Key Laboratory. of Oral Diseases Research of Anhui Province, College & Hospital of Stomatology, Anhui Medical University, Hefei, China.
Department of Periodontology, Anhui Stomatology Hospital Affiliated to Anhui Medical University, Hefei, China.
Front Immunol. 2024 Nov 1;15:1438998. doi: 10.3389/fimmu.2024.1438998. eCollection 2024.
Recent 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.
By 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.
Single-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.
Fibroblasts 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.
最近的研究表明，细胞死亡可能参与牙周炎中的骨质流失或炎症消退。免疫原性细胞死亡（ICD）是一种新发现的细胞死亡途径，可能与该疾病的发展有关。
通过分析牙周炎的单细胞 RNA 测序（scRNA-seq）和评分基因集活性，我们确定了与 ICD 相关的细胞群体，并用 qPCR、酶联免疫吸附测定（ELISA）和免疫荧光（IF）染色进一步验证。通过结合批量转录组并应用机器学习方法，我们确定了几个潜在的 ICD 相关枢纽基因，然后用于构建诊断模型。随后，进行共识聚类分析以确定与 ICD 相关的亚型，并使用多种生物信息学算法来研究亚型之间免疫细胞和通路的差异。最后，进行 qPCR 和免疫组织化学染色以验证模型的准确性。
单细胞基因集活性分析发现，在非免疫细胞中，成纤维细胞的 ICD 活性评分较高，KEGG 结果显示成纤维细胞富集在多种 ICD 相关途径中。qPCR、ELISA 和 IF 进一步验证了结果的准确性。从批量转录组中，我们确定了 11 个与 ICD 相关的差异表达基因（DEGs），机器学习方法进一步确定了 5 个与 ICD 相关的枢纽基因。基于这 5 个基因的共识聚类分析表明，与 ICD 相关的亚型之间存在免疫细胞和免疫功能的差异。最后，qPCR 和免疫组化证实了这 5 个基因作为牙周炎诊断生物标志物的能力。
成纤维细胞可能是牙周炎中 ICD 的主要细胞来源。由 ICD 驱动的适应性免疫反应可能是牙周炎的发病机制之一。与 ICD 相关的 5 个关键基因（ENTPD1、TLR4、LY96、PRF1 和 P2RX7）可能是牙周炎的诊断生物标志物和未来的治疗靶点。
