Spatiotemporal interplay between epithelial and mesenchymal cells drives human dentinogenesis

Domain: neuroscience
Published: 2026-02-14
Editorial outcome: major_revision

Abstract
Tooth dentin, secreted by odontoblasts, constitutes most of the tooth structure and provides support and sensory function. However, dentin defects are common and irreparable once they exceed a critical threshold. Human dentin develops from dental papilla (DP) cells under the guidance of the dental epithelium (DE). Here, we present a human tooth development atlas from initiation to erupted stage using single-cell RNA sequencing and spatial transcriptomics, focusing on epithelial-mesenchymal interactions. This atlas reveals that DE orchestrates DP differentiation in a WNT-NOTCH sequential activation model and identifies the key signaling molecules. DLX6-AS1+ DP cells respond to dental epithelial signals and can be isolated from adult dental pulp stem cells (DPSCs). Notably, DLX6-AS1+ DPSCs successfully generate tubular dentin in an in vivo disease model of dentin defects. This research provides valuable information on human tooth development and establishes a basis for repairing regenerative dental tissue.

Introduction
Dental hard tissues comprise enamel, dentin, and cementum, with dentin forming the primary structural component of teeth. Produced by odontoblasts, dentin provides mechanical support and sensory function essential for tooth integrity1. Pathological conditions such as dental caries trigger odontoblast apoptosis2, prompting the pulp to form reparative dentin as a protective barrier. However, aging or chronic inflammation impair this regenerative capacity3. Understanding the cellular origin and molecular mechanisms underlying dentin development and regeneration remains a critical challenge.

During organogenesis, the orchestrated regulation of conserved signaling pathways governs cell fate determination, proliferation, and differentiation. Tooth development, a paradigm of ectoderm-derived organogenesis, exemplifies such complexity. Congenital genetic diseases such as dentinogenesis imperfecta can lead to dentin loss, along with injuries and damage4. Additionally, patients with ectodermal dysplasia frequently exhibit dentin loss and deformities due to epithelial cell dysfunction5. The development and homeostasis of dental tissues depend on reciprocal interactions between dental epithelium (DE) and dental mesenchyme derived tissues, such as dental papilla (DP), which collectively determine tooth morphology, dimensions, and functional integrity6–8. Within this interaction network, dental epithelial signals guide some DP cells to differentiate into odontoblasts, while others remain undifferentiated in adult pulp, serving as a reservoir for reparative dentin formation upon pulp exposure. This highlights the importance of DE signaling in determining DP cell fate. Our recent research has shown the crucial role of DE in early human tooth development9, but further studies are needed to understand how DE guides odontoblast differentiation throughout tooth development from initiation to erupted stage and whether these signals could be utilized in tooth regeneration.

Molecular studies have revealed that multiple signaling cascades, including WNT10,11, NOTCH12, BMP13,14, TGF-β15, and FGF16,17, orchestrate epithelial-mesenchymal interactions during tooth development. However, the precise mechanisms governing these interactions in human odontogenesis remain to be fully elucidated. In particular, DE-derived WNT signals initiate mesenchymal cell proliferation and differentiation, as demonstrated in mouse models where disruption of epithelial WNT secretion impairs odontoblast differentiation18,19. The NOTCH signaling system, through its classical roles in cell fate determination via ligand-receptor interactions between adjacent cells, contributes to tooth development20,21. These molecular networks work together rather than separately, creating a complex regulatory system, yet the spatiotemporal regulation network of these crucial signaling pathways still needs to be explored22. The WNT-NOTCH signaling axis has emerged as a critical regulator of progenitor cell maintenance, terminal differentiation, and regeneration in human tissues23,24. However, its precise operational mode in human tooth development and regeneration requires further elucidation. This understanding is particularly crucial given that odontogenic stem cells in monophyodont rodents exhibit distinct functional characteristics from their human counterparts25, necessitating human-specific investigations into tooth development and regeneration mechanisms.

To advance our understanding of human tooth structures, we established a comprehensive developmental atlas of human teeth from initiation to erupted stage using integrated single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST). By mapping the interactions between ligands and receptors of tooth-related signaling molecules, we identified key DE-derived molecules that play a crucial role in directing DP differentiation. Functional validation revealed their dentinogenic superiority over controls. Moreover, we identified previously unknown subgroups of dental pulp stem cells (DPSCs); notably, these subgroups can regenerate dentin to repair dentin defects, as demonstrated in a relevant animal model. These results not only progress our insights into human development, but also present a blueprint for regenerative solutions.

Results
Spatiotemporal cellular dynamics and molecular regulation in tooth development
To construct a comprehensive developmental timeline of human tooth formation, we collected and analyzed scRNA-seq and ST data spanning multiple developmental stages, from initiation to erupted stage9,25–28 (Fig. 1a). Following quality control and integration of scRNA-seq data, we performed systematic cell type classification and marker gene identification to establish a tooth developmental atlas. Uniform Manifold Approximation and Projection (UMAP) analysis revealed 11 cell clusters and their temporal distribution patterns (Fig. 1b and Supplementary Fig. 1a). Cell populations were identified based on canonical marker gene expression: DE (KRT19, KRT5, KRT14), DP lineage (DPL) (FGF3, RSPO4, DLX6-AS1), and dental follicle (DF) (COL12A1, POSTN, OGN). Endothelial cells (ESAM, CLDN5, VWF), fibroblasts (PDGFRB, RGS5, BGN), monocytes (TYROBP, AIF1, CD14), lymphocytes (TRBC2, CD3E, CD48), erythroblasts (ALAS2, HBG1, HBA1), vascular smooth muscle cells (ACTC1, SGCA, TNNT1), and neuronal cells (S100B, MPZ, PLP1) were defined according to their specific markers9,26,29 (Fig. 1c). The consistent expression patterns of key markers were observed across all batches, as depicted in both FeaturePlots and violin plots (VlnPlots) (Supplementary Fig. 1b). A detailed list of the top 10 differentially expressed genes defining each major cell lineage can be found in Supplementary Fig. 2a. Integration of scRNA-seq and ST analyses provided detailed insights into the spatial distribution and gene expression profiles of two key cell populations: DE and DPL (Fig. 1d). Spatial heatmap analysis and immunofluorescence (IF) staining further validated our cell clustering approach (Fig. 1e). KRT14 specifically labeled the DE, while FGF3 was localized to the DPL, as illustrated in Fig. 1e. To confirm the specificity of these signals, co-staining was performed with the differentiation markers AMELX (for DE) and DSPP (for DPL). Isotype negative controls demonstrated the absence of non-specific signals (Supplementary Fig. 2b).

To understand the molecular mechanisms involved in human tooth development, we analyzed the expression patterns of key developmental signaling pathways in DE and DPL. We isolated DE and DPL cell populations and identified genes related to ligands and receptors from signaling pathways crucial for tooth development, such as WNT, NOTCH, BMP/TGF-β, FGF, SHH, and EDA signaling pathways6. We examined the expression levels of these genes in the two cell populations, calculated the total expression of each signaling pathway, and represented the data in pie charts to show the expression proportions of each pathway in DE and DPL (Fig. 1f). Our analysis revealed that WNT (WNT1/3A/4/5A/5B/6/7A/7B/8B/10A/10B/11), NOTCH (JAG1/2, DLL1/3/4), SHH (SHH, IHH, DHH), and EDA ligands were predominantly expressed in DE, while their corresponding receptors were mainly expressed in DPL. Conversely, BMP (BMP2/3/4/5/6/7/10/15), TGF-β (TGF-β1/2/3), and FGF (FGF2/3/8/10) ligands showed higher expression levels in DPL. Detailed gene-level statistics and absolute expression values are provided in Supplementary Data 1. Moreover, to evaluate the functional significance of these pathways, pathway activity scoring was conducted. The results indicated increased activity in the EDA pathway in DE and in the BMP, TGF-β, and FGF pathways in DPL, suggesting their specific roles within each lineage (Supplementary Fig. 2c).

ScRNA-seq data revealed that WNT and NOTCH ligands were main signals secreted by the DE throughout all developmental stages. However, their relative abundance varied at different stages. In early stages, WNT ligand expression was predominant while NOTCH ligand expression was low. As development progressed, there was a shift towards increased NOTCH expression and decreased WNT expression (Fig. 1g). The overall activity of key signaling pathways was further assessed across various time points (Supplementary Fig. 2d). Consistent with the trend shown in Fig. 1g, the WNT and NOTCH pathways exhibited a relay-like dynamic during development, whereas the SHH pathway maintained activity throughout the 12–24 (post-conception week, PCW) period. The BMP and TGF-β pathways displayed sustained expression throughout all stages of development. To investigate interaction details at specific timepoints, we focused on the 15-17 PCW window, utilizing CellChat to predict signaling communication between DE and DPL cells. At 17 PCW, the NOTCH pathway showed robust interaction, predominantly initiated by DE cells and directed towards DPL cells, suggesting a mechanism of epithelial-to-mesenchymal NOTCH signaling (Supplementary Fig. 2e–g). The integrated analysis of scRNA-seq and ST data indicated that the human DE orchestrated DPL development by regulating multiple developmental pathways in a coordinated manner. This initial analysis laid the groundwork for our subsequent hypothesis, which was then substantiated through further validation.

Temporal and spatial orchestration of WNT and NOTCH signaling during tooth development
Analysis of scRNA-seq data revealed that WNT and NOTCH pathways components were not only predominantly expressed by DE but also exhibited distinct temporal expression patterns during tooth development. To delineate the relationship between these pathways, we performed comprehensive analysis using scatter plots and pie charts. Notably, the gene expression of WNT family ligands showed tissue specificity. Ligands associated with the canonical WNT/β-catenin pathway30, such as WNT1, WNT3A, WNT4, WNT8B, WNT10A, and WNT10B were predominantly expressed in the DE. In the non-canonical WNT pathway31, WNT5A, WNT5B, WNT6, WNT7A, WNT7B, and WNT11 exhibited distinct distribution patterns. Specifically, WNT6, WNT7A, and WNT7B were predominantly expressed in the DE, while WNT5A, WNT5B, and WNT11 showed higher levels of expression in the DPL. The sequencing data revealed that the DE was the primary source of NOTCH pathway components JAG1 and JAG2 (Fig. 2a).

Temporal analysis using bubble heatmap visualization uncovered a unique “relay” mechanism between the WNT and NOTCH signaling pathways in tooth development. In the early stages (7–15 PCW), the DE exhibited high expression of WNT ligands, while the DPL displayed elevated expression of WNT receptors. However, post 15 PCW, WNT ligand expression declined, accompanied by a rise in NOTCH ligand expression in DE, NOTCH receptor expression in DPL, and WNT antagonist expression in DPL (Fig. 2b). To gain a deeper insight into the possible cellular crosstalk, we analyzed the temporal dynamics of ligands in DPL and their corresponding receptors in DE (Supplementary Fig. 3a). Our analysis suggested a potential autocrine/paracrine regulatory loops within the DE. Figure 2c displayed the most prominently expressed WNT and NOTCH ligands in DE based on previous sequencing data. To distinguish the potential activities of canonical versus non-canonical WNT signaling, we plotted the expression profile for each ligand individually (Supplementary Fig. 3b). This analysis revealed dynamic expression patterns for each ligand, suggesting a spatiotemporal regulation of different signaling activities during DE development. To investigate the heterogeneity within the DE, we performed a detailed sub-clustering analysis, identifying several subpopulations (Supplementary Fig. 3c). Each subpopulation was characterized by a distinct molecular signature based on its top differentially expressed genes9 (Supplementary Fig. 3d). Notably, FeaturePlots revealed highly specific expression patterns of key signaling ligands: WNT6, WNT7B and JAG1 were predominantly expressed in the outer enamel epithelium (OEE), cervical loop (CL), inner enamel epithelium (IEE), and enamel knot (EK). WNT10B expression was specifically localized to the EK (Fig. 2d and Supplementary Fig. 3e). We conducted ST mapping of the key molecules identified in Fig. 2c to spatially validate the findings, offering detailed insights into their tissue distribution patterns (Supplementary Fig. 3f). To validate our transcriptomic findings at the protein level, we performed IF staining. This analysis not only verified the precise protein localization (Fig. 2d) but also illustrated the activation of pathways by showing robust expression of the downstream targets LEF132 and HEY133. The subsequent quantification of these signals further emphasized the complex spatiotemporal coordination of WNT and NOTCH signaling during human tooth development (Supplementary Fig. 3g).

DLX6-AS1+ DP cells exhibit a specific expression pattern in human fetal teeth and potentially receive WNT-NOTCH signaling from the DE
In our recent study, we classified DP cells in the early stage into two subpopulations according to their location9. By incorporating late-stage data, we further subclustered DPL cells into three clusters: DLX6-AS1+ DP, odontoblasts and NR2F1+TWIST1+ DP based on their markers and developmental trajectory analysis34 (Fig. 3a, b). Histograms were generated to assess the temporal stability of distinct fates by categorizing cells according to their developmental stage and key marker genes (Supplementary Fig. 4a). DLX6-AS1+ DP cells, characterized by high DLX6-AS1 expression, were located in close proximity to DE cells (Fig. 3c, d). In mice, the expression of Dlx6os1, the murine homolog of human DLX6-AS1, showed minimal expression in the mouse molar at E16.5, a stage coinciding with the emergence of CDP in mice35, suggesting distinctive developmental markers and patterns in human tooth development (Supplementary Fig. 4b, c).

Based on the analysis of their location and trajectory analysis, we proposed that DLX6-AS1+ DP cells may be responsive to DE signals and act as potential progenitors for odontoblasts (Fig. 3d). To investigate this hypothesis, differential gene expression analysis was conducted to characterize the molecular signatures of DLX6-AS1+ and NR2F1+ TWIST1+ DP cells, revealing different transcriptomic profiles for each subpopulation (Fig. 3e). Pathway enrichment analysis on the upregulated genes in each group was performed to computationally investigate potential functional consequences, supporting the hypothesis of a functional divergence between the two subpopulations (Supplementary Fig. 4d). We found increased expression of LEF1 and HEY1, which are target genes of the WNT and NOTCH signaling pathways respectively, in the DLX6-AS1+ DP cells (Fig. 3e). Key receptors and target genes of WNT and NOTCH signaling pathways were confirmed through scRNA-seq and ST data (Fig. 3f and Supplementary Fig. 4e).

The temporal gene expression profile of DLX6-AS1+ DP cells indicated distinct gene expression patterns at different time points. Early-stage genes such as MEIS2 and TFAP2B were linked to the WNT signaling pathway, while late-stage genes like ZBTB16 were associated with the NOTCH signaling pathway (Fig. 3g). A comparison of scRNA-seq data from 15 PCW and 17 PCW further illustrated this transition via volcano plot. DLX6-AS1+ DP cells of 15 PCW exhibited upregulated EGR1 and FOXO1 expression, which are related to the WNT signaling pathway, while DLX6-AS1+ DP cells of 17 PCW showed increased expression of HEY1 and IFITM5, which are associated with the NOTCH signaling pathway (Fig. 3g). Furthermore, the expression patterns of WNT, NOTCH, and odontogenesis-related genes were compared between DLX6-AS1+ DP cells and NR2F1+TWIST1+ DP cells using ST data (Supplementary Fig. 4f). Correlation analysis revealed a positive co-expression between DLX6-AS1 and odontogenesis-related molecules36,37, including DLX1, BMP4, RUNX2, DSPP and DMP1, and WNT/NOTCH pathway components, such as LEF1, LRP5, NOTCH2 and HEY1 (Fig. 3h). The role of DLX6-AS1 in odontogenesis was indicated in our recent study38 as well as in other research39.

Application of WNT-NOTCH signaling in odontogenic differentiation of DLX6-AS1+ cells
DLX6-AS1+ DMSCs and DPSCs, representing different differentiation statuses of DP cells, were isolated using a lentiviral reporter system with the DLX6-AS1 promoter-driven EGFP to investigate their odontogenic potential and suitability as seed cells for dentin regeneration. A negative control group was included by transducing cells with an empty lentiviral vector (Supplementary Fig. 5a–d). Both experimental and control groups underwent puromycin selection in vitro. DLX6-AS1+ DMSCs exhibited elevated expression of the odontoblast differentiation marker DSPP, as well as the WNT/NOTCH target genes LEF1 and HEY1, after a 21-day odontogenic induction (Fig. 4a), consistent with the gene signatures identified in precious analysis. Alkaline phosphatase (ALP) and Alizarin red S (ARS) staining confirmed our observations, showing enhanced odontogenic differentiation in DLX6-AS1+ DMSCs (Fig. 4b). Next, we assessed whether a WNT-NOTCH sequential treatment could enhance the odontogenic potential of DLX6-AS1+ DMSCs and DLX6-AS1+ DPSCs. Using scRNA-seq and ST data, we developed a protocol for odontoblast differentiation. DLX6-AS1+ DMSCs were initially cultured in a medium supplemented with WNTs (WNT6, WNT7B, and WNT10B at a concentration of 10 ng/ml) from day 1 to day 7. Subsequently, on day 8, the culture medium was changed to one containing JAG1 (at 10 ng/ml), which was maintained until day 14. The growth factor-supplemented medium was refreshed every two days throughout the induction period to ensure continuous stimulation, as indicated by increased expression of target genes for both pathways in the western blot analysis (Fig. 4c). By day 21, an enhancement in odontogenic differentiation was observed in the group subjected to sequential WNT-NOTCH stimulation, as evidenced by ALP and ARS staining (Fig. 4d). Our findings, detailed in Supplementary Fig. 5e, show that the sequential WNT-NOTCH stimulation leads to a significantly higher level of mineralization compared to the conventional odontogenic induction protocol. IF staining showed increased expression of odontogenic markers DSPP and KI67 in this group (Fig. 4e). Additionally, WNT-NOTCH sequential stimulation upregulated DLX6-AS1 expression in DMSC (Supplementary Fig. 5f). To confirm our analysis that epithelial-derived WNT-NOTCH signaling directed DP odontogenic differentiation, we established a co-culture system with DE and DLX6-AS1+ DMSCs. When we inhibited WNT and NOTCH signaling from DE cells, we observed decreased expression of odontogenic markers DMP1 and DSPP in the DLX6-AS1+ DMSCs (Fig. 4f). These findings underscored the importance of DE-derived WNT-NOTCH signaling in DP differentiation.

