An open multimodal spatial resource integrating same-tissue transcriptomics, proteomics, and histology

Authors

Duchini, E., Tsao, C., Madore, J., Ashhurst, T. M., De Almeida Silva, J., Shin, J.-S., Gupta, R., McCaughan, G., Palendira, U., Liu, K., Ferguson, A., Marsh-Wakefield, F.

Abstract

Spatial transcriptomic and proteomic technologies provide complementary insights into tissue organisation, cellular phenotype and function, yet integrating these modalities on the same tissue section remains technically challenging. Sequential workflows must preserve RNA integrity, antigenicity and tissue morphology while maintaining accurate spatial registration. At present, publicly available multimodal datasets suitable for computational method development remain limited. Here, we present a workflow for sequential 10x Genomics Xenium spatial transcriptomics, COMET cyclic immunofluorescence, and haematoxylin and eosin (H&E) histological staining on the same formalin-fixed paraffin-embedded tissue section. We demonstrate this approach across multiple biologically distinct human tissues, including tonsil, hepatocellular adenoma, and matched tumour and non-tumour hepatocellular carcinoma, illustrating the widespread applicability of the workflow beyond a single tissue type. Following image registration, Xenium-derived cell segmentations were applied to protein images to generate integrated single-cell transcriptomic and proteomic measurements for downstream analyses. To facilitate community reuse, we publicly release four representative aligned tissue cores together with transcript coordinates, multiplex protein images, H&E images, cell segmentations, and integrated single-cell datasets. We additionally introduce UnumLocalia, an open-source visualisation and data extraction tool that enables interactive exploration of aligned multimodal images, supports user-defined cell segmentation, and allows export of integrated single-cell data for downstream analyses. Together, this technical protocol, workflow, software, and openly available dataset provide a reusable resource for multimodal spatial biology, supporting advances in biological discovery, computational method development, multimodal data integration, and validation of emerging analytical approaches across complementary spatial technologies.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 22 Aug 2026.

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