MSstatsTMT Improves Accuracy of Thermal Proteome Profiling

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.