通过高重数空间多组学解码人类癌症组织中的免疫和肿瘤生态位

The tumor microenvironment (TME) is defined by extensive spatial and molecular heterogeneity, and single-biomarker assays fail to capture the complexity of cellular interactions that drive tumor progression and therapeutic response. To overcome these limitations, we present a spatial multi-omics strategy that integrates Vizgen's spatial transcriptomics and proteomics technologies to enable deeper characterization of the TME. The MERSCOPE® Ultra™ Platform, powered by MERFISH 2.0, currently supports high-resolution spatial profiling of up to 1,000 genes and 6 proteins in a single assay. Here, we introduce an expanded multi-omics workflow that increases protein detection capacity up to 30 targets while maintaining simultaneous spatial detection of up to 1,000 RNA transcripts. The workflow is initiated by staining with conjugated antibodies linked to optimized oligonucleotide tags, engineered to ensure high sensitivity and minimal background noise, followed by MERFISH 2.0 RNA imaging. The integrated spatial multi-omics approach seamlessly couples high-plex protein detection with MERFISH 2.0 transcriptomics. The robustness and utility of these integrated methodologies were extensively validated across a broad range of indications and antibody panels. We applied this multi-omics assay to cancer specimens, achieving simultaneous quantification of an up to 30-plex immuno-oncology (IO) protein signature and a comprehensive, pre-designed 815 IO transcript inventory which supports its powerful application in translational oncology. Our results demonstrated robust performance, characterized by high detection sensitivity and minimal background interference for both molecular modalities. A key advantage of the presented assay is the full user customization of both the protein biomarker panels and RNA transcripts, enabling researchers to optimally tailor panel design to specific mechanistic inquiries. Leveraging the subcellular resolution of the MERSCOPE Ultra Platform, we executed granular single-cell spatial profiling, successfully delineating cellular neighborhoods and characterizing cell-to-cell communication within the tumor microenvironment. The resulting multi-omic datasets allowed us to characterize the specific gene signatures and immune cell subsets enriched within distinct tumor regions, illuminating the spatial determinants governing tumor-immune interactions. Therefore, this integrated, expanded spatial multi-omics solution represents a powerful framework to enable comprehensive co-profiling of immune and tumor biomarkers within a single tissue section. By simultaneously capturing protein and RNA expression at single-cell resolution, it provides an unparalleled framework for dissecting cellular interactions, identifying spatially organized biomarkers, and accelerating translational studies aimed at predicting therapeutic response.