cjbrCascadia Journal of Biomedical ResearchCascadia J. Biomed. Res.2693-8510Cascadia Health Sciences Publishing10.58302/44s62m79Distinct immune landscapes between murine and human periodontitisZhaoYiWangQianLinWeiminSongWeiZhangZhongYangXiaoyuHuangDenghaoWangFeiyuWuQinghengJiangShuangLiYuyuTsukasakiMasayukiYuanQuanYinQiState Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, ChinaState Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China; Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, ChinaDepartment of Biochemistry, School of Dentistry, Showa University, Shinagawa, Tokyo, Japan15102025173114073300620250109202512112025https://creativecommons.org/licenses/by-nc/4.0/
Periodontitis (PD) affects over 1 billion people globally, underscoring the need for valid animal models. This study aimed to (1) systematically compare single-cell immune landscapes between human PD and murine ligature models, and (2) evaluate whether Porphyromonas gingivalis (Pg) supplementation improves model translatability. Using single-cell RNA sequencing (scRNA-seq) of murine gingival tissues from ligature, ligature+Pg, and control groups, integrated with human data, distinct immune profiles between murine and human PD was identified: (1) Murine models showed significantly lower B/plasma cell Δ(PD-Ctrl) proportions than human (murine 0.10% vs. human 22.39%, p < 0.001) and attenuated activation states; (2) Conversely, the change in macrophages Δ(PD-Ctrl) is significantly greater in mice (murine 28.53% vs. human −6.603%, p < 0.0001) and exhibited hyperactivation; (3) Co-administration of Pg did not reduce the immunological distinctions between mice and humans. (p > 0.05). These findings reveal immunological disparities in murine ligature models from human PD, suggesting to approach with caution when using these models for studying immune interactions in PD.
DentistryImmunologyTranscriptomicsIntroduction
Periodontitis (PD) is a global health concern, affecting over one billion individuals worldwide, with its prevalence steadily increasing over the past three decades.1 PD is characterized by the destruction of the periodontal ligament and alveolar bone, resulting in tooth loss in advanced stages. It is also associated with other systemic diseases, such as cardiovascular disease, diabetes, and autoimmune diseases.2 A recent bibliometric analysis further quantified these associations, identifying key mechanistic pathways shared between periodontitis and systemic conditions.3 The pathogenesis of PD is complex, involving intricate host-microbe interactions, dysregulated immune responses, and disruptions in tissue homeostasis.4 Thus, developing PD animal models that accurately reflect these multifaceted pathological processes is crucial for advancing our understanding and treatment of PD.
The murine ligature model is one of the most widely used models for studying PD. It induces localized PD by ligating a suture (size 3-0 to 9-0) around the molars of mice.5,6 This model resembles the alveolar bone loss in human PD.6,7 Typically, suture durations range from 1 to 2 weeks, as previous studies have shown that bone loss stabilizes by nine days after ligature, coinciding with peak neutrophil infiltration and a robust immune response.6 It has enabled numerous discoveries in PD research, including insights into bone loss mechanisms, microbial interactions, and potential therapeutic strategies.8,9 However, several key limitations have been gradually noted: (1) the acute ligature-induced trauma differs from human chronic plaque-mediated pathogenesis; (2) the model’s sterile environment contrasts with human polymicrobial infections; and (3) the 1–2 weeks time frame is distinct from years-long disease progression in humans. These differences particularly affect immune cell dynamics and microbial interactions. The extent to which the murine immune response, at both cellular and transcriptional levels, mirrors the immune response in human PD remains to be fully elucidated.
Single-cell RNA sequencing (scRNA-seq) provides a powerful tool for uncovering cellular heterogeneity within the complex tissue microenvironment.10,11 scRNA-seq of human gingival mucosa in individuals with or without periodontitis reveals that enhanced neutrophil infiltration and the NETs-related neutrophil subpopulation are critical for the pathogenesis of PD.12,13 Moreover, scRNA-seq analyses in human PD have demonstrated enhanced B/plasma cell infiltration,12,14 and revealed that tooth-associated keratinocytes and their stem cells exert essential inflammatory effects.15 In mice, a single-cell gingival transcriptomic atlas has identified a novel fibroblast subpopulation activated to guide oral barrier immunity.16 And studies have characterized immune cell transcriptional profiles in murine oral mucosa during PD at single cell resolution.17,18 While both human PD samples and murine ligature models have been analyzed, cross-species single-cell comparisons to identify transcriptional similarities and differences remain lacking. Moreover, the potential impact of Porphyromonas gingivalis (Pg) on refining the murine model has not yet been explored using single-cell profiling.
In this study, we provided a direct scRNA-seq comparison between murine and human PD, coupled with the first single-cell assessment of Pg co-administration. The objective of this research is to: (1) systematically compare single-cell immune landscapes between human PD and murine ligature models, and (2) evaluate whether Pg supplementation improves model translatability.
