Deep Learning for Cross-Domain Spatial Transcriptomic Modeling of Tissue Repair

Abstract

Spatial transcriptomics enables investigation of tissue organization while preserving molecular and spatial information within intact tissues. However, existing computational methods primarily focus on clustering and batch integration and provide limited characterization of higher-order spatial organization and transferable tissuestate dynamics across heterogeneous biological systems. This study introduces a cross-domain spatial transcriptomic framework centered on recurrence-based latent tissuestate analysis, pathological fragmentation quantification, and transferable representation learning between wound repair and tumor-associated microenvironments. Human spatial transcriptomic datasets spanning cutaneous wound healing, oral squamous cell carcinoma, and head and neck squamous cell carcinoma were integrated within a graph-based latent embedding framework. Recurrence analysis was applied within latent transcriptomic space to characterize spatial organization and remodeling dynamics. A pathological fragmentation index quantified intra-tissue spatial disorganization from recurrence structure. The learned latent embeddings achieved a mean silhouette score of 0.79, demonstrating coherent separation of tissue states. Recurrence analysis revealed progressive restoration of spatial organization during wound remodeling, whereas tumor-associated tissues exhibited increased fragmentation and heterogeneous recurrence structure. Independent single-cell RNA-seq reference atlases demonstrated reproducible multicellular enrichment patterns within latent spatial niches. The proposed framework demonstrates that recurrence-inspired latent spatial analysis may provide biologically interpretable characterization of tissue organization and pathological remodeling across heterogeneous biological systems.