DPSCs, a type of mesenchymal stem cells, are easily obtained from extracted third molars or exfoliated deciduous teeth. Their low immunogenicity make them suitable candidates for both autologous and allogeneic therapeutic applications. Our group has conducted extensive research on DPSC-based therapies40,41, leading to their advancement to phase I clinical trial and investigator-initiated trial. DPSCs have the potential to differentiate into odontoblasts for pulp-dentin complex regeneration42, but enhancing their dentinogenic inducing ability is a key challenge that needs to be addressed. Advances in differentiation and selection protocols could contribute to making DPSCs more effective in clinical applications. We applied the same WNT-NOTCH sequential stimulation on DLX6-AS1+ DPSCs and observed significantly enhanced odontogenic differentiation effects in the WNT-NOTCH sequential stimulation group (Fig. 4g–j).

Utilizing sequential WNT-NOTCH signaling in a recombinant model promotes dentinogenesis
To validate the odontogenic differentiation of DLX6-AS1+ cells mediated by WNT-NOTCH signal in vivo, we utilized a tissue recombinant strategy. DMSCs or DLX6-AS1+ DMSCs were combined with non-dental human keratinocytes and transplanted beneath the renal capsule. In the Pre-WNTs+JAG1 treatment group, DLX6-AS1+ DMSCs were pre-treated with WNTs (WNT6, WNT7B, and WNT10B) before being combined with keratinocytes to replicate the developmental signaling milieu. JAG1-soaked beads, as pre-established protocols43, were transplanted in close proximity to the recombinants (Fig. 5a).

Histological analysis was conducted to evaluate the recombinants under different treatment conditions. Hematoxylin and Eosin (H&E) staining revealed the overall tissue organization and morphological features (Supplementary Fig. 6a). Masson’s Trichrome staining was employed to assess extracellular matrix composition, tissue fibrosis, and collagen fiber arrangement (Fig. 5b). Among all experimental groups, only the recombinants from the DLX6-AS1+ DMSCs with Pre-WNTs+JAG1 treatment group generated tooth-like structures, characterized by finely deposited pre-dentin and dentin of tubular structure arranged parallel to each other. DSPP, a marker for odontoblast differentiation, was employed to evaluate differentiation in the recombinants (Fig. 5c). The expression of these proteins indicated functional and structural maturation of odontoblasts. Notably, the recombinants from DLX6-AS1+ DMSCs with Pre-WNTs+JAG1 treatment group showed significantly upregulated expression of the odontogenic marker DSPP, as confirmed by quantitative analysis (Fig. 5c and Supplementary Fig. 6b). The specificity of this staining was confirmed with an isotype control, which showed only negligible background fluorescence (Supplementary Fig. 6c). To confirm that the transplanted cells were responsible for this new tissue formation, we tracked GFP-labeled DLX6-AS1+ DMSCs. Indeed, these GFP-positive cells (green) were observed to be actively secreting DSPP (red), directly linking our target cell population to the process of dentinogenesis (Fig. 5e). We also assessed Micro-CT analysis of the recombinants by evaluating its mineralization. Micro-CT analysis revealed the formation of robust mineralized tissue in the recombinants from the DLX6-AS1+ DMSCs with Pre-WNTs+JAG1 treatment group (Fig. 5d).

In conclusion, only the WNT-NOTCH sequential stimulation was capable of directing DLX6-AS1+ DMSCs towards generating dentin-like structures ex vivo, showing the therapeutic potential of DLX6-AS1+ DMSCs under WNT-NOTCH sequential stimulation for applications in dental tissue and other ectodermal-derived organ engineering.

Repair of dentin defects by DLX6-AS1+ DPSCs using the sequential application of WNT-NOTCH signaling
With the ongoing clinical trials of DPSC as a promising cell type for clinical applications, we next investigate the dentin regeneration capacity of DLX6-AS1+ DPSCs by establishing a nude mouse molar tooth injury model. DLX6-AS1+ DPSCs were isolated from extracted third molars and were pre-treated with WNTs (WNT6, WNT7B and WNT10B), then injected into the pulp exposure site along with JAG1 (Fig. 6a). This intervention showed no adverse effects on vital organs, including the liver, spleen, and kidneys (Supplementary Fig. 7a–c). With the assistance of micro-CT analysis, it was clearly demonstrated that the DLX6-AS1+ DPSCs treated with WNT-NOTCH sequential stimulation could promote dentin regeneration (Fig. 6b). Quantitative analysis verified a notable increase in mineralized tissue volume fraction (BV/TV) and tissue mineral density (TMD) at the damage sites compared to the controls (Fig. 6c). 28 days post-injury, histological assessment was performed using Masson’s Trichrome staining and IF analysis. The results showed that in the group treated with DLX6-AS1+ DPSCs receiving WNT-NOTCH sequential stimulation, a dentine bridge was formed at the damaged site of the mouse molar (Fig. 6d). In contrast to the disorganized pattern typically seen in reparative dentin, our regenerated dentin exhibited a well-arranged tubular structure, as indicated by the arrows in the zoomed-in image. Expression of DSPP was detected beneath the dentine bridge in the group treated with DLX6-AS1+ DPSCs receiving WNT-NOTCH sequential stimulation (Fig. 6d). SEM analyses showed that the tissue developed in the DLX6-AS1+ DPSCs with WNT-NOTCH sequential stimulation group exhibited a well-organized, tubular ultrastructure resembling natural dentin (Fig. 6d). qPCR results indicated that the molars treated with DLX6-AS1+ DPSCs receiving WNT-NOTCH sequential stimulation highly expressed DMP1, ALP, and MEPE (Fig. 6e). Notably, using the co-labeling of human DLX6-AS1 and DSPP proteins with RNAscope technology, it was shown that human DLX6-AS1+ DPSCs (red-stained) were arranged in a single row on the side close to the regenerated dentin. Strong DSPP expression was observed in these well-arranged DLX6-AS1+ DPSCs, indicating their ability to differentiate into regenerative odontoblasts (Fig. 6f). IF analysis confirmed the origin of the reparative dentin from the transplanted progenitors, demonstrating GFP-labeled DLX6-AS1+ DPSCs co-expressing the odontoblast marker DSPP within the newly formed matrix (Supplementary Fig. 7d). These findings demonstrate the potential of developmentally-guided DLX6-AS1+ DPSCs for therapeutic dentin regeneration, offering a strategy for utilizing discarded dental pulp tissue. A model for odontoblast differentiation and regeneration was established based on previous analysis and studies. During tooth development, undifferentiated ectomesenchymal cells in the dental pulp divide under the influence of WNT signals from the DE to become DLX6-AS1+ DP cells. One daughter cell differentiates into an odontoblast in response to NOTCH signals from the DE, while the other daughter cell remains as a subodontoblast cell. DLX6-AS1+ DPSCs can be isolated and, when exposed to sequential WNT-NOTCH signaling, can generate human dentin in dentin defects (Fig. 6g).

Methods
Ethical approval and consent
This study was conducted under approval from the Ethics Commissions of Beijing Friendship Hospital (2021-P2-198-02), Beijing Laboratory of Oral Health (Z2022SY064), and Beijing Obstetrics and Gynecology Hospital (2023-KY-082-01), all affiliated with Capital Medical University (CMU). All research involving human biological materials was performed in accordance with the Declaration of Helsinki and the 2021 ISSCR Guidelines for Stem Cell Research and Clinical Translation. Written informed consent was obtained from all donors for the use of human embryonic tissues, and no financial compensation was provided. All animal experiments were performed in accordance with relevant guidelines and regulations and were approved by the Ethics Committee of Capital Medical University (AEEI-2023-087).

Tissue collection
Human embryonic tissues were obtained from the Beijing Obstetrics and Gynecology Hospital following legally and ethically approved protocols. A total of eight embryos were analyzed. Specifically, scRNA-seq was performed on two replicates per stage across 7–8, 9-10, 12, and 15 PCW, while ST was performed on two replicates per stage across 9-10, 12, and 15 PCW. In all cases, each replicate (n=2 per stage) comprised one incisor and one molar from a distinct embryo. Due to the early developmental stage of the tissues, the sex of the embryos was not determined. All collected human embryonic tooth germ samples were entirely consumed during the single-cell and spatial transcriptomic experimental procedures and were therefore destroyed. Sample processing followed previously established protocols9,28. In brief, incisor and molar tooth germs were dissected in DEPC-treated ice-cold phosphate buffered saline (PBS) (Gibco, 10010023; Invitrogen, AM9906). The isolated specimens were washed in cold PBS and processed for scRNA-seq. For ST analysis, dissected mandibles were immersed in pre-cooled isopentane (SigmaAldrich, 4583) at −80°C and embedded in O.C.T. compounds (Sakura, 4583). Single-cell transcriptomic data for stages 17–24 PCW were obtained from Shi et al.26 (available at: https://data.mendeley.com/datasets/v3wgx8pm5y/1). The sex of the embryos was not reported in the original study. Young pulp single-cell data were accessed from Gene Expression Omnibus (GEO) datasets. This included dataset GSE20247627, derived from an immature third molar of a 13-year-old male donor, and dataset GSM436560925, from a tooth apical papilla with approximately two-thirds root completion where the donor’s sex was not reported. ST data at 20 PCW were acquired from Shi et al.26. Consistent with the SAGER guidelines, we report that sex was not considered as a biological variable in our study design because it was either not determined for early-stage embryonic samples or not reported in the integrated external datasets. Consequently, no sex-based analyses were performed, and data could not be disaggregated by sex.

Cell culture
Primary DE and mesenchymal cells were isolated from 12 PCW tooth germs. Tooth germs were enzymatically dissociated using 1.2 U/ml dispase II (Gibco, 17105041) and 20 U/ml DNase I (Gibco, 10104159001) for 10 min at room temperature. Epithelial and mesenchymal tissues were mechanically separated using fine needles. The DE tissues were dissociated and cultured in calcium chloride-free Keratinocyte Serum-free Medium (Gibco, A15170-01) supplemented with 15% FBS (Gibco, A5670401).

According to previous method9,63, DMSCs were enzymatically isolated using 3 mg/ml collagenase type I (Sigma-Aldrich, C0130) and 4 mg/ml dispase II for 1 h at 37°C. Cells were maintained in α-MEM medium (Gibco, 12561072) supplemented with 15% FBS and antimicrobial agents (100 units/ml penicillin, 100 mg/ml streptomycin, 25 μg/ml Amphotericin B; Gibco, 15070063) at 37°C in 5% CO2.

DPSCs were isolated following established protocols64. The pulp chamber was exposed after surface cleaning, and pulp tissue was enzymatically dissociated with collagenase at 37°C for 1 h. Dissociated cells were filtered through a 70-μm mesh and cultured in supplemented α-MEM medium under standard conditions.

Human keratinocytes were obtained from Shanghai Jinyuan Biotechnology Co., Ltd (JY-J068) and cultured in Keratinocyte Serum-free Medium (Gibco, A15170-01) according to established protocols65. Cells were cultured for 36 h prior to tissue recombination experiments.

scRNA-seq
Data preprocessing and integration. ScRNA-seq was performed at CapitalBio Technology (Beijing) using the Single Cell 3’ GEM, Library & Gel Bead Kit V3.1 and Chromium Single Cell B Chip Kit. Cell suspensions were loaded onto a Chromium controller for single-cell gel bead generation. Each channel was loaded with approximately 6000 cells, yielding ~3000 target cells for amplification. Library quality was assessed using an Agilent 2400 bioanalyzer. Libraries were constructed according to manufacturer’s protocols and sequenced on an Illumina Novaseq 6000 platform with paired-end 150 bp reads, targeting ≥100,000 reads per cell. Illumina NovaSeq 6000 software (v1.7) was used for sequencing data collection.

To ensure robust and unbiased comparison, all datasets underwent a standardized pre-processing and integration workflow using the Seurat R package (v4.4.0). For each dataset independently, the raw gene expression matrix was used to create a Seurat object. Low-quality cells were filtered out based on the following criteria: cells with fewer than 200 detected genes, more than 2500 detected genes, or a mitochondrial gene content exceeding 5% were removed. Following quality control, data was normalized using the NormalizeData function with a “LogNormalize” method and a scale factor of 10,000. The top 2000 most highly variable genes (HVGs) were identified for each dataset using the FindVariableFeatures function.

For integration, the pre-processed Seurat objects were merged. The merged data was then scaled using the ScaleData function. To correct for technical batch effects arising from different sequencing platforms, sample preparations, and laboratories, we utilized the Harmony algorithm (v0.1.1)66. Principal Component Analysis (PCA) was performed on the scaled data, and the RunHarmony function was executed on the resulting PCA embeddings, using the dataset of origin (orig.ident) as the batch variable.

Dimensionality reduction, clustering, and cell type annotation. All subsequent downstream analyses were performed on the Harmony-corrected embeddings. Uniform Manifold Approximation and Projection (UMAP) was run on the top 30 Harmony dimensions for 2D visualization. Cell clustering was performed by first running FindNeighbors on the same Harmony dimensions, followed by FindClusters with a resolution of 0.4. Cluster-specific markers were identified using the FindMarkers function (Wilcoxon rank-sum test). Cell type annotation was manually curated based on the expression of known canonical markers, tooth-specific genes, and published reference data. The quality of data integration was visually validated by coloring the UMAP by developmental stage to confirm proper mixing of cells, and by generating VlnPlots and FeaturePlots for key markers to verify consistent expression patterns across original batches.

Differential expression and functional enrichment analysis. Differentially expressed genes (DEGs) between clusters were identified using the FindMarkers function on the original, uncorrected normalized counts. DEG selection criteria included a Bonferroni-adjusted P value < 0.05 and an average log2(FoldChange) > 0.25. Functional annotation of significant genes was performed using the clusterProfiler package (v4.2.2) for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. To analyze specific populations, DE and DP cells were subset using the WhichCells function. Following the identification of pathway-specific ligands and receptors, gene expression data were retrieved using Seurat’s FetchData function. Expression patterns were visualized through dot plots, pie charts, violin plots, and Sankey diagrams using Seurat’s DotPlot, VlnPlot, and R’s ggplot functions. To quantify the relative contribution of signaling pathways, we selected only the ligand and receptor genes that were stably detected in our single-cell data for the DE or DPL. Genes not detected in either population were excluded from the calculation and display. For a given gene set, we first extracted the normalized expression values for each gene in all individual cells within the DE and DPL. We then calculated a total expression score for each population by summing the expression values of all genes in the set across all cells of that population. Finally, the proportions shown in the pie charts were calculated.

Developmental trajectory analysis. Cell development trajectories were analyzed using VECTOR methodology as described by Zhang et al.34. Code is available at: https://github.com/jumphone/Vector. The analysis pipeline comprised: (1) dimensionality reduction through principal component analysis (PCA), with component selection based on explained variance; (2) identification of starting cells (SCs) using quantile polarization (QP) scores; (3) data visualization via UMAP; and (4) inference of RNA dynamics directions based on UMAP positioning and network distances from SCs. The UMAP space was segmented into pixels, and developmental trajectories were reconstructed by integrating positional information, QP scores, and inter-pixel distances.

Branching trajectory analysis. Branching trajectory analysis of DP cells was performed using Monocle 2 software (v2.18.0). Branch-specific gene expression programs were identified through Branched Expression Analysis Modeling (BEAM). This approach enabled the detection of genes with branch-dependent regulation patterns along the DP cell development trajectory.

ST analysis
ST data generation. ST analysis was conducted at CapitalBio Technology (Beijing). Tissue cryosections (10-μm) were mounted on GEX arrays and processed through sequential steps: incubation at 37°C, fixation at −20°C, and H&E staining. Sample wells were prepared using a Slide Cassette. Permeabilization was performed with 70 μL enzyme solution, followed by SSC washing and reverse transcription. After first-strand synthesis, samples underwent KOH treatment and second-strand synthesis. cDNA amplification was performed on a S1000TM Touch Thermal Cycler. Visium spatial libraries were prepared according to standard protocols and sequenced on an Illumina NovaSeq 6000 with paired-end 150 bp reads, targeting ≥100,000 reads per spot. Illumina NovaSeq 6000 software (v1.7) was used for sequencing data collection.