ResultsSingle-cell RNA sequencing reveals different immune responses between human periodontitis and murine ligature model
To gain insights into the murine ligature model,6 we collected gingival tissue from ligature-induced PD mice and healthy mice (Figure S1A) and performed scRNA-seq using the 10X Genomics platform. Micro-CT and histological analyses confirmed successful establishment of the ligature model, with periodontal bone loss (Figures S1B–S1D). After quality control (Figure S1E), a total of 66,873 high quality cells were retained for downstream analysis. Cell clusters were visualized using uniform manifold approximation and projection (UMAP) (Figures 1A and 1B), revealing six major cell types based on specific marker genes: fibroblasts (e.g., Col1a2, Lum), epithelial cells (e.g., Krt5, Krt6a), endothelial cells (e.g., Pecam1, Cdh5), immune cells (e.g., Ptprc, Cd52), myocytes (e.g., Acta2, Tagln), and Schwann & neuron cells (e.g., Plp1, Kcna1) (Figure 1C).12,19,20
Single-cell profiling reveals immunological differences between murine and human PD
(A) UMAP plot shows six major cell types identified in murine gingiva.
(B) Bar chart of general cell proportions in murine Ctrl and PD groups.
(C) UMAP plots show the representative gene expression for each murine cell type.
(D) UMAP plot shows 6 major cell types identified in human gingiva.
(E) Bar chart of general cell proportions in human Ctrl and PD groups. Refer to Methods for statistical tests used. Human Δ(PD-Ctrl) vs. Murine Δ(PD-Ctrl), ∗∗∗p < 0.001.
(F) UMAP plots show the representative gene expression for each human cell type.
(G) Venn diagrams show the shared up-regulated (upper) and down-regulated (lower) DEGs comparing PD with Ctrl group, between mouse and human. Ratios in parentheses show the proportion of overlapping genes to the total genes.
(H) Volcano plot shows up-regulated (red) and down-regulated (blue) DEGs between murine Ctrl and PD groups (adjust. p < 0.05); gray indicates no statistical significance (adjust.p > 0.05).
(I) Volcano plot shows up-regulated (red) and down-regulated (blue) DEGs between human Ctrl and PD groups (adjust.p < 0.05); gray indicates no statistical significance (adjust.p > 0.05). Color labels of (H) and (I), right of the row.
(J) Dot plot of murine representative GO terms of up-regulated DEGs in each cell type comparing PD with Ctrl group.
(K) Dot plot of human representative GO terms of up-regulated DEGs in each cell type comparing PD with Ctrl group.
To compare the murine ligature model with human PD, we analyzed published scRNA-seq datasets (GEO: GSE164241), including gingival tissue from healthy individuals and patients with PD.12 Utilizing the matching marker genes, we identified six major cell types in human PD (Figures 1D–1F). We observed a profound infiltration of immune cells in the gingival tissue of human PD (Figure 1E), reflecting exaggerated and destructive host immune response in human PD. In contrast, the immune cell populations showed a modest increase in the murine ligature model (Figure 1B), resulting distinct difference between the two species (p < 0.001). Across all samples, changes in cell proportions were consistent (Figures S1F and S1G).
Analysis of differentially expressed genes (DEGs) between PD and Ctrl groups revealed limited overlap in DEGs across human PD and murine ligature model, reflecting distinct host responses (Figure 1G). The top 5 DEGs in mice were associated with macrophage and neutrophil functions (S100a9, S100a8, Lyz2, Ccl4, and Cxcl2) (Figure 1H), whereas in humans, they were related to antibody secretion and pro-inflammatory (IGHG4, IGHG1, IGHG2, IGKC, and IL1B) (Figure 1I). Further GO analysis of DEGs in each cell type revealed that the up-regulated genes in mice PD were primarily associated with ATP metabolic processes and energy metabolism in fibroblast, endothelial cells, and myocytes; while in immune cells, up-regulated DEGs were enriched in response to wounding (Figure 1J). In contrast, in humans, the up-regulated genes were enriched in “B cell activation,” “T cell activation,” and “neutrophil activation” between the PD and Ctrl groups (Figure 1K).
Identification of immune subpopulations reveals distinct immune cell infiltration
We next focused on the immune cell populations. Ten immune clusters were identified based on classic markers in both murine ligature model and human PD (Figures 2A–2D, S2A, and S2B),12,19,20 revealing similar immune cell subsets but with notable differences in proportions of each cell type between species. In both human and mice, T cells (including CD4+, CD8+, double-positive, double-negative αβT cells, and γδT cells) were the dominant immune population in healthy gingival tissue and decreased in both species during PD (Figures 2B and 2D). In the murine ligature model, dendritic cells (DCs) moderately decreased (24.8%–19.2%, p = 0.057), while macrophages expanded significantly (3.8%–32.9%, p = 0.001) (Figure 2B). In contrast, human PD had a smaller proportion of DCs and macrophages, with both populations decreasing in PD (DCs, from 6.5% to 3.1%, p = 0.001; macrophages, from 8.0% to 5.9%, p = 0.021) (Figure 2D). The change in macrophages (ΔPD-Ctrl) is significantly greater in mice than in humans (p < 0.0001). While B/plasma cells remained scarce in both healthy and ligature-induced murine gingival tissue (around 1.8%, compared p > 0.05) (Figure 2B), they were markedly increased in PD human tissue (from 19.5% to 36.7%, p = 0.002) (Figure 2D). The murine B/plasma Δ(PD-Ctrl) is significantly lower than that in humans (p < 0.001). Neutrophils increased both in the murine ligature model and human PD (mouse, from 3.6% to 18.0%, p < 0.001; human, from 2.4% to 10.3%, p = 0.021) (Figures 2B and 2D).
