Automated Annotation of Untargeted All-Ion Fragmentation LC-MS Metabolomics Data with MetaboAnnotatoR

Area of Science:

• Analytical Chemistry
• Biochemistry
• Computational Biology

Background:

• Untargeted metabolomics and lipidomics using LC-MS generate vast datasets requiring extensive annotation.
• All-ion fragmentation (AIF) LC-MS/MS offers fragmentation data without extra experimental time.
• Analyzing AIF data necessitates parent-fragment relationship reconstruction and pseudo-MS/MS spectrum annotation.

Purpose of the Study:

• To develop a novel, automated approach for annotating isotopologues, adducts, and in-source fragments from AIF LC-MS datasets.
• To improve the efficiency and accuracy of metabolite and lipid identification in complex biological samples.
• To provide an open-source R package for widespread accessibility and application.

Main Methods:

• Combined correlation-based parent-fragment linking with molecular fragment matching.
• Focused workflow on a subset of features for enhanced efficiency.
• Validated the approach on human serum datasets with expert-annotated features.

Main Results:

• Achieved high precision (82-92%) and recall (82-85%) for top-ranked features in human serum datasets.
• Outperformed current state-of-the-art software (MS-DIAL) for AIF data annotation.
• Demonstrated variable but acceptable performance across different biological matrices and instrument types.

Conclusions:

• The proposed workflow, MetaboAnnotatoR, provides an efficient and accurate method for automated AIF LC-MS/MS data annotation.
• The open-source R package facilitates broader adoption and advancement in metabolomics and lipidomics research.
• Further validation and refinement are needed for datasets rich in non-lipid metabolites.