Skip to main content

Advertisement

SpringerLink
Log in

An insight into iTRAQ: where do we stand now?

  • Review
  • Published:
Analytical and Bioanalytical Chemistry Aims and scope Submit manuscript

Abstract

The iTRAQ (isobaric tags for relative and absolute quantification) technique is widely employed in proteomic workflows requiring relative quantification. Here, we review the iTRAQ literature; in particular, we focus on iTRAQ usage in relation to other commonly used quantitative techniques e.g. stable isotope labelling in culture (SILAC), label-free methods and selected reaction monitoring (SRM). As a result, we identify several issues arising with respect to iTRAQ. Perhaps frustratingly, iTRAQ’s attractiveness has been undermined by a number of technical and analytical limitations: it may not be truly quantitative, as the changes in abundance reported will generally be underestimated. We discuss weaknesses and strengths of iTRAQ as a methodology for relative quantification in the light of this and other technical issues. We focus on technical developments targeted at iTRAQ accuracy and precision, use of 4-plex over 8-plex reagents and application of iTRAQ to post-translational modification (PTM) workflows. We also discuss iTRAQ in relation to label-free approaches, to which iTRAQ is losing ground.

 

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

CID:

collision-induced dissociation

COFRADIC:

combined fractional diagonal chromatography

CV:

coefficient of variation

ETD:

electron transfer dissociation

FDR:

false discovery rate

HCD:

higher-energy collisional dissociation

HILIC:

hydrophilic interaction liquid chromatography

IMAC:

immobilized metal ion affinity chromatography

IPTL:

isobaric peptide termini labelling

iTRAQ:

isobaric tags for relative and absolute quantification

NHS:

N-hydroxysuccinimide

PQD:

pulsed Q dissociation

PTM:

post-translational modification

RP-HPLC:

reverse-phase high-performance liquid chromatography

SCX:

strong cation exchange

SILAC:

stable isotope labelling with amino acids in cell culture

TMT:

tandem mass tag

UHPLC:

ultra-high-performance liquid chromatography

VSN:

variance-stabilizing normalization

References

  1. Treumann A, Thiede B (2010) Isobaric protein and peptide quantification: perspectives and issues. Expert Rev Proteomics 7(5):647–653

    Article  CAS  Google Scholar 

  2. Noirel J, Evans C, Salim M, Mukherjee J, Ow SY, Pandhal J, Pham TK, Biggs CA, Wright PC (2011) Methods in quantitative proteomics: setting iTRAQ on the right track. Curr Proteomics 8(1):17–30

    Article  CAS  Google Scholar 

  3. Ross PL, Huang YN, Marchese JN, Williamson B, Parker K, Hattan S, Khainovski N, Pillai S, Dey S, Daniels S, Purkayastha S, Juhasz P, Martin S, Bartlet-Jones M, He F, Jacobson A, Pappin DJ (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 3(12):1154–1169

    Article  CAS  Google Scholar 

  4. Ow SY, Cardona T, Taton A, Magnuson A, Lindblad P, Stensjo K, Wright PC (2008) Quantitative shotgun proteomics of enriched heterocysts from Nostoc sp. PCC 7120 using 8-plex isobaric peptide tags. J Proteome Res 7(4):1615–1628

    Article  CAS  Google Scholar 

  5. Pierce A, Unwin RD, Evans CA, Griffiths S, Carney L, Zhang L, Jaworska E, Lee CF, Blinco D, Okoniewski MJ, Miller CJ, Bitton DA, Spooncer E, Whetton AD (2008) Eight-channel iTRAQ enables comparison of the activity of six leukemogenic tyrosine kinases. Mol Cell Proteomics 7(5):853–863

    Article  CAS  Google Scholar 

  6. Aggarwal K, Choe LH, Lee KH (2006) Shotgun proteomics using the iTRAQ isobaric tags. Brief Funct Genomic Proteomic 5(2):112–120

    Article  CAS  Google Scholar 

  7. 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(11):5347–5355

    Article  CAS  Google Scholar 

  8. Casado-Vela J, Martinez-Esteso MJ, Rodriguez E, Borras E, Elortza F, Bru-Martinez R (2010) iTRAQ-based quantitative analysis of protein mixtures with large fold change and dynamic range. Proteomics 10(2):343–347

