Skip to main content

Introduction to Proteomics Technologies

  • Protocol
Statistical Analysis in Proteomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1362))

Abstract

Compared to genomics or transcriptomics, proteomics is often regarded as an “emerging technology,” i.e., as not having reached the same level of maturity. While the successful implementation of proteomics workflows and technology still requires significant levels of expertise and specialization, great strides have been made to make the technology more powerful, streamlined and accessible. In 2014, two landmark studies published the first draft versions of the human proteome.

We aim to provide an introduction specifically into the background of mass spectrometry (MS)-based proteomics. Within the field, mass spectrometry has emerged as a core technology. Coupled to increasingly powerful separations and data processing and bioinformatics solution, it allows the quantitative analysis of whole proteomes within a matter of days, a timescale that has made global comparative proteome studies feasible at last. We present and discuss the basic concepts behind proteomics mass spectrometry and the accompanying topic of protein and peptide separations, with a focus on the properties of datasets emerging from such studies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wilhelm M, Schlegl J, Hahne H et al (2014) Mass-spectrometry-based draft oft he human proteome. Nature 509:582–587

    Article  CAS  PubMed  Google Scholar 

  2. Kim MS, Pinto SM, Getnet D et al (2014) A draft map of the human proteome. Nature 509:575–581

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  3. Tyers M, Mann M (2003) From genomics to proteomics. Nature 422:193–197

    Article  CAS  PubMed  Google Scholar 

  4. Pandey A, Mann M (2000) Proteomics to study genes and genomes. Nature 405:837–846

    Article  CAS  PubMed  Google Scholar 

  5. Rappsilber J, Mann M (2002) What does it mean to identify a protein in proteomics? Trends Biochem Sci 27:74–78

    Article  CAS  PubMed  Google Scholar 

  6. Smith LM, Kelleher NL (2013) Proteoform: a single term describing protein complexity. Nat Methods 10:186–187

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  7. Schlüter H, Apweiler R, Holzhütter HG et al (2009) Finding one’s way in proteomics: a protein species nomenclature. Chem Cent J. doi:10.1186/1752-153X-3-11

    PubMed Central  PubMed  Google Scholar 

  8. Lenz C, Urlaub H (2014) Separation methodology to improve proteome coverage depth. Expert Rev Proteomics 11:409–414

    Article  CAS  PubMed  Google Scholar 

  9. Catherman AD, Skinner OS, Kelleher NL (2014) Top Down proteomics: facts and perspectives. Biochem Biophys Res Commun 445:683–693

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  10. Ahlf DR, Thomas PM, Kelleher NL (2013) Developing top down proteomics to maximize proteome and sequence coverage from cells and tissues. Curr Opin Chem Biol 17:787–794

    Article  CAS  PubMed  Google Scholar 

  11. Weber K, Osborn M (1969) The reliability of molecular weight determinations by dodecyl sulfate-polyacrylamide gel electrophoresis. J Biol Chem 244:4406–4412

    CAS  PubMed  Google Scholar 

  12. Hanash SM (2001) 2D or not 2D is there a future for 2D gels in proteomics? Proteomics 1:635–637

    CAS  PubMed  Google Scholar 

  13. Dihazi H, Müller GA (2007) The urinary proteome: a tool to discover biomarker of kidney diseases. Expert Rev Proteomics 4:39–50

    Article  CAS  PubMed  Google Scholar 

  14. O’Farrell PH (1975) High resolution two-dimensional electrophoresis of proteins. J Biol Chem 250:4007–4021

    PubMed Central  PubMed  Google Scholar 

  15. O’Farrell PZ, Goodman HM, O’Farrell PH (1977) High resolution two-dimensional electrophoresis of basic as well as acidic proteins. Cell 12:1133–1141

    Article  PubMed  Google Scholar 

  16. Klose J (1975) Protein mapping by combined isoelectric focusing and electrophoresis of mouse tissues. A novel approach to testing for induced point mutations in mammals. Humangenetik 26:231–243

