  Rhee JW, Lee KW, Kim D, Lee Y, Jeon OH et al. 2007. NF-κB-dependent regulation of matrix metalloproteinase-9 gene expression by lipopolysaccharide in a macrophage cell line RAW 264.7. J. Biochem. Mol. Biol. 40:88–94
    [Google Scholar]
  17. 17.  Wang L, Xie L, Ramachandran S, Lee Y, Yan Z et al. 2015. Non-canonical bromodomain within DNA-PKcs promotes DNA damage response and radioresistance through recognizing an IR-induced acetyl-lysine on H2AX. Chem. Biol. 22:849–61
    [Google Scholar]
  18. 18.  Xie L, Liu C, Wang L, Gunawardena HP, Yu Y et al. 2013. Protein phosphatase 2A catalytic subunit alpha plays a MyD88-dependent, central role in the gene-specific regulation of endotoxin tolerance. Cell Rep 3:678–88
    [Google Scholar]
  19. 19.  Zhu H, Hunter TC, Pan S, Yau PM, Bradbury EM, Chen X 2002. Residue-specific mass signatures for the efficient detection of protein modifications by mass spectrometry. Anal. Chem. 74:1687–94
    [Google Scholar]
  20. 20.  Chen X, Sun L, Yu Y, Xue Y, Yang P 2007. Amino acid-coded tagging approaches in quantitative proteomics. Expert Rev. Proteom. 4:25–37
    [Google Scholar]
  21. 21.  Liu C, Yu Y, Liu F, Wei X, Wrobel JA et al. 2014. A chromatin activity-based chemoproteomic approach reveals a transcriptional repressome for gene-specific silencing. Nat. Commun. 5:5733
    [Google Scholar]
  22. 22.  Merrill AE, Hebert AS, MacGilvray ME, Rose CM, Bailey DJ et al. 2014. NeuCode labels for relative protein quantification. Mol. Cell. Proteom. 13:2503–12
    [Google Scholar]
  23. 23.  Potts GK, Voigt EA, Bailey DJ, Rose CM, Westphall MS et al. 2016. Neucode labels for multiplexed, absolute protein quantification. Anal. Chem. 88:3295–303
    [Google Scholar]
  24. 24.  Rhoads TW, Prasad A, Kwiecien NW, Merrill AE, Zawack K et al. 2015. NeuCode labeling in nematodes: proteomic and phosphoproteomic impact of ascaroside treatment in Caenorhabditis elegans. . Mol. Cell. Proteom. 14:2922–35
    [Google Scholar]
  25. 25.  Baughman JM, Rose CM, Kolumam G, Webster JD, Wilkerson EM et al. 2016. NeuCode proteomics reveals Bap1 regulation of metabolism. Cell Rep 16:583–95
    [Google Scholar]
  26. 26.  Gauthier NP, Soufi B, Walkowicz WE, Pedicord VA, Mavrakis KJ et al. 2013. Cell-selective labeling using amino acid precursors for proteomic studies of multicellular environments. Nat. Methods 10:768–73
    [Google Scholar]
  27. 27.  Tape CJ, Norrie IC, Worboys JD, Lim L, Lauffenburger DA, Jørgensen C 2014. Cell-specific labeling enzymes for analysis of cell–cell communication in continuous co-culture. Mol. Cell. Proteom. 13:1866–76
    [Google Scholar]
  28. 28.  Tape CJ, Ling S, Dimitriadi M, McMahon KM, Worboys JD et al. 2016. Oncogenic KRAS regulates tumor cell signaling via stromal reciprocation. Cell 165:910–20
    [Google Scholar]
  29. 29.  Li Z, Zhu Y, Sun Y, Qin K, Liu W et al. 2016. Nitrilase-activatable noncanonical amino acid precursors for cell-selective metabolic labeling of proteomes. ACS Chem. Biol. 11:3273–77
    [Google Scholar]
  30. 30.  Reynolds KJ, Fenselau C 2004. Quantitative protein analysis using proteolytic [18O] water labeling. Curr. Protoc. Protein Sci. 76:23.4.1–4.9
    [Google Scholar]
  31. 31.  Ye X, Luke B, Andresson T, Blonder J 2009. 18O stable isotope labeling in MS-based proteomics. Brief. Funct. Genom. Proteom. 8:136–44
    [Google Scholar]
  32. 32.  Zhang S, Liu X, Kang X, Sun C, Lu H et al. 2012. iTRAQ plus 18O: a new technique for target glycoprotein analysis. Talanta 91:122–27
    [Google Scholar]
  33. 33.  Eckel-Passow JE, Oberg AL, Therneau TM, Mason C, Mahoney DW et al. 2006. Regression analysis for comparing protein samples with 16O/18O stable-isotope labeled mass spectrometry. Bioinformatics 22:2739–45
    [Google Scholar]
  34. 34.  Mason CJ, Therneau TM, Eckel-Passow JE, Johnson KL, Oberg AL et al. 2007. A method for automatically interpreting mass spectra of 18O-labeled isotopic clusters. Mol. Cell. Proteom. 6:305–18
