ArticleAnalytical chemistry2022
Referenced Kendrick Mass Defect Annotation and Class-Based Filtering of Imaging MS Lipidomics Experiments.
Article in Analytical chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- Chemical Informatics Combined with Kendrick Mass Analysis to Enhance Annotation and Identify Pathways in Soybean Metabolomics.Metabolites · 2025Article
- Spatial pharmacology using mass spectrometry imaging.Trends in pharmacological sciences · 2024Review
- Spatial probabilistic mapping of metabolite ensembles in mass spectrometry imaging.Nature communications · 2023Article
- Emerging Computational Methods in Mass Spectrometry Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
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5 authors.
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Abstract
Because of their diverse functionalities in cells, lipids are of primary importance when characterizing molecular profiles of physiological and disease states. Imaging mass spectrometry (IMS) provides the spatial distributions of lipid populations in tissues. Referenced Kendrick mass defect (RKMD) analysis is an effective mass spectrometry (MS) data analysis tool for classification and annotation of lipids. Herein, we extend the capabilities of RKMD analysis and demonstrate an integrated method for lipid annotation and chemical structure-based filtering for IMS datasets. Annotation of lipid features with lipid molecular class, radyl carbon chain length, and degree of unsaturation allows image reconstruction and visualization based on each structural characteristic. We show a proof-of-concept application of the method to a computationally generated IMS dataset and validate that the RKMD method is highly specific for lipid components in the presence of confounding background ions. Moreover, we demonstrate an application of the RKMD-based annotation and filtering to matrix-assisted laser desorption/ionization (MALDI) IMS lipidomic data from human kidney tissue analysis.
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