Dissecting B/Plasma Cells in Periodontitis at Single-Cell/Bulk Resolution

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.

The role of MIF in periodontitis: A potential pathogenic driver, biomarker, and therapeutic target

“…During the inflammatory response to periodontitis, the invasion of periodontal pathogens directly stimulates periodontal ligament cells and APCs to produce inflammatory cytokines (Nilsson, 2021). In our previous single‐cell transcriptomic study, although targeting extensively on different immune cells, MIF was only expressed in plasma cells in periodontitis, in contrast to its expression in monocytes, myeloid dendritic cells, and immune cell progenitors in healthy gingival tissue (Liu et al, 2022). Subsequently, MIF can facilitate the recruitment of B or T cells depending on the co‐receptors CXCR4 or CXCR2 and CXCR4, respectively, with receptor CD74 (Klasen et al, 2014).…”

“…However, since the initial symptoms of periodontal diseases are often mild and tolerable, diagnostic schemes requiring patients to visit clinics rather than self‐testing may often lead to inadequate patients for long‐term follow‐up (Echeverría et al, 2019). In addition, current periodontal classifications are mainly based on diagnostic methods that reflect findings at the gross/histological level, but the most significant biological events have been proven to first occur at the molecular level (Liu et al, 2022). The molecular‐level evidence of the MIF role in periodontal disease (Table 1) may have the potential to eliminate the above‐mentioned disadvantages in large‐scale screening or patient self‐testing, which enhances patient adherence and compliance and, in turn, improves periodontal disease‐related outcomes (Ganganna et al, 2020; Grande et al, 2020; Heidari et al, 2017; Li et al, 2013; Lira‐Junior et al, 2017; Ortiz‐García et al, 2019; Yilmaz et al, 2021; Zhang et al, 2021).…”

Single‐cell sequencing, spatial transcriptome ad periodontitis: Rethink pathogenesis and classification

“…Using machine learning methods to further narrow down potential gene candidates, they defined three diagnostic models. One of model showed good clinical performance which was validated by bulk tissue transcriptomics and low‐throughput quantitative real‐time polymerase chain reaction (Liu et al, 2022). Caetano et al focused on the cellular landscape in periodontitis.…”