    [Google Scholar]
  35. 35.  Bezstarosti K, Ghamari A, Grosveld FG, Demmers JA 2010. Differential proteomics based on 18O labeling to determine the cyclin dependent kinase 9 interactome. J. Proteome Res. 9:4464–75
    [Google Scholar]
  36. 36.  Qian W-J, Petritis BO, Nicora CD, Smith RD 2011. Trypsin-catalyzed oxygen-18 labeling for quantitative proteomics. Gel-Free Proteom 753:43–54
    [Google Scholar]
  37. 37.  Gevaert K, Impens F, Ghesquière B, Van Damme P, Lambrechts A, Vandekerckhove J 2008. Stable isotopic labeling in proteomics. Proteomics 8:4873–85
    [Google Scholar]
  38. 38.  Ke M, Wu H, Zhu Z, Zhang C, Zhang Y, Deng Y 2017. Differential proteomic analysis of white adipose tissues from T2D KKAy mice by LC‐ESI‐QTOF. Proteomics 17:1600219
    [Google Scholar]
  39. 39.  Liu Y, Liu K, Qin W, Liu C, Zheng X et al. 2016. Effects of stem cell therapy on protein profile of parkinsonian rats using an 18O‐labeling quantitative proteomic approach. Proteomics 16:1023–32
    [Google Scholar]
  40. 40.  Smeekens JM, Chen W, Wu R 2015. Mass spectrometric analysis of the cell surface N-glycoproteome by combining metabolic labeling and click chemistry. J. Am. Soc. Mass Spectrom. 26:604–14
    [Google Scholar]
  41. 41.  Gao Y, Cao Z, Yang X, Abdelmegeed MA, Sun J et al. 2017. Proteomic analysis of acetaminophen‐induced hepatotoxicity and identification of heme oxygenase 1 as a potential plasma biomarker of liver injury. Proteomics Clin. Appl. 11:1600123
    [Google Scholar]
  42. 42.  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. 17:994–99
    [Google Scholar]
  43. 43.  Nakamura T, Oda Y 2007. Mass spectrometry-based quantitative proteomics. Biotechnol. Genet. Eng. Rev. 24:147–64
    [Google Scholar]
  44. 44.  Tao WA, Aebersold R 2003. Advances in quantitative proteomics via stable isotope tagging and mass spectrometry. Curr. Opin. Biotechnol. 14:110–18
    [Google Scholar]
  45. 45.  Li J, Steen H, Gygi SP 2003. Protein profiling with cleavable isotope-coded affinity tag (cICAT) reagents the yeast salinity stress response. Mol. Cell. Proteom. 2:1198–204
    [Google Scholar]
  46. 46.  Schmidt A, Kellermann J, Lottspeich F 2005. A novel strategy for quantitative proteomics using isotope‐coded protein labels. Proteomics 5:4–15
    [Google Scholar]
  47. 47.  Blonder J, Yu L-R, Radeva G, Chan KC, Lucas DA et al. 2006. Combined chemical and enzymatic stable isotope labeling for quantitative profiling of detergent-insoluble membrane proteins isolated using Triton X-100 and Brij-96. J. Proteome Res. 5:349–60
    [Google Scholar]
  48. 48.  Maccarrone G, Lebar M, Martins-de-Souza D 2014. Brain quantitative proteomics combining GeLC-MS and isotope-coded protein labeling (ICPL). Shotgun Proteom 1156:175–85
    [Google Scholar]
  49. 49.  Rainczuk A, Condina M, Pelzing M, Dolman S, Rao J et al. 2013. The utility of isotope-coded protein labeling for prioritization of proteins found in ovarian cancer patient urine. J. Proteome Res. 12:4074–88
    [Google Scholar]
  50. 50.  Leroy B, Rosier C, Erculisse V, Leys N, Mergeay M, Wattiez R 2010. Differential proteomic analysis using isotope‐coded protein‐labeling strategies: comparison, improvements and application to simulated microgravity effect on Cupriavidus metallidurans CH34. Proteomics 10:2281–91
    [Google Scholar]
  51. 51.  Paradela A, Marcilla M, Navajas R, Ferreira L, Ramos-Fernandez A et al. 2010. Evaluation of isotope-coded protein labeling (ICPL) in the quantitative analysis of complex proteomes. Talanta 80:1496–502
    [Google Scholar]
  52. 52.  Lottspeich F, Kellermann J 2011. ICPL labeling strategies for proteome research. Gel-Free Proteom 753:55–64
    [Google Scholar]
  53. 53.  Shi Y, Elmets CA, Smith JW, Liu YT, Chen YR et al. 2007. Quantitative proteomes and in vivo secretomes of progressive and regressive UV‐induced fibrosarcoma tumor cells: mimicking tumor microenvironment using a dermis‐based cell‐trapped system linked to tissue chamber. Proteomics 7:4589–600