    Article  CAS  Google Scholar 

  9. DeSouza LV, Romaschin AD, Colgan TJ, Siu KW (2009) Absolute quantification of potential cancer markers in clinical tissue homogenates using multiple reaction monitoring on a hybrid triple quadrupole/linear ion trap tandem mass spectrometer. Anal Chem 81(9):3462–3470

    Article  CAS  Google Scholar 

  10. Zhang C (2010) Proteomic studies on the development of the central nervous system and beyond. Neurochem Res 35(10):1487–1500

    Article  CAS  Google Scholar 

  11. Mandal N, Heegaard S, Prause JU, Honore B, Vorum H (2010) Ocular proteomics with emphasis on two-dimensional gel electrophoresis and mass spectrometry. Biol Proced Online 12(1):56–88

    Article  CAS  Google Scholar 

  12. Keshamouni VG, Michailidis G, Grasso CS, Anthwal S, Strahler JR, Walker A, Arenberg DA, Reddy RC, Akulapalli S, Thannickal VJ, Standiford TJ, Andrews PC, Omenn GS (2006) Differential protein expression profiling by iTRAQ-2DLC-MS/MS of lung cancer cells undergoing epithelial-mesenchymal transition reveals a migratory/invasive phenotype. J Proteome Res 5(5):1143–1154

    Article  CAS  Google Scholar 

  13. Glen A, Gan CS, Hamdy FC, Eaton CL, Cross SS, Catto JW, Wright PC, Rehman I (2008) iTRAQ-facilitated proteomic analysis of human prostate cancer cells identifies proteins associated with progression. J Proteome Res 7(3):897–907

    Article  CAS  Google Scholar 

  14. DeSouza LV, Taylor AM, Li W, Minkoff MS, Romaschin AD, Colgan TJ, Siu KW (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(8):3525–3534

    Article  CAS  Google Scholar 

  15. Bantscheff M, Boesche M, Eberhard D, Matthieson T, Sweetman G, Kuster B (2008) Robust and sensitive iTRAQ quantification on an LTQ Orbitrap mass spectrometer. Mol Cell Proteomics 7(9):1702–1713

    Article  CAS  Google Scholar 

  16. Kuzyk MA, Ohlund LB, Elliott MH, Smith D, Qian H, Delaney A, Hunter CL, Borchers CH (2009) A comparison of MS/MS-based, stable-isotope-labeled, quantitation performance on ESI-quadrupole TOF and MALDI-TOF/TOF mass spectrometers. Proteomics 9(12):3328–3340

    Article  CAS  Google Scholar 

  17. Chong PK, Gan CS, Pham TK, Wright PC (2006) Isobaric tags for relative and absolute quantitation (iTRAQ) reproducibility: implication of multiple injections. J Proteome Res 5(5):1232–1240

    Article  CAS  Google Scholar 

  18. Gan CS, Chong PK, Pham TK, Wright PC (2007) Technical, experimental, and biological variations in isobaric tags for relative and absolute quantitation (iTRAQ). J Proteome Res 6(2):821–827

    Article  CAS  Google Scholar 

  19. Griffin TJ, Xie H, Bandhakavi S, Popko J, Mohan A, Carlis JV, Higgins L (2007) iTRAQ reagent-based quantitative proteomic analysis on a linear ion trap mass spectrometer. J Proteome Res 6(11):4200–4209

    Article  CAS  Google Scholar 

  20. Olsen JV, Macek B, Lange O, Makarov A, Horning S, Mann M (2007) Higher-energy C-trap dissociation for peptide modification analysis. Nat Methods 4(9):709–712

    Article  CAS  Google Scholar 

  21. Kocher T, Pichler P, Schutzbier M, Stingl C, Kaul A, Teucher N, Hasenfuss G, Penninger JM, Mechtler K (2009) High precision quantitative proteomics using iTRAQ on an LTQ Orbitrap: a new mass spectrometric method combining the benefits of all. J Proteome Res 8(10):4743–4752

    Article  CAS  Google Scholar 

  22. Dayon L, Pasquarello C, Hoogland C, Sanchez JC, Scherl A (2010) Combining low- and high-energy tandem mass spectra for optimized peptide quantification with isobaric tags. J Proteomics 73(4):769–777

    Article  CAS  Google Scholar 

  23. Mischerikow N, van Nierop P, Li KW, Bernstein HG, Smit AB, Heck AJ, Altelaar AF (2010) Gaining efficiency by parallel quantification and identification of iTRAQ-labeled peptides using HCD and decision tree guided CID/ETD on an LTQ Orbitrap. Analyst 135(10):2643–2652

