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Related Experiment Video

Updated: Feb 17, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Compositional Proteomics: Effects of Spatial Constraints on Protein Quantification Utilizing Isobaric Tags.

Jonathon J O'Brien1, Jeremy D O'Connell1, Joao A Paulo1

  • 1Department of Cell Biology, Harvard Medical School , Boston, Massachusetts 02115, United States.

Journal of Proteome Research
|December 3, 2017
PubMed
Summary

Isobaric labeling mass spectrometry (MS) data is compositional, impacting protein abundance analysis. A new statistical model and software improve accuracy and detection of changes, especially for infinite changes in proteomics.

Keywords:
Bayesian hierarchical modelingSPS-FTMS3infinite changesinterferenceisobaric tags for relative and absolute quantitation (iTRAQ)mass spectrometrypartially pooled varianceratio compressionsignal detectiontandem mass tags (TMT)

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Bioinformatics

Background:

  • Mass spectrometry (MS) enables whole proteome quantitation.
  • Isobaric labeling MS allows simultaneous multi-sample quantification using reporter ions.
  • Current methods may face limitations due to constrained signals in spectral features.

Purpose of the Study:

  • To demonstrate that isobaric tag proteomics data are inherently compositional.
  • To highlight the implications of compositional data for analysis and interpretation.
  • To present a novel statistical model and software for improved protein abundance analysis.

Main Methods:

  • Experimental validation of compositional nature of isobaric tag MS data.
  • Development of a new statistical model for compositional data analysis.
  • Application of accompanying software for enhanced estimation accuracy.

Main Results:

  • Isobaric tag proteomics data exhibit inherent compositional properties.
  • A new statistical model significantly improves estimation accuracy.
  • The model enhances the detection of protein abundance changes, including infinite changes.
  • Magnitude of estimates for infinite changes is highly dependent on experimental design.

Conclusions:

  • Isobaric tag MS data requires compositional data analysis approaches.
  • The developed statistical model and software offer improved accuracy and detection capabilities.
  • Understanding compositional effects is crucial for accurate interpretation of protein quantification studies.