This workflow integrates single-cell maldi-msi with spatial transcriptomics, improving metabolic heterogeneity assessment in mouse and human tissues.
Understanding tissue complexity requires spatially resolved multiomic data at single-cell resolution. Here, we present a workflow that integrates high-resolution matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) with Xenium spatial transcriptomics (SPT) on a single tissue section. This one-section strategy ensures exact spatial correspondence between metabolic and transcriptomic features, avoiding the misalignment issues of serial sections, where even minor offsets can result in sampling different cells. We validate compatibility of MALDI-MSI with downstream SPT, preserving transcriptomic quality despite semi-destructive ionization. Using mouse brain and human glioblastoma tissues, we achieve pixel-perfect modality coregistration, enabling per-cell MALDI spectra extraction aligned with gene expression. Integrated clustering reveals enhanced cell-type resolution and identifies metabolic heterogeneity within transcriptionally defined populations. This enables a direct and precise correlation between what a cell is doing (gene expression) and its biochemical state (e.g., signaling activity, metabolic status), providing a more holistic and accurate picture of cellular function, heterogeneity, and interaction in health and disease. Our workflow provides a scalable path to multiomic atlases of disease and development, advancing both data integration and translational research.
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Hendriks et al. (2025) studied this question.
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