Integrating spatial transcriptomics and Imaging Mass Cytometry™ for multi-omic mapping of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a heterogeneous malignancy, requiring spatially resolved multi-omic approaches to advance therapeutic strategies. Spatial transcriptomics using the Xenium™ platform enables high-throughput mapping of hundreds to thousands of RNA targets within intact tissue architecture. While transcript-level insights provide critical context for understanding gene expression patterns, integrating proteomic data adds a complementary layer that enables direct validation of biomarker expression. Spatial proteomics technologies, such as Imaging Mass Cytometry™ (IMC™), complement transcriptomics by providing high-dimensional protein expression data at subcellular resolution. IMC leverages metal-tagged antibodies and laser ablation to simultaneously quantify over 40 protein markers with 5 orders of magnitude linear dynamic range, surpassing traditional immunohistochemistry and immunofluorescence. We demonstrate the feasibility and biological insights gained from applying IMC to same tissue sections previously processed with Xenium, integrating transcriptomic and proteomic data through computational co-registration. Formalin-fixed, paraffin-embedded HCC tissue sections were profiled using a custom Xenium v1 transcriptomic panel, followed by IMC with a 43-marker immuno-oncology themed antibody panel on the same section. IMC was also performed on serial sections without prior Xenium processing for performance comparison. Data integration was achieved using Xenium Explorer software, which employs a computational co-registration algorithm to align nuclei across modalities, enabling overlay of transcriptomic and proteomic biomarkers for spatial correlation analysis. IMC performed post-Xenium processing generated high-quality data comparable to IMC alone, preserving tumor and immune cell phenotyping capabilities. Both techniques localized macrophages, neutrophils, B cells, cytotoxic T cells, and T helper cells and their activation states within distinct tissue regions. Computational integration of transcriptomic and proteomic datasets revealed subpopulations of immune cells and activation states, as well as discrepancies between RNA and protein localization for several markers, underscoring the importance of multi-modal validation. This integrated approach provided a more nuanced view of HCC microenvironmental complexity. Overall, we demonstrate concurrent application of spatial transcriptomics and proteomics at the cellular level on the same tissue section. This integrated workflow, enabled by computational co-registration, delivers a multidimensional perspective of tumor biology and uncovers spatial relationships between unique cell populations with varying activation states offering novel insights into HCC heterogeneity and informing the development of precision therapeutic strategies.