Identification of immune cell types reveals different immune cell infiltration
(A) UMAP plot shows immune cell-types identified in murine Ctrl and PD groups.
(B) Bar chart of immune cell proportions in murine Ctrl and PD groups. Refer to Methods for statistical tests used. Human Δ(PD-Ctrl) vs. Murine Δ(PD-Ctrl), ∗∗∗∗p < 0.0001.
(C) UMAP plot shows immune cell-types identified in human Ctrl and PD groups.
(D) Bar chart of immune cell proportions in human Ctrl and PD groups. Refer to Methods for statistical tests used. Human Δ(PD-Ctrl) vs. murine Δ(PD-Ctrl), ∗∗∗p < 0.001.
(E) Representative SSC-CD79A flow cytometry scatterplots (left) and cell percentage statistical charts (right) of murine Ctrl and PD groups. Data are presented as mean ± SEM.
(F) Representative CD11B-F4/80 flow cytometry scatterplots (left) and cell percentage statistical charts (right) of murine Ctrl and PD groups. Data are presented as mean ± SEM.
(G) Immunofluorescence slices of CD19, CD68, CD45, and MPO in murine groups. Scale bars: 50 μm (left) and 5 μm (right). Color labels are shown in the figure. (Abbreviations: D, dentin/enamel; S, gingival sulcus; G, gingiva).
(H) Immunofluorescence slices of CD19, CD68, CD45, and MPO in human groups. CD19 and CD68 were co-stained on one slide; CD45 and MPO on another. Scale bars: 50 μm (left) and 5 μm (right). Color labels are shown in the figure.
To verify that the differences are not affected by the individuals from diverse backgrounds, an East Asian population (GEO: GSE171213) was also compared to the murine dataset, and a similar consequence was observed (Figure S2C). The change in B/plasma (ΔPD-Ctrl) is significantly greater in humans (14.8% increase) than in mice (0.1% increase) (p < 0.05). And also, the change in macrophages (ΔPD-Ctrl) is significantly greater in mice (28.5% increase) than in humans (2.6% increase) (p < 0.01). There is no significant difference between human and murine neutrophil (ΔPD-Ctrl) (p > 0.05).
To validate these findings, we performed flow cytometry analysis (Figure S2D) and observed an increase in CD45+ immune cells in the murine PD group (Figure S2E). B/plasma cells (CD79A+) were slightly increased (Figure 2E), while macrophages (CD11B+F4/80+) (Figure 2F) and neutrophils (CD11B+LY6G+) (Figure S2F) significantly increased. Immunofluorescence staining also validated these observations, showing that B/plasma cells and macrophages were the most prominent differences between the two species, while neutrophil infiltration remained relatively consistent (Figures 2G and 2H).
Moreover, neutrophil subgroups were identified based on established markers (Table S3).21 Significant differences between human and mice were observed in: elevated Neutro b1 subgroup (related to myeloid leukocyte migration and chemotaxis) and decreased Neutro c subgroup (associated with ribonucleoprotein complex biogenesis and cytoplasmic translation) in human PD, while no comparable changes were noted in mice (Figure S2G). Notably, overall neutrophil proportions and trends of cellular differentiation pseudotime revealed relatively consistent results between human and mice (Figures S2H and S2I).
B cells in the murine ligature model are fewer and primarily in an early inactive state
To further investigate the B/plasma cell compartments, we integrated murine and human datasets by FindIntegrationAnchors. Through SingleR and manual annotation (Table S1), we identified 8 distinct sub-clusters, including naive B cells, activated B cells, transitional B cells, conventional memory B cells, CD11c+CD27−memory B cells, atypical memory B cells, regulatory B cells (Breg), and plasma cells (Figures 3A and 3B). Through pseudotime ordering of integrated B/plasma cells (Figure 3C), we observed that the distribution of eight B/plasma subsets was consistent with the trajectory of differentiation (Figure 3D). In the murine model, B/plasma cells were predominantly located at the early stages of the pseudotime trajectory (Figure 3E), with a composition primarily of naive B cells (Figure 3A). In the murine PD group, naive B cells showed an increased trend, but plasma cells remained scarce (Figure 3A). In human PD, the increased plasma cells were predominantly located at the late stages of the pseudotime trajectory (Figure 3E). Compared to the murine model, the human B/plasma Δ(PD-Ctrl) is significantly higher in the plasma cell subset (p = 0.001) (Figure 3A). The trends of plasma cells (CD138+), naive B cells (IgD+), and conventional memory B cells (CD27+) were verified by immunofluorescence staining (Figure 3F).
Murine B/Plasma cells are fewer in number and predominantly in an early, inactive state
(A) UMAP plot (left) and proportion bar chart (right) of B/plasma cell types identified in the integrated gingival dataset of mice and humans. Refer to Methods for statistical tests used. Human Δ(PD-Ctrl) vs. Murine Δ(PD-Ctrl), ∗∗p < 0.01. (Abbreviations: mB, memory B cells; Breg, regulatory B cells).
(B) UMAP plot (left) and bar chart (right) of murine and human groups in the integrated B/plasma dataset.
(C) Trajectory plot of cellular pseudotime from Monocle analysis.
(D) Trajectory plot of B/plasma subsets in pseudotime.
(E) Trajectory plot of murine and human groups in pseudotime.
