Abstract 6276: Comparative analysis and integration of image-based and high-resolution sequence-based spatial transcriptomics platforms for tumor microenvironment analysis

Abstract Background: Advances in spatial transcriptomics (ST) enable precise gene expression analysis within the tumor microenvironment (TME). VisiumHD and Xenium Prime 5K are cutting-edge platforms offering unique strengths in spatial resolution, sensitivity, and multiplexity. This study evaluates their performance metrics in heterogeneous tumor tissues from the same slide, focusing on technical performance, spatial resolution, and cell type identification. Additionally, post-Xenium VisiumHD datasets were generated and integrated to harness the complementary strengths of both platforms. We aimed to investigate a head-to-head comparison in spatially registered ST to provide guidance for researchers in selecting the appropriate platform for specific cancer research objectives. Methods: ST data were analyzed from tumor samples processed with Xenium Prime 5K followed by VisiumHD. Evaluations included transcript capture efficiency, gene detection sensitivity, and signal-to-noise ratios. Spatial resolution was assessed using cell- and transcript-level metrics, gene expression correlations, and spatial mapping accuracy. Cell type identification focused on assignment accuracy and rare population detection. Custom workflows integrated post-Xenium VisiumHD datasets, combining platform strengths for detailed TME analysis. Results: Gene expression correlations were highly consistent across platforms. VisiumHD provided superior transcriptome-wide coverage (∼18, 085 genes), making it ideal for exploratory studies, while Xenium offered 2-3x greater per-gene sensitivity for detecting low-abundance transcripts. Xenium Prime 5K excelled in spatial resolution, with precise cell-level annotations, whereas VisiumHD mapped broader spatial patterns via deconvolution. Integration of Xenium cell segmentation with VisiumHD enabled single-cell transcript analysis with comprehensive gene expression data. This approach improved rare cell population detection and overcame limitations of individual platforms. Conclusions: VisiumHD is ideal for transcriptome-wide exploratory studies, while Xenium Prime 5K excels in high-resolution, focused cellular analyses. Cross-platform integration bridges gaps between resolution and coverage, enabling robust analysis of complex TMEs. This study provides critical guidance for platform selection and highlights the potential of integrated approaches to advance cancer research through spatial transcriptomics. Citation Format: Dongjoo Lee, Yeonjae Jung, Jae Eun Lee, Myunghyun Lim, Daeseung Lee, Kwon Joong Na, Hongyoon Choi. Comparative analysis and integration of image-based and high-resolution sequence-based spatial transcriptomics platforms for tumor microenvironment analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6276.