Single-Cell and Spatial Multi-Omics Unravel Estrogen-Driven Remodeling of the Prolapsed Uuterine Microenvironment via Fibroblast Reprogramming and Intercellular Communication

Single-Cell and Spatial Multi-Omics Unravel Estrogen-Driven Remodeling of the Prolapsed Uuterine Microenvironment via Fibroblast Reprogramming and Intercellular Communication | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Single-Cell and Spatial Multi-Omics Unravel Estrogen-Driven Remodeling of the Prolapsed Uuterine Microenvironment via Fibroblast Reprogramming and Intercellular Communication Xiao Liu, Lin Wang, Shuyu Wang, Lingyun Wei, Mengyu Geng, Wenzhen Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7983273/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The clinical paradox of local estrogen therapy in pelvic organ prolapse—its widespread application juxtaposed with inconsistent therapeutic outcomes—highlights a critical gap in our understanding of its actions within the native tissue architecture. To resolve this, we employed high-definition spatial transcriptomics, achieving a near-cellular resolution map of the postmenopausal vaginal wall. This spatial atlas directly visualized estrogen’s core mechanism: the orchestration of a structured HAS1 + fibroblast-pericyte reparative niche around vasculature. Estrogen directs the recruitment of POLR3G-driven, HAS1 + fibroblasts into this precise micro-anatomical location, enabling their functional coupling with pericytes. This co-localization facilitates a rewired fibroblast-pericyte signaling axis, enhancing pro-repair communication. Computational pharmacology further affirms this niche as a druggable functional unit. Our findings establish a new paradigm: estrogen's efficacy is not mediated by broad tissue stimulation, but through the precise spatial engineering of a multicellular repair unit, a mechanism unveiled only through high-definition spatial mapping and one that redefines the future of targeted therapeutic strategies. Biological sciences/Molecular biology/Transcriptomics Health sciences/Diseases/Urogenital diseases/Urinary incontinence Health sciences/Signs and symptoms/Reproductive signs and symptoms Pelvic organ prolapse Estrogen therapy Single-cell RNA-seq Spatial transcriptomics Fibroblast reprogramming Perivascular niche Tissue remodeling Cell-cell communication Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Pelvic organ prolapse (POP) is a common pelvic floor disorder that severely impacts the quality of life for middle-aged and elderly women and represents a significant public health burden. Epidemiological studies reveal that nearly half of all women exhibit some degree of anatomical POP 1 . For patients with severe symptoms, surgery is the primary treatment; however, the choice of surgical procedure is complex and carries a risk of recurrence 2 , with approximately 11–19% of patients requiring surgical intervention 3 . Notably, about 30% of surgical cases may require reoperation due to recurrence 1 , 4 , reflecting the chronic and refractory nature of the disease. Clinically, POP often presents with a range of symptoms such as pelvic pressure, urinary dysfunction, and decreased sexual quality of life, which not only harm patients' physical and mental health but also impose a substantial socioeconomic burden—for instance, the annual direct medical cost for POP in the United States exceeds $ 1.5 billion 5 . With the accelerating global aging population, the total number of patients is projected to increase by 46% to approximately 156 million by 2050 6 , underscoring the urgent need to elucidate its pathogenesis and develop effective prevention and treatment strategies. Although local estrogen therapy is incorporated into comprehensive POP management, its role presents a complex "double-edged sword" profile. Epidemiological data indicate that the postmenopausal low-estrogen state is associated with an increased risk of POP 7 , and local estrogen application can improve vaginal tissue thickness and collagen metabolism to some extent 8 . However, some clinical studies, particularly randomized controlled trials (RCTs), demonstrate limited efficacy in improving patients' subjective symptoms or achieving long-term anatomical restoration 9 , 10 . This disparity between basic research and clinical observation suggests an incomplete understanding of estrogen's mechanisms of action within the complex in vivo tissue microenvironment. A key bottleneck lies in the inability of traditional methodologies, such as tissue homogenate analysis or in vitro cell models, to resolve the highly heterogeneous cellular composition, cell-specific responses, and spatial interaction networks within the primary POP target tissue—the vaginal wall 11 – 13 . The pelvic floor connective tissue constitutes a complex ecosystem comprising various cell types, including fibroblasts, immune cells, and vascular cells. Estrogen receptor (ER) expression varies significantly across these cell types 11 , and the disrupted ERα/ERβ ratio observed in POP patients may predict differential responses to estrogen signaling among distinct cellular populations 11 , 14 . Fibroblasts, as the primary producers of the extracellular matrix (ECM) and core maintainers of tissue architecture, are considered one of the most critical targets for estrogen in regulating connective tissue homeostasis. This cellular heterogeneity is a major contributor to the individual variation in response to estrogen therapy. Yet, a systematic characterization of the functional heterogeneity of fibroblasts, particularly within the postmenopausal POP vaginal wall at single-cell resolution, is currently lacking. Furthermore, tissue function depends not only on cellular composition but also critically on its three-dimensional spatial architecture. Currently, knowledge regarding two pivotal questions remains limited: (1) How does estrogen influence the precise spatial localization of different cell types, especially functionally distinct fibroblast subpopulations, to reconstruct a reparative microenvironment? (2) How does estrogen remodel the short-range intercellular communication network to coordinate multicellular repair programs? A mechanistic understanding of these spatiotemporal dimensions is key to breaking through the current therapeutic impasse. Recent breakthroughs in single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics have provided powerful tools for systematically parsing the cellular atlas, state transitions, spatial neighborhoods, and cell-cell communication within complex tissues at a systems level 13 , 15 . Notably, technologies like Visium HD, with their near-cellular spatial resolution (e.g., 2 µm), enable unprecedented precision in mapping the in situ distribution of cellular subpopulations, thereby revealing fine structural units within the microenvironment. Moreover, emerging computational pharmacology approaches can link single-cell transcriptomic data with drug sensitivity, allowing direct prediction of potential targeted strategies based on disease mechanisms and significantly accelerating the translation from basic discovery to clinical application. Therefore, this study aims to construct the first integrated multi-omics atlas of the anterior vaginal wall in postmenopausal POP patients by leveraging 10x Genomics scRNA-seq and Visium HD spatial transcriptomics. To achieve this, we profiled the anterior vaginal wall tissues from a cohort of postmenopausal POP patients, either untreated or treated with local estrogen. Our study aims to systematically characterize the cellular heterogeneity and functional states of the major cell types, with a particular focus on fibroblast subpopulations, at single-cell resolution. We further seek to delineate the specific regulatory impact of estrogen on the proportion, differentiation trajectory, and transcriptional activity of these fibroblast subsets. Moreover, we endeavor to elucidate how estrogen reshapes the spatial distribution of cells and reprograms the intercellular signaling network. Finally, by integrating computational pharmacology, we will predict the targetability of the identified repair programs, thereby providing a robust cellular and molecular foundation for developing precise estrogen-targeted and novel therapeutic strategies beyond estrogen. 2. Results 2.1 Construction of a Multi-omics Atlas and Estrogen-Mediated Global Remodeling of the Cellular Community To systematically decipher the pathological microenvironment of postmenopausal pelvic organ prolapse (POP) and the intervention mechanisms of estrogen therapy at single-cell and spatial resolution, we performed 10x Genomics single-cell RNA sequencing (scRNA-seq) and Visium HD spatial transcriptomic sequencing on anterior vaginal wall tissues from POP patients. These included individuals either treated with topical estrogen (once daily for 6 weeks; E group, n = 6) or left untreated (C group, control, n = 5). Using Visium HD technology for in situ sequencing, we acquired spatial gene expression information by merging 2 x 2 µm barcoded spots into 8 x 8 µm analysis units. Subsequent multi-omics integration analysis (MIA) enabled the precise mapping of cell identities onto their tissue spatial locations, constructing the first single-cell and spatial integrated atlas of the postmenopausal POP vaginal wall (Fig. 1 A). All enrolled POP patients were classified as POP-Q stage III–IV (Table 1 ). Histological examination (H&E staining) confirmed the acquisition of full-thickness vaginal wall structures (Fig. 1 B), allowing us to capture cellular dynamics before and after treatment. Table 1 Baseline Characteristics of the Study Cohort Group POP(Control Group) POP with Estrogen(Estrogen-treated Group) P Patient P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 - Age (years) 67 69 79 65 70 66 66 79 73 72 68 0.838 Height (m) 1.58 1.58 1.58 1.64 1.57 1.62 1.60 1.52 1.55 1.58 1.59 0.510 Weight (kg) 53 60 59 56 65 66 86 53 64 54 64 0.301 Body mass index ( kg/m 2 ) 21.23 24.03 23.63 20.82 26.37 25.15 33.59 22.94 26.64 21.63 25.32 0.792 Menopause Duration (years) 15 14 28 10 20 16 24 25 20 20 10 0.657 G (Gravidity) 4 5 5 5 4 6 4 5 6 4 4 0.633 P (Parity) 3 5 5 3 4 4 4 5 5 4 2 1.000 Hypertension Yes Yes Yes No No Yes Yes No No Yes No 1.000 Diabetes Yes No Yes No No Yes Yes Yes No Yes No 0.782 Note: A total of 11 postmenopausal women with POP-Q stage III-IV were enrolled, including 5 in the control group (Group C) and 6 in the estrogen-treated group (Group E). Continuous variables (e.g., Age, BMI) were compared using the independent samples t-test. Categorical variables (e.g., Hypertension, Diabetes) were compared using the Chi-square test. All baseline characteristics showed no statistically significant differences between the two groups ( P > 0.05), indicating that the groups were comparable. Following stringent quality control, the scRNA-seq data yielded transcriptomes from 116,775 high-quality cells. Principal component analysis and UMAP clustering identified seven major cell types: epithelial cells, endothelial cells, fibroblasts, pericytes, T cells, mast cells, and mononuclear phagocytic cells (Fig. 1 C). Further subcluster analysis was performed to resolve finer heterogeneity (Fig. 1 D). The accuracy of cell type annotation was confirmed by differential expression gene analysis and the expression of canonical markers, such as high levels of COL1A1 in fibroblasts and PECAM1 in endothelial cells (Fig. 1 E, F; refer to Supplementary Table 1 for a complete list of cell types and their marker genes). Global cell proportion analysis revealed that estrogen treatment induced a significant remodeling of the cellular community. Compared to the control group, the estrogen-treated group exhibited a marked increase in the relative proportions of fibroblasts and T cells, while the proportion of pericytes was significantly decreased (Fig. 1 G) [8]. This shift was quantitatively confirmed by Ro/e analysis: estrogen treatment promoted the enrichment of fibroblasts (Ro/e: 0.80 vs. 1.15) and T cells (Ro/e: 0.42 vs. 1.45), while reversing the enrichment of pericytes observed in the POP disease state (Ro/e: 1.40 vs. 0.69) (Fig. 1 H). These results demonstrate that estrogen does not uniformly affect all cell populations but rather specifically modulates the balance between stromal cells (e.g., fibroblasts and pericytes) and immune cells (e.g., T cells) 16 , 17 . The significant change in the abundance of fibroblasts, as the primary producers of the extracellular matrix, highlights their role as a core target of estrogen in regulating tissue homeostasis 18 . This finding provides a clear rationale for our subsequent in-depth investigation into fibroblast heterogeneity and functional reprogramming. 2.2 Focusing on a Core Target: Estrogen Reprograms Fibroblasts and Induces a Shift Toward a Pro-Repair Phenotype Based on the observed overall expansion of fibroblasts, we hypothesized that estrogen's effect might be subpopulation-specific. To test this, we performed unsupervised sub-clustering on fibroblasts, which successfully partitioned them into five transcriptionally distinct subpopulations (Fig. 2 A). Crucially, cell proportion analysis revealed that estrogen did not uniformly promote the expansion of all subpopulations but exhibited high selectivity: the proportion of the Fibroblasts_HAS1 subpopulation significantly increased post-treatment, whereas the proportion of the canonical myofibroblast subpopulation, Fibroblasts_ACTA2, relatively decreased (Fig. 2 B, C) 19,20 . This indicates that the estrogen-driven expansion of fibroblasts essentially instigates a selective shift in cell state prevalence. To understand the functional implications of this proportional shift, we systematically annotated the functions of each subpopulation. Marker gene analysis showed that the Fibroblasts_HAS1 subpopulation highly expresses hyaluronic acid synthase 1 (HAS1), identifying it as a primary cellular source for hyaluronic acid synthesis in the tissue. This suggests a potential role in improving tissue tension through enhanced matrix hydration and modulating the immune microenvironment (Fig. 2 D, E). In contrast, the Fibroblasts_ACTA2 subpopulation exhibited typical myofibroblast characteristics, expressing α-smooth muscle actin (ACTA2), which is associated with tissue contraction and fibrosis. This estrogen-induced shift strongly implies that the treatment promotes the generation of a reparative matrix while concurrently suppressing aberrant fibrotic processes. To further confirm this functional switch at the global transcriptome level, we analyzed differentially expressed genes (DEGs) in fibroblasts upon estrogen treatment. A volcano plot showed significant expression changes in 543 genes (Fig. 2 F). Gene Ontology (GO) pathway enrichment analysis revealed that upregulated genes were significantly enriched in protein synthesis pathways such as "cytoplasmic translation" and "ribosomal structural constituent" (Fig. 2 G). Conversely, downregulated genes were enriched in fibrosis-related pathways, including "cellular