(F) Immunofluorescence slices of CD138 (plasma cells), IgD (naive B cells), and CD27 (conventional memory B cells) in murine and human groups. Scale bars: 25 μm (left) and 5 μm (right). Color labels are shown in the figure. (Abbreviations: E, epithelium; L, lamina propria).
(G) Combined plots show cell density changes of murine and human groups (above), B/plasma subsets (median), and gene expression changes (below) aligned with pseudotime.
(H) Violin plot shows the expression level of transcription factors BTG1 and XBP1 in human groups. Data are presented as the median with interquartile range.
(I) Violin plot shows the expression level of transcription factor Btg1 and Xbp1 in murine groups. Data are presented as the median with interquartile range.
Gene expression analysis further highlighted these differences. Murine B/plasma cells expressed genes associated with early differentiation (e.g., Lyn, Cd24, Ms4a1), whereas human cells demonstrated higher expression of late-stage, antibody-related genes such as MZB1, IGKC, and JCHAIN. Despite this distinction, both murine and human PD groups showed a general trend of later-stage gene expression compared to their respective Ctrl groups (Figure 3G).
Interestingly, transcription factor (TF) analysis revealed species-specific changes. In human PD, BTG1, a key TF that inhibits hyperactive B cell proliferation,22 was significantly downregulated (p < 0.001), while XBP1, a hallmark TF that promotes B cells transforming into antibody-secreting phenotype,23 was significantly upregulated (p < 0.001) (Figure 3H). These transcription factors showed no significant changes in the murine ligature model (Btg1, p = 0.173; Xbp1, p = 0.780), underscoring key differences in B/plasma cell regulation between species (Figure 3I).
Macrophages in the murine ligature model show elevated differentiation
We performed cross-species integration of monocytes/macrophages between the murine ligature model and human PD samples using FindIntegrationAnchors. Through combined SingleR prediction and manual annotation (Table S2), we identified nine distinct subpopulations: classical monocytes, intermediate monocytes, non-classical monocytes, two M1-like macrophage subsets (Macro 1 and 2), and four M2-like macrophage subsets (Micro 3, 4, 5, and 6) (Figures 4A–4D). The trends of monocytes (classical, CD14+CCR2+; non-classical, CD14+CX3CR1+), and macrophages (CD206+ for M2-like; iNOS+ for M1-like) were verified by immunofluorescence staining (Figures 4E and 4F). Pseudotime ordering revealed their differentiation trajectory (Figures 4G–4J).
Murine macrophages exhibit enhanced differentiation and immune activity compared to humans
(A) UMAP plot shows monocyte (Mono)/macrophage (Macro) cell-types identified in integrated gingival dataset of mice and humans.
(B) Bar chart of Mono/Macro cell proportions in the integrated gingival dataset of mice and humans. Refer to Methods for statistical tests used. Human Δ(PD-Ctrl) vs. Murine Δ(PD-Ctrl), ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001.
(C) UMAP plot of murine and human groups in the integrated Mono/Macro dataset.
(D) Bar chart of murine and human group proportions in integrated Mono/Macro dataset.
(E) Immunofluorescence slices of CD14 (monocytes), CCR2 (classical monocytes), and CX3CR1 (non-classical monocytes) in murine and human groups. Scale bars: 25 μm (left) and 5 μm (right). Color labels are shown in the figure. (Abbreviations: E, epithelium; L, lamina propria; T, tooth).
(F) Immunofluorescence slices of CD206 (M2-like macrophages) and iNOS (M1-like macrophages) in murine and human groups. Scale bars: 25 μm (left) and 5 μm (right). Color labels are shown in the figure. (Abbreviations: E, epithelium; L, lamina propria; T, tooth).
(G) Trajectory plot of cellular pseudotime from Monocle analysis.
(H) Combined plot shows cell density changes (above) and the matched trajectory plot (below) of Mono/Macro sub-types in pseudotime.
(I) Trajectory plot of murine PD and Ctrl groups in pseudotime.
(J) Trajectory plot of human PD and Ctrl groups in pseudotime. Color labels of (I) and (J), right of the row.
(K) Combined plot shows murine cell density (above) and matched gene expression changes in a heatmap (below) aligned with pseudotime. Red-colored genes, related to acute inflammation.
(L) Combined plot shows human cell density (above) and matched gene expression changes in a heatmap (below) aligned with pseudotime. Blue-colored genes, related to energy metabolism and antioxidation; Violet-colored genes, related to protein homeostasis. Color labels of (K)-(L), right of the row.
In mice, macrophages from the Ctrl group were primarily located at the early stages of pseudotime, and shifted toward the later stages in the PD group (Figure 4I). In the murine PD group, classical monocytes markedly increased (p = 0.002), while non-classical monocytes were significantly reduced (p = 0.002) (Figures 4B and 4E). This likely indicated that: 1) concurrent recruitment and M1-polarization of classical monocytes during acute inflammation; 2) exhaustion of non-classical monocyte reserves to M2-like cells. Compared to humans, the murine Δ(PD-Ctrl) is significantly higher in M1-like Micro 1 (pro-inflammatory, p = 0.032) and M2-like Micro 3 (tissue repair, p < 0.001) (Figures 4B and 4F), reflecting the acute inflammatory features associated with mechanical injury.
