Rapid Development of Improved Data-Dependent Acquisition Strategies.

Area of Science:

• Analytical Chemistry
• Metabolomics
• Mass Spectrometry

Background:

• Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is crucial for untargeted metabolomics.
• Current data-dependent acquisition (DDA) methods are inefficient, fragmenting only a small ion subset.
• Developing new DDA strategies is challenging due to high experimental costs.

Purpose of the Study:

• To theoretically assess potential improvements in DDA strategies.
• To develop a cost-efficient in silico framework for DDA method optimization.
• To introduce and validate novel DDA methods with advanced ion prioritization.

Main Methods:

• Theoretical analysis of DDA strategy improvements.
• Development of a virtual metabolomics mass spectrometer (ViMMS) framework.
• Integration of ViMMS with an Instrument Application Programming Interface (IAPI) for simulation and real-world application.
• Design and optimization of two new DDA methods with advanced ion prioritization.

Main Results:

• Theoretical framework demonstrates potential for DDA improvement.
• ViMMS framework enables fast and cost-efficient DDA strategy development.
• New DDA methods successfully fragmented more unique ions compared to standard DDA in complex mixtures.

Conclusions:

• The developed in silico framework significantly accelerates DDA method optimization.
• Novel DDA strategies enhance ion coverage in untargeted metabolomics.
• This approach offers a more efficient path to identifying a broader range of metabolites.