Integration of scRNA-seq and ST data. To overcome the multi-cellular resolution and accurately map cell populations in a spatial context, we implemented an integrative analysis strategy combining our high-resolution scRNA-seq data with the spatial transcriptomics data9,67. Using the FindTransferAnchors and TransferData functions within the Seurat toolkit, we projected the cell type annotations derived from our scRNA-seq atlas (the ‘reference’) onto each spatial spot of the ST data (the ‘query’). This process calculates a prediction score for each cell type at every spot, effectively deconvoluting the mixed cellular transcriptomes. The dominant cell type with the highest score was then used to assign an identity to each spot, enabling the visualization of cell population distributions. Gene expression patterns were visualized using the SpatialFeaturePlot function.

RNAscope in situ hybridization
RNAscope Multiplex Fluorescent Assay (Advanced Cell Diagnostics, 320850) was performed on 5-μm cryosections of tissues fixed overnight in 4% paraformaldehyde at room temperature. Sections underwent target retrieval at 95–100°C for 5 min, followed by protease treatment (Advanced Cell Diagnostics, 310-100) at 40°C for 7 min. DLX6-AS1 probe (Advanced Cell Diagnostics, 569491-C2) hybridization was conducted at 40°C for 2 h. Signal amplification was performed using RNAscope Multiplex Fluorescent Reagent Kit, with detection via TSA Plus Cyanine 3 (PerkinElmer, NEL751A). Nuclei were counterstained with 4’,6 - diamidino − 2 - phenylindole (DAPI). Nikon NIS-Elements (v5.4) was used for image acquisition.

DLX6-AS1+DMSCs and DLX6-AS1+DPSCs
A lentiviral vector was constructed by replacing the CMV promoter in GV814 vector (CMV-EGFP-MCS-SV40-Puromycin) with the DLX6-AS1 promoter sequence (NR_015448-promoter, positions 4836-6835). The construction and packaging of the resultant lentiviral vector (NR_015448-promoter-EGFP-SV40-Puromycin) were performed by GeneChem. The full nucleotide sequence of the constructed vector and the inserted promoter region are provided in Supplementary Data 2. The viral particles were produced by co-transfecting the lentiviral construct with packaging plasmids into 293T cells. The target cells were infected with the lentivirus, and the cells positive for the reporter gene were screened to identify the cell population with an active promoter of the target gene. Under endogenous conditions, when the promoter is active in cells, it drives the expression of the reporter gene. As a corresponding control, the empty vector pLenti-Ubi-MCS-EGFP-IRES-Puromycin was used. DMSCs or DPSCs were seeded at 1×105 cells/well in 12-well plates and infected at 30% confluence using an MOI of 50. After 16 h, medium was replaced and cells were cultured for 72 h. Stable transformants were selected using puromycin (1 μg/ml, Sigma Aldrich, P8833) for 6 days, with medium changes every 2 days. Infection efficiency was confirmed by fluorescence microscopy.

WNT-NOTCH sequential stimulation
DLX6-AS1+ cells were cultured in the α-MEM medium (Gibco, 12561072) supplemented with either WNT proteins (WNT6, OriGene, TP762653; WNT7B, CUSABIO, CSB-EP026142HU; WNT10B, CUSABIO, CSB-EP026130HU; all at 10 ng/ml) or JAG1 (10 ng/ml, MCE, HY-P70399). Sequential stimulation consisted of 7 days WNT treatment followed by 7 days JAG1 treatment, with medium changes every 2 days.

Co-culture system
To ensure the robustness of our knockdown experiments, we initially designed three distinct siRNA sequences for each target gene (WLS and JAG1). We then tested all three in vitro to screen for the one that demonstrated the highest knockdown efficiency (Supplementary Fig. 5g). DE cells were transfected with 40 nmol/L WLS or JAG1 siRNAs (Sangon Biotech) using Lipofectamine RNAiMAX (Invitrogen, 13778030) in Opti-MEM (Invitrogen, 31985070). After 12 h, medium was replaced with α-MEM (Gibco, 12561072). DLX6-AS1+ DMSCs (2.5×104 cells/well) were seeded in transwell upper chambers and co-cultured with transfected epithelial cells for 7 days. The ‘pre+ and post+‘ condition specifically involved a transfer of the upper chamber: Days 0-3.5 (‘pre+‘ phase): The transwell upper chambers containing DLX6-AS1+ DMSCs were first co-cultured for 3.5 days with the DE cells that had been pre-treated with siRNA-WLS. Days 3.5-7 (‘post+‘ phase): Subsequently, these same upper chambers were physically transferred to new wells containing DE cells that had been pre-treated with siRNA-JAG1 and were co-cultured for the remaining 3.5 days.

All siRNA oligonucleotides were chemically synthesized and purified by Sangon Biotech. The sequences were as follows:
si-WLS-1: 5′-UCAUGUUUUCUAUAAUUGCCC-3′
si-WLS-2: 5′-ACAUUUCACCGACAUGUAGGA-3′
si-WLS-3: 5′-ACCAUUUUGUCUUGUGAUGGU-3′
si-JAG1-1: 5′-UUGAACAAGCUCACAUACCUG-3′
si-JAG1-2: 5′-UAAUUGAUAACUUUCCAGCUG-3′
si-JAG1-3: 5′-UAAAGUCGUAGUUAACACCAG-3′
siRNA-NC: 5′-ACGUGACACGUUCGGAGAA-3′

Western blot
Protein samples were separated on Mini-PROTEAN TGX Gels (Bio-Rad, 4561093) and transferred to membranes using the Trans-Blot Turbo system (Bio-Rad, 1704156). After blocking, membranes were probed with primary antibodies against DSPP (1:1,000, Proteintech, 55396-1-AP), LEF1 (1:1,000, Proteintech, 14972-1-AP), HEY1 (1:1,000, Proteintech, 19929-1-AP), and GAPDH (Proteintech, 60004-1-Ig, Clone 1E6D9). Following HRP-conjugated secondary antibody incubation, signals were detected using the ChemiDoc MP Imaging System (Bio-Rad Image Lab software v6.1). Unprocessed scans of all blots are provided in the Source Data file.

Alkaline phosphatase (ALP) staining and Alizarin red S staining
For ALP activity assessment, cells were fixed in 4% paraformaldehyde after 14 days of induction and stained using an ALP Assay Kit (Sigma Aldrich, MAK530). Mineralization was evaluated after 21 days of induction using 2% Alizarin red S solution (Sigma Aldrich, A5533). Leica Application Suite X (LAS X) software (v3.7) was used for brightfield image acquisition. For quantification, stained minerals were dissolved in 10% cetylpyridinium chloride (Sigma Aldrich, C5911) and measured spectrophotometrically at 562 nm.

IF staining
Samples were processed through sequential steps of trypsin treatment, permeabilization (0.5% PBST), hydrogen peroxide treatment, and goat serum blocking. Primary antibodies were used at a dilution of 1:200. Primary antibodies were applied against: DSPP (Abcam, ab216892), Ki67 (Cell Signaling Technology, 9129, Clone D3B5), KRT14 (Abcam, ab119695, Clone LL002), WNT6 (Abcam, ab50030), WNT7B (Proteintech, 10605-1-AP), WNT10B (Abcam, ab70816), LRP6 (Santa Cruz, sc-25317, Clone C-10), JAG1 (Abcam, ab300561, Clone EPR26388-51), NOTCH2 (Abcam, ab313453, Clone EPR28701-38), DKK1 (Abcam, ab61034, Clone EPR4759), SFRP1 (Proteintech, 26460-1-AP), HEY1 (Proteintech, 19929-1-AP), LEF1 (Abcam, ab137872, Clone EPR2029Y), BMP2 (Abcam, ab214821), BMP4 (Abcam, ab39973), GFP (CST, 2956, Clone D5.1) and rabbit IgG isotype control (CST, 3900, Clone DA1E) were added. Sections were subsequently incubated with appropriate secondary antibodies and counterstained with DAPI. Regions of interest (ROIs) were delineated based on tissue architecture visualized by DAPI staining. For Supplementary Fig. 3g, the Mean Fluorescence Intensity (MFI) of WNT6, WNT7B, WNT10B, and JAG1 was measured within the DE, while the MFI of LRP6, NOTCH2, DKK1, SFRP1, HEY1, and LEF1 was measured within the DPL. Data are presented as mean ± SEM. Nikon NIS-Elements (v5.4) was used for image acquisition.

qRT-PCR analysis
RNA was isolated using the RNeasy Kit (Qiagen, 74104) and reverse transcribed using the Transcriptor First Strand cDNA Synthesis Kit (Roche, 04379012001). Quantitative PCR was performed using SsoAdvanced Universal SYBR Green Supermix (Bio-Rad, 172-5270). Bio-Rad CFX Maestro software (v2.0) was used for data collection.
The primers used for RT-qPCR were synthesized by Sangon Biotech and as follows:
GAPDH:
Forward: 5′-GCACCGTCAAGGCTGAGAAC-3′
Reverse: 5′-ATGGTGGTGAAGACGCCAGT-3′
DMP1:
Forward: 5′-GCAGAGTGATGACCCAGAG-3′
Reverse: 5′-GCTCGCTTCTGTCATCTTCC-3′
DSPP:
Forward: 5′-GGGATGTTGGCGATGCA-3′
Reverse: 5′-CCAGCTACTTGAGGTCCATCTTC-3′
DLX6-AS1:
Forward: 5′-AGTTTCTCTCTAGATTGCCTT-3′
Reverse: 5′-ATTGACATGTTAGTGCCCTT-3′
MEPE:
Forward: 5′-GGCCAGTGACTGCGATTAAAC-3′
Forward: 5′-CCTTCGAGTGTGCTTTAGCAT-3′
ALP:
Forward: 5′-AACATCAGGGACATTGACGTG-3′
Reverse: 5′-GTATCTCGGTTTGAAGCTCTTCC-3′

Animals
Male BALB/c nude mice (Mus musculus), 8 weeks of age, were obtained from Charles River Laboratories. These mice possess a BALB/c genetic background carrying the Foxn1nu mutation The mice were housed in a specific pathogen-free facility at Capital Medical University under controlled environmental conditions. Specifically, housing conditions included a 12-h light/12-h dark cycle, a constant ambient temperature of 22±2°C, and a relative humidity of 55±10%. The animals were housed in individually ventilated cages with free access to standard sterilized rodent chow and water. For all in vivo experiments, mice were randomly assigned to experimental groups (n=5 mice per group). Only male mice were used in this study to avoid potential confounding effects from hormonal fluctuations associated with the female estrous cycle, which could influence tissue regeneration and inflammatory responses.

Recombination and subrenal culture
Human keratinocyte cells and mesenchymal cells (DMSCs, DLX6-AS1+ DMSCs, or WNTs-pretreated DLX6-AS1+ DMSCs) were pelleted separately at 150xg for 5 min. Cell pellets (1×105 cells/0.25 μL each) were sequentially injected into Cell matrix Type I-A (5 μL, Nitta Gelatin Inc, 631-00651) and incubated at 37°C for 30 min for gelation68. Recombinants were cultured in supplemented DMEM (20% FBS, 100 U/mL penicillin/streptomycin, 0.18 mg/mL L-ascorbic acid) for 3 days before transplantation under the kidney capsule of adult male nude mice. WNTs or JAG1-loaded PLGA (75:25) (MCE, HY-B2247A) was co-transplanted. For the control group, PLGA microspheres loaded with Bovine Serum Albumin (BSA) were used69. Recombinants were harvested after 3 months for histological analysis.

H&E staining and Masson’s trichrome staining
Paraffin-embedded samples were processed for H&E staining (Servicebio, G1076) and Masson’s trichrome staining (Servicebio, G1006) following manufacturers’ protocols.

Tooth damage model
Under anesthesia, cavities were created in the maxillary first molars of adult male nude mice until pulp visibility. Pulp exposure was achieved using a 27G 3/4 gauge needle70. After pulp exposure, different treatment interventions were applied according to experimental groups, with one group receiving WNTs-pretreated DLX6-AS1+ DPSCs combined with JAG1 injection at the exposure site. Cavities were sealed with glass ionomer cement (3M ESPE, 56906). Samples were collected 21 days post-operation.

MicroCT analysis
Samples were scanned using a high-resolution micro-computed tomography (μCT) system (SkyScan 1276, Bruker, Kontich, Belgium) located at the Core Facility Center, Capital Medical University. The scans were performed with an isotropic voxel size of 7 μm. Two- and three-dimensional reconstructions were generated for dental defect analysis.

Scanning electron microscopy (SEM) analysis
Mouse tooth samples were fixed in 2.5% glutaraldehyde. Dried samples were mounted on aluminum stubs, sputter-coated with a 15 nm gold-palladium layer and imaged using a scanning electron microscope. image were acquired using a Thermo Scientific Apreo 2C SEM controlled by Thermo Scientific microscope control software.

Combined detection by RNAscope® multiplex fluorescent assay and IF
A combined RNAscope® and immunofluorescence (IF) protocol was performed following the manufacturer’s guidelines for the RNAscope® Multiplex Fluorescent v2 Assay kit (Advanced Cell Diagnostics, 323100). Briefly, after tissue preparation and target retrieval, the IF portion of the protocol was initiated. Sections were blocked and then incubated with primary antibodies overnight. Following primary antibody incubation, sections were treated with Protease Plus. The RNAscope® portion was then continued with probe hybridization, signal amplification, and signal development using the TSA detection system (PerkinElmer, NEL751A). Finally, nuclei were counterstained with DAPI.