    [Google Scholar]
  54. 54.  Fleron M, Greffe Y, Musmeci D, Massart A-C, Hennequière V et al. 2010. Novel post-digest isotope coded protein labeling method for phospho-and glycoproteome analysis. J. Proteom. 73:1986–2005
    [Google Scholar]
  55. 55.  Croner RS, Sevim M, Metodiev MV, Jo P, Ghadimi M et al. 2016. Identification of predictive markers for response to neoadjuvant chemoradiation in rectal carcinomas by proteomic isotope coded protein label (ICPL) analysis. Int. J. Mol. Sci. 17:209
    [Google Scholar]
  56. 56.  Thompson A, Schäfer J, Kuhn K, Kienle S, Schwarz J et al. 2003. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 75:1895–904
    [Google Scholar]
  57. 57.  Werner T, Becher I, Sweetman G, Doce C, Savitski MM, Bantscheff M 2012. High-resolution enabled TMT 8-plexing. Anal. Chem. 84:7188–94
    [Google Scholar]
  58. 58.  Altelaar AM, Frese CK, Preisinger C, Hennrich ML, Schram AW et al. 2013. Benchmarking stable isotope labeling based quantitative proteomics. J. Proteom. 88:14–26
    [Google Scholar]
  59. 59.  Rauniyar N, Yates JR III 2014. Isobaric labeling-based relative quantification in shotgun proteomics. J. Proteome Res. 13:5293–309
    [Google Scholar]
  60. 60.  Ow SY, Salim M, Noirel J, Evans C, Wright P 2011. Minimising iTRAQ ratio compression through understanding LC‐MS elution dependence and high‐resolution HILIC fractionation. Proteomics 11:2341–46
    [Google Scholar]
  61. 61.  DeSouza LV, Grigull J, Ghanny S, Dubé V, Romaschin AD et al. 2007. Endometrial carcinoma biomarker discovery and verification using differentially tagged clinical samples with multidimensional liquid chromatography and tandem mass spectrometry. Mol. Cell. Proteom. 6:1170–82
    [Google Scholar]
  62. 62.  Ma Y, Xiao T, Xu Q, Shao X, Wang H 2016. iTRAQ-based quantitative analysis of cancer-derived secretory proteome reveals TPM2 as a potential diagnostic biomarker of colorectal cancer. Front. Med. 10:278–85
    [Google Scholar]
  63. 63.  Xin Q-L, Deng C-L, Chen X, Wang J, Wang S-B et al. 2017. Quantitative proteomic analysis of mosquito C6/36 cells reveals host proteins involved in Zika virus infection. J. Virol. 91:e00554–17
    [Google Scholar]
  64. 64.  Wang P, Joberty G, Buist A, Vanoosthuyse A, Stancu I-C et al. 2017. Tau interactome mapping based identification of Otub1 as Tau deubiquitinase involved in accumulation of pathological Tau forms in vitro and in vivo. Acta Neuropathol 133:731–49
    [Google Scholar]
  65. 65.  Ow SY, Salim M, Noirel J, Evans C, Rehman I, Wright PC 2009. iTRAQ underestimation in simple and complex mixtures: “the good, the bad and the ugly. .” J. Proteome Res. 8:5347–55
    [Google Scholar]
  66. 66.  Karp NA, Huber W, Sadowski PG, Charles PD, Hester SV, Lilley KS 2010. Addressing accuracy and precision issues in iTRAQ quantitation. Mol. Cell. Proteom. 9:1885–97
    [Google Scholar]
  67. 67.  Ting L, Rad R, Gygi SP, Haas W 2011. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat. Methods 8:937–40
    [Google Scholar]
  68. 68.  McAlister GC, Huttlin EL, Haas W, Ting L, Jedrychowski MP et al. 2012. Increasing the multiplexing capacity of TMTs using reporter ion isotopologues with isobaric masses. Anal. Chem. 84:7469–78
    [Google Scholar]
  69. 69.  Radhakrishnan A, Nanjappa V, Raja R, Sathe G, Chavan S et al. 2016. Dysregulation of splicing proteins in head and neck squamous cell carcinoma. Cancer Biol. Therapy 17:219–29
    [Google Scholar]
  70. 70.  Nie S, Lo A, Wu J, Zhu J, Tan Z et al. 2014. Glycoprotein biomarker panel for pancreatic cancer discovered by quantitative proteomics analysis. J. Proteome Res. 13:1873–84
    [Google Scholar]
  71. 71.  Wang Z, Liang S, Lian X, Liu L, Zhao S et al. 2015. Identification of proteins responsible for adriamycin resistance in breast cancer cells using proteomics analysis. Sci. Rep. 5:9301
