Single-cell Resolution Spatial Transcriptomics Delineates the Inflammatory Landscape of Human Dental Pulp: Regional Crosstalk and Therapeutic Implications

Single-cell Resolution Spatial Transcriptomics Delineates the Inflammatory Landscape of Human Dental Pulp: Regional Crosstalk and Therapeutic Implications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Single-cell Resolution Spatial Transcriptomics Delineates the Inflammatory Landscape of Human Dental Pulp: Regional Crosstalk and Therapeutic Implications Jianmao Zheng, Fengyuan Zhang, Yuanyuan Kong, Xiaobin Fu, Jiyuan Zuo, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7096435/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study utilized single-cell resolution spatial transcriptomics (Visium HD) to investigate the spatial cellular architecture and molecular interactions in healthy and inflamed dental pulp, aiming to explore the pathological mechanisms of pulpitis and identify novel targets for vital pulp therapy. Spatial transcriptomic sequencing was performed on dental pulp tissues from two healthy individuals and two pulpitis patients, with integrated analyses including Seurat clustering, cell trajectory inference, GO enrichment, CellphoneDB interaction network modeling, and PROGENy pathway activity assessment to compare cellular heterogeneity and signaling regulation. Nine major cell types (fibroblasts, progenitor cells, endothelial cells, neural cells, plasma cells, B cells, T cells, monocytes, and macrophages) were identified, and their spatial distribution was mapped. Subclustering and differential expression analysis revealed that fibroblast (e.g., APOL2 + / CCN2 + ) and progenitor cell (e.g., CDK5R1 + / CCRL2 + ) subclusters exacerbated fibrosis and immune activation, while TMPRSS4 + / CST5 + fibroblasts were critical for homeostasis. Pro-inflammatory endothelial subclusters ( IGHG1 + / CXCL13 + ) expanded, while anti-inflammatory subclusters ( SERPINA5 + / SERPINA3 + ) diminished, leading to vascular-immune imbalance. Upregulation of immunoglobulin genes and downregulation of MBP disrupted neural function, while inflamed pulp showed increased B cells and macrophages, decreased T cells and monocytes, and downregulated PTN . Inflammatory pathways (PI3K, EGFR, TGFβ, MAPK, Estrogen, NF-κB) were upregulated, with enhanced TGFβ signaling in endothelial cells. Intercellular interaction analysis showed altered APP - CD74 signaling in endothelial-macrophage interactions and disrupted CXCL14 -mediated communication between immune and endothelial cells. These findings implicate cellular remodeling, including PTN downregulation, APP suppression, CXCL14 deficiency, CXCR4 upregulation, and TGFβ activation, as key drivers of pulpitis progression. Biological sciences/Molecular biology/Proteomics/Protein–protein interaction networks Biological sciences/Biological techniques/Gene expression analysis/Microarray analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction The current primary clinical treatment for irreversible pulpitis (IP) is root canal therapy (RCT), which eliminates infection but inevitably compromises the fracture resistance of dental tissues and results in the loss of pulp vitality. However, histopathological studies have consistently demonstrated a weak correlation between clinical and histological diagnoses of pulpitis. A clinical diagnosis of "irreversible pulpitis" does not necessarily indicate irreversible inflammatory damage. In 2021, the American Association of Endodontists (AAE) proposed that conservative vital pulp therapy (VPT) could be considered for IP cases 1 . Current VPT protocols require complete removal of infected pulp, yet clinically distinguishing inflamed from healthy pulp tissue remains challenging. Consequently, developing novel drugs or targeted therapies to promote reparative processes in inflamed pulp is urgently needed 2 . Identifying biomarkers of pulpitis is critical for advancing such therapies. However, existing biomarker research primarily focuses on clinical examinations and lacks direct histopathological validation 2 .Therefore, resolving the spatial distribution of cells and spatiotemporal dynamics of biomarkers in pulpitis lesions is imperative. Recent studies have applied single-cell RNA sequencing (scRNA-seq) to analyze the temporal-spatial landscape of dental pulp 3 – 8 , but scRNA-seq fails to preserve spatial context. Spatial transcriptomics (ST) overcomes this limitation by retaining spatial resolution 9 . The 10× Genomics Visium HD system enhances the resolution of the original Visium platform from 55 µm spots to 2×2 µm squares, enabling spatial organization analysis at single-cell resolution. Compared to previous ST technologies, Visium HD provides superior spatial structural reconstruction, particularly advantageous for analyzing small tissues 10 . The aim of this study is to leverage the spatial localization and visualization capabilities of Visium HD to perform comparative analyses of healthy and inflamed pulp samples. By integrating gene expression profiles with microstructural features, we aim to map the spatial distribution of diverse cell types, elucidate spatial interactions among pulp cells and molecules, decode intercellular communication patterns, and delineate tissue organizational functions, thereby refining our understanding of pulpitis pathogenesis. To achieve this, we first identified major cell populations in healthy and inflamed pulp, including fibroblasts, progenitor cells, endothelial cells, neural cells, plasma cells, B cells, T cells, monocytes, and macrophages, along with subclusters of these cells. Furthermore, we performed cell differentiation trajectory analysis and differential gene expression analysis (DGE) on fibroblasts, progenitor cells, endothelial cells, neural cells, and immune cells. Additionally, we investigated inflammation-related signaling pathways in pulp tissues from pulpitis patients and healthy controls. Finally, we analyzed the cell-cell interaction networks in normal versus inflamed pulp. These findings provide novel insights into therapeutic targets for VPT and advance diagnostic and treatment strategies for pulpitis. Result Spatially resolved single-cell transcriptomic mapping of dental pulp in health and pulpitis via Visium HD To investigate the spatial transcriptomic profiles of dental pulp in healthy individuals and pulpitis patients, we collected fresh pulp tissues from two healthy volunteers and two pulpitis patients. The tissues underwent fixation, embedding, sectioning, and staining. Following sample validation, tissue mounting, spatial transcriptomic library preparation, sequencing, and data analysis, the results were visualized. Single-cell resolution was achieved using the 10× Genomics Visium HD platform (Fig. 1 a, b). Spatial transcriptomics identifies 9 major cell types in pulp from patients with irreversible pulpitis and healthy individuals Based on established cell marker genes 11 – 19 and spatial localization, we classified cell clusters into 9 major categories using the Space Ranger pipeline: fibroblasts, progenitor cells, endothelial cells, neural cells, plasma cells, B cells, T cells, monocytes, and macrophages. Canonical marker genes included COL1A2 for fibroblasts, NES for progenitor cells, VWF for endothelial cells, MPZ for neural cells, IGKC for plasma cells, MS4A1 for B cells, TRBC2 for T cells, LYZ for monocytes, and CD68 for macrophages (Fig. 2 a). Hematoxylin and eosin (H&E) staining demonstrated predominant inflammatory cell infiltration in pulpitis tissues, spatial maps confirmed that cell clustering aligned with anatomical structures and exhibited single-cell resolution (Fig. 2 b). Uniform Manifold Approximation and Projection (UMAP) analysis revealed distinct expression patterns of COL1A2 , NES , VWF , MPZ , IGKC , MS4A1 , TRBC2 , LYZ , and CD68 in fibroblasts, progenitor cells, endothelial cells, neural cells, plasma cells, B cells, T cells, monocytes, and macrophages, respectively (Fig. 2 c). Furthermore, spatial maps highlighted the regional dominance of these marker genes within their corresponding cell type territories (Fig. 2 d). Notably, consistent with prior single-cell RNA sequencing (scRNA-seq) studies, odontoblasts (a pulp-specific cell population) were not detected in our analysis. This limitation is presumably attributable to: (i) the high anesthesia sensitivity of odontoblasts during sample collection, and (ii) the low-abundance transcripts of established odontoblast markers (dentin sialo-phosphoprotein, DSPP ; dentin matrix acidic phosphoprotein 1, DMP1 ) 20 , 21 . Fibroblasts We performed subclustering of fibroblasts using the Seurat package. Visualization revealed that fibroblasts were divided into 11 subclusters (0–10), with distinct proportion distributions between healthy and inflamed dental pulps. Notably, subcluster 0 showed increased proportion in inflamed pulp, while subclusters 1, 7, and 9 were exclusively present in healthy pulp (Fig. 3 a, b). Cellular differentiation trajectory analysis demonstrated 8 differentiation trajectories originating from subcluster 0, which exhibited the earliest differentiation pseudotime. Subclusters 1 and 9 were located along the same trajectory, with subcluster 1 positioned at the trajectory terminus displaying the latest differentiation pseudotime. Subcluster 7 showed relatively late differentiation pseudotime and was situated at the midpoint of another trajectory (Fig. 3 c). Differential gene expression analysis across subclusters identified APOL2 (associated with apoptosis and autophagy 22 ) and CCN2 (implicated in tissue repair, fibrosis, and TGFβ signaling 23 , 24 ) as highly expressed in Subcluster 0. Subclusters 7 and 9 exhibited elevated expression of TMPRSS4 (Transmembrane Serine Protease 4, a regulator of inflammatory responses 25 – 27 ) and CST5 (an anti-inflammatory mediator 28 , 29 ), respectively(Fig. 3 d). These findings suggest a dual role for fibroblast subclusters in pulp homeostasis and inflammatory responses: under healthy conditions, the TMPRSS4 + / CST5 + subclusters maintain tissue integrity through protease-antiprotease balance, whereas under inflammatory stimulation, the APOL2 + / CCN2 + subclusters drive pathological repair via collagen fibril organization pathways. This not only refines our understanding of pulp repair mechanisms but also highlights potential therapeutic strategies targeting the CCN2 pathway or TMPRSS4 / CST5 balance in specific fibroblast subclusters to modulate pulpitis progression. Subsequently, we analyzed differentially expressed genes (DEGs) in fibroblasts from inflamed versus healthy pulp. Volcano plots demonstrated significant upregulation of inflammation-related genes ( IGHG1 , IGHA1 , MS4A1 30,31 ) in inflamed pulp (Fig. 3 e). Gene Ontology (GO) enrichment analysis revealed biological processes (BP) and cellular components (CC) significantly associated with fibroblast DEGs in inflamed pulp. These BP/CC terms were closely linked to four other cell populations: progenitor cells (e.g., connective tissue development, skeletal system development, endothelial cells (e.g., angiogenesis, blood vessel development), neural cells (e.g., tube morphogenesis), and immune cells (e.g., response to bacterium, inflammatory response) (Fig. 3 f). Spatial co-localization analysis further confirmed upregulated expression of these BP-associated genes in fibroblasts from inflamed pulp (Fig. 3 g). These results suggest enhanced interactions between fibroblasts and the four cell populations under inflammatory conditions, potentially amplifying pathological cascades in pulpitis. Progenitor Cells Utilizing Seurat software, we categorized progenitor cells into 6 subclusters (0 − 5) and visualized their spatial distribution and proportional representation in both healthy and inflamed dental pulp. The analysis revealed an increased proportion of subcluster 0 in inflamed pulp, while subclusters 4 and 5 exhibited reduction and complete absence in pathological conditions (Fig. 4 a, b). Cellular differentiation trajectory analysis of cellular differentiation demonstrated two distinct pathways originating from subcluster 0 as the initial state. Subcluster 0 exhibited the earliest differentiation timing, while subclusters 4 and 5 were positioned along separate trajectories, with subcluster 4 displaying later differentiation timing and subcluster 5 representing the terminal stage of differentiation (Fig. 4 c). Differential gene analysis revealed that Subcluster 0 exhibited high expression of CDK5R1 (involved in signal transduction and apoptosis induction 32 , 33 ) and CCRL2 (a pro-inflammatory mediator regulating immune modulation, inflammatory responses, and cell migration 34 , 35 ). Subcluster 4 showed elevated expression of NEUROD6 (Neurogenic Differentiation factor 6, critical for neural development and function 36 , 37 ), whereas Subcluster 5 was marked by GDF10 (implicated in tissue repair, regeneration, and neural axon regeneration 38 , 39 ) and ZIC1 (essential for embryonic development, particularly neurogenesis 40 , 41 ) (Fig. 4 d). These findings suggest that progenitor cell subclusters may act as “bidirectional functional modulators” in pulpitis: dynamic shifts in subcluster proportions could dictate inflammatory outcomes. Specifically, suppressing the pro-apoptotic/immune-recruiting properties of Subcluster 0 or supplementing GDF10 + / ZIC1 + subclusters exogenously may represent novel therapeutic strategies to reverse pathological inflammation and promote pulp neural regeneration. Volcano plots demonstrated significant upregulation of inflammation-related genes ( IGHG1 , IGKC , IGHA1 31 ) in progenitor cells from inflamed pulp compared to healthy controls (Fig. 4 e). Gene Ontology (GO) enrichment analysis via Sankey diagrams revealed biological processes (BP) and cellular components (CC) associated with four other cell populations: integrin-mediated signaling pathway linked to fibroblasts, blood microparticle to endothelial cells, regulation of neuron projection development to neural cells, and inflammatory response to immune cells (Fig. 4 f). Spatial co-localization analysis visualized the distribution and co-expression of these enriched BP/CC terms with the progenitor cell marker gene NES . Results indicated significant upregulation of blood microparticle and inflammatory response-related genes in progenitor cells from inflamed pulp, alongside downregulation of neuron projection development-associated genes (Fig. 4 g). Collectively, these data suggest that progenitor cells in inflamed pulp may drive local inflammation by activating immune pathways (e.g., IGHG1/IGKC/IGHA1 upregulation) and blood microparticle-mediated endothelial signaling, while suppressing neural repair mechanisms through impaired neurodevelopmental gene expression. Endothelial cells Endothelial cells underwent subclustering via Seurat, yielding 4 distinct subclusters (0 − 3) as visualized in our analysis. In inflammatory dental pulp tissues, subclusters 0 and 2 showed significant proportional reductions, whereas subcluster 1 demonstrated marked expansion (Fig. 5 a, b). Cellular differentiation trajectory analysis revealed a unidirectional path