Statistical analysis
ImageJ (v1.53) was used for quantification and processing of confocal, widefield, and SEM images. GraphPad Prism (v9.0) was used for statistical analysis and graphing. Results were expressed as mean ± SEM. Statistical significance for two-group comparisons was determined by a two-sided unpaired Student’s t test. For multiple-group comparisons, a one-way ANOVA followed by Tukey’s multiple comparison test was used. A P value < 0.05 was considered statistically significant.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors utilize new and existing single-cell RNA sequencing and spatial transcriptomics datasets to characterize signaling pathways between epithelium and mesenchyme during human tooth development. They specifically show a temporal relationship between WNT and NOTCH signaling in the development of both dental epithelium and mesenchyme, as well as the identification of a subpopulation of mesenchymal cells with roles in odontogenesis. Then, they utilized their findings to successfully show an improved dentin repair strategy in a mouse model.\nWhile the roles of WNT and NOTCH signaling pathways have been widely studied in the development of teeth, the authors specifically examine human data and the provide evidence of a sequential order between WNT and NOTCH signaling in human tooth development. Furthermore, this study identifies a mesenchymal cell subpopulation with roles in dentin formation. Lastly, the authors provide evidence of a specific mesenchymal cell type that can direct dentin repair upon sequential WNT and NOTCH signaling, which has therapeutic implications.\nMajor points:\n• Several aspects of Fig. 1 are unclear which affects interpretability of the data:\no Resolution of spatial transcriptomics in some Figs is not sufficient to draw conclusions …what do the empty spots (regions without circles) in Fig. 1D mean? Do we assume empty regions do not have any expression of genes from that cluster?\no Immunofluorescence validation of the spatial transcriptomics data should be done at the same timepoints. Fig. 1E appears to show 15 PCW timepoint for spatial transcriptomics but 17PCW for the immunofluorescence staining. Perhaps authors can provide 17PCW spatial transcriptomics data if they have it?\no Might be helpful to clearly describe how the data, which originated from multiple soirces, was integrated and/or re-analyzed.\no What is different between the two pie charts for BMP pathway in Fig. 1F?\no It is not clear how the relative proportions of ligand vs receptor are quantified in Fig. 1F. If authors show that DE expresses 37% of the four FGF receptors, does this mean 37% of the 4 receptors listed are only expressed by DE and not by DPL, and 67% of the 4 receptors are expressed by DPL and not DE?\no Does Fig. 1G include the expression levels between only DE and DPL clusters or all clusters?\n• Supplemental Fig. 1 is supposed to show the expression of WNT and NOTCH ligands relative to the dental epithelial subpopulations. However, the UMAPs for the epithelial subpopulations (panel a) and the WNT/NOTCH ligands expression (panel b) are related but different. This makes it difficult to compare ligand expression and epithelial subpopulations.\n• Quantifications of IF stainings from Fig. 2D would improve interpretability of the images and support the main idea in this figure. What is the sample size of these stainings?\n• Authors explain that in mice, Dlx6os1 expression did not overlap with Lmo1+/Fgf3+/Smpd3+ coronal papilla cells, which are known as odontoblast progenitors in mice. How does this affect the interpretation of the in vivo experiments performed in mice in the paper? Do the known odontoblast progenitor cells respond to the exogenous JAG1 and support dentin repair in Fig. 5?\n• How do the protocols compare between odontogenic induction in Fig. 4a-b and WNT/NOTCH sequential stimulation in Fig. 4c-e? It appears that the alizarin red stainings at the end of the two induction protocols are similar. If the odontogenic induction does not specifically include the WNT first, then NOTCH sequence, this which would question the significance of the sequential order of signaling. Authors should also consider labeling the cells in 4c-e as DLX6-AS1+ as well.\n• In Fig. 5, how do the authors explain the more obvious levels of collagen in the DMSC only samples vs DLX6-AS1+ DMSC samples when the DLX6-AS1+ DMSCs are thought to have superior odontogenic potential?\n• The role of Notch in reparative dentinogenesis (dentin repair following injury) has been reported in the literature (Wang et al., 2014). In the dentin repair in vivo experiments, an appropriate control would be the addition of JAG1 alone after injury to assess the extent to which Notch signaling affects dentin repair by endogenous cells.\n• How epithelium and mesenchyme undergo the transition from WNT to NOTCH signaling remains to be explored. It would be worth examining the roles of negative regulators of WNT pathway (i.e. DKKs, SFRPs described in Fig. 2b) in vivo\nMinor points:\n• Fig. 3e appears to be cropped at the top, was there any reason to exclude those genes?\n• In Fig. 3, additional in vivo validation of the WNT/NOTCH target genes would be helpful.\n• Diagram showing the time-course of the tissue recombination experiments would be helpful.\n• In Fig. 6d, authors describe an improvement in dentin that results from (DLX6-AS1+ cells + WNTs+JAG1) mediated dentinogenesis compared to reparative dentinogenesis. A zoomed-in image of the dentin formed through reparative dentinogenesis would be helpful for comparison.", "reviewer_2": "This study characterizes the spatiotemporal dynamics of Wnt and Notch signaling during human tooth development, revealing an early predominance of Wnt activity that transitions to Notch signaling at later stages. The author concluded that both pathways exhibit a coordinated spatial distribution, with Wnt and Notch ligands enriched in the dental epithelium and receptors localized in the underlying dental papilla, thereby highlighting a key epithelial–mesenchymal signaling axis. Within the dental papilla, a distinct population of DLX6-AS1⁺ cells was identified as a potential odontoblast progenitor, responsive to Wnt and Notch signals. These cells demonstrated functional odontogenic capacity by generating tubular dentin in both an in vivo dentin defect model, underscoring their potential in regenerative dental therapies. This manuscript presents interesting and novel findings that offer new insight into the molecular regulation of odontogenesis; however, its current form is limited by the following points:\n1. Based on the single-cell RNA-seq data presented in Figure 2, it appears that the expression of Wnt receptors is not exclusively located in the dental papilla population but is also present in the dental epithelium. This observation is further supported by the immunofluorescence data showing LRP6 localization in the dental epithelium. Given this, it would be helpful if the authors could clarify how these findings fit into their proposed model of epithelial-mesenchymal signaling dynamics.\n2. The authors present single-cell RNA-seq and spatial transcriptomic data, with a primary focus on the dental epithelium and dental papilla. However, in the functional validation experiments — including both in vitro odontogenic differentiation and an ex vivo tissue recombination approach —the authors used DMSCs rather than directly testing DPSCs. The rationale behind this choice is unclear. The authors are encouraged to clarify why DMSCs were used instead of DPSCs, and to discuss how this decision may affect the interpretation and specificity of their findings.\n3. The authors clearly demonstrate impressive reparative dentin formation in the injury model, supporting the therapeutic potential of DLX6-AS1⁺ DPSCs for dentin regeneration. However, it remains possible that implantation of pre-Wnt-treated DLX6-AS1⁺ cells combined with JAG1 may influence additional signaling pathways beyond Wnt and Notch that contribute to dentinogenesis. Could the authors clarify whether they have investigated the involvement of other signaling pathways in their experimental system, and if so, please provide details of these analyses?", "reviewer_3": "The study “Spatiotemporal Interplay Between Epithelial and Mesenchymal Cells Drives Human Dentinogenesis” by Wei et al. presents a comprehensive single-cell and spatial transcriptomic atlas of developing human permanent teeth, uncovering a sequential WNT-NOTCH signaling mechanism presumably from the dental epithelium that guides odontoblast differentiation from DLX6-AS1⁺ dental papilla cells. These primary cell progenitors, isolated from both dental mesenchymal stem cells (DMSCs) and dental pulp stem cells (DPSCs), demonstrated enhanced odontogenic potential upon WNT-NOTCH stimulation, forming organized, tubular dentin-like material both in vitro and in vivo. Notably, DLX6-AS1⁺ DPSCs were able to regenerate structured dentin in a mouse molar injury model following sequential ligand treatment. While the developmental atlas can be a valuable resource for the field, many of the study’s conclusions remain premature. The functional role of DLX6-AS1, the causal relationship between WNT and NOTCH signaling, and definitive lineage tracing of proposed progenitors are not adequately demonstrated, leaving key mechanistic questions unresolved.\nSpecific Comments:\n• Related to Figure 1\n1. The Single-cell and spatial transcriptomic were produced from limited sample diversity: Only two biological replicates per timepoint (incisor and molar) were used for scRNA-seq and ST. This raises concerns about representativeness and reproducibility.\n2. Batch effects and technical artifacts: Integration of data from different developmental stages and platforms (scRNA-seq + ST) could introduce batch effects, especially without detailed normalization controls. How did the authors integrate the data from previous studies? Which specific methods were used?\n3. Spatial resolution constraints: ST has limited spatial resolution (~50 µm spot diameter), so unfortunately fine-scale (cellular level) epithelial-mesenchymal interfaces may not be precisely captured complicating the analysis of signal senders vs receivers.\n4. No functional validation of inferred ligand-receptor interactions at this stage, interpretations are based on expression correlations only. Differentiation studies are required to make further claims.\n5. UMAP visualizations depend on parameter tuning and dimensionality reduction. Please show confirmation that cell types remain stable across batch corrections.\n6. The Marker gene heatmap uses only a few canonical markers shown; not sufficient to resolve subtypes (e.g., epithelial subpopulations like IEE, OEE, SR, etc.). Please increase resolution and depth in the analysis.\n7. Please validate the IF in Fig.1e by co-staining with lineage markers. Please also show negative controls.\n8. Fig.1f. The pathway ligand/receptor pie charts relative proportions lack statistical significance. Please add information on absolute expression levels or functional relevance of pathways like EDA or BMP.\n9. Fig.1g. The Sankey diagram of ligand-receptor dynamics is descriptive only, not enough for predicted signaling interactions or temporal changes without perturbation experiments. It shows that Shh has almost no ligands at all in the early stage even though it’s known to be functionally important for tooth development. How were the percentage between pathways calculated? A pathway that has only few ligands known might have small percentage relative to others but still be more relevant and effective in cell fate determination. Furthermore, for example Notch pathway is known to act multiple times during developmental trajectories. Please add resolution by analyzing specific timepoints.\n• Related to Figure 2\n10. Fig.2b. Please show also temporal distribution of receptor in epithelium and ligands in mesenchyme.\n11. Fig.2c. Temporal line plots of top ligands do not distinguish canonical vs. non-canonical WNT activity; co-expression of multiple ligands complicates pathway specificity. Please separate the individual ligands in independent temporal blots.\n12. Fig.1d. Please show higher resolution images. It is unclear if ligands are membrane-bound or secreted. Please analyze functionality receptor activation status. For example: in what cell types are the downstream target genes of the receptors upregulated?\n13. In Supplementary Figure 1: The clustering utilized in the gene plot (b) is different than the UMAP plot in (a). Wnt10B does not look enriched in the CL, IEE as quoted in the paragraph line 147-149. Please cite Figure 2d in line 149. Please re-consider the clustering in the supplementary figure.\n• Related to Figure 3\n14. Fig.3e. The differential expression analysis comparing DLX6-AS1⁺ DP and NR2F1⁺TWIST1⁺ DP is associative; functional differences between these subpopulations are not directly tested.\n15. The role of DLX6-AS1 (a long non-coding RNA) in regulating odontogenic potential is not mechanistically investigated. It's unclear whether DLX6-AS1 is functionally required or simply a marker. No evidence is provided for how DLX6-AS1 interacts with WNT/NOTCH pathways at a molecular level (e.g., chromatin remodeling, RNA-protein interactions).\n16. UMAP + cell proportions - relative proportions of subclusters across time are shown but may be biased by sample quality or dissociation efficiency.\n17. Marker gene violin plots - Marker overlap between subpopulations (e.g., DLX6-AS1⁺ DP vs. NR2F1⁺ DP) is not quantitatively compared; could be transient states rather than distinct fates.\n18. Fig.3g. Please describe how the predicted interaction networks in panel g was analyzed and created, and what database was used in this analysis.\n19. In Supplementary Figure 3\n(b) mention the cluster name identified by Jing et al, 2022.\n(c) mention column names for the pathway enrichment analysis plot.\n(d) should be across three dental papilla subpopulations not three dental pulp population\n• Related to Figure 4\n20. Cell sorting method (DLX6-AS1 promoter–driven EGFP lentiviral reporter) may alter cell behavior. The WNT-NOTCH sequential stimulation is optimized using artificial, recombinant ligands. There is no attempt to mimic physiological gradients or signal durations. Jagged needs to be tethered or immobilized to activate Notch (to generate force needed for Notch cleavage). Did the authors utilize some type of Jagged-tethering system? Did they use Jagged soaked beads here (as they did in Fig.5)? Did the ligand (Jagged) activate Notch pathway in sensor cells.\n21. In line 199, the reference for the previous results from human tooth data is missing.\n22. Fig.4g. HEY1 protein quantification does not match the Western blot band intensity.\n23. Functional assays (e.g., ALP/ARS staining) show bulk mineralization, but do not confirm odontoblast-specific function or polarized secretion of dentin matrix proteins. Please add quantification for figure panel Fig.4d? Please mention the sample size, N=? normalized to?\n24. The WNT-JAG1 timeline requires further clarification. It does not match with the text in line 207. In the text, it was indicated that the treatment happened only one day for each WNT and JAGGED1. The control band of 4c does not match with 4a for the western blot.\n25. Fig.4j. Please add quantification (e.g., % positive cells); Ki67 increase could reflect proliferation unrelated to odontogenic commitment.\n26. Co-culture experiments (panel f) use siRNA knockdown of WNT/NOTCH ligands in DE cells. How many siRNAs were designed for each? There might be off targets or other confounding factors, having multiple guides. Explain in the Fig4f what pre+ and post+ group samples mean? The treatment timeline is not clearly mentioned.\n27. In figure panel (g) and (i) Why the western blot graphs for fig 4a when compared to fig 4g look so different even though the expression patterns were more intense DPSC results. There was 3-fold increase in the DMSC for DLX6-AS1 when compared to DLX6-AS1-DPSC. The representative image for the western blot does not match the quantification.\n28. Fig.4i: What is the control sample?\n29. Similar issues as in DMSCs: functional maturity of odontoblasts is not confirmed.\n30. In Supplementary Figure 3c: When was the WNT and Jagged1 treatment given in this analysis? Mention whether it is pre or continuous.\n• Related to Figure 5\n31. Histological evidence of dentin-like structure is based on morphology and staining (Masson’s, DMP1/DSPP), but no mechanical testing is done to assess dentin quality.\nPlease perform microCT analysis to prove calcification or the dentin formation in the DLX6-AS1 DMSC pre-WNT+JAG1.\n32. Lack of negative controls: please test the effects of JAG1 or WNT alone (vs. sequential application).\n33. IF for DMP1 and DSPP, while positive staining is shown, quantitative comparison across conditions is absent, no control for nonspecific expression.\n34. Why wasn’t the JAG1 bead–only treatment included as a separate group? It is possible that JAG1 alone is sufficient to induce dentin formation.\n• Related to Figure 6\n35. Please track after the delivery of JAG1 its bioavailability, degradation, and localization.\n36. Micro-CT images lack volumetric or density measurements to confirm that mineralized tissue matches natural dentin.\n37. Bone volume fraction quantification BV/TV is a coarse metric; does not distinguish between dentin, osteodentin, or calcified scar tissue. Please use SEM/TEM to confirm tubular organization of regenerated dentin.\n38. Histology and IF, while organized dentin bridge is reported, higher-resolution imaging (e.g., SEM/TEM) is needed to confirm tubular microstructure.\n39. Fig.6e: Confirm primers target human transcripts. Consider single-cell or lineage-resolved validation.\n40. Please site the reference (PMID: 40346090: Profiling and functional characterization of long noncoding RNAs during human tooth development) for the relevance of DLX6-AS1."}, "author_response": "Response: We appreciate the reviewer's acknowledgment of the importance of our findings and their therapeutic implications. We have thoroughly reviewed all comments received during the peer review process and have made revisions to enhance the clarity and significance of the manuscript. We are confident that the manuscript has been strengthened as a result of this valuable feedback.\nMajor points:\n1. Several aspects of Fig. 1 are unclear which affects interpretability of the data:\n(1) Resolution of spatial transcriptomics in some Figs is not sufficient to draw conclusions …what do the empty spots (regions without circles) in Fig. 1D mean? Do we assume empty regions do not have any expression of genes from that cluster?\nResponse:\nWe sincerely thank the reviewer for raising this important and insightful question. We fully agree with the reviewer regarding the resolution limitations of ST technology. To systematically overcome this limitation, we applied an integrative analysis approach that combines our high-resolution single-cell RNA sequencing (scRNA-seq) data with spatial transcriptomics (ST) data, a strategy utilized in several prior studies (1. Cell 2021; 2. Cell 2019; 3. Nat Commun 2021). Our analytical workflow can be summarized: First, we utilized scRNA-seq data to define the various cell types and their molecular signatures at the single-cell level. Second, using computational methods in Seurat, specifically the FindTransferAnchors and TransferData functions, we \"mapped\" these high-fidelity cell type annotations onto each ST capture spot. This algorithm calculates a probability score for each scRNA-seq-defined cell type within every spot. Moreover, to validate the computational inferences, we conducted IF staining for key cell-type markers. The spatial patterns of KRT14 (a DE marker) and FGF3 (a DPL marker) shown in Fig. 1e align well with the spatial distributions of the DE and DPL populations inferred from the integrated analysis. An explanation of the integrated analysis strategy has been incorporated into the Methods section on Page 15, Lines 20-29.