In contrast, human macrophages displayed a more even distribution across pseudotime in both the Ctrl and PD groups (Figure 4J). Unlike the murine ligature model, the human PD group did not show a marked increase in classical monocytes or M1-like macrophages (p > 0.05), but a significant increase in non-classical monocytes (p = 0.046) (Figures 4B, 4E, and 4F). Compared to mice, the human Δ(PD-Ctrl) is significantly lower in Micro 4 (metabolic tissue protection, p = 0.039), and higher in Micro 5 (lysosome function, p = 0.002) (Figure 4B), suggesting differences in the macrophage responses and functional dynamics between the two species.
Additionally, the cell density of different groups and representative genes gradually changed with pseudotime (Figures 4K and L). In the murine Ctrl group, the cell density distribution was primarily concentrated in the early-stage monocytes, whereas the PD group exhibited a broader distribution across all stages, with upregulated genes associated with acute immune responses (e.g., Sell, Ereg, C1qb, and C2). In contrast, the cell density distribution in human Ctrl and PD groups differed from that in mice, with genes gradually expressed along pseudotime being linked to energy metabolism (e.g., ATP5F1E, NDUFS7), antioxidation (e.g., SELENOK, ME1), and protein homeostasis (e.g., TENT5A, CREG1). These findings suggested distinct transcriptional profiles existed in PD between the two species.
Application of <italic toggle="yes">Porphyromonas gingivalis</italic> induces moderate changes in the immune response of the murine ligature model
Given the limitations of the simple ligature (Lig) model in mimicking human PD, we investigated whether combining ligature with Porphyromonas gingivalis (Pg) application (LigPg) could better recapitulate the immune response of PD.24 PCR validation confirmed Pg colonization in the LigPg samples (Figure S3A). Furthermore, LigPg mice exhibited elevated serum TNF-α and IL-6 levels, reflecting a systemic inflammatory impact (Figure S3B). LigPg group showed a similar level of local inflammation and osteoclastic bone resorption to that of the Lig group (Figures S3C and S3D).
After quality control (Figure S3E), single-cell RNA sequencing of Lig and LigPg murine gingival tissue yielded a total of 20,122 high quality cells (Figure 5A). Compared with the Lig group, Pg application did not induce an expansion of the immune cells (p > 0.05) (Figure 5B). And 9,045 immune cells were then classified into subgroups for further analysis (Figure 5C). Notably, Pg application did not significantly increase the proportions of B/plasma cells or macrophages (p > 0.05), and neutrophils showed an elevated trend with no statistical significance (p > 0.05) (Figure 5D). Flow cytometry analysis demonstrated no significant changes in B/plasma cell or macrophage populations (Figures 5E, 5F, and S3F–S3H). Consistent with these findings, immunofluorescence staining revealed comparable CD19+ and CD68+ signals between the LigPg and Lig groups (Figure 5G). These results suggest that Pg exposure exerts minimal impact on mimicking human PD immunity in this model.
Moderate changes at the cellular and transcriptomic level between Lig and LigPg groups
(A) UMAP plot shows general cell types identified in the gingiva of murine Lig and LigPg groups.
(B) Bar chart of general cell proportions in murine Lig and LigPg groups. Refer to Methods for statistical tests used. LigPg vs. Lig, ns p > 0.05.
(C) UMAP plot shows immune cell types identified in the gingiva of murine Lig and LigPg groups.
(D) Bar chart of immune cell proportions in murine Lig and LigPg groups. Refer to Methods for statistical tests used. LigPg vs. Lig, ns p > 0.05.
(E) Representative SSC-CD79A flow cytometry scatterplots (left) and cell percentage statistical charts (right) of Lig and LigPg groups. Data are presented as mean ± SEM.
(F) Representative CD11B-F4/80 flow cytometry scatterplots (left) and cell percentage statistical charts (right) of Lig and LigPg groups. Data are presented as mean ± SEM.
(G) Immunofluorescence slices of CD19, CD68, CD45, and MPO in the gingiva of Lig and LigPg groups. Scale bars: 50 μm (left) and 5 μm (right). Color labels are shown in the figure. (Abbreviations: D, dentin/enamel; S, gingival sulcus; G, gingiva).
(H) Venn diagrams show the shared up-regulated (left) and down-regulated (right) DEGs in immune cells, separately comparing Lig and LigPg groups to the Ctrl group. Ratios in parentheses show the proportion of overlapping genes to the total genes.
(I) Multi-group volcano plot displays up-regulated (red, adjust.p < 0.05) and down-regulated (blue, adjust.p < 0.05) DEGs in immune subsets, comparing LigPg to Lig group; gray indicates no statistical significance (adjust.p > 0.05). The numbers above (or below) the bars represent the proportion of up-regulated (or down-regulated) DEGs in the total gene set (%) in each sub-group.
When comparing DEGs between Lig vs. Ctrl and LigPg vs. Ctrl separately, most DEGs showed overlap between the Lig&Ctrl and LigPg&Ctrl comparisons (Figure 5H). And comparing LigPg with Lig group, most genes showed no significant differences, especially B/plasma cell (up-regulated DEGs, 0.09%; down-regulated DEGs, none). DC, macrophages, and neutrophils displayed higher percentage of DEGs (up-regulated, about 0.49%–2.68%; down-regulated, about 0.14%–0.79%) (Figure 5I). Up-regulated DEGs were mainly linked to inflammatory responses and cellular stress (e.g., Mt4, Perp, Lagls7, and Ly6d), while down-regulated DEGs were mostly associated with mitochondrial metabolism (e.g., mt-Nd2, mt-Nd4 l, and mt-Nd5).
