Background The tumor microenvironment is composed of highly heterogeneous structures and cell types that dynamically influence and communicate with each other. While examination of singular biospecimens is sufficient for diagnostic purposes, it is inadequate and cost prohibitive when scaling for complex and overarching studies. High-density multi-tumor tissue microarrays (TMAs) have proven to be a practical and effective solution for high-throughput molecular analysis of tissues.

Methods In this study, we paired the 10x Genomics Visium Cytassist Spatial Gene Expression Solution and Xenium In-Situ Platform on multi-tumor TMAs to screen for common biomarkers among a cohort of samples. Visium maps the whole transcriptome with spatial context, while Xenium enables high-throughput cellular characterization at single-cell resolution. The CytAssist platform expands the standard Visium solution by facilitating the retrieval of RNA transcriptomic information from tissues placed on standard or archival slides. Xenium complements Visium data by assigning transcripts to particular cells with spatial context and subcellular resolution.

Results Combining spatial transcriptomics and targeted in situ data with FFPE TMAs, we established a high-throughput method to uncover molecular signatures suitable for understanding the tumor microenvironment. We demonstrated the ability to spatially and comprehensively resolve individual oncogenes and tumor suppressor genes across multiple tumors from a cohort of cancer patients and various tumor samples. These markers were mapped back to distinct morphological features within each tissue core, using differential gene expression data to identify specific cell types throughout different patient tissues.

Conclusions By combining the throughput of TMA samples with the depth of the Visium and Xenium platforms, this strategy enables greater insights into cell-type specifics while expanding the spectrum of biospecimen types that can be analyzed. This integrated approach accelerates the identification and spatial resolution of oncological biomarkers, enhancing our understanding of the tumor microenvironment.