Referenced Kendrick Mass Defect Annotation and Class-Based Filtering of Imaging MS Lipidomics Experiments

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) a...

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Bibliographic Details
Published in:Analytical chemistry (Washington) 2022-04, Vol.94 (14), p.5504-5513
Main Authors: Richardson, Luke T, Neumann, Elizabeth K, Caprioli, Richard M, Spraggins, Jeffrey M, Solouki, Touradj
Format: Article
Language:English
Subjects:
Analytical chemistry
Annotations
Cefotaxime
Chemistry
Data analysis
Datasets
Filtration
Humans
Image processing
Image reconstruction
Ionization
Ions
Lipidomics
Lipids
Lipids - analysis
Mass spectrometry
Mass spectroscopy
Molecular chains
Scientific imaging
Spatial distribution
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods
Spectroscopy
Tissue analysis
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Summary: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.
ISSN:0003-2700
1520-6882
DOI:10.1021/acs.analchem.1c03715