An integrative spatial multi-omic workflow for unified analysis of tumor tissue

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Combining molecular profiling with imaging techniques has advanced the field of spatial biology, offering new insights into complex biological processes. Focusing on diffuse IDH -mutated low-grade glioma, this study presents a workflow for Spatial Multi-omics Integration, SMINT, specifically combining spatial transcriptomics and spatial metabolomics. Our workflow incorporates both existing and custom-developed computational tools to enable cell segmentation and registration of spatial coordinates from both modalities to a common coordinate framework. During our investigation of cell segmentation strategies, we found that nuclei-only segmentation, while containing only 40% of segmented cell transcripts, enables accurate cell type annotation, but does not account for multinucleated cells. Our integrative workflow including cell-morphology segmentation identified distinct cellular neighborhoods at the infiltrating edge of gliomas, which were enriched in multinucleated and oligodendrocyte-lineage tumor cells, that may drive tumor invasion into the normal cortical layers of the brain. Highlights Alignment and integrated analysis of spatial transcriptomic and metabolomic data Nuclei-only and cell-morphology segmentations are concordant for cell annotation Spatially distinct regions are conserved in transcriptomic and metabolomic datasets Multi-omic exploration of glioma leading edge identifies novel biological features