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bioRxiv · 10.64898/2026.04.27.721059

SMEW: An interactive multi-scale toolkit for cross-condition and network-based analysis of spatial metabolomics data

Williams, E.·Hulme, H.·Zakirov, A.·Buszta, D.·Hamm, G.·Flint, L.·Franzen, L.·Olsson Lindvall, M.·Stamou, M.·Andersson, P.·Tan, J.·Ling, S.·Mohorianu, I.
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Abstract

Spatial metabolomics, measured through mass spectrometry imaging (MSI), provides high-throughput, spatially resolved information on metabolite distributions within tissues, including endogenous metabolites and exogenous compounds. This offers a direct readout of cellular biochemical activity and phenotypes, not fully captured by transcriptomics or proteomic profiling. However, inferring biologically meaningful patterns from noisy, high-dimensional MSI data, particularly across multiple samples and complex experimental designs, remains challenging, and often requires substantial programming expertise. Here we introduce SMEW (Spatial Metabolomics Enhanced Workflow), a flexible, interactive and shareable Shiny-based platform designed to enable code-free downstream analysis of spatial metabolomics MSI data. SMEW provides a unified environment for hierarchical analysis across bulk-, region- and pixel-level resolutions, allowing comparisons between experimental conditions like disease or treatment groups while highlighting coherent metabolic patterns and linking these patterns to biological pathways. The workflow leverages local spatial covariation to robustly summarise MSI data through dimensionality reduction, clustering and identification of spatially variable metabolites. In addition, metabolite co-localisation and covariation network analysis, together with spatially resolved pathway enrichment facilitate the biological interpretation of cross-condition datasets within a single integrated interface. SMEW is applicable across MSI technologies and mass resolutions, as illustrated through case studies on DESI and MALDI-ToF datasets from lung, liver, and kidney. By complementing existing MSI processing and visualisation tools with an accessible, multi-sample, and biologically interpretable analysis framework, SMEW enables functional, flexible, rigorous and intuitive exploration of spatial metabolomics datasets. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/721059v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1e2abaeorg.highwire.dtl.DTLVardef@753ee9org.highwire.dtl.DTLVardef@1756fc1org.highwire.dtl.DTLVardef@fbedc7_HPS_FORMAT_FIGEXP M_FIG C_FIG Key PointsO_LISMEW provides a flexible, interactive and shareable Shiny-based platform designed to enable code-free downstream analysis of spatial metabolomics MSI data C_LIO_LIThe SMEW framework enables hierarchical analysis at bulk-, region- and pixel levels within a unified framework without relying on extensive programming expertise C_LIO_LIThe pipeline integrates spatially aware clustering, pathway analysis and identification of metabolite co-localisation modules C_LIO_LIThe workflow facilitates flexible comparison of multi-sample experimental conditions through multivariate modelling, differential testing and covariation networks to study treatment- and disease-associated metabolite dynamics C_LIO_LISMEW has been applied to interrogate diverse biological questions, including characterising disease-associated remodelling in a mouse bleomycin model of pulmonary fibrosis, exploring the therapeutic index of antisense oligonucleotides in the liver and assessing metabolic heterogeneity in a small molecule-treated mouse renal tumour model C_LI

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Williams, E., Hulme, H., Zakirov, A., Buszta, D., Hamm, G., Flint, L., Franzen, L., Olsson Lindvall, M., Stamou, M., Andersson, P., Tan, J., Ling, S., Mohorianu, I.. 2026-04-29. SMEW: An interactive multi-scale toolkit for cross-condition and network-based analysis of spatial metabolomics data. https://doi.org/10.64898/2026.04.27.721059

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