initiating from subcluster 1, with subcluster 0 displaying intermediate differentiation timing and subcluster 2 representing the terminal differentiation phase (Fig. 5 c). Differential gene analysis identified SERPINA5 and SERPINA3 (both serine protease inhibitors) as highly expressed in Subcluster 0. SERPINA5 regulates coagulation-anticoagulation balance and immune modulation 42 , 43 , whereas SERPINA3 suppresses excessive protease activity at inflammatory sites to mitigate tissue damage 44 . Subcluster 2 exhibited elevated expression of pro-inflammatory genes such as IL9R 45 . while Subcluster 1 showed upregulated expression of inflammation-related genes ( IGHG1 , IGKC , IGHA1 , CSF3 , MZB1 , and CXCL13 ) 31 , 46 – 50 (Fig. 5 d). These results suggest that endothelial remodeling in inflamed pulp exacerbates pathological injury through three mechanisms: (i) Expansion of pro-inflammatory subclusters: Upregulated genes in Subcluster 1 ( IGHG1 , CXCL13 ) may recruit immune cells and amplify inflammatory cascades 31 , 50 ; (ii) Depletion of anti-inflammatory/protective subclusters: Reduced proportions of Subclusters 0 (Serpin family genes) and 2 ( IL9R ) indicate impaired anti-protease and immunoregulatory capacities in endothelial cells under inflammation, aggravating tissue damage 42 – 45 ; (iii) Enhanced endothelial-immune crosstalk: Immunoglobulins ( IGHG1 ) and chemokines ( CXCL13 ) in Subcluster 1 may potentiate endothelial cell interactions with B cells and neutrophils, driving chronic inflammation. Volcano plots demonstrated significant upregulation of inflammation-related genes ( IGHG1 , IGKC , IGHA1 ) and downregulation of reparative genes ( PTN , TF ) in endothelial cells from inflamed pulp (Fig. 5 e). Gene Ontology (GO) enrichment analysis revealed biological processes (BP) and cellular components (CC) associated with endothelial cell DEGs, including collagen fibril organization and extracellular matrix linked to fibroblasts, cell population proliferation and positive regulation of cell development to progenitor cells, synapse organization and synaptic membrane to neural cells, and regulation of inflammatory response to immune cells (Fig. 5 f). Spatial co-localization of these BP/CC-associated genes with the endothelial cell marker VWF confirmed upregulated expression of collagen fibril organization and regulation of inflammatory response genes in inflamed pulp endothelial cells (Fig. 5 g). These findings suggest that endothelial cells in inflamed pulp participate in pathological progression via multicellular synergy. The evidence chain includes: (i) Endothelial cells adopt a pro-inflammatory phenotype (e.g., upregulated IGHG1 , downregulated PTN 51 – 53 ); (ii) GO analysis highlights functional linkages with fibroblasts, progenitor cells, neural cells, and immune cells (Fig. 5 e); (iii) Spatial co-localization verifies endothelial cell involvement in both collagen fibril organization and regulation of inflammatory response. This cross-cellular synergy likely establishes an "inflammation-repair imbalance" vicious cycle, perpetuating pulpitis progression. Neural cells Employing Seurat-based subclustering analysis on neural cells, we identified 7 distinct subclusters (0 − 6). Subcluster 3 exhibited significant proportional expansion in inflammatory dental pulp tissues, while subclusters 0, 1, 2, 4, and 6 showed marked reductions. Notably, subclusters 1, 2, 4, and 6 were exclusively present in healthy controls (Fig. 6 a, b). Cellular differentiation trajectory analysis revealed 2 distinct differentiation branches originating from subcluster 3. Subcluster 6 occupied the terminal position of one branch, representing an early differentiation state, while subclusters 1, 2, and 4 localized to the endpoint of the alternative branch, corresponding to terminal differentiation stages (Fig. 6 c). Differential gene analysis demonstrated that Subcluster 0 exhibited high expression of MPRIP (implicated in neuronal migration or synaptic plasticity 54 , 55 ) and LAMTOR4 (or C7orf59 , regulating cell growth, metabolism, and autophagy 56 – 58 . Subcluster 3 showed elevated expression of inflammation-associated immunoglobulin genes ( IGKC , IGHG1 31 ), while Subcluster 6 was marked by SERPINB9 , a granzyme B inhibitor that neutralizes cytotoxic immune cell activity to maintain tissue homeostasis 59 (Fig. 6 d). These findings suggest functional polarization of neural cells under inflammatory conditions: pro-inflammatory subclusters (e.g., Subcluster 3) engage in immune responses via immunoglobulin gene upregulation, whereas homeostatic subclusters (e.g., Subclusters 0 and 6) preserve tissue integrity through neuronal migration, autophagy, and immune tolerance regulation. The imbalance between pro-inflammatory expansion and homeostatic decline likely disrupts neuro-immune crosstalk, exacerbating pulp inflammatory injury. Differential gene expression analysis of neural cells in inflamed versus healthy pulp revealed significant upregulation of IGKC , IGHG1 , and APOE , alongside downregulation of MBP (Fig. 6 e). Gene Ontology (GO) enrichment analysis highlighted biological processes (BP) and cellular components (CC) associated with neural cell DEGs, including extracellular matrix and extracellular matrix organization linked to fibroblasts, positive regulation of cell development and cell population proliferation to progenitor cells, blood vessel development and angiogenesis to endothelial cells, and immunoglobulin-mediated immune response to immune cells (Fig. 6 f). Spatial co-localization of these BP/CC-associated genes with the neural cell marker MPZ demonstrated pronounced upregulation of immunoglobulin-mediated immune response-related genes in inflamed pulp neural cells (Fig. 6 g). Collectively, these results indicate that neural cells in inflamed pulp may interact with immune cells via immunoglobulin-mediated responses (e.g., IGKC/IGHG1 upregulation), while being modulated by fibroblasts, progenitor cells, and endothelial cells to establish a pro-inflammatory microenvironment. The upregulation of APOE might compensate for neural repair 60 , whereas MBP downregulation suggests myelin damage, collectively contributing to neuronal dysfunction and pain hypersensitivity. Immune cells We performed Uniform Manifold Approximation and Projection (UMAP) and spatial co-localization analyses on immune cells, including plasma cells, B cells, T cells, monocytes, and macrophages. In inflamed pulp, the proportions of B cells and macrophages were significantly increased, while T cells and monocytes were reduced (Fig. 7 a, b). Differential gene expression analysis revealed a predominant downregulation of immune cell-related genes in inflamed pulp compared to healthy controls, with notable downregulation of PTN , SERPINA3 , TF , CXCL14 , and GPX3 , alongside upregulation of CXCL13 , CCL18 , MS4A1 , and PI3 (Fig. 7 c, e). Gene Ontology (GO) enrichment analysis of differentially expressed genes (DEGs) identified biological processes (BP) and cellular components (CC) associated with four other cell populations: extracellular matrix organization and collagen fibril organization linked to fibroblasts; stem cell differentiation and hemopoiesis to progenitor cells; blood vessel development and angiogenesis to endothelial cells; and regulation of neuron projection development and neural crest cell differentiation to neural cells (Fig. 7 d). Spatial co-localization of these BP/CC-associated genes with the immune cell marker IGKC demonstrated significant downregulation of regulation of neuron projection development-related genes in immune cells from inflamed pulp (Fig. 7 f). These findings suggest that the immune microenvironment in inflamed pulp undergoes functional remodeling characterized by: (i) Adaptive immune activation: Elevated B cell/macrophage ratios 61 , 62 and reduced T cell/monocyte proportions indicate immune response dysregulation 61 , 63 , 64 ; (ii) Impaired immune-neural crosstalk: Coordinated downregulation of antioxidant/neurotrophic genes (e.g., PTN 51 – 53 , GPX3 65 ) and spatial suppression of regulation of neuron projection development pathways (Fig. 7 f); (iii) Enhanced inflammatory cell recruitment: Upregulated chemokines ( CXCL13/CCL18 ) synergistically promote leukocyte infiltration 50 , 66 – 68 . Collectively, these data highlight that pathological progression in pulpitis may be driven by a neuro-immune regulatory network, where disrupted intercellular communication exacerbates disease severity. Spatial Heterogeneity of Inflammation-Related Pathway Activity in Inflamed Pulp of Pulpitis Abnormal activation of signaling pathways has been implicated in pulpitis 69 , 70 . We employed PROGENy to evaluate pathway activity by quantifying expression changes of downstream genes associated with specific pathways 71 , 72 . We assessed the activity of 14 pathways critically involved in inflammation and tissue regeneration. We observed elevated activity of PI3K, EGFR, TGFβ, MAPK, Estrogen, and NF-κB pathways in inflamed pulp cells, while JAK-STAT, Androgen, and p53 pathways exhibited reduced activity. Specific pathways including NF-κB and TRAIL demonstrated high activity in immune cells of inflamed pulp (Fig. 8 a). Given the predominant pathway activation in inflamed cells, we focused on TGFβ pathway activity across cell populations, revealing significant upregulation in all 5 major cell types (Fig. 8 b). Further comparison showed that endothelial cells had significantly higher TGFβ activity than fibroblasts, neural cells, and immune cells in inflamed pulp (Fig. 8 c). To validate TGFβ pathway activation in endothelial cells, we evaluated serial sections of healthy and inflamed pulp. Spatial co-localization of TGFβ with VWF -positive endothelial cells confirmed enhanced TGFβ signaling in pulpitis-derived endothelial cells (Fig. 8 d). In summary, our findings successfully delineate the activation states of distinct signaling pathways in inflamed pulp and highlight significant upregulation of the TGFβ pathway in pulpitis endothelial cells. Remodeling of Cell-Cell Interaction Networks in Dental Pulp of Healthy Individuals and Pulpitis Patients To further investigate spatial intercellular relationships in healthy and inflamed pulp, we performed cell-cell interaction analysis using the Cellphone DB software. Our results revealed extensive interactions across cell types in both states, with the APP-CD74 gene pair exhibiting the most prominent connectivity (Fig. 9 a–f). Previous studies have demonstrated that extensive intercellular communication occurs between macrophages and other cell types in healthy dental pulp via the CD74 - APP ligand–receptor pair, with endothelial cells being the most frequent initiators of such interactions through CD74 ligands 20 . In healthy pulp, bidirectional interactions ( APP-CD74 and CD74-APP ) were observed between endothelial cells and macrophages, whereas inflamed pulp exhibited only unidirectional APP-CD74 interactions (Fig. 9 a, d). To validate the spatial relationship between endothelial cells and macrophages, we visualized the co-localization of APP -high endothelial cells and CD74 -high macrophages in healthy and inflamed pulp sections. Inflamed pulp showed significantly reduced spatial proximity between CD74 -high endothelial cells and APP -high macrophages compared to healthy controls (Fig. 9 g, h), further supporting weakened unidirectional CD74-APP interactions in inflamed pulp. Subsequent analysis of APP and CD74 expression across cell populations demonstrated significant downregulation of APP in macrophages from inflamed pulp (Fig. 9 i), whereas CD74 expression in endothelial cells and macrophages remained unaltered (Fig. 9 j). This explains the diminished bidirectional CD74-APP crosstalk in inflamed pulp, attributable to APP suppression in macrophages. In summary, our study successfully uncovers remodeling of the endothelial cell- macrophages interaction network in pulpitis, characterized by attenuated CD74-APP signaling due to APP downregulation in macrophages under inflammatory conditions. In healthy pulp, no intercellular interactions occurred via the CXCL14 - CXCR4 ligand-receptor pair. Conversely, in inflamed pulp, fibroblasts, progenitor cells, and neural cells—but not endothelial cells—engaged with 5 immune cell types through CXCL14 - CXCR4 signaling (Fig. S1A, B). Notably, CXCL14 was significantly downregulated in immune cells and endothelial cells, while CXCR4 was upregulated in immune cells compared to healthy pulp (Fig. S1C, D). These findings suggest a functional dichotomy wherein endothelial cells diverge from fibroblasts, progenitor cells, and neural cells in inflammation, implicating the CXCL14 - CXCR4 axis as a critical regulator of pulpitis progression that merits further investigation. Discussion Due to the minute size of dental pulp tissues and challenges in obtaining intact inflamed pulp samples, spatial transcriptomic profiling of human pulp has remained unexplored. While existing studies have applied scRNA-seq and single-cell transcriptomics to dental pulp 20 , 73 , these approaches fail to integrate spatial contextualization with cellular signatures. In this study, we present the first comprehensive atlas of human healthy and inflamed pulp using single-cell-resolution spatial transcriptomics. Our work provides an unprecedented characterization of spatiotemporal reprogramming across pulp cell types during inflammation, delivering novel biological insights for developing innovative therapeutic strategies in vital pulp preservation. Previous studies demonstrate that Pleiotrophin ( PTN ) enhances proliferative and osteogenic/odontogenic capacities of dental pulp stem cells (DPSCs), conferring protection against senescence, while potentially inhibiting chondrogenic differentiation and preventing H 2 O 2 -induced senescent injury in DPSCs 74 – 76 . However, no evidence exists regarding PTN roles in pulpitis. Our analysis of immune cells revealed significant downregulation of PTN in inflamed pulp. Subsequent evaluation across cell types confirmed ubiquitous PTN suppression in non-neural populations during inflammation (Fig. S2). Based on these findings, we propose that PTN —beyond its established anti-senescence functions—may constitute a previously unrecognized regulatory mechanism suppressing pulpal inflammation. Previous studies have established associations between pulpitis and activation of PI3K/AKT, NF-κB, and MAPK pathways 77 – 80 . Our PROGENy analysis further revealed elevated activity of TGFβ signaling in inflamed pulp cells. These findings implicate TGFβ as previously unrecognized pathological dimensions of pulpal inflammation, expanding the mechanistic landscape beyond canonical pathways. Our study reveals that neural cells exhibit no significant interactions with other cell types in healthy pulp, whereas inflamed pulp demonstrates extensive neuro-immune interplay (Fig. 9 b, e). Further analysis identifies APP , CD74 , CD44 , and CXCL14 as dominant ligands mediating neural cell communication in inflamed pulp (Fig. S3). These findings suggest that neural cells actively engage in immune responses during pulpitis, implicating a previously unrecognized neuro-immunomodulatory