    [Google Scholar]
  72. 72.  Ross PL, Huang YN, Marchese JN, Williamson B, Parker K et al. 2004. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteom. 3:1154–69
    [Google Scholar]
  73. 73.  Choe L, D'Ascenzo M, Relkin NR, Pappin D, Ross P et al. 2007. 8‐Plex quantitation of changes in cerebrospinal fluid protein expression in subjects undergoing intravenous immunoglobulin treatment for Alzheimer's disease. Proteomics 7:3651–60
    [Google Scholar]
  74. 74.  Hsu J-L, Huang S-Y, Chow N-H, Chen S-H 2003. Stable-isotope dimethyl labeling for quantitative proteomics. Anal. Chem. 75:6843–52
    [Google Scholar]
  75. 75.  Xiang F, Ye H, Chen R, Fu Q, Li L 2010. N,N-dimethyl leucines as novel isobaric tandem mass tags for quantitative proteomics and peptidomics. Anal. Chem. 82:2817–25
    [Google Scholar]
  76. 76.  Frost DC, Greer T, Li L 2014. High-resolution enabled 12-plex DiLeu isobaric tags for quantitative proteomics. Anal. Chem. 87:1646–54
    [Google Scholar]
  77. 77.  Greer T, Li L 2016. Isotopic N,N-dimethyl leucine (iDiLeu) for absolute quantification of peptides using a standard curve approach. Quant. Proteom. Mass Spectrom. 1410:195–206
    [Google Scholar]
  78. 78.  Yu Q, Shi X, Greer T, Lietz CB, Kent KC, Li L 2016. Evaluation and application of dimethylated amino acids as isobaric tags for quantitative proteomics of the TGF-β/Smad3 signaling pathway. J. Proteome Res. 15:3420–31
    [Google Scholar]
  79. 79.  Greer T, Hao L, Nechyporenko A, Lee S, Vezina CM et al. 2015. Custom 4-plex DiLeu isobaric labels enable relative quantification of urinary proteins in men with lower urinary tract symptoms (LUTS). PLOS ONE 10:e0135415
    [Google Scholar]
  80. 80.  Hao L, Johnson J, Lietz CB, Buchberger A, Frost D et al. 2017. Mass defect-based N,N-dimethyl leucine labels for quantitative proteomics and amine metabolomics of pancreatic cancer cells. Anal. Chem. 89:1138–46
    [Google Scholar]
  81. 81.  Koehler CJ, Strozynski M, Kozielski F, Treumann A, Thiede B 2009. Isobaric peptide termini labeling for MS/MS-based quantitative proteomics. J. Proteome Res. 8:4333–41
    [Google Scholar]
  82. 82.  Koehler CJ, Arntzen , Strozynski M, Treumann A, Thiede B 2011. Isobaric peptide termini labeling utilizing site-specific N-terminal succinylation. Anal. Chem. 83:4775–81
    [Google Scholar]
  83. 83.  Koehler CJ, Arntzen , de Souza GA, Thiede B 2013. An approach for triplex-isobaric peptide termini labeling (triplex-IPTL). Anal. Chem. 85:2478–85
    [Google Scholar]
  84. 84.  Arntzen , Koehler CJ, Barsnes H, Berven FS, Treumann A, Thiede B 2010. IsobariQ: software for isobaric quantitative proteomics using IPTL, iTRAQ, and TMT. J. Proteome Res. 10:913–20
    [Google Scholar]
  85. 85.  Tomazella GG, Kassahun H, Nilsen H, Thiede B 2012. Quantitative proteome analysis reveals RNA processing factors as modulators of ionizing radiation-induced apoptosis in the C. elegans germline. J. Proteome Res. 11:4277–88
    [Google Scholar]
  86. 86.  Zhang S, Chen L, Shan Y, Sui Z, Wu Q et al. 2016. Pseudo isobaric peptide termini labelling for relative proteome quantification by SWATH MS acquisition. Analyst 141:4912–18
    [Google Scholar]
  87. 87.  Old WM, Meyer-Arendt K, Aveline-Wolf L, Pierce KG, Mendoza A et al. 2005. Comparison of label-free methods for quantifying human proteins by shotgun proteomics. Mol. Cell. Proteom. 4:1487–502
    [Google Scholar]
  88. 88.  Bantscheff M, Schirle M, Sweetman G, Rick J, Kuster B 2007. Quantitative mass spectrometry in proteomics: a critical review. Anal. Bioanal. Chem. 389:1017–31
    [Google Scholar]
  89. 89.  Rappsilber J, Ryder U, Lamond AI, Mann M 2002. Large-scale proteomic analysis of the human spliceosome. Genome Res 12:1231–45
    [Google Scholar]
  90. 90.  Sanders SL, Jennings J, Canutescu A, Link AJ, Weil PA 2002. Proteomics of the eukaryotic transcription machinery: identification of proteins associated with components of yeast TFIID by multidimensional mass spectrometry. Mol. Cell. Biol. 22:4723–38
