Dissecting B/Plasma Cells in Periodontitis at Single-Cell/Bulk Resolution
Dissecting B/Plasma Cells in Periodontitis at Single-Cell/Bulk Resolution
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DOI：
10.1177/00220345221099442
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发表时间：
2022-05-26
影响因子：
7.6
通讯作者：
Huang, D.
中科院分区：
文献类型：
--
作者：
Liu, L.;Chen, Y.;Huang, D.
近几十年来，我们对牙周炎的认识已经从大体/组织学水平发展到细胞/分子水平。以前的景观研究探讨了牙周炎的分子亚型，诊断和牙龈组织细胞分解，并在转录组水平上获得了有意义的结果。然而，目前的牙周炎转录组学研究缺乏免疫细胞和特定免疫细胞亚型的生物学过程之间的相互作用的更精细的解剖。在这项研究中，我们在单细胞水平上对牙周炎中的15种免疫细胞类型进行了分类，并基于多中心整合的单细胞转录组谱进行了细胞通讯分析，其中浆细胞产生的巨噬细胞迁移抑制因子可以与大多数其他免疫细胞进行通讯牙周炎。牙周炎中B/浆细胞浸润的伪时间分析揭示了B/浆细胞的2种不同的细胞命运（CF）。此外，在大块组织水平上，单样本基因集富集分析显示了类似的免疫细胞浸润趋势，加权基因共表达网络分析确定了免疫相关基因模块。结合上述发现，我们使用机器学习方法进一步缩小潜在的候选基因，以开发和验证牙周炎的分子诊断模型。对大型公共队列（68名健康人与235名牙周炎患者）和独立验证队列（12名健康人与7名牙周炎患者）的多变量逻辑回归显示，CF 1特征提供了良好的区分和校准性能，并在适当的阈值概率下具有临床益处。此外，在速冻牙龈组织和龈沟液中进行候选基因的定量实时聚合酶链反应验证。我们的转录组景观分析在单细胞和散装组织的分辨率，从而说明了B/浆细胞浸润过程中的牙周炎，并揭示了基因签名，可能有助于分子诊断的疾病。
In recent decades, our understanding of periodontitis has evolved from that based on a gross/histologic level to one on a cellular/molecular level. Previous landscape studies have explored molecular subtyping, diagnosis, and gingival tissue cell decomposition in periodontitis, and meaningful results have been obtained at a transcriptomic level. However, current periodontitis transcriptomic studies lack a finer dissection of the intercommunication between immune cells and the biological processes of specific immune cell subtypes. In this study, we classified 15 immune cell types in periodontitis at a single-cell level and conducted a cell communication analysis based on a multicenter integrated single-cell transcriptome profile, in which plasma cell-generated macrophage migration inhibitory factor can communicate with most other immune cells in periodontitis. A pseudotime analysis focusing on B/plasma cell infiltration in periodontitis revealed 2 distinct cell fates (CFs) for B/plasma cells. In addition, at a bulk tissue level, a single-sample gene set enrichment analysis showed a similar immune cell infiltration trend, and a weighted gene coexpression network analysis identified an immune-related gene module. Combined with the above findings, we used machine learning methods to further narrow down potential gene candidates for developing and validating molecular diagnostic models of periodontitis. Multivariable logistic regression of a large public cohort (68 healthy vs. 235 periodontitis) and an independent validation cohort (12 healthy vs. 7 periodontitis) showed the CF1 signature provides a good discrimination and calibration performance with clinical benefits at a proper threshold probability. Furthermore, quantitative real-time polymerase chain reaction validation of the gene candidates was performed in both snap-frozen gingival tissues and gingival crevicular fluids. Our transcriptomic landscape analysis at both single-cell and bulk tissue resolutions thereby illustrates the B/plasma cell infiltration process in periodontitis and reveals a gene signature that may assist in molecular diagnosis of the disease.