InSitu Untargeted Metabolite Identification and Spatial Mapping by Clustering Resampling Mass Spectrometry Imaging

Abstract Mass spectrometry imaging enables the spatially resolved analysis of molecular distributions in biological tissues. However, in situ untargeted metabolite identification remains challenging due to the inherent trade-off between spatial resolution and in situ tandem mass spectrometry (MS2) acquisition. Here, we developed clustering resampling mass spectrometry imaging (CR-MSI), a method that innovatively converts in situ MS2 acquisition from countless pixel scans to a few representative points testing. CR-MSI allows the acquisition of both spatial localization and structural fragmentation data for metabolites from a single tissue section. Additionally, we have engineered an integrated device that automates the CR-MSI workflow, transforming MS2 acquisition from an empirical, operator-dependent procedure to an objective, data-driven process. Moreover, the synchronization between MSI data acquisition and processing has improved the efficiency and overall MSI throughput. When applied to rat brain, CR-MSI collected 1,175 MS2 species, representing a 410% increase compared with conventional techniques. This approach will vastly enhance the structural elucidation of untargeted mass spectrometry imaging.