Dissecting the iTRAQ Data Analysis

  • Suruchi Aggarwal
  • Amit Kumar Yadav
Part of the Methods in Molecular Biology book series (MIMB, volume 1362)


In the era of large-scale quantitative biology, mass spectrometry-based quantitative proteomics is progressively becoming indispensable for gaining insights into the biological systems at molecular level. Various quantitative study designs rely on chemical tagging approaches to study disease, stress, or drug response and temporal studies aiming at disease/developmental progression in a biological system. Isobaric tags for relative and absolute quantitation (iTRAQ) is one of the most popular chemical labeling techniques which allows four, six, or eight samples to be multiplexed in a single run. As the iTRAQ tag has a balancer group to equalize all states of a labeled peptide to same mass, the differentially labeled iTRAQ peptides are mixed before chromatography and elute as a single combined peak in MS. This enhances the peptide signal and quantitation is performed during MS/MS along with sequencing, where reporter ions of different masses are released to give relative quantitation. Known amount of a spiked-in protein can also help in absolute quantitation of the proteins in a sample.

Key words

iTRAQ Quantitative proteomics Statistics Relative protein quantitation Chemical labeling 



S.A. is supported by SRF grant and A.K.Y. is supported by Innovative Young Biotechnologist Award (IYBA) grant and DDRC-SFC grant from Department of Biotechnology (DBT), India. Authors acknowledge Nazmuddin Saquib for critically reviewing the manuscript, and Manu Kandpal and Vivek Arora for proofreading the manuscript.


