Diagnosis of Periodontitis via Neutrophil Degranulation Signatures Identified by Integrated scRNA-Seq and Deep Learning

<b>Background and objective</b>: Periodontitis, a chronic inflammatory disease driven by host immune dysregulation, leads to progressive destruction of periodontal tissues. This study employed an integrative approach combining single-cell transcriptomics, hierarchical weighted gene co-expression network analysis (hdWGCNA), and deep learning algorithms to identify key biomarkers associated with neutrophil degranulation in periodontitis, aiming to establish diagnostic models for early detection and precision interventions. <b>Methods</b>: We integrated single-cell RNA sequencing (scRNA-seq) data from human gingival tissues with bulk transcriptomic datasets. Pathogenic neutrophil subsets were characterized via pseudotime trajectory and cell-cell communication analyses. Hierarchical weighted gene co-expression network analysis (hdWGCNA) identified functional modules linked to degranulation. Machine learning and a convolutional neural network (CNN) model combining gene expression and immune cell profiles were developed for diagnosis. <b>Results</b>: scRNA-seq revealed a neutrophil subpopulation significantly increased infiltration in periodontitis, with cell-cell communication and pseudotime trajectory analyses demonstrating amplified inflammatory crosstalk. hdWGCNA identified the turquoise module enriched in PD-KEY-Neutrophils, containing hub genes linked to neutrophil degranulation and complement activation. Immune infiltration and non-negative matrix factorization linked high-degranulation neutrophil signatures to the periodontal immunity microenvironment. Machine learning demonstrated that the neutrophil degranulation-associated genes effectively distinguish diseased gingival tissue, suggesting their potential to predict periodontitis. Finally, integrating transcriptomic and immunological data, we developed a gene-immune CNN deep learning model accurately diagnosed periodontitis in diverse cohorts (AUC = 0.922). <b>Conclusions</b>: Our study identified a pathogenic neutrophil subpopulation driving periodontitis through degranulation and inflammation. The neutrophil degranulation genes serve as critical biomarkers, offering new insights for clinical diagnosis and treatment of periodontitis.