    [Google Scholar]
  91. 91.  Ishihama Y, Oda Y, Tabata T, Sato T, Nagasu T 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. Proteom. 4:1265–72
    [Google Scholar]
  92. 92.  Dowle AA, Wilson J, Thomas JR 2016. Comparing the diagnostic classification accuracy of iTRAQ, peak-area, spectral-counting, and emPAI methods for relative quantification in expression proteomics. J. Proteome Res. 15:3550–62
    [Google Scholar]
  93. 93.  Liu H, Sadygov RG, Yates JR3rd 2004. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal. Chem. 76:4193–201
    [Google Scholar]
  94. 94.  Zhang Y, Wen Z, Washburn MP, Florens L 2009. Effect of dynamic exclusion duration on spectral count based quantitative proteomics. Anal. Chem. 81:6317–26
    [Google Scholar]
  95. 95.  Lu P, Vogel C, Wang R, Yao X, Marcotte EM 2007. Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat. Biotechnol. 25:117–24
    [Google Scholar]
  96. 96.  Zybailov B, Mosley AL, Sardiu ME, Coleman MK, Florens L, Washburn MP 2006. Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae. . J. Proteome Res. 5:2339–47
    [Google Scholar]
  97. 97.  Powell DW, Weaver CM, Jennings JL, McAfee KJ, He Y et al. 2004. Cluster analysis of mass spectrometry data reveals a novel component of SAGA. Mol. Cell. Biol. 24:7249–59
    [Google Scholar]
  98. 98.  Zhou JY, Schepmoes AA, Zhang X, Moore RJ, Monroe ME et al. 2010. Improved LC-MS/MS spectral counting statistics by recovering low-scoring spectra matched to confidently identified peptide sequences. J. Proteome Res. 9:5698–704
    [Google Scholar]
  99. 99.  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. 82:2272–81
    [Google Scholar]
  100. 100.  Chelius D, Bondarenko PV 2002. Quantitative profiling of proteins in complex mixtures using liquid chromatography and mass spectrometry. J. Proteome Res. 1:317–23
    [Google Scholar]
  101. 101.  Cox J, Hein MY, Luber CA, Paron I, Nagaraj N, Mann M 2014. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteom. 13:2513–26
    [Google Scholar]
  102. 102.  Callister SJ, Barry RC, Adkins JN, Johnson ET, Qian WJ et al. 2006. Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics. J. Proteome Res. 5:277–86
    [Google Scholar]
  103. 103.  Nahnsen S, Bielow C, Reinert K, Kohlbacher O 2013. Tools for label-free peptide quantification. Mol. Cell. Proteom. 12:549–56
    [Google Scholar]
  104. 104.  Blein-Nicolas M, Zivy M 2016. Thousand and one ways to quantify and compare protein abundances in label-free bottom-up proteomics. Biochim. Biophys. Acta 1864:883–95
    [Google Scholar]
  105. 105.  Bantscheff M, Lemeer S, Savitski MM, Kuster B 2012. Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Anal. Bioanal. Chem. 404:939–65
    [Google Scholar]
  106. 106.  Ahrné E, Molzahn L, Glatter T, Schmidt A 2013. Critical assessment of proteome-wide label-free absolute abundance estimation strategies. Proteomics 13:2567–78
    [Google Scholar]
  107. 107.  Trudgian DC, Ridlova G, Fischer R, Mackeen MM, Ternette N et al. 2011. Comparative evaluation of label-free SINQ normalized spectral index quantitation in the central proteomics facilities pipeline. Proteomics 11:2790–97
    [Google Scholar]
  108. 108.  Dephoure N, Gygi SP 2012. Hyperplexing: a method for higher-order multiplexed quantitative proteomics provides a map of the dynamic response to rapamycin in yeast. Sci. Signal. 5:rs2
    [Google Scholar]
  109. 109.  Kumar A, Jamwal S, Midha MK, Hamza B, Aggarwal S et al. 2016. Dataset generated using hyperplexing and click chemistry to monitor temporal dynamics of newly synthesized macrophage secretome post infection by mycobacterial strains. Data Brief 9:349–54
    [Google Scholar]
  110. 110.  Welle KA, Zhang T, Hryhorenko JR, Shen S, Qu J, Ghaemmaghami S 2016. Time-resolved analysis of proteome dynamics by tandem mass tags and stable isotope labeling in cell culture (TMT-SILAC) hyperplexing. Mol. Cell. Proteom. 15:3551–63
