Spatial Transcriptomics Super-Resolution via Dual-Adaptive Multimodal Learning

Conventional sequencing-based spatial transcriptomics offers broad gene coverage but aggregates signals from multiple cells, motivating histology-guided super-resolution reconstruction of finer spatial expression. Existing methods often use fixed context combinations or uniform-capacity prediction branches, limiting explicit adaptation of scale contributions and mapping capacity to local tissue and molecular information. To address this limitation, we introduce Dual-Adaptive Spatial Transcriptomics (DAST), which jointly adapts multiscale context integration and expression mapping to predict superpixel-level gene expression under spot-aggregated supervision. Specifically, DAST constructs multiscale contexts after pathology feature encoding, interacts each scale with local expression features, and dynamically learns scale contributions from the resulting multimodal representations. The resulting fused content and scale weights then jointly guide selection among experts with different hidden widths, adapting mapping capacity to the context composition. Across ten Visium HD and Xenium datasets, DAST achieves the lowest RMSE and highest SSIM compared with six other methods. Factorial comparisons and ablations provide evidence of synergistic gains from dynamic scale fusion and heterogeneous expert mapping. Downstream analyses further explore spatial domain identification and candidate tertiary lymphoid structure localization.