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Updated: Sep 19, 2025

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

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msqrob2TMT: Robust Linear Mixed Models for Inferring Differential Abundant Proteins in Labeled Experiments With

Stijn Vandenbulcke¹, Christophe Vanderaa², Oliver Crook³

¹Department of Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium; CompOmics, VIB Center for Medical Biotechnology, VIB, Ghent, Belgium; Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.

Molecular & Cellular Proteomics : MCP

|June 1, 2025

Summary

This summary is machine-generated.

New workflows called msqrob2TMT improve differential abundance analysis for complex mass spectrometry-based proteomics experiments. This tool enhances statistical inference and biomarker discovery by effectively handling intricate sample correlations.

Keywords:

TMT labeling differential proteomics mass spectrometry mixed models statistics

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Area of Science:

Proteomics
Computational Biology
Statistical Modeling

Background:

Mass spectrometry-based proteomics utilizes labeling strategies to increase sample throughput via multiplexed runs.
Complex experimental designs in proteomics often exceed single-run capacity, leading to correlation structures that complicate statistical inference and biomarker discovery.

Purpose of the Study:

To introduce msqrob2TMT, a suite of mixed model-based workflows for differential abundance analysis in labeled mass spectrometry-based proteomics data.
To provide a flexible and modular tool that accommodates complex experimental designs and corrects for feature-specific covariates.

Main Methods:

Development of mixed model-based workflows (msqrob2TMT) for differential abundance analysis.
Accommodation of sample-specific and feature-specific covariates for complex experimental designs.
Benchmarking against DEqMS, MSstatsTMT, and msTrawler using spike-in studies and a real mouse study.

Main Results:

msqrob2TMT demonstrates greater flexibility, improved modularity, and enhanced performance compared to existing tools.
Robust ridge regression application contributes to improved performance.
Successful application in a real mouse study, effectively accounting for complex correlation structures.

Conclusions:

msqrob2TMT is a powerful and flexible tool for differential abundance analysis in complex mass spectrometry-based proteomics studies.
The workflows facilitate reliable statistical inference and biomarker discovery in challenging experimental designs.
The tool effectively addresses complex correlation structures inherent in large-scale proteomics data.