Revealing Hidden Variables in DESI-based Spatial Metabolomics: Solvent Composition and Tissue Type as Critical Drivers

ABSTRACT: In the development of a desorption electrospray ionization (DESI) workflow for spatial metabolomics, we investigated the impact of two commonly used solvent systems, 90% acetonitrile (ACN) and 90% methanol (MeOH), on the spatial metabolomic profiling of multiple murine tissues. The performance of both solvents was evaluated across several metabolite classes (central carbon metabolites, amino acids, and fatty acids). While the ACN-based solvent system led to higher signal intensities of small polar metabolites involved in glycolysis, the tricarboxylic acid (TCA) cycle, and amino acid metabolism, the MeOH-based solvent system provided superior signal intensities of fatty acids. These findings demonstrate that solvent composition differentially influences metabolite extraction and ionization processes in DESI and should be carefully matched to the biological question and metabolite classes of interest. To illustrate the utility of the optimized workflow, the ACN solvent system was applied to a rat model of renal ischemic injury. Spatial mapping of metabolites across distinct kidney regions (cortex, outer medulla, and inner medulla) revealed pronounced region-specific metabolic changes between normoxic and ischemic conditions. Together, these results demonstrate the importance of solvent selection in DESI-based spatial metabolomics and showcase the ability of this approach to uncover spatially resolved metabolic adaptations associated with tissue injury.

INSTRUMENT(S): MS Imaging - negative

PROVIDER: MTBLS14771 | MetaboLights | 2026-09-11

REPOSITORIES: MetaboLights