Open AccessHERMES: a molecular-formula-oriented method to target the metabolome
Author(s) -
Roger Giné,
Jordi Capellades,
Josep M. Badia,
Dennis Vughs,
Michaela Schwaiger-Haber,
Theodore Alexandrov,
María Vinaixa,
Andrea M. Brunner,
Gary J. Patti,
Óscar Yanes
Publication year - 2021
Publication title -
nature methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 19.469
H-Index - 318
eISSN - 1548-7105
pISSN - 1548-7091
DOI - 10.1038/s41592-021-01307-z
Subject(s) - metabolome , metabolomics , computational biology , visualization , data acquisition , mass spectrometry , identification (biology) , annotation , biological system , chromatography , chemistry , computer science , data mining , biology , bioinformatics , operating system , botany
Comprehensive metabolome analyses are essential for biomedical, environmental, and biotechnological research. However, current MS1- and MS2-based acquisition and data analysis strategies in untargeted metabolomics result in low identification rates of metabolites. Here we present HERMES, a molecular-formula-oriented and peak-detection-free method that uses raw LC/MS1 information to optimize MS2 acquisition. Investigating environmental water, Escherichia coli, and human plasma extracts with HERMES, we achieved an increased biological specificity of MS2 scans, leading to improved mass spectral similarity scoring and identification rates when compared with a state-of-the-art data-dependent acquisition (DDA) approach. Thus, HERMES improves sensitivity, selectivity, and annotation of metabolites. HERMES is available as an R package with a user-friendly graphical interface for data analysis and visualization.