mechanism that merits in-depth investigation. CXCL14 promotes cell migration and angiogenesis 81 and is implicated as a potent mediator of pulp regenerative potential acting via CXCR4 82 . It also recruits M2-polarized macrophages to facilitate tissue repair 83 . Conversely, CXCR4 upregulation in immune cells drives pathological immune retention, exacerbating inflammation 84 . Thus, strategically enhancing CXCL14 expression in pulp cells while antagonizing CXCR4 in immune cells may concurrently promote tissue regeneration and resolve inflammation. CXCL14 acts as a natural antagonist of CXCL12 , inhibiting the CXCL12 - CXCR4 axis 81 —a pathway critical for B cell development and function 85 . In pulpitis, B cells leverage CXCL12 - CXCR4 signaling to interact with other immune cells (including autocrine loops) (Fig. S1B). Given the significant expansion of B cells in our inflamed pulp samples, elevating CXCL14 expression and modulating CXCR4 activity could: Suppressing B cell maturation, promoting angiogenesis and tissue regeneration, attenuating immune cell retention and accelerating inflammatory resolution. CONSLUSION We present the first single-cell-resolution spatial transcriptomic dataset of healthy and inflamed human dental pulp, combining cellular clustering, subcluster profiling, pathway activity analysis, and cell-cell interaction mapping to establish spatiotemporal landscapes. This integrated approach reveals putative roles of specific cell subpopulations in pulpitis pathogenesis and highlights PTN , APP , the CXCL14 - CXCR4 ligand-receptor axis and the TGFβ pathway as potential key regulators meriting further investigation. Materials and methods Tissue Acquisition All dental pulp specimens utilized in this study were obtained from non-functional third molars and orthodontically extracted premolars of patients undergoing Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Sun Yat-sen University, Guangzhou. Each sample collection procedure complied with institutional regulations and was approved by the Medical Ethics Committee of Hospital of Stomatology, Sun Yat-sen University. Written informed consent was obtained from all participants prior to their inclusion in the study, and consent documentation was archived accordingly. Patients presenting with systemic diseases other than pulpitis were excluded from the study. Pulpitis-affected specimens were characterized by deep carious cavities with pulp exposure and typical clinical symptoms, including spontaneous pain and nocturnal pain. Healthy control samples exhibited intact tooth structure without caries or enamel demineralization, and donors reported no subjective symptoms of dental discomfort. FFPE(Formalin-Fixed Paraffin-Embedded)section preparation The isolated dental pulp tissues were aseptically collected and fixed in 4% paraformaldehyde solution for 12–24 hours, followed by overnight storage in 75% ethanol. A graded ethanol dehydration series was subsequently implemented as follows: Dehydration: Sequential immersion in ethanol solutions of increasing concentrations (30%, 50%, 70%, 80%, 95%, and 100%), with each concentration maintained for 45–60 minutes to achieve complete dehydration; Clearing: Residual ethanol was eliminated through xylene immersion; Paraffin infiltration: Tissues were treated with low-melting-point paraffin to remove residual xylene; Embedding: Infiltrated tissues were immersed in molten paraffin within embedding cassettes and rapidly solidified on a pre-chilled platform; Sectioning: 10 µm-thick sections were prepared using a Leica CM1950 cryostat. Visium HD The isolation of total RNA from FFPE tissue blocks was achieved using the RNeasy FFPE kit (73504, Qiagen). The quality assessment of the extracted RNA was performed by calculating DV200. Tissue sections passing the quality control (DV200 > 30%) were subjected to ST assay. The Visium HD workflow requires new HD slides that require thawing, washes, and equilibration in appropriate buffers. Visium HD slides also feature high-resolution fiducials for subpixel image alignment, and a dispensing pad and spacer for CytAssist compatibility. We first placed FFPE tissue sections on plain glass slides for deparaffinization, H&E staining and imaging following the Visium HD FFPE Tissue Preparation Handbook (CG000684). Subsequently, the sections were stained with H&E and imaged at 20× magnification in brightfield using PANNORAMIC MIDIⅡ Digital Scanner (3DHISTECH). Probe hybridization, probe ligation, slide preparation, probe release, extension, library construction, and sequencing followed the Visium HD Spatial Gene Expression Reagent Kits User Guide (CG000685). Sequencing was performed on an Illumina NovaSeq 6000 with paired-end reads (43 cycles Read 1, 10 cycles i7, 10 cycles i5, 50 cycles Read 2). We used Space Ranger v3.0 to map FASTQ files to the human reference, detect the tissue section, align the sequencing data to the microscope image and the CytAssist image, and output gene-barcode matrices for further analysis. Seurat objects (version 5.2.0) were initialized for each sample using the 8 × 8 µm filtered feature barcode matrices generated by Space Ranger. For DP sample, HD bins with fewer than 100 UMI counts and 50 detected genes were filtered out. The raw counts were independently normalized using log-normalization. Clustering of the Visium HD data was conducted within Seurat. The initial clustering round identified clusters at a resolution of 0.3. Additionally, we used the DoubletFinder software (version 2.0.4) to remove potential doublets. After filtering out low-quality cells and doublets, the data were normalized using the NormalizeData function, high-variable genes were identified with the FindVariableFeatures function, and the data were scaled using the ScaleData function. Subsequently, dimensionality reduction was performed using RunPCA for principal component analysis. Clustering was conducted using the FindNeighbors and FindClusters functions. Finally, the clustering results were visualized using DimPlot. The FindAllMarkers function was utilized to identify characteristic genes for each cluster. Subsequently, we performed enrichment analysis on the characteristic genes of each cluster using the clusterProfiler R package (version 4.12.6) with default settings. The human annotation information was sourced from the org.Hs.eg.db database. Visualization of Cell Clustering Bins were clustered based on gene expression levels using Space Ranger (v3.0.0). First, UMI counts were normalized, followed by dimensionality reduction through principal component analysis (PCA). The top 10 principal components were selected for clustering using both graph-based methods 86 . The PCA-reduced data were further processed using t-SNE 87 and UMAP 88 for visualization purposes. Customized analysis and visualization of the clustering results from Space Ranger were performed using Loupe Browser (v8.1.1), the official software provided by 10x Genomics. Cell Differentiation Trajectory Analysis The UMAP coordinates of the subclusters were utilized as input for Monocle3. We constructed the trajectories across cell types with the following parameters: learn_graph_control = list (minimal_branch_len = 50), use_partition = FALSE, close_loop = FALSE. The root node was caculated by CytoTRACE. We employed the graph_test function in Monocle3, specifying the parameter neighbor_graph = "principal_graph", to discern genes with differential expression along the trajectory of a particular lineage. Genes with q value less than 0.05 were selected. Gene Ontology (GO) Enrichment Analysis Differential gene expression analysis was performed using the “FindMarkers” function in Seurat with the bimod likelihood-ratio test. Differentially expressed genes (DEGs) were filtered using thresholds of absolute log 2 -fold change > 1 and P Value 0.5 and P Value < 0.05). The filtered DEGs were subjected to GO enrichment analysis on the Metascape platform ( http://metascape.org ) with a P value cutoff of 0.05. Significantly enriched GO terms (q-value < 0.05) were visualized using the CNSknowall platform ( https://cnsknowall.com ). Finally, the spatial expression patterns of these enriched pathways were mapped onto dental pulp tissue sections using Loupe Browser software (10x Genomics). PROGENy analysis To investigate the expression patterns of signaling pathways in distinct cellular populations within dental pulp tissues under healthy and inflammatory conditions, we performed pathway activity quantification using the R package progeny 89 . This study systematically analyzed pathway activities in spatial transcriptomic datasets of five cell types (fibroblasts, progenitor cells, endothelial cells, neural cells and immune cells) under healthy and inflamed states through the PROGENy algorithm. Utilizing the top 100 genes from transcriptional footprints of each experimental group, we constructed a unified expression matrix encompassing 10 experimental conditions (5 cell types × 2 states) via data integration. A zero-imputation strategy was implemented to generate expression vectors, followed by normalization. Pathway activity scores were computed using PROGENy, with hierarchical clustering heatmaps generated through Complex Heatmap to comprehensively visualize inter-group biological associations. Bar plots delineated pathway activity levels across cellular populations, while Loupe Browser facilitated the generation of pathway expression violin plots. Furthermore, spatially resolved mapping of pathway expression patterns was achieved through visualization on dental pulp tissue sections. CellphoneDB Cell − Cell Communication Analysis Cellular interactions between cell types were computed based on ligand-receptor co-expression using the CellPhoneDB (v4.0.0) with default settings. The analysis of interactions among fibroblasts, progenitor cells, endothelial cells, neural cells, and immune cells was conducted. Only genes expressed in more than 10% of total cells were included. To calculate the average number of interactions, we summed all significant (p-value < 0.05) interactions between two cell types per sample/donor and averaged this number over samples/donors. To calculate the interaction strength between two cell types across patients, we summed up the mean expression of all ligand and receptor pairs as calculated by CellPhoneDB across all samples. Declarations Acknowledgements This work was financial supported by the Guangdong Basic and Applied Basic Research Foundation (No.2022A1515011266, 2022A1515110601), National Natural Science Foundation of China (No.81700950, No. 82370943). Author contributions J.Z. and X.W. design the study. Y.K. and F.Z. contributed equally. X.F., J.Z., Q.G., J.W., M.X., and Q.J. collected and processed the samples. F.Z. and X.F. performed the experiments and analyzed the data. F.Z., Q.Z., and Y.Z. wrote the manuscript. J.L. and all other co-authors critically revised the manuscript. J.Z. and X.W. provided financial support. 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Additional Declarations There is no conflict of interest Supplementary Files Fig.S1.tif Fig. S1 Part of cell-cell communication networks in healthy and inflamed pulp. A Dot plot of interaction strength in healthy pulp: dot size = -log₁₀(adjusted P value); color = mean scaled expression (log₂) of ligand-receptor pairs; x-axis: interacting cell types; y-axis: ligand-receptor pairs. B Corresponding analyses for inflamed pulp (as in A). C Violin plots of scaled CXCL14 expression (log₂) in endothelial cells and immune cells (Benjamini-Hochberg test; *: P < 0.05). DViolin plots of scaled CXCR4 expression (log₂) in immune cells (Benjamini-Hochberg test; *: P < 0.05). Fig.S2.tif Fig. S2 Violin plots of scaled PTN expression (log₂) across cell types (Benjamini-Hochberg test; *: P < 0.05). Fig.S3.tif Fig. S3 Dot plot of interaction strength between neural cell and other cells in inflamed pulp: dot size = -log₁₀(adjusted P value); color = mean scaled expression (log₂) of ligand-receptor pairs; x-axis: interacting cell types; y-axis: ligand-receptor pairs. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7096435","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":490201283,"identity":"9a77ea85-fc21-4618-b3cd-6b1d5655623a","order_by":0,"name":"Jianmao Zheng","email":"data:image/png;base64,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","orcid":"","institution":"Sun Yat Sen Univ, Guangdong Prov Key Lab Stomatol, Guangzhou, Guangdong, Peoples R China","correspondingAuthor":true,"prefix":"","firstName":"Jianmao","middleName":"","lastName":"Zheng","suffix":""},{"id":490201284,"identity":"ae8427fb-47a5-4c80-b54c-2f5e0ed1d2dd","order_by":1,"name":"Fengyuan Zhang","email":"","orcid":"","institution":"Sun Yat-sen university","correspondingAuthor":false,"prefix":"","firstName":"Fengyuan","middleName":"","lastName":"Zhang","suffix":""},{"id":490201285,"identity":"ad30df4f-5eec-4462-8f3b-590783680333","order_by":2,"name":"Yuanyuan Kong","email":"","orcid":"","institution":"Stomatology Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Kong","suffix":""},{"id":490201286,"identity":"f055e1b2-4aed-4bc7-a667-ef1a2f5c8f35","order_by":3,"name":"Xiaobin Fu","email":"","orcid":"","institution":"Sun Yat-sen university","correspondingAuthor":false,"prefix":"","firstName":"Xiaobin","middleName":"","lastName":"Fu","suffix":""},{"id":490201287,"identity":"9d66cc4f-a06f-4431-aff5-8605f8c1fca5","order_by":4,"name":"Jiyuan Zuo","email":"","orcid":"","institution":"Sun Yat-sen university","correspondingAuthor":false,"prefix":"","firstName":"Jiyuan","middleName":"","lastName":"Zuo","suffix":""},{"id":490201288,"identity":"7b99fa25-224f-41d9-bd20-acf9a8a71c02","order_by":5,"name":"Qining Guo","email":"","orcid":"","institution":"Sun Yat-sen university","correspondingAuthor":false,"prefix":"","firstName":"Qining","middleName":"","lastName":"Guo","suffix":""},{"id":490201289,"identity":"3de0b99e-3ec3-456e-b92a-f034520eb3ab","order_by":6,"name":"Jiayi Wang","email":"","orcid":"","institution":"Sun Yat-sen university","correspondingAuthor":false,"prefix":"","firstName":"Jiayi","middleName":"","lastName":"Wang","suffix":""},{"id":490201290,"identity":"0f94202c-b221-4ecb-922b-c5dac37361b3","order_by":7,"name":"Manlin Xu","email":"","orcid":"","institution":"Sun Yat-sen university","correspondingAuthor":false,"prefix":"","firstName":"Manlin","middleName":"","lastName":"Xu","suffix":""},{"id":490201291,"identity":"a434849a-2f51-4b2d-95c4-f68e226ba368","order_by":8,"name":"Qian Zeng","email":"","orcid":"","institution":"Sun Yat-sen university","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Zeng","suffix":""},{"id":490201292,"identity":"f6c4b656-fb63-443d-bbb2-2f80ef17db2e","order_by":9,"name":"Yuejiao Zhang","email":"","orcid":"","institution":"Sun Yat-sen university","correspondingAuthor":false,"prefix":"","firstName":"Yuejiao","middleName":"","lastName":"Zhang","suffix":""},{"id":490201293,"identity":"679d29c7-4fc7-46a7-9cdd-53f0a97eb924","order_by":10,"name":"Junqi Ling","email":"","orcid":"","institution":"Guanghua School of Stomatology, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Junqi","middleName":"","lastName":"Ling","suffix":""}],"badges":[],"createdAt":"2025-07-10 23:40:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7096435/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7096435/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87825754,"identity":"bd937760-2281-4e1f-8a5d-fd6f0cbe6905","added_by":"auto","created_at":"2025-07-29 11:48:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":574802,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic Diagram of Visium HD Workflow. \u003cstrong\u003ea\u003c/strong\u003e Sample preparation and imaging; probe hybridization and ligation; CytAssist probe capture; probe extension; library construction; sequencing; data processing and visualization. \u003cstrong\u003eb\u003c/strong\u003e The capture area consists of a 6.5 × 6.5 mm square region formed by a continuous array of 2 × 2 μm oligos. Each oligo contains a capture probe that binds to the target sequence and includes a spatially informative barcode. The commonly analyzed unit, or bin, is an 8 × 8 μm rectangular region composed of a grid of 2 × 2 μm barcoded squares.