response to TGF-β stimulus" and "extracellular matrix structural constituent" (Fig. 2 H) 8 . This opposing enrichment pattern provides compelling molecular functional evidence that estrogen successfully redirects the functional state of fibroblasts from "pro-fibrotic" to "pro-repair" by simultaneously activating protein anabolic metabolism and inhibiting TGF-β-driven fibrotic programs 19 , 21 2.3 Tracing Cell Fate: Estrogen Guides Fibroblast Differentiation via a Core Transcription Factor Having established the estrogen-induced transition in fibroblast states, we next sought to determine whether the acquisition of this reparative phenotype was a stochastic event or a precisely regulated differentiation process. To address this, we first assessed the developmental potential of cells using the CytoTRACE algorithm. The results demonstrated that fibroblasts from the estrogen-treated group exhibited a significantly lower overall differentiation state compared to the control group, presenting a "younger," more plastic phenotype (Fig. 3 A). This suggests that estrogen may preserve progenitor-like characteristics, thereby providing a ample cellular reservoir for differentiation into reparative phenotypes. We next employed Monocle3 to construct a pseudotemporal trajectory, providing a visual representation of the cell state transition paths. The trajectory analysis revealed that cells diverged from a relatively centralized starting point towards multiple distinct terminal states (Fig. 3 B). Critically, cells from the estrogen-treated group were significantly enriched in specific differentiation branches leading to reparative subpopulations, such as Fibroblasts_HAS1 (Fig. 3 C). This observation directly confirms that estrogen actively guides the direction of fibroblast differentiation, rather than passively selecting for pre-existing states 22 , 23 . Along this estrogen-guided trajectory, we systematically identified core gene clusters whose expression dynamically changed during the differentiation process (Fig. 3 D). These genes formed clear temporal expression modules: genes such as ACKR3 and PI16 decreased along the trajectory, constituting a "stemness/plasticity maintenance module"; whereas the expression of genes like APOD and MT-CO1 increased, forming a "terminal functional execution module." Genes associated with this trajectory were significantly enriched in pathways including "extracellular matrix organization" and "collagen metabolic process" (Fig. 3 E), indicating that this differentiation path is functionally coupled to tissue repair and remodeling. To identify the upstream drivers of this differentiation process, we analyzed the transcription factor (TF) regulatory network using SCENIC. The results revealed highly specific TF activation patterns across different fibroblast subpopulations (Fig. 3 F). Among these, POLR3G displayed the most prominent regulatory activity in the Fibroblasts_HAS1 subpopulation. Further regulatory specificity scoring confirmed that the regulatory module centered on POLR3G was the most definitive "identity determinant" for this subpopulation (Fig. 3 G). This indicates that estrogen precisely orchestrates the fate conversion of fibroblasts along the pseudotime trajectory towards a reparative phenotype by activating a specific transcriptional program dominated by POLR3G. 2.4 Locating the Repair Unit: Spatial Transcriptomics Reveals an Estrogen-Induced HAS1 + Fibroblast-Pericyte Niche The transition in cell state must ultimately be located within the three-dimensional space of the tissue to fulfill its physiological function. To validate the fibroblast state transition revealed by single-cell analysis in situ and to investigate whether estrogen restructures cellular spatial organization, we integrated Visium HD spatial transcriptomic data. Unsupervised clustering of the spatial data first revealed that tissue regions from the estrogen-treated group exhibited a transcriptomically distinct clustering pattern compared to the control group (Fig. 4 A), indicating that estrogen induces a systematic remodeling of the vaginal wall tissue, from its overall transcriptional state to its spatial architecture. Through spatial cell type deconvolution, we precisely mapped the in situ distribution of major cell types. Quantitative analysis confirmed that the relative proportion of the reparative Fibroblasts_HAS1 subpopulation significantly increased in the estrogen-treated group, while the proportion of the terminally differentiated Fibroblasts_C7 subpopulation correspondingly decreased (Fig. 4 B), a finding consistent with our scRNA-seq results. The most critical spatial evidence came from the precise localization of cellular subpopulations. We found that estrogen treatment induced a significant spatial repositioning of the Fibroblasts_HAS1 subpopulation: it shifted from a relatively dispersed distribution in the control group to a specific aggregation in regions highly enriched with pericytes, forming a tight spatial co-localization between the two (Fig. 4 C) 16 , 24 . Concurrently, the spatial distribution of the ACTA2-expressing myofibroblast subpopulation was markedly contracted. This spatial remodeling—"moving towards the repair center and away from fibrotic areas"—establishes a physical foundation for functional cellular collaboration. At the molecular level, our analysis of spatially variable genes revealed cell-type-specific molecular reprogramming induced by estrogen (Fig. 4 D). The expression signals of repair-related signature genes were significantly enhanced in situ in the Fibroblasts_HAS1 subpopulation, while some pro-fibrotic genes in the Fibroblasts_ACTA2 subpopulation showed a downregulation trend. Notably, pericytes spatially adjacent to Fibroblasts_HAS1 also exhibited coordinated changes in their gene expression profiles, suggesting functional coupling achieved through spatial proximity. To further characterize the spatial microenvironment of this repair unit, we performed spatial neighborhood analysis. The results showed that the Fibroblasts_HAS1 subpopulation formed stable spatial adjacencies with pericytes, mononuclear phagocytes, and T cells within the tissue (Fig. 4 E), collectively constituting a multicellular spatial niche. Based on the functional heterogeneity of fibroblasts and the potential for a repair phenotype switch suggested by our prior single-cell and spatial transcriptomic analyses, we hypothesized that estrogen induces a reparative fibroblast subpopulation possessing dual functional capabilities: "matrix synthesis" and "environmental sensing." To test this, we designed a multiplex immunofluorescence (mIF) experiment targeting the key hyaluronic acid synthase HAS1, the collagen receptor DDR2, and the myofibroblast marker α-SMA. The mIF results confirmed our hypothesis. A key spatial restructuring phenomenon was observed in the estrogen-treated group: within the luminal outlines formed by α-SMA + vascular structures, there was clear enrichment of DDR2 (yellow) and HAS1 (green) signals (Fig. 4 F). This indicates that the estrogen-induced HAS1 + DDR2 + reparative fibroblasts are not randomly distributed but are specifically localized to the perivascular region, congregating around structures constituted by vascular smooth muscle cells (α-SMA+). This location is a known hotspot for intercellular interaction and signal exchange. This phenomenon strongly suggests that estrogen activates the perivascular microenvironment and guides the recruitment of reparative cells to this specific anatomical site, thereby morphologically and spatially constructing a stem cell niche-like "perivascular repair niche." Within this unit, HAS1 + DDR2 + fibroblasts can simultaneously leverage nutritional support from the vasculature and, via DDR2 acting as a key sensor of the collagen microenvironment, couple with the chemotactic behavior of HAS1 + cells to form a structure-function integrated microenvironmental remodeling event. This provides crucial in situ protein-level evidence for the synergistic mechanism of the "fibroblast-pericyte axis" in POP tissue repair. Functionally, this niche resembles stem cell microenvironments reported in various tissues, providing necessary spatial anchoring and signaling support for repair cells 24 . Furthermore, in the estrogen-treated group, we successfully identified a cell population matching the predicted profile: they concurrently highly expressed HAS1 and DDR2 but did not express α-SMA (Fig. 4 F). This phenotypic signature confirmed at the protein level the existence of the hypothesized "functionally coupled repair cell." This cell type not only possesses the ability to construct a reparative matrix via hyaluronic acid synthesis but can also sense collagen microenvironment signals through DDR2, enabling dynamic regulation of its functional state. Their α-SMA-negative character further confirms that this population has not differentiated into pro-fibrotic myofibroblasts, thereby ensuring the repair process steers towards functional tissue reconstruction rather than pathological fibrosis. By integrating spatial transcriptomics with in situ immunofluorescence validation, this study systematically elucidates across multiple dimensions—tissue architecture, cellular composition, spatial distribution, and molecular expression—that estrogen not only reprograms fibroblast states at the single-cell level but also guides HAS1 + fibroblasts and pericytes to collectively build a structured repair niche within the tissue space. This discovery organically links cell state transition with spatial restructuring, providing solid spatial biological evidence for understanding the mechanism of estrogen-mediated microenvironment remodeling. 2.5 Decoding Intercellular Crosstalk: Estrogen Activates a Fibroblast-Pericyte Signaling Axis Building upon the spatial adjacency revealed by spatial transcriptomics, we next investigated whether this physical proximity translates into functional molecular crosstalk. Through systematic cell-cell communication analysis, we found that fibroblasts serve as a central hub within the global interaction network of the vaginal wall. Visualization analysis revealed dense and complex connection networks formed between various fibroblast subpopulations and pericytes, epithelial cells, and immune cells (Fig. 5 A, B). In-depth analysis of the functional properties of fibroblasts within this signaling network identified a dual role: they act as active signal senders, expressing various ligands (e.g., PI16, CXCL12) to transmit regulatory cues to neighboring cells, and as critical signal receivers, sensing microenvironmental signals through a rich repertoire of receptors (e.g., FTL, FTH1) (Fig. 5 C, D). Network centrality analysis further indicated that the signaling axis formed by fibroblasts and pericytes was particularly prominent within the overall communication network, suggesting this cellular pair may be a key signaling unit for maintaining tissue homeostasis. To quantify the impact of estrogen on specific cellular interactions, we performed a detailed ligand-receptor pair analysis. Although the Ro/e analysis indicated an overall decrease in the proportion of pericytes, it was surprising to find that in the estrogen-treated group, the number of interactions between Fibroblasts_HAS1 and pericytes significantly increased by approximately 25%, with a marked enhancement in interaction strength (Fig. 5 E, F, Supplementary Table 4) 19 , 25 . This finding perfectly correlates with the observed increase in their spatial proximity. Further molecular mechanism dissection revealed key signaling pathways mediating this enhanced interaction. We identified several specifically regulated ligand-receptor pairs, including TGFB1-TGFBR1, FTH1-SCARA5, among others (Fig. 5 G) 26 . Among these, the regulation of the TGF-β signaling pathway may influence the cellular fibrotic phenotype and the balance of tissue repair, while the interaction between FTH1 (ferritin heavy chain) and SCARA5 (scavenger receptor class A member 5) may be involved in iron metabolism homeostasis and cytoprotective processes. 2.6 Toward Precision Therapy: Computational Pharmacology Validates the Drug Targeting Potential of the Repair Unit Based on our preceding findings—that estrogen induces fibroblast differentiation into a HAS1 + reparative phenotype, guides its spatial positioning, and facilitates niche formation with pericytes—we posed a critical question: Does this highly coordinated program of cellular and spatial remodeling possess druggable potential? To address this, we utilized the BeyondCell computational platform to predict drug sensitivity based on our single-cell transcriptomic data. Initially, in the global drug sensitivity space, cells from the control and estrogen-treated groups exhibited markedly distinct distribution patterns (Fig. 6 A). This finding indicates that the estrogen-remodeled cellular microenvironment not only possesses a unique transcriptomic signature but also a specific "pharmacological identity," theoretically enabling targeted intervention. In-depth analysis of the global drug sensitivity patterns (Fig. 6 B) revealed that estrogen treatment significantly reshaped the cellular sensitivity profiles toward non-estrogen drugs. Crucially, however, cells in both treated and control groups consistently exhibited "low sensitivity" toward the three natural estrogens (estriol, estrone, and estradiol). This phenomenon strongly suggests that the overall therapeutic effect of estrogen is not achieved through transient, potent agonism of intracellular estrogen receptor signaling. Instead, it likely involves inducing a sustained, programmed remodeling of cell states and the microenvironment, whereby cells, after completing reprogramming, no longer exhibit high sensitivity to additional estrogen stimulation. This may represent a hallmark of repair program completion and the establishment of a new homeostasis. Focusing further on fibroblasts and pericytes, which were identified as core targets of estrogen regulation, we analyzed the cell-specific action patterns of different estrogen subtypes. In fibroblasts, all three natural estrogens showed low sensitivity, consistent with a mechanism where estrogen primarily induces state transition rather than direct, strong activation. Strikingly, in pericytes, estrone demonstrated clear high sensitivity (Fig. 6 C). This discovery is highly instructive, indicating that the overall therapeutic effect of estrogen may stem from the synergistic action of different subtypes on distinct cell populations: estradiol primarily drives fibroblast state reprogramming, whereas estrone, by acting directly on pericytes, indirectly supports the cooperative interactions of the fibroblast-pericyte axis. Further dissection of the sensitivity distribution patterns for different estrogen subtypes at single-cell resolution revealed that the biosensitivity score (BCS) distribution of estradiol exhibited unique characteristics: high BCS values were primarily enriched in control group cells, while low BCS values were significantly enriched in estrogen-treated group cells (Fig. 6 D). Quantitative analysis confirmed that compared to the control group, the BCS score for estradiol significantly decreased in the treated group, whereas the BCS scores for estrone and estriol showed opposite trends (Fig. 6 E). This differential pharmacological signature reveals a functional division of labor among estrogen subtypes within the repair program 27 . To validate the biological basis of the computational pharmacology predictions, we analyzed the key cell populations targeted by the sensitivity predictions via spatial transcriptomics. Spatial cell localization analysis confirmed the clear co-localization of HAS1 + fibroblasts and pericytes in estrogen-treated tissues (Fig. 6 F). In-depth spatial weight analysis further revealed the specialized spatial structures constructed by these cell populations during the repair process (Fig. 6 G), where pericytes exhibited focal enrichment around tubular structures, and Fibroblasts_HAS1 displayed a distribution pattern highly coordinated with that of pericytes. These findings complete a full scientific cycle from single-cell atlas to spatial localization, and finally to pharmacological validation. The results demonstrate that the therapeutic effect of estrogen is achieved by creating a structured and functional "HAS1 + fibroblast-pericyte" spatial functional unit. Furthermore, the computational pharmacology analysis confirms that this repair unit possesses tangible therapeutic potential for specific targeting, providing new targets and a theoretical basis for developing precision strategies that move beyond traditional estrogen therapy. 