\nThe color of each circle in Fig. 1d corresponds to the cell type with the highest probability score at that spatial location. The Fig. 1d illustrates the inferred spatial distribution of cell populations resulting from computational analysis, rather than the expression of a single gene. Empty regions in the Fig. do not indicate areas where cells lack gene expression from a specific cluster, but rather signify minimal expression in those areas. A Fig. caption explaining the circles symbolizing data collection points has been included on Page 26, Lines 1-3.\n(2) Immunofluorescence validation of the spatial transcriptomics data should be done at the same timepoints. Fig. 1E appears to show 15 PCW timepoint for spatial transcriptomics but 17PCW for the immunofluorescence staining. Perhaps authors can provide 17PCW spatial transcriptomics data if they have it?\nResponse:\nWe acknowledge the reviewer's insightful feedback and have accordingly updated Fig. 1e by replacing the 15 PCW ST data with the 17 PCW data to align with the IF validation timepoint. Furthermore, we have improved the Fig. by including additional co-staining with lineage markers at the 17 PCW stage. These modifications are elaborated in the revised Fig. 1e, and corresponding Fig. legends have been appended on Page 26, Lines 4-7.\nThe revised Fig. 1e is presented below.\n(3) Might be helpful to clearly describe how the data, which originated from multiple sources, was integrated and/or re-analyzed.\nResponse:\nWe appreciate the reviewer's insightful suggestion. In response, we have provided a detailed explanation in the Methods section of our revised manuscript regarding the integration and re-analysis of data from multiple sources. Our analytical approach involved standardized pre-processing, batch correction utilizing Harmony, and subsequent analysis of the adjusted data. The detailed protocol, delineating the specific functions and parameters utilized in the Seurat and Harmony R packages, is now included in the Methods section of our revised manuscript on Page 13, Lines 36-39 and Page 14, Lines 1-32.\n(4) What is different between the two pie charts for BMP pathway in Fig. 1F?\nResponse:\nWe appreciate the reviewer's inquiry and we apologize for the lack of clarity in our previous explanation. One pie chart depicted the ligands and receptors of the BMP pathway, while the other focused on members of the core TGF-β pathway. In response, we have revised Fig. 1f. The two pathways are now delineated by a dotted line in the updated Fig. 1f, and the Fig. legend has been appropriately adjusted on Page 26, Lines 8-11.\n(5) It is not clear how the relative proportions of ligand vs receptor are quantified in Fig. 1F. If authors show that DE expresses 37% of the four FGF receptors, does this mean 37% of the 4 receptors listed are only expressed by DE and not by DPL, and 67% of the 4 receptors are expressed by DPL and not DE?\nResponse:\nThe reviewer's feedback on the ambiguity is appreciated, and we apologize for any resulting confusion. The pie chart percentages represent the relative contribution of the DE and DPL subpopulations to the total expression of specific ligand or receptor gene sets, enabling the assessment of their signaling potential. Only genes stably detected in single-cell data for DE or DPL were included, with undetected genes excluded. Normalized expression values for each gene in individual cells within DE and DPL were extracted, summed across all cells to obtain total expression scores for each population, and then displayed in the pie chart. For instance, the 37% attributed to FGF receptors in DE indicates that DE contributes 37% of the total FGF receptor gene set expression, with DPL contributing the remaining 63%. This comparison reflects the overall signaling contribution from DE and DPL, rather than exclusive gene expression. The legend for Fig. 1f has been revised on Page 26, Lines 8-11, and corresponding descriptions in the main text have been updated on Page 14, Lines 26-32.\n(6) Does Fig. 1G include the expression levels between only DE and DPL clusters or all clusters?\nResponse:\nWe acknowledge the reviewer's query regarding the Sankey diagram in Fig. 1g, which specifically elucidates the signaling interactions between the DE and DPL clusters. We are sorry for the lack of clarity in the Fig. 1g and have addressed this in the revised Fig. 1g, along with an updated figure legend as detailed in the revised manuscript on Page 26, Lines 12-13.\n2. Supplemental Fig. 1 is supposed to show the expression of WNT and NOTCH ligands relative to the dental epithelial subpopulations. However, the UMAPs for the epithelial subpopulations (panel a) and the WNT/NOTCH ligands expression (panel b) are related but different. This makes it difficult to compare ligand expression and epithelial subpopulations.\nResponse:\nWe appreciate the reviewer's feedback on the lack of clarity in the presentation of Supplementary Fig. 1. In response, we have revised the figure to facilitate a direct comparison. The gene expression UMAPs in panel now align with the cell clustering UMAP in panel on the revised Supplementary Fig. 3c and 3e, thereby ensuring a direct comparation of ligand expression and epithelial subpopulations.\nThe revised Supplementary Fig. 3c and Supplementary Fig. 3e are presented below.\n3. Quantifications of IF stainings from Fig. 2D would improve interpretability of the images and support the main idea in this Fig. What is the sample size of these stainings?\nResponse:\nWe express our gratitude to the reviewer for the insightful and constructive feedback. We have added a new Supplementary Fig. 3g that presents bar graphs of the quantitative analysis for all IF staining from Fig. 2d, with details provided in the Methods section on Page 17, Lines 25-29.\nThe staining sample size is n=3. This information has been included in both the figure legends and the Methods section accordingly.\nThe revised Supplementary Fig. 3g are presented below.\n4. Authors explain that in mice, Dlx6os1 expression did not overlap with Lmo1+/Fgf3+/Smpd3+ coronal papilla cells, which are known as odontoblast progenitors in mice. How does this affect the interpretation of the in vivo experiments performed in mice in the paper? Do the known odontoblast progenitor cells respond to the exogenous JAG1 and support dentin repair in Fig. 5?\nResponse:\nWe appreciate the reviewer's insightful question. Our study suggests that DLX6-AS1 can serve as a specific marker in humans for identifying odontoblast progenitor cells. In our xenotransplantation model, our aim was not to activate endogenous mouse progenitors but to assess the regenerative potential of human DLX6-AS1+ cells in vivo. To confirm that mouse odontoblast progenitor cells were not involved in dentin repair, we genetically labeled the transplanted human cells with GFP. Upon sequential WNT and JAG1 stimulation, the transplanted GFP-labeled DLX6-AS1⁺ DMSCs (green) exhibited active secretion of the odontoblast marker DSPP (red), indicating successful differentiation and proving their role in dentinogenesis. These changes are presented in the revised Fig. 5e and described on Page 8, Lines 31-34.\nThe revised Fig. 5e is presented below.\n5. How do the protocols compare between odontogenic induction in Fig. 4a-b and WNT/NOTCH sequential stimulation in Fig. 4c-e? It appears that the alizarin red stainings at the end of the two induction protocols are similar. If the odontogenic induction does not specifically include the WNT first, then NOTCH sequence, this which would question the significance of the sequential order of signaling. Authors should also consider labeling the cells in 4c-e as DLX6-AS1+ as well.\nResponse:\nWe appreciate the reviewer's insightful question. In the revised manuscript, we have included a comparison of ALP and ARS staining for odontogenic induction alone and sequential induction in the revised Supplementary Fig. 5e, along with quantification. Both sets of data demonstrate the superior inductive capacity of sequential induction.\nWe agree with the reviewer's suggestion that cell labeling is necessary for improving data clarity. Therefore, we have incorporated labeling into the revised Fig. 4c-e.\nThe revised Supplementary Fig. 5e is presented below.\n6. In Fig. 5, how do the authors explain the more obvious levels of collagen in the DMSC only samples vs DLX6-AS1+ DMSC samples when the DLX6-AS1+ DMSCs are thought to have superior odontogenic potential?\nResponse:\nWe are grateful to the reviewer for addressing this issue. The blue-staining structure seen in the control group (DMSC only) in the Masson's Trichrome staining of Fig. 5b was the PLGA beads utilized in our study. In the updated version, we have altered the representative image of the DMSC group to prevent any confusion.\nThe revised Fig. 5b is presented below.\n7. The role of Notch in reparative dentinogenesis (dentin repair following injury) has been reported in the literature (Wang et al., 2014). In the dentin repair in vivo experiments, an appropriate control would be the addition of JAG1 alone after injury to assess the extent to which Notch signaling affects dentin repair by endogenous cells.\nResponse:\nWe express our gratitude to the reviewer for the valuable suggestion. In response, we have included the following groups: DLX6-AS1⁺ DPSCs with WNTs treatment group and DLX6-AS1⁺ DPSCs with JAG1 treatment group. Compared with these two groups, the DLX6-AS1⁺ DPSCs with Pre-WNTs+JAG1 treatment group generated a thick, well-organized, and continuous dentin bridge with a distinct tubular structure that closely mimics native dentin. The revised Fig. 6 illustrates these groups.\n8. How epithelium and mesenchyme undergo the transition from WNT to NOTCH signaling remains to be explored. It would be worth examining the roles of negative regulators of WNT pathway (i.e. DKKs, SFRPs described in Fig. 2b) in vivo.\nResponse:\nWe are grateful to the reviewer for this highly insightful comment. Our data (Fig. 2b) had suggested that the expression of WNT pathway inhibitors in the DPL was upregulated as WNT signaling activity declined. To validate this finding at the protein level and investigate their spatial localization, we followed the reviewer's excellent suggestion and performed immunofluorescence staining for key antagonists (DKK1 and SFRP1) on sections of human tooth germs (revised Fig. 2d).\nOur findings are in agreement with the research on the role of WNT inhibitors in both mouse and human odontogenesis. The fine-tuning of WNT pathway by its inhibitors is known to be essential for proper tooth development. Excessive Wnt/β-catenin signaling impairs odontoblast maturation and root formation (4. J Periodontal Res 2013). This crucial regulatory step is mediated by WNT antagonists. The expression dynamics of DKK1 perfectly align with this function, as it is specifically upregulated in preodontoblasts at the onset of differentiation when WNT activity must be reduced (5. Dev Dyn 2005). As previously reported, both DKK1 and SFRP1 are expressed in the dental mesenchyme and odontoblasts, suggesting a key role in this self-regulation process (6. Development 2011).\nTaken together, the spatial and temporal localization of DKK1 and SFRP1 proteins we observed is consistent with a conserved and functionally critical mechanism. It provides in situ evidence for our model where these upregulated negative regulators might orchestrate the \"handover\" of signaling dominance from the WNT to the NOTCH pathway.\nWhile a full in vivo functional perturbation study would be a significant undertaking for future work, we believe this new expression data provides evidence for the in vivo relevance of this mechanism. We thank the reviewer for encouraging us to pursue this line of investigation, as it has added a significant layer of mechanistic depth to our manuscript.\nThe revised Fig. 2d is presented below.\nMinor points:\n• Fig. 3e appears to be cropped at the top, was there any reason to exclude those genes?\nResponse:\nWe are very grateful to the reviewer for this comment on Fig. 3e. To address the issue, we have revised the plot. The data points for genes with extremely significant P values (adjusted P values = 0) are now represented by upward-pointing arrows at the top of the y-axis. These arrows clearly signify that the adjusted P values for these genes are exceptionally significant, resulting in -log adjusted P values that are effectively infinite. This approach unambiguously communicates their extreme statistical importance. We have also updated the figure legend to explain the meaning of these symbols on the Page 30, Lines 4-8.\nThe revised Fig. 3e is presented below.\n• In Fig. 3, additional in vivo validation of the WNT/NOTCH target genes would be helpful.\nResponse:\nThe reviewer's suggestion has been acknowledged and implemented. Immunofluorescence staining was conducted on sections of human tooth germ to detect LEF1, a canonical WNT pathway target, and HEY1, a canonical NOTCH pathway target. The expression patterns observed aligned well with our data: LEF1 exhibited high expression in the dental papilla during early development stages (correlating with elevated WNT activity), whereas HEY1 expression was prominent in the pre-odontoblast layer during later stages (correlating with increased NOTCH activity). To visually represent the expression data cohesively, the new images have been incorporated into the revised Fig. 2d, alongside the staining for the pathway components themselves.\nThe revised Fig. 2d is presented below.\n• Diagram showing the time-course of the tissue recombination experiments would be helpful.\nResponse:\nWe appreciate the reviewer's suggestion. To enhance the clarity of our experimental workflow, we have developed a schematic diagram depicting the timeline of the tissue recombination experiments. This diagram delineates the essential stages, starting from cell preparation and a 3-day in vitro culture period to a 3-month in vivo growth phase post-transplantation under the kidney capsule in nude mice, as shown in the revised Fig. 5a.\nThe revised Fig. 5a is presented below.\n• In Fig. 6d, authors describe an improvement in dentin that results from (DLX6-AS1+ cells + WNTs+JAG1) mediated dentinogenesis compared to reparative dentinogenesis. A zoomed-in image of the dentin formed through reparative dentinogenesis would be helpful for comparison.\nResponse:\nWe extend our sincere thanks to the reviewer for the invaluable suggestion. In response, a zoom-in image depicting the dentin formed through reparative dentinogenesis has been incorporated into the revised Fig. 6d.\nThe revised Fig. 6d is presented below.\nReference:\n1. Fawkner-Corbett D, et al. Spatiotemporal analysis of human intestinal development at single-cell resolution. Cell 184, 810-826 e823 (2021).\n2. Asp M, et al. A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart. Cell 179, 1647-1660 e1619 (2019).\n3. Mantri M, et al. Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis. Nat Commun 12, 1771 (2021).\n4. Bae CH, et al. Excessive Wnt/β-catenin signaling disturbs tooth-root formation. J Periodontal Res 48, 405-410 (2013).\n5. Fjeld K, et al. Dynamic expression of Wnt signaling-related Dickkopf1, -2, and -3 mRNAs in the developing mouse tooth. Dev Dyn 233, 161-166 (2005).\n6. Li J, et al. SMAD4-mediated WNT signaling controls the fate of cranial neural crest cells during tooth morphogenesis. Development 138, 1977-1989 (2011).\nReviewer #2\nThis study characterizes the spatiotemporal dynamics of Wnt and Notch signaling during human tooth development, revealing an early predominance of Wnt activity that transitions to Notch signaling at later stages. The author concluded that both pathways exhibit a coordinated spatial distribution, with Wnt and Notch ligands enriched in the dental epithelium and receptors localized in the underlying dental papilla, thereby highlighting a key epithelial–mesenchymal signaling axis. Within the dental papilla, a distinct population of DLX6-AS1⁺ cells was identified as a potential odontoblast progenitor, responsive to Wnt and Notch signals. These cells demonstrated functional odontogenic capacity by generating tubular dentin in both an in vivo dentin defect model, underscoring their potential in regenerative dental therapies. This manuscript presents interesting and novel findings that offer new insight into the molecular regulation of odontogenesis; however, its current form is limited by the following points:\nResponse:\nWe appreciate the feedback provided to enhance our research. Every comment has been thoroughly reviewed, and the manuscript has been revised accordingly. Herein, we present our detailed responses to each point raised.\n1. Based on the single-cell RNA-seq data presented in Figure 2, it appears that the expression of Wnt receptors is not exclusively located in the dental papilla population but is also present in the dental epithelium. This observation is further supported by the immunofluorescence data showing LRP6 localization in the dental epithelium. Given this, it would be helpful if the authors could clarify how these findings fit into their proposed model of epithelial-mesenchymal signaling dynamics.\nResponse:\nWe thank the reviewer for the insightful query. Both DE and DPL rely on WNT signaling for development as a fundamental signaling pathway (1. J Biol Chem 2013; 2. Int J Biol Sci 2013). Our central model focuses on the epithelial-mesenchymal signaling axis, where DE-derived WNTs act on the DPL to drive its differentiation. Prior research has demonstrated that conditional knockout of Wntless by Shh-Cre leads to impaired ameloblast and odontoblast differentiation. To demonstrate the impact of DE-derived WNTs on DPL differentiation, we presented a co-culture model in Fig. 4f and in vivo recombination assays in Fig 5. Our data also showed WNT and NOTCH receptor expression within the DE itself, consistent with reported roles for intra-epithelial WNT signaling in maintaining stemness. We included this point in the discussion section on Page 10, Lines 35-37 and Page 11, Lines 6-10.\n2. The authors present single-cell RNA-seq and spatial transcriptomic data, with a primary focus on the dental epithelium and dental papilla. However, in the functional validation experiments — including both in vitro odontogenic differentiation and an ex vivo tissue recombination approach —the authors used DMSCs rather than directly testing DPSCs. The rationale behind this choice is unclear. The authors are encouraged to clarify why DMSCs were used instead of DPSCs, and to discuss how this decision may affect the interpretation and specificity of their findings.\nResponse:\nWe would like to thank the reviewer for their question. In the functional validation experiments, we used DPSCs to test odontogenic induction in vitro. However, as DMSCs exhibit lower differentiation compared to DPSCs and align more closely with the developmental model, we utilized DMSCs to assess the developmental signals identified from the bioinformatics data in the ex vivo tissue recombination model. We also have included this explanation in the revised manuscript on Page 11, Lines 13-18.\n3. The authors clearly demonstrate impressive reparative dentin formation in the injury model, supporting the therapeutic potential of DLX6-AS1⁺ DPSCs for dentin regeneration. However, it remains possible that implantation of pre-Wnt-treated DLX6-AS1⁺ cells combined with JAG1 may influence additional signaling pathways beyond Wnt and Notch that contribute to dentinogenesis. Could the authors clarify whether they have investigated the involvement of other signaling pathways in their experimental system, and if so, please provide details of these analyses?\nResponse:\nWe appreciate the reviewer’s insightful question. Previous studies have indicated the involvement of classical signaling pathways, such as the BMP pathway, in dentinogenesis and dentin regeneration, demonstrating increased expression of BMP2 and BMP4 in various studies (3. J Dent Res 2012; 4. Arch Oral Biol 2018). Additionally, ectopic overexpression of WNT signaling in dental epithelium has been shown to induce the expression of BMP2 and BMP4 in the underlying mesenchymal cells (5. Organogenesis 2019). In light of this background, we further assessed the expression of BMP2 and BMP4 in the DLX6-AS1⁺ WNTs+JAG1 group (Supplementary Fig. 7e). We noted elevated levels of BMP2 and BMP4 compared the control group, suggesting a potential involvement of other classical signaling pathways for dentin regeneration. We also included this in the discussion on Page 11, Lines 10-12.