SpatialMOC: Accurate reconstruction of spatial multi-omics landscapes through cross-modality prediction

Abstract

Spatial multi-omics technologies provide unprecedented opportunities to characterize tissue organization by measuring complementary molecular layers within their native spatial context. However, simultaneous profiling of multiple molecular modalities remains technically challenging, limiting the widespread application of spatial multi-omics and leaving most studies reliant on single-modality measurements. Here, we present Spatial Multi-Omics Cross-prediction (Spatial-MOC), a computational framework that reconstructs missing spatial molecular modalities by integrating spatial context with cross-modality representation learning. Across multiple tissues, molecular modalities, developmental stages, sequencing platforms, and degraded datasets, SpatialMOC consistently outper-formed existing computational approaches in bidirectional molecular prediction. Beyond accurate prediction, SpatialMOC faithfully reconstructed tissue architecture, preserved dynamic molecular and regulatory heterogeneity, and recovered biologically meaningful spatial landscapes from technically compromised measurements. Together, these results establish SpatialMOC as a general framework for spatial multi-omics reconstruction, extending the analytical value of existing spatial omics datasets and facilitating comprehensive investigations of tissue organization, development, and disease.