Optimizing MS Parameters for Data-Independent Acquisition (DIA) to Enhance Untargeted Metabolomics.

Area of Science:

• Analytical Chemistry
• Biochemistry
• Mass Spectrometry

Background:

• Data-Independent Acquisition (DIA) is a key mass spectrometry (MS) technique for metabolomics.
• Optimizing MS parameters is crucial for maximizing metabolite coverage and data quality.

Purpose of the Study:

• To develop and optimize a novel, short-gradient, nanosensitive analytical method for human plasma analysis using DIA LC-MS/MS.
• To systematically tune MS parameters for enhanced sensitivity, specificity, and metabolite identification.

Main Methods:

• Development of a 13-minute gradient nano-LC-MS/MS method.
• Systematic optimization of MS parameters: scan speed, isolation window width, resolution, automatic gain control, and collision energy.
• Comparison of DIA with Data-Dependent Acquisition (DDA) MS for untargeted metabolomics.

Main Results:

• Detection of 2,907 features, with 675 compounds annotated.
• Achieved a balance between sensitivity and specificity, minimizing interferences.
• Demonstrated the potential for integrating proteomics and metabolomics analyses.

Conclusions:

• The optimized DIA LC-MS/MS method provides robust and reproducible results for human plasma metabolomics.
• This advancement enhances the potential for significant discoveries in various biological fields.
• The method supports multiomics applications by enabling combined proteomic and metabolomic analyses.