International dental journal · 2025
Single-Cell and Machine Learning Analysis Reveal Novel Inflammatory Macrophage Subtypes and Biomarkers in Periodontitis.
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Chen Shaoyong, Zhao Jun, Hang Jiayi, Tang Jianjia, Xiang Rong, Zhang Siqin
Аннотация
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.
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.