Single-Cell Transcriptomic Analysis of Dental Pulp and Periodontal Ligament Stem Cells

Abstract

The regeneration of periodontal, periapical, and pulpal tissues is a complex process requiring the direct involvement of cells derived from pluripotent stem cells in the periodontal ligament and dental pulp. Dental pulp stem cells (DPSCs) and periodontal ligament stem cells (PDLSCs) are spatially distinct with the potential to differentiate into similar functional and phenotypic cells. We aimed to identify the cell heterogeneity of DPSCs and PDLSCs and explore the differentiation potentials of their specialized organ-specific functions using single-cell transcriptomic analysis. Our results revealed 7 distinct clusters, with cluster 3 showing the highest potential for differentiation. Clusters 0 to 2 displayed features similar to fibroblasts. The trajectory route of the cell state transition from cluster 3 to clusters 0, 1, and 2 indicated the distinct nature of cell differentiation. PDLSCs had a higher proportion of cells (78.6%) at the G1 phase, while DPSCs had a higher proportion of cells at the S and G2/M phases (36.1%), mirroring the lower cell proliferation capacity of PDLSCs than DPSCs. Our study suggested the heterogeneity of stemness across PDLSCs and DPSCs, the similarities of these 2 stem cell compartments to be potentially integrated for regenerative strategies, and the distinct features between them potentially particularized for organ-specific functions of the dental pulp and periodontal ligament for a targeted regenerative dental tissue repair and other regeneration therapies.

Keywords: fibroblast; osteoblast; regenerative medicine, stem cell, tooth; single-cell RNA-seq.

Figure 1.

Human periodontal ligament and dental pulp cell isolation, bulk and single-cell RNA sequencing workflow. (A) Extracted human teeth were obtained from 3 healthy donors (aged 21–27), and the dental pulp and periodontal ligament were harvested. (B–D) Samples were enzymatically digested, and the isolated primary cells were processed through single-cell RNA sequencing or (E–G) characterized through Fluorescence Activated Cell Sorting (FACS), differentiated into osteoblasts and fibroblasts, and bulk RNA sequencing was performed. (H) After data acquisition, gene expression features, cell differentiation potentials, and cell ontogeny were investigated.

Figure 2.