  1. 1.
    Altelaar AF, Munoz J, Heck AJ (2012) Next-generation proteomics: towards an integrative view of proteome dynamics. Nat Rev Genet 14:35–48CrossRefPubMedGoogle Scholar
  2. 2.
    Cox J, Mann M (2011) Quantitative, high-resolution proteomics for data-driven systems biology. Annu Rev Biochem 80:273–299CrossRefPubMedGoogle Scholar
  3. 3.
    Bantscheff M, Hopf C, Savitski MM et al (2011) Chemoproteomics profiling of HDAC inhibitors reveals selective targeting of HDAC complexes. Nat Biotechnol 29:255–265CrossRefPubMedGoogle Scholar
  4. 4.
    Boehm AM, Putz S, Altenhofer D et al (2007) Precise protein quantification based on peptide quantification using iTRAQ. BMC Bioinformatics 8:214PubMedCentralCrossRefPubMedGoogle Scholar
  5. 5.
    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 1:376–386CrossRefPubMedGoogle Scholar
  6. 6.
    Hsu JL, Huang SY, Chow NH et al (2003) Stable-isotope dimethyl labeling for quantitative proteomics. Anal Chem 75:6843–6852CrossRefPubMedGoogle Scholar
  7. 7.
    Yao X, Freas A, Ramirez J et al (2001) Proteolytic 18O labeling for comparative proteomics: model studies with two serotypes of adenovirus. Anal Chem 73:2836–2842CrossRefPubMedGoogle Scholar
  8. 8.
    Ross PL, Huang YN, Marchese JN et al (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 3:1154–1169CrossRefPubMedGoogle Scholar
  9. 9.
    Thompson A, Schafer J, Kuhn K et al (2003) Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 75:1895–1904CrossRefPubMedGoogle Scholar
  10. 10.
    Ong SE, Mann M (2005) Mass spectrometry-based proteomics turns quantitative. Nat Chem Biol 1:252–262CrossRefPubMedGoogle Scholar
  11. 11.
    Bantscheff M, Lemeer S, Savitski MM et al (2012) Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Anal Bioanal Chem 404:939–965CrossRefPubMedGoogle Scholar
  12. 12.
    Bantscheff M, Schirle M, Sweetman G et al (2007) Quantitative mass spectrometry in proteomics: a critical review. Anal Bioanal Chem 389:1017–1031CrossRefPubMedGoogle Scholar
  13. 13.
    Vaudel M, Sickmann A, Martens L (2010) Peptide and protein quantification: a map of the minefield. Proteomics 10:650–670CrossRefPubMedGoogle Scholar
  14. 14.
    Glibert P, Van SK, Dhaenens M et al (2014) iTRAQ as a method for optimization: enhancing peptide recovery after gel fractionation. Proteomics 14:680–684PubMedCentralCrossRefPubMedGoogle Scholar
  15. 15.
    Burkhart JM, Vaudel M, Zahedi RP et al (2011) iTRAQ protein quantification: a quality-controlled workflow. Proteomics 11:1125–1134CrossRefPubMedGoogle Scholar
  16. 16.
    Pichler P, Kocher T, Holzmann J et al (2011) Improved precision of iTRAQ and TMT quantification by an axial extraction field in an Orbitrap HCD cell. Anal Chem 83:1469–1474PubMedCentralCrossRefPubMedGoogle Scholar
  17. 17.
    Collins BC, Lau TY, Pennington SR et al (2011) Differential proteomics incorporating iTRAQ labeling and multi-dimensional separations. Methods Mol Biol 691:369–383CrossRefPubMedGoogle Scholar
  18. 18.
    Unwin RD (2010) Quantification of proteins by iTRAQ. Methods Mol Biol 658:205–215CrossRefPubMedGoogle Scholar
  19. 19.
    Ow SY, Salim M, Noirel J et al (2009) iTRAQ underestimation in simple and complex mixtures: “the good, the bad and the ugly”. J Proteome Res 8:5347–5355CrossRefPubMedGoogle Scholar
  20. 20.
    Phanstiel D, Zhang Y, Marto JA et al (2008) Peptide and protein quantification using iTRAQ with electron transfer dissociation. J Am Soc Mass Spectrom 19:1255–1262PubMedCentralCrossRefPubMedGoogle Scholar
  21. 21.
    Bantscheff M, Boesche M, Eberhard D et al (2008) Robust and sensitive iTRAQ quantification on an LTQ Orbitrap mass spectrometer. Mol Cell Proteomics 7:1702–1713PubMedCentralCrossRefPubMedGoogle Scholar
  22. 22.
    Aggarwal K, Choe LH, Lee KH (2006) Shotgun proteomics using the iTRAQ isobaric tags. Brief Funct Genomic Proteomic 5:112–120CrossRefPubMedGoogle Scholar
  23. 23.
    Luo R, Zhao H (2012) Protein quantitation using iTRAQ: review on the sources of variations and analysis of nonrandom missingness. Stat Interface 5:99–107PubMedCentralCrossRefPubMedGoogle Scholar
  24. 24.