    CAS  PubMed  Google Scholar 

  17. Lilley KS, Razzaq A, Dupree P (2002) Two-dimensional gel electrophoresis: recent advances in sample preparation, detection and quantitation. Curr Opin Chem Biol 6:46–50

    Article  CAS  PubMed  Google Scholar 

  18. Switzer RC III, Merril CR, Shifrin S (1979) A highly sensitive silver stain for detecting proteins and peptides in polyacrylamide gels. Anal Biochem 98:231–237

    Article  CAS  PubMed  Google Scholar 

  19. Steinberg TH, Jones LJ, Haugland RP, Singer VL (1996) SYPRO Orange and SYPRO Red protein gel stains: one-step fluorescent staining of denaturing gels for detection of nanogram levels of protein. Anal Biochem 239:223–237

    Article  CAS  PubMed  Google Scholar 

  20. Patton WF (2002) Detection technologies in proteome analysis. J Chromatogr B Analyt Technol Biomed Life Sci 771:3–31

    Article  CAS  PubMed  Google Scholar 

  21. Unlu M, Morgan ME, Minden JS (1997) Difference gel electrophoresis: a single gel method for detecting changes in protein extracts. Electrophoresis 18:2071–2077

    Article  CAS  PubMed  Google Scholar 

  22. Gharbi S, Gaffney P, Yang A et al (2002) Evaluation of two-dimensional differential gel electrophoresis for proteomic expression analysis of a model breast cancer cell system. Mol Cell Proteomics 1:91–98

    Article  CAS  PubMed  Google Scholar 

  23. Dihazi H, Dihazi GH, Jahn O et al (2011) Multipotent adult germline stem cells and embryonic stem cells functional proteomics revealed an important role of eukaryotic initiation factor 5A (Eif5a) in stem cell differentiation. J Proteome Res 10:1962–1973

    Article  CAS  PubMed  Google Scholar 

  24. Dihazi H, Dihazi GH, Nolte J et al (2009) Differential proteomic analysis of multipotent adult germline stem cells and embryonic stem cells reveals high proteome similarity. J Proteome Res 8:5497–5510

    Article  CAS  PubMed  Google Scholar 

  25. Zuo X, Speicher DW (2002) Comprehensive analysis of complex proteomes using microscale solution isoelectrofocusing prior to narrow pH range two-dimensional electrophoresis. Proteomics 2:58–68

    Article  CAS  PubMed  Google Scholar 

  26. Lin D, Tabb DL, Yates JR III (2003) Large-scale protein identification using MS. Biochim Biophys Acta 1646:1–10

    Article  CAS  PubMed  Google Scholar 

  27. Link AJ, Eng J, Schieltz DM et al (1999) Direct analysis of protein complexes using MS. Nat Biotechnol 17:676–682

    Article  CAS  PubMed  Google Scholar 

  28. Issaq HJ, Chan KC, Janini GM et al (2005) Multidimensional separation of peptides for effective proteomic analysis. J Chromatogr B 817:35–47

    Article  CAS  Google Scholar 

  29. Majors RE (1980) Multidimensional high performance liquid chromatography. J Chromatogr Sci 18:571–580

    Article  CAS  Google Scholar 

  30. Giddings JC (1984) Twodimensional separations: concept and promise. Anal Chem 56:1258A–1264A

    Article  CAS  PubMed  Google Scholar 

  31. Cortes HJ (ed) (1990) Multidimensional chromatography. Techniques and applications. Marcel Dekker, New York

    Google Scholar 

  32. Anderegg RJ, Wagner DS, Blackburn RK, Opiteck GJ, Jorgenson JW (1997) A multidimensional approach to protein characterization. J Protein Chem 16:523–526

    Article  CAS  PubMed  Google Scholar 

  33. Neverova I, Van Eyk JE (2005) Role of chromatographic techniques in proteomic analysis. J Chromatogr B 815:51–63

    Article  CAS  Google Scholar 

  34. Neverova I, Van Eyk JE (2002) Application of reversed phase high performance liquid chromatography for subproteomic analysis of cardiac muscle. Proteomics 2:22–31

