Single-Cell and Machine Learning Analysis Reveal Novel Inflammatory Macrophage Subtypes and Biomarkers in Periodontitis.

BACKGROUND

Periodontitis (PD) is a chronic inflammatory disease marked by immune dysregulation and progressive tissue destruction. Macrophages play a pivotal role in PD pathogenesis; however, their heterogeneity, molecular characteristics and clinical relevance remain incompletely understood.

OBJECTIVE

To identify and characterise novel subpopulations of macrophages associated with PD and explore their diagnostic and prognostic significance using single-cell RNA sequencing and machine learning.

METHODS

Single-cell RNA sequencing (scRNA-seq) was performed on gingival tissues from PD patients and healthy controls to identify macrophage subtypes. Pseudotime trajectory and cell-cell communication analyses were conducted to investigate functional states and intercellular interactions. Metabolic pathway analysis assessed the metabolic features of PD-related macrophages (PD-MΦ). Machine learning algorithms were used to identify key diagnostic genes and construct a PD-MΦ-related gene signature (PMRGS). The model was validated using ROC analysis and in vitro experiments in THP-1-derived macrophages under inflammatory stimulation.

RESULTS

Distinct PD-MΦ subpopulations were identified, exhibiting pro-inflammatory and immunometabolic alterations. Five diagnostic biomarkers - CXCR4, ATF3, TXN, CBX3 and MBP - were selected to develop the PMRGS. The gene signature showed strong diagnostic performance (area under the curve = 0.88). In vitro validation confirmed differential gene expression patterns consistent with scRNA-seq results.

CONCLUSION

This study reveals novel PD-associated macrophage subtypes and identifies a predictive gene signature with potential clinical utility in early diagnosis and disease monitoring. These findings provide new insights into PD immunopathogenesis and suggest therapeutic targets for macrophage-directed interventions.