\nThe Supplementary Fig. 7e is presented below.\nReference:\n1. Zhu X, et al. Intra-epithelial requirement of canonical Wnt signaling for tooth morphogenesis. J Biol Chem 288, 12080-12089 (2013).\n2. Zhang R, Yang G, Wu X, Xie J, Yang X, Li T. Disruption of Wnt/beta-catenin signaling in odontoblasts and cementoblasts arrests tooth root development in postnatal mouse teeth. Int J Biol Sci 9, 228-236 (2013).\n3. Yang W, Harris MA, Cui Y, Mishina Y, Harris SE, Gluhak-Heinrich J. Bmp2 is required for odontoblast differentiation and pulp vasculogenesis. J Dent Res 91, 58-64 (2012).\n4. Jani P, Liu C, Zhang H, Younes K, Benson MD, Qin C. The role of bone morphogenetic proteins 2 and 4 in mouse dentinogenesis. Arch Oral Biol 90, 33-39 (2018).\n5. Zhou N, et al. Persistent Wnt/beta-catenin signaling in mouse epithelium induces the ectopic Dspp expression in cheek mesenchyme. Organogenesis 15, 1-12 (2019).\nReviewer #3\nThe study “Spatiotemporal Interplay Between Epithelial and Mesenchymal Cells Drives Human Dentinogenesis” by Wei et al. presents a comprehensive single-cell and spatial transcriptomic atlas of developing human permanent teeth, uncovering a sequential WNT-NOTCH signaling mechanism presumably from the dental epithelium that guides odontoblast differentiation from DLX6-AS1⁺ dental papilla cells. These primary cell progenitors, isolated from both dental mesenchymal stem cells (DMSCs) and dental pulp stem cells (DPSCs), demonstrated enhanced odontogenic potential upon WNT-NOTCH stimulation, forming organized, tubular dentin-like material both in vitro and in vivo. Notably, DLX6-AS1⁺ DPSCs were able to regenerate structured dentin in a mouse molar injury model following sequential ligand treatment. While the developmental atlas can be a valuable resource for the field, many of the study’s conclusions remain premature. The functional role of DLX6-AS1, the causal relationship between WNT and NOTCH signaling, and definitive lineage tracing of proposed progenitors are not adequately demonstrated, leaving key mechanistic questions unresolved.\nResponse:\nWe appreciate the reviewer's insightful evaluation of our manuscript. The involvement of DLX6-AS1 in enhancing dentinogenesis has been noted in our research and in other studies (1. Int Endod J 2024; 2. Int J Oral Sci 2025). A recent study on human endometrium development also highlighted the sequential WNT-NOTCH stimulation for cell lineage specification (3. Nat Genet 2021). Previous research has proposed a negative feedback mechanism of the WNT pathway involving its antagonists like DKK1 (4. Proc Natl Acad Sci U S A 2004). This mechanism plays a crucial role in maintaining signaling output within a physiological range by preventing excessive or prolonged pathway activation. Similar feedback regulation has been observed in various embryonic development, tissue repair and regeneration processes (5. J Cell Sci 2006; 6. PLoS Med 2007). In order to trace the lineage of the proposed progenitors, cell-labeling experiments were performed to confirm the presence of GFP in the regenerative odontoblasts. All these concerns have been addressed in our revised manuscript, and we will provide a more detailed explanation in the point-to-point response.\nSpecific Comments:\n• Related to Figure 1\n1. The Single-cell and spatial transcriptomic were produced from limited sample diversity: Only two biological replicates per timepoint (incisor and molar) were used for scRNA-seq and ST. This raises concerns about representativeness and reproducibility.\nResponse:\nWe thank the reviewer for highlighting the importance of increasing biological replicates for improved representativeness and reproducibility.\nThe primary constraint we faced is the exceptional difficulty in obtaining these specific human developmental tissues. The collection of fresh, high-quality samples, particularly across distinct developmental timepoints (incisor and molar), is subject to significant logistical and ethical constraints, making each sample exceedingly rare and precious.\nWe note that contending with small sample size is an accepted challenge in foundational human developmental and tissue atlas studies. Our approach is therefore consistent with other landmark papers in the field that have successfully generated high-impact data from a limited number of biological replicates (7. Cell 2021; 8. Cell 2019; 9. Cell Res 2021). To overcome this constraint, we have increased our sample size for subsequent IF/in situ staining and functional experiments, as detailed in the Methods section.\n2. Batch effects and technical artifacts: Integration of data from different developmental stages and platforms (scRNA-seq + ST) could introduce batch effects, especially without detailed normalization controls. How did the authors integrate the data from previous studies? Which specific methods were used?\nResponse:\nWe thank the reviewer for emphasizing this point. We acknowledge the complexity of integrating data from different developmental stages and platforms could introduce a batch effect. To address this, we implemented a documented integration workflow utilized in previous studies (10. Nature 2021; 11. Science 2021; 12. Science 2020), comprising three main steps: (1) Standardized Pre-processing; (2) Batch Correction using Harmony (13. Nat Methods 2019); (3) Validation of Integration Effectiveness (14. Genome Biol 2020).\nTo validate the integration of our data, we also conducted a computational validation. We assessed the preservation of true biological signals during batch correction by examining the stability of canonical marker genes for major cell populations. A new supplementary figure (Supplementary Fig. 1) has been included to visually present our results. The UMAP of the integrated atlas, colored by developmental stage, demonstrates thorough mixing of cells from different batches within shared clusters (Supplementary Fig. 1a). Major cell lineages were found to form distinct and well-separated clusters, annotated based on the expression of established canonical marker genes (Fig. 1b and c). FeaturePlots and VlnPlots for key markers were generated to address batch-specific distortion, stratified by their original batch (Supplementary Fig. 1b). These plots reveal conserved expression of key markers across all developmental stages, providing evidence that our integration approach successfully harmonized the datasets without introducing artifacts or compromising essential biological features. Details of the integration methods have been included in the Method section on Page 13, Lines 36-39 and Page 14, Lines 1-14.\nThe Supplementary Fig. 1 is presented below.\n3. Spatial resolution constraints: ST has limited spatial resolution (~50 µm spot diameter), so unfortunately fine-scale (cellular level) epithelial-mesenchymal interfaces may not be precisely captured complicating the analysis of signal senders vs receivers.\nResponse:\nWe acknowledge the reviewer's insightful comment on the spatial resolution of the Visium platform, recognizing that the approximately 50 µm spot diameter of ST data limits the platform's capacity to analyse signals from the epithelial-mesenchymal interfaces.\nThe analysis in our study integrates scRNA-seq and ST data within a comprehensive framework. We do not rely solely on ST data for inferring signaling interactions. Initially, we utilize our detailed scRNA-seq atlas to precisely determine the cell types expressing ligands and receptors. This single-cell resolution enables us to confidently assign \"sender\" and \"receiver\" identities, eliminating mixed-cell signal uncertainties. Subsequently, we employ the scRNA-seq data as a guide to map the spatial co-localization of these cell types in the ST data. Importantly, we validate these computational interactions through IF/ RNA scope staining, offering experimental support for the proximity of the interacting cell populations.\nThis integrative strategy is a well-established approach for surpassing the resolution limits of ST. Previous studies have employed this method to examine signals from the epithelial-mesenchymal interfaces (7. Cell 2021; 15. Nat Commun 2021; 16. Nat Genet 2021; 17. Cell Rep 2022). In response to the reviewer's feedback, we have included the workflow details in our revised manuscript on Page 15, Lines 20-29.\n4. No functional validation of inferred ligand-receptor interactions at this stage, interpretations are based on expression correlations only. Differentiation studies are required to make further claims.\nResponse:\nWe appreciate the reviewer's insightful comment. Initially, we utilized bioinformatics data in conjunction with immunofluorescence staining to map the expression of crucial ligand-receptor pairs. The analysis of the interaction between DE and DPL was previously performed using CellPhoneDB for early-stage data (18. Cell Rep 2025), and the assessment of NOTCH signaling interaction is presented in the Supplementary Fig. 2e-g. Most of these interactions are well-established based on previous studies, such as the WNT (WNT6/7B/10B)-LRP6 (19. Cell Stem Cell 2022) and JAG1-NOTCH2 (20. Cell Stem Cell 2022; 21. Proc Natl Acad Sci U S A 2013) signaling axis. Additionally, validation of target gene expression, specifically LEF1 for WNT signaling (22. Cell 2009) and HEY1 for NOTCH signaling (23. Nat Commun 2024), was conducted using single-cell RNA sequencing, spatial transcriptomics, and immunofluorescence staining. Subsequent in vitro experiments included the detection of target genes through western blot analysis in Fig.4. Following the reviewer's feedback, the manuscript has been revised accordingly, with the specific revisions located on Page 4, Lines 28-32 and Page 5, Lines 35-38.\n5. UMAP visualizations depend on parameter tuning and dimensionality reduction. Please show confirmation that cell types remain stable across batch corrections.\nResponse:\nWe appreciate the reviewer for highlighting this crucial point. To address this rigorously, we systematically assessed the expression consistency of the canonical marker genes that underpin our cell type definitions. The results of this analysis are presented in the revised Supplementary Fig. 1b. For each major cell lineage identified (e.g., DE, DPL, DF), we generated FeaturePlots and VlnPlots for a set of key defining marker genes, categorized by their original batch (developmental stage). These plots illustrate that the expression patterns and levels of these identity-defining genes are consistent across all integrated datasets. The consistency observed in multiple canonical markers provides compelling evidence for the robustness and stability of our cell type definitions. It confirms that a cell classified as a specific type shares the similar fundamental biological identity with other cells of the same type, irrespective of its batch origin. Additionally, the biological validity of our integrated atlas is well supported by downstream functional experiments. The high level of agreement between findings from our single-cell data and results from functional assays serves as a validation of the accuracy and biological relevance of the integration. In combination, these two independent validation approaches – systematic marker gene examination and validation through functional experiments – offer evidence that our cell type annotations are stable. A description of these validation steps has been included in the Method section on revised Page 14, Lines 11-14.\n6. The Marker gene heatmap uses only a few canonical markers shown; not sufficient to resolve subtypes (e.g., epithelial subpopulations like IEE, OEE, SR, etc.). Please increase resolution and depth in the analysis.\nResponse:\nWe are very grateful for the reviewer's valuable suggestion. To comprehensively address the reviewer's suggestion, we have strengthened our analysis on two fronts:\nRegarding the major cell lineages: To provide more robust support for the 11 primary clusters shown in Fig. 1c, we have now included a new Supplementary Fig. 2a that lists the top 10 differentially expressed genes for each major cell lineage.\nRegarding the epithelial subtypes: To directly address the reviewer's point on subtype resolution, we conducted a detailed sub-clustering of the DE population. To delineate these subtypes by their molecular signatures, we have generated another Supplementary Fig. 3d, which displays the top differentially expressed genes for each subpopulation.\nWe have incorporated both of these additions into the supplementary materials and have updated the Results section of the main text with the corresponding citations and descriptions on Page 3, Lines 33-34 and Page 5, Lines 28-29.\nThe Supplementary Fig. 2a and Supplementary Fig. 3d are presented below.\n7. Please validate the IF in Fig.1e by co-staining with lineage markers. Please also show negative controls.\nResponse:\nWe sincerely thank the reviewer for this question. Lineage markers (AMELX for ameloblasts in DE (24. Int J Mol Sci 2021); DSPP for odontoblasts in DPL (25. J Dent Res 2010)) were co-stained in Figure 1e, with negative controls included in Supplementary Fig. 2b. Details on differentiation markers and expression patterns can be found in the revised manuscript on Page 3, Lines 37-39 and Page 4, Lines 1-2.\nThe revised Fig. 1e is presented below.\n8. Fig.1f. The pathway ligand/receptor pie charts relative proportions lack statistical significance. Please add information on absolute expression levels or functional relevance of pathways like EDA or BMP.\nResponse:\nWe appreciate the reviewer for highlighting this point. We agree that Fig. 1f's pie charts require additional information on absolute expression levels, statistical significance, and functional relevance. To address this, two new analyses have been incorporated:\n(1) We have created a new supplementary table (Supplementary Table 1) which presents comprehensive quantitative data on the principal ligands and receptors illustrated in Fig. 1f. This table includes the average normalized expression levels, log-fold changes, and adjusted p-values for each gene in both the DE and DPL lineages, offering statistical confirmation for the identified proportional variances.\n(2) In order to evaluate the functional significance of crucial pathways such as EDA and BMP, a pathway activity analysis was performed. Module scores were computed for each cell using the \"Hallmark\" gene sets from MSigDB. These scores are depicted in Supplementary Fig. 2c, employing violin plots to contrast pathway activity between the DE and DPL populations. We have updated the manuscript accordingly on Page 4, Lines 17-20.\nThe Supplementary Fig. 2c is presented below.\n9. Fig.1g. The Sankey diagram of ligand-receptor dynamics is descriptive only, not enough for predicted signaling interactions or temporal changes without perturbation experiments. It shows that Shh has almost no ligands at all in the early stage even though it’s known to be functionally important for tooth development. How were the percentage between pathways calculated? A pathway that has only few ligands known might have small percentage relative to others but still be more relevant and effective in cell fate determination. Furthermore, for example Notch pathway is known to act multiple times during developmental trajectories. Please add resolution by analyzing specific timepoints.\nResponse:\nWe sincerely thank the reviewer for these insightful points.\n(1) The percentages in Fig. 1g's Sankey diagram were calculated as follows: Firstly, the normalized expression values of all ligand (or receptor) genes from the seven key pathways within the DE and DPL cell populations were extracted for each specified time point. Subsequently, the expression of these genes was summed to establish a \"total signaling expression pool\" for that particular time point within the context of DE-DPL interactions. Lastly, the total expression for each individual pathway was computed and compared to this \"total signaling expression pool\" to determine its relative contribution percentage at that specific time. It is acknowledged that this diagram serves as a descriptive visualization. Its primary objective was not to quantify the absolute activity of each pathway but rather to address the query: \"At any given developmental stage, how is the relative share of expression distributed among the major signaling pathways that constitute the potential DE-DPL communication landscape?\n(2) We concur with the reviewer regarding the limitations of the calculation method used, which may not accurately represent the biological activity of a pathway. To address this concern, we conducted a more comprehensive analysis by evaluating the activity of each pathway at various time points using the AUCell algorithm (Supplementary Fig. 2d). Our updated assessment of SHH pathway activity indicates that despite the relatively low total expression flux of SHH ligands in our DE-DPL interaction model, the pathway's activity score, as determined by AUCell, is notably elevated during the 12-24 PCW developmental stages (Supplementary Fig. 2d). This suggests that although the SHH pathway may have a smaller overall contribution in terms of total ligand expression, its functional activity remains substantial. It is important to note that our original Sankey data does not diminish the significance of the SHH pathway in other cellular interactions or at different developmental time points.\n(3) To address the reviewer's request for higher resolution regarding the NOTCH pathway, we conducted a comprehensive cell-cell communication analysis using CellChat at the critical time points of 15 PCW and 17 PCW. Signaling networks between DE and DPL cell populations at these time points are presented separately in Supplementary Fig. 2e-g. Our analysis revealed no statistically significant NOTCH signaling interactions at 15 PCW. However, at 17 PCW, the NOTCH pathway exhibited significant interactions (Supplementary Fig. 2e-g). Additionally, we have included a detailed network plot illustrating the specific ligand-receptor pairs predicted for the NOTCH pathway at 17 PCW. This offers a molecular hypothesis for NOTCH activation at this stage, supporting the findings presented in Figure 2. We have updated the manuscript accordingly on Page 4, Lines 23-32.\nThe Supplementary Fig. 2d-g is presented below.\n• Related to Figure 2\n10. Fig.2b. Please show also temporal distribution of receptor in epithelium and ligands in mesenchyme.\nResponse:\nWe thank the reviewer for the suggestion and have generated a new supplementary figure (Supplementary Fig. 3a) depicting the temporal expression patterns of crucial ligands in the DPL and their associated receptors in the DE across all developmental stages. This supplementary analysis is now referenced and discussed in the Results section on Page 5, Lines 18-21.\nThe Supplementary Fig. 3a is presented below.\n11. Fig.2c. Temporal line plots of top ligands do not distinguish canonical vs. non-canonical WNT activity; co-expression of multiple ligands complicates pathway specificity. Please separate the individual ligands in independent temporal blots.\nResponse:\nWe appreciate the reviewer's insightful feedback. In response, we have implemented the suggestion by creating a new supplementary figure (Supplementary Fig. 3b). This updated figure presents individual temporal plots for each WNT and NOTCH ligand, enabling a precise evaluation of their expression patterns over time. This enhanced visualization facilitates a clearer interpretation of the potential signaling pathway activities at distinct developmental stages. The main text has been revised to cite the figures on Page 5, Lines 23-24.\nThe Supplementary Fig. 3b is presented below.\n12. Fig.2d. Please show higher resolution images. It is unclear if ligands are membrane-bound or secreted. Please analyze functionality receptor activation status. For example: in what cell types are the downstream target genes of the receptors upregulated?\nResponse:\nWe appreciate the reviewer's suggestions and have implemented the recommended revisions, thereby enhancing the robustness of our findings.\n(1) We have substituted the images in Fig. 2d with higher-resolution versions, which now better depict the subcellular localization of the ligands. Specifically, the staining pattern for JAG1 is prominently concentrated at the cell-cell junctions between the dental epithelium and dental papilla.