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7096435/v1/a1fc479271963310ac36454d.png"},{"id":87825300,"identity":"5962a3b8-ece9-4518-8d34-7e31662fc234","added_by":"auto","created_at":"2025-07-29 11:40:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1801274,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial transcriptomics identifies 9 major cell types in healthy and inflamed dental pulp. \u003cstrong\u003ea\u003c/strong\u003e Violin plots display the expression of specific marker genes for the 9 major cell types. The height and width of each violin represent the expression level (Log\u003csub\u003e2\u003c/sub\u003e) and the number of cells at that expression level, respectively. \u003cstrong\u003eb\u003c/strong\u003e H\u0026amp;E staining of dental pulp sections from healthy individuals and pulpitis patients used in the Visium HD assay; spatial transcriptomic maps show the spatial distribution of major cell populations within the pulp tissue. Scale bar = 200 μm. \u003cstrong\u003ec\u003c/strong\u003e UMAP visualization reveals 9 major cell types in the dental pulp; feature plots display the expression of cluster-specific marker genes, with high expression shown in red and low expression in gray. \u003cstrong\u003ed\u003c/strong\u003e Spatial localization of \u003cem\u003eCOL1A2, NES, VWF, MPZ, IGKC, MS4A1, TRBC2, LYZ\u003c/em\u003e and\u003cem\u003e CD68\u003c/em\u003e genes in the Visium HD dataset. Scale bar = 200 μm.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7096435/v1/caea59d2f53a715da32b8fd9.png"},{"id":87825305,"identity":"04918e04-4399-4173-b114-2eee7afa56bc","added_by":"auto","created_at":"2025-07-29 11:40:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2470797,"visible":true,"origin":"","legend":"\u003cp\u003eFibroblast subclustering, differentiation trajectories, differential gene expression, and GO enrichment in healthy and inflamed pulp. \u003cstrong\u003ea\u003c/strong\u003e\u0026nbsp;Spatial co-localization maps and pie charts depict spatial distribution and proportional composition of fibroblast subclusters.\u0026nbsp;\u003cstrong\u003eb\u003c/strong\u003e\u0026nbsp;UMAP visualization of 11 fibroblast subclusters (res=0.3).\u0026nbsp;\u003cstrong\u003ec\u003c/strong\u003e Cellular differentiation trajectory analysis revealing 8 differentiation paths.\u0026nbsp;\u003cstrong\u003ed\u003c/strong\u003e\u0026nbsp;Heatmap of subcluster-specific DEGs (Log\u003csub\u003e2\u003c/sub\u003e-scaled):\u0026nbsp;\u003cem\u003eAPOL2\u003c/em\u003e↑ in SC0;\u0026nbsp;\u003cem\u003eTMPRSS4\u003c/em\u003e↑ in SC7;\u0026nbsp;\u003cem\u003eCST5\u003c/em\u003e↑ in SC9.\u0026nbsp;\u003cstrong\u003ee\u003c/strong\u003e\u0026nbsp;Volcano plot of fibroblast DEGs in pulpitis (|log\u003csub\u003e2\u003c/sub\u003eFC|\u0026gt;0.5,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), highlighting upregulated\u0026nbsp;\u003cem\u003eIGHG1\u003c/em\u003e,\u003cem\u003e IGHA1\u003c/em\u003e,\u003cem\u003e MS4A1\u003c/em\u003e.\u0026nbsp;\u003cstrong\u003ef\u003c/strong\u003e\u0026nbsp;Sankey bubble diagram showing GO pathways enriched in fibroblast DEGs (|log\u003csub\u003e2\u003c/sub\u003eFC|\u0026gt;0.5,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) with gene-pathway linkages.\u0026nbsp;\u003cstrong\u003eg\u003c/strong\u003e\u0026nbsp;Spatial co-localization of the fibroblast marker\u0026nbsp;\u003cem\u003eCOL1A2\u003c/em\u003e\u0026nbsp;(green) and representative DEGs (red) from 4 GO terms: connective tissue development, angiogenesis, tube morphogenesis, and response to bacterium; yellow indicates co-expression, bar=200 μm.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7096435/v1/f1a03a635c15b3f02c588c7c.png"},{"id":87827428,"identity":"0f179660-21cd-4232-bb21-4183a49f92d8","added_by":"auto","created_at":"2025-07-29 11:56:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1890178,"visible":true,"origin":"","legend":"\u003cp\u003eProgenitor cell subclustering, differentiation trajectories, differential gene expression, and GO enrichment in healthy and inflamed pulp. \u003cstrong\u003ea\u003c/strong\u003e\u0026nbsp;Spatial co-localization maps and pie charts depict spatial distribution and proportional composition of progenitor cell subclusters.\u0026nbsp;\u003cstrong\u003eb\u003c/strong\u003e\u0026nbsp;UMAP visualization of 6 progenitor cell subclusters (res=0.3).\u0026nbsp;\u003cstrong\u003ec\u003c/strong\u003e Cellular differentiation trajectory analysis revealing 2 differentiation paths.\u0026nbsp;\u003cstrong\u003ed\u003c/strong\u003e\u0026nbsp;Heatmap of subcluster-specific DEGs (Log\u003csub\u003e2\u003c/sub\u003e-scaled):\u0026nbsp;\u003cem\u003eCDK5R1\u003c/em\u003e↑\u003cem\u003e \u003c/em\u003eand \u003cem\u003eCCRL2\u003c/em\u003e↑ in SC0;\u0026nbsp;\u003cem\u003eNEUROD6\u003c/em\u003e↑ in SC4;\u0026nbsp;\u003cem\u003eGDF10\u003c/em\u003e↑\u003cem\u003e \u003c/em\u003eand \u003cem\u003eZIC1\u003c/em\u003e↑ in SC5.\u0026nbsp;\u003cstrong\u003ee\u003c/strong\u003e\u0026nbsp;Volcano plot of progenitor cell DEGs in pulpitis (|log\u003csub\u003e2\u003c/sub\u003eFC|\u0026gt;0.5,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), highlighting upregulated\u0026nbsp;\u003cem\u003eIGHG1\u003c/em\u003e,\u003cem\u003e IGKC\u003c/em\u003e,\u003cem\u003e IGHA1\u003c/em\u003e.\u0026nbsp;\u003cstrong\u003ef\u003c/strong\u003e\u0026nbsp;Sankey bubble diagram showing GO pathways enriched in progenitor cell DEGs (|log\u003csub\u003e2\u003c/sub\u003eFC|\u0026gt;0.5,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) with gene-pathway linkages.\u0026nbsp;\u003cstrong\u003eg\u003c/strong\u003e\u0026nbsp;Spatial co-localization of the progenitor cell marker\u0026nbsp;\u003cem\u003eNES\u003c/em\u003e\u0026nbsp;(green) and representative DEGs (red) from 4 GO terms: integrin-mediated signaling pathway, blood microparticle, regulation of neuron projection development, and inflammatory response; yellow indicates co-expression, bar=200 μm.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7096435/v1/a48bfa904aad06e5b8235fe0.png"},{"id":87825762,"identity":"2f23f8f5-ec07-4f09-9248-da69d8502a49","added_by":"auto","created_at":"2025-07-29 11:48:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2393915,"visible":true,"origin":"","legend":"\u003cp\u003eEndothelial cell subclustering, differentiation trajectories, differential gene expression, and GO enrichment in healthy and inflamed pulp. \u003cstrong\u003ea\u003c/strong\u003e\u0026nbsp;Spatial co-localization maps and pie charts depict spatial distribution and proportional composition of endothelial cell subclusters.\u0026nbsp;\u003cstrong\u003eb\u003c/strong\u003e\u0026nbsp;UMAP visualization of 4 endothelial cell subclusters (res=0.3).\u0026nbsp;\u003cstrong\u003ec\u003c/strong\u003e Cellular differentiation trajectory analysis revealing 1 differentiation paths.\u0026nbsp;\u003cstrong\u003ed\u003c/strong\u003e\u0026nbsp;Heatmap of subcluster-specific DEGs (Log\u003csub\u003e2\u003c/sub\u003e-scaled):\u0026nbsp;\u003cem\u003eSERPINA5\u003c/em\u003e↑ and \u003cem\u003eSERPINA3\u003c/em\u003e↑ in SC0;\u0026nbsp;\u003cem\u003eIGHG1\u003c/em\u003e↑,\u003cem\u003e IGKC\u003c/em\u003e↑,\u003cem\u003e IGHA1\u003c/em\u003e↑,\u003cem\u003e CSF3\u003c/em\u003e↑,\u003cem\u003e MZB1\u003c/em\u003e↑\u003cem\u003e \u003c/em\u003eand\u003cem\u003e CXCL13\u003c/em\u003e↑ in SC1;\u0026nbsp;\u003cem\u003eIL9R\u003c/em\u003e↑ in SC2.\u0026nbsp;\u003cstrong\u003ee\u003c/strong\u003e\u0026nbsp;Volcano plot of endothelial cell DEGs in pulpitis (|log\u003csub\u003e2\u003c/sub\u003eFC|\u0026gt;1,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), highlighting upregulated\u0026nbsp;\u003cem\u003eIGHG1\u003c/em\u003e,\u003cem\u003e IGKC \u003c/em\u003eand\u003cem\u003e IGHA1\u003c/em\u003e; downregulated \u003cem\u003ePTN \u003c/em\u003eand\u003cem\u003e TF\u003c/em\u003e.\u0026nbsp;\u003cstrong\u003ef\u003c/strong\u003e\u0026nbsp;Sankey bubble diagram showing GO pathways enriched in endothelial cell DEGs (|log\u003csub\u003e2\u003c/sub\u003eFC|\u0026gt;1,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) with gene-pathway linkages.\u0026nbsp;\u003cstrong\u003eg\u003c/strong\u003e\u0026nbsp;Spatial co-localization of the endothelial cell marker\u0026nbsp;\u003cem\u003eVWF\u003c/em\u003e\u0026nbsp;(green) and representative DEGs (red) from 4 GO terms: collagen fibril organization (GO:0030199), cell population proliferation (GO:0008283), synapse organization (GO:0050808), and regulation of inflammatory response (GO:0050727); yellow indicates co-expression, bar=200 μm.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7096435/v1/4241272a13658758b5428297.png"},{"id":87825758,"identity":"2625b4b1-3004-4f7a-9600-1a9d0c866640","added_by":"auto","created_at":"2025-07-29 11:48:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1738826,"visible":true,"origin":"","legend":"\u003cp\u003eNeural cell subclustering, differentiation trajectories, differential gene expression, and GO enrichment in healthy and inflamed pulp. \u003cstrong\u003ea\u003c/strong\u003e\u0026nbsp;Spatial co-localization maps and pie charts depict spatial distribution and proportional composition of neural cell subclusters.\u0026nbsp;\u003cstrong\u003eb\u003c/strong\u003e\u0026nbsp;UMAP visualization of 4 neural cell subclusters (res=0.3).\u0026nbsp;\u003cstrong\u003ec\u003c/strong\u003e Cellular differentiation trajectory analysis revealing 2 differentiation paths.\u0026nbsp;\u003cstrong\u003ed\u003c/strong\u003e\u0026nbsp;Heatmap of subcluster-specific DEGs (Log\u003csub\u003e2\u003c/sub\u003e-scaled):\u0026nbsp;\u003cem\u003eLAMTOR4\u003c/em\u003e↑ in SC0;\u0026nbsp;\u003cem\u003eIGKC\u003c/em\u003e↑\u003cem\u003e \u003c/em\u003eand\u003cem\u003e IGHG1\u003c/em\u003e↑ in SC3;\u0026nbsp;\u003cem\u003eSERPINB9\u003c/em\u003e↑ in SC6.\u0026nbsp;\u003cstrong\u003ee\u003c/strong\u003e\u0026nbsp;Volcano plot of neural cell DEGs in pulpitis (|log\u003csub\u003e2\u003c/sub\u003eFC|\u0026gt;0.5,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), highlighting upregulated\u0026nbsp;\u003cem\u003eIGKC\u003c/em\u003e,\u003cem\u003e IGHG1 \u003c/em\u003eand\u003cem\u003e APOE\u003c/em\u003e.\u0026nbsp;\u003cstrong\u003ef\u003c/strong\u003e\u0026nbsp;Sankey bubble diagram showing GO pathways enriched in neural cell DEGs (|log\u003csub\u003e2\u003c/sub\u003eFC|\u0026gt;0.5,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) with gene-pathway linkages.\u0026nbsp;\u003cstrong\u003eg\u003c/strong\u003e\u0026nbsp;Spatial co-localization of the neural cell marker\u0026nbsp;\u003cem\u003eCOL1A2\u003c/em\u003e\u0026nbsp;(green) and representative DEGs (red) from 4 GO terms: extracellular matrix, positive regulation of cell development, blood vessel development, and immunoglobulin-mediated immune response; yellow indicates co-expression, bar=200 μm.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-7096435/v1/9de02090c30f8ee6567669a5.png"},{"id":87825755,"identity":"a6a2bfd8-d926-4ddb-9f06-5e5ebc2afa6a","added_by":"auto","created_at":"2025-07-29 11:48:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2946223,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential gene expression and GO enrichment in immune cells of healthy versus inflamed pulp. \u003cstrong\u003ea\u003c/strong\u003e UMAP visualization of 5 major immune cell clusters. \u003cstrong\u003eb\u003c/strong\u003e Spatial co-localization maps and pie charts showing spatial distribution and proportional changes of immune cell populations. \u003cstrong\u003ec\u003c/strong\u003e Volcano plot of immune cell DEGs in pulpitis (|log₂FC|\u0026gt;1, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), highlighting downregulated \u003cem\u003ePTN\u003c/em\u003e, \u003cem\u003eSERPINA3\u003c/em\u003e, \u003cem\u003eTF\u003c/em\u003e, \u003cem\u003eCXCL14\u003c/em\u003e, and \u003cem\u003eGPX3\u003c/em\u003e. \u003cstrong\u003ed\u003c/strong\u003e Sankey bubble diagram of enriched GO pathways for immune cell DEGs (|log₂FC|\u0026gt;1, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05): bubble color indicates significance (blue → red: \u003cem\u003eP\u003c/em\u003e value decreasing); bubble size reflects DEG count per pathway. \u003cstrong\u003ee\u003c/strong\u003e Heatmap of immune cell DEG expression (log₂-scaled) across conditions (red: high; blue: low). \u003cstrong\u003ef\u003c/strong\u003e Spatial co-localization of immune marker \u003cem\u003eIGKC\u003c/em\u003e (green) and representative DEGs (red) from extracellular matrix organization, stem cell differentiation, blood vessel development and regulation of neuron projection development; yellow denotes co-expression regions. bar=200 μm.\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-7096435/v1/d5b1fa0002c5a54ddc18c693.png"},{"id":87825319,"identity":"21d0896a-8240-44bf-adc2-0f50275e51c7","added_by":"auto","created_at":"2025-07-29 11:40:57","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2279581,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial activation landscape of inflammation-related signaling pathways in pulpitis. \u003cstrong\u003ea\u003c/strong\u003e Heatmap of PROGENy pathway activity scores in healthy and pulpitis groups (red: high; blue: low). \u003cstrong\u003eb\u003c/strong\u003e Violin plots depicting expression of TGFβ pathway genes across 5 cell populations in healthy versus inflamed pulp, the Wilcoxon test, *, P\u0026lt;0.05. \u003cstrong\u003ec\u003c/strong\u003e Statistical analysis of the pathway activity of TGFβ in endothelial cells compared with fibroblasts, progenitor cells, neural cells and immune cells in the inflamed pulp, the Wilcoxon test, *, P\u0026lt;0.05. \u003cstrong\u003ed\u003c/strong\u003e Spatial co-localization of TGFβ pathway genes (red) and endothelial cell marker \u003cem\u003eVWF\u003c/em\u003e (green) in healthy (H1, H2) and inflamed pulp (P1, P2); yellow indicates co-expression. bar=200 μm.