Enhancing Metabolome Coverage in Data-Dependent LC-MS/MS Analysis through an Integrated Feature Extraction Strategy.

Area of Science:

• Metabolomics
• Mass Spectrometry
• Bioinformatics

Background:

• Untargeted metabolomics relies on software like XCMS, MZmine 2, and MS-DIAL for metabolic feature extraction.
• These conventional tools often fail to detect low-abundance metabolic features, limiting comprehensive metabolome coverage.
• This limitation hinders the complete understanding of biological systems through metabolomic analysis.

Purpose of the Study:

• To enhance metabolome coverage in untargeted metabolomics by rescuing low-abundance metabolic features missed by conventional software.
• To develop and validate a data-preprocessing pipeline for integrating these previously unrecognized features.
• To improve the accuracy and completeness of metabolic profiling from liquid chromatography-tandem mass spectrometry (LC-MS/MS) data.

Main Methods:

• Categorization of metabolic features based on chromatographic peak shape and MS/MS spectra.
• Assessment of false positives and quantitative accuracy for features missed by conventional software.
• Development of an integrated data-preprocessing pipeline to combine conventional results with rescued low-abundance features.

Main Results:

• Metabolic features missed by conventional software contain valid and important biological information.
• The integrated feature extraction approach increased the number of significant features by 24.4% in test data.
• Five additional biologically significant metabolites were identified using the enhanced strategy.

Conclusions:

• The developed integrated feature extraction strategy significantly improves metabolome coverage compared to conventional methods.
• This approach facilitates the confirmation of metabolites of interest and increases the success rate of de novo metabolite identification.
• Rescuing low-abundance features is crucial for achieving a more complete and accurate metabolic profile.