3. Discussion Pelvic organ prolapse (POP), a highly prevalent pelvic floor disorder, has long faced challenges in pharmacological treatment due to inconsistent therapeutic outcomes. Although epidemiological evidence strongly supports an association between the postmenopausal low-estrogen state and increased POP risk 7 , 28 , and local estrogen therapy is widely incorporated into clinical management guidelines, randomized controlled trials (RCTs) reveal considerable variability in its efficacy for improving patients' subjective symptoms and achieving long-term anatomical restoration, presenting a notable "double-edged sword" profile 9 , 10 , 29 , 30 . This disparity between basic research and clinical observation indicates persistent critical gaps in our understanding of estrogen's mechanisms of action within the complex, heterogeneous tissue microenvironment. Traditional research has largely relied on molecular biological analyses of tissue homogenates or simplified in vitro cell models 31 , 32 . While these approaches have successfully uncovered estrogen's effects on macroscopic processes such as collagen metabolism and fibroblast proliferation 18 , 33 , 34 , their fundamental limitation lies in the inability to resolve cellular heterogeneity within the tissue and their neglect of the three-dimensional spatial architecture essential for intercellular communication. Consequently, while we can observe the "net effect" following estrogen treatment, we remain unable to determine which specific cell populations execute these effects, what state transitions these populations undergo, or how they are spatially organized to coordinately achieve repair functions. The advent of single-cell transcriptome sequencing has recently provided new perspectives for analyzing tissue heterogeneity. Studies have identified significant individual variations in estrogen receptor expression in the pelvic floor tissues of POP patients 11 , 14 , as well as tissue-specific alterations in membrane receptors such as GPER 35 , 36 . More importantly, scRNA-seq studies have definitively identified functionally distinct fibroblast subpopulations within the vaginal wall 13 , while also revealing that infiltrating immune cells and their different polarization states in pelvic floor tissues are closely associated with estrogen regulation and tissue repair outcomes 16 , 37 . These findings highlight the limitations of traditional two-dimensional culture models in recapitulating complex cellular interactions 38 . Building on this knowledge, our study employed single-cell RNA sequencing and Visium HD spatial transcriptomics to overcome the limitations of traditional approaches 31 , 32 , 38 , marking a paradigm shift from "holistic observation" to "programmatic dissection." We not only systematically defined the cellular composition of the vaginal wall microenvironment but also precisely mapped their functional states 12 , 13 , 39 , differentiation trajectories, spatial coordinates, and communication networks 16 . Through systematic multi-omics integration, we propose a novel mechanistic model: estrogen exerts its therapeutic effect not by broadly stimulating tissue regeneration 18 , 34 , but by executing a set of precisely coordinated cellular programs—reprogramming key cell states 19 , reconstructing functional spatial niches at specific anatomical sites, and rewiring intercellular communication 15 , 16 —thereby guiding the disordered pathological microenvironment into an orderly repair program. 3.1 Functional Heterogeneity and State Reprogramming of Fibroblasts Within the pelvic floor connective tissue, fibroblasts, as the predominant cellular component and primary producers of the extracellular matrix (ECM), have long been a central focus in understanding POP pathophysiology 39 . However, a fundamental limitation of previous research has been the treatment of fibroblasts as a functionally homogeneous population that becomes uniformly activated in the pathological state. Substantial evidence indicates an increase in fibroblasts exhibiting myofibroblast characteristics in the pelvic support tissues of POP patients 19 , accompanied by aberrant activation of the TGF-β signaling pathway 26 , 40 , 41 and disordered collagen metabolism 31 , 42 , 43 . While this "pro-fibrotic" perspective partially explains the phenomenon of ECM remodeling imbalance in POP tissues, it creates a cognitive paradox: if estrogen merely acts as a simple stimulator of fibroblast proliferation 18 , 34 , it should logically exacerbate, rather than ameliorate, this fibrotic tendency. Our scRNA-seq analysis successfully unveiled the "heterogeneous veil" of fibroblasts in the POP vaginal wall, systematically identifying five transcriptionally distinct fibroblast subpopulations with divergent functional orientations. By comparing estrogen-treated and untreated groups, we found that estrogen does not function as an indiscriminate "global activator" 18,34 , but rather as a precise "cell state selector." Specifically, estrogen selectively and significantly enriched the Fibroblasts_HAS1 subpopulation, characterized by high expression of hyaluronic acid synthase 1, while relatively suppressing the canonical, ACTA2-rich myofibroblast subpopulation 19 . This finding is significant because hyaluronic acid, as a crucial ECM component, enhances tissue hydration, modulates immune responses, and promotes tissue repair—properties starkly contrasting with the mere collagen deposition that leads to fibrosis 42 , 43 . Consequently, our study fundamentally revises the simplistic view that "fibroblasts solely play a pro-fibrotic role in POP" 31,39 , revealing the existence of functionally antagonistic subpopulations within them and, for the first time, identifying the Fibroblasts_HAS1 subpopulation as a key cellular executor of estrogen-mediated tissue repair. To delve deeper into the mechanism of this estrogen-guided cell state conversion, CytoTRACE analysis revealed that fibroblasts in the estrogen-treated group overall maintained greater developmental potential and plasticity. The pseudotemporal trajectory constructed by Monocle3 further clearly delineated paths differentiating from a relatively primitive state towards multiple terminal subpopulations. Cells from the estrogen-treated group were significantly enriched in the branch leading to Fibroblasts_HAS1, strongly demonstrating that estrogen's role is to actively guide cell fate rather than passively select for pre-existing states. More importantly, through SCENIC transcription factor regulatory network analysis, we identified POLR3G as the most specific and active core transcription factor for the Fibroblasts_HAS1 subpopulation. These findings collectively outline a clear regulatory hierarchy: estrogen, by activating a specific transcriptional program dominated by POLR3G 21 – 23 , precisely navigates multipotent fibroblast precursor cells towards the differentiation track of a reparative phenotype. 3.2 Construction and Functional Specialization of the Spatial Niche While single-cell transcriptomics has revolutionarily revealed cellular heterogeneity 13 , a fundamental question remains unresolved: Where are these transcriptionally defined, repair-potent cell subpopulations precisely located within the complex tissue architecture? The current understanding of POP treatment mechanisms is almost entirely built upon the dimension of "altered cellular composition and state" 18,19,34 , critically lacking spatial validation. This gap prevents us from answering where precisely reparative events occur within the tissue and from understanding how different cell types couple physically and functionally through spatial proximity 16 . Our study, by integrating Visium HD spatial transcriptomics with multiplex immunofluorescence staining, successfully bridged this gap, linking the cell state changes revealed by single-cell analysis to their precise spatial localization within the tissue. Unsupervised clustering of the spatial transcriptomic data showed a significant spatial restructuring of the overall transcriptional state in estrogen-treated tissues. Through cell type deconvolution, we made a compelling discovery: under estrogen influence, the reparative Fibroblasts_HAS1 subpopulation did not diffuse uniformly but specifically aggregated towards perivascular regions highly enriched with pericytes, forming a tight spatial co-localization between the two 16 . Concurrently, the spatial distribution of the pro-fibrotic Fibroblasts_ACTA2 subpopulation was relatively contracted 19 . This dynamic spatial repositioning—"selective aggregation and concomitant contraction"—strongly indicates that estrogen actively reconfigures cellular spatial distribution, constructing a specialized "perivascular repair niche" in situ. To dissect the cellular and molecular basis of this niche, we performed in situ validation at the protein level. Immunofluorescence results demonstrated that the cells specifically enriched around vasculature post-estrogen treatment were a population concurrently expressing high levels of HAS1 and the collagen receptor DDR2, but lacking expression of α-SMA. This unique HAS1 + DDR2 + α-SMA- phenotype represents a novel class of repair cells functionally equipped with dual capabilities—"matrix synthesis" and "environmental sensing"—while successfully avoiding the "fibrotic" terminal fate. Specifically, HAS1 empowers these cells to synthesize hyaluronic acid, constructing a hydrated, elastic reparative matrix to improve tissue tension. DDR2, as a key collagen receptor, enables them to continuously sense mechanical and chemical signals from the surrounding collagen microenvironment, facilitating dynamic dialogue between the cell and the ECM. The α-SMA- phenotype clearly distinguishes them from terminally differentiated, contractile, tissue-hardening-driving myofibroblasts 19 . The combination of these three characteristics allows this cell type to achieve a complete in situ "sensing-response" functional loop at the perivascular hub, a nexus for nutrient and signal exchange. 3.3 Remodeling of the Cellular Communication Network and Coordinated Regulation While physical proximity between cells is a necessary condition for functional collaboration, it is not sufficient. We therefore investigated whether the estrogen-guided spatial co-localization translates into functional molecular crosstalk that coordinates the execution of the repair program. Previous research into the molecular mechanisms of POP, including extensive reports on the aberrant activation of classic signaling pathways such as transforming growth factor-β (TGF-β) 26,40,41 , has largely operated under an implicit assumption: that activity changes in these pathways are primarily cell-autonomous behaviors. For instance, studies have predominantly focused on expression changes of TGF-β receptors or downstream SMAD proteins within fibroblasts themselves 19 , 44 . This "cell-centric" perspective largely overlooks the crucial role of intercellular communication as an upstream regulator of signaling pathways. Our cell-cell communication analysis broke through this limitation, shifting the mechanistic understanding from the intracellular to the intercellular realm. The results demonstrated that fibroblasts act as the central hub of the signaling network within the vaginal wall tissue 39 . Most importantly, we found that estrogen's regulation of key signaling pathways is fundamentally achieved by remodeling the communication relationships between specific cell pairs. Taking the canonical TGF-β pathway as an example, although the overall proportion of pericytes decreased after estrogen treatment, the number and strength of interactions between the reparative Fibroblasts_HAS1 subpopulation and pericytes increased significantly by approximately 25%. This implies that estrogen does not simply increase or decrease the overall "volume" of TGF-β signaling across the tissue. Instead, akin to adjusting the "bandwidth" of a communication network, it specifically enhances the signal flux through the particular "communication channel" of the "fibroblast-pericyte" pair. Further molecular mechanism dissection through ligand-receptor pair analysis not only confirmed the specific regulation of known pathways, such as TGFB1-TGFBR1, within specific cell pairs but also identified a series of novel ligand-receptor pairs, like FTH1-SCARA5, previously underappreciated in the POP field. The interaction between FTH1, a key protein for iron storage and antioxidant stress, and the scavenger receptor SCARA5 suggests that iron metabolism homeostasis and cytoprotective mechanisms may be as integral to maintaining the function of the repair niche as the ECM remodeling we previously focused on 45 . The discovery of these non-canonical pathways indicates that the stability of the estrogen-constructed repair niche is supported by a diversified signaling network. 