Single-cell RNA landscape of the osteoimmunology microenvironment in periodontitis

Single-cell RNA sequencing (scRNA-seq) enables specific profiling of cell populations at single-cell resolution. The osteoimmunology microenvironment in the occurrence and development of periodontitis remains poorly understood at the single-cell level. In this study, we used single-cell transcriptomics to comprehensively reveal the complexities of the molecular components and differences with counterparts residing in periodontal tissues. Methods: We performed scRNA-seq to identify 51248 single cells from healthy controls (n=4), patients with severe chronic periodontitis (n=5), and patients with severe chronic periodontitis after initial periodontal therapy within 1 month (n=3). Uniform manifold approximation and projection (UMAP) were further conducted to explore the cellular composition of periodontal tissues. Pseudotime cell trajectory and RNA velocity analysis, combined with gene enrichment analysis were used to reveal the molecular pathways underlying cell fate decisions. CellPhoneDB were performed to identify ligand-receptor pairs among the major cell types in the osteoimmunology microenvironment of periodontal tissues. Results: A cell atlas of the osteoimmunology microenvironment in periodontal tissues was characterized and included ten major cell types, such as fibroblasts, monocytic cells, endothelial cells, and T and B cells. The enrichment of TNFRSF21+ fibroblasts with high expression of CXCL1, CXCL2, CXCL5, CXCL6, CXCL13, and IL24 was detected in patients with periodontitis compared to healthy individuals. The fractions of CD55+ mesenchymal stem cells (MSCs), APOE+ pre-osteoblasts (pre-OBs), and IBSP+ osteoblasts decreased significantly in response to initial periodontal therapy. In addition, CXCL12+ MSC-like pericytes could convert their identity into a pre-OB state during inflammatory responses even after initial periodontal therapy confirmed by single-cell trajectory. Moreover, we portrayed the distinct subtypes of monocytic cells and abundant endothelial cells significantly involved in the immune response. The heterogeneity of T and B cells in periodontal tissues was characterized. Finally, we mapped osteoblast/osteoclast differentiation mediators to their source cell populations by identifying ligand-receptor pairs and highlighted the effects of Ephrin-Eph signaling on bone regeneration after initial periodontal therapy. Conclusions: Our analyses uncovered striking spatiotemporal dynamics in gene expression, population composition, and cell-cell interactions during periodontitis progression. These findings provide insights into the cellular and molecular underpinning of periodontal bone regeneration.

(D) Representative images of immunofluorescence staining of periodontal tissues from HC (left panels) and PD samples (right panels) for double fluorescent analysis of Collagen I (green) and CD358 (red) expression. Top panel: Merged; bottom left: Collagen I; bottom right: CD358. Scale bar = 50μm.
(E) Bubble plot showing expressions (dots) of the selected marker genes (columns) in each subcluster (rows, as in Figure 2B). Dot colored by the average expression level, and dot size proportional to the percentage expression.
(E) Pathways enrichment analysis associated with genes clusters in Figure 4E -Log 10 (P-value)   Figure 5A.

(B)
The box plots showing the percentage of cells for each subcluster of CD4 + T cells from HC (blue, n=2266), PD (red, n=1637), and PDT (green, n=1163) samples with plot center, box, whiskers, and points corresponding to the median, IQR, 1.5 × IQR and >1.5× IQR, respectively. Data were analyzed using t test (two-tailed, two sample, equal variance). Figure 6D.

(D)
The box plots showing the percentage of cells for each subcluster of CD8 + T cells from HC (blue, n=2604), PD (red, n=1695), and PDT (green, n=1652) samples with plot center, box, whiskers, and points corresponding to the median, IQR, 1.5 × IQR and >1.5× IQR, respectively. Data were analyzed using t test (two-tailed, two sample, equal variance).
(E) Heatmap of the QuSAGE activity for the gene modules (labels on the right) that associated with cell functional genes among seven subclusters of CD8 + T cells from HC, PD, and PDT samples (labels on the top). Red indicates increased average expression of genes in the modules.   Figure 6G. Figure 6G to I, from (blue, n=1039), PD (red, n=1078), and PDT (green, n=1131) samples with plot center, box, whiskers, and points corresponding to the median, IQR, 1.5 × IQR and >1.5× IQR, respectively. Data were analyzed using t test (two-tailed, two sample, equal variance).
(B) Heatmap of the relative expression level of the top 8-10 differentially expressed maker genes identified in the fibroblast clusters in (A).

(C)
The box plots showing the percentage of cells for each of six clusters as in (A) from HC (blue, n=37), PD (red, n=449), and PDT samples (green, n=543) with plot center, box, whiskers, and points corresponding to the median, IQR, 1.5 × IQR and >1.5× IQR, respectively. Data were analyzed using t-tests (two-tailed, two sample, equal variance).
(D) Heatmap of the QuSAGE activity for GO terms that are associated with differentially expressed marker genes among six clusters (labels on the bottom). Red indicating increased average expression of genes in GO term. Epi cells: Epithelial cells.