    Article  CAS  PubMed  Google Scholar 

  35. Zhu H, Klemic JF, Chang S et al (2000) Analysis of yeast protein kinases using protein chips. Nat Genet 26:283–289

    Article  CAS  PubMed  Google Scholar 

  36. Lueking A, Horn M, Eickhoff H et al (1999) Protein microarrays for gene expression and antibody screening. Anal Biochem 270:103–111

    Article  CAS  PubMed  Google Scholar 

  37. MacBeath G (2002) Protein microarrays and proteomics. Nat Genet 32:S526–S532

    Article  CAS  Google Scholar 

  38. Schmidt A, Karas M, Dülcks T (2003) Effect of different solution flow rates on analyte ion signals in nano-ESI MS, or: when does ESI turn into nano-ESI? J Am Soc Mass Spectrom 14:492–500

    Article  CAS  PubMed  Google Scholar 

  39. Luo Q, Gu Y, Wu S-L et al (2008) Two-dimensional strong cation exchange/porous layer open tubular/mass spectrometry for ultratrace proteomic analysis using a 10 μm id poly(styrene-divinylbenzen porous layer open tubular column with an on-line triphasic trapping column. Electrophoresis 29:1804–1811

    Article  CAS  Google Scholar 

  40. Sandra K, Moshir M, D’hondt F et al (2008) Highly efficient peptide separations in proteomics. Part 1. Unidimensional high performance liquid chromatography. J Chromatogr B 866:48–63

    Article  CAS  Google Scholar 

  41. Köcher T, Pichler P, Swart R et al (2012) Analysis of protein mixtures from whole-cell extracts by single-run nanoLC-MS/MS using ultralong gradients. Nat Protoc 7:882–890

    Article  PubMed  CAS  Google Scholar 

  42. Hsieh EJ, Bereman MS, Durand S et al (2013) Effects of column and gradient lengths on peak capacity and peptide identification in nanoflow LC-MS/MS of complex proteomics samples. J Am Soc Mass Spectrom 24:148–153

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  43. Sandra K, Moshir M, D’hondt F et al (2009) Highly efficient peptide separations in proteomics. Part 2. Bi- and multidimensional liquid-based separation techniques. J Chromatogr B 877:1019–1039

    Article  CAS  Google Scholar 

  44. Haubitz M, Wittke S, Weissinger EM et al (2005) Urine protein patterns can serve as diagnostic tools in patients with IgA nephropathy. Kidney Int 67:2313–2320

    Article  CAS  PubMed  Google Scholar 

  45. Weissinger EM, Wittke S, Kaiser T et al (2004) Proteomic patterns established with capillary electrophoresis and mass spectrometry for diagnostic purposes. Kidney Int 65:2426–2434

    Article  CAS  PubMed  Google Scholar 

  46. Wittke S, Fliser D, Haubitz M et al (2003) Determination of peptides and proteins in human urine with capillary electrophoresis-mass spectrometry, a suitable tool for the establishment of new diagnostic markers. J Chromatogr A 1013:173–181

    Article  CAS  PubMed  Google Scholar 

  47. Karas M, Hillenkamp F (1988) Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons. Anal Chem 60:2299–2301

    Article  CAS  PubMed  Google Scholar 

  48. Karas M, Glückmann M, Schäfer J (2000) Ionization in matrix-assisted laser desorption/ionization: singly charged molecular ions are the lucky survivors. J Mass Spectrom 35:1–12

    Article  CAS  PubMed  Google Scholar 

  49. Stevenson E, Breuker K, Zenobi R (2000) Internal energies of analyte ions generated from different matrix-assisted laser desorption/ionization matrices. J Mass Spectrom 35:1035–1041

    Article  CAS  PubMed  Google Scholar 

  50. Krüger R, Pfenninger A, Fournier I et al (2000) Analyte incorporation and ionization in matrix-assisted laser desorption/ionization visualized by pH indicator molecular probes. Anal Chem 73:5812–5821