Discussion
Recently, scRNA-seq has significantly advanced our understanding of PD pathogenesis. In human studies, gingival scRNA-seq has elucidated disease-specific cellular dynamics, including enhanced neutrophil infiltration linked to NETosis,12,13 expansion of B/plasma cell populations,12,14 and inflammatory reprogramming of keratinocytes.15 Parallel efforts in murine models have identified ligature-induced signatures, such as the activation of a novel fibroblast subpopulation regulating barrier immunity,16 and definitive immune cell states in oral mucosa.17,18 While these studies established foundational insights, no systematic cross-species comparisons were designed to evaluate model translatability. And also, methodological disparities—particularly inconsistent enzymatic digestion protocols25—compromise direct comparisons of cell proportions between studies. Our study constructed a single-cell transcriptomic atlas of murine gingiva from ligature-induced PD models (± Porphyromonas gingivalis, Pg) and compared it to human PD datasets by integrated analysis. Specifically, our scRNA-seq analysis demonstrated that murine models exhibited significantly reduced B/plasma cell proportions with attenuated activation states, while showing heightened macrophage differentiation and immune activity compared to human PD, with Pg supplementation failing to rescue these disparities. These findings highlight limitations of murine ligature models in mimicking human PD immunity.
Heightened macrophage activity and reduced B/plasma cell responses were identified in murine models compared to human chronic PD. This could be attributed to the potential influence of the acute inflammatory nature of the ligature injuries. Notably, the observed macrophage hyperactivation likely underestimates the chronic oxidative stress seen in human PD, where sustained inflammation and antioxidant depletion drive tissue damage. Furthermore, the attenuated B-cell responses in mice may fail to replicate immunoglobulin-mediated redox regulation characteristic of human periodontal immunity. The murine model’s short-term ligation (9 days) would trigger mechanical trauma and early-phase immune responses. Interestingly, a recent study revealed B/plasma cell emergence at Days 1–4 after ligature, followed by a marked reduction by Day 7, aligning with our Day-9 observations.16 The early increase in B/plasma cells reflected an acute, short-term, and non-specific immune response, while the significant decline by Day 7–9 might indicate a lack of long-term durability. This highlighted a notable difference from the B/plasma cell continuous infiltration typically observed in chronic human PD.
Given the distinct differences between mice and humans, several potential factors for further improvement need to be considered, including time, age, microbiota, and species-specific biology.
Firstly, the duration of disease progression might play a critical role. Ligature ranging from 1 to 2 weeks is widely used in murine model,6 while human PD typically develops over months or years.26,27 Considering similar triggering mechanisms in both species, such as the ligature-induced accumulation of bacteria in mice and food debris accumulation in humans,27,28 it suggests that murine models may represent only the early stages of PD. Notably, human patients with PD present at various disease stages (initial/moderate/severe/advanced), each with distinct molecular signatures, which should be considered when constructing animal models and verifying cross-species simulation. Secondly, aging could influence immune responses. Most current studies employed young mice (8–12 weeks old) for the ligature model as in our research, while chronic PD predominantly affects elderly populations.29 Previous studies comparing young and aged mice demonstrated that older mice (50 weeks old) developed more severe bone loss30 and significantly higher accumulation of inflammatory B cells31—more closely resembling human PD.
Additionally, different oral microbiota composition between species may be another cause. Clinical evidence shows that effective antimicrobials such as chlorhexidine reduced efficacy in patients with high-pathogen loads (e.g., P. gingivalis), underscoring the importance of microbiome composition in PD.32 So, we introduced Pg, the most widely used human pathogen for the murine model in our study. While ligature models offer methodological advantages for acute-phase studies, integrating chronic pathogen Pg could enhance immune-microbial interactions and exacerbate inflammation responses in previous studies. But unexpectedly, our results showed no significant improvement. Given that the human oral microbiome always forms complex spatial structures,33 alternative approaches utilizing transplanted human periodontal plaque or prolonged infection time might be further considered.34,35 Lastly, inherent interspecies differences should also be taken into account. It is reported that the proportion of serum-resident monocytes in different differentiation stages showed significant difference between humans and mice.36 Alternative animal models may offer better simulation for human PD, such as beagle dogs,37,38 pigs39,40 and rhesus monkeys.41
Limitations of the study
In our study, a transcriptional-level comparison was made between murine 9-day-ligature model and human PD, and proposed it may not be fully suitable for exploring more complex immune responses, especially in B/plasma cells and macrophages. However, our study merely highlights differences at the immune cellular transcription level, yet the ligature model remains a classic system for alveolar bone resorption and acute inflammation, requiring further validation with advancing technologies. Beyond this, potential factors for further improvement including time, age, microbiota, and species-specific biology might be considered and explored in the future studies to identify more appropriate animal models that accurately recapitulate the immune responses and microbial dynamics observed in human PD.
Resource availabilityLead contact
Requests for further information and resources should be directed to and will be fulfilled by the lead contact, Quan Yuan (yuanquan@scu.edu.cn).
Materials availability
This study did not generate new unique reagents.
