High-Parameter Spatial Multi-Omics through Histology-Anchored Integration

Recent advances in spatial omics technologies enablein situmolecular profiling while preserving spatial context but face fundamental challenges in achieving high-parameter and multi-omics co-profiling. Spatially resolving complementary panels or distinct omics layers across serial tissue sections circumvents technical trade-offs but introduces thespatial diagonal integration problem: reconstructing unified multi-omics states when datasets lack shared molecular features. To address this critical challenge, we presentSpatialExand its extensionSpatialEx+, computational frameworks that leverage histology as a universal anchor to integrate spatial molecular data across tissue sections. The foundational SpatialEx model combines a pre-trained H&E foundation model with hypergraph learning and contrastive learning to predict single-cell omics profiles from histology, encoding multi-neighborhood spatial dependencies and global tissue context. Building upon SpatialEx, SpatialEx+ introduces anomics cycle modulethat encourages cross-omics consistency across adjacent sections via slice-invariant mapping functions, achieving seamless diagonal integration without requiring co-measured multi-omics data for training. Through rigorous validation across three key applications, we demonstrate: (1)H&E-to-omics predictionat single-cell resolution, characterizing tumor microenvironments beyond sequencing borders in breast cancer; (2)panel diagonal integration, merging non-overlapping spatial gene panels from different slices to resolve immune-stromal boundaries lost by individual panels; and (3)omics diagonal integration, revealing Parkinson’s disease anatomical domains and context-specific tissue pathologies through integrated transcriptomic-metabolic analysis. The framework scales to datasets exceeding one million cells, maintains robustness with non-overlapping or heterogeneous sections, and supports unlimited omics layersin principle. By transforming highly feasible spatial single-omics assays with histology into a holistic spatial multi-omics map, our work democratizes systems-level tissue analysis, bridging fundamental spatial biology and scalable multi-omics research with minimal experimental overhead.