Comprehensive Evaluation and Optimization of the Data-Dependent LC-MS/MS Workflow for Deep Proteome Profiling.

Area of Science:

• Proteomics
• Analytical Chemistry
• Biochemistry

Background:

• Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a cornerstone of modern proteomics.
• Current performance evaluations of LC-MS/MS workflows often rely on limited metrics like peptide and protein counts.
• A comprehensive assessment is needed to fully understand and optimize complex LC-MS/MS workflows.

Purpose of the Study:

• To develop MSRefine, a novel computational tool for evaluating and optimizing LC-MS/MS performance in data-dependent acquisition (DDA) proteomics.
• To provide a comprehensive suite of metrics beyond simple identification counts.
• To assist researchers in troubleshooting and improving their proteomics experiments.

Main Methods:

• MSRefine extracts and analyzes 47 distinct metrics from LC-MS/MS data.
• The tool scores these metrics and presents them through visual reports.
• Performance evaluation involved comparative analysis of diverse datasets across different samples, methods, and instruments.

Main Results:

• MSRefine successfully demonstrated its capability to evaluate diverse LC-MS/MS experiments.
• Comprehensive visual metrics and scores provided actionable insights into workflow performance.
• The tool identified areas for optimization, leading to strategies for increased peptide and protein identification.

Conclusions:

• MSRefine offers a robust and comprehensive approach to LC-MS/MS performance evaluation in DDA proteomics.
• The tool's detailed metrics and visualizations facilitate workflow optimization.
• Utilizing MSRefine can significantly enhance the depth and quality of proteomic profiling.