Data and code availability
Original raw data from the gingival tissue in murine ligature models (with or without Pg) was uploaded to the GSA database (CRA021219). Human PD gingival scRNA-seq data were retrieved from the GEO database (GSE164241 and GSE171213).
The custom codes are online (https://github.com/15332720088/Periodontitis_GitHub_Code_ZY.git) and also available from the corresponding authors.
Microscopy data reported in this article will be shared by the lead contact upon request.
Any additional information required to reanalyze the data reported in this article is available from the lead contact upon request.
Acknowledgments
This work was supported by the 10.13039/501100001809National Natural Science Foundation of China (NSFC 82125006; 823B2018) and the 10.13039/501100018542Natural Science Foundation of Sichuan Province (NSFSC 25NSFSC0265). The authors would like to acknowledge the assistance of Xiao Zhang for providing technical support on Micro-CT and histological sections, and Shuqin Cao for maintaining laboratory regulations and ensuring order in the lab. The authors also acknowledged the assistance of Berry Genomics Co., Ltd. and OE Biotech Co., Ltd. for providing technical support on the 10X Genomics and Illumina NovaSeq system. The graphical abstract was created using templates from BioRender, and is licensed under BioRender’s Academic License Terms.
Author contributions
Conceptualization, Y.Z., Q.Y., and Q.Y.; methodology, Y.Z., Q.W., W.L., Z.Z., X.Y., D.H., M.T., Q.Y., and Q.Y.; formal analysis, Y.Z., Q.W., W.L., and W.S.; investigation, Y.Z., W.S., Z.Z., X.Y., D.H., F.W., Q.W., S.J., and Y.L.; writing – original draft, Y.Z.; writing – review and editing, Y.Z., M.T., Q.Y., and Q.Y.; supervision, M.T., Q.Y., and Q.Y.; project administration, M.T., Q.Y., and Q.Y.; funding acquisition, W.L., Q.Y. and Q.Y.
Declaration of interests
The authors declare no competing interests.
Declaration of generative AI and AI-assisted technologies in the writing process
The authors utilized DeepSeek and ChatGPT to assist in polishing the language and improving the readability of an initial draft. Following the use of these tools, the authors meticulously reviewed, revised, and approved the final article. The authors accept full and sole responsibility for the content and integrity of the published work.
Experimental model and study participant detailsAnimal models
This study was approved by the Sichuan University Research and Animal Care Committee (Approval No: WCHSIRB-D-2021-335) and all experimental procedures complied with animal ethics guidelines. And this study also conformed to the updated ARRIVE 2.0 guidelines. Male C57/BL6J mice (22-26 g, 8-10 weeks old) were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. Mice were housed in a specific-pathogen-free environment (SPF), with standard temperature and humidity controls and ad libitum access to food and water. To establish the ligature model, mice were anesthetized with sodium pentobarbital, and 6-0 non-resorbable sutures were ligated around the maxillary second molars and mandibular first molars for 9 days6 Following euthanasia, the maxillae and gingival tissue were collected, excluding any tissue with detached sutures (integrated gingiva of 6 mice as a sample; Ctrl group, n = 3; Lig group, n=6). For the ligature model with Porphyromonas gingivalis (Pg) tropical application, ligated molars were treated with 100 μL of PBS containing 1.5 × 109 CFU of Pg daily by cotton swabs for 30 s during 9 days (integrated gingiva of 6 mice as a sample; LigPg group, n=3). Afterward, sutures were removed, and bacterial DNA was extracted for PCR detection. In all steps, confounders were controlled through systematic randomization using a computer-generated random number table. Animals were assigned to treatment groups using block randomization, stratified by baseline weight (±5 g) and sex. The randomization scheme also controlled for measurement sequence and cage location. Allocation concealment was maintained using sequentially numbered opaque envelopes.
Participant biopsy collection
The procedures were approved by the Ethics Committee of West China Hospital of Stomatology, Sichuan University (Approval No: WCHSIRB-D-2022-250). Written informed consent was obtained from all participants prior to sample collection. Gingival biopsies were acquired under local anesthesia from healthy controls (probing depths <3 mm, no bleeding on probing, non-smokers, without systemic diseases or recent antibiotic use) during implant phase II surgery and from periodontitis patients (probing depths ≥5 mm with bleeding on probing, radiographic bone loss >30%, diagnosed stage III/IV periodontitis, excluding acute infections or pregnant individuals) during flap surgery or gingivectomy procedures. The patients enrolled in the two groups (n = 4) were matched for key demographic characteristics, including age and sex, to ensure comparability. All samples were processed using standardized protocols to ensure consistency. Details of all human samples are included in Table S4.