    Gan CS, Chong PK, Pham TK et al (2007) Technical, experimental, and biological variations in isobaric tags for relative and absolute quantitation (iTRAQ). J Proteome Res 6:821–827CrossRefPubMedGoogle Scholar
  25. 25.
    Mahoney DW, Therneau TM, Heppelmann CJ et al (2011) Relative quantification: characterization of bias, variability and fold changes in mass spectrometry data from iTRAQ-labeled peptides. J Proteome Res 10:4325–4333PubMedCentralCrossRefPubMedGoogle Scholar
  26. 26.
    Herbrich SM, Cole RN, West KP Jr et al (2013) Statistical inference from multiple iTRAQ experiments without using common reference standards. J Proteome Res 12:594–604PubMedCentralCrossRefPubMedGoogle Scholar
  27. 27.
    Choe L, D’Ascenzo M, Relkin NR et al (2007) 8-plex quantitation of changes in cerebrospinal fluid protein expression in subjects undergoing intravenous immunoglobulin treatment for Alzheimer’s disease. Proteomics 7:3651–3660PubMedCentralCrossRefPubMedGoogle Scholar
  28. 28.
    Dayon L, Hainard A, Licker V et al (2008) Relative quantification of proteins in human cerebrospinal fluids by MS/MS using 6-plex isobaric tags. Anal Chem 80:2921–2931CrossRefPubMedGoogle Scholar
  29. 29.
    Wiese S, Reidegeld KA, Meyer HE et al (2007) Protein labeling by iTRAQ: a new tool for quantitative mass spectrometry in proteome research. Proteomics 7:340–350CrossRefPubMedGoogle Scholar
  30. 30.
    Shadforth IP, Dunkley TP, Lilley KS et al (2005) i-Tracker: for quantitative proteomics using iTRAQ. BMC Genomics 6:145PubMedCentralCrossRefPubMedGoogle Scholar
  31. 31.
    Schwacke JH, Hill EG, Krug EL et al (2009) iQuantitator: a tool for protein expression inference using iTRAQ. BMC Bioinformatics 10:342PubMedCentralCrossRefPubMedGoogle Scholar
  32. 32.
    Rodriguez-Suarez E, Gubb E, Alzueta IF et al (2010) Virtual expert mass spectrometrist: iTRAQ tool for database-dependent search, quantitation and result storage. Proteomics 10:1545–1556CrossRefPubMedGoogle Scholar
  33. 33.
    Gatto L, Lilley KS (2012) MSnbase-an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation. Bioinformatics 28:288–289CrossRefPubMedGoogle Scholar
  34. 34.
    Wang P, Yang P, Yang JY (2012) OCAP: an open comprehensive analysis pipeline for iTRAQ. Bioinformatics 28:1404–1405CrossRefPubMedGoogle Scholar
  35. 35.
    Gruhler A, Matthiesen R (2007) Quantitation with virtual expert mass spectrometrist. Methods Mol Biol 367:139–152PubMedGoogle Scholar
  36. 36.
    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 26:1367–1372CrossRefPubMedGoogle Scholar
  37. 37.
    Yadav AK, Kumar D, Dash D (2011) MassWiz: a novel scoring algorithm with target-decoy based analysis pipeline for tandem mass spectrometry. J Proteome Res 10:2154–2160CrossRefPubMedGoogle Scholar
  38. 38.
    Perkins DN, Pappin DJ, Creasy DM et al (1999) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20:3551–3567CrossRefPubMedGoogle Scholar
  39. 39.
    Geer LY, Markey SP, Kowalak JA et al (2004) Open mass spectrometry search algorithm. J Proteome Res 3:958–964CrossRefPubMedGoogle Scholar
  40. 40.
    Craig R, Beavis RC (2004) TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20:1466–1467CrossRefPubMedGoogle Scholar
  41. 41.
    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 6:1638–1655CrossRefPubMedGoogle Scholar
  42. 42.
    Yadav AK, Kadimi PK, Kumar D et al (2013) ProteoStats—a library for estimating false discovery rates in proteomics pipelines. Bioinformatics 29:2799–2800CrossRefPubMedGoogle Scholar
  43. 43.
    Reiter L, Claassen M, Schrimpf SP et al (2009) Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry. Mol Cell Proteomics 8:2405–2417PubMedCentralCrossRefPubMedGoogle Scholar
  44. 44.
    Lin WT, Hung WN, Yian YH et al (2006) Multi-Q: a fully automated tool for multiplexed protein quantitation. J Proteome Res 5:2328–2338CrossRefPubMedGoogle Scholar
  45. 45.
    Pan C, Kora G, Tabb DL et al (2006) Robust estimation of peptide abundance ratios and rigorous scoring of their variability and bias in quantitative shotgun proteomics. Anal Chem 78:7110–7120CrossRefPubMedGoogle Scholar
  46. 46.