    Article  CAS  Google Scholar 

  24. Han H, Pappin DJ, Ross PL, McLuckey SA (2008) Electron transfer dissociation of iTRAQ labeled peptide ions. J Proteome Res 7(9):3643–3648

    Article  CAS  Google Scholar 

  25. Phanstiel D, Unwin R, McAlister GC, Coon JJ (2009) Peptide quantification using 8-plex isobaric tags and electron transfer dissociation tandem mass spectrometry. Anal Chem 81(4):1693–1698

    Article  CAS  Google Scholar 

  26. Bantscheff M, Schirle M, Sweetman G, Rick J, Kuster B (2007) Quantitative mass spectrometry in proteomics: a critical review. Anal Bioanal Chem 389(4):1017–1031

    Article  CAS  Google Scholar 

  27. Ow SY, Salim M, Noirel J, Evans C, Wright PC (2011) Minimising iTRAQ ratio compression through understanding LC-MS elution dependence and high-resolution HILIC fractionation. Proteomics 11(11):2341–2346

    Article  CAS  Google Scholar 

  28. Karp NA, Huber W, Sadowski PG, Charles PD, Hester SV, Lilley KS (2010) Addressing accuracy and precision issues in iTRAQ quantitation. Mol Cell Proteomics 9(9):1885–1897

    Article  CAS  Google Scholar 

  29. Mahoney DW, Therneau TM, Heppelmann CJ, Higgins L, Benson LM, Zenka RM, Jagtap P, Nelsestuen GL, Bergen HR, Oberg AL (2011) Relative quantification: characterization of bias, variability and fold changes in mass spectrometry data from iTRAQ-labeled peptides. J Proteome Res 10(9):4325–4333

    Article  CAS  Google Scholar 

  30. Breitwieser FP, Muller A, Dayon L, Kocher T, Hainard A, Pichler P, Schmidt-Erfurth U, Superti-Furga G, Sanchez JC, Mechtler K, Bennett KL, Colinge J (2011) General statistical modeling of data from protein relative expression isobaric tags. J Proteome Res 10(6):2758–2766

    Article  CAS  Google Scholar 

  31. Ting L, Rad R, Gygi SP, Haas W (2011) MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat Methods 8(11):937–940

    Article  CAS  Google Scholar 

  32. Wenger CD, Lee MV, Hebert AS, McAlister GC, Phanstiel DH, Westphall MS, Coon JJ (2011) Gas-phase purification enables accurate, multiplexed proteome quantification with isobaric tagging. Nat Methods 8(11):933–935

    Article  CAS  Google Scholar 

  33. Savitski MM, Fischer F, Mathieson T, Sweetman G, Lang M, Bantscheff M (2010) Targeted data acquisition for improved reproducibility and robustness of proteomic mass spectrometry assays. J Am Soc Mass Spectrom 21(10):1668–1679

    Article  CAS  Google Scholar 

  34. Yang Y, Qiang X, Owsiany K, Zhang S, Thannhauser TW, Li L (2011) Evaluation of different multidimensional LC-MS/MS pipelines for isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomic analysis of potato tubers in response to cold storage. J Proteome Res 10(10):4647–4660

    Article  CAS  Google Scholar 

  35. Bortner JD Jr, Richie JP Jr, Das A, Liao J, Umstead TM, Stanley A, Stanley BA, Belani CP, El-Bayoumy K (2011) Proteomic profiling of human plasma by iTRAQ reveals down-regulation of ITI-HC3 and VDBP by cigarette smoking. J Proteome Res 10(3):1151–1159

    Article  CAS  Google Scholar 

  36. Pham TK, Roy S, Noirel J, Douglas I, Wright PC, Stafford GP (2010) A quantitative proteomic analysis of biofilm adaptation by the periodontal pathogen Tannerella forsythia. Proteomics 10(17):3130–3141

    Article  CAS  Google Scholar 

  37. Jankova L, Chan C, Fung CL, Song X, Kwun SY, Cowley MJ, Kaplan W, Dent OF, Bokey EL, Chapuis PH, Baker MS, Robertson GR, Clarke SJ, Molloy MP (2011) Proteomic comparison of colorectal tumours and non-neoplastic mucosa from paired patient samples using iTRAQ mass spectrometry. Mol Biosyst 7(11):2997–3005

