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Updated: Jun 28, 2026
Isolation, Processing and Analysis of Murine Gingival Cells
Published on: July 2, 2013
Single-Cell and Machine Learning Analysis Reveal Novel Inflammatory Macrophage Subtypes and Biomarkers in
Shaoyong Chen1, Jun Zhao2, Jiayi Hang2
1Department of Preventive Dentistry, College & Hospital of Stomatology, Guangxi Medical University, Nanning, Guangxi, China; Guangxi Key Laboratory of Oral and Maxillofacial Rehabilitation and Reconstruction, Nanning, Guangxi, China.
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.
Insights
Researchers identified new macrophage subtypes in periodontitis (PD) and developed a gene signature for early PD diagnosis. This discovery offers potential for targeted therapies and improved disease management.
Area of Science:
- Immunology
- Genomics
- Computational Biology
Background:
- Periodontitis (PD) is a chronic inflammatory disease characterized by immune dysregulation and tissue damage.
- Macrophages are key players in PD pathogenesis, but their subtypes and roles are not fully understood.
Purpose of the Study:
- To identify and characterize novel macrophage subpopulations in periodontitis.
- To explore their diagnostic and prognostic value using single-cell RNA sequencing and machine learning.
Main Methods:
- Single-cell RNA sequencing (scRNA-seq) of gingival tissues from PD patients and controls.
- Pseudotime, cell-cell communication, and metabolic pathway analyses.
- Machine learning for diagnostic gene identification and a predictive gene signature (PMRGS) development.
Main Results:
- Distinct PD-associated macrophage (PD-MΦ) subpopulations with pro-inflammatory and immunometabolic alterations were identified.
- A five-gene signature (CXCR4, ATF3, TXN, CBX3, MBP) demonstrated strong diagnostic performance (AUC=0.88).
- In vitro validation confirmed the gene expression patterns.
Conclusions:
- Novel PD-associated macrophage subtypes and a predictive gene signature for early diagnosis and monitoring were discovered.
- Findings offer insights into PD immunopathogenesis and potential therapeutic targets for macrophage-directed interventions.
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