Automated metabolite formula ranking using formula subset analysis for LC-MS/MS-based metabolomics

Area of Science:

• Metabolomics
• Mass Spectrometry
• Computational Chemistry

Background:

• Metabolite identification is a critical challenge in untargeted liquid chromatography-tandem mass spectrometry (LC-MS) metabolomics.
• Existing methods struggle when metabolites are not present in tandem mass spectrometry (MS/MS) databases.

Purpose of the Study:

• To develop and evaluate Formula Subset Analysis (FSA), a novel approach for prescreening and ranking chemical formula candidates from MS/MS spectra.
• To address the bottleneck in metabolite identification for untargeted metabolomics.

Main Methods:

• FSA computes mother-daughter relationships (MDRs) between precursor and fragment ions.
• Characteristic fragments (CFs) with unique MDRs are identified.
• Precursor formula candidates are ranked based on the number of MDRs.

Main Results:

• FSA correctly identified the top chemical formula candidate in 85.28% of cases and top-5 in 97.35% across 30,690 spectra.
• The average processing time per spectrum was only 0.024 seconds.
• FSA demonstrated high accuracy without requiring training data or MS/MS databases.

Conclusions:

• FSA offers significant advantages in speed, expandability, and applicability for untargeted LC-MS metabolomics.
• While not using structural information, its performance makes it ideal for prescreening metabolite candidates.
• The method is adaptable to various chemical elements and species identification.