Dynamic Regulatory Graph Learning for Histology-to-Spatial Transcriptomics

Virtual spatial transcriptomics aims to predict spatial gene expression from histopathology images. However, gene expression is not governed by morphology alone; regulatory relationships are structured, context-dependent, and shaped by the local tissue microenvironment. Existing visual-to-expression models and fixed-prior graph methods struggle to align molecular dependencies with spatially varying histological contexts. We propose DragH2ST, a dual-stage retrieval-augmented dynamic regulatory graph learning framework that constructs task-specific regulatory manifolds and rewires them at the spot level according to local histology. During training, DragH2ST retrieves and integrates heterogeneous biomedical knowledge to build a task-specific regulatory prior, guiding gene representations toward biologically plausible and context-adaptive regulatory structures. To capture microenvironment-dependent regulation, context-gated graph rewiring performs spot-level soft rewiring of gene-regulatory edges conditioned on histology-derived context, enabling adaptive graph reasoning without dense graph reconstruction. At inference, DragH2ST retrieves morphologically similar historical spot prototypes as case-level references, providing evidence-based support for interpretation and biological plausibility assessment. Experiments on multiple benchmarks show that DragH2ST improves prediction accuracy, preserves gene-gene regulatory topology, and captures spatially heterogeneous expression patterns.