    Zhang Y, Askenazi M, Jiang J et al (2010) A robust error model for iTRAQ quantification reveals divergent signaling between oncogenic FLT3 mutants in acute myeloid leukemia. Mol Cell Proteomics 9:780–790PubMedCentralCrossRefPubMedGoogle Scholar
  47. 47.
    D’Ascenzo M, Choe L, Lee KH (2008) iTRAQPak: an R based analysis and visualization package for 8-plex isobaric protein expression data. Brief Funct Genomic Proteomic 7:127–135CrossRefPubMedGoogle Scholar
  48. 48.
    Savitski MM, Mathieson T, Zinn N et al (2013) Measuring and managing ratio compression for accurate iTRAQ/TMT quantification. J Proteome Res 12:3586–3598CrossRefPubMedGoogle Scholar
  49. 49.
    Ting L, Rad R, Gygi SP et al (2011) MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat Methods 8:937–940PubMedCentralCrossRefPubMedGoogle Scholar
  50. 50.
    Ow SY, Salim M, Noirel J et al (2011) Minimising iTRAQ ratio compression through understanding LC-MS elution dependence and high-resolution HILIC fractionation. Proteomics 11:2341–2346CrossRefPubMedGoogle Scholar
  51. 51.
    Karp NA, Huber W, Sadowski PG et al (2010) Addressing accuracy and precision issues in iTRAQ quantitation. Mol Cell Proteomics 9:1885–1897PubMedCentralCrossRefPubMedGoogle Scholar
  52. 52.
    Pascovici D, Song X, Solomon PS et al (2014) Combining protein ratio p-values as a pragmatic approach to the analysis of multi-run iTRAQ experiments. J Proteome Res 6:738–746Google Scholar
  53. 53.
    Bouyssie D, de Gonzalez PA, Mouton E et al (2007) Mascot file parsing and quantification (MFPaQ), a new software to parse, validate, and quantify proteomics data generated by ICAT and SILAC mass spectrometric analyses: application to the proteomics study of membrane proteins from primary human endothelial cells. Mol Cell Proteomics 6:1621–1637CrossRefPubMedGoogle Scholar
  54. 54.
    Deutsch EW, Shteynberg D, Lam H et al (2010) Trans-proteomic pipeline supports and improves analysis of electron transfer dissociation data sets. Proteomics 10:1190–1195PubMedCentralCrossRefPubMedGoogle Scholar
  55. 55.
    Arntzen MO, Koehler CJ, Barsnes H et al (2011) IsobariQ: software for isobaric quantitative proteomics using IPTL, iTRAQ, and TMT. J Proteome Res 10:913–920CrossRefPubMedGoogle Scholar
  56. 56.
    Matthiesen R, Lundsgaard M, Welinder KG et al (2003) Interpreting peptide mass spectra by VEMS. Bioinformatics 19:792–793CrossRefPubMedGoogle Scholar
  57. 57.
    Park SK, Yates JR, III (2010) Census for proteome quantification. Curr Protoc Bioinformatics Chapter 13:Unit-11Google Scholar
  58. 58.
    Breitwieser FP, Muller A, Dayon L et al (2011) General statistical modeling of data from protein relative expression isobaric tags. J Proteome Res 10:2758–2766CrossRefPubMedGoogle Scholar
  59. 59.
    Pan C, Kora G, McDonald WH et al (2006) ProRata: a quantitative proteomics program for accurate protein abundance ratio estimation with confidence interval evaluation. Anal Chem 78:7121–7131CrossRefPubMedGoogle Scholar
  60. 60.
    Valot B, Langella O, Nano E et al (2011) MassChroQ: a versatile tool for mass spectrometry quantification. Proteomics 11:3572–3577CrossRefPubMedGoogle Scholar
  61. 61.
    Kohlbacher O, Reinert K, Gropl C et al (2007) TOPP—the OpenMS proteomics pipeline. Bioinformatics 23:e191–e197CrossRefPubMedGoogle Scholar
  62. 62.
    Forshed J, Johansson HJ, Pernemalm M et al (2011) Enhanced information output from shotgun proteomics data by protein quantification and peptide quality control (PQPQ). Mol Cell Proteomics 10:M111PubMedCentralCrossRefPubMedGoogle Scholar
  63. 63.
    Zou X, Zhao M, Shen H et al (2012) MilQuant: a free, generic software tool for isobaric tagging-based quantitation. J Proteomics 75:5516–5522CrossRefPubMedGoogle Scholar
  64. 64.
    Onsongo G, Stone MD, Van Riper SK et al (2010) LTQ-iQuant: a freely available software pipeline for automated and accurate protein quantification of isobaric tagged peptide data from LTQ instruments. Proteomics 10:3533–3538PubMedCentralCrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Immunology Group, International Centre for Genetic Engineering and BiotechnologyNew DelhiIndia
  2. 2.Drug Discovery Research Center (DDRC), Translational Health Science and Technology InstituteFaridabadIndia

Personalised recommendations