Analysis of immunogenic cell death in periodontitis based on scRNA-seq and bulk RNA-seq data

BackgroundRecent studies have suggested that cell death may be involved in bone loss or the resolution of inflammation in periodontitis. Immunogenic cell death (ICD), a recently identified cell death pathway, may be involved in the development of this disease.MethodsBy analyzing single-cell RNA sequ...

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Main Authors: Erli Wu, Xuan Yin, Feng Liang, Xianqing Zhou, Jiamin Hu, Wanting Yuan, Feihan Gu, Jingxin Zhao, Ziyang Gao, Ming Cheng, Shouxiang Yang, Lei Zhang, Qingqing Wang, Xiaoyu Sun, Wei Shao
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Language:English
Published: Frontiers Media S.A. 2024-11-01
Series:Frontiers in Immunology
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Online Access:https://www.frontiersin.org/articles/10.3389/fimmu.2024.1438998/full
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author Erli Wu
Xuan Yin
Feng Liang
Xianqing Zhou
Jiamin Hu
Wanting Yuan
Feihan Gu
Jingxin Zhao
Ziyang Gao
Ming Cheng
Shouxiang Yang
Lei Zhang
Qingqing Wang
Qingqing Wang
Xiaoyu Sun
Xiaoyu Sun
Wei Shao
Wei Shao
author_facet Erli Wu
Xuan Yin
Feng Liang
Xianqing Zhou
Jiamin Hu
Wanting Yuan
Feihan Gu
Jingxin Zhao
Ziyang Gao
Ming Cheng
Shouxiang Yang
Lei Zhang
Qingqing Wang
Qingqing Wang
Xiaoyu Sun
Xiaoyu Sun
Wei Shao
Wei Shao
author_sort Erli Wu
collection DOAJ
description BackgroundRecent studies have suggested that cell death may be involved in bone loss or the resolution of inflammation in periodontitis. Immunogenic cell death (ICD), a recently identified cell death pathway, may be involved in the development of this disease.MethodsBy analyzing single-cell RNA sequencing (scRNA-seq) for periodontitis and scoring gene set activity, we identified cell populations associated with ICD, which were further verified by qPCR, enzyme linked immunosorbent assay (ELISA) and immunofluorescence (IF) staining. By combining the bulk transcriptome and applying machine learning methods, we identified several potential ICD-related hub genes, which were then used to build diagnostic models. Subsequently, consensus clustering analysis was performed to identify ICD-associated subtypes, and multiple bioinformatics algorithms were used to investigate differences in immune cells and pathways between subtypes. Finally, qPCR and immunohistochemical staining were performed to validate the accuracy of the models.ResultsSingle-cell gene set activity analysis found that in non-immune cells, fibroblasts had a higher ICD activity score, and KEGG results showed that fibroblasts were enriched in a variety of ICD-related pathways. qPCR, Elisa and IF further verified the accuracy of the results. From the bulk transcriptome, we identified 11 differentially expressed genes (DEGs) associated with ICD, and machine learning methods further identified 5 hub genes associated with ICD. Consensus cluster analysis based on these 5 genes showed that there were differences in immune cells and immune functions among subtypes associated with ICD. Finally, qPCR and immunohistochemistry confirmed the ability of these five genes as biomarkers for the diagnosis of periodontitis.ConclusionFibroblasts may be the main cell source of ICD in periodontitis. Adaptive immune responses driven by ICD may be one of the pathogenesis of periodontitis. Five key genes associated with ICD (ENTPD1, TLR4, LY96, PRF1 and P2RX7) may be diagnostic biomarkers of periodontitis and future therapeutic targets.
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spelling doaj-art-64e1b2a13796444aa79213ffb7d106042025-08-20T01:47:25ZengFrontiers Media S.A.Frontiers in Immunology1664-32242024-11-011510.3389/fimmu.2024.14389981438998Analysis of immunogenic cell death in periodontitis based on scRNA-seq and bulk RNA-seq dataErli Wu0Xuan Yin1Feng Liang2Xianqing Zhou3Jiamin Hu4Wanting Yuan5Feihan Gu6Jingxin Zhao7Ziyang Gao8Ming Cheng9Shouxiang Yang10Lei Zhang11Qingqing Wang12Qingqing Wang13Xiaoyu Sun14Xiaoyu Sun15Wei Shao16Wei Shao17Key Laboratory. of Oral Diseases Research of Anhui Province, College & Hospital of Stomatology, Anhui Medical University, Hefei, ChinaKey Laboratory. of Oral Diseases Research of Anhui Province, College & Hospital of Stomatology, Anhui Medical University, Hefei, ChinaKey Laboratory. of Oral Diseases Research of Anhui Province, College & Hospital of Stomatology, Anhui Medical University, Hefei, ChinaKey Laboratory. of Oral Diseases Research of Anhui Province, College & Hospital of Stomatology, Anhui Medical University, Hefei, ChinaKey Laboratory. of Oral Diseases Research of Anhui Province, College & Hospital of Stomatology, Anhui Medical University, Hefei, ChinaKey Laboratory. of Oral Diseases Research of Anhui Province, College & Hospital of Stomatology, Anhui Medical University, Hefei, ChinaKey Laboratory. of Oral Diseases Research of Anhui Province, College & Hospital of Stomatology, Anhui Medical