Spatial transcriptomics enables localized gene expression profiling within histological regions. Current supervised methods struggle to infer patterns for novel gene types beyond their training scope, while existing zero-shot frameworks partially address this by incorporating gene semantics, the “independent learning” paradigms hamper their usage in zero-shot gene expression prediction. Specifically, they learn tissue morphology and gene semantics (inter-modality) independently, and treat gene functions (intra-modality) as independent entities. In this paper, we present a deep association multimodal framework which bridges pathological image with gene functionality semantics for zero-shot expression prediction. Concretely, our framework achieves generalized expression prediction by integrating nuclei-aware spatial modeling that preserves tissue microarchitecture, cross-modal alignment of pathological features with gene functionality semantics via iterative vision-language prompt learning, and gene interaction modeling that dynamically captures relationships across gene descriptions. On standard benchmark datasets, we demonstrate competitive zero-shot performance compared to other competitors (e.g., outperforms 16.3 \(\%\) in mean Pearson Correlation Coefficient on cSCC dataset), and we show clinical interpretability of our method. Codes is publicly available at https://github.com/DeepMed-Lab-ECNU/ALIGN-ST .