Revealing Hidden Variables in DESI-Based Spatial Metabolomics: Solvent Composition and Tissue Type as Critical Drivers
Marco Giampà, Peter D. E. M. Verhaert, Jan Claereboudt, Emmanuelle Claude, Bernard Drotleff, Geert Goeminne, Veerle Heedfeld, Jakub Idkowiak, Emrys A. Jones, Alexander Muck, Janick Mathys, Alaa Othmann, Nina Ravoet, Julio Lopes-Sampaio, Johannes V. Swinnen, Ina Jochmans, Bart GhesquièreAbstract
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 various murine tissues. The performance of both solvents was evaluated across several metabolite classes (central carbon metabolites, amino acids, and fatty acids). Although the ACN-based solvent system led to higher signal intensities for small polar metabolites involved in glycolysis, the tricarboxylic acid (TCA) cycle, and amino acid metabolism, the MeOH-based solvent system provided superior signal intensities for fatty acids. These findings demonstrate that the solvent composition differentially influences metabolite extraction and ionization processes in DESI and should be carefully matched to the biological questions and metabolite classes of interest. As a proof-of-principle, the ACN solvent system was applied to a pilot study based on a rat model of renal ischemic injury, revealing 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.