\n(2) To address pathway activation, we conducted immunofluorescence co-staining experiments to detect the expression of canonical downstream target genes. Specifically, we stained for LEF1 and HEY1 at critical developmental stages. This data offers evidence of active WNT and NOTCH signaling pathways during tooth development. Please refer to the revised Fig. 2d and the manuscript (Page 5, Lines 35-38 and Page 6, Lines 1-2).\nThe revised Fig. 2d is presented below.\n13. In Supplementary Figure 1: The clustering utilized in the gene plot (b) is different than the UMAP plot in (a). Wnt10B does not look enriched in the CL, IEE as quoted in the paragraph line 147-149. Please cite Figure 2d in line 149. Please re-consider the clustering in the supplementary figure.\nResponse:\nWe thank the reviewer for their valuable feedback. To address the issue of inconsistent coordinates, Supplementary Fig. 3c and 3e have been regenerated. In the revised version, the gene expression UMAPs now utilize the same cell coordinates as the main UMAP, enabling a direct and unambiguous comparison. We acknowledge the error in our previous description; the correct statement is that WNT10B is predominantly expressed in the EK, which has been rectified in the main text. Additionally, the requested citation for Fig. 2d has been included in the revised manuscript on Page 5, Line 32-33. Moreover, to strengthen the evidence for our DE sub-clustering and to delineate these subtypes by their molecular signatures, a new figure (Supplementary Fig. 3d) has been created. This figure illustrates the top differentially expressed genes for each subpopulation, using markers consistent with our prior publication in Cell Reports (18. Cell Rep 2025).\nThe Supplementary Fig. 3c-e are presented below.\n• Related to Figure 3\n14. Fig.3e. The differential expression analysis comparing DLX6-AS1⁺ DP and NR2F1⁺TWIST1⁺ DP is associative; functional differences between these subpopulations are not directly tested.\nResponse:\nWe are very grateful for this crucial comment. To computationally explore the potential functional differences between these subpopulations, we performed a pathway enrichment analysis on the genes upregulated in each group (Supplementary Fig. 4d). This analysis indicated that the DLX6-AS1⁺ and NR2F1⁺TWIST1⁺ DP subpopulations were enriched for distinct and functionally relevant biological pathways. Concurrently, we noted that the NR2F1⁺TWIST1⁺ DP subpopulation is located distally from the epithelium and exhibits downregulated expression of key WNT and NOTCH pathway receptors and transcription factors (Fig. 3e). The figure below illustrates this spatial arrangement.\nThis observation leads us to hypothesize that this subpopulation may have a reduced responsiveness to the primary morphogenetic signals emanating from the epithelium. We agree that this is an associative finding and not a direct functional experiment. We believe that definitively establishing these functional differences is a valuable and necessary direction for our subsequent research.\n15. The role of DLX6-AS1 (a long non-coding RNA) in regulating odontogenic potential is not mechanistically investigated. It's unclear whether DLX6-AS1 is functionally required or simply a marker. No evidence is provided for how DLX6-AS1 interacts with WNT/NOTCH pathways at a molecular level (e.g., chromatin remodeling, RNA-protein interactions).\nResponse:\nWe sincerely thank the reviewer for this insightful and thought-provoking comment.\nFirst, to address whether DLX6-AS1 is functionally required or simply a marker, we would like to refer to our recent publication where we performed siRNA-mediated knockdown of DLX6-AS1 (2. Int J Oral Sci 2025). The results showed that downregulation of DLX6-AS1 led to a reduction in odontoblastic differentiation capacity. This evidence suggested that DLX6-AS1 was not only a marker but was also required for odontoblastic differentiation.\nTo further investigate its mechanism and its interaction with the WNT/NOTCH pathways, we firstly performed an RNA-seq analysis to test whether DLX6-AS1 was essential for cells to respond to WNT/NOTCH signaling. We transfected DMSCs with either a negative control (NC) siRNA or a DLX6-AS1 siRNA, and then subjected both groups to WNT/NOTCH pathway stimulation. The RNA-seq pathway enrichment analysis revealed that key odontogenic processes, including the “Calcium signaling pathway”, “ECM-receptor interaction”, and “chromatin remodeling” were significantly suppressed in the DLX6-AS1 knockdown group. The induction of WNT/NOTCH downstream target genes and key odontogenic markers was blunted in the knockdown cells compared to the control group. Key results from the RNA-seq analysis are shown below.\nBuilding on this functional link, we sought to explore a potential direct molecular interaction. Our staining results revealed its presence in both the nucleus and cytoplasm. The representative image is shown below.\nThis observation that a portion of DLX6-AS1 resided in the nucleus suggested it might function there, possibly by interacting with chromatin or nuclear transcription factors. To explore this possibility in the context of the WNT pathway, we performed molecular docking simulations between DLX6-AS1 and the key transcription factor LEF1 using the HDOCKlite server, with structures predicted by AlphaFold. The simulation predicted a possible binding interface, yielding a favorable docking score of -451.51 kcal/mol. Visualization of the top-ranked model in PyMOL proved that this interaction is primarily stabilized by extensive hydrogen bonds between the two molecules.\nThese preliminary data suggest that DLX6-AS1 might act as a functional mediator for WNT/NOTCH-driven differentiation. Accordingly, we have revised the Discussion section of our manuscript on Page 10, Lines 37-39 and Page 11, Lines 1-5.\n16. UMAP + cell proportions - relative proportions of subclusters across time are shown but may be biased by sample quality or dissociation efficiency.\nResponse:\nThe reviewer's critical point is greatly appreciated. To address this concern, we implemented a comprehensive approach involving standardized protocols, computational correction, and experimental validation. All samples underwent uniform processing and rigorous quality control measures. The Harmony algorithm was then applied to mitigate technical batch effects. By integrating cells based on biological characteristics rather than sample origin, we are confident that the observed post-correction differences are not influenced by technical variability. Our RNA scope analysis of tooth germ development revealed a notable increase in DLX6-AS1⁺ cell numbers from the cap to the bell stage (Fig. 3d), supporting the expansion of this specific subpopulation identified in our scRNA-seq analysis. In conclusion, despite the limitations of dissociation-based methods, the combination of robust computational correction and experimental validation offers compelling evidence for the biological significance of the observed changes in cell proportions.\n17. Marker gene violin plots - Marker overlap between subpopulations (e.g., DLX6-AS1⁺ DP vs. NR2F1⁺ DP) is not quantitatively compared; could be transient states rather than distinct fates.\nResponse:\nWe thank the reviewer for the insightful question raised. To explore whether these subpopulations represent transient states or distinct fates, we merged our current analyses with a time-resolved quantitative comparison.\nOur developmental trajectory analysis (Fig. 3a) modeled cellular transitions and unveiled their dynamic relationship. The vector field depicted a bifurcation event, indicating the divergence of cells from a shared progenitor region into two separate trajectories.\nOur Volcano Plot analysis (Fig. 3e) revealed distinct transcriptional profiles between the DLX6-AS1⁺ DP and NR2F1⁺TWIST1⁺ DP populations, showcasing differences in the expression of hundreds of genes, particularly the exclusive expression of critical fate-determining factors.\nTo confirm the temporal stability of these distinct fates, bar plots were generated for key marker genes (DLX6-AS1, NR2F1, TWIST1, etc.), with cells stratified by developmental stage (Supplementary Fig. 4a). The analysis revealed significant and specific enrichment of DLX6-AS1 expression in the DLX6-AS1⁺ DP cluster at both early and late stages. In contrast, NR2F1 and TWIST1 expression remained consistently confined to the NR2F1⁺TWIST1⁺ DP cluster at both time points.\nIn conclusion, the integration of volcano plot, developmental trajectory analysis, and the demonstrated temporal stability of their unique marker profiles provides evidence that these subpopulations represent distinct cellular fates. The manuscript has been revised to incorporate this new analysis on Page 6, Lines 9-11.\nThe Supplementary Fig. 4a is presented below.\n18. Fig.3g. Please describe how the predicted interaction networks in panel g was analyzed and created, and what database was used in this analysis.\nResponse:\nWe express our gratitude to the reviewer for the inquiry. Fig. 3g depicts the temporal dynamics of differentially expressed genes within the DLX6-AS1⁺ DP population, not a predicted interaction network. The analysis aimed to identify genes significantly up- or down-regulated at specific developmental time points compared to others. The analysis utilized the Wilcoxon Rank Sum test in the Seurat package (26. Cell 2019), based solely on our dataset without external databases.\nRegarding the query on \"predicted interaction networks,\" it likely pertains to Fig. 3h. We acknowledge any ambiguity and provide a detailed methodology for Fig. 3h. This figure represents a gene co-expression analysis, not a database-derived interaction network. The analysis aimed to explore potential functional relationships by assessing co-expression of genes within the DLX6-AS1⁺ DP cells. Pairwise Pearson correlation coefficients were calculated for a curated gene list, and a chord diagram was generated using the circlize R package to visualize the correlation matrix. The links in the diagram indicate the strength of positive co-expression, with no external interaction database used.\nIn summary, Fig. 3g illustrates gene expression changes over developmental time, while Fig. 3h reveals genes that change together within the same cell population. The manuscript and figure legends have been revised to accurately describe Fig. 3h as a 'Gene Co-expression Network' on Page 6, Lines 38-39 and Page 30, Lines 12-13.\n19. In Supplementary Figure 3\n(b) mention the cluster name identified by Jing et al, 2022.\n(c) mention column names for the pathway enrichment analysis plot.\n(d) should be across three dental papilla subpopulations not three dental pulp population\nResponse:\nWe sincerely thank the reviewer for their meticulous review and constructive suggestions regarding Supplementary Fig. 3. We have carefully addressed each point and have revised the figure accordingly.\n(1) Supplementary Fig. 3b: We added a new UMAP plot that illustrates the dental papilla subpopulations (revised Supplementary Fig. 4b). The clusters have been annotated based on the established marker genes from Jing et al., 2022 (27. Nat Commun 2022).\n(2) Supplementary Fig. 3c: We have updated the pathway enrichment analysis plot to include the column names (revised Supplementary Fig. 4d).\n(3) Supplementary Fig. 3d: We have corrected the figure legend to accurately state \"three dental papilla subpopulations\" instead of \"three dental pulp populations\" (revised Supplementary Fig. 4e).\nThe revised Supplementary Fig. 4 is presented below.\n• Related to Figure 4\n20. Cell sorting method (DLX6-AS1 promoter–driven EGFP lentiviral reporter) may alter cell behavior. The WNT-NOTCH sequential stimulation is optimized using artificial, recombinant ligands. There is no attempt to mimic physiological gradients or signal durations. Jagged needs to be tethered or immobilized to activate Notch (to generate force needed for Notch cleavage). Did the authors utilize some type of Jagged-tethering system? Did they use Jagged soaked beads here (as they did in Fig.5)? Did the ligand (Jagged) activate Notch pathway in sensor cells.\nResponse:\nWe sincerely thank the reviewer for this series of critical and insightful questions regarding our experimental design. We have addressed each point below and revised the manuscript accordingly.\n(1) The potential effects of the cell sorting method: We agree that lentiviral transduction itself can potentially alter cell behavior. To rigorously control for this, we employed a specific empty vector, pLenti-Ubi-MCS-EGFP-IRES-Puromycin, as our control in the relevant experiments. It contained the same strong ubiquitin (Ubi) promoter and expresses the same reporter (EGFP) and selection marker (Puromycin resistance gene). Therefore, cells transduced with this empty vector underwent the same lentiviral infection, genomic integration, and puromycin selection pressure as the experimental group. Our results consistently showed that the phenotypic changes were observed only in the DLX6-AS1+ cells, but not in the negative control group transduced with the empty vector. This demonstrated that the observed effects were not the artifacts of the lentiviral system, EGFP expression, or the selection process. We have clarified this in the Methods section (Page 16, Lines 10-11) and relevant figure legends.\n(2) The artificial nature of the stimulation: This reductionist approach was intentionally chosen to isolate the specific roles of the WNT and NOTCH pathways and to dissect their sequential relationship, which would be difficult to achieve in a more complex environment. We have now explicitly acknowledged this limitation in our discussion section and highlighted that future studies using organoid models or advanced microfluidic systems could provide deeper insights into the spatio-temporal dynamics of this signaling crosstalk on Page 11, Lines 24-27.\n(3) The mechanism of NOTCH activation by Jagged1: In our experiments, we used a commercially available, soluble recombinant human Jagged-1 protein fused to a C-terminal human Fc tag (MCE, HY-P70399), expressed in HEK293 cells. We specifically selected this Fc-fusion protein because it is designed to overcome the low activity of soluble, monomeric ligands. The Fc tag induces spontaneous dimerization of the JAG1 protein. This dimeric structure is critical for efficiently clustering Notch receptors on the cell surface, thereby better mimicking the physiological presentation of the ligand and leading to robust pathway activation. This approach, using soluble Fc-chimeric ligands, is a well-established method for activating NOTCH signaling and has been successfully used to drive the expansion of hematopoietic stem cells (28. J Exp Med 2000) and to induce adipogenesis (29. Cell Prolif 2012). Crucially, we confirmed that this method was functionally effective in our specific cellular context.\nWe fully appreciate the nuance of the reviewer's point regarding the canonical, force-dependent activation model. To address this, we have expanded our Discussion. We now acknowledge the limitations of our approach while contextualizing it within the field's latest advancements. We highlight two recent, groundbreaking studies that are paving the way for even more sophisticated methods: the work by Daley, Baker, et al. on AI-designed soluble NOTCH agonists (30. Cell 2025), and a recent preprint from Ruohola-Baker's group employing similar tools.\nIn our revised Discussion on Page 11, Lines 27-30, we now state that while our study used an established method, the future of precisely manipulating this pathway likely lies in these next-generation, engineered soluble agonists. This represents an exciting future direction for the field and our own research.\n21. In line 199, the reference for the previous results from human tooth data is missing.\nResponse:\nWe appreciate the reviewer for highlighting the ambiguity in our phrasing and apologize for any confusion caused. Our aim was not to refer to a previously published paper but to establish a connection between these functional outcomes and the single-cell transcriptomic findings presented earlier in this manuscript. The expression \"our previous results from human tooth data\" was intended to denote our own preceding analysis (e.g., in Figures 1-3).\nTo eliminate this ambiguity and clarify the logical flow, we have revised the sentence in the manuscript on Page 7, Lines 11-14. The revised sentence now reads: \" DLX6-AS1+ DMSCs exhibited elevated expression of the odontoblast differentiation marker DSPP, as well as the WNT/NOTCH target genes LEF1 and HEY1, after a 21-day odontogenic induction (Fig. 4a), consistent with the gene signatures identified in precious analysis.\"\n22. Fig.4g. HEY1 protein quantification does not match the Western blot band intensity.\nResponse:\nWe appreciate the reviewer for raising this question. Upon thorough re-evaluation of our initial figure and the corresponding statistical outcomes, we confirm that the original bar graph depicted the mean ± SD derived from three independent biological experiments (n=3), with the image representing a singular representative result.\nTo address this point and to significantly enhance the clarity and transparency of our data presentation, we have revised Figure 4g. Specifically, we have now:\n(1) Replaced the single representative image with the Western blots from all three independent experiments.\n(2) Updated the bar graph to display the mean ± SEM.\n(3) Overlaid individual data points (dots) onto the bar graph to show the full data distribution from each replicate.\nThe updated Figure 4g and its corresponding legend have been incorporated into the revised manuscript.\nThe updated Figure 4g is presented below.\n23. Functional assays (e.g., ALP/ARS staining) show bulk mineralization, but do not confirm odontoblast-specific function or polarized secretion of dentin matrix proteins. Please add quantification for figure panel Fig.4d? Please mention the sample size, N=? normalized to?\nResponse:\nThe reviewer's insightful comment is appreciated. In response, quantitative analysis has been incorporated into our functional assays, based on three independent biological replicates (n=3) and normalized to the control group. In Figure 4d, both ALP and ARS staining were quantified in the mineralization assays. Additionally, the expression of the odontoblast marker DSPP was quantified to provide conclusive evidence of odontoblast-specific maturation, as depicted in Fig. 4e. New quantitative bar graphs have been included in both Fig. 4d and Fig. 4e, along with updated figure legends containing sample sizes and statistical details.\nThe new quantitative bar graphs are presented below.\n24. The WNT-JAG1 timeline requires further clarification. It does not match with the text in line 207. In the text, it was indicated that the treatment happened only one day for each WNT and JAGGED1. The control band of 4c does not match with 4a for the western blot.\nResponse:\nWe thank the reviewer for the inquiry and regret the previous lack of clarity. Our WNT-NOTCH sequential treatment involved continuous stimulation over multiple days, rather than a single-day intervention. Specifically, WNTs (WNT6, WNT7B, and WNT10B) were administered from day 1 and sustained for 7 days. Subsequently, JAG1 was added from day 8 and maintained for another 7 days, with regular medium replenishment every 2 days. To eliminate any confusion, we have revised the relevant passage on Page 7, Lines 18-24: “DLX6-AS1⁺ DMSCs were initially cultured in a medium supplemented with WNTs (WNT6, WNT7B, and WNT10B at a concentration of 10 ng/ml) from day 1 to day 7. Subsequently, on day 8, the culture medium was changed to one containing JAG1 (at 10 ng/ml), which was maintained until day 14. The growth factor-supplemented medium was refreshed every two days throughout the induction period to ensure continuous stimulation, as indicated by increased expression of target genes for both pathways in the western blot analysis (Fig. 4c).”