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Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

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Related Experiment Video

Updated: Sep 20, 2025

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

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

Published on: November 15, 2017

MSstatsTMT Improves Accuracy of Thermal Proteome Profiling.

Amanda M Figueroa-Navedo¹, Rohan Kapre², Tushita Gupta²

¹Barnett Institute of Chemical and Biological Analysis, Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts, USA.

Molecular & Cellular Proteomics : MCP

|May 29, 2025

Summary

Thermal proteome profiling analyzes protein interactions using mass spectrometry. This study advocates for the MSstatsTMT R package for robust statistical analysis, improving accuracy and sensitivity in detecting protein-drug interactions.

Keywords:

MSstats MSstatsTMT non-parametric response curves proteomics thermal proteome profiling

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Last Updated: Sep 20, 2025

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Area of Science:

Biochemistry
Proteomics
Computational Biology

Background:

Thermal proteome profiling (TPP) is a technique to study molecular interactions.
TPP involves treating biological samples with small molecules and exposing them to varying temperatures.
Data analysis, particularly curve fitting, is crucial for interpreting TPP results.

Purpose of the Study:

To review and compare statistical methods for data processing and curve fitting in TPP.
To advocate for the MSstatsTMT R package as a superior statistical analysis strategy.
To demonstrate the advantages of MSstatsTMT over existing methods using simulated and experimental data.

Main Methods:

Liquid chromatography coupled with mass spectrometry (LC-MS) in data-dependent acquisition mode.
Tandem Mass Tag (TMT) labeling for quantitative proteomic analysis across different temperatures.
Statistical analysis using the open-source R package MSstatsTMT.

Main Results:

Current statistical methods for TPP can produce significantly different results.
MSstatsTMT provides a robust statistical analysis strategy that avoids subjective pre-filtering and curve fitting.
MSstatsTMT appropriately accounts for all sources of variation and enhances the sensitivity of results.

Conclusions:

The MSstatsTMT R package offers a more reliable and sensitive approach for thermal proteome profiling data analysis.
This strategy supports complex experimental designs, including varying temperatures and drug concentrations.
A documented, reproducible workflow using MSstatsTMT is available for the scientific community.