Dissecting B/Plasma Cells in Periodontitis at Single-Cell/Bulk Resolution. (October 2022)

• Record Type: Journal Article
• Authors: Liu, L.; Chen, Y.; Wang, L.; Yang, F.; Li, X.; Luo, S.; Yang, L.; Wang, T.; Song, D.; Huang, D.
• Is Part Of: Journal of dental research. Volume 101:Number 11(2022)
• Page Start: 1388
• Page End: 1397
• Publication Date: 2022-10
• Subjects: single-cell sequencing -- bioinformatics -- oral diagnosis -- periodontal disease(s)/periodontitis -- immunity -- logistic models; Dentistry -- Periodicals; Dentistry -- Social aspects -- Periodicals; Dentistry -- Periodicals; Research -- Periodicals; 617.6005
• DOI: 10.1177/00220345221099442
• Languages: English
• ISSNs: 0022-0345

Abstract

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.