Comparison of spatial transcriptomics technologies across six cancer types

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Comparison of spatial transcriptomics technologies across six cancer types

Authors

Cervilla, S.; Grases, D.; Perez, E.; Musulen, E.; Real, F. X.; Esteller, M.; Porta-Pardo, E.

Abstract

Spatial biology experiments integrate the molecular and histological landscape of tissues to provide a previously inaccessible view of tissue biology, unlocking the architecture of complex multicellular tissues. Within spatial biology, spatial transcriptomics platforms are among the most advanced, allowing researchers to characterize the expression of thousands of genes across space. These new technologies are transforming our understanding of how cells are organized in space and communicate with each other to determine emergent phenotypes with unprecedented granularity. This is particularly important in cancer research, as it is becoming evident that tumor evolution is shaped not only by the genetic properties of cancer cells but also by how they interact with the tumor microenvironment and their spatial organization. While many platforms can generate spatial transcriptomics profiles, it is still unclear in which context each platform better suits the needs of its users. Here we compare the results obtained using 4 different spatial transcriptomics (VISIUM, VISIUM CytAssist, Xenium and CosMx) and one spatial proteomics (VISIUM CytAssist) platforms across serial sections of 6 FFPE samples from primary human tumors covering some of the most common forms of the disease (lung, breast, colorectal, bladder, lymphoma and ovary). We observed that the VISIUM platform with CytAssist chemistry yielded superior data quality. Xenium consistently produced more reliable results for in situ platforms, with better gene clustering and fewer false positives than CosMx. Interestingly, these platform-based variations didn\'t significantly affect cell type identification. Finally, by comparing VISIUM protein profiles with the spatial transcriptomics data from all four platforms on each sample, we identified several genes with mismatched RNA and protein expression patterns, highlighting the importance of multi-omics profiling for a more comprehensive understanding.

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