    Article  CAS  Google Scholar 

  38. Zhang H, Zhao C, Li X, Zhu Y, Gan CS, Wang Y, Ravasi T, Qian PY, Wong SC, Sze SK (2010) Study of monocyte membrane proteome perturbation during lipopolysaccharide-induced tolerance using iTRAQ-based quantitative proteomic approach. Proteomics 10(15):2780–2789

    Article  CAS  Google Scholar 

  39. Wang L, Dai Y, Peng W, Qi S, Ouyang X, Tu Z (2011) Differential expression of serine-threonine kinase receptor-associated protein in patients with systemic lupus erythematosus. Lupus 20(9):921–927

    Article  CAS  Google Scholar 

  40. Chan LS, Hansra G, Robinson PJ, Graham ME (2010) Differential phosphorylation of dynamin I isoforms in subcellular compartments demonstrates the hidden complexity of phosphoproteomes. J Proteome Res 9(8):4028–4037

    Article  CAS  Google Scholar 

  41. Bennett KL, Funk M, Tschernutter M, Breitwieser FP, Planyavsky M, Ubaida Mohien C, Muller A, Trajanoski Z, Colinge J, Superti-Furga G, Schmidt-Erfurth U (2011) Proteomic analysis of human cataract aqueous humour: comparison of one-dimensional gel LCMS with two-dimensional LCMS of unlabelled and iTRAQ(R)-labelled specimens. J Proteomics 74(2):151–166

    Article  CAS  Google Scholar 

  42. Newton BW, Cologna SM, Moya C, Russell DH, Russell WK, Jayaraman A (2011) Proteomic analysis of 3T3-L1 adipocyte mitochondria during differentiation and enlargement. J Proteome Res 10(10):4692–4702

    Article  CAS  Google Scholar 

  43. Chen YT, Chen CL, Chen HW, Chung T, Wu CC, Chen CD, Hsu CW, Chen MC, Tsui KH, Chang PL, Chang YS, Yu JS (2010) Discovery of novel bladder cancer biomarkers by comparative urine proteomics using iTRAQ technology. J Proteome Res 9(11):5803–5815

    Article  CAS  Google Scholar 

  44. Choe L, D'Ascenzo M, Relkin NR, Pappin D, Ross P, Williamson B, Guertin S, Pribil P, Lee KH (2007) 8-plex quantitation of changes in cerebrospinal fluid protein expression in subjects undergoing intravenous immunoglobulin treatment for Alzheimer’s disease. Proteomics 7(20):3651–3660

    Article  CAS  Google Scholar 

  45. 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(2):127–135

    Article  CAS  Google Scholar 

  46. Zieske LR (2006) A perspective on the use of iTRAQ reagent technology for protein complex and profiling studies. J Exp Bot 57(7):1501–1508

    Article  CAS  Google Scholar 

  47. Gehrig PM, Hunziker PE, Zahariev S, Pongor S (2004) Fragmentation pathways of N(G)-methylated and unmodified arginine residues in peptides studied by ESI-MS/MS and MALDI-MS. J Am Soc Mass Spectrom 15(2):142–149

    Article  CAS  Google Scholar 

  48. Houel S, Abernathy R, Renganathan K, Meyer-Arendt K, Ahn NG, Old WM (2010) Quantifying the impact of chimera MS/MS spectra on peptide identification in large-scale proteomics studies. J Proteome Res 9(8):4152–4160

    Article  CAS  Google Scholar 

  49. Hao P, Qian J, Ren Y, Sze SK (2011) Electrostatic repulsion-hydrophilic interaction chromatography (ERLIC) versus strong cation exchange (SCX) for fractionation of iTRAQ-labeled peptides. J Proteome Res 10(12):5568–5574

    CAS  Google Scholar 

  50. Phillips HL, Williamson JC, van Elburg KA, Snijders AP, Wright PC, Dickman MJ (2010) Shotgun proteome analysis utilising mixed mode (reversed phase-anion exchange chromatography) in conjunction with reversed phase liquid chromatography mass spectrometry analysis. Proteomics 10(16):2950–2960

    Article  CAS  Google Scholar 

  51. Christoforou A, Lilley KS (2011) Taming the isobaric tagging elephant in the room in quantitative proteomics. Nat Methods 8(11):911–913

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  53. Ow SY, Noirel J, Salim M, Evans C, Watson R, Wright PC (2010) Balancing robust quantification and identification for iTRAQ: application of UHR-ToF MS. Proteomics 10(11):2205–2213

