Advertisement

Practical Integration of Multi-Run iTRAQ Data

  • Dana Pascovici
  • Xiaomin Song
  • Jemma Wu
  • Thiri Zaw
  • Mark MolloyEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1977)

Abstract

In this chapter, we describe some of the approaches we employ in the analysis of iTRAQ data in our group, with an emphasis on practical issues that can occur in larger multi-run projects. Our pipeline starts with a well-established iTRAQ workflow, makes use of protein level quantitation using ProteinPilot, and continues either via a global analysis in the presence of a common reference, or by identifying pairwise comparisons of interest and applying a method taking the protein ratios and protein ratio confidence measures into consideration. Additionally we describe what issues can occur in the more subtle scenarios involving composite databases in multi-run situations, and an approach applicable in that setting.

Key words

Mass spectrometry Quantitative proteomics iTRAQ Data processing Replication 

References

  1. 1.
    Ross PL, Huang YN, Marchese JN et al (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 3:1154–1169CrossRefGoogle Scholar
  2. 2.
    Thompson A, Schäfer 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 75:1895–1904CrossRefGoogle Scholar
  3. 3.
    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:O111.016717CrossRefGoogle Scholar
  4. 4.
    Braun CR, Bird GH, Wühr M et al (2015) Generation of multiple reporter ions from a single isobaric reagent increases multiplexing capacity for quantitative proteomics. Anal Chem 87:9855–9863CrossRefGoogle Scholar
  5. 5.
    Boehm AM, Pütz S, Altenhöfer D et al (2007) Precise protein quantification based on peptide quantification using iTRAQ. BMC Bioinformatics 8:214CrossRefGoogle Scholar
  6. 6.
    Shadforth IP, Dunkley TP, Lilley KS et al (2005) i-Tracker: for quantitative proteomics using iTRAQ. BMC Genomics 6:145CrossRefGoogle Scholar
  7. 7.
    Arntzen MØ, Koehler CJ, Barsnes H et al (2010) IsobariQ: software for isobaric quantitative proteomics using IPTL, iTRAQ, and TMT. J Proteome Res 10:913–920CrossRefGoogle Scholar
  8. 8.
    Nesvizhskii AI, Aebersold R (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol Cell Proteomics 4:1419–1440CrossRefGoogle Scholar
  9. 9.
    Rauniyar N, Yates JR 3rd (2014) Isobaric labeling-based relative quantification in shotgun proteomics. J Proteome Res 13:5293–5309CrossRefGoogle Scholar
  10. 10.
    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 8:5347–5355CrossRefGoogle Scholar
  11. 11.
    Karp NA, Huber W, Sadowski PG et al (2010) Addressing accuracy and precision issues in iTRAQ quantitation. Mol Cell Proteomics 9:1885–1897CrossRefGoogle Scholar
  12. 12.
    Bräutigam A, Shrestha RP, Whitten D et al (2008) Low-coverage massively parallel pyrosequencing of cDNAs enables proteomics in non-model species: comparison of a species-specific database generated by pyrosequencing with databases from related species for proteome analysis of pea chloroplast envelopes. J Biotechnol 136:44–53CrossRefGoogle Scholar
  13. 13.
    Kamath KS, Pascovici D, Penesyan A et al (2016) Pseudomonas aeruginosa cell membrane protein expression from phenotypically diverse cystic fibrosis isolates demonstrates host-specific adaptations. J Proteome Res 15:2152–2163CrossRefGoogle Scholar
  14. 14.
    Padliya ND, Garrett WM, Campbell KB et al (2007) Tandem mass spectrometry for the detection of plant pathogenic fungi and the effects of database composition on protein inferences. Proteomics 7:3932–3942CrossRefGoogle Scholar
  15. 15.
    Seymour SL (2010) Assessing and interpreting protein identifications. J Biomol Tech 21:S12PubMedCentralGoogle Scholar
  16. 16.
    Pascovici D, Gardiner DM, Song X et al (2013) Coverage and consistency: bioinformatics aspects of the analysis of multirun iTRAQ experiments with wheat leaves. J Proteome Res 12:4870–4881CrossRefGoogle Scholar
  17. 17.
    Hill EG, Schwacke JH, Comte-Walters S et al (2008) A statistical model for iTRAQ data analysis. J Proteome Res 7:3091–3101CrossRefGoogle Scholar
  18. 18.
    Oberg AL, Mahoney DW, Eckel-Passow JE et al (2008) Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA. J Proteome Res 7:225–233CrossRefGoogle Scholar
  19. 19.
    Whitlock MC (2005) Combining probability from independent tests: the weighted Z-method is superior to Fisher's approach. J Evol Biol 18:1368–1373CrossRefGoogle Scholar
  20. 20.
    Pascovici D, Song X, Solomon PS et al (2015) Combining protein ratio p-values as a pragmatic approach to the analysis of multirun iTRAQ experiments. J Proteome Res 14:738–7461CrossRefGoogle Scholar
  21. 21.
    Winterberg B, Fall LAD, Song X et al (2014) The necrotrophic effector protein SnTox3 re-programs metabolism and elicits a strong defence response in susceptible wheat leaves. BMC Plant Biol 14:215CrossRefGoogle Scholar
  22. 22.
    Ullrich M, Liang V, Chew YL et al (2014) Bio-orthogonal labeling as a tool to visualize and identify newly synthesized proteins in Caenorhabditis elegans. Nat Protoc 9:2237–2255CrossRefGoogle Scholar
  23. 23.
    Song X, Bandow J, Sherman J et al (2008) iTRAQ experimental design for plasma biomarker discovery. J Proteome Res 7:2952–2958CrossRefGoogle Scholar
  24. 24.
    Martinez-Val A, Garcia F, Ximénez-Embún P et al (2016) On the statistical significance of compressed ratios in isobaric labeling: a cross-platform comparison. J Proteome Res 15:3029–3038CrossRefGoogle Scholar
  25. 25.
    Freue GVC, Sasaki M, Meredith A et al (2010) Proteomic signatures in plasma during early acute renal allograft rejection. Mol Cell Proteomics 9:1954–1967CrossRefGoogle Scholar
  26. 26.
    Navarro P, Trevisan-Herraz M, Bonzon-Kulichenko E et al (2014) General statistical framework for quantitative proteomics by stable isotope labeling. J Proteome Res 13:1234–1247CrossRefGoogle Scholar
  27. 27.
    Choi M, Chang CY, Clough T et al (2014) MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 30:2524–2526CrossRefGoogle Scholar
  28. 28.
    Zhou C, Walker MJ, Williamson AJK et al (2013) A hierarchical statistical modeling approach to analyze proteomic isobaric tag for relative and absolute quantitation data. Bioinformatics 30:549–558CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Dana Pascovici
    • 1
  • Xiaomin Song
    • 1
  • Jemma Wu
    • 1
  • Thiri Zaw
    • 1
  • Mark Molloy
    • 1
    Email author
  1. 1.Australian Proteome Analysis FacilityMacquarie UniversitySydneyAustralia

Personalised recommendations