Characterization of Electrospray Ionization Complexity in Untargeted Metabolomic Studies

• Published in: Analytical chemistry (Washington) Vol. 96; no. 27; pp. 10935 - 10942
• Main Authors: Nash, William J., Ngere, Judith B., Najdekr, Lukas, Dunn, Warwick B.
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 09.07.2024
• Subjects: Adducts; Animals; Annotations; Chromatography, Liquid; Complexity; Datasets; Electrospraying; Fragments; Humans; Ionization; Mammals; Metabolites; Metabolomics; Metabolomics - methods; Phase composition; Retention; Retention time; Spectrometry, Mass, Electrospray Ionization - methods
• ISSN: 0003-2700; 1520-6882; 1520-6882
• DOI: 10.1021/acs.analchem.4c00966

The annotation of metabolites detected in LC-MS-based untargeted metabolomics studies routinely applies accurate m/z of the intact metabolite (MS1) as well as chromatographic retention time and MS/MS data. Electrospray ionization and transfer of ions through the mass spectrometer can result in the generation of multiple “features” derived from the same metabolite with different m/z values but the same retention time. The complexity of the different charged and neutral adducts, in-source fragments, and charge states has not been previously and deeply characterized. In this paper, we report the first large-scale characterization using publicly available data sets derived from different research groups, instrument manufacturers, LC assays, sample types, and ion modes. 271 m/z differences relating to different metabolite feature pairs were reported, and 209 were annotated. The results show a wide range of different features being observed with only a core 32 m/z differences reported in >50% of the data sets investigated. There were no patterns reporting specific m/z differences that were observed in relation to ion mode, instrument manufacturer, LC assay type, and mammalian sample type, although some m/z differences were related to study group (mammal, microbe, plant) and mobile phase composition. The results provide the metabolomics community with recommendations of adducts, in-source fragments, and charge states to apply in metabolite annotation workflows.