    Article  CAS  Google Scholar 

  51. Patel R (2015) MALDI-TOF MS for the diagnosis of infectious diseases. Clin Chem 61:100–111

    Article  CAS  PubMed  Google Scholar 

  52. Whitehouse CM, Dreyer RN, Yamashita M et al (1985) Electrospray interface for liquid chromatographs and mass spectrometers. Anal Chem 57:675–679

    Article  CAS  PubMed  Google Scholar 

  53. Fenn JB, Mann M, Meng CK et al (1989) Electrospray ionization for mass spectrometry of large biomolecules. Science 246:64–71

    Article  CAS  PubMed  Google Scholar 

  54. Emmett MR, Caprioli R (1994) Micro-electrospray mass spectrometry: ultra-high-sensitivity analysis of peptides and proteins. J Am Soc Mass Spectrom 5:605–613

    Article  CAS  PubMed  Google Scholar 

  55. Schwartz JC, Jardine I (1996) Quadrupole ion trap mass spectrometry. Methods Enzymol 270:552–586

    Article  CAS  PubMed  Google Scholar 

  56. Chernushevich IV, Loboda AV, Thomson BA (2001) An introduction to quadrupole-time-of-flight mass spectrometry. J Mass Spectrom 36:849–865

    Article  CAS  PubMed  Google Scholar 

  57. Hines WM, Parker K, Peltier J et al (1998) Protein identification and protein characterization by high-performance time-of-flight mass spectrometry. J Protein Chem 17:525–526

    CAS  PubMed  Google Scholar 

  58. Beinvenut WV, Daon C, Pasquarello C et al (2002) Matrix-assisted laser desorption/ionization-tandem mass spectrometry with high resolution and sensitivity for identification and characterization of proteins. Proteomics 2:868–876

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  60. Zubarev R, Makarov AA (2013) Orbitrap mass spectrometry. Anal Chem 85:5288–5296

    Article  CAS  PubMed  Google Scholar 

  61. Roepstorff P, Fohlman J (1984) Proposal for a common nomenclature for sequence ions in mass spectra of peptides. Biomed Mass Spectrom 11:601

    Article  CAS  PubMed  Google Scholar 

  62. Steen H, Mann M (2004) The ABC’s (and XYZ’s) of peptide sequencing. Nat Rev Mol Cell Biol 5:699–711

    Article  CAS  PubMed  Google Scholar 

  63. Huddleston MJ, Bean MF, Carr SA (1993) Collisional fragmentation of glycopeptides by electrospray ionization LC/MS and LC/MS/MS: methods for selective detection of glycopeptides in protein digests. Anal Chem 65:877–884

    Article  CAS  PubMed  Google Scholar 

  64. Carr SA, Huddleston MJ, Annan RS (1996) Selective detection and sequencing of phosphopeptides at the femtomole level by mass spectrometry. Anal Biochem 239:180–192

    Article  CAS  PubMed  Google Scholar 

  65. 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 10:1785–1793

    Article  CAS  PubMed  Google Scholar 

  66. Huang EC, Henion JD (1990) LC/MS and LC/MS/MS determination of protein tryptic digests. J Am Soc Mass Spectrom 1:158–165

    Article  CAS  PubMed  Google Scholar 

  67. Covey TR, Huang EC, Henion JD (1991) Structural characterization of protein tryptic peptides via liquid chromatography/mass spectrometry and collision-induced dissociation of their doubly charged molecular ions. Anal Chem 63:1193–1200

    Article  CAS  PubMed  Google Scholar 

  68. Zubarev A (2013) The challenge of the proteome dynamic range and its implications for in-depth proteomics. Proteomics 13:723–726

    Article  CAS  PubMed  Google Scholar 

  69. Hebert AS, Richards AL, Bailey DJ et al (2014) The one hour yeast proteome. Mol Cell Proteomics 13:339–347

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  70. Picotti P, Aebersold R (2012) Selected reaction monitoring-based proteomics: workflows, potentials, pitfalls and future directions. Nat Methods 9:555–566

