A new spatial multi-omics approach to deeply characterize human cancer tissue using a single tissue section

Adimi, Y.; Levin, C.; Lombardi, E. P.; Morin, B.; Gervais, C.; Tolou, S.; Noirot-Jagu, A.-C.; Hauret, J.; Thomas, C.; Tchepikoff, P.; Grigorev, F.; Djafri, A.; Longuemaux, S.; Caron, A.; Naimi, S.; Cesaroni, M.; Tartour, E.; Classe, M.; Badoual, C.

doi:10.1101/2024.10.03.616510

bioRxiv · 10.1101/2024.10.03.616510

A new spatial multi-omics approach to deeply characterize human cancer tissue using a single tissue section

Adimi, Y.·Levin, C.·Lombardi, E. P.·Morin, B.·Gervais, C.·Tolou, S.·Noirot-Jagu, A.-C.·Hauret, J.·Thomas, C.·Tchepikoff, P.·Grigorev, F.·Djafri, A.·Longuemaux, S.·Caron, A.·Naimi, S.·Cesaroni, M.·Tartour, E.·Classe, M.·Badoual, C.

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Abstract

In the ever-changing world of digital pathology, being able to extract a maximum amount of information from a patient tissue sample is of paramount importance for better diagnosis, disease characterization, and therapeutic strategies. Recent technologies such as multiplex immunofluorescence imaging and spatial transcriptomic now enable a deep analysis of protein and gene expression while retaining the spatial context of the tissue. Here, we describe an innovative approach combining a 34-protein Phenocycler panel and transcriptome analysis using Visium on a single head and neck squamous cell carcinoma section. While protein analysis reveals the complexity of the immune phenotypes involved in the disease, transcriptome analysis reveals the intricate cellular states of cancer cells that coexist within the patients tumor. Finally, integrating both omics modalities, we uncover unique comparison of gene and protein expression of spatially resolved cellular subspaces.

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Adimi, Y., Levin, C., Lombardi, E. P., Morin, B., Gervais, C., Tolou, S., Noirot-Jagu, A.-C., Hauret, J., Thomas, C., Tchepikoff, P., Grigorev, F., Djafri, A., Longuemaux, S., Caron, A., Naimi, S., Cesaroni, M., Tartour, E., Classe, M., Badoual, C.. 2024-10-04. A new spatial multi-omics approach to deeply characterize human cancer tissue using a single tissue section. https://doi.org/10.1101/2024.10.03.616510

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