    [Google Scholar]
  111. 111.  Gunawardena HP, O'Brien J, Wrobel JA, Xie L, Davies SR et al. 2016. QuantFusion: novel unified methodology for enhanced coverage and precision in quantifying global proteomic changes in whole tissues. Mol. Cell. Proteom. 15:740–51
    [Google Scholar]
  112. 112.  Surinova S, Schiess R, Hüttenhain R, Cerciello F, Wollscheid B, Aebersold R 2011. On the development of plasma protein biomarkers. J. Proteome Res. 10:5–16
    [Google Scholar]
  113. 113.  Shi T, Fillmore TL, Sun X, Zhao R, Schepmoes AA et al. 2012. Antibody-free, targeted mass-spectrometric approach for quantification of proteins at low picogram per milliliter levels in human plasma/serum. PNAS 109:15395–400
    [Google Scholar]
  114. 114.  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. 10:1785–93
    [Google Scholar]
  115. 115.  Lescuyer P, Hochstrasser D, Rabilloud T 2007. How shall we use the proteomics toolbox for biomarker discovery?. J. Proteome Res. 6:3371–76
    [Google Scholar]
  116. 116.  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. Proteom. 11:1475–88
    [Google Scholar]
  117. 117.  Lange V, Picotti P, Domon B, Aebersold R 2008. Selected reaction monitoring for quantitative proteomics: a tutorial. Mol. Syst. Biol. 4:222
    [Google Scholar]
  118. 118.  Picotti P, Aebersold R 2012. Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nat. Methods 9:555–66
    [Google Scholar]
  119. 119.  Shi T, Su D, Liu T, Tang K, Camp DG2nd et al. 2012. Advancing the sensitivity of selected reaction monitoring-based targeted quantitative proteomics. Proteomics 12:1074–92
    [Google Scholar]
  120. 120.  Lesur A, Domon B 2015. Advances in high-resolution accurate mass spectrometry application to targeted proteomics. Proteomics 15:880–90
    [Google Scholar]
  121. 121.  Hüttenhain R, Soste M, Selevsek N, Röst H, Sethi A et al. 2012. Reproducible quantification of cancer-associated proteins in body fluids using targeted proteomics. Sci. Transl. Med. 4:142ra94
    [Google Scholar]
  122. 122.  Hollander Z, Lazarova M, Lam KK, Ignaszewski A, Oudit GY et al. 2014. Proteomic biomarkers of recovered heart function. Eur. J. Heart Fail. 16:551–59
    [Google Scholar]
  123. 123.  Heywood WE, Galimberti D, Bliss E, Sirka E, Paterson RW et al. 2015. Identification of novel CSF biomarkers for neurodegeneration and their validation by a high-throughput multiplexed targeted proteomic assay. Mol. Neurodegener. 10:64
    [Google Scholar]
  124. 124.  Wildsmith KR, Schauer SP, Smith AM, Arnott D, Zhu Y et al. 2014. Identification of longitudinally dynamic biomarkers in Alzheimer's disease cerebrospinal fluid by targeted proteomics. Mol. Neurodegener. 9:22
    [Google Scholar]
  125. 125.  Zhang Q, Fillmore TL, Schepmoes AA, Clauss TR, Gritsenko MA et al. 2013. Serum proteomics reveals systemic dysregulation of innate immunity in type 1 diabetes. J. Exp. Med. 210:191–203
    [Google Scholar]
  126. 126.  Birse CE, Lagier RJ, FitzHugh W, Pass HI, Rom WN et al. 2015. Blood-based lung cancer biomarkers identified through proteomic discovery in cancer tissues, cell lines and conditioned medium. Clin. Proteom. 12:18
    [Google Scholar]
  127. 127.  Kume H, Muraoka S, Kuga T, Adachi J, Narumi R et al. 2014. Discovery of colorectal cancer biomarker candidates by membrane proteomic analysis and subsequent verification using selected reaction monitoring (SRM) and tissue microarray (TMA) analysis. Mol. Cell. Proteom. 13:1471–84
    [Google Scholar]
  128. 128.  Wolf-Yadlin A, Hautaniemi S, Lauffenburger DA, White FM 2007. Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks. PNAS 104:5860–65
    [Google Scholar]
  129. 129.  Sabido E, Quehenberger O, Shen Q, Chang CY, Shah I et al. 2012. Targeted proteomics of the eicosanoid biosynthetic pathway completes an integrated genomics-proteomics-metabolomics picture of cellular metabolism. Mol. Cell. Proteom. 11: https://doi.org/10.1074/mcp.M111.014746