University, Hefei, ChinaKey Laboratory. of Oral Diseases Research of Anhui Province, College & Hospital of Stomatology, Anhui Medical University, Hefei, ChinaKey Laboratory. of Oral Diseases Research of Anhui Province, College & Hospital of Stomatology, Anhui Medical University, Hefei, ChinaKey Laboratory. of Oral Diseases Research of Anhui Province, College & Hospital of Stomatology, Anhui Medical University, Hefei, ChinaKey Laboratory. of Oral Diseases Research of Anhui Province, College & Hospital of Stomatology, Anhui Medical University, Hefei, ChinaKey Laboratory. of Oral Diseases Research of Anhui Province, College & Hospital of Stomatology, Anhui Medical University, Hefei, ChinaKey Laboratory. of Oral Diseases Research of Anhui Province, College & Hospital of Stomatology, Anhui Medical University, Hefei, ChinaDepartment of Periodontology, Anhui Stomatology Hospital Affiliated to Anhui Medical University, Hefei, ChinaKey Laboratory. of Oral Diseases Research of Anhui Province, College & Hospital of Stomatology, Anhui Medical University, Hefei, ChinaDepartment of Periodontology, Anhui Stomatology Hospital Affiliated to Anhui Medical University, Hefei, ChinaKey Laboratory. of Oral Diseases Research of Anhui Province, College & Hospital of Stomatology, Anhui Medical University, Hefei, ChinaDepartment of Microbiology and Parasitology, Anhui Provincial Laboratory of Pathogen Biology, School of Basic Medical Sciences, Anhui Medical University, Hefei, Anhui, ChinaBackgroundRecent studies have suggested that cell death may be involved in bone loss or the resolution of inflammation in periodontitis. Immunogenic cell death (ICD), a recently identified cell death pathway, may be involved in the development of this disease.MethodsBy analyzing single-cell RNA sequencing (scRNA-seq) for periodontitis and scoring gene set activity, we identified cell populations associated with ICD, which were further verified by qPCR, enzyme linked immunosorbent assay (ELISA) and immunofluorescence (IF) staining. By combining the bulk transcriptome and applying machine learning methods, we identified several potential ICD-related hub genes, which were then used to build diagnostic models. Subsequently, consensus clustering analysis was performed to identify ICD-associated subtypes, and multiple bioinformatics algorithms were used to investigate differences in immune cells and pathways between subtypes. Finally, qPCR and immunohistochemical staining were performed to validate the accuracy of the models.ResultsSingle-cell gene set activity analysis found that in non-immune cells, fibroblasts had a higher ICD activity score, and KEGG results showed that fibroblasts were enriched in a variety of ICD-related pathways. qPCR, Elisa and IF further verified the accuracy of the results. From the bulk transcriptome, we identified 11 differentially expressed genes (DEGs) associated with ICD, and machine learning methods further identified 5 hub genes associated with ICD. Consensus cluster analysis based on these 5 genes showed that there were differences in immune cells and immune functions among subtypes associated with ICD. Finally, qPCR and immunohistochemistry confirmed the ability of these five genes as biomarkers for the diagnosis of periodontitis.ConclusionFibroblasts may be the main cell source of ICD in periodontitis. Adaptive immune responses driven by ICD may be one of the pathogenesis of periodontitis. Five key genes associated with ICD (ENTPD1, TLR4, LY96, PRF1 and P2RX7) may be diagnostic biomarkers of periodontitis and future therapeutic targets.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1438998/fullimmunogenic cell death (ICD)periodontitisfibroblastsmachine learningbiomarker
spellingShingle Erli Wu
Xuan Yin
Feng Liang
Xianqing Zhou
Jiamin Hu
Wanting Yuan
Feihan Gu
Jingxin Zhao
Ziyang Gao
Ming Cheng
Shouxiang Yang
Lei Zhang
Qingqing Wang
Qingqing Wang
Xiaoyu Sun
Xiaoyu Sun
Wei Shao
Wei Shao
Analysis of immunogenic cell death in periodontitis based on scRNA-seq and bulk RNA-seq data
Frontiers in Immunology
immunogenic cell death (ICD)
periodontitis
fibroblasts
machine learning
biomarker
title Analysis of immunogenic cell death in periodontitis based on scRNA-seq and bulk RNA-seq data
title_full Analysis of immunogenic cell death in periodontitis based on scRNA-seq and bulk RNA-seq data
title_fullStr Analysis of immunogenic cell death in periodontitis based on scRNA-seq and bulk RNA-seq data
title_full_unstemmed Analysis of immunogenic cell death in periodontitis based on scRNA-seq and bulk RNA-seq data
title_short Analysis of immunogenic cell death in periodontitis based on scRNA-seq and bulk RNA-seq data
title_sort analysis of immunogenic cell death in periodontitis based on scrna seq and bulk rna seq data
topic immunogenic cell death (ICD)
periodontitis
fibroblasts
machine learning
biomarker
url https://www.frontiersin.org/articles/10.3389/fimmu.2024.1438998/full
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