3.4 Therapeutic Paradigm Shift and Translational Prospects Building upon these discoveries, we face a critical translational question: how can these mechanistic insights be converted into actionable therapeutic strategies? Traditional POP drug development has long focused on the single dimension of "pathology suppression," for instance, by striving to develop TGF-β signaling inhibitors or matrix metalloproteinase inhibitors to block excessive ECM degradation or abnormal deposition 26 , 33 , 40 . However, such "anti-fibrotic" or "anti-catabolic" strategies have repeatedly encountered setbacks in clinical development. The fundamental reason lies in their sole aim to "block the bad" while failing to "promote the good" 20,46 —that is, they do not actively activate and support the endogenous, orderly repair programs within the tissue. Our BeyondCell computational pharmacology analysis offers a new perspective on this challenge. The analysis revealed that the cell microenvironment remodeled by estrogen exhibits a distribution in the global drug sensitivity space that is markedly distinct from the untreated group, implying it has acquired a unique "pharmacological identity." At single-cell resolution, different natural estrogen subtypes showed differential predicted sensitivities towards the distinct cell types constituting the repair unit: fibroblasts, which play a central role in driving repair, exhibited low sensitivity to estradiol, estrone, and estriol, consistent with a mechanism where estrogen primarily induces cell state reprogramming rather than directly and potently agonizing their proliferation 19 . In contrast, pericytes, acting as "collaborative partners" within the repair niche, displayed clear high sensitivity to estrone. This highly instructive discovery suggests that the overall therapeutic effect of estrogen may stem from the synergistic action of different subtypes on different cell populations: estradiol likely serves as the primary "instructional signal" initiating fibroblast reprogramming 22 , 47 , whereas estrone, by acting directly on pericytes, indirectly consolidates and supports the cooperative interactions and niche stability of the "fibroblast-pericyte axis". This cell type-specific pharmacological profile not only provides a fresh perspective for understanding the complex actions of estrogen but also theoretically demonstrates that the discovered "HAS1 + fibroblast-pericyte" repair unit is itself a biological entity with tangible potential for precise targeting 23 . Based on this new paradigm, future therapeutic strategies need no longer be confined to broad-spectrum hormone replacement or single-pathway inhibition. We can envision more precise interventions, including: developing small-molecule drugs capable of specifically activating POLR3G or its downstream network to directly drive fibroblast differentiation towards the reparative HAS1 + phenotype; utilizing biomaterial or chemokine strategies to mimic or enhance the "homing" of HAS1 + DDR2 + reparative cells to the perivascular niche 48 , 49 ; and designing combination regimens incorporating different estrogen subtypes or specific receptor modulators based on cell-specific pharmacological profiles 27 . 4. Conclusion and Perspectives This study, through the integration of single-cell and spatial multi-omics, has for the first time systematically delineated a holistic picture of estrogen-mediated remodeling of the vaginal wall microenvironment in postmenopausal POP patients. Based on these findings, we have constructed a novel and coherent mechanistic model. This model clearly delineates the sequential chain of estrogen's therapeutic action: it is initiated by the precise reprogramming of cell states, where estrogen activates a specific transcriptional program centered on POLR3G to direct fibroblast differentiation from a pro-fibrotic fate towards the HAS1 + reparative phenotype 19 . Subsequently, in the spatial dimension, it actively organizes these reparative cells, guiding their specific homing to the perivascular region to co-construct a structured "repair niche" with pericytes 16 . Finally, it enhances cooperative cellular communication, specifically strengthening interactions at key signaling hubs like the "fibroblast-pericyte axis," thereby converting spatial proximity into functional synergy to efficiently execute the tissue repair program 26 , 40 . This multi-level integrated model not only provides a new theoretical explanation for understanding the individual variation in the clinical efficacy of estrogen from cellular and spatial perspectives but, more importantly, represents a fundamental paradigm shift in concept. Future research should focus on: (1) utilizing tools such as gene editing and organoids in cellular and animal models to validate the causal roles of core regulatory factors in driving reparative differentiation and spatial homing 50 ; (2) developing precision strategies capable of specifically targeting the repair niche, including designing small-molecule agonists targeting POLR3G, developing biomaterial scaffolds that mimic cellular homing behavior 48 , 49 , and designing optimal estrogen subtype combination therapies based on cell-specific pharmacological profiles 27 ; and (3) validating the multi-omics signature established in this study across larger cohorts 13 to construct a biomarker system capable of predicting patient responsiveness to estrogen therapy 9 , 29 . Ultimately, the value of this study lies in completing a full scientific cycle from mechanistic dissection to theoretical innovation, and further to pathway planning. It not only provides a new spatial biological framework for understanding estrogen's action but, more significantly, lays a solid theoretical foundation and points towards promising clinical translation directions for developing next-generation pelvic floor regenerative medicine strategies that move beyond traditional hormone replacement towards truly etiology-targeted therapies. Methods Ethics statement and sample collection This study was approved by the Institutional Review Board of Shanxi Bethune Hospital, and written informed consent was obtained from all participants. A total of 11 postmenopausal women with pelvic organ prolapse (POP) were enrolled, including 5 untreated patients (Prolapse group) and 6 patients treated with topical promestriene (Prolapse + Estrogen group) once daily for 6 weeks. Vaginal anterior wall tissues were collected during surgery and immediately processed for single-cell and spatial transcriptomic analysis. Tissue dissociation and single-cell suspension preparation Fresh tissue samples were stored in sCelLiveTM Tissue Preservation Solution (Singleron) on ice within 30 minutes after surgery. Tissues were washed three times with Hanks Balanced Salt Solution (HBSS), minced into small pieces, and digested with 3 mL sCelLiveTM Tissue Dissociation Solution (Singleron) using the Singleron PythoNTM Tissue Dissociation System at 37°C for 15 minutes. The cell suspension was filtered through a 40-µm sterile strainer. Red blood cells were lysed using GEXSCOPE® Red Blood Cell Lysis Buffer (RCLB, Singleron) at a volume ratio of 1:2 (cell pellet:RCLB) for 5–8 minutes at room temperature. The mixture was centrifuged at 300 × g at 4°C for 5 minutes, and the pellet was resuspended in PBS. Cell viability was assessed using Trypan Blue staining and microscopic evaluation. Single-cell RNA sequencing library preparation Single-cell suspensions were adjusted to a concentration of 2 × 10^5 cells/mL in PBS. Cells were loaded onto a microwell chip using the Singleron Matrix® Single Cell Processing System. Barcoding beads were collected from the chip, and reverse transcription was performed to synthesize cDNA from mRNA captured by the beads. The cDNA was amplified by PCR, fragmented, and ligated with sequencing adapters using the GEXSCOPE® Single Cell RNA Library Kit (Singleron). Libraries were quantified, diluted to 4 nM, pooled, and sequenced on an Illumina NovaSeq 6000 platform with 150 bp paired-end reads. Spatial transcriptomics library preparation Fresh frozen vaginal tissue sections (10 µm thickness) were mounted on Visium HD slides (10x Genomics). Tissue permeabilization was optimized to release RNA, which was captured by spatially barcoded oligonucleotides on the slide. cDNA synthesis, amplification, and library construction were performed according to the Visium HD Spatial Gene Expression protocol (10x Genomics). Libraries were quantified and sequenced on an Illumina NovaSeq 6000 platform. Sequencing Both scRNA-seq and spatial transcriptomics libraries were sequenced on the Illumina NovaSeq 6000 system using 150 bp paired-end reads. Sequencing depth aimed for at least 50,000 reads per cell for scRNA-seq and 50,000 reads per spot for spatial transcriptomics. Single-cell RNA-seq data processing Raw sequencing data were processed using the CeleScope pipeline (v1.9.0, Singleron). Briefly, low-quality reads and adapter sequences were trimmed using Cutadapt (v1.17). Cell barcodes and UMIs were extracted and corrected. Clean reads were aligned to the GRCh38 reference genome (Ensembl version 92) using STAR (v2.6.1a). Gene expression counts were generated using featureCounts (v2.0.1). The output was a gene-by-cell expression matrix for downstream analysis. Quality control and clustering of scRNA-seq data The Seurat R package (v5.0) was used for quality control and clustering. Cells with fewer than 200 genes or more than the top 2% of gene counts were filtered out. Mitochondrial gene content exceeding 10% was also excluded. After filtering, 116,775 high-quality cells were retained. Gene expression was normalized using the NormalizeData function, and variable genes were identified with FindVariableFeatures (top 2000 genes). Principal component analysis (PCA) was performed, and the top 20 principal components were used for clustering with the FindClusters function (resolution = 0.5). Cell clusters were visualized using UMAP. Cell type annotation Cell types were annotated based on the expression of canonical marker genes from the SynEcoSys™ database (Singleron) and published references. Major cell types identified included epithelial cells, endothelial cells, fibroblasts, Pericytes, T cells, mast cells, and mononuclear phagocytes. Fibroblast subclusters were further annotated using subsetting and reclustering. Differential expression and pathway enrichment analysis Differentially expressed genes (DEGs) between groups were identified using the FindMarkers function in Seurat with the Wilcoxon rank-sum test. Genes expressed in more than 10% of cells in both groups and with an average log2 fold change > 0.25 were considered significant. Adjusted p-values were calculated using Bonferroni correction. Pathway enrichment analysis was performed using the clusterProfiler R package (v3.16.1) for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. Pathways with adjusted p-value < 0.05 were considered significantly enriched. Trajectory inference analysis Cell differentiation trajectories were inferred using CytoTRACE (v0.3.3) to estimate differentiation potential. Pseudotime analysis was performed with Monocle2 (v2.10.0) or Monocle3 (v1.0.0) on fibroblast subpopulations. Highly variable genes were used to order cells along trajectories, and branch points were visualized using UMAP. Cell-cell interaction analysis CellChat (v0.0.2) was used to infer intercellular communication networks. Ligand-receptor interactions were evaluated based on a curated database. Interaction strength was calculated and visualized using circle plots or heatmaps. Specific interactions between fibroblast subpopulations and Pericytes were highlighted. Spatial transcriptomics data processing Spatial gene expression data were processed using Space Ranger (10x Genomics) to generate count matrices. Data were analyzed in Seurat (v5.0) using the Load10X_Spatial function. Spots with fewer than 10 UMI counts were filtered out. Gene expression was normalized and scaled. Dimensionality reduction was performed using PCA, and clustering was done with FindClusters. Spatial gene expression was visualized using SpatialFeaturePlot. Integration of scRNA-seq and spatial transcriptomics data Integration was performed using the Seurat integration pipeline. Anchor points between scRNA-seq and spatial data were identified with FindTransferAnchors. Cell type labels from scRNA-seq were transferred to spatial spots using TransferData. The proportion of cell types per spot was calculated and visualized with SpatialDimPlot. Spatial deconvolution was also validated using RCTD (Robust Cell Type Decomposition) in doublet mode. Statistical analysis All statistical analyses were performed in R (v4.1.0). Group comparisons were conducted using non-parametric tests (Wilcoxon test for two groups). P-values < 0.05 were considered statistically significant. Visualization was done using ggplot2, pheatmap, and Seurat plotting functions. Single-cell drug susceptibility assessment To assess drug sensitivity profiles at single-cell resolution, we performed computational analysis using the R package Beyondcell (version 1.2.1). Drug perturbation signatures from the built-in Beyondcell database were applied to our scRNA-seq dataset. Data preprocessing, including normalization and correction for the number of detected genes per cell, was conducted following the developer's recommendations. The analysis generated a Beyondcell score for each cell-drug pair, which quantifies the predicted sensitivity. Histological Analysis (H&E Staining) For histological evaluation, portions of fresh vaginal anterior wall tissues were fixed in 4% paraformaldehyde for 24 hours at room temperature, followed by dehydration, clearing in xylene, and embedding in paraffin. Tissue sections were cut at 5 µm thickness using a microtome (Leica, Germany). After deparaffinization and rehydration, sections were stained with Harris Hematoxylin (Sigma-Aldrich) for 5 minutes, rinsed, differentiated in 1% acid ethanol, and blued in Scott's tap water. Eosin Y (Sigma-Aldrich) was applied for 2 minutes as a counterstain. Sections were then dehydrated, cleared, and mounted with neutral balsam (Sigma-Aldrich). Slides were scanned using a digital slide scanner (e.g., Nikon Eclipse Ci-L) or imaged with a bright-field microscope (e.g., Olympus BX53). H&E staining was used to verify the integrity of full-thickness vaginal wall architecture, including the epithelium, lamina propria, and muscularis layers. Multiplex Immunofluorescence Staining and Imaging To validate transcriptomic findings, multiplex immunofluorescence (mIF) was performed on formalin-fixed, paraffin-embedded vaginal wall sections using tyramide signal amplification (TSA). Sections underwent deparaffinization, rehydration, and antigen retrieval. After blocking, a sequential staining protocol was applied for three targets: HAS1, α-SMA, and DDR2. Each staining cycle included incubation with a primary antibody (1:5000) at 4°C overnight, followed by an HRP-conjugated secondary antibody and a fluorophore-conjugated TSA reagent. Antibodies were eluted between cycles to prevent cross-reactivity. Nuclei were counterstained with DAPI, autofluorescence was quenched, and sections were mounted with anti-fade medium. Slides were scanned using a digital slide scanner, and high-resolution multispectral images were acquired for co-localization analysis. Declarations Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions Lin Wang and Xiaochun Liu conceived and designed the study. Lin Wang, Shuyu Wang, Lingyun Wei, and Mengyu Geng were responsible for sample collection and processing. Lin Wang, Shuyu Wang, and Wenzhen Wang performed the experimental work and data acquisition. Lin Wang, Shuyu Wang, Mengyu Geng, and Xiaochun Liu contributed to data analysis and interpretation. The manuscript was drafted by Lin Wang and critically reviewed and revised by all authors. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work. Funding This work was supported by the Shanxi Provincial Science and Technology Innovation Talent Team Project (No. 202204051002031), the 2025 Central Guidance Fund for Local Science and Technology Development Projects (No. YDZJSX2025D076), the National Natural Science Foundation of China (Grant No. 81971365), the Shanxi Province Selective Funding Program for Scientific and Technological Activities of Returned Scholars (Grant No. 2025058), and the 2024 Traditional Chinese Medicine Research Project (Approval No.: 2024ZYY2C037). Acknowledgments The authors gratefully acknowledge the patients who participated in this study and the clinical staff involved in sample collection. We also thank our colleagues from the Third Hospital of Shanxi Medical University and Shanxi Bethune Hospital for their insightful discussions and technical support. Special thanks are extended to the staff at the Singleron and 10x Genomics sequencing platforms for their professional assistance. Data Availability Statement The raw sequencing data (single-cell RNA-seq and spatial transcriptomics) generated in this study have been deposited in the NCBI BioProject database under the accession number PRJNA1347381. All other data supporting the findings of this paper are available within the article and its supplementary materials, or from the corresponding author upon reasonable request. AI-Generated Content The authors declare that no AI-generated content was used in the preparation of this manuscript. References Weintraub, A. Y., Glinter, H. & Marcus-Braun, N. Narrative review of the epidemiology, diagnosis and pathophysiology of pelvic organ prolapse. Int. Braz. J. Urol. 46 , 5–14 (2020). Jeon, M. J. Surgical decision making for symptomatic pelvic organ prolapse: evidence-based approach. Obstet. Gynecol. Sci. 62 , 307 (2019). 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Recreating the female reproductive tract in vitro using iPSC technology in a linked microfluidics environment. Stem Cell Res. Ther. 4 , S13 (2013). Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7983273","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":537654274,"identity":"e23faf13-41e1-4861-a90d-2a698190c203","order_by":0,"name":"Xiao Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYJACZoYCCQZ+ZubDD4hSzgPWYiDBINnOlmZAihYGBoPzPAoSRGmxl8gx/FxgYJG4+TAPUGeNTTRhWyTSkqVnGEgkbjvMe+ABw7G03AbCWpKPMfOAtfAlGDA2HCZGS2IbWMvmZiBJpBaoLRuYidZy5lmyNFCx8YzDwEBOIMYv7O3AEOOpqJPt7z98+MGHGhvCWhgEEpA4CTgUoQL+A0QpGwWjYBSMgpEMACnwNfbKYxDVAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-4137-9366","institution":"Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, 030032, China","correspondingAuthor":true,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Liu","suffix":""},{"id":537654275,"identity":"2a133ff6-58d7-4038-8831-5136320a24af","order_by":1,"name":"Lin Wang","email":"","orcid":"","institution":"Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, 030032, China","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Wang","suffix":""},{"id":537654276,"identity":"6a3180c7-ead5-4503-ab05-1f21cef2e950","order_by":2,"name":"Shuyu Wang","email":"","orcid":"","institution":"Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, 030032, China","correspondingAuthor":false,"prefix":"","firstName":"Shuyu","middleName":"","lastName":"Wang","suffix":""},{"id":537654277,"identity":"3efa497d-2b85-417f-9227-5bc17fc9e253","order_by":3,"name":"Lingyun Wei","email":"","orcid":"","institution":"Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, 030032, China","correspondingAuthor":false,"prefix":"","firstName":"Lingyun","middleName":"","lastName":"Wei","suffix":""},{"id":537654278,"identity":"6cc385c1-dddc-4ad4-84ca-51fab5aa2908","order_by":4,"name":"Mengyu Geng","email":"","orcid":"","institution":"Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, 030032, China","correspondingAuthor":false,"prefix":"","firstName":"Mengyu","middleName":"","lastName":"Geng","suffix":""},{"id":537654279,"identity":"3473f673-7936-4e19-a612-3615dfa5d896","order_by":5,"name":"Wenzhen Wang","email":"","orcid":"","institution":"Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, 030032, China","correspondingAuthor":false,"prefix":"","firstName":"Wenzhen","middleName":"","lastName":"Wang","suffix":""},{"id":537654280,"identity":"3d1ca194-acbc-4a7e-a99f-d27a9fb90ef6","order_by":6,"name":"Nan Jia","email":"","orcid":"","institution":"Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, 030032, China","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Jia","suffix":""}],"badges":[],"createdAt":"2025-10-29 21:05:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7983273/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7983273/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94984779,"identity":"a296fd18-c9b5-4e46-bd00-f0a0007c91cc","added_by":"auto","created_at":"2025-11-03 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08:57:56","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":202511,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7983273/v1/17bcd60363da173ade23426c.html"},{"id":94838783,"identity":"eadffbb0-c4c5-4907-b1ab-e062464d6f0d","added_by":"auto","created_at":"2025-10-31 08:57:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":352766,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of a multi-omics atlas of the postmenopausal POP vaginal wall and estrogen-mediated global remodeling of the cellular community. \u003c/strong\u003e(A) Schematic overview of the experimental and analytical workflow for constructing the single-cell and spatial integrated atlas. Anterior vaginal wall tissues from postmenopausal POP patients, either untreated (Control, Group C, n=5) or treated with topical estrogen (Estrogen-treated, Group E, n=6), were subjected to scRNA-seq and Visium HD spatial transcriptomics, followed by multi-omics integration analysis. (B) Representative H\u0026amp;E staining of a full-thickness vaginal wall tissue section, confirming the preservation of tissue architecture. (C) UMAP projection of 116,775 high-quality cells from integrated scRNA-seq data, colored by the seven major cell types identified. (D) UMAP projection of cells following unsupervised sub-clustering of major cell types, revealing multiple functionally distinct cellular subpopulations, including epithelial, fibroblast, and immune cell subsets. (E) Heatmap displaying the expression of top canonical marker genes used for annotating the major cell types. (F) Violin plots showing the expression distribution of representative marker genes across the annotated cell types. (G) Stacked bar plot illustrating the global shifts in the relative proportions of major cell types between Group C and Group E. (H) Ro/e (Ratio of observed to expected) analysis quantifying the enrichment or depletion of specific cell types in response to estrogen treatment. An Ro/e \u0026gt;1 indicates enrichment, while \u0026lt;1 indicates depletion.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7983273/v1/56f44d5abc468614e27c57c8.jpg"},{"id":94984998,"identity":"fd15d116-aa8c-44de-987d-a74573fc25c7","added_by":"auto","created_at":"2025-11-03 06:57:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":347681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstrogen reprograms fibroblast heterogeneity and induces a shift toward a pro-repair phenotype.\u003c/strong\u003e (A) UMAP projection of fibroblasts from the integrated dataset, revealing five transcriptionally distinct subpopulations. (B) Stacked bar plot showing the relative abundance of each fibroblast subpopulation across all 11 individual samples. (C) Stacked bar plot depicting the composition of fibroblast subpopulations in control (Group C) and estrogen-treated (Group E) groups. (D) Heatmap of the top marker genes defining the five fibroblast subpopulations. (E) Violin plots showing the expression distribution of representative marker genes (e.g., HAS1, ACTA2) across the fibroblast subpopulations. (F) Volcano plot displaying the differentially expressed genes (DEGs) in fibroblasts after estrogen treatment. (G) Biological processes significantly enriched among the upregulated genes. (H) Biological processes significantly enriched among the downregulated genes.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7983273/v1/f0149798d9787400048b46b9.jpg"},{"id":94838785,"identity":"26beb43c-99ee-4aaf-a2e6-a68e9dc92790","added_by":"auto","created_at":"2025-10-31 08:57:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":372596,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstrogen guides fibroblast differentiation towards a pro-repair phenotype via the core transcription factor POLR3G. \u003c/strong\u003e(A) Box plot comparing the CytoTRACE-predicted differentiation states of fibroblasts between Group C and Group E, indicating that estrogen treatment maintains fibroblasts in a less differentiated, more plastic state. (B) Pseudotemporal trajectory of fibroblasts inferred by Monocle3, showing the transition paths from a central starting point to multiple distinct terminal states. (C) Distribution of cells from the five distinct states along the pseudotemporal trajectory, showing the specific enrichment patterns of cells from Group C and Group E across different states. (D) Heatmap of core gene clusters with expression patterns dynamically changing along the pseudotime trajectory. (E) Gene Ontology (GO) enrichment analysis of the trajectory-associated genes, revealing significant enrichment in pathways related to extracellular matrix organization and collagen metabolic process. (F) Transcription factor (TF) regulatory activity across fibroblast subpopulations analyzed by SCENIC, highlighting specific TF activation patterns. (G) Regulatory specificity scores of key TFs.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7983273/v1/f605fb1c6adf216bcf467698.jpg"},{"id":94838788,"identity":"5bf58d36-8306-4942-b276-be2d71d11f66","added_by":"auto","created_at":"2025-10-31 08:57:56","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":420226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial transcriptomics and immunofluorescence validation reveal an estrogen-induced HAS1+ fibroblast-pericyte repair niche. \u003c/strong\u003e(A) Spatial clustering of vaginal wall tissues from representative control (Group C, left) and estrogen-treated (Group E, right) samples, showing distinct transcriptomic patterning induced by estrogen. (B) Quantitative analysis of cell type proportions deconvoluted from spatial transcriptomics data, confirming the increase in Fibroblasts_HAS1 and decrease in Fibroblasts_C7 after estrogen treatment. (C) Spatial distribution maps of key cell types. Estrogen induces co-localization of Fibroblasts_HAS1 (red) with pericytes (blue), forming a structured niche, while contracting the distribution of Fibroblasts_ACTA2+ myofibroblasts. (D) Heatmaps of spatially variable genes in control and estrogen-treated tissues, showing cell-type-specific molecular reprogramming in the repair niche. (E) Spatial weight analysis illustrating the specialized microenvironment constructed by Fibroblasts_HAS1, pericytes, mononuclear phagocytes (MPs), and T cells. (F) Multiplex immunofluorescence (mIF) validation. Co-localization of HAS1 (green, matrix synthesis) and DDR2 (yellow, environmental sensing) signals around α-SMA+ (magenta, vasculature) structures in the estrogen-treated group, confirming the formation of a HAS1+DDR2+α-SMA- reparative fibroblast population within a perivascular repair niche.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7983273/v1/ff8a8a28227213ae860bf60a.jpg"},{"id":94984624,"identity":"754c2d20-cbd4-4336-9cac-003d30b05431","added_by":"auto","created_at":"2025-11-03 06:54:26","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":389054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstrogen activates a fibroblast-pericyte signaling axis by remodeling intercellular communication. \u003c/strong\u003e(A) Circle plot visualizing the global cell-cell communication network among major cell types, demonstrating fibroblasts as a central signaling hub. (B) Heatmap summarizing the number of significant ligand-receptor interactions between different cell type pairs. (C, D) Functional characterization of fibroblast signaling capabilities. (C) Dot plot showing the top ligands significantly expressed by fibroblast subpopulations. (D) Dot plot showing the top receptors significantly expressed by fibroblast subpopulations. (E) Chord diagrams comparing the specific ligand-receptor interactions between fibroblast subpopulations and Pericytes (including pericytes) in control (left) and estrogen-treated (right) conditions. (F) Heatmaps comparing the interaction strength between fibroblast subpopulations and Pericytes in control (left) and estrogen-treated (right) groups, showing enhanced Fibroblasts_HAS1-pericyte interactions. (G) Dot plot analyzing the expression of key ligand-receptor pairs (including TGFB1-TGFBR1 and FTH1-SCARA5) between fibroblast subpopulations and Pericytes across experimental groups.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7983273/v1/5f93deda8f2668d2dc47c23b.jpg"},{"id":94838793,"identity":"825faee3-aaa6-444e-960e-15c54b5f2f54","added_by":"auto","created_at":"2025-10-31 08:57:56","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":291689,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComputational pharmacology validates the drug targeting potential of the estrogen-induced repair unit. \u003c/strong\u003e(A) UMAP visualization of the global drug sensitivity space, showing distinct distribution patterns between cells from Group C (control) and Group E (estrogen-treated). (B) Drug sensitivity profiles of major cell types (fibroblasts, mural cells, mononuclear phagocytes, and endothelial cells) to various pharmacological agents. (C) Sensitivity patterns of key cell types to natural estrogen subtypes (estrone, estradiol, estriol) in Group C (left) and Group E (right), showing cell type-specific responses. (D) UMAP distribution of biosensitivity scores (BCS) for individual natural estrogen subtypes across all cells, demonstrating differential sensitivity patterns. (E) Quantitative comparison of BCS for the three natural estrogen subtypes between Group C and Group E, confirming significant changes in sensitivity profiles. (F) Spatial validation of the repair unit, showing co-localization of HAS1+ fibroblasts (red) and pericytes (blue) in estrogen-treated tissues. (G) Spatial weight analysis mapping the precise anatomical distribution of pericytes and Fibroblasts_HAS1 within the vascular niche.