\nRegarding the Western blots, Figures 4a and 4c represent two distinct and independent experiments designed, whereas Figure 4a was an experiment comparing DMSCs with DLX6-AS1+ DMSCs under a standard mineralization induction protocol. Figure 4c was an experiment designed to validate the effects of our novel sequential WNT-NOTCH sequential treatment specifically on DLX6-AS1+ DMSCs. Therefore, the \"control\" lane in Figure 4c represents DLX6-AS1+ DMSCs that received no treatment at all (i.e., no WNTs, JAG1, or standard mineralization induction). As the experimental setups vary, a direct comparison of these bands is deemed inappropriate. In order to avoid potential confusion, we have revised the figure legend for Fig. 4c to explicitly elucidate the meaning of the term \"control\" on Page 32, Lines 5-6.\n25. Fig.4j. Please add quantification (e.g., % positive cells); Ki67 increase could reflect proliferation unrelated to odontogenic commitment.\nResponse:\nWe appreciate the reviewer's valuable input. In compliance with the reviewer's suggestion, we have quantified the Ki67 immunofluorescence staining depicted in Fig. 4j. A new bar graph illustrating this quantitative information has been included in Fig. 4j, and the figure legend has been revised accordingly.\nIn the context of this proliferation, we posit that a moderate increase in progenitor cell proliferation during early differentiation stages is crucial for subsequent tissue development. The observed rise in proliferation coincided with a significant upregulation of the odontogenic marker DSPP. Hence, we interpret this expansion as a preparatory phase to ensure an adequate cell population for functional maturation. This clarification has been incorporated into the Discussion section of our manuscript to enhance the contextualization of the Ki67 data on Page 10, Lines 33-35.\n26. Co-culture experiments (panel f) use siRNA knockdown of WNT/NOTCH ligands in DE cells. How many siRNAs were designed for each? There might be off targets or other confounding factors, having multiple guides. Explain in the Fig4f what pre+ and post+ group samples mean? The treatment timeline is not clearly mentioned.\nResponse:\nWe thank the reviewer for these important questions. We have addressed each point below and made corresponding revisions to the manuscript.\n(1) To ensure the robustness of our knockdown experiments, we initially designed and synthesized three distinct siRNA sequences for each target gene (WLS and JAG1). Subsequently, we conducted in vitro testing on all three sequences to identify the most efficient one. The siRNA demonstrating the highest knockdown efficiency was chosen for all co-culture experiments depicted in Fig. 4f. The complete siRNA efficiency validation data has been included as Supplementary Fig. 5g.\nThe Supplementary Fig. 5g is presented below.\n(2) We sincerely apologize for the lack of clarity regarding the 'pre+ and post+' groups and the experimental timeline in the original manuscript. We have now added a detailed protocol to the Methods section on Page 16, Lines 32-37 and have comprehensively revised the legend for Figure 4f on Page 32, Lines 15-17 to provide a full explanation.\nThe 'pre+ and post+' group represents a sequential co-culture experiment designed to test the temporal requirement of WNT signaling followed by NOTCH signaling for DMSC differentiation. First, DE cells were seeded in lower wells and pre-treated with either siRNA targeting WLS (siRNA-WLS) or siRNA targeting JAG1 (siRNA-JAG1). The 'pre+ and post+' condition specifically involved a transfer of the upper chamber: Days 0-3.5 ('pre+' phase): The transwell upper chambers containing DLX6-AS1+ DMSCs were first co-cultured for 3.5 days with the DE cells that had been pre-treated with siRNA-WLS. Days 3.5-7 ('post+' phase): Subsequently, these same upper chambers were physically transferred to new wells containing DE cells that had been pre-treated with siRNA-JAG1 and were co-cultured for the remaining 3.5 days.\n27. In figure panel (g) and (i) Why the western blot graphs for fig 4a when compared to fig 4g look so different even though the expression patterns were more intense DPSC results. There was 3-fold increase in the DMSC for DLX6-AS1 when compared to DLX6-AS1-DPSC. The representative image for the western blot does not match the quantification.\nResponse:\nWe sincerely thank the reviewer for the meticulous examination of our data. Firstly, we apologize for the ambiguity in the presentation of Supplementary Fig. 4, which may have led to the misinterpretation of a \"3-fold increase in the DMSC for DLX6-AS1 when compared to DLX6-AS1-DPSC\". It is important to clarify that the experiments in Supplementary Fig. 4a and b were intended as separate validations, not for direct comparison. Each panel illustrates successful transduction in relation to its respective untreated control. Therefore, any perceived difference stems from comparing independently normalized fold-changes that are not directly comparable. For further clarification, please refer to the revised Supplementary Fig. 5c-d.\nSecondly, the original Fig. 4a and 4g displayed a single representative image from one experiment and a bar graph showing the mean ± SD from three biological replicates, potentially causing a perceived mismatch. To address this issue, we have made significant revisions to both figures. Specifically, we have:\n(1) Replaced the single representative image with Western blots from all three independent experiments. (2) Updated the bar graph to show the mean ± SEM. (3) Included individual data points overlaid onto the bar graph. The revised Fig. 4a and 4g, along with their respective legends, have been included in the revised manuscript.\n28. Fig.4i: What is the control sample?\nResponse:\nWe thank the reviewer for this important question. The \"control\" sample in Figure 4i represents DLX6-AS1+ DPSCs that received no subsequent treatment (i.e., no WNTs, JAG1, or mineralization induction medium). We have revised the legend for Figure 4i to clearly state the nature of this control group (Page 32, Lines 22-23).\n29. Similar issues as in DMSCs: functional maturity of odontoblasts is not confirmed.\nResponse:\nWe appreciate the reviewer for highlighting this crucial issue. In our previous manuscript, we conducted immunofluorescence staining for DSPP, a widely recognized marker for fully developed odontoblasts, to validate the functional maturation of DPSCs. In order to strengthen our findings, we have conducted a quantitative assessment of the DSPP fluorescence intensity derived from these experiments. This newly acquired quantitative data has been incorporated into the revised manuscript in the revised Fig. 5j.\n30. In Supplementary Figure 3c: When was the WNT and Jagged1 treatment given in this analysis? Mention whether it is pre or continuous.\nResponse:\nWe thank the reviewer for pointing out the need for clarification of our original Supplementary Fig. 4c. The 'WNTs' group received continuous stimulation with WNTs for 14 days. The 'JAG1' group received continuous stimulation with JAG1 for 14 days. The 'Pre WNTs Post JAG1' group was treated sequentially: first with WNTs for 7 days, and then with JAG1 for the following 7 days. We have now amended the legend to include this precise information, ensuring clarity for the reader. Please refer to the revised Supplementary Fig. 5f’s legend.\n• Related to Figure 5\n31. Histological evidence of dentin-like structure is based on morphology and staining (Masson’s, DMP1/DSPP), but no mechanical testing is done to assess dentin quality. Please perform microCT analysis to prove calcification or the dentin formation in the DLX6-AS1 DMSC pre-WNT+JAG1.\nResponse:\nWe sincerely thank the reviewer for this excellent and constructive suggestion. Following the reviewer's advice, we have performed micro-CT analysis on the recombinants.\nMicro-CT analysis of the recombinants revealed robust mineralized tissue formation exclusively in the DLX6-AS1⁺ DMSCs with Pre-WNTs+JAG1 treatment group (Fig. 5d). Quantitative analysis confirmed that mineral density was significantly elevated in this group compared to all other groups (Fig. 5e), highlighting the efficacy of the sequential stimulation strategy. We have now incorporated the micro-CT analysis and the corresponding quantitative data as a new figure panel in the manuscript (Fig. 5d). The detailed description of these findings has been added to the Results section on Page 8, Lines 35-37.\nThe Fig. 5d is presented below.\n32. Lack of negative controls: please test the effects of JAG1 or WNT alone (vs. sequential application).\nResponse:\nWe are very grateful to the reviewer for this critical question regarding our experimental design. To address this point, we have now conducted and analyzed experiments where DLX6-AS1⁺ DMSCs were treated under the following conditions: (1) with WNTs alone (WNTs group), and (2) with JAG1 alone (JAG1 group). Our findings clearly highlight the unique efficacy of the sequential protocol when compared to these single-treatment controls:\nJAG1 group: Treatment with JAG1 alone failed to induce any significant signs of odontogenic differentiation.\nWNTs group: The cells failed to progress to terminal differentiation and did not form substantial mineralized nodules.\nThese results demonstrate that WNTs and NOTCH signaling play distinct and non-interchangeable roles in a specific temporal order. We have now included this comparative data in Figure 5.\nThe Fig. 5 is presented below.\n33. IF for DMP1 and DSPP, while positive staining is shown, quantitative comparison across conditions is absent, no control for nonspecific expression.\nResponse:\nWe sincerely thank the reviewer for this important point. In response to this suggestion, we have now performed a quantitative analysis, and the results are presented as bar graphs in Supplementary Fig. 6b.\nTo address the reviewer's valid concern regarding nonspecific expression, we have also performed a crucial isotype control. As shown in Supplementary Fig. 6c, the isotype control group displayed only negligible background fluorescence.\nWe have updated the Results section and the corresponding figure legend to reflect these new data on Page 8, Lines 24-31.\nThe Supplementary Fig. 6b-c are presented below.\n34. Why wasn’t the JAG1 bead–only treatment included as a separate group? It is possible that JAG1 alone is sufficient to induce dentin formation.\nResponse:\nWe appreciate the reviewer for highlighting this important issue. To investigate whether JAG1 alone is effective, we treated DLX6-AS1⁺ DMSCs with JAG1 alone (JAG1 group). Our findings demonstrate that JAG1 alone does not suffice to stimulate dentin formation. The comparative data has been included in Fig. 5.\n• Related to Figure 6\n35. Please track after the delivery of JAG1 its bioavailability, degradation, and localization.\nResponse:\nWe sincerely thank the reviewer for this insightful question regarding the pharmacokinetics of the delivered JAG1. Our primary goal in the current study was to validate the biological principle of the sequential WNT/NOTCH signaling cascade in vivo. To achieve this, we employed a commercially available, soluble recombinant human JAG1 protein fused to a human Fc domain. It is well-established that the Fc domain significantly extends the in vivo half-life of recombinant proteins (31. Nat Rev Immunol 2007; 32. Nat Biotechnol 2005), and this direct administration approach is a common practice for validating pathway activation in vivo (28. J Exp Med 2000).\nTo address the questions, we conducted immunofluorescence staining for both the delivered JAG1 protein and its canonical downstream target, HEY1, at the site of administration. Our results show that JAG1 was localized to the target tissue, which was accompanied by upregulation of HEY1, providing the evidence that the delivered JAG1 was not only present but also biologically active and effectively engaging the NOTCH signaling pathway. The staining image is shown below.\nWe acknowledge its limitations for a full pharmacokinetic profile. We have addressed this point in our revised Discussion section, stating that the development of advanced delivery systems to improve localization and control release profiles is a crucial next step. Furthermore, we have contextualized this by highlighting the exciting future possibilities offered by next-generation biologics. As we mention, recent groundbreaking work on AI-designed soluble NOTCH agonists may provide molecules with enhanced stability and potency, offering a promising alternative to overcome the limitations of conventional protein delivery (30. Cell 2025). This discussion has been incorporated into the manuscript on Page 11, Lines 27-30.\n36. Micro-CT images lack volumetric or density measurements to confirm that mineralized tissue matches natural dentin.\nResponse:\nWe appreciate the reviewer for the suggestion. As advised, we have conducted an analysis on dentin mineralization. The new quantitative findings, accompanied by relevant graphs and statistical evaluations, are now included in Fig. 6c and detailed in the corresponding section of Results on Page 9, Lines 13-15.\nThe revised Fig. 6c is shown below.\n37. Bone volume fraction quantification BV/TV is a coarse metric; does not distinguish between dentin, osteodentin, or calcified scar tissue. Please use SEM/TEM to confirm tubular organization of regenerated dentin.\nResponse:\nWe express our gratitude to the reviewer for this insightful comment. To address this limitation, we conducted SEM analysis to examine the ultrastructure of the newly formed tissue. Our SEM images illustrate that the tissue developed in the DLX6-AS1⁺ DPSCs subjected to WNT-NOTCH sequential stimulation displayed a well-organized, tubular ultrastructure characteristic of natural dentin. These SEM images are included as Figure 6d and are elaborated upon in the Results section on Page 9, Lines 23-25.\nThe revised Fig. 6d is shown below.\n38. Histology and IF, while organized dentin bridge is reported, higher-resolution imaging (e.g., SEM/TEM) is needed to confirm tubular microstructure.\nResponse:\nWe thank the reviewer for emphasizing this crucial point. To address this limitation, we performed SEM. These SEM analyses provided a high-resolution view that confirmed the presence of a well-organized, tubular microstructure within the dentin bridge that was observed in our H&E and immunofluorescence images. This evidence can be found in the newly added Figure 6d and its corresponding description in the Results section on Page 9, Lines 23-25.\n39. Fig.6e: Confirm primers target human transcripts. Consider single-cell or lineage-resolved validation.\nResponse:\nWe thank the reviewer for these important points. We have addressed them as follows:\nTo confirm the species-specificity of the primers used in Figure 6, we have now performed a validation experiment. We tested the human-specific primers on cDNA templates derived from both human DPSCs and mouse cells. The results showed that a single, sharp melting curve peak was observed exclusively for the human cDNA template. No amplification signal was detected from the mouse cDNA template. This confirmed that our primers were specific to human transcripts and that the qPCR data accurately reflect gene expression changes within the transplanted human cells.\nRegarding the lineage-resolved validation, we would like to highlight a key feature of our experimental design that directly provides this level of evidence. The transplanted human DPSCs were genetically labeled with GFP, which serves as a definitive and permanent lineage tracer. Our immunofluorescence analysis demonstrates clear co-localization of the GFP signal with the DSPP signal (Supplementary Figure 7d). This GFP⁺/DSPP⁺ co-localization provides direct, lineage-resolved evidence at the protein level, conclusively showing that the newly formed reparative dentin originates directly from the differentiation of our transplanted human cells.\n40. Please site the reference (PMID: 40346090: Profiling and functional characterization of long noncoding RNAs during human tooth development) for the relevance of DLX6-AS1.\nResponse:\nWe sincerely thank the reviewer for highlighting this highly relevant reference. We are pleased to clarify that this paper is indeed our group's own previous foundational work that established the role of DLX6-AS1 in human tooth development.\nFollowing the reviewer's suggestion, we have now explicitly cited this work on Page 7, Line 1 to provide a stronger rationale for our current investigation. 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Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods 16, 1289-1296 (2019).\n14. Tran HTN, et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol 21, 12 (2020).\n15. Mantri M, et al. Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis. Nat Commun 12, 1771 (2021).\n16. Wu SZ, et al. A single-cell and spatially resolved atlas of human breast cancers. Nat Genet 53, 1334-1347 (2021).\n17. Zhao L, Song W, Chen YG. Mesenchymal-epithelial interaction regulates gastrointestinal tract development in mouse embryos. Cell Rep 40, 111053 (2022).\n18. Zhang R, et al. Integrated multi-omics profiling characterizes the crucial role of human dental epithelium during tooth development. Cell Rep 44, 115437 (2025).\n19. Seong J, et al. Epiblast inducers capture mouse trophectoderm stem cells in vitro and pattern blastoids for implantation in utero. Cell Stem Cell 29, 1102-1118 e1108 (2022).\n20. Roos FJM, et al. Human branching cholangiocyte organoids recapitulate functional bile duct formation. Cell Stem Cell 29, 776-794 e713 (2022).\n21. Zhou Y, et al. Notch2 regulates BMP signaling and epithelial morphogenesis in the ciliary body of the mouse eye. Proc Natl Acad Sci U S A 110, 8966-8971 (2013).\n22. Nguyen DX, et al. WNT/TCF signaling through LEF1 and HOXB9 mediates lung adenocarcinoma metastasis. Cell 138, 51-62 (2009).\n23. Cai S, et al. Nlrc3 signaling is indispensable for hematopoietic stem cell emergence via Notch signaling in vertebrates. Nat Commun 15, 226 (2024).\n24. Miao X, et al. Stage-Specific Role of Amelx Activation in Stepwise Ameloblast Induction from Mouse Induced Pluripotent Stem Cells. Int J Mol Sci 22, (2021).\n25. Sun Y, et al. Key proteolytic cleavage site and full-length form of DSPP. J Dent Res 89, 498-503 (2010).\n26. Stuart T, et al. Comprehensive Integration of Single-Cell Data. Cell 177, 1888-1902 e1821 (2019).\n27. Jing J, et al. Spatiotemporal single-cell regulatory atlas reveals neural crest lineage diversification and cellular function during tooth morphogenesis. Nat Commun 13, 4803 (2022).\n28. Karanu FN, et al. The notch ligand jagged-1 represents a novel growth factor of human hematopoietic stem cells. J Exp Med 192, 1365-1372 (2000).\n29. Ba K, et al. Jagged-1-mediated activation of notch signalling induces adipogenesis of adipose-derived stem cells. Cell Prolif 45, 538-544 (2012).\n30. Mout R, et al. Design of soluble Notch agonists that drive T cell development and boost immunity. Cell, (2025).\n31. Roopenian DC, Akilesh S. FcRn: the neonatal Fc receptor comes of age. Nat Rev Immunol 7, 715-725 (2007).\n32. Vaccaro C, Zhou J, Ober RJ, Ward ES. Engineering the Fc region of immunoglobulin G to modulate in vivo antibody levels. Nat Biotechnol 23, 1283-1288 (2005)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have adequately addressed my previous concerns.", "reviewer_2": "The authors clearly answered my questions and concerns. The current form of the revised manuscript was significantly improved and reads really well. Congratulations on your nice work."}, "author_response": "Response to reviewers\nReviewer #1\nThe authors have adequately addressed my previous concerns.\nResponse:\nWe would like to express our sincere gratitude to you for your rigorous review and thoughtful feedback on our manuscript. Your insights have been instrumental in strengthening the scientific rigor and clarity of our study, and we are pleased to learn that our revisions have adequately addressed your previous concerns.\nReviewer #2\nThe authors clearly answered my questions and concerns. The current form of the revised manuscript was significantly improved and reads really well. Congratulations on your nice work.\nResponse:\nThank you for your encouraging feedback and thoughtful evaluation. We are delighted that our responses to your questions and concerns have enhanced the manuscript, making it more coherent and impactful. Your recognition of the improved quality of the revised version is highly motivating, and we are grateful for your support of our work."}]

Decision letter