Method detailsBiopsy preparation and 10X genomics sequencing
Murine gingival tissue was processed identically to Williams et al., involving collagenase/DNase digestion, 70 μm filtration, RBC lysis, and 10X Chromium single-cell capture, followed by v3.0 library preparation and Illumina sequencing.12 Murine gingival tissue was minced and digested in 2 mg/mL type II collagenase (Worthington) and 0.04 mg/ml DNase I (Sigma) at 37°C for 45 min. The cell suspension was filtered through a 70 μm filter to remove debris. Red blood cell lysis and subsequent processing followed the same protocols used for the human gingiva data analysis.12,42 Single-cell suspensions were loaded onto the 10X Chromium Controller (10X Genomics) for single-cell capture following standard protocols. Libraries were generated using the Chromium Single Cell v3.0 reagents according to the manufacturer’s protocol. The libraries were sequenced using the Illumina NovaSeq system, ensuring high coverage and data quality for downstream scRNA-seq analysis.43,44
scRNA-seq data analysis
Human gingival scRNA-seq data (GSE164241) was obtained from GEO, with all subsequent re-analyses performed identically to the murine data. Another human scRNA-seq data (GSE171213) was treated the same way to verify geographic factors. Murine scRNA-seq data were aligned to the mm10 genome using the Cell Ranger 3.0.1 pipeline. Both datasets were processed using Seurat v4.1 and R v4.3.0. After doublet removal, we filtered out genes detected in fewer than three cells, cells with >20% mitochondrial reads, >30% ribosomal reads, and cells expressing <200 genes. The gene expression matrix was normalized using Seurat’s “NormalizeData,” and highly variable genes were selected via “FindVariableFeatures.” Principal component analysis (PCA) was performed, followed by cross-condition batch correction using Harmony v1.1. Finally, uniform manifold approximation and projection (UMAP) was applied for dimensionality reduction and visualization.
For cross-species analysis, we converted mouse gene symbols (MGI) to human orthologs (HGNC) using BioMart. Cross-species integration and batch correction were performed with “FindIntegrationAnchors” and “IntegrateData.” Cell clusters were annotated by SingleR v2.2 (for reference only) and manual curation preferentially based on conserved marker genes across species. Species-specific markers were used only when orthologs were unavailable. The annotated cell types and their canonical marker genes included: general cell populations (Figures 1C and 1F), immune cell subsets (Figures S2A and S2B), B/plasma cell lineages (Table S1), macrophage/monocyte populations (Table S2), and neutrophil clusters (Table S3). Differentially expressed genes (DEGs) were identified with criteria of |Log2FoldChange| > 0.5 and adjusted P<0.05. GO terms were annotated using the Gene Ontology database, and cell type comparisons were conducted based on the top 100 DEGs (adjusted P<0.05 and FDR < 0.05).
For balanced, we performed random sampling for 1,500 cells per group, retaining all cells for groups with fewer than 1,500 cells. And then, pseudotime trajectory analysis was performed using Monocle2 v2.28.0 for DDRTree dimensional reduction. Cells were ordered along trajectories after selecting highly variable genes (mean expression≥0.1) and filtering low-quality cells (detected genes >200). Trajectory-dependent gene expression was analyzed using Monocle2’s BEAM algorithm.43,44
Flow cytometry analysis
Murine gingiva samples were minced and digested with collagenase IV (Gibco™) and DNase I (Sigma) for 50 minutes at 37°C. Tissue digests were mechanically dissociated through a 70 μm cell strainer to generate single-cell suspensions.45 Information of antibody used for flow cytometry is described in the key resources table. A fixation/permeabilization kit (eBioscience) was used for fixation. After staining, cell acquisition was performed on a BD Biosciences flow cytometer, and data were analyzed by FlowJo software.46
Histological preparation and staining
Human tissues were fixed in 4% paraformaldehyde (PFA) and embedded in paraffin. Murine gingival samples were fixed in 4% PFA, decalcified in 10% EDTA for six weeks, and embedded in paraffin. Sections (5 μm) underwent antigen retrieval in citrate buffer, followed by staining with diluted (1:100) primary antibodies. Information of antibody used for staining is described in the key resources table. The next day, the sections were stained with Goat-anti-Rat Cy3 and Goat-anti-Rabbit Alexa Fluor 488 secondary antibody, and then stained with DAPI for nuclear labeling. Images were captured using an Olympus FV3000 confocal microscope. H&E, TRAP and Masson’s staining were then performed following established protocols.47,48
Enzyme-linked immunosorbent assay (ELISA)
Serum was isolated from retro-orbital plexus bleeds. Cytokine concentrations (TNF-α, IL-6) were determined by species-specific ELISA kits (TNF-α: Jonln JL10484; IL-6: NeoBioScience EMC004) following the manufacturers' standardized protocols.
Micro-CT analysis
Maxillary bone tissue was fixed in 4% PFA and scanned by Venus micro-CT system (VNC-102, PingSeng Healthcare, China) with a spatial resolution of 8 μm. Three-dimensional reconstructions were performed using Avatar software.48
Quantification and statistical analysis
To evaluate cross-species proportion changes, the differences between PD and control groups Δ(PD-Ctrl) in both human and murine samples were calculated for comparative analysis. For murine PD vs. Ctrl, human PD vs. Ctrl, and human Δ(PD-Ctrl) vs. murine Δ(PD-Ctrl), statistical analysis was performed as follows: normality was assessed by the Shapiro–Wilk test, and homogeneity of variance was assessed with Levene’s test (α = 0.05); data with a normal distribution were analyzed using an unpaired Welch’s t-test, while non-normally distributed data were analyzed using the Mann–Whitney U test. Flow cytometry data underwent unpaired t-tests following normality and variance confirmation. Differential gene expression analysis employed Seurat’s Wilcoxon rank sum tests. Statistical significance was defined as P<0.05 for all analyses.
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Document S1. Figures S1–S3 and Tables S1–S4
Supplemental information can be found online at https://doi.org/10.1016/j.isci.2025.114073.