Tissue-based absolute quantification using large-scale TMT and LFQ experiments.

Area of Science:

• Proteomics
• Bioinformatics
• Systems Biology

Background:

• Publicly available proteomic datasets, including cell lines, tissue atlases, and tumor data, facilitate research into protein existence, localization, abundance, and RNA correlation.
• Label-free quantification (LFQ) using MS1 features is common, but isobaric tandem mass tags (TMT) datasets are underutilized.
• Existing methods for protein quantification have limitations in comparing different dataset types.

Purpose of the Study:

• To compare traditional intensity-based absolute quantification (iBAQ) with a novel reporter ion proteome abundance ranking method using TMT data.
• To evaluate the applicability of the new TMT-based iBAQ method on large-scale tissue atlas datasets.
• To provide a robust workflow for proteomic identification, normalization, and quantification applicable to both LFQ and TMT data.

Main Methods:

• Developed a TMT-based reporter ion proteome abundance ranking method analogous to the iBAQ framework.
• Applied the new TMT method to samples analyzed with both LFQ and TMT.
• Validated the TMT-iBAQ approach on two independent large-scale tissue atlas datasets (one LFQ, one TMT) using established proteomic workflows.

Main Results:

• The TMT-based reporter ion abundance ranking method effectively substitutes MS1 feature intensities within the iBAQ framework.
• Direct comparison of LFQ-iBAQ and TMT-iBAQ values from the same samples showed comparable results.
• The TMT-iBAQ method demonstrated robustness when applied to large-scale tissue atlas datasets.

Conclusions:

• A novel TMT-based protein quantification method analogous to iBAQ is presented, expanding the utility of TMT datasets.
• This TMT-iBAQ approach provides a reliable means to compare protein abundance across diverse proteomic datasets.
• The developed workflow enhances the analysis of large-scale proteomic atlases, enabling deeper biological insights.