TissueField: Reconstructing Spatial Gene Expression with Reusable Conditional Neural Fields

Spatial transcriptomics samples gene expression at discrete locations, creating a need to reconstruct spatial expression patterns from partial and irregular observations. Implicit neural representations offer a continuous model of these patterns, but fitting a separate field for each tissue section limits the reuse of learned structure. We present TissueField, a conditional neural field that learns from source sections to reconstruct expression on a new section from partial observations. TissueField constructs local context–query episodes and uses a Transformer hypernetwork to infer window-specific modulation of a shared coordinate network. The network predicts compact expression embeddings, which a shared decoder maps to gene expression. On a new section, conditioning and reconstruction require only forward computation, with modulation reused across queries within each window. Across Stereo-seq MOSTA, MERFISH, Visium, and Slide-seqV2, within-platform cross-section evaluations show consistent reconstruction gains over directly transferred baselines. Spatial analyses show preservation of characteristic expression patterns and tissue organization. These findings support reusing learned reconstruction structure to recover expression on new sections without repeated model fitting.