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

Kriel, J.; Moffet, J. J.; Lu, T.; Fatunla, O. E.; Narayana, V. K.; Valkovic, A.; Maluenda, A.; McConville, M. J.; Tsui, E.; Whittle, J. R.; Best, S. A.; Freytag, S.

doi:10.1101/2024.10.15.618574

bioRxiv · 10.1101/2024.10.15.618574

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

Kriel, J.·Moffet, J. J.·Lu, T.·Fatunla, O. E.·Narayana, V. K.·Valkovic, A.·Maluenda, A.·McConville, M. J.·Tsui, E.·Whittle, J. R.·Best, S. A.·Freytag, S.

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Abstract

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. HighlightsO_LIAlignment and integrated analysis of spatial transcriptomic and metabolomic data C_LIO_LINuclei-only and cell-morphology segmentations are concordant for cell annotation C_LIO_LISpatially distinct regions are conserved in transcriptomic and metabolomic datasets C_LIO_LIMulti-omic exploration of glioma leading edge identifies novel biological features C_LI

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Kriel, J., Moffet, J. J., Lu, T., Fatunla, O. E., Narayana, V. K., Valkovic, A., Maluenda, A., McConville, M. J., Tsui, E., Whittle, J. R., Best, S. A., Freytag, S.. 2024-10-18. An integrative spatial multi-omic workflow for unified analysis of tumor tissue. https://doi.org/10.1101/2024.10.15.618574

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