Cluster composition and gene signatures in periodontal ligament stem cells (PDLSCs) and dental pulp stem cells (DPSCs). (A) Uniform Manifold Approximation and Projection (UMAP) visualization of PDLSCs and DPSCs, colored with clusters (upper panel) and sample groups (lower panel). Cluster 0 was characterized by HSPA5, ID1, and ID3 genes. ID1 and ID3 are transcription factors. In mice, Id1 and Id3 kept a steady state of hematopoiesis (Gadomski et al. 2020) and were found to play a role in bone formation (Maeda et al. 2004). Cluster 1, near cluster 0 in the UMAP (A), exhibited enriched expressions of ACTA2, MYL9, MYL12A, CALD1, TPM1, TPM2, TAGLN, DSTN, SYNPO2, SPARC, and WNT5A linked to tissue development pathways and considered signature genes in stem-myofibroblastic cell populations ( Appendix Table 2). ACTA2 encodes the smooth muscle α2 actin protein in smooth muscle cells (Talele et al. 2015); MYL9 and MYL12A regulate muscle contraction (Oya et al. 2021; Sun et al. 2020); CALD1 is responsible for tonically inhibiting the ATPase activity of myosin in smooth muscle (Gusev 2001); tropomyosin 1 and 2 (TPM1 and TPM2) regulate muscle contraction and relaxation by interacting with actin, myosin, and the troponin complex (Bai et al. 2013); TAGLN is an actin cross-linking/gelling protein found in smooth muscle (Li et al. 1996); DSTN is an actin-binding component protein (Liao et al. 2021); and SPARC/osteonectin regulates periodontal/osteogenesis homeostasis and collagen content. Cluster 2 was defined by enriched expression for IGFBP5, TIMP3, COL3A1, DCN, IFITM3, C1R, CTSD, FTL, C1S, NUPR1, SOX4, LUM, NNMT, FTH1, CXCL12, IGFBP6, GAS5, SFRP4, and HTRA1 genes. The pathways of ossification, response to hormone or growth factors, and a different set of tissue formation genes showed top enrichment in cluster 2. Cluster 3 showed enriched expression of genes related to cell proliferation, migration, and pluripotency, such as TOP2A, UBE2C, HMGB2, HIST1H4C, KPNA2, CENPF, CKS2, and TYMS. Cluster 4 displayed wide variability in gene expression, including genes related to cell adhesion, cytokinesis, and oncogenes, with high expression levels of CCNB1, PTTG1, and UBE2S. Clusters 5 and 6 were isolated in the UMAP composition and exhibited high expression levels of apoptotic markers such as TP53, BAX, and FADD (cluster 5) and CASP3 and CYCS (cluster 6). In addition, they displayed enriched signatures for inflammasome-related pyroptotic pathways, such as CASP1, CASP4, and IL18 (cluster 5) and CASP1 and GASDME (cluster 6). Therefore, clusters 5 and 6 were linked to either undergoing cell death or the inflammasome-related pyroptotic pathways ( Appendix Fig. 1). (B) Heatmap showing the top 10 (ranked by adjusted P value) significant differentially expressed genes (DEGs) among 7 clusters (rows for genes and columns for clusters). If the number of significant DEGs is fewer than 10 in 1 cluster, we only use fewer than 10 genes for that cluster in the heatmap. Feature genes are highlighted by black dots with the corresponding gene names on the right side of the heatmap. (C) Dot plots showing the expression profiles of canonical markers among 7 clusters. The color scale represents the expression level of genes, and the dot size refers to the percentage of cells expressing the gene. Genes expressed in more than 75% of the cells and/or had an average expression ≥1 were included for comparison purposes. The genes with a log-scaled fold change >0.25 and adjusted P < 0.05 were considered statistically significant (DEGs). (D) Schematic depiction of subpopulation expression signature score (S score). Given a list of the DEGs in cell type A (yellow) over cell type B (purple) identified from bulk RNA sequencing data, a high S score represents the testing cell cluster displaying a higher similarity to cell type A, while a low S score indicates a low similarity between the testing cell cluster and cell type A. (E) Box plot showing the S score among 7 clusters, where the clusters with higher scores are more similar to fibroblasts. In contrast, those with lower scores are more like osteoblasts. (F) Schematic depiction of single-cell entropy (scEntropy) score, where higher scEntropy scores indicate less differentiated cell clusters/types, while low entropy values represent more differentiated cell clusters/types. (G) Box plot showing the scEntropy score among 7 clusters. (H) Pseudotime trajectory of cells from 7 clusters. Left, colored by clusters; right, colored by pseudotime. (I) Heatmap for the DEGs along the trajectory (pseudotime). The pseudotime was split into 3 bins (intervals), and the average gene expression was computed for each bin. Genes are grouped into 2 categories based on their expression profiles, one highly expressed at the initial stage and the other at the late stage. Blue-red colors within the heatmap represent the standardized gene expression z scores.

Figure 3.

Annotation of the cell clusters in periodontal ligament stem cells (PDLSCs) and dental pulp stem cells (DPSCs). (A) Pie chart illustrating cluster composition in PDLSCs and DPSCs. The percentage of cells in each cluster is listed in the parentheses behind the cluster ID. (B) Uniform Manifold Approximation and Projection visualization of the PDLSCs and DPSCs, respectively. Cells are colored according to cluster labels. (C) Dot plots show canonical marker expression profiles among 7 clusters in DPSCs and PDLSCs. The color scale represents the expression level of genes, and the dot size refers to the percentage of cells expressing the gene. (D, E) Heatmap showing the top 10 cluster-specific differentially expressed genes (DEGs) distributed in each of the 7 clusters within each PDLSC (D) and DPSC (E). If the number of biomarker DEGs is fewer than 10 in 1 cluster, we only use fewer than 10 genes for that cluster in the heatmap. Clusters 5 and 6 were not included in the heatmaps to avoid the dominant effects of their apoptosis genes. The clusters (columns) with similar overall expression profiles were grouped (relation shown on the top of the heatmap) based on the Euclidean distance in hierarchical clustering.

Figure 4.

Differentiation potentials of cells trajectory analysis of the cell clusters in periodontal ligament stem cells (PDLSCs) and dental pulp stem cells (DPSCs). (A) Box plot showing the illustration of S score among 7 clusters in PDLSCs (left panel) and DPSCs (right panel), where the clusters with higher scores are more similar to fibroblasts, while those with lower scores are more like osteoblasts. (B) Uniform Manifold Approximation and Projection visualization of cells in PDLSCs (left panel) and DPSCs (right panel), colored by cell cycle phase (G1, G2M, and S). (C) Box plot showing the illustration of single-cell entropy (scEntropy) score among 7 clusters in PDLSCs (left panel) and DPSCs (right panel).