An isotopically-labelled temporal mass spectrometry imaging data analysis workflow to reveal glucose spatial metabolism patterns in bovine lens tissue

• Published in: Scientific reports Vol. 14; no. 1; pp. 18843 - 14
• Main Authors: Shi, Dingchang, Grey, Angus C, Guo, George
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group 14.08.2024; Nature Publishing Group UK; Nature Portfolio
• Subjects: Bioinformatics; Glucose; Glucose metabolism; Ionization; Kinetic MALDI imaging; Lens; MALDI imaging; Mass spectrometry; Mass spectroscopy; Metabolic pathways; Metabolism; Metabolites; Metabolomics; Scientific imaging; Spatial analysis; Stable isotope
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-69507-z

Application of stable isotopically labelled (SIL) molecules in Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) over a series of time points allows the temporal and spatial dynamics of biochemical reactions to be tracked in a biological system. However, these large kinetic MSI datasets and the inherent variability of biological replicates presents significant challenges to the rapid analysis of the data. In addition, manual annotation of downstream SIL metabolites involves human input to carefully analyse the data based on prior knowledge and personal expertise. To overcome these challenges to the analysis of spatiotemporal MALDI-MSI data and improve the efficiency of SIL metabolite identification, a bioinformatics pipeline has been developed and demonstrated by analysing normal bovine lens glucose metabolism as a model system. The pipeline consists of spatial alignment to mitigate the impact of sample variability and ensure spatial comparability of the temporal data, dimensionality reduction to rapidly map regional metabolic distinctions within the tissue, and metabolite annotation coupled with pathway enrichment modules to summarise and display the metabolic pathways induced by the treatment. This pipeline will be valuable for the spatial metabolomics community to analyse kinetic MALDI-MSI datasets, enabling rapid characterisation of spatio-temporal metabolic patterns from tissues of interest.