SpacePath enables pathway and molecular-family analysis beyond annotation ambiguity in spatial metabolomics and lipidomics

Abstract

Mass spectrometry imaging (MSI) maps metabolites and lipids in tissue with spatial resolution, but molecular identity remains partly resolved: single ions match multiple candidate compounds, and most detected metabolites and lipids fall outside canonical biochemical pathways. We developed SpacePath, a computational framework that retains ion-to-compound ambiguity, scores canonical pathways across tissue locations, and groups assignments that were unmapped to pathways into de novo molecular families by chemical similarity. Pathway mappings covered on average only 1% of molecular assignments, whereas the combined SpacePath representation covered 24% of pooled assignments. Benchmarking on four public spatial lipidomics datasets (healthy mouse brain, cerebellum, a demyelination model, and human bladder cancer) recovered previously reported spatial patterns. In newly acquired glioblastoma xenograft datasets, SpacePath identified lipid programs specific to tumor and surrounding brain, and SpiderMass-MS/MS independently confirmed four tumor-associated features from a de novo family, refining the initial MS1 assignments. SpacePath extends the spatial interpretation of MSI data beyond canonical pathways while preserving annotation uncertainty.