Isobaric Labeling Update in MaxQuant

Area of Science:

• Proteomics and Mass Spectrometry
• Computational Biology and Bioinformatics

Background:

• Isobaric labeling techniques like TMT Pro are crucial for quantitative proteomics.
• Accurate analysis of complex proteomic data requires robust software tools.
• Challenges include reporter ion impurities and batch effects in large-scale studies.

Purpose of the Study:

• To present an updated version of the MaxQuant software for isobaric labeling data analysis.
• To evaluate the performance of new features using benchmark datasets.
• To improve the accuracy and reduce systematic errors in quantitative proteomics.

Main Methods:

• Implementation of impurity correction factors for isobaric labels (e.g., TMT Pro).
• Direct analysis of TMT data combined with FAIMS separation.
• Application of weighted median normalization to diverse datasets, including human body atlas data.

Main Results:

• Impurity correction significantly improves accuracy, demonstrated on a single-cell multispecies mixture.
• Weighted median normalization effectively removes or reduces batch effects, leading to biologically meaningful clustering.
• Normalization performs well even without using reference channels, simplifying experimental design.

Conclusions:

• The updated MaxQuant software offers enhanced capabilities for analyzing isobaric labeling data.
• New features improve data accuracy, reduce batch effects, and simplify analysis workflows.
• MaxQuant provides a powerful, freely available tool for quantitative proteomics research.