Integrated Prior Knowledge by Bayesian Inference for Spatial Metabolomics Annotation in Mass Spectrometry Imaging
Baolin Xiong, Jishuai Wang, Yuanqing Zhang, Lei Wang, Peipei Zhou, Fangfang Wang, Xin Qi, Yu Guo, Wenchang Xu, Yibo Chen, Zhi Sun, Wenbo ChengAbstract
Mass spectrometry imaging (MSI) reveals molecular distributions in tissue, but exact-mass searching often assigns multiple metabolite candidates to a single feature. We developed a Bayesian inference model for spatial metabolomics annotation (BIMSM) that prioritizes these candidates by integrating initial matching probabilities with adduct and biochemical-reaction relationships calibrated by ion-image similarity. The method was evaluated using simulated MSI data, mouse-brain MSI datasets with an external LC−MS/MS reference, and kidney-tissue MSI datasets. Among evaluable mouse-brain reference candidates, Top-1, Top-3, and Top-5 coverage increased from 6.5%, 14.4%, and 20.0% to 17.9%, 27.4%, and 32.9%, respectively. The kidney-tissue application produced less redundant candidate lists for descriptive pathway enrichment analysis. These results support BIMSM as a downstream candidate-prioritization tool that uses spatial and chemical context to guide analytical follow-up, while orthogonal validation remains necessary for definitive metabolite identification.