AtlasS2V: Organ Atlas–Guided Prediction of 3D Tissue Distributions from a Single Section

Three-dimensional tissue reconstruction typically relies on multiple spatially pro- filed sections from the same specimen, limiting its applicability to valuable datasets represented by a single section. We introduce ATLASS2V, which combines one observed 2D tissue section with a spatially registered 3D reference atlas to pre- dict cell-density and cell-type distributions above and below the section. The atlas supplies depth-resolved anatomical structure and spatial context beyond the observed plane, while the observed section captures specimen-specific devi- ations from this population prior. ATLASS2V calibrates atlas abundance to the observed section and transfers its residual through a reference-trained distance gate, retaining more individual information nearby and relying increasingly on the atlas at greater distances. On an independent mouse, ATLASS2V improves reconstruction over central-copy and Atlas-only across near-to-intermediate offsets and converges toward the atlas at longer distances. Strict outer-animal validation shows consistent gains over central-copy in all four held-out mice, increasing mean composition Pearson from 0.392 to 0.629. A marker-density adaptation further improves distance-integrated correlation over each animal’s best fixed expert in 58 of 60 held-out mice. Organ-specific studies demonstrate feasibility in human lung, breast, and kidney. Atlas-derived context also improves sparse-panel inference: with ten measured genes, hidden-gene correlation increases from 0.6755 to 0.7268, while 34-class cell-typing macro-F1 increases from 0.3058 with 2D atlas context to 0.4707 with correct 3D context. These results support reference-conditioned prediction as a route to extending single-section datasets into virtual 3D tissue distributions without collecting serial sections from each target specimen.