利用Xenium转录组学与Hyperion XTi成像质谱流式对肿瘤-免疫微环境进行综合多组学分析

Spatial multi-omics has become a powerful strategy for resolving cellular heterogeneity and precisely characterizing the tissue microenvironment, surpassing the biological resolution achievable with RNA-only or protein-only spatial assays. To address the limitations of single-modality profiling, we established an integrated workflow that combines Xenium in situ transcriptomics with Standard BioTools' Hyperion XTi Imaging Mass Cytometry (IMC) system. This approach is designed to enable coordinated, high-parameter analysis of transcriptomic and proteomic features within the same tissue regions, supporting deeper investigation of microenvironmental structure and function. Using a human primary FFPE multi-tissue panel comprising stomach large cell carcinoma, colon adenocarcinoma, invasive ductal carcinoma, and lung squamous cell carcinoma, BioChain generated spatially resolved Xenium transcriptomic data and mapped cell populations using lineage-defining markers. Major epithelial, stromal, endothelial, lymphoid, and myeloid compartments were identified, and architectural features-including tumor nests, stromal boundaries, tertiary lymphoid structures, and mixed immune aggregates-were delineated. For each region of interest, transcriptomic readouts were distilled into concise microenvironmental summaries reflecting immune infiltration intensity, lymphoid organization, cytotoxic activity, stromal composition, and checkpoint-associated transcriptional programs. These region-level profiles enabled cross-tissue comparison of microenvironmental classes across heterogeneous tumor types. Hyperion XTi IMC profiling of the same tissue regions provided a complementary proteomic view with high spatial and phenotypic resolution. The expanded protein panel resolved functional states associated with immune activation, suppression, proliferation, and tissue remodeling. Overlapping immune and stromal markers supported alignment of proteomic signals with the Xenium transcriptomic map, allowing corresponding cellular neighborhoods to be referenced across modalities. Together, these datasets established a multi-omic spatial framework that links cellular identity to protein-level functional dynamics within shared tissue coordinates. Overall, this combined transcriptomic-proteomic strategy establishes a scalable and practical template for comprehensive spatial profiling using limited FFPE material. The workflow is compatible with existing analysis tools, readily adaptable to new panels or tissue types, and well positioned for future development of spatially informed biomarkers and mechanistic insights into complex tissue ecosystems.