\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-7096435/v1/885c3aa3dab300f147ae6149.png"},{"id":87825764,"identity":"9886d58c-6fef-44e7-91e7-c17735acb39e","added_by":"auto","created_at":"2025-07-29 11:48:57","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":913286,"visible":true,"origin":"","legend":"\u003cp\u003eCell-cell communication networks in healthy and inflamed pulp. \u003cstrong\u003ea\u003c/strong\u003e Dot plot of interaction strength in healthy pulp: dot size = -log₁₀(adjusted \u003cem\u003eP\u003c/em\u003e value); color = mean scaled expression (log₂) of ligand-receptor pairs; x-axis: interacting cell types; y-axis: ligand-receptor pairs. \u003cstrong\u003eb\u003c/strong\u003e Heatmap of interaction frequency between cell types in healthy pulp (red: high; blue: low). \u003cstrong\u003ec\u003c/strong\u003e Network diagram visualizing interaction frequency in healthy pulp (edge weight reflects interaction count). \u003cstrong\u003ed-f\u003c/strong\u003e, Corresponding analyses for inflamed pulp (as in \u003cstrong\u003ea-c\u003c/strong\u003e). \u003cstrong\u003eg\u003c/strong\u003e Spatial co-localization of \u003cem\u003eAPP\u003c/em\u003e⁺ endothelial cells (green) and \u003cem\u003eCD74\u003c/em\u003e⁺ macrophages (red) in healthy/inflamed pulp. \u003cstrong\u003eh\u003c/strong\u003e Co-localization of \u003cem\u003eCD74\u003c/em\u003e⁺ endothelial cells (green) and \u003cem\u003eAPP\u003c/em\u003e⁺ macrophage (red). \u003cstrong\u003ei\u003c/strong\u003e Violin plots of scaled \u003cem\u003eAPP\u003c/em\u003e expression (log₂) across cell types (Benjamini-Hochberg test; *: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). \u003cstrong\u003ej\u003c/strong\u003e Violin plots of scaled \u003cem\u003eCD74\u003c/em\u003e expression (log₂).\u003c/p\u003e","description":"","filename":"Fig.9.png","url":"https://assets-eu.researchsquare.com/files/rs-7096435/v1/bc136e97012f5790c7dc4e61.png"},{"id":90055857,"identity":"31943b0d-9c34-4938-ad23-b2351796fcc0","added_by":"auto","created_at":"2025-08-28 01:08:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16384910,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7096435/v1/aceac1cd-db59-48c3-8e6a-88e10f1530da.pdf"},{"id":87825756,"identity":"b7df0b04-04cd-409e-ac2b-0e237761c6cb","added_by":"auto","created_at":"2025-07-29 11:48:57","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":523418,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S1\u003c/strong\u003e Part of cell-cell communication networks in healthy and inflamed pulp. \u003cstrong\u003eA\u003c/strong\u003e Dot plot of interaction strength in healthy pulp: dot size = -log₁₀(adjusted \u003cem\u003eP\u003c/em\u003e value); color = mean scaled expression (log₂) of ligand-receptor pairs; x-axis: interacting cell types; y-axis: ligand-receptor pairs. \u003cstrong\u003eB\u003c/strong\u003e Corresponding analyses for inflamed pulp (as in \u003cstrong\u003eA\u003c/strong\u003e). \u003cstrong\u003eC\u003c/strong\u003e Violin plots of scaled \u003cem\u003eCXCL14\u003c/em\u003e expression (log₂) in endothelial cells and immune cells (Benjamini-Hochberg test; *: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). \u003cstrong\u003eD\u003c/strong\u003eViolin plots of scaled \u003cem\u003eCXCR4\u003c/em\u003e expression (log₂) in immune cells (Benjamini-Hochberg test; *: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Fig.S1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7096435/v1/3b40aa3bdfb076ae8b316b49.tif"},{"id":87825310,"identity":"bde2b117-f995-450b-8dfa-a6fc6c3fb2cc","added_by":"auto","created_at":"2025-07-29 11:40:57","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":121944,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S2\u003c/strong\u003e Violin plots of scaled \u003cem\u003ePTN\u003c/em\u003e expression (log₂) across cell types (Benjamini-Hochberg test; *: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Fig.S2.tif","url":"https://assets-eu.researchsquare.com/files/rs-7096435/v1/e1b41fa314303c834996d72e.tif"},{"id":87825303,"identity":"949999a3-893c-4c0b-9a3f-36039cbe7d48","added_by":"auto","created_at":"2025-07-29 11:40:57","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":51330,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. S3\u003c/strong\u003e Dot plot of interaction strength between neural cell and other cells in inflamed pulp: dot size = -log₁₀(adjusted \u003cem\u003eP\u003c/em\u003e value); color = mean scaled expression (log₂) of ligand-receptor pairs; x-axis: interacting cell types; y-axis: ligand-receptor pairs.\u003c/p\u003e","description":"","filename":"Fig.S3.tif","url":"https://assets-eu.researchsquare.com/files/rs-7096435/v1/a4ccbe8b02def8003bddbbb4.tif"}],"financialInterests":"There is no conflict of interest","formattedTitle":"Single-cell Resolution Spatial Transcriptomics Delineates the Inflammatory Landscape of Human Dental Pulp: Regional Crosstalk and Therapeutic Implications","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe current primary clinical treatment for irreversible pulpitis (IP) is root canal therapy (RCT), which eliminates infection but inevitably compromises the fracture resistance of dental tissues and results in the loss of pulp vitality. However, histopathological studies have consistently demonstrated a weak correlation between clinical and histological diagnoses of pulpitis. A clinical diagnosis of \"irreversible pulpitis\" does not necessarily indicate irreversible inflammatory damage. In 2021, the American Association of Endodontists (AAE) proposed that conservative vital pulp therapy (VPT) could be considered for IP cases\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Current VPT protocols require complete removal of infected pulp, yet clinically distinguishing inflamed from healthy pulp tissue remains challenging. Consequently, developing novel drugs or targeted therapies to promote reparative processes in inflamed pulp is urgently needed\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Identifying biomarkers of pulpitis is critical for advancing such therapies. However, existing biomarker research primarily focuses on clinical examinations and lacks direct histopathological validation\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.Therefore, resolving the spatial distribution of cells and spatiotemporal dynamics of biomarkers in pulpitis lesions is imperative. Recent studies have applied single-cell RNA sequencing (scRNA-seq) to analyze the temporal-spatial landscape of dental pulp\u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, but scRNA-seq fails to preserve spatial context. Spatial transcriptomics (ST) overcomes this limitation by retaining spatial resolution\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe 10× Genomics Visium HD system enhances the resolution of the original Visium platform from 55 µm spots to 2×2 µm squares, enabling spatial organization analysis at single-cell resolution. Compared to previous ST technologies, Visium HD provides superior spatial structural reconstruction, particularly advantageous for analyzing small tissues\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe aim of this study is to leverage the spatial localization and visualization capabilities of Visium HD to perform comparative analyses of healthy and inflamed pulp samples. By integrating gene expression profiles with microstructural features, we aim to map the spatial distribution of diverse cell types, elucidate spatial interactions among pulp cells and molecules, decode intercellular communication patterns, and delineate tissue organizational functions, thereby refining our understanding of pulpitis pathogenesis. To achieve this, we first identified major cell populations in healthy and inflamed pulp, including fibroblasts, progenitor cells, endothelial cells, neural cells, plasma cells, B cells, T cells, monocytes, and macrophages, along with subclusters of these cells. Furthermore, we performed cell differentiation trajectory analysis and differential gene expression analysis (DGE) on fibroblasts, progenitor cells, endothelial cells, neural cells, and immune cells. Additionally, we investigated inflammation-related signaling pathways in pulp tissues from pulpitis patients and healthy controls. Finally, we analyzed the cell-cell interaction networks in normal versus inflamed pulp. These findings provide novel insights into therapeutic targets for VPT and advance diagnostic and treatment strategies for pulpitis.\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cb\u003eSpatially resolved single-cell transcriptomic mapping of dental pulp in health and pulpitis via Visium HD\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate the spatial transcriptomic profiles of dental pulp in healthy individuals and pulpitis patients, we collected fresh pulp tissues from two healthy volunteers and two pulpitis patients. The tissues underwent fixation, embedding, sectioning, and staining. Following sample validation, tissue mounting, spatial transcriptomic library preparation, sequencing, and data analysis, the results were visualized. Single-cell resolution was achieved using the 10× Genomics Visium HD platform (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, b).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpatial transcriptomics identifies 9 major cell types in pulp from patients with irreversible pulpitis and healthy individuals\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on established cell marker genes\u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17 CR18\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e–\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and spatial localization, we classified cell clusters into 9 major categories using the Space Ranger pipeline: fibroblasts, progenitor cells, endothelial cells, neural cells, plasma cells, B cells, T cells, monocytes, and macrophages. Canonical marker genes included \u003cem\u003eCOL1A2\u003c/em\u003e for fibroblasts, \u003cem\u003eNES\u003c/em\u003e for progenitor cells, \u003cem\u003eVWF\u003c/em\u003e for endothelial cells, \u003cem\u003eMPZ\u003c/em\u003e for neural cells, \u003cem\u003eIGKC\u003c/em\u003e for plasma cells, \u003cem\u003eMS4A1\u003c/em\u003e for B cells, \u003cem\u003eTRBC2\u003c/em\u003e for T cells, \u003cem\u003eLYZ\u003c/em\u003e for monocytes, and \u003cem\u003eCD68\u003c/em\u003e for macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Hematoxylin and eosin (H\u0026amp;E) staining demonstrated predominant inflammatory cell infiltration in pulpitis tissues, spatial maps confirmed that cell clustering aligned with anatomical structures and exhibited single-cell resolution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Uniform Manifold Approximation and Projection (UMAP) analysis revealed distinct expression patterns of \u003cem\u003eCOL1A2\u003c/em\u003e, \u003cem\u003eNES\u003c/em\u003e, \u003cem\u003eVWF\u003c/em\u003e, \u003cem\u003eMPZ\u003c/em\u003e, \u003cem\u003eIGKC\u003c/em\u003e, \u003cem\u003eMS4A1\u003c/em\u003e, \u003cem\u003eTRBC2\u003c/em\u003e, \u003cem\u003eLYZ\u003c/em\u003e, and \u003cem\u003eCD68\u003c/em\u003e in fibroblasts, progenitor cells, endothelial cells, neural cells, plasma cells, B cells, T cells, monocytes, and macrophages, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Furthermore, spatial maps highlighted the regional dominance of these marker genes within their corresponding cell type territories (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003eNotably, consistent with prior single-cell RNA sequencing (scRNA-seq) studies, odontoblasts (a pulp-specific cell population) were not detected in our analysis. This limitation is presumably attributable to: (i) the high anesthesia sensitivity of odontoblasts during sample collection, and (ii) the low-abundance transcripts of established odontoblast markers (dentin sialo-phosphoprotein, \u003cem\u003eDSPP\u003c/em\u003e; dentin matrix acidic phosphoprotein 1, \u003cem\u003eDMP1\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFibroblasts\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe performed subclustering of fibroblasts using the Seurat package. Visualization revealed that fibroblasts were divided into 11 subclusters (0–10), with distinct proportion distributions between healthy and inflamed dental pulps. Notably, subcluster 0 showed increased proportion in inflamed pulp, while subclusters 1, 7, and 9 were exclusively present in healthy pulp (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b). Cellular differentiation trajectory analysis demonstrated 8 differentiation trajectories originating from subcluster 0, which exhibited the earliest differentiation pseudotime. Subclusters 1 and 9 were located along the same trajectory, with subcluster 1 positioned at the trajectory terminus displaying the latest differentiation pseudotime. Subcluster 7 showed relatively late differentiation pseudotime and was situated at the midpoint of another trajectory (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Differential gene expression analysis across subclusters identified \u003cem\u003eAPOL2\u003c/em\u003e (associated with apoptosis and autophagy\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e) and \u003cem\u003eCCN2\u003c/em\u003e (implicated in tissue repair, fibrosis, and TGFβ signaling\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e) as highly expressed in Subcluster 0. Subclusters 7 and 9 exhibited elevated expression of \u003cem\u003eTMPRSS4\u003c/em\u003e (Transmembrane Serine Protease 4, a regulator of inflammatory responses\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e–\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e) and \u003cem\u003eCST5\u003c/em\u003e (an anti-inflammatory mediator\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e ), respectively(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). These findings suggest a dual role for fibroblast subclusters in pulp homeostasis and inflammatory responses: under healthy conditions, the \u003cem\u003eTMPRSS4\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e/\u003cem\u003eCST5\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e subclusters maintain tissue integrity through protease-antiprotease balance, whereas under inflammatory stimulation, the \u003cem\u003eAPOL2\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e/\u003cem\u003eCCN2\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e subclusters drive pathological repair via collagen fibril organization pathways. This not only refines our understanding of pulp repair mechanisms but also highlights potential therapeutic strategies targeting the \u003cem\u003eCCN2\u003c/em\u003e pathway or \u003cem\u003eTMPRSS4\u003c/em\u003e/\u003cem\u003eCST5\u003c/em\u003e balance in specific fibroblast subclusters to modulate pulpitis progression.