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Proteomics with Enhanced In-Source Fragmentation/Annotation: Applying XCMS-EISA Informatics and Q-MRM

Jingchuan Xue1,2, Rico J E Derks3, Linh Hoang1

  • 1Scripps Center for Metabolomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States.

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Summary

Enhanced in-source fragmentation/annotation (EISA) generates peptide fragment ions comparable to tandem mass spectrometry, improving proteomic analysis. This method enhances peptide identification and quantification, particularly for post-translational modifications.

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Area of Science:

  • Proteomics and Mass Spectrometry
  • Analytical Chemistry
  • Biochemistry

Background:

  • Enhanced in-source fragmentation/annotation (EISA) produces fragment ions matching tandem mass spectrometry (MS/MS) data for small molecules.
  • EISA supports various acquisition methods like data-dependent acquisition (DDA), data-independent acquisition (DIA), and multiple-reaction monitoring (MRM).
  • Existing methods facilitate molecular identification in untargeted metabolomics and targeted quantitative analyses.

Purpose of the Study:

  • To apply EISA to peptide-based proteomic analysis using optimized in-source fragmentation.
  • To compare EISA-generated fragmentation patterns with traditional tandem MS experiments.
  • To develop automated proteomic data analysis software utilizing EISA.

Main Methods:

  • Optimized in-source fragmentation conditions were used to generate fragmentation patterns for a mixture of 38 peptides.
  • Comparison of EISA with automated data-dependent acquisition (DDA) on a quadrupole time-of-flight (QTOF-MS) instrument.
  • Development of an XCMS-EISA annotation/deconvolution program for automated proteomic data analysis.

Main Results:

  • EISA generated peptide fragmentation patterns comparable to b- and y-type fragment ions from tandem MS.
  • Optimal EISA conditions yielded a significantly higher fragment percentage of peptides compared to DDA on QTOF-MS and linear ion trap instruments.
  • EISA demonstrated utility in post-translational modification (PTM) characterization and peptide quantification via Q-MRM.

Conclusions:

  • EISA is effective for peptide-based proteomic analysis, generating comparable fragmentation to traditional MS/MS.
  • EISA enhances peptide identification and quantification, particularly for PTMs and in targeted quantitative analyses.
  • Further development of EISA informatics will advance sensitive autonomous identification and quantitative proteomics.