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7983273/v1/314f6d2c70bbff6c31547b8d.jpg"},{"id":95227275,"identity":"b37c5162-a386-4f0c-9c09-64a964f9ec69","added_by":"auto","created_at":"2025-11-05 16:32:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4005208,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7983273/v1/bc3c9919-fb18-405e-b998-5e71f9cd81cf.pdf"},{"id":94838784,"identity":"1f79b4eb-72ac-4719-b932-c3143fd206e3","added_by":"auto","created_at":"2025-10-31 08:57:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29682,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7983273/v1/94c013223df19b336f4335d2.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Single-Cell and Spatial Multi-Omics Unravel Estrogen-Driven Remodeling of the Prolapsed Uuterine Microenvironment via Fibroblast Reprogramming and Intercellular Communication","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePelvic organ prolapse (POP) is a common pelvic floor disorder that severely impacts the quality of life for middle-aged and elderly women and represents a significant public health burden. Epidemiological studies reveal that nearly half of all women exhibit some degree of anatomical POP \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. For patients with severe symptoms, surgery is the primary treatment; however, the choice of surgical procedure is complex and carries a risk of recurrence \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, with approximately 11\u0026ndash;19% of patients requiring surgical intervention \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Notably, about 30% of surgical cases may require reoperation due to recurrence \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, reflecting the chronic and refractory nature of the disease. Clinically, POP often presents with a range of symptoms such as pelvic pressure, urinary dysfunction, and decreased sexual quality of life, which not only harm patients' physical and mental health but also impose a substantial socioeconomic burden\u0026mdash;for instance, the annual direct medical cost for POP in the United States exceeds \u003cspan\u003e$\u003c/span\u003e1.5\u0026nbsp;billion \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. With the accelerating global aging population, the total number of patients is projected to increase by 46% to approximately 156\u0026nbsp;million by 2050 \u003csup\u003e6\u003c/sup\u003e, underscoring the urgent need to elucidate its pathogenesis and develop effective prevention and treatment strategies.\u003c/p\u003e\u003cp\u003eAlthough local estrogen therapy is incorporated into comprehensive POP management, its role presents a complex \"double-edged sword\" profile. Epidemiological data indicate that the postmenopausal low-estrogen state is associated with an increased risk of POP \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and local estrogen application can improve vaginal tissue thickness and collagen metabolism to some extent \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, some clinical studies, particularly randomized controlled trials (RCTs), demonstrate limited efficacy in improving patients' subjective symptoms or achieving long-term anatomical restoration \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This disparity between basic research and clinical observation suggests an incomplete understanding of estrogen's mechanisms of action within the complex in vivo tissue microenvironment. A key bottleneck lies in the inability of traditional methodologies, such as tissue homogenate analysis or in vitro cell models, to resolve the highly heterogeneous cellular composition, cell-specific responses, and spatial interaction networks within the primary POP target tissue\u0026mdash;the vaginal wall \u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe pelvic floor connective tissue constitutes a complex ecosystem comprising various cell types, including fibroblasts, immune cells, and vascular cells. Estrogen receptor (ER) expression varies significantly across these cell types \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and the disrupted ERα/ERβ ratio observed in POP patients may predict differential responses to estrogen signaling among distinct cellular populations \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Fibroblasts, as the primary producers of the extracellular matrix (ECM) and core maintainers of tissue architecture, are considered one of the most critical targets for estrogen in regulating connective tissue homeostasis. This cellular heterogeneity is a major contributor to the individual variation in response to estrogen therapy. Yet, a systematic characterization of the functional heterogeneity of fibroblasts, particularly within the postmenopausal POP vaginal wall at single-cell resolution, is currently lacking.\u003c/p\u003e\u003cp\u003eFurthermore, tissue function depends not only on cellular composition but also critically on its three-dimensional spatial architecture. Currently, knowledge regarding two pivotal questions remains limited: (1) How does estrogen influence the precise spatial localization of different cell types, especially functionally distinct fibroblast subpopulations, to reconstruct a reparative microenvironment? (2) How does estrogen remodel the short-range intercellular communication network to coordinate multicellular repair programs? A mechanistic understanding of these spatiotemporal dimensions is key to breaking through the current therapeutic impasse.\u003c/p\u003e\u003cp\u003eRecent breakthroughs in single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics have provided powerful tools for systematically parsing the cellular atlas, state transitions, spatial neighborhoods, and cell-cell communication within complex tissues at a systems level \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Notably, technologies like Visium HD, with their near-cellular spatial resolution (e.g., 2 \u0026micro;m), enable unprecedented precision in mapping the in situ distribution of cellular subpopulations, thereby revealing fine structural units within the microenvironment. Moreover, emerging computational pharmacology approaches can link single-cell transcriptomic data with drug sensitivity, allowing direct prediction of potential targeted strategies based on disease mechanisms and significantly accelerating the translation from basic discovery to clinical application. Therefore, this study aims to construct the first integrated multi-omics atlas of the anterior vaginal wall in postmenopausal POP patients by leveraging 10x Genomics scRNA-seq and Visium HD spatial transcriptomics.\u003c/p\u003e\u003cp\u003eTo achieve this, we profiled the anterior vaginal wall tissues from a cohort of postmenopausal POP patients, either untreated or treated with local estrogen. Our study aims to systematically characterize the cellular heterogeneity and functional states of the major cell types, with a particular focus on fibroblast subpopulations, at single-cell resolution. We further seek to delineate the specific regulatory impact of estrogen on the proportion, differentiation trajectory, and transcriptional activity of these fibroblast subsets. Moreover, we endeavor to elucidate how estrogen reshapes the spatial distribution of cells and reprograms the intercellular signaling network. Finally, by integrating computational pharmacology, we will predict the targetability of the identified repair programs, thereby providing a robust cellular and molecular foundation for developing precise estrogen-targeted and novel therapeutic strategies beyond estrogen.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Construction of a Multi-omics Atlas and Estrogen-Mediated Global Remodeling of the Cellular Community\u003c/h2\u003e\u003cp\u003eTo systematically decipher the pathological microenvironment of postmenopausal pelvic organ prolapse (POP) and the intervention mechanisms of estrogen therapy at single-cell and spatial resolution, we performed 10x Genomics single-cell RNA sequencing (scRNA-seq) and Visium HD spatial transcriptomic sequencing on anterior vaginal wall tissues from POP patients. These included individuals either treated with topical estrogen (once daily for 6 weeks; E group, n\u0026thinsp;=\u0026thinsp;6) or left untreated (C group, control, n\u0026thinsp;=\u0026thinsp;5). Using Visium HD technology for in situ sequencing, we acquired spatial gene expression information by merging 2 x 2 \u0026micro;m barcoded spots into 8 x 8 \u0026micro;m analysis units. Subsequent multi-omics integration analysis (MIA) enabled the precise mapping of cell identities onto their tissue spatial locations, constructing the first single-cell and spatial integrated atlas of the postmenopausal POP vaginal wall (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). All enrolled POP patients were classified as POP-Q stage III\u0026ndash;IV (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Histological examination (H\u0026amp;E staining) confirmed the acquisition of full-thickness vaginal wall structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), allowing us to capture cellular dynamics before and after treatment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics of the Study Cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003ePOP(Control Group)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c12\" namest=\"c7\"\u003e\u003cp\u003ePOP with Estrogen(Estrogen-treated Group)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePatient\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eP8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eP9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eP10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eP11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.838\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHeight (m)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWeight (kg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.301\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBody mass index ( kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e33.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e22.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e26.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e21.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e25.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.792\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMenopause Duration (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eG (Gravidity)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.633\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eP (Parity)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.782\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003eNote: A total of 11 postmenopausal women with POP-Q stage III-IV were enrolled, including 5 in the control group (Group C) and 6 in the estrogen-treated group (Group E). Continuous variables (e.g., Age, BMI) were compared using the independent samples t-test. Categorical variables (e.g., Hypertension, Diabetes) were compared using the Chi-square test. All baseline characteristics showed no statistically significant differences between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that the groups were comparable.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFollowing stringent quality control, the scRNA-seq data yielded transcriptomes from 116,775 high-quality cells. Principal component analysis and UMAP clustering identified seven major cell types: epithelial cells, endothelial cells, fibroblasts, pericytes, T cells, mast cells, and mononuclear phagocytic cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Further subcluster analysis was performed to resolve finer heterogeneity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The accuracy of cell type annotation was confirmed by differential expression gene analysis and the expression of canonical markers, such as high levels of COL1A1 in fibroblasts and PECAM1 in endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, F; refer to Supplementary Table\u0026nbsp;1 for a complete list of cell types and their marker genes).\u003c/p\u003e\u003cp\u003eGlobal cell proportion analysis revealed that estrogen treatment induced a significant remodeling of the cellular community. Compared to the control group, the estrogen-treated group exhibited a marked increase in the relative proportions of fibroblasts and T cells, while the proportion of pericytes was significantly decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG) [8]. This shift was quantitatively confirmed by Ro/e analysis: estrogen treatment promoted the enrichment of fibroblasts (Ro/e: 0.80 vs. 1.15) and T cells (Ro/e: 0.42 vs. 1.45), while reversing the enrichment of pericytes observed in the POP disease state (Ro/e: 1.40 vs. 0.69) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH).\u003c/p\u003e\u003cp\u003eThese results demonstrate that estrogen does not uniformly affect all cell populations but rather specifically modulates the balance between stromal cells (e.g., fibroblasts and pericytes) and immune cells (e.g., T cells) \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The significant change in the abundance of fibroblasts, as the primary producers of the extracellular matrix, highlights their role as a core target of estrogen in regulating tissue homeostasis \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This finding provides a clear rationale for our subsequent in-depth investigation into fibroblast heterogeneity and functional reprogramming.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.2 Focusing on a Core Target: Estrogen Reprograms Fibroblasts and Induces a Shift Toward a Pro-Repair Phenotype\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on the observed overall expansion of fibroblasts, we hypothesized that estrogen's effect might be subpopulation-specific. To test this, we performed unsupervised sub-clustering on fibroblasts, which successfully partitioned them into five transcriptionally distinct subpopulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Crucially, cell proportion analysis revealed that estrogen did not uniformly promote the expansion of all subpopulations but exhibited high selectivity: the proportion of the Fibroblasts_HAS1 subpopulation significantly increased post-treatment, whereas the proportion of the canonical myofibroblast subpopulation, Fibroblasts_ACTA2, relatively decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, C) \u003csup\u003e19,20\u003c/sup\u003e. This indicates that the estrogen-driven expansion of fibroblasts essentially instigates a selective shift in cell state prevalence.