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. MS(2) 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 MS(2) data. Multipass IM experiments yielded excellent CCS accuracy (<0.4%) relative to database values for differential lipids found in RCC tissues, improving the filtering threshold used in previous CCS-based annotation workflows. High-accuracy multipass CCS measurements enabled the correct annotation of isobaric lipid database matches, even in the absence of MS(2) data. Additionally, MS(2) 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.