    Article  CAS  PubMed  Google Scholar 

  71. Gillet LC, Navarro P, Tate S 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, O111.016717. doi:10.1074/mcp.O111.016717

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  72. Selevsek N, Chang CY, Gillet LC et al (2015) Reproducible and consistent quantification of the Saccharomyces cerevisiae proteome by SWATH-mass spectrometry. Mol Cell Proteomics 14:739–749

    Article  CAS  PubMed  Google Scholar 

  73. Sleno L, Volmer DA (2004) Ion activation methods for tandem mass spectrometry. J Mass Spectrom 39:1091–1112

    Article  CAS  PubMed  Google Scholar 

  74. Wells JM, McLuckey SA (2005) Collision-induced dissociation (CID) of peptides and proteins. Methods Enzymol 402:148–185

    Article  CAS  PubMed  Google Scholar 

  75. Olsen JV, Macek B, Lange O et al (2007) Higher-energy C-trap dissociation for peptide modification analysis. Nat Methods 4:709–712

    Article  CAS  PubMed  Google Scholar 

  76. Syka JE, Coon JJ, Schroeder MJ et al (2004) Peptide and protein sequence analysis by electron transfer dissociation mass spectrometry. Proc Natl Acad Sci U S A 101:9528–9533

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  77. Mikesh LM, Ueberheide B, Chi A et al (2006) The utility of ETD mass spectrometry in proteomic analysis. Biochim Biophys Acta 1764:1811–1822

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  78. Medzihradsky KF, Chalkley RJ (2015) Lessons in de novo peptide sequencing by tandem mass spectrometry. Mass Spectrom Rev 34:43–63

    Article  CAS  Google Scholar 

  79. Mann M, Wilm M (1994) Error-tolerant identification of peptides in sequence databases by peptide sequence tags. Anal Chem 66:4390–4399

    Article  CAS  PubMed  Google Scholar 

  80. MacCoss MJ, Wu CC, Yates JR 3rd (2002) Probability-based validation of protein identifications using a modified SEQUEST algorithm. Anal Chem 74:5593–5599

    Article  CAS  PubMed  Google Scholar 

  81. Perkins DN, Pappin DJ, Creasy DM et al (1999) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20:3551–3567

    Article  CAS  PubMed  Google Scholar 

  82. Geer LY, Markey SP, Kowalak JA (2004) Open mass spectrometry search algorithm. J Proteome Res 3:958–964

    Article  CAS  PubMed  Google Scholar 

  83. Shilov IV, Seymour SL, Patel AA et al (2007) The Paragon Algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra. Mol Cell Proteomics 6:1638–1655

    Article  CAS  PubMed  Google Scholar 

  84. Cox J, Neuhauser N, Michalski A et al (2011) Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res 10:1794–1805

    Article  CAS  PubMed  Google Scholar 

  85. Beausoleil SA, Villén J, Gerber SA et al (2006) A probability-based approach for high-throughput protein phosphorylation analysis and site localization. Nat Biotechnol 24:1285–1292

    Article  CAS  PubMed  Google Scholar 

  86. Savitski MM, Lemeer S, Boesche M et al (2011) Confident phosphorylation site localization using the Mascot Delta Score. Mol Cell Proteomics 10, M110.003830. doi:10.1074/mcp.M110.003830

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  87. Taus T, Köcher T, Pichler P et al (2011) Universal and confident phosphorylation site localization using phosphoRS. J Proteome Res 10:5354–5362

    Article  CAS  PubMed  Google Scholar 

  88. Jeong K, Kim S, Bandeira N (2012) False discovery rates in spectral identification. BMC Bioinformatics 13 Suppl 16:S2. doi: 10.1186/1471-2105-13-S16-S2

    Google Scholar 

  89. Käll L, Canterbury JD, Weston J (2007) Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat Methods 4:923–925

    Article  PubMed  CAS  Google Scholar 

  90. Bantscheff M, Schirle M, Sweetman G et al (2007) Quantitative mass spectrometry in proteomics: a critical review. Anal Bioanal Chem 389:1017–1031

