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rMSIfragment: improving MALDI-MSI lipidomics through automated in-source fragment annotation.
- Source :
-
Journal of Cheminformatics . 11/18/2023, Vol. 15 Issue 1, p1-14. 14p. - Publication Year :
- 2023
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DESORPTION ionization mass spectrometry
*LIPIDOMICS
*ANNOTATIONS
Subjects
Details
- Language :
- English
- ISSN :
- 17582946
- Volume :
- 15
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Journal of Cheminformatics
- Publication Type :
- Academic Journal
- Accession number :
- 173723383
- Full Text :
- https://doi.org/10.1186/s13321-023-00756-2