Depth-Resolved Gene Expression Estimation from Tissue Morphology and Sparse Spatial Transcriptomics

Most spatial transcriptomics assays measure gene expression on a limited number of two-dimensional tissue sections, leaving molecular variation across depth largely unobserved. We investigate whether volumetric tissue imaging can provide the structural context needed to estimate this missing molecular information from sparse 2D measurements. We introduce VISTA (Volumetric Inference from Sparse Transcriptomic Anchors), a structure-guided framework for estimating depth-resolved gene-expression fields from 3D tissue imaging using 2D spatial transcriptomics measurements as molecular anchors. VISTA combines a frozen pathology foundation encoder with multi-scale structural encoding, depth-aware attention, and 3D graph propagation to integrate local and long-range tissue context, while a Schr\"odinger-bridge objective along depth encourages coherent predictions that remain consistent with observed ST measurements and preserve 3D structural organization. A single formulation covers three settings that differ in how directly depth-resolved prediction can be checked. On registered serial sections, predictions are checked against held-out measured sections, under supervision as thin as a single revealed plane. On a serial volume in which every section is sequenced, depth accuracy is measured directly against held-out expression and reported against a split-half noise ceiling that bounds what is recoverable; VISTA attains roughly half of it, against a few percent for interpolation. On continuous volumes, where only one plane is sequenced and no method can report per-depth accuracy, we isolate the contribution of depth with a control that replaces the volume by its own projection. Across settings, VISTA improves agreement with measured ST and produces more coherent and depth-sensitive molecular estimates than existing baselines. These results support structure-guided volumetric modeling as a practical approach for extending sparse sectional transcriptomic measurements into depth-resolved molecular representations.