rMSIfragment: improving MALDI-MSI lipidomics through automated in-source fragment annotation

• Published in: Journal of cheminformatics Vol. 15; no. 1; p. 80
• Main Authors: Baquer, Gerard, Sementé, Lluc, Ràfols, Pere, Martín-Saiz, Lucía, Bookmeyer, Christoph, Fernández, José A., Correig, Xavier, García-Altares, María
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 15-09-2023; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Adducts; Annotation; Annotations; Biological activity; Chemical composition; Chemistry; Chemistry and Materials Science; Computational Biology/Bioinformatics; Computer Applications in Chemistry; Documentation and Information in Chemistry; Fragmentation; High performance liquid chromatography; In-source decay; In-source fragmentation; Ionization; Ions; Lipids; Liquid chromatography; MALDI; Mass spectrometry; Mass spectrometry imaging; Mass spectroscopy; Scientific imaging; Theoretical and Computational Chemistry; In-source decay; Cheminformatics; Computation; MALDI; Mass spectrometry imaging; In-source fragmentation; Lipids; Bioinformatics; Annotation
• ISSN: 1758-2946; 1758-2946
• DOI: 10.1186/s13321-023-00756-2

Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) spatially resolves the chemical composition of tissues. Lipids are of particular interest, as they influence important biological processes in health and disease. However, the identification of lipids in MALDI-MSI remains a challenge due to the lack of chromatographic separation or untargeted tandem mass spectrometry. Recent studies have proposed the use of MALDI in-source fragmentation to infer structural information and aid identification. Here we present rMSIfragment, an open-source R package that exploits known adducts and fragmentation pathways to confidently annotate lipids in MALDI-MSI. The annotations are ranked using a novel score that demonstrates an area under the curve of 0.7 in ROC analyses using HPLC–MS and Target-Decoy validations. rMSIfragment applies to multiple MALDI-MSI sample types and experimental setups. Finally, we demonstrate that overlooking in-source fragments increases the number of incorrect annotations. Annotation workflows should consider in-source fragmentation tools such as rMSIfragment to increase annotation confidence and reduce the number of false positives.