    [Crossref] [Google Scholar]
  130. 130.  Bisson N, James DA, Ivosev G, Tate SA, Bonner R et al. 2011. Selected reaction monitoring mass spectrometry reveals the dynamics of signaling through the GRB2 adaptor. Nat. Biotechnol. 29:653–58
    [Google Scholar]
  131. 131.  Gallien S, Duriez E, Crone C, Kellmann M, Moehring T, Domon B 2012. Targeted proteomic quantification on quadrupole-orbitrap mass spectrometer. Mol. Cell. Proteom. 11:1709–23
    [Google Scholar]
  132. 132.  Kim YJ, Gallien S, El-Khoury V, Goswami P, Sertamo K et al. 2015. Quantification of SAA1 and SAA2 in lung cancer plasma using the isotype-specific PRM assays. Proteomics 15:3116–25
    [Google Scholar]
  133. 133.  Ronsein GE, Reyes-Soffer G, He Y, Oda M, Ginsberg H, Heinecke JW 2016. Targeted proteomics identifies paraoxonase/arylesterase 1 (PON1) and apolipoprotein Cs as potential risk factors for hypoalphalipoproteinemia in diabetic subjects treated with fenofibrate and rosiglitazone. Mol. Cell Proteom. 15:1083–93
    [Google Scholar]
  134. 134.  Lawrence RT, Searle BC, Llovet A, Villen J 2016. Plug-and-play analysis of the human phosphoproteome by targeted high-resolution mass spectrometry. Nat. Methods 13:431–34
    [Google Scholar]
  135. 135.  Majovsky P, Naumann C, Lee CW, Lassowskat I, Trujillo M et al. 2014. Targeted proteomics analysis of protein degradation in plant signaling on an LTQ-Orbitrap mass spectrometer. J. Proteome Res. 13:4246–58
    [Google Scholar]
  136. 136.  Venable JD, Dong MQ, Wohlschlegel J, Dillin A, Yates JR 2004. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat. Methods 1:39–45
    [Google Scholar]
  137. 137.  Gillet LC, Navarro P, Tate S, Röst H, Selevsek N 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. Proteom. 11: https://doi.org/10.1074/mcp.O111.016717
    [Crossref] [Google Scholar]
  138. 138.  Chen X, Iliopoulos D, Zhang Q, Tang Q, Greenblatt MB et al. 2014. XBP1 promotes triple-negative breast cancer by controlling the HIF1α pathway. Nature 508:103–7
    [Google Scholar]
  139. 139.  Liu Y, Huttenhain R, Surinova S, Gillet LC, Mouritsen J et al. 2013. Quantitative measurements of N-linked glycoproteins in human plasma by SWATH-MS. Proteomics 13:1247–56
    [Google Scholar]
  140. 140.  Selevsek N, Chang CY, Gillet LC, Navarro P, Bernhardt OM et al. 2015. Reproducible and consistent quantification of the Saccharomyces cerevisiae proteome by SWATH-mass spectrometry. Mol. Cell. Proteom. 14:739–49
    [Google Scholar]
  141. 141.  Krautkramer KA, Reiter L, Denu JM, Dowell JA 2015. Quantification of SAHA-dependent changes in histone modifications using data-independent acquisition mass spectrometry. J. Proteome Res. 14:3252–62
    [Google Scholar]
  142. 142.  Ortea I, Rodríguez-Ariza A, Chicano-Gálvez E, Arenas Vacas MS, Jurado Gámez B 2016. Discovery of potential protein biomarkers of lung adenocarcinoma in bronchoalveolar lavage fluid by SWATH MS data-independent acquisition and targeted data extraction. J. Proteom. 138:106–14
    [Google Scholar]
  143. 143.  Haverland NA, Fox HS, Ciborowski P 2014. Quantitative proteomics by SWATH-MS reveals altered expression of nucleic acid binding and regulatory proteins in HIV-1-infected macrophages. J. Proteome Res. 13:2109–19
    [Google Scholar]
  144. 144.  Huang Q, Yang L, Luo J, Guo L, Wang Z et al. 2015. SWATH enables precise label-free quantification on proteome scale. Proteomics 15:1215–23
    [Google Scholar]
  145. 145.  Streng AS, de Boer D, Bouwman FG, Mariman EC, Scholten A et al. 2016. Development of a targeted selected ion monitoring assay for the elucidation of protease induced structural changes in cardiac troponin T. J. Proteom. 136:123–32
    [Google Scholar]
  146. 146.  Gerber SA, Rush J, Stemman O, Kirschner MW, Gygi SP 2003. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. PNAS 100:6940–45
