A unified spatial AI framework for cross-domain tissue-state analysis in wound healing, oral, and cardiovascular pathology
Tuan D. PhamObjective
To develop a cross-domain spatial AI framework for identifying conserved tissue-state organisation across wound healing, oral pathology, and cardiovascular tissue using spatial transcriptomic data.
Methods
Four public spatial transcriptomic datasets spanning wound healing, periodontitis, oral squamous cell carcinoma, and cardiac tissue were integrated using recurrence modelling, graph-based spatial learning, fuzzy tissue-state analysis, and tensor decomposition. Cross-domain coupling, spatial fragmentation, recurrence structure, and permutation-based topological validation were evaluated.
Results
Six conserved fuzzy tissue states were identified, dominated by extracellular matrix remodelling, fibroblast/stromal activation, endothelial signalling, and inflammatory pathways. Latent embedding analysis demonstrated strong overlap between wound healing and oral domains, while cardiovascular tissue exhibited more compact spatial organisation. Across the four datasets, oral squamous cell carcinoma showed the highest fragmentation and spatial entropy. Tensor decomposition identified conserved stromal-remodelling programmes across domains. Permutation testing showed significantly higher graph modularity and lower spatial entropy relative to null distributions in all four datasets, whereas mean local recurrence was not significantly elevated in any dataset.
Conclusion
The proposed framework identified conserved spatial tissue-state architecture linking wound healing, oral pathology, and cardiovascular tissue despite differences in tissue origin, pathology, and acquisition technology.
Significance
These findings demonstrate the potential of spatial AI for investigating conserved stromal and inflammatory microenvironmental organisation across clinically related disease systems and may support spatial biology research in wound healing, oral, and systemic disease.