Abstract 5505: A unified framework for cross-platform spatial omics analysis and deep spatial profiling.

Abstract Background: Spatial multi-omics has advanced rapidly in resolution, multiplexing, 3D modeling, and multi-modality. Platforms rely on distinct chemistries and assay architectures, yielding heterogeneous outputs but complementary strengths. These spatially indexed readouts enable neighborhood/niche inference, cell-cell communication, and 3D reconstruction, but demand rigorous normalization, uncertainty modeling, and scalable computation. Despite progress with foundation models for imputation and integration, a domain-specific suite that unifies these models for spatial omics is lacking. Methods: We developed Spyrrow (Spatially Resolved Multi-omics Data Visualization and Analytic Framework), a Python toolkit for cross-platform analysis, visualization, multi-modality integration and enhancement, spatial registration, and 3D reconstruction (https://github.com/WangLab-ComputationalBiology/spyrrow). Spyrrow ingests heterogeneous outputs and standardizes them into a unified spatial data model. Modules span raw-data processing (cell segmentation to cell-level matrices; single-cell enhancement for Visium; Visium HD single-cell transformation using registered H&E), multimodal registration, and a foundation-model hub (UNI, KRONOS, LOKI) for panel expansion, imputation, and cross-modality retrieval. With harmonized data and z-axis registration, Spyrrow builds 3D models linking serial sections. On the unified object, we provide clustering, graph-based neighborhood retrieval and niche identification, region detection (tumor compartments, TLS), spatial label transfer, and a comprehensive cell-cell communication module. Visualization includes pseudo-H&E/pseudo-fluorescence, ligand-receptor quiver plots, and 3D voxel renderings. Results: Spyrrow has been applied across multiple spatial-omics datasets and projects. The visualization suite produces clear, interpretable displays of niches, regions, and interaction patterns. Across benchmarks, integration with state-of-the-art models improves spatially aware clustering, label transfer, registration, and communication inference. Region-detection performance was corroborated by experimental validation. Together these results demonstrate robust performance, scalability, and cross-platform integration that are difficult to achieve with existing tools. Conclusions: Spyrrow addresses a critical need in spatial multi-omics by providing a unified, adaptable, and cross-platform analytical framework. It facilitates both biological discovery and methodological innovation by supporting a wide range of spatial omics platforms. Importantly, it offers a scalable and interpretable approach to spatial data analysis—from broad tissue-level context to detailed subcellular features-empowering deeper insights into the tumor microenvironment and advancing immune-oncology research. Citation Format: Yunhe Liu, Edoardo L. Draetta, Kayla R. Caughlin, Alexandria Lau, Andre Fonseca, Tian Chu, Kyung Serk Cho, Yibo Dai, Yang Liu, Jun Wang, Jiahui Jiang, Yinyin Yuan, Futreal Andrew, Mingyao Li, Linghua Wang. A unified framework for cross-platform spatial omics analysis and deep spatial profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5505.