Robust integration and annotation of single-cell and spatial omics data using interpretable gene programs

Cellular identity emerges from the dynamic coordination of context-aware gene programs that encode biological functions across molecular layers. To decode this complexity, we present SSpMosaic, a computational framework that establishes metaprograms (higher-order, cross-dataset aligned gene program representations) as universal anchors for biological state representation. Leveraging these metaprograms, SSpMosaic enables consistent, accurate integration across batches, modalities, and species. Critically, SSpMosaic accurately annotates cell types within query datasets, enabling discovery and annotation of novel cell states through metaprogram-based transfer learning. The framework achieves resolution-agnostic spatial transcriptomics deconvolution, precisely mapping cell-type distributions from spot-level (Visium) to subcellular scales (CosMx/Visium HD). As a paradigm-shifting application, we integrate single-nucleus transcriptomics, chromatin accessibility, and spatial transcriptomics to resolve multi-stage spatial domain dynamics across tissue slices. Finally, SSpMosaic enables reference-free spatial characterization, identifying conserved spatial ecotypes across tissue slices and annotating cellular niches without requiring matched single-cell data.