Abstract
Understanding tissue complexity requires spatially resolved multi-omics data at single-cell resolution. Here, we present a workflow integrating high-resolution matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) with Xenium spatial transcriptomics (SPT) on a single tissue section. This strategy ensures pixel-scale spatial correspondence between metabolic and transcriptomic features, avoiding misalignment issues of serial sections, where even minor offsets result in sampling different cells. We investigated MALDI-MSI compatibility with downstream SPT revealing that the number of transcripts per cell decreased by ~ 30% after MSI, whilst cell recovery and cell-type assignments are preserved. Validated using mouse brain and demonstrated using human glioblastoma tissues, we achieved pixel-scale modality co-registration, enabling per-cell MALDI spectra extraction aligned with gene expression. Integrated clustering revealed enhanced cell-type resolution and identified metabolic heterogeneity within transcriptionally defined populations. This facilitates precise correlations of a cell's function and its biochemical state, providing a holistic view of cellular function, heterogeneity, and interaction in health and disease. Our workflow provides a scalable path to multi-omic atlases, advancing both data integration and translational research.