HyperBridge: From Sparse Spatial Omics Observations to Dynamic 3D Tissue States

Spatial omics profiles molecular states within their native tissue context, yet dense measurements across three-dimensional space and biological time remain costly and experimentally challenging. Available datasets therefore contain sparse tissue sections or observations from only a few biological stages, leaving spatial regions and intermediate tissue states unmeasured. Although these forms of missingness arise in different experimental settings, both require inference of unobserved spatial–molecular distributions from sparse tissue snapshots. We introduce HyperBridge, a generative method that reconstructs unobserved tissue states as an unbalanced stochastic bridge between endpoint distributions. A dynamic three-dimensional hypergraph captures higher-order relationships among observed cells or spots and provides endpoint-conditioned structural signals for the bridge control, while activity probabilities allow represented mass to vary without requiring particle-wise correspondence. The same formulation supports reconstruction between tissue sections and between three-dimensional states at different biological stages. Experiments across diverse spatial-omics platforms show that HyperBridge improves held-out section reconstruction and preserves tissue organization. Large-scale reconstruction further produces coherent dense tissue atlases, while experiments on planarian regeneration demonstrate reconstruction and forecasting across unobserved biological stages.