    [Google Scholar]
  147. 147.  Kirkpatrick DS, Gerber SA, Gygi SP 2005. The absolute quantification strategy: a general procedure for the quantification of proteins and post-translational modifications. Methods 35:265–73
    [Google Scholar]
  148. 148.  Jedrychowski MP, Wrann CD, Paulo JA, Gerber KK, Szpyt J et al. 2015. Detection and quantitation of circulating human irisin by tandem mass spectrometry. Cell Metab 22:734–40
    [Google Scholar]
  149. 149.  Hochleitner EO, Sondermann P, Lottspeich F 2004. Determination of the stoichiometry of protein complexes using liquid chromatography with fluorescence and mass spectrometric detection of fluorescently labeled proteolytic peptides. Proteomics 4:669–76
    [Google Scholar]
  150. 150.  Beynon RJ, Doherty MK, Pratt JM, Gaskell SJ 2005. Multiplexed absolute quantification in proteomics using artificial QCAT proteins of concatenated signature peptides. Nat. Methods 2:587–89
    [Google Scholar]
  151. 151.  Carroll KM, Simpson DM, Eyers CE, Knight CG, Brownridge P et al. 2011. Absolute quantification of the glycolytic pathway in yeast: deployment of a complete QconCAT approach. Mol. Cell. Proteom. 10: https://doi.org/10.1074/mcp.M111.007633
    [Crossref] [Google Scholar]
  152. 152.  Chen J, Wang M, Turko IV 2012. Mass spectrometry quantification of clusterin in the human brain. Mol. Neurodegener. 7:41
    [Google Scholar]
  153. 153.  Brun V, Dupuis A, Adrait A, Marcellin M, Thomas D et al. 2007. Isotope-labeled protein standards: toward absolute quantitative proteomics. Mol. Cell. Proteom. 6:2139–49
    [Google Scholar]
  154. 154.  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. 7:1118–30
    [Google Scholar]
  155. 155.  Wang Q, Zhang S, Guo L, Busch CM, Jian W et al. 2015. Serum apolipoprotein A-1 quantification by LC-MS with a SILAC internal standard reveals reduced levels in smokers. Bioanalysis 7:2895–911
    [Google Scholar]
  156. 156.  Singh S, Springer M, Steen J, Kirschner MW, Steen H 2009. FLEXIQuant: a novel tool for the absolute quantification of proteins, and the simultaneous identification and quantification of potentially modified peptides. J. Proteome Res. 8:2201–10
    [Google Scholar]
  157. 157.  Zeiler M, Straube WL, Lundberg E, Uhlen M, Mann M 2012. A Protein Epitope Signature Tag (PrEST) library allows SILAC-based absolute quantification and multiplexed determination of protein copy numbers in cell lines. Mol. Cell. Proteom. 11: https://doi.org/10.1074/mcp.O111.009613
    [Crossref] [Google Scholar]
  158. 158.  Uhlén M, Bjorling E, Agaton C, Szigyarto CA, Amini B et al. 2005. A human protein atlas for normal and cancer tissues based on antibody proteomics. Mol. Cell. Proteom. 4:1920–32
    [Google Scholar]
  159. 159.  Schwanhäusser B, Busse D, Li N, Dittmar G, Schuchhardt J et al. 2011. Global quantification of mammalian gene expression control. Nature 473:337–42
    [Google Scholar]
  160. 160.  Silva JC, Gorenstein MV, Li GZ, Vissers JP, Geromanos SJ 2006. Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol. Cell. Proteom. 5:144–56
    [Google Scholar]
  161. 161.  Wilhelm M, Schlegl J, Hahne H, Gholami AM, Lieberenz M et al. 2014. Mass-spectrometry-based draft of the human proteome. Nature 509:582–87
    [Google Scholar]
  162. 162.  Zhao S, Xu W, Jiang W, Yu W, Lin Y et al. 2010. Regulation of cellular metabolism by protein lysine acetylation. Science 327:1000–4
    [Google Scholar]
  163. 163.  Wang L, Xie L, Ramachandran S, Lee Y, Yan Z et al. 2015. Non-canonical bromodomain within DNA-PKcs promotes DNA damage response and radioresistance through recognizing an IR-induced acetyl-lysine on H2AX. Chem. Biol. 22:849–61
    [Google Scholar]
/content/journals/10.1146/annurev-anchem-061516-045357
Loading
/content/journals/10.1146/annurev-anchem-061516-045357
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error