    Article  CAS  Google Scholar 

  54. Shvartsburg AA, Creese AJ, Smith RD, Cooper HJ (2011) Separation of a set of peptide sequence isomers using differential ion mobility spectrometry. Anal Chem 83(18):6918–6923

    Article  CAS  Google Scholar 

  55. Marko-Varga G (2004) Proteomics principles and challenges. Pure Appl Chem 76(4):829–837

    Article  CAS  Google Scholar 

  56. Choe LH, Aggarwal K, Franck Z, Lee KH (2005) A comparison of the consistency of proteome quantitation using two-dimensional electrophoresis and shotgun isobaric tagging in Escherichia coli cells. Electrophoresis 26(12):2437–2449

    Article  CAS  Google Scholar 

  57. Gan CS, Reardon KF, Wright PC (2005) Comparison of protein and peptide prefractionation methods for the shotgun proteomic analysis of Synechocystis sp. PCC 6803. Proteomics 5(9):2468–2478

    Article  CAS  Google Scholar 

  58. Lin WT, Hung WN, Yian YH, Wu KP, Han CL, Chen YR, Chen YJ, Sung TY, Hsu WL (2006) Multi-Q: a fully automated tool for multiplexed protein quantitation. J Proteome Res 5(9):2328–2338

    Article  CAS  Google Scholar 

  59. Boehm AM, Putz S, Altenhofer D, Sickmann A, Falk M (2007) Precise protein quantification based on peptide quantification using iTRAQ. BMC Bioinformatics 8:214

    Article  CAS  Google Scholar 

  60. Oberg AL, Mahoney DW, Eckel-Passow JE, Malone CJ, Wolfinger RD, Hill EG, Cooper LT, Onuma OK, Spiro C, Therneau TM, Bergen HR 3rd (2008) Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA. J Proteome Res 7(1):225–233

    Article  CAS  Google Scholar 

  61. Schwacke JH, Hill EG, Krug EL, Comte-Walters S, Schey KL (2009) iQuantitator: a tool for protein expression inference using iTRAQ. BMC Bioinformatics 10:342

    Article  CAS  Google Scholar 

  62. Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, individualized ppb-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26:1367

    Google Scholar 

  63. Hundertmark C, Fischer R, Reinl T, May S, Klawonn F, Jansch L (2009) MS-specific noise model reveals the potential of iTRAQ in quantitative proteomics. Bioinformatics 25(8):1004–1011

    Article  CAS  Google Scholar 

  64. Pichler P, Kocher T, Holzmann J, Mazanek M, Taus T, Ammerer G, Mechtler K (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(15):6549–6558

    Article  CAS  Google Scholar 

  65. Thingholm TE, Palmisano G, Kjeldsen F, Larsen MR (2010) Undesirable charge-enhancement of isobaric tagged phosphopeptides leads to reduced identification efficiency. J Proteome Res 9(8):4045–4052

    Article  CAS  Google Scholar 

  66. Patel VJ, Thalassinos K, Slade SE, Connolly JB, Crombie A, Murrell JC, Scrivens JH (2009) A comparison of labeling and label-free mass spectrometry-based proteomics approaches. J Proteome Res 8(7):3752–3759

    Article  CAS  Google Scholar 

  67. Neilson KA, Mariani M, Haynes PA (2011) Quantitative proteomic analysis of cold-responsive proteins in rice. Proteomics 11(9):1696–1706

    Article  Google Scholar 

  68. Wang H, Alvarez S, Hicks LM (2011) 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. doi:10.1021/pr2008225

  69. Usaite R, Wohlschlegel J, Venable JD, Park SK, Nielsen J, Olsson L, Yates Iii JR (2008) Characterization of global yeast quantitative proteome data generated from the wild-type and glucose repression Saccharomyces cerevisiae strains: the comparison of two quantitative methods. J Proteome Res 7(1):266–275

    Article  CAS  Google Scholar 

  70. Collier TS, Sarkar P, Franck WL, Rao BM, Dean RA, Muddiman DC (2010) Direct comparison of stable isotope labeling by amino acids in cell culture and spectral counting for quantitative proteomics. Anal Chem 82(20):8696–8702

    Article  CAS  Google Scholar 

  71. Li GZ, Vissers JP, Silva JC, Golick D, Gorenstein MV, Geromanos SJ (2009) Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures. Proteomics 9(6):1696–1719