\u003c/p\u003e\u003cp\u003eSubsequently, we analyzed differentially expressed genes (DEGs) in fibroblasts from inflamed versus healthy pulp. Volcano plots demonstrated significant upregulation of inflammation-related genes (\u003cem\u003eIGHG1\u003c/em\u003e, \u003cem\u003eIGHA1\u003c/em\u003e, \u003cem\u003eMS4A1\u003c/em\u003e \u003csup\u003e30,31\u003c/sup\u003e) in inflamed pulp (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Gene Ontology (GO) enrichment analysis revealed biological processes (BP) and cellular components (CC) significantly associated with fibroblast DEGs in inflamed pulp. These BP/CC terms were closely linked to four other cell populations: progenitor cells (e.g., connective tissue development, skeletal system development, endothelial cells (e.g., angiogenesis, blood vessel development), neural cells (e.g., tube morphogenesis), and immune cells (e.g., response to bacterium, inflammatory response) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Spatial co-localization analysis further confirmed upregulated expression of these BP-associated genes in fibroblasts from inflamed pulp (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg). These results suggest enhanced interactions between fibroblasts and the four cell populations under inflammatory conditions, potentially amplifying pathological cascades in pulpitis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eProgenitor Cells\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUtilizing Seurat software, we categorized progenitor cells into 6 subclusters (0 − 5) and visualized their spatial distribution and proportional representation in both healthy and inflamed dental pulp. The analysis revealed an increased proportion of subcluster 0 in inflamed pulp, while subclusters 4 and 5 exhibited reduction and complete absence in pathological conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, b). Cellular differentiation trajectory analysis of cellular differentiation demonstrated two distinct pathways originating from subcluster 0 as the initial state. Subcluster 0 exhibited the earliest differentiation timing, while subclusters 4 and 5 were positioned along separate trajectories, with subcluster 4 displaying later differentiation timing and subcluster 5 representing the terminal stage of differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Differential gene analysis revealed that Subcluster 0 exhibited high expression of \u003cem\u003eCDK5R1\u003c/em\u003e (involved in signal transduction and apoptosis induction\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e ) and \u003cem\u003eCCRL2\u003c/em\u003e (a pro-inflammatory mediator regulating immune modulation, inflammatory responses, and cell migration\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e ). Subcluster 4 showed elevated expression of \u003cem\u003eNEUROD6\u003c/em\u003e (Neurogenic Differentiation factor 6, critical for neural development and function \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e ), whereas Subcluster 5 was marked by \u003cem\u003eGDF10\u003c/em\u003e (implicated in tissue repair, regeneration, and neural axon regeneration\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e ) and \u003cem\u003eZIC1\u003c/em\u003e (essential for embryonic development, particularly neurogenesis\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e ) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). These findings suggest that progenitor cell subclusters may act as “bidirectional functional modulators” in pulpitis: dynamic shifts in subcluster proportions could dictate inflammatory outcomes. Specifically, suppressing the pro-apoptotic/immune-recruiting properties of Subcluster 0 or supplementing \u003cem\u003eGDF10\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e/\u003cem\u003eZIC1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e subclusters exogenously may represent novel therapeutic strategies to reverse pathological inflammation and promote pulp neural regeneration.\u003c/p\u003e\u003cp\u003eVolcano plots demonstrated significant upregulation of inflammation-related genes (\u003cem\u003eIGHG1\u003c/em\u003e, \u003cem\u003eIGKC\u003c/em\u003e, \u003cem\u003eIGHA1\u003c/em\u003e \u003csup\u003e31\u003c/sup\u003e ) in progenitor cells from inflamed pulp compared to healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). Gene Ontology (GO) enrichment analysis via Sankey diagrams revealed biological processes (BP) and cellular components (CC) associated with four other cell populations: integrin-mediated signaling pathway linked to fibroblasts, blood microparticle to endothelial cells, regulation of neuron projection development to neural cells, and inflammatory response to immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef). Spatial co-localization analysis visualized the distribution and co-expression of these enriched BP/CC terms with the progenitor cell marker gene \u003cem\u003eNES\u003c/em\u003e. Results indicated significant upregulation of blood microparticle and inflammatory response-related genes in progenitor cells from inflamed pulp, alongside downregulation of neuron projection development-associated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg). Collectively, these data suggest that progenitor cells in inflamed pulp may drive local inflammation by activating immune pathways (e.g., \u003cem\u003eIGHG1/IGKC/IGHA1\u003c/em\u003e upregulation) and blood microparticle-mediated endothelial signaling, while suppressing neural repair mechanisms through impaired neurodevelopmental gene expression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEndothelial cells\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEndothelial cells underwent subclustering via Seurat, yielding 4 distinct subclusters (0 − 3) as visualized in our analysis. In inflammatory dental pulp tissues, subclusters 0 and 2 showed significant proportional reductions, whereas subcluster 1 demonstrated marked expansion (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, b). Cellular differentiation trajectory analysis revealed a unidirectional path initiating from subcluster 1, with subcluster 0 displaying intermediate differentiation timing and subcluster 2 representing the terminal differentiation phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Differential gene analysis identified \u003cem\u003eSERPINA5\u003c/em\u003e and \u003cem\u003eSERPINA3\u003c/em\u003e (both serine protease inhibitors) as highly expressed in Subcluster 0. \u003cem\u003eSERPINA5\u003c/em\u003e regulates coagulation-anticoagulation balance and immune modulation\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, whereas \u003cem\u003eSERPINA3\u003c/em\u003e suppresses excessive protease activity at inflammatory sites to mitigate tissue damage\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Subcluster 2 exhibited elevated expression of pro-inflammatory genes such as \u003cem\u003eIL9R\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. while Subcluster 1 showed upregulated expression of inflammation-related genes (\u003cem\u003eIGHG1\u003c/em\u003e, \u003cem\u003eIGKC\u003c/em\u003e, \u003cem\u003eIGHA1\u003c/em\u003e, \u003cem\u003eCSF3\u003c/em\u003e, \u003cem\u003eMZB1\u003c/em\u003e, and \u003cem\u003eCXCL13\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan additionalcitationids=\"CR47 CR48 CR49\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e–\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). These results suggest that endothelial remodeling in inflamed pulp exacerbates pathological injury through three mechanisms: (i) Expansion of pro-inflammatory subclusters: Upregulated genes in Subcluster 1 (\u003cem\u003eIGHG1\u003c/em\u003e, \u003cem\u003eCXCL13\u003c/em\u003e) may recruit immune cells and amplify inflammatory cascades\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e ; (ii) Depletion of anti-inflammatory/protective subclusters: Reduced proportions of Subclusters 0 (Serpin family genes) and 2 (\u003cem\u003eIL9R\u003c/em\u003e) indicate impaired anti-protease and immunoregulatory capacities in endothelial cells under inflammation, aggravating tissue damage\u003csup\u003e\u003cspan additionalcitationids=\"CR43 CR44\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e–\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e ; (iii) Enhanced endothelial-immune crosstalk: Immunoglobulins (\u003cem\u003eIGHG1\u003c/em\u003e) and chemokines (\u003cem\u003eCXCL13\u003c/em\u003e) in Subcluster 1 may potentiate endothelial cell interactions with B cells and neutrophils, driving chronic inflammation.\u003c/p\u003e\u003cp\u003eVolcano plots demonstrated significant upregulation of inflammation-related genes (\u003cem\u003eIGHG1\u003c/em\u003e, \u003cem\u003eIGKC\u003c/em\u003e, \u003cem\u003eIGHA1\u003c/em\u003e) and downregulation of reparative genes (\u003cem\u003ePTN\u003c/em\u003e, \u003cem\u003eTF\u003c/em\u003e) in endothelial cells from inflamed pulp (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). Gene Ontology (GO) enrichment analysis revealed biological processes (BP) and cellular components (CC) associated with endothelial cell DEGs, including collagen fibril organization and extracellular matrix linked to fibroblasts, cell population proliferation and positive regulation of cell development to progenitor cells, synapse organization and synaptic membrane to neural cells, and regulation of inflammatory response to immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). Spatial co-localization of these BP/CC-associated genes with the endothelial cell marker \u003cem\u003eVWF\u003c/em\u003e confirmed upregulated expression of collagen fibril organization and regulation of inflammatory response genes in inflamed pulp endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg). These findings suggest that endothelial cells in inflamed pulp participate in pathological progression via multicellular synergy. The evidence chain includes: (i) Endothelial cells adopt a pro-inflammatory phenotype (e.g., upregulated \u003cem\u003eIGHG1\u003c/em\u003e, downregulated \u003cem\u003ePTN\u003c/em\u003e\u003csup\u003e\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e–\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e); (ii) GO analysis highlights functional linkages with fibroblasts, progenitor cells, neural cells, and immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee); (iii) Spatial co-localization verifies endothelial cell involvement in both collagen fibril organization and regulation of inflammatory response. This cross-cellular synergy likely establishes an \"inflammation-repair imbalance\" vicious cycle, perpetuating pulpitis progression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNeural cells\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEmploying Seurat-based subclustering analysis on neural cells, we identified 7 distinct subclusters (0 − 6). Subcluster 3 exhibited significant proportional expansion in inflammatory dental pulp tissues, while subclusters 0, 1, 2, 4, and 6 showed marked reductions. Notably, subclusters 1, 2, 4, and 6 were exclusively present in healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, b). Cellular differentiation trajectory analysis revealed 2 distinct differentiation branches originating from subcluster 3. Subcluster 6 occupied the terminal position of one branch, representing an early differentiation state, while subclusters 1, 2, and 4 localized to the endpoint of the alternative branch, corresponding to terminal differentiation stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Differential gene analysis demonstrated that Subcluster 0 exhibited high expression of \u003cem\u003eMPRIP\u003c/em\u003e (implicated in neuronal migration or synaptic plasticity\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e ) and \u003cem\u003eLAMTOR4\u003c/em\u003e (or \u003cem\u003eC7orf59\u003c/em\u003e, regulating cell growth, metabolism, and autophagy\u003csup\u003e\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e–\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Subcluster 3 showed elevated expression of inflammation-associated immunoglobulin genes (\u003cem\u003eIGKC\u003c/em\u003e, \u003cem\u003eIGHG1\u003c/em\u003e\u003csup\u003e31\u003c/sup\u003e ), while Subcluster 6 was marked by \u003cem\u003eSERPINB9\u003c/em\u003e, a granzyme B inhibitor that neutralizes cytotoxic immune cell activity to maintain tissue homeostasis\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). These findings suggest functional polarization of neural cells under inflammatory conditions: pro-inflammatory subclusters (e.g., Subcluster 3) engage in immune responses via immunoglobulin gene upregulation, whereas homeostatic subclusters (e.g., Subclusters 0 and 6) preserve tissue integrity through neuronal migration, autophagy, and immune tolerance regulation. The imbalance between pro-inflammatory expansion and homeostatic decline likely disrupts neuro-immune crosstalk, exacerbating pulp inflammatory injury.\u003c/p\u003e\u003cp\u003eDifferential gene expression analysis of neural cells in inflamed versus healthy pulp revealed significant upregulation of \u003cem\u003eIGKC\u003c/em\u003e, \u003cem\u003eIGHG1\u003c/em\u003e, and \u003cem\u003eAPOE\u003c/em\u003e, alongside downregulation of \u003cem\u003eMBP\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee). Gene Ontology (GO) enrichment analysis highlighted biological processes (BP) and cellular components (CC) associated with neural cell DEGs, including extracellular matrix and extracellular matrix organization linked to fibroblasts, positive regulation of cell development and cell population proliferation to progenitor cells, blood vessel development and angiogenesis to endothelial cells, and immunoglobulin-mediated immune response to immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef). Spatial co-localization of these BP/CC-associated genes with the neural cell marker \u003cem\u003eMPZ\u003c/em\u003e demonstrated pronounced upregulation of immunoglobulin-mediated immune response-related genes in inflamed pulp neural cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eg). Collectively, these results indicate that neural cells in inflamed pulp may interact with immune cells via immunoglobulin-mediated responses (e.g., \u003cem\u003eIGKC/IGHG1\u003c/em\u003e upregulation), while being modulated by fibroblasts, progenitor cells, and endothelial cells to establish a pro-inflammatory microenvironment. The upregulation of \u003cem\u003eAPOE\u003c/em\u003e might compensate for neural repair\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, whereas \u003cem\u003eMBP\u003c/em\u003e downregulation suggests myelin damage, collectively contributing to neuronal dysfunction and pain hypersensitivity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImmune cells\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe performed Uniform Manifold Approximation and Projection (UMAP) and spatial co-localization analyses on immune cells, including plasma cells, B cells, T cells, monocytes, and macrophages. In inflamed pulp, the proportions of B cells and macrophages were significantly increased, while T cells and monocytes were reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, b). Differential gene expression analysis revealed a predominant downregulation of immune cell-related genes in inflamed pulp compared to healthy controls, with notable downregulation of \u003cem\u003ePTN\u003c/em\u003e, \u003cem\u003eSERPINA3\u003c/em\u003e, \u003cem\u003eTF\u003c/em\u003e, \u003cem\u003eCXCL14\u003c/em\u003e, and \u003cem\u003eGPX3\u003c/em\u003e, alongside upregulation of \u003cem\u003eCXCL13\u003c/em\u003e, \u003cem\u003eCCL18\u003c/em\u003e, \u003cem\u003eMS4A1\u003c/em\u003e, and \u003cem\u003ePI3\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec, e). Gene Ontology (GO) enrichment analysis of differentially expressed genes (DEGs) identified biological processes (BP) and cellular components (CC) associated with four other cell populations: extracellular matrix organization and collagen fibril organization linked to fibroblasts; stem cell differentiation and hemopoiesis to progenitor cells; blood vessel development and angiogenesis to endothelial cells; and regulation of neuron projection development and neural crest cell differentiation to neural cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed). Spatial co-localization of these BP/CC-associated genes with the immune cell marker \u003cem\u003eIGKC\u003c/em\u003e demonstrated significant downregulation of regulation of neuron projection development-related genes in immune cells from inflamed pulp (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef).