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

Authors

Galvis, J., Moreau, M., Lillo, S., Bomont, D., Ledoux, L., Zirem, Y., Salzet, M., Fournier, I., Desbenoit, N., Daubon, T., Nikolski, M.

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.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 23 Oct 2026.

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