Spatial Transcriptomic Analysis Identifying Immune Stromal Niches Associated with Inflammatory and Fibrotic Signatures in Periodontitis: A Single-sample Proof-ofConcept Exploratory Analysis

Introduction: Periodontitis progressively destroys toothsupporting tissues through spatially heterogeneous immunestromal interactions. Whether healing results in regeneration or fibrotic scarring remains unpredictable because bulk analyses obscure the microenvironmental complexity underlying these outcomes. Aim: To characterise spatial immune-stromal microenvironments in periodontitis and identify molecular determinants of regenerative versus fibrotic healing using spatial transcriptomics and causal AI in a single-sample, proof-of-concept exploratory study. Materials and Methods: A retrospective secondary computational analysis of publicly available spatial transcriptomic data was conducted from 1st to 30th June 2026 at Saveetha Dental College and Hospitals, Chennai, Tamil Nadu, India. The analysed 10× Visium data from periodontitis-affected human oral mucosa ((National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) accession GSE206621, sample B2) were analysed. K-means clustering (k=4) identified four immune-stromal microenvironments, termed “spatial niches.” Pathway scores across six programs, Ligand–Receptor (L-R) analysis of 14 signalling pairs and spatial autocorrelation (Moran’s I) defined zone-specific biology. Random Forest (RF) and Neural-Network (NN) classifiers assessed niche separability. Directed Acyclic Graphs (DAGs) and Structural Causal Models (SCMs) distinguished regenerative from fibrotic healing. Results: Four niches were identified: quiescent epithelial-rich, destructive immune-fibroblast, transitional/remodelling, and moderate-activity zones. The destructive zone showed the greatest inflammation, immune infiltration, Extracellular matrix (ECM) remodelling, fibroblast activation, and L-R signalling, indicating a strongly pro-inflammatory, fibrotic environment. The transitional zone showed substantial stromal remodelling but less inflammatory signalling, suggesting a potential regenerative state. Machine-learning analysis showed insample niche separability. Causal modelling implicated proinflammatory immune-stromal signalling and fibroblast activation as key determinants of fibrotic healing, whereas Transforming Growth Factor Beta-1 (TGFB1)-TGFBR-axis modulation at the transitional boundary was associated with regenerative outcomes. Conclusion: In the present single-sample, proof-of-concept analysis, periodontitis lesions contained spatially organised niches with distinct immune-stromal programs. Causal Artificial Intelligence (AI) identified actionable signalling nodes distinguishing regenerative from fibrotic fates, providing a hypothesis-generating framework for spatially targeted periodontal therapies requiring validation in multi-sample studies.