Large-Scale and Deep Quantitative Proteome Profiling Using Isobaric Labeling Coupled with Two-Dimensional LC–MS/MS

  • Marina A. Gritsenko
  • Zhe Xu
  • Tao LiuEmail author
  • Richard D. SmithEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1410)


Comprehensive, quantitative information on abundances of proteins and their posttranslational modifications (PTMs) can potentially provide novel biological insights into diseases pathogenesis and therapeutic intervention. Herein, we introduce a quantitative strategy utilizing isobaric stable isotope-labeling techniques combined with two-dimensional liquid chromatography–tandem mass spectrometry (2D-LC–MS/MS) for large-scale, deep quantitative proteome profiling of biological samples or clinical specimens such as tumor tissues. The workflow includes isobaric labeling of tryptic peptides for multiplexed and accurate quantitative analysis, basic reversed-phase LC fractionation and concatenation for reduced sample complexity, and nano-LC coupled to high resolution and high mass accuracy MS analysis for high confidence identification and quantification of proteins. This proteomic analysis strategy has been successfully applied for in-depth quantitative proteomic analysis of tumor samples and can also be used for integrated proteome and PTM characterization, as well as comprehensive quantitative proteomic analysis across samples from large clinical cohorts.

Key words

Quantitative proteomics Isobaric labeling iTRAQ Two-dimensional liquid chromatography Mass spectrometry 



Portions of this work were supported by the grant U24CA160019, from the National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC) and National Institutes of Health grant P41GM103493. The experimental work described herein was performed in the Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by the DOE and located at Pacific Northwest National Laboratory, which is operated by Battelle Memorial Institute for the DOE under Contract DE-AC05-76RL0 1830.


  1. 1.
    Baker ES, Liu T, Petyuk VA et al (2012) Mass spectrometry for translational proteomics: progress and clinical implications. Genome Med 4:63PubMedCentralCrossRefPubMedGoogle Scholar
  2. 2.
    Rifai N, Gillette MA, Carr SA (2006) Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol 24:971–983CrossRefPubMedGoogle Scholar
  3. 3.
    Domon B, Aebersold R (2006) Mass spectrometry and protein analysis. Science 312:212–217CrossRefPubMedGoogle Scholar
  4. 4.
    Zhang B, Wang J, Wang X et al (2014) Proteogenomic characterization of human colon and rectal cancer. Nature 513:382–387PubMedCentralCrossRefPubMedGoogle Scholar
  5. 5.
    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
  6. 6.
    Wang Y, Yang F, Gritsenko MA et al (2011) Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells. Proteomics 11:2019–2026PubMedCentralCrossRefPubMedGoogle Scholar
  7. 7.
    Qian WJ, Liu T, Petyuk VA et al (2009) Large-scale multiplexed quantitative discovery proteomics enabled by the use of an (18)O-labeled “universal” reference sample. J Proteome Res 8:290–299PubMedCentralCrossRefPubMedGoogle Scholar
  8. 8.
    Mertins P, Yang F, Liu T et al (2014) Ischemia in tumors induces early and sustained phosphorylation changes in stress kinase pathways but does not affect global protein levels. Mol Cell Proteomics 13:1690–1704PubMedCentralCrossRefPubMedGoogle Scholar
  9. 9.
    Mertins P, Qiao JW, Patel J et al (2013) Integrated proteomic analysis of post-translational modifications by serial enrichment. Nat Methods 10:634–637PubMedCentralCrossRefPubMedGoogle Scholar
  10. 10.
    Kim S, Gupta N, Pevzner PA (2008) Spectral probabilities and generating functions of tandem mass spectra: a strike against decoy databases. J Proteome Res 7:3354–3363PubMedCentralCrossRefPubMedGoogle Scholar
  11. 11.
    Elias JE, Gygi SP (2007) Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat Methods 4:207–214CrossRefPubMedGoogle Scholar
  12. 12.
    Qian WJ, Liu T, Monroe ME et al (2005) Probability-based evaluation of peptide and protein identifications from tandem mass spectrometry and SEQUEST analysis: the human proteome. J Proteome Res 4:53–62CrossRefPubMedGoogle Scholar
  13. 13.
    Zhang B, Chambers MC, Tabb DL (2007) Proteomic parsimony through bipartite graph analysis improves accuracy and transparency. J Proteome Res 6:3549–3557PubMedCentralCrossRefPubMedGoogle Scholar
  14. 14.
    Monroe ME, Shaw JL, Daly DS et al (2008) MASIC: a software program for fast quantitation and flexible visualization of chromatographic profiles from detected LC-MS(/MS) features. Comput Biol Chem 32:215–217PubMedCentralCrossRefPubMedGoogle Scholar
  15. 15.
    Dayon L, Nunez Galindo A, Corthesy J et al (2014) Comprehensive and scalable highly automated MS-based proteomic workflow for clinical biomarker discovery in human plasma. J Proteome Res 13:3837–3845Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Biological Sciences Division and Environmental Molecular Sciences LaboratoryPacific Northwest National LaboratoryRichlandUSA

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