\u003c/p\u003e\u003cp\u003eThese findings suggest that the immune microenvironment in inflamed pulp undergoes functional remodeling characterized by: (i) Adaptive immune activation: Elevated B cell/macrophage ratios\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e and reduced T cell/monocyte proportions indicate immune response dysregulation\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e; (ii) Impaired immune-neural crosstalk: Coordinated downregulation of antioxidant/neurotrophic genes (e.g., \u003cem\u003ePTN\u003c/em\u003e\u003csup\u003e\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e–\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eGPX3\u003c/em\u003e\u003csup\u003e65\u003c/sup\u003e ) and spatial suppression of regulation of neuron projection development pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef); (iii) Enhanced inflammatory cell recruitment: Upregulated chemokines (\u003cem\u003eCXCL13/CCL18\u003c/em\u003e) synergistically promote leukocyte infiltration \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan additionalcitationids=\"CR67\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e–\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Collectively, these data highlight that pathological progression in pulpitis may be driven by a neuro-immune regulatory network, where disrupted intercellular communication exacerbates disease severity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpatial Heterogeneity of Inflammation-Related Pathway Activity in Inflamed Pulp of Pulpitis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAbnormal activation of signaling pathways has been implicated in pulpitis\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. We employed PROGENy to evaluate pathway activity by quantifying expression changes of downstream genes associated with specific pathways\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e,\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. We assessed the activity of 14 pathways critically involved in inflammation and tissue regeneration. We observed elevated activity of PI3K, EGFR, TGFβ, MAPK, Estrogen, and NF-κB pathways in inflamed pulp cells, while JAK-STAT, Androgen, and p53 pathways exhibited reduced activity. Specific pathways including NF-κB and TRAIL demonstrated high activity in immune cells of inflamed pulp (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eGiven the predominant pathway activation in inflamed cells, we focused on TGFβ pathway activity across cell populations, revealing significant upregulation in all 5 major cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). Further comparison showed that endothelial cells had significantly higher TGFβ activity than fibroblasts, neural cells, and immune cells in inflamed pulp (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec). To validate TGFβ pathway activation in endothelial cells, we evaluated serial sections of healthy and inflamed pulp. Spatial co-localization of TGFβ with \u003cem\u003eVWF\u003c/em\u003e-positive endothelial cells confirmed enhanced TGFβ signaling in pulpitis-derived endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed). In summary, our findings successfully delineate the activation states of distinct signaling pathways in inflamed pulp and highlight significant upregulation of the TGFβ pathway in pulpitis endothelial cells.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRemodeling of Cell-Cell Interaction Networks in Dental Pulp of Healthy Individuals and Pulpitis Patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further investigate spatial intercellular relationships in healthy and inflamed pulp, we performed cell-cell interaction analysis using the Cellphone DB software. Our results revealed extensive interactions across cell types in both states, with the \u003cem\u003eAPP-CD74\u003c/em\u003e gene pair exhibiting the most prominent connectivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea–f). Previous studies have demonstrated that extensive intercellular communication occurs between macrophages and other cell types in healthy dental pulp via the \u003cem\u003eCD74\u003c/em\u003e-\u003cem\u003eAPP\u003c/em\u003e ligand–receptor pair, with endothelial cells being the most frequent initiators of such interactions through \u003cem\u003eCD74\u003c/em\u003e ligands\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In healthy pulp, bidirectional interactions (\u003cem\u003eAPP-CD74\u003c/em\u003e and \u003cem\u003eCD74-APP\u003c/em\u003e) were observed between endothelial cells and macrophages, whereas inflamed pulp exhibited only unidirectional \u003cem\u003eAPP-CD74\u003c/em\u003e interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea, d). To validate the spatial relationship between endothelial cells and macrophages, we visualized the co-localization of \u003cem\u003eAPP\u003c/em\u003e-high endothelial cells and \u003cem\u003eCD74\u003c/em\u003e-high macrophages in healthy and inflamed pulp sections. Inflamed pulp showed significantly reduced spatial proximity between \u003cem\u003eCD74\u003c/em\u003e-high endothelial cells and \u003cem\u003eAPP\u003c/em\u003e-high macrophages compared to healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eg, h), further supporting weakened unidirectional \u003cem\u003eCD74-APP\u003c/em\u003e interactions in inflamed pulp.\u003c/p\u003e\u003cp\u003eSubsequent analysis of \u003cem\u003eAPP\u003c/em\u003e and \u003cem\u003eCD74\u003c/em\u003e expression across cell populations demonstrated significant downregulation of \u003cem\u003eAPP\u003c/em\u003e in macrophages from inflamed pulp (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ei), whereas \u003cem\u003eCD74\u003c/em\u003e expression in endothelial cells and macrophages remained unaltered (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ej). This explains the diminished bidirectional \u003cem\u003eCD74-APP\u003c/em\u003e crosstalk in inflamed pulp, attributable to \u003cem\u003eAPP\u003c/em\u003e suppression in macrophages. In summary, our study successfully uncovers remodeling of the endothelial cell- macrophages interaction network in pulpitis, characterized by attenuated \u003cem\u003eCD74-APP\u003c/em\u003e signaling due to \u003cem\u003eAPP\u003c/em\u003e downregulation in macrophages under inflammatory conditions.\u003c/p\u003e\u003cp\u003eIn healthy pulp, no intercellular interactions occurred via the \u003cem\u003eCXCL14\u003c/em\u003e-\u003cem\u003eCXCR4\u003c/em\u003e ligand-receptor pair. Conversely, in inflamed pulp, fibroblasts, progenitor cells, and neural cells—but not endothelial cells—engaged with 5 immune cell types through \u003cem\u003eCXCL14\u003c/em\u003e-\u003cem\u003eCXCR4\u003c/em\u003e signaling (Fig. S1A, B). Notably, CXCL14 was significantly downregulated in immune cells and endothelial cells, while CXCR4 was upregulated in immune cells compared to healthy pulp (Fig. S1C, D). These findings suggest a functional dichotomy wherein endothelial cells diverge from fibroblasts, progenitor cells, and neural cells in inflammation, implicating the \u003cem\u003eCXCL14\u003c/em\u003e-\u003cem\u003eCXCR4\u003c/em\u003e axis as a critical regulator of pulpitis progression that merits further investigation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDue to the minute size of dental pulp tissues and challenges in obtaining intact inflamed pulp samples, spatial transcriptomic profiling of human pulp has remained unexplored. While existing studies have applied scRNA-seq and single-cell transcriptomics to dental pulp\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e, these approaches fail to integrate spatial contextualization with cellular signatures. In this study, we present the first comprehensive atlas of human healthy and inflamed pulp using single-cell-resolution spatial transcriptomics. Our work provides an unprecedented characterization of spatiotemporal reprogramming across pulp cell types during inflammation, delivering novel biological insights for developing innovative therapeutic strategies in vital pulp preservation.\u003c/p\u003e\u003cp\u003ePrevious studies demonstrate that \u003cem\u003ePleiotrophin\u003c/em\u003e (\u003cem\u003ePTN\u003c/em\u003e) enhances proliferative and osteogenic/odontogenic capacities of dental pulp stem cells (DPSCs), conferring protection against senescence, while potentially inhibiting chondrogenic differentiation and preventing H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e-induced senescent injury in DPSCs\u003csup\u003e\u003cspan additionalcitationids=\"CR75\" citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. However, no evidence exists regarding \u003cem\u003ePTN\u003c/em\u003e roles in pulpitis. Our analysis of immune cells revealed significant downregulation of \u003cem\u003ePTN\u003c/em\u003e in inflamed pulp. Subsequent evaluation across cell types confirmed ubiquitous \u003cem\u003ePTN\u003c/em\u003e suppression in non-neural populations during inflammation (Fig. S2). Based on these findings, we propose that \u003cem\u003ePTN\u003c/em\u003e\u0026mdash;beyond its established anti-senescence functions\u0026mdash;may constitute a previously unrecognized regulatory mechanism suppressing pulpal inflammation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePrevious studies have established associations between pulpitis and activation of PI3K/AKT, NF-κB, and MAPK pathways\u003csup\u003e\u003cspan additionalcitationids=\"CR78 CR79\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. Our PROGENy analysis further revealed elevated activity of TGFβ signaling in inflamed pulp cells. These findings implicate TGFβ as previously unrecognized pathological dimensions of pulpal inflammation, expanding the mechanistic landscape beyond canonical pathways.\u003c/p\u003e\u003cp\u003eOur study reveals that neural cells exhibit no significant interactions with other cell types in healthy pulp, whereas inflamed pulp demonstrates extensive neuro-immune interplay (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb, e). Further analysis identifies \u003cem\u003eAPP\u003c/em\u003e, \u003cem\u003eCD74\u003c/em\u003e, \u003cem\u003eCD44\u003c/em\u003e, and \u003cem\u003eCXCL14\u003c/em\u003e as dominant ligands mediating neural cell communication in inflamed pulp (Fig. S3). These findings suggest that neural cells actively engage in immune responses during pulpitis, implicating a previously unrecognized neuro-immunomodulatory mechanism that merits in-depth investigation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eCXCL14\u003c/em\u003e promotes cell migration and angiogenesis\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e and is implicated as a potent mediator of pulp regenerative potential acting via \u003cem\u003eCXCR4\u003c/em\u003e\u003csup\u003e82\u003c/sup\u003e. It also recruits M2-polarized macrophages to facilitate tissue repair\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. Conversely, \u003cem\u003eCXCR4\u003c/em\u003e upregulation in immune cells drives pathological immune retention, exacerbating inflammation\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. Thus, strategically enhancing \u003cem\u003eCXCL14\u003c/em\u003e expression in pulp cells while antagonizing \u003cem\u003eCXCR4\u003c/em\u003e in immune cells may concurrently promote tissue regeneration and resolve inflammation.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCXCL14\u003c/em\u003e acts as a natural antagonist of \u003cem\u003eCXCL12\u003c/em\u003e, inhibiting the \u003cem\u003eCXCL12\u003c/em\u003e-\u003cem\u003eCXCR4\u003c/em\u003e axis\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e \u0026mdash;a pathway critical for B cell development and function\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. In pulpitis, B cells leverage \u003cem\u003eCXCL12\u003c/em\u003e-\u003cem\u003eCXCR4\u003c/em\u003e signaling to interact with other immune cells (including autocrine loops) (Fig. S1B). Given the significant expansion of B cells in our inflamed pulp samples, elevating \u003cem\u003eCXCL14\u003c/em\u003e expression and modulating \u003cem\u003eCXCR4\u003c/em\u003e activity could: Suppressing B cell maturation, promoting angiogenesis and tissue regeneration, attenuating immune cell retention and accelerating inflammatory resolution.\u003c/p\u003e"},{"header":"CONSLUSION","content":"\u003cp\u003eWe present the first single-cell-resolution spatial transcriptomic dataset of healthy and inflamed human dental pulp, combining cellular clustering, subcluster profiling, pathway activity analysis, and cell-cell interaction mapping to establish spatiotemporal landscapes. This integrated approach reveals putative roles of specific cell subpopulations in pulpitis pathogenesis and highlights \u003cem\u003ePTN\u003c/em\u003e, \u003cem\u003eAPP\u003c/em\u003e, the \u003cem\u003eCXCL14\u003c/em\u003e-\u003cem\u003eCXCR4\u003c/em\u003e ligand-receptor axis and the TGFβ pathway as potential key regulators meriting further investigation.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eTissue Acquisition\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll dental pulp specimens utilized in this study were obtained from non-functional third molars and orthodontically extracted premolars of patients undergoing Department of Oral and Maxillofacial Surgery, Hospital of Stomatology, Sun Yat-sen University, Guangzhou. Each sample collection procedure complied with institutional regulations and was approved by the Medical Ethics Committee of Hospital of Stomatology, Sun Yat-sen University. Written informed consent was obtained from all participants prior to their inclusion in the study, and consent documentation was archived accordingly. Patients presenting with systemic diseases other than pulpitis were excluded from the study.\u003c/p\u003e\u003cp\u003ePulpitis-affected specimens were characterized by deep carious cavities with pulp exposure and typical clinical symptoms, including spontaneous pain and nocturnal pain. Healthy control samples exhibited intact tooth structure without caries or enamel demineralization, and donors reported no subjective symptoms of dental discomfort.