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo understand the functional implications of this proportional shift, we systematically annotated the functions of each subpopulation. Marker gene analysis showed that the Fibroblasts_HAS1 subpopulation highly expresses hyaluronic acid synthase 1 (HAS1), identifying it as a primary cellular source for hyaluronic acid synthesis in the tissue. This suggests a potential role in improving tissue tension through enhanced matrix hydration and modulating the immune microenvironment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, E). In contrast, the Fibroblasts_ACTA2 subpopulation exhibited typical myofibroblast characteristics, expressing α-smooth muscle actin (ACTA2), which is associated with tissue contraction and fibrosis. This estrogen-induced shift strongly implies that the treatment promotes the generation of a reparative matrix while concurrently suppressing aberrant fibrotic processes.\u003c/p\u003e\u003cp\u003eTo further confirm this functional switch at the global transcriptome level, we analyzed differentially expressed genes (DEGs) in fibroblasts upon estrogen treatment. A volcano plot showed significant expression changes in 543 genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Gene Ontology (GO) pathway enrichment analysis revealed that upregulated genes were significantly enriched in protein synthesis pathways such as \"cytoplasmic translation\" and \"ribosomal structural constituent\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). Conversely, downregulated genes were enriched in fibrosis-related pathways, including \"cellular response to TGF-β stimulus\" and \"extracellular matrix structural constituent\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH) \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. This opposing enrichment pattern provides compelling molecular functional evidence that estrogen successfully redirects the functional state of fibroblasts from \"pro-fibrotic\" to \"pro-repair\" by simultaneously activating protein anabolic metabolism and inhibiting TGF-β-driven fibrotic programs \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Tracing Cell Fate: Estrogen Guides Fibroblast Differentiation via a Core Transcription Factor\u003c/h2\u003e\u003cp\u003eHaving established the estrogen-induced transition in fibroblast states, we next sought to determine whether the acquisition of this reparative phenotype was a stochastic event or a precisely regulated differentiation process. To address this, we first assessed the developmental potential of cells using the CytoTRACE algorithm. The results demonstrated that fibroblasts from the estrogen-treated group exhibited a significantly lower overall differentiation state compared to the control group, presenting a \"younger,\" more plastic phenotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). This suggests that estrogen may preserve progenitor-like characteristics, thereby providing a ample cellular reservoir for differentiation into reparative phenotypes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe next employed Monocle3 to construct a pseudotemporal trajectory, providing a visual representation of the cell state transition paths. The trajectory analysis revealed that cells diverged from a relatively centralized starting point towards multiple distinct terminal states (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Critically, cells from the estrogen-treated group were significantly enriched in specific differentiation branches leading to reparative subpopulations, such as Fibroblasts_HAS1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). This observation directly confirms that estrogen actively guides the direction of fibroblast differentiation, rather than passively selecting for pre-existing states \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlong this estrogen-guided trajectory, we systematically identified core gene clusters whose expression dynamically changed during the differentiation process (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). These genes formed clear temporal expression modules: genes such as ACKR3 and PI16 decreased along the trajectory, constituting a \"stemness/plasticity maintenance module\"; whereas the expression of genes like APOD and MT-CO1 increased, forming a \"terminal functional execution module.\" Genes associated with this trajectory were significantly enriched in pathways including \"extracellular matrix organization\" and \"collagen metabolic process\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), indicating that this differentiation path is functionally coupled to tissue repair and remodeling.\u003c/p\u003e\u003cp\u003eTo identify the upstream drivers of this differentiation process, we analyzed the transcription factor (TF) regulatory network using SCENIC. The results revealed highly specific TF activation patterns across different fibroblast subpopulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Among these, POLR3G displayed the most prominent regulatory activity in the Fibroblasts_HAS1 subpopulation. Further regulatory specificity scoring confirmed that the regulatory module centered on POLR3G was the most definitive \"identity determinant\" for this subpopulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). This indicates that estrogen precisely orchestrates the fate conversion of fibroblasts along the pseudotime trajectory towards a reparative phenotype by activating a specific transcriptional program dominated by POLR3G.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Locating the Repair Unit: Spatial Transcriptomics Reveals an Estrogen-Induced HAS1\u0026thinsp;+\u0026thinsp;Fibroblast-Pericyte Niche\u003c/h2\u003e\u003cp\u003eThe transition in cell state must ultimately be located within the three-dimensional space of the tissue to fulfill its physiological function. To validate the fibroblast state transition revealed by single-cell analysis in situ and to investigate whether estrogen restructures cellular spatial organization, we integrated Visium HD spatial transcriptomic data.\u003c/p\u003e\u003cp\u003eUnsupervised clustering of the spatial data first revealed that tissue regions from the estrogen-treated group exhibited a transcriptomically distinct clustering pattern compared to the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), indicating that estrogen induces a systematic remodeling of the vaginal wall tissue, from its overall transcriptional state to its spatial architecture. Through spatial cell type deconvolution, we precisely mapped the in situ distribution of major cell types. Quantitative analysis confirmed that the relative proportion of the reparative Fibroblasts_HAS1 subpopulation significantly increased in the estrogen-treated group, while the proportion of the terminally differentiated Fibroblasts_C7 subpopulation correspondingly decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), a finding consistent with our scRNA-seq results.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe most critical spatial evidence came from the precise localization of cellular subpopulations. We found that estrogen treatment induced a significant spatial repositioning of the Fibroblasts_HAS1 subpopulation: it shifted from a relatively dispersed distribution in the control group to a specific aggregation in regions highly enriched with pericytes, forming a tight spatial co-localization between the two (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Concurrently, the spatial distribution of the ACTA2-expressing myofibroblast subpopulation was markedly contracted. This spatial remodeling\u0026mdash;\"moving towards the repair center and away from fibrotic areas\"\u0026mdash;establishes a physical foundation for functional cellular collaboration.\u003c/p\u003e\u003cp\u003eAt the molecular level, our analysis of spatially variable genes revealed cell-type-specific molecular reprogramming induced by estrogen (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The expression signals of repair-related signature genes were significantly enhanced in situ in the Fibroblasts_HAS1 subpopulation, while some pro-fibrotic genes in the Fibroblasts_ACTA2 subpopulation showed a downregulation trend. Notably, pericytes spatially adjacent to Fibroblasts_HAS1 also exhibited coordinated changes in their gene expression profiles, suggesting functional coupling achieved through spatial proximity. To further characterize the spatial microenvironment of this repair unit, we performed spatial neighborhood analysis. The results showed that the Fibroblasts_HAS1 subpopulation formed stable spatial adjacencies with pericytes, mononuclear phagocytes, and T cells within the tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), collectively constituting a multicellular spatial niche.\u003c/p\u003e\u003cp\u003eBased on the functional heterogeneity of fibroblasts and the potential for a repair phenotype switch suggested by our prior single-cell and spatial transcriptomic analyses, we hypothesized that estrogen induces a reparative fibroblast subpopulation possessing dual functional capabilities: \"matrix synthesis\" and \"environmental sensing.\" To test this, we designed a multiplex immunofluorescence (mIF) experiment targeting the key hyaluronic acid synthase HAS1, the collagen receptor DDR2, and the myofibroblast marker α-SMA.\u003c/p\u003e\u003cp\u003eThe mIF results confirmed our hypothesis. A key spatial restructuring phenomenon was observed in the estrogen-treated group: within the luminal outlines formed by α-SMA\u0026thinsp;+\u0026thinsp;vascular structures, there was clear enrichment of DDR2 (yellow) and HAS1 (green) signals (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). This indicates that the estrogen-induced HAS1\u0026thinsp;+\u0026thinsp;DDR2\u0026thinsp;+\u0026thinsp;reparative fibroblasts are not randomly distributed but are specifically localized to the perivascular region, congregating around structures constituted by vascular smooth muscle cells (α-SMA+). This location is a known hotspot for intercellular interaction and signal exchange. This phenomenon strongly suggests that estrogen activates the perivascular microenvironment and guides the recruitment of reparative cells to this specific anatomical site, thereby morphologically and spatially constructing a stem cell niche-like \"perivascular repair niche.\" Within this unit, HAS1\u0026thinsp;+\u0026thinsp;DDR2\u0026thinsp;+\u0026thinsp;fibroblasts can simultaneously leverage nutritional support from the vasculature and, via DDR2 acting as a key sensor of the collagen microenvironment, couple with the chemotactic behavior of HAS1\u0026thinsp;+\u0026thinsp;cells to form a structure-function integrated microenvironmental remodeling event. This provides crucial in situ protein-level evidence for the synergistic mechanism of the \"fibroblast-pericyte axis\" in POP tissue repair. Functionally, this niche resembles stem cell microenvironments reported in various tissues, providing necessary spatial anchoring and signaling support for repair cells \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFurthermore, in the estrogen-treated group, we successfully identified a cell population matching the predicted profile: they concurrently highly expressed HAS1 and DDR2 but did not express α-SMA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). This phenotypic signature confirmed at the protein level the existence of the hypothesized \"functionally coupled repair cell.\" This cell type not only possesses the ability to construct a reparative matrix via hyaluronic acid synthesis but can also sense collagen microenvironment signals through DDR2, enabling dynamic regulation of its functional state. Their α-SMA-negative character further confirms that this population has not differentiated into pro-fibrotic myofibroblasts, thereby ensuring the repair process steers towards functional tissue reconstruction rather than pathological fibrosis.\u003c/p\u003e\u003cp\u003eBy integrating spatial transcriptomics with in situ immunofluorescence validation, this study systematically elucidates across multiple dimensions\u0026mdash;tissue architecture, cellular composition, spatial distribution, and molecular expression\u0026mdash;that estrogen not only reprograms fibroblast states at the single-cell level but also guides HAS1\u0026thinsp;+\u0026thinsp;fibroblasts and pericytes to collectively build a structured repair niche within the tissue space. This discovery organically links cell state transition with spatial restructuring, providing solid spatial biological evidence for understanding the mechanism of estrogen-mediated microenvironment remodeling.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Decoding Intercellular Crosstalk: Estrogen Activates a Fibroblast-Pericyte Signaling Axis\u003c/h2\u003e\u003cp\u003eBuilding upon the spatial adjacency revealed by spatial transcriptomics, we next investigated whether this physical proximity translates into functional molecular crosstalk. Through systematic cell-cell communication analysis, we found that fibroblasts serve as a central hub within the global interaction network of the vaginal wall. Visualization analysis revealed dense and complex connection networks formed between various fibroblast subpopulations and pericytes, epithelial cells, and immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn-depth analysis of the functional properties of fibroblasts within this signaling network identified a dual role: they act as active signal senders, expressing various ligands (e.g., PI16, CXCL12) to transmit regulatory cues to neighboring cells, and as critical signal receivers, sensing microenvironmental signals through a rich repertoire of receptors (e.g., FTL, FTH1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, D). Network centrality analysis further indicated that the signaling axis formed by fibroblasts and pericytes was particularly prominent within the overall communication network, suggesting this cellular pair may be a key signaling unit for maintaining tissue homeostasis.\u003c/p\u003e\u003cp\u003eTo quantify the impact of estrogen on specific cellular interactions, we performed a detailed ligand-receptor pair analysis. Although the Ro/e analysis indicated an overall decrease in the proportion of pericytes, it was surprising to find that in the estrogen-treated group, the number of interactions between Fibroblasts_HAS1 and pericytes significantly increased by approximately 25%, with a marked enhancement in interaction strength (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, F, Supplementary Table\u0026nbsp;4) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. This finding perfectly correlates with the observed increase in their spatial proximity.\u003c/p\u003e\u003cp\u003eFurther molecular mechanism dissection revealed key signaling pathways mediating this enhanced interaction. We identified several specifically regulated ligand-receptor pairs, including TGFB1-TGFBR1, FTH1-SCARA5, among others (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG) \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Among these, the regulation of the TGF-β signaling pathway may influence the cellular fibrotic phenotype and the balance of tissue repair, while the interaction between FTH1 (ferritin heavy chain) and SCARA5 (scavenger receptor class A member 5) may be involved in iron metabolism homeostasis and cytoprotective processes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Toward Precision Therapy: Computational Pharmacology Validates the Drug Targeting Potential of the Repair Unit\u003c/h2\u003e\u003cp\u003eBased on our preceding findings\u0026mdash;that estrogen induces fibroblast differentiation into a HAS1\u0026thinsp;+\u0026thinsp;reparative phenotype, guides its spatial positioning, and facilitates niche formation with pericytes\u0026mdash;we posed a critical question: Does this highly coordinated program of cellular and spatial remodeling possess druggable potential? To address this, we utilized the BeyondCell computational platform to predict drug sensitivity based on our single-cell transcriptomic data.\u003c/p\u003e\u003cp\u003eInitially, in the global drug sensitivity space, cells from the control and estrogen-treated groups exhibited markedly distinct distribution patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). This finding indicates that the estrogen-remodeled cellular microenvironment not only possesses a unique transcriptomic signature but also a specific \"pharmacological identity,\" theoretically enabling targeted intervention.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn-depth analysis of the global drug sensitivity patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB) revealed that estrogen treatment significantly reshaped the cellular sensitivity profiles toward non-estrogen drugs. Crucially, however, cells in both treated and control groups consistently exhibited \"low sensitivity\" toward the three natural estrogens (estriol, estrone, and estradiol). This phenomenon strongly suggests that the overall therapeutic effect of estrogen is not achieved through transient, potent agonism of intracellular estrogen receptor signaling. Instead, it likely involves inducing a sustained, programmed remodeling of cell states and the microenvironment, whereby cells, after completing reprogramming, no longer exhibit high sensitivity to additional estrogen stimulation. This may represent a hallmark of repair program completion and the establishment of a new homeostasis.\u003c/p\u003e\u003cp\u003eFocusing further on fibroblasts and pericytes, which were identified as core targets of estrogen regulation, we analyzed the cell-specific action patterns of different estrogen subtypes. In fibroblasts, all three natural estrogens showed low sensitivity, consistent with a mechanism where estrogen primarily induces state transition rather than direct, strong activation. Strikingly, in pericytes, estrone demonstrated clear high sensitivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). This discovery is highly instructive, indicating that the overall therapeutic effect of estrogen may stem from the synergistic action of different subtypes on distinct cell populations: estradiol primarily drives fibroblast state reprogramming, whereas estrone, by acting directly on pericytes, indirectly supports the cooperative interactions of the fibroblast-pericyte axis.\u003c/p\u003e\u003cp\u003eFurther dissection of the sensitivity distribution patterns for different estrogen subtypes at single-cell resolution revealed that the biosensitivity score (BCS) distribution of estradiol exhibited unique characteristics: high BCS values were primarily enriched in control group cells, while low BCS values were significantly enriched in estrogen-treated group cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Quantitative analysis confirmed that compared to the control group, the BCS score for estradiol significantly decreased in the treated group, whereas the BCS scores for estrone and estriol showed opposite trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). This differential pharmacological signature reveals a functional division of labor among estrogen subtypes within the repair program \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo validate the biological basis of the computational pharmacology predictions, we analyzed the key cell populations targeted by the sensitivity predictions via spatial transcriptomics. Spatial cell localization analysis confirmed the clear co-localization of HAS1\u0026thinsp;+\u0026thinsp;fibroblasts and pericytes in estrogen-treated tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). In-depth spatial weight analysis further revealed the specialized spatial structures constructed by these cell populations during the repair process (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG), where pericytes exhibited focal enrichment around tubular structures, and Fibroblasts_HAS1 displayed a distribution pattern highly coordinated with that of pericytes.\u003c/p\u003e\u003cp\u003eThese findings complete a full scientific cycle from single-cell atlas to spatial localization, and finally to pharmacological validation. The results demonstrate that the therapeutic effect of estrogen is achieved by creating a structured and functional \"HAS1\u0026thinsp;+\u0026thinsp;fibroblast-pericyte\" spatial functional unit. Furthermore, the computational pharmacology analysis confirms that this repair unit possesses tangible therapeutic potential for specific targeting, providing new targets and a theoretical basis for developing precision strategies that move beyond traditional estrogen therapy.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003ePelvic organ prolapse (POP), a highly prevalent pelvic floor disorder, has long faced challenges in pharmacological treatment due to inconsistent therapeutic outcomes. Although epidemiological evidence strongly supports an association between the postmenopausal low-estrogen state and increased POP risk \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, and local estrogen therapy is widely incorporated into clinical management guidelines, randomized controlled trials (RCTs) reveal considerable variability in its efficacy for improving patients' subjective symptoms and achieving long-term anatomical restoration, presenting a notable \"double-edged sword\" profile \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. This disparity between basic research and clinical observation indicates persistent critical gaps in our understanding of estrogen's mechanisms of action within the complex, heterogeneous tissue microenvironment.\u003c/p\u003e\u003cp\u003eTraditional research has largely relied on molecular biological analyses of tissue homogenates or simplified in vitro cell models \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. While these approaches have successfully uncovered estrogen's effects on macroscopic processes such as collagen metabolism and fibroblast proliferation \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, their fundamental limitation lies in the inability to resolve cellular heterogeneity within the tissue and their neglect of the three-dimensional spatial architecture essential for intercellular communication. Consequently, while we can observe the \"net effect\" following estrogen treatment, we remain unable to determine which specific cell populations execute these effects, what state transitions these populations undergo, or how they are spatially organized to coordinately achieve repair functions.\u003c/p\u003e\u003cp\u003eThe advent of single-cell transcriptome sequencing has recently provided new perspectives for analyzing tissue heterogeneity. Studies have identified significant individual variations in estrogen receptor expression in the pelvic floor tissues of POP patients \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, as well as tissue-specific alterations in membrane receptors such as GPER \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. More importantly, scRNA-seq studies have definitively identified functionally distinct fibroblast subpopulations within the vaginal wall \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, while also revealing that infiltrating immune cells and their different polarization states in pelvic floor tissues are closely associated with estrogen regulation and tissue repair outcomes \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. These findings highlight the limitations of traditional two-dimensional culture models in recapitulating complex cellular interactions \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBuilding on this knowledge, our study employed single-cell RNA sequencing and Visium HD spatial transcriptomics to overcome the limitations of traditional approaches \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, marking a paradigm shift from \"holistic observation\" to \"programmatic dissection.\" We not only systematically defined the cellular composition of the vaginal wall microenvironment but also precisely mapped their functional states \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, differentiation trajectories, spatial coordinates, and communication networks \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Through systematic multi-omics integration, we propose a novel mechanistic model: estrogen exerts its therapeutic effect not by broadly stimulating tissue regeneration \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, but by executing a set of precisely coordinated cellular programs\u0026mdash;reprogramming key cell states \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, reconstructing functional spatial niches at specific anatomical sites, and rewiring intercellular communication \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u0026mdash;thereby guiding the disordered pathological microenvironment into an orderly repair program.\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Functional Heterogeneity and State Reprogramming of Fibroblasts\u003c/h2\u003e\u003cp\u003eWithin the pelvic floor connective tissue, fibroblasts, as the predominant cellular component and primary producers of the extracellular matrix (ECM), have long been a central focus in understanding POP pathophysiology \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. However, a fundamental limitation of previous research has been the treatment of fibroblasts as a functionally homogeneous population that becomes uniformly activated in the pathological state. Substantial evidence indicates an increase in fibroblasts exhibiting myofibroblast characteristics in the pelvic support tissues of POP patients \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, accompanied by aberrant activation of the TGF-β signaling pathway \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e and disordered collagen metabolism \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. While this \"pro-fibrotic\" perspective partially explains the phenomenon of ECM remodeling imbalance in POP tissues, it creates a cognitive paradox: if estrogen merely acts as a simple stimulator of fibroblast proliferation \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, it should logically exacerbate, rather than ameliorate, this fibrotic tendency.\u003c/p\u003e\u003cp\u003eOur scRNA-seq analysis successfully unveiled the \"heterogeneous veil\" of fibroblasts in the POP vaginal wall, systematically identifying five transcriptionally distinct fibroblast subpopulations with divergent functional orientations. By comparing estrogen-treated and untreated groups, we found that estrogen does not function as an indiscriminate \"global activator\" \u003csup\u003e18,34\u003c/sup\u003e, but rather as a precise \"cell state selector.\" Specifically, estrogen selectively and significantly enriched the Fibroblasts_HAS1 subpopulation, characterized by high expression of hyaluronic acid synthase 1, while relatively suppressing the canonical, ACTA2-rich myofibroblast subpopulation \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. This finding is significant because hyaluronic acid, as a crucial ECM component, enhances tissue hydration, modulates immune responses, and promotes tissue repair\u0026mdash;properties starkly contrasting with the mere collagen deposition that leads to fibrosis \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Consequently, our study fundamentally revises the simplistic view that \"fibroblasts solely play a pro-fibrotic role in POP\" \u003csup\u003e31,39\u003c/sup\u003e, revealing the existence of functionally antagonistic subpopulations within them and, for the first time, identifying the Fibroblasts_HAS1 subpopulation as a key cellular executor of estrogen-mediated tissue repair.\u003c/p\u003e\u003cp\u003eTo delve deeper into the mechanism of this estrogen-guided cell state conversion, CytoTRACE analysis revealed that fibroblasts in the estrogen-treated group overall maintained greater developmental potential and plasticity. The pseudotemporal trajectory constructed by Monocle3 further clearly delineated paths differentiating from a relatively primitive state towards multiple terminal subpopulations. Cells from the estrogen-treated group were significantly enriched in the branch leading to Fibroblasts_HAS1, strongly demonstrating that estrogen's role is to actively guide cell fate rather than passively select for pre-existing states. More importantly, through SCENIC transcription factor regulatory network analysis, we identified POLR3G as the most specific and active core transcription factor for the Fibroblasts_HAS1 subpopulation. These findings collectively outline a clear regulatory hierarchy: estrogen, by activating a specific transcriptional program dominated by POLR3G \u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, precisely navigates multipotent fibroblast precursor cells towards the differentiation track of a reparative phenotype.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Construction and Functional Specialization of the Spatial Niche\u003c/h2\u003e\u003cp\u003eWhile single-cell transcriptomics has revolutionarily revealed cellular heterogeneity \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, a fundamental question remains unresolved: Where are these transcriptionally defined, repair-potent cell subpopulations precisely located within the complex tissue architecture? The current understanding of POP treatment mechanisms is almost entirely built upon the dimension of \"altered cellular composition and state\" \u003csup\u003e18,19,34\u003c/sup\u003e, critically lacking spatial validation. This gap prevents us from answering where precisely reparative events occur within the tissue and from understanding how different cell types couple physically and functionally through spatial proximity \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur study, by integrating Visium HD spatial transcriptomics with multiplex immunofluorescence staining, successfully bridged this gap, linking the cell state changes revealed by single-cell analysis to their precise spatial localization within the tissue. Unsupervised clustering of the spatial transcriptomic data showed a significant spatial restructuring of the overall transcriptional state in estrogen-treated tissues. Through cell type deconvolution, we made a compelling discovery: under estrogen influence, the reparative Fibroblasts_HAS1 subