ArticleJournal of cheminformatics2023
rMSIfragment: improving MALDI-MSI lipidomics through automated in-source fragment annotation.
Article in Journal of cheminformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- MALDI Mass Spectrometry Imaging in Alzheimer's Disease Lipidomics: Matrix Selection, Spatial Lipid Pathology and Emerging Analytical Strategies.International journal of molecular sciences · 2026Review
- Polyethylene Mulch Emissions Differentially Impact the Soil Metabolome and Microbial Community in Field Pea (Journal of xenobiotics · 2026Article
- The MALDI Method to Analyze the Lipid Profile, Including Cholesterol, Triglycerides and Other Lipids.Current issues in molecular biology · 2026Review
- Review
- Article
- Structural annotation of full-scan MS data: A unified solution for LC-MS and MS imaging analyses.bioRxiv : the preprint server for biology · 2025Article
- Spatially resolved metabolomics: From metabolite mapping to function visualising.Clinical and translational medicine · 2024Review
- Identification of Plant Compounds with Mass Spectrometry Imaging (MSI).Metabolites · 2024Review
- Development of a New Binary Matrix for the Comprehensive Analysis of Lipids and Pigments in Micro- and Macroalgae Using MALDI-ToF/ToF Mass Spectrometry.International journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.