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFFPE(Formalin-Fixed Paraffin-Embedded)section preparation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe isolated dental pulp tissues were aseptically collected and fixed in 4% paraformaldehyde solution for 12\u0026ndash;24 hours, followed by overnight storage in 75% ethanol. A graded ethanol dehydration series was subsequently implemented as follows: Dehydration: Sequential immersion in ethanol solutions of increasing concentrations (30%, 50%, 70%, 80%, 95%, and 100%), with each concentration maintained for 45\u0026ndash;60 minutes to achieve complete dehydration; Clearing: Residual ethanol was eliminated through xylene immersion; Paraffin infiltration: Tissues were treated with low-melting-point paraffin to remove residual xylene; Embedding: Infiltrated tissues were immersed in molten paraffin within embedding cassettes and rapidly solidified on a pre-chilled platform; Sectioning: 10 \u0026micro;m-thick sections were prepared using a Leica CM1950 cryostat.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVisium HD\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe isolation of total RNA from FFPE tissue blocks was achieved using the RNeasy FFPE kit (73504, Qiagen). The quality assessment of the extracted RNA was performed by calculating DV200. Tissue sections passing the quality control (DV200\u0026thinsp;\u0026gt;\u0026thinsp;30%) were subjected to ST assay. The Visium HD workflow requires new HD slides that require thawing, washes, and equilibration in appropriate buffers. Visium HD slides also feature high-resolution fiducials for subpixel image alignment, and a dispensing pad and spacer for CytAssist compatibility. We first placed FFPE tissue sections on plain glass slides for deparaffinization, H\u0026amp;E staining and imaging following the Visium HD FFPE Tissue Preparation Handbook (CG000684). Subsequently, the sections were stained with H\u0026amp;E and imaged at 20\u0026times; magnification in brightfield using PANNORAMIC MIDIⅡ Digital Scanner (3DHISTECH). Probe hybridization, probe ligation, slide preparation, probe release, extension, library construction, and sequencing followed the Visium HD Spatial Gene Expression Reagent Kits User Guide (CG000685). Sequencing was performed on an Illumina NovaSeq 6000 with paired-end reads (43 cycles Read 1, 10 cycles i7, 10 cycles i5, 50 cycles Read 2). We used Space Ranger v3.0 to map FASTQ files to the human reference, detect the tissue section, align the sequencing data to the microscope image and the CytAssist image, and output gene-barcode matrices for further analysis.\u003c/p\u003e\u003cp\u003eSeurat objects (version 5.2.0) were initialized for each sample using the 8 \u0026times; 8 \u0026micro;m filtered feature barcode matrices generated by Space Ranger. For DP sample, HD bins with fewer than 100 UMI counts and 50 detected genes were filtered out. The raw counts were independently normalized using log-normalization. Clustering of the Visium HD data was conducted within Seurat. The initial clustering round identified clusters at a resolution of 0.3. Additionally, we used the DoubletFinder software (version 2.0.4) to remove potential doublets. After filtering out low-quality cells and doublets, the data were normalized using the NormalizeData function, high-variable genes were identified with the FindVariableFeatures function, and the data were scaled using the ScaleData function. Subsequently, dimensionality reduction was performed using RunPCA for principal component analysis. Clustering was conducted using the FindNeighbors and FindClusters functions. Finally, the clustering results were visualized using DimPlot.\u003c/p\u003e\u003cp\u003eThe FindAllMarkers function was utilized to identify characteristic genes for each cluster. Subsequently, we performed enrichment analysis on the characteristic genes of each cluster using the clusterProfiler R package (version 4.12.6) with default settings. The human annotation information was sourced from the org.Hs.eg.db database.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVisualization of Cell Clustering\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBins were clustered based on gene expression levels using Space Ranger (v3.0.0). First, UMI counts were normalized, followed by dimensionality reduction through principal component analysis (PCA). The top 10 principal components were selected for clustering using both graph-based methods\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. The PCA-reduced data were further processed using t-SNE\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e and UMAP\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e for visualization purposes. Customized analysis and visualization of the clustering results from Space Ranger were performed using Loupe Browser (v8.1.1), the official software provided by 10x Genomics.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCell Differentiation Trajectory Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe UMAP coordinates of the subclusters were utilized as input for Monocle3. We constructed the trajectories across cell types with the following parameters: learn_graph_control\u0026thinsp;=\u0026thinsp;list (minimal_branch_len\u0026thinsp;=\u0026thinsp;50), use_partition\u0026thinsp;=\u0026thinsp;FALSE, close_loop\u0026thinsp;=\u0026thinsp;FALSE. The root node was caculated by CytoTRACE. We employed the graph_test function in Monocle3, specifying the parameter neighbor_graph = \"principal_graph\", to discern genes with differential expression along the trajectory of a particular lineage. Genes with q value less than 0.05 were selected.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGene Ontology (GO) Enrichment Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDifferential gene expression analysis was performed using the \u0026ldquo;FindMarkers\u0026rdquo; function in Seurat with the bimod likelihood-ratio test. Differentially expressed genes (DEGs) were filtered using thresholds of absolute log\u003csub\u003e2\u003c/sub\u003e-fold change\u0026thinsp;\u0026gt;\u0026thinsp;1 and \u003cem\u003eP\u003c/em\u003e Value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, followed by volcano plot visualization. Marker genes for each cluster were further screened with relaxed criteria (absolute log\u003csub\u003e2\u003c/sub\u003e-fold change\u0026thinsp;\u0026gt;\u0026thinsp;0.5 and \u003cem\u003eP\u003c/em\u003e Value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The filtered DEGs were subjected to GO enrichment analysis on the Metascape platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://metascape.org\u003c/span\u003e\u003cspan address=\"http://metascape.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a \u003cem\u003eP\u003c/em\u003e value cutoff of 0.05. Significantly enriched GO terms (q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were visualized using the CNSknowall platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cnsknowall.com\u003c/span\u003e\u003cspan address=\"https://cnsknowall.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Finally, the spatial expression patterns of these enriched pathways were mapped onto dental pulp tissue sections using Loupe Browser software (10x Genomics).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePROGENy analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate the expression patterns of signaling pathways in distinct cellular populations within dental pulp tissues under healthy and inflammatory conditions, we performed pathway activity quantification using the R package progeny\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. This study systematically analyzed pathway activities in spatial transcriptomic datasets of five cell types (fibroblasts, progenitor cells, endothelial cells, neural cells and immune cells) under healthy and inflamed states through the PROGENy algorithm.\u003c/p\u003e\u003cp\u003eUtilizing the top 100 genes from transcriptional footprints of each experimental group, we constructed a unified expression matrix encompassing 10 experimental conditions (5 cell types \u0026times; 2 states) via data integration. A zero-imputation strategy was implemented to generate expression vectors, followed by normalization. Pathway activity scores were computed using PROGENy, with hierarchical clustering heatmaps generated through Complex Heatmap to comprehensively visualize inter-group biological associations. Bar plots delineated pathway activity levels across cellular populations, while Loupe Browser facilitated the generation of pathway expression violin plots. Furthermore, spatially resolved mapping of pathway expression patterns was achieved through visualization on dental pulp tissue sections.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCellphoneDB Cell\u0026thinsp;\u0026minus;\u0026thinsp;Cell Communication Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCellular interactions between cell types were computed based on ligand-receptor co-expression using the CellPhoneDB (v4.0.0) with default settings. The analysis of interactions among fibroblasts, progenitor cells, endothelial cells, neural cells, and immune cells was conducted. Only genes expressed in more than 10% of total cells were included. To calculate the average number of interactions, we summed all significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) interactions between two cell types per sample/donor and averaged this number over samples/donors. To calculate the interaction strength between two cell types across patients, we summed up the mean expression of all ligand and receptor pairs as calculated by CellPhoneDB across all samples.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financial supported by the Guangdong Basic and Applied Basic Research Foundation (No.2022A1515011266, 2022A1515110601), National Natural Science Foundation of China (No.81700950, No. 82370943).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.Z. and X.W. design the study. Y.K. and F.Z. contributed equally. X.F., J.Z., Q.G., J.W., M.X., and Q.J. collected and processed the samples. F.Z. and X.F. performed the experiments and analyzed the data. F.Z., Q.Z., and Y.Z. wrote the manuscript. J.L. and all other co-authors critically revised the manuscript. J.Z. and X.W. provided financial support.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003eThe authors have declared no conflict of interest. The manuscript has been seen and approved by all authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAAE position statement on vital pulp therapy. J Endod \u003cstrong\u003e47\u003c/strong\u003e, 1340\u0026ndash;1344 (2021).\u003c/li\u003e\n\u003cli\u003eDuncan, H. F. Present status and future directions-vital pulp treatment and pulp preservation strategies. Int Endod J \u003cstrong\u003e55 Suppl 3\u003c/strong\u003e, 497\u0026ndash;511 (2022).\u003c/li\u003e\n\u003cli\u003eOpasawatchai, A. et al. Single-cell transcriptomic profiling of human dental pulp in sound and carious teeth: A pilot study. Front. Dent. 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Nat Commun \u003cstrong\u003e13\u003c/strong\u003e, 828 (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7096435/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7096435/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study utilized single-cell resolution spatial transcriptomics (Visium HD) to investigate the spatial cellular architecture and molecular interactions in healthy and inflamed dental pulp, aiming to explore the pathological mechanisms of pulpitis and identify novel targets for vital pulp therapy. Spatial transcriptomic sequencing was performed on dental pulp tissues from two healthy individuals and two pulpitis patients, with integrated analyses including Seurat clustering, cell trajectory inference, GO enrichment, CellphoneDB interaction network modeling, and PROGENy pathway activity assessment to compare cellular heterogeneity and signaling regulation. Nine major cell types (fibroblasts, progenitor cells, endothelial cells, neural cells, plasma cells, B cells, T cells, monocytes, and macrophages) were identified, and their spatial distribution was mapped. Subclustering and differential expression analysis revealed that fibroblast (e.g., \u003cem\u003eAPOL2\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e/\u003cem\u003eCCN2\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) and progenitor cell (e.g., \u003cem\u003eCDK5R1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e/\u003cem\u003eCCRL2\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) subclusters exacerbated fibrosis and immune activation, while \u003cem\u003eTMPRSS4\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e/\u003cem\u003eCST5\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e fibroblasts were critical for homeostasis. Pro-inflammatory endothelial subclusters (\u003cem\u003eIGHG1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e/\u003cem\u003eCXCL13\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) expanded, while anti-inflammatory subclusters (\u003cem\u003eSERPINA5\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e/\u003cem\u003eSERPINA3\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) diminished, leading to vascular-immune imbalance. Upregulation of immunoglobulin genes and downregulation of \u003cem\u003eMBP\u003c/em\u003e disrupted neural function, while inflamed pulp showed increased B cells and macrophages, decreased T cells and monocytes, and downregulated \u003cem\u003ePTN\u003c/em\u003e. Inflammatory pathways (PI3K, EGFR, TGFβ, MAPK, Estrogen, NF-κB) were upregulated, with enhanced TGFβ signaling in endothelial cells. Intercellular interaction analysis showed altered \u003cem\u003eAPP\u003c/em\u003e-\u003cem\u003eCD74\u003c/em\u003e signaling in endothelial-macrophage interactions and disrupted \u003cem\u003eCXCL14\u003c/em\u003e-mediated communication between immune and endothelial cells. These findings implicate cellular remodeling, including \u003cem\u003ePTN\u003c/em\u003e downregulation, \u003cem\u003eAPP\u003c/em\u003e suppression, \u003cem\u003eCXCL14\u003c/em\u003e deficiency, \u003cem\u003eCXCR4\u003c/em\u003e upregulation, and TGFβ activation, as key drivers of pulpitis progression.\u003c/p\u003e","manuscriptTitle":"Single-cell Resolution Spatial Transcriptomics Delineates the Inflammatory Landscape of Human Dental Pulp: Regional Crosstalk and Therapeutic Implications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 11:40:52","doi":"10.21203/rs.3.rs-7096435/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"02457415-db7f-46ab-a914-5b02d1f87059","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52048217,"name":"Biological sciences/Molecular biology/Proteomics/Protein\u0026#x2013;protein interaction networks"},{"id":52048218,"name":"Biological sciences/Biological techniques/Gene expression analysis/Microarray analysis"}],"tags":[],"updatedAt":"2025-08-28T01:00:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-29 11:40:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7096435","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7096435","identity":"rs-7096435","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}