Li, W.; Mao, L.; Liu, Y.; Peng, F.; Sachs, N.; Wu, W.; Yiu, S. P. T.; Yan, H.; Schroeder, A.; Yu, X.; Jin, K.; Jiang, S.; Chen, Z.; Loth, M.; Gomez, L.; Lubo, I.; Blank, N.; Samarah, L.; Basak, A.; Cho, Y. W.; Chen, C.-Y.; Kim, D. M.; Shalek, A. K.; Solis Soto, L. M.; Rabinowitz, J.; Reilly, M.; Qian, X.; Thaiss, C.; Maegdefessel, L.; Wang, L.; Kadara, H.; Jiang, S.; Deng, Y.; Li, M.

Multimodal spatial omics has transformed biology by mapping molecular complexity within intact tissues, yet current technologies remain limited in the number of modalities measured simultaneously and often produce lower-quality data than single-modality assays. We present COSIE, a computational framework that generates high-resolution, multilayered molecular landscapes across tissue sections, individuals, and platforms. COSIE integrates histology, epigenome, transcriptome, proteome, and metabolome into a unified representation. Applied to 12 datasets spanning 10 spatial technologies, eight modalities, and nine tissue types, ranging from thousands of spots to millions of cells, COSIE outperforms existing methods. It resolves tissue structures, enhances noisy measurements, predicts unmeasured modalities, and captures dynamic processes. In human tumors, COSIE identifies invasive subregions linked to clinical outcomes and predicts spatial gene expression in TCGA samples using only histology images. By transforming fragmented data into comprehensive spatial maps, COSIE advances computationally complete spatial omics and the creation of digital tissue twins for biomedicine.