A Spatial Metabolomics Annotation Workflow Leveraging Cyclic Ion Mobility and Machine Learning-Predicted Collision Cross Sections

In nontargeted spatial metabolomics, accurate annotation is crucial for understanding metabolites’ biological roles and spatial patterns. MS2 mass spectrometry imaging (MSI) coverage is often incomplete or nonexistent, resulting in many unknown features that represent an untapped source of biological information. Ion mobility-derived collision cross sections (CCS) have been leveraged as valuable descriptors for confirming putative metabolite annotations, distinguishing isomers, and aiding in unknown structural elucidation. In this study, desorption electrospray ionization cyclic ion mobility mass spectrometry imaging (DESI-cIM-MSI) data from human renal cell carcinoma (RCC) tissues is used as a testbed to explore the extent to which CCS measurements enhance MSI lipid annotation confidence when combined with machine learning CCS predictions and SIRIUS analysis of MS2 data. Multipass IM experiments yielded excellent CCS accuracy (2 data. Additionally, MS2 data from differential RCC features were uploaded to SIRIUS, and the predicted CCS values for SIRIUS candidates were compared to experimental CCS data to filter out unlikely candidates. Finally, CCS measurements contributed to the annotation of two spatially correlated unknown features, differential between tumor and control kidney tissues. Both features were assigned to rocuronium, a surgical muscle relaxant that had not been previously reported in MSI studies. Overall, these results underscore the potential of high-accuracy CCS values to enhance metabolite annotations in MSI-based spatial metabolomics.