    Article  CAS  PubMed  Google Scholar 

  91. Lundgren DH, Hwang SI, Wu L et al (2010) Role of spectral counting in quantitative proteomics. Expert Rev Proteomics 7:39–53

    Article  CAS  PubMed  Google Scholar 

  92. Florens L, Carozza MJ, Swanson SK (2006) Analyzing chromatin remodeling complexes using shotgun proteomics and normalized spectral abundance factors. Methods 40:303–311

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  93. Ishihama Y, Oda Y, Tabata T (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 4:1265–1272

    Article  CAS  PubMed  Google Scholar 

  94. Vogel C, Marcotte EM (2012) Label-free protein quantitation using weighted spectral counting. Methods Mol Biol 893:321–341

    Article  CAS  PubMed  Google Scholar 

  95. Neilson KA, Ali NA, Muralidharan S et al (2011) Less label, more free: approaches in label-free quantitative mass spectrometry. Proteomics 11:535–553

    Article  CAS  PubMed  Google Scholar 

  96. Wong JW, Cagney G (2010) An overview of label-free quantitation methods in proteomics by mass spectrometry. Methods Mol Biol 604:273–283

    Article  CAS  PubMed  Google Scholar 

  97. Silva JC, Gorenstein MV, Li GZ et al (2006) Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol Cell Proteomics 5:144–156

    Article  CAS  PubMed  Google Scholar 

  98. Schwanhäusser B, Busse D, Li N et al (2011) Global quantification of mammalian gene expression control. Nature 473:337–342

    Article  PubMed  CAS  Google Scholar 

  99. Smits AH, Jansen PW, Poser I et al (2013) Stoichiometry of chromatin-associated protein complexes revealed by label-free quantitative mass spectrometry-based proteomics. Nucleic Acids Res 41, e28

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  100. Gerber SA, Rush J, Stemman O et al (2003) Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc Natl Acad Sci U S A 100:6940–6945

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  101. Ross PL, Huang YN, Marchese JN et al (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 3:1154–1169

    Article  CAS  PubMed  Google Scholar 

  102. Liang HC, Lahert E, Pike I et al (2015) Quantitation of protein post-translational modifications using isobaric tandem mass tags. Bioanalysis 7:383–400

    Article  CAS  PubMed  Google Scholar 

  103. Hsu JL, Huang SY, Chow NH et al (2003) Stable-isotope dimethyl labeling for quantitative proteomics. Anal Chem 75:6843–6852

    Article  CAS  PubMed  Google Scholar 

  104. Smolka MB, Zhou H, Purkayastha S et al (2001) Optimization of the isotope-coded affinity tag-labeling procedure for quantitative proteome analysis. Anal Biochem 297:25–31

    Article  CAS  PubMed  Google Scholar 

  105. Fenselau C, Yao X (2009) 18O2-labeling in quantitative proteomic strategies: a status report. J Proteome Res 8:2140–2143

    Article  CAS  PubMed  Google Scholar 

  106. Ong SE, Blagoev B, Kratchmarova I 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 1:376–386

    Article  CAS  PubMed  Google Scholar 

  107. Geiger T, Cox J, Ostasiewicz P et al (2010) Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat Methods 7:383–385

    Article  CAS  PubMed  Google Scholar 

  108. Krijgsveld J, Ketting RF, Mahmoudi T et al (2003) Metabolic labeling of C. elegans and D. melanogaster for quantitative proteomics. Nat Biotechnol 21:927–931

    Article  CAS  PubMed  Google Scholar 

  109. Nesvizhskii A (2014) Proteogenomics: concepts, applications and computational strategies. Nat Methods 11:1114–1125

    Article  PubMed Central  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hassan Dihazi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media New York

About this protocol

Cite this protocol

Lenz, C., Dihazi, H. (2016). Introduction to Proteomics Technologies. In: Jung, K. (eds) Statistical Analysis in Proteomics. Methods in Molecular Biology, vol 1362. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3106-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-3106-4_1

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-3105-7

  • Online ISBN: 978-1-4939-3106-4

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics