IsobaricQuant enables cross-platform quantification, visualization, and filtering of isobarically-labeled peptides.

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Isobaric mass tags (e.g., iTRAQ, TMT) enhance throughput in quantitative proteomics but can suffer from inaccurate quantification due to co-eluting interferences.
• Current methods to address interference, like MS3 or ion mobility, have limitations in auditability and instrumentation cost.

Purpose of the Study:

• To introduce IsobaricQuant, an open-source software for accurate quantification and filtering of isobaric mass tag labeled peptides.
• To specifically address and mitigate precursor interference in mass spectrometry-based quantitative proteomics.

Main Methods:

• Developed IsobaricQuant, an open-source software tool compatible with MS2 and MS3 acquisition strategies.
• Incorporated a viewer for assessing interference and developed quality control (QC) scores for filtering scans with interference.
• Validated IsobaricQuant accuracy against existing software and assessed its performance in filtering inaccurate quantifications.

Main Results:

• IsobaricQuant demonstrates accurate quantification comparable to commonly used software.
• The tool's QC scores effectively filter scans with reduced quantitative accuracy at both MS2 and MS3 levels.
• Application to a PISA dataset showed improved sensitivity for identifying kinase inhibitor targets.

Conclusions:

• IsobaricQuant provides a robust, auditable solution for improving quantitative accuracy in isobaric mass tag-based proteomics.
• The software's QC metrics are valuable for removing unreliable peptide quantifications and reducing protein coefficient of variation (CV).
• IsobaricQuant enhances the sensitivity of biological discovery, particularly in complex datasets like kinase inhibitor studies.