Biology subjects

Duchini, E.

Publications and source records attributed to Duchini, E..

2 recordsLinked to original sources

bioRxiv2026

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

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.

immunology↗

bioRxiv2026

Combining xenium in situ spatial transcriptomics and imaging mass cytometry on a single tissue section

Spatial imaging technologies provide an expansive view of tissue microenvironments through high-plex profiling of protein and molecular targets in situ. Imaging mass cytometry (IMC; Standard BioTools) is a trusted method for defining immune phenotypes based on up to 40 protein targets, whilst Xenium in situ spatial transcriptomics (Xenium; 10x Genomics) is an emerging platform that can measure up to 5000 mRNA markers simultaneously. Although these platforms can reveal valuable insights on their own, there is an increasing need to analyse samples using a multi-omics approach to further our understanding of complex biological processes. To address this, we have assessed a novel dual-platform workflow that combines Xenium and IMC on a single formalin-fixed paraffin-embedded tissue section to enable the spatial profiling of both mRNA and protein targets at single-cell resolution. The feasibility of the workflow was determined by comparing the staining quality of IMC performed after Xenium to that of IMC performed alone on an adjacent tissue section, confirming that Xenium has little to no negative impact on subsequent IMC protein staining. Although the location of transcripts picked up by Xenium correlated with the corresponding proteins picked up by IMC at a global scale, discrepancies between the two technologies were apparent at the single-cell level. This is to be expected, as biologically transcript expression does not always correlate with protein, and both platforms have their own technical limitations. However, when we analyse T cells identified by both technologies, as opposed to T cells identified by Xenium or IMC alone, it produces the most biologically meaningful results at both the transcript and protein level for specific T cell markers. These results highlight how integration of the two platforms, identifying the presence of both RNA and protein, can foster a more comprehensive view of cellular landscapes and provide a greater depth of functional capabilities and cellular interactions.

immunology↗