    Article  CAS  Google Scholar 

  72. Levin Y, Hradetzky E, Bahn S (2011) Quantification of proteins using data-independent analysis (MSE) in simple and complex samples: a systematic evaluation. Proteomics 11(16):3273–3287

    Article  CAS  Google Scholar 

  73. Li Z, Adams RM, Chourey K, Hurst GB, Hettich RL, Pan C (2011) Systematic comparison of label-free, metabolic labeling, and isobaric chemical labeling for quantitative proteomics on LTQ Orbitrap Velos. J Proteome Res. doi:10.1021/pr200748h

  74. Ueda K, Takami S, Saichi N, Daigo Y, Ishikawa N, Kohno N, Katsumata M, Yamane A, Ota M, Sato TA, Nakamura Y, Nakagawa H (2010) Development of serum glycoproteomic profiling technique; simultaneous identification of glycosylation sites and site-specific quantification of glycan structure changes. Mol Cell Proteomics 9(9):1819–1828

    Article  CAS  Google Scholar 

  75. Zhang Y, Askenazi M, Jiang J, Luckey CJ, Griffin JD, Marto JA (2010) A robust error model for iTRAQ quantification reveals divergent signaling between oncogenic FLT3 mutants in acute myeloid leukemia. Mol Cell Proteomics 9(5):780–790

    Article  CAS  Google Scholar 

  76. Wu J, Warren P, Shakey Q, Sousa E, Hill A, Ryan TE, He T (2010) Integrating titania enrichment, iTRAQ labeling, and Orbitrap CID-HCD for global identification and quantitative analysis of phosphopeptides. Proteomics 10(11):2224–2234

    Article  CAS  Google Scholar 

  77. Hoffert JD, Pisitkun T, Saeed F, Song JH, Chou CL, Knepper MA (2011) Dynamics of the G protein-coupled vasopressin V2 receptor signaling network revealed by quantitative phosphoproteomics. Mol Cell Proteomics. doi:10.1074/mcp.M111.014613

  78. Rosenzweig D, Smith D, Myler PJ, Olafson RW, Zilberstein D (2008) Post-translational modification of cellular proteins during Leishmania donovani differentiation. Proteomics 8(9):1843–1850

    Article  CAS  Google Scholar 

  79. Prudova A, auf dem Keller U, Butler GS, Overall CM (2010) Multiplex N-terminome analysis of MMP-2 and MMP-9 substrate degradomes by iTRAQ-TAILS quantitative proteomics. Mol Cell Proteomics 9(5):894–911

    Article  CAS  Google Scholar 

  80. Kleifeld O, Doucet A, Prudova A, auf dem Keller U, Gioia M, Kizhakkedathu JN, Overall CM (2011) Identifying and quantifying proteolytic events and the natural N terminome by terminal amine isotopic labeling of substrates. Nat Protoc 6(10):1578–1611

    Article  CAS  Google Scholar 

  81. Choudhary C, Kumar C, Gnad F, Nielsen ML, Rehman M, Walther TC, Olsen JV, Mann M (2009) Lysine acetylation targets protein complexes and co-regulates major cellular functions. Science 325(5942):834–840

    Article  CAS  Google Scholar 

  82. Pandey A, Podtelejnikov AV, Blagoev B, Bustelo XR, Mann M, Lodish HF (2000) Analysis of receptor signaling pathways by mass spectrometry: identification of vav-2 as a substrate of the epidermal and platelet-derived growth factor receptors. Proc Natl Acad Sci U S A 97(1):179–184

    Article  CAS  Google Scholar 

  83. Gronborg M, Kristiansen TZ, Stensballe A, Andersen JS, Ohara O, Mann M, Jensen ON, Pandey A (2002) A mass spectrometry-based proteomic approach for identification of serine/threonine-phosphorylated proteins by enrichment with phospho-specific antibodies: identification of a novel protein, Frigg, as a protein kinase A substrate. Mol Cell Proteomics 1(7):517–527

    Article  CAS  Google Scholar 

  84. Chiappetta G, Corbo C, Palmese A, Galli F, Piroddi M, Marino G, Amoresano A (2009) Quantitative identification of protein nitration sites. Proteomics 9(6):1524–1537

    Article  CAS  Google Scholar 

  85. Przybylski C, Junger MA, Aubertin J, Radvanyi F, Aebersold R, Pflieger D (2010) Quantitative analysis of protein complex constituents and their phosphorylation states on a LTQ-Orbitrap instrument. J Proteome Res 9(10):5118–5132

    Article  CAS  Google Scholar 

  86. Skorobogatko YV, Deuso J, Adolf-Bergfoyle J, Nowak MG, Gong Y, Lippa CF, Vosseller K (2011) Human Alzheimer’s disease synaptic O-GlcNAc site mapping and iTRAQ expression proteomics with ion trap mass spectrometry. Amino Acids 40(3):765–779

    Article  CAS  Google Scholar 

  87. Tenga MJ, Lazar IM (2011) Impact of peptide modifications on the isobaric tags for relative and absolute quantitation method accuracy. Anal Chem 83(3):701–707

    Article  CAS  Google Scholar 

  88. Wiktorowicz JE, English RD, Wu Z, Kurosky A (2012) Model studies on iTRAQ modification of peptides: sequence-dependent reaction specificity. J Proteome Res. doi:10.1021/pr2003165

  89. Hill EG, Schwacke JH, Comte-Walters S, Slate EH, Oberg AL, Eckel-Passow JE, Therneau TM, Schey KL (2008) A statistical model for iTRAQ data analysis. J Proteome Res 7(8):3091–3101

    Article  CAS  Google Scholar 

  90. Mertins P, Udeshi ND, Clauser KR, Mani DR, Patel J, Ong SE, Jaffe JD, Carr SA (2011) iTRAQ labeling is superior to mTRAQ for quantitative global proteomics and phosphoproteomics. Mol Cell Proteomics. doi:10.1074/mcp.M111.014423

  91. Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, Mann M (2006) Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127(3):635–648

    Article  CAS  Google Scholar 

  92. Martin DM, Nett IR, Vandermoere F, Barber JD, Morrice NA, Ferguson MA (2010) Prophossi: automating expert validation of phosphopeptide-spectrum matches from tandem mass spectrometry. Bioinformatics 26(17):2153–2159

    Article  CAS  Google Scholar 

  93. Langlais P, Mandarino LJ, Yi Z (2010) Label-free relative quantification of co-eluting isobaric phosphopeptides of insulin receptor substrate-1 by HPLC-ESI-MS/MS. J Am Soc Mass Spectrom 21(9):1490–1499

    Article  CAS  Google Scholar 

  94. Zhao Y, Jia W, Wang J, Ying W, Zhang Y, Qian X (2011) Fragmentation and site-specific quantification of core fucosylated glycoprotein by multiple reaction monitoring-mass spectrometry. Anal Chem 83(22):8802–8809

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  96. Tambor V, Hunter CL, Seymour SL, Kacerovsky M, Stulik J, Lenco J (2011) CysTRAQ—a combination of iTRAQ and enrichment of cysteinyl peptides for uncovering and quantifying hidden proteomes. J Proteomics 75(3):857–867

    Article  CAS  Google Scholar 

  97. Giron P, Dayon L, Turck N, Hoogland C, Sanchez JC (2011) Quantitative analysis of human cerebrospinal fluid proteins using a combination of cysteine tagging and amine-reactive isobaric labeling. J Proteome Res 10(1):249–258

    Article  CAS  Google Scholar 

  98. Forrester MT, Thompson JW, Foster MW, Nogueira L, Moseley MA, Stamler JS (2009) Proteomic analysis of S-nitrosylation and denitrosylation by resin-assisted capture. Nat Biotechnol 27(6):557–559

    Article  CAS  Google Scholar 

  99. Murray CI, Uhrigshardt H, O'Meally RN, Cole RN, Van Eyk JE (2012) Identification and quantification of S-nitrosylation by cysteine reactive tandem mass tag switch assay. Mol Cell Proteomics. doi:10.1074/mcp.M111.013441

  100. Forrester MT, Hess DT, Thompson JW, Hultman R, Moseley MA, Stamler JS, Casey PJ (2011) Site-specific analysis of protein S-acylation by resin-assisted capture. J Lipid Res 52(2):393–398

    Article  CAS  Google Scholar 

  101. Carrillo B, Yanofsky C, Laboissiere S, Nadon R, Kearney RE (2010) Methods for combining peptide intensities to estimate relative protein abundance. Bioinformatics 26(1):98–103

    Article  CAS  Google Scholar 

  102. Neilson KA, Ali NA, Muralidharan S, Mirzaei M, Mariani M, Assadourian G, Lee A, van Sluyter SC, Haynes PA (2011) Less label, more free: approaches in label-free quantitative mass spectrometry. Proteomics 11(4):535–553

    Article  CAS  Google Scholar 

  103. Filiou MD, Martins-de-Souza D, Guest PC, Bahn S, Turck CW (2012) To label or not to label: applications of quantitative proteomics in neuroscience research. Proteomics. doi:10.1002/pmic.201100350

  104. Puetz SM, Boehm AM, Stiewe T, Sickmann A (2012) iTRAQ analysis of a cell culture model for malignant transformation, including comparison with 2D-PAGE and SILAC. J Proteome Res. doi:10.1021/pr200881c

  105. Sohn CH, Lee JE, Sweredoski MJ, Graham RL, Smith GT, Hess S, Czerwieniec G, Loo JA, Deshaies RJ, Beauchamp JL (2012) Click chemistry facilitates formation of reporter ions and simplified synthesis of amine-reactive multiplexed isobaric tags for protein quantification. J Am Chem Soc 134(5):2672–2680

    Article  CAS  Google Scholar 

  106. Kolb HC, Finn MG, Sharpless KB (2001) Click chemistry: diverse chemical function from a few good reactions. Angew Chem Int Ed Engl 40(11):2004–2021

    Article  CAS  Google Scholar 

  107. Koehler CJ, Arntzen MO, Strozynski M, Treumann A, Thiede B (2011) Isobaric peptide termini labeling utilizing site-specific N-terminal succinylation. Anal Chem 83(12):4775–4781

    Article  CAS  Google Scholar 

  108. Kirchberger J, Kopperschlager G (1990) An improved purification procedure of alkaline phosphatase from calf intestine by applying partition in aqueous two-phase systems and dye-ligand chromatography. Bioseparation 1(1):33–41

    CAS  Google Scholar 

  109. Jia W, Andaya A, Leary JA (2012) Novel mass spectrometric method for phosphorylation quantification using cerium oxide nanoparticles and tandem mass tags. Anal Chem. doi:10.1021/ac203248s

  110. Ong SE, Mann M (2005) Mass spectrometry-based proteomics turns quantitative. Nat Chem Biol 1(5):252–262

    Article  CAS  Google Scholar 

  111. Schwanhausser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, Chen W, Selbach M (2011) Global quantification of mammalian gene expression control. Nature 473(7347):337–342

    Article  CAS  Google Scholar 

  112. Pandhal J, Ow SY, Noirel J, Wright PC (2011) Improving N-glycosylation efficiency in Escherichia coli using shotgun proteomics, metabolic network analysis, and selective reaction monitoring. Biotechnol Bioeng 108(4):902–912

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank the EPSRC for funding (EP/E053556/1) and the ChELSI initiative (EP/E036252/1). We thank Bruker Daltonik Bremen for help with the FT-ICR work (Drs Jens Fuchser and Matthias Witt).

Author information

Authors and Affiliations

  1. Biological and Environmental Systems Group, The ChELSI Institute, Chemical and Biological Engineering, The University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    Caroline Evans, Josselin Noirel, Saw Yen Ow, Malinda Salim, Ana G. Pereira-Medrano, Narciso Couto, Jagroop Pandhal, Trong Khoa Pham, Esther Karunakaran, Xin Zou, Catherine A. Biggs & Phillip C. Wright

  2. MBCF Mass Spectrometry Facility, Manchester Paterson Institute for Cancer Research, The University of Manchester, Wilmslow Road, Manchester, M20 4BX, UK

    Duncan Smith

  3. The ChELSI Institute, Department of Chemical and Biological Engineering, The University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    Phillip C. Wright

Authors
  1. Caroline Evans
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Josselin Noirel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Saw Yen Ow
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Malinda Salim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ana G. Pereira-Medrano
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Narciso Couto
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jagroop Pandhal
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Duncan Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Trong Khoa Pham
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Esther Karunakaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Xin Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Catherine A. Biggs
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Phillip C. Wright
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Phillip C. Wright.

Additional information

Published in the topical issue Quantitative Mass Spectrometry in Proteomics with guest editors Bernhard Kuster and Marcus Bantscheff.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(PDF 1161 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Evans, C., Noirel, J., Ow, S.Y. et al. An insight into iTRAQ: where do we stand now?. Anal Bioanal Chem 404, 1011–1027 (2012). https://doi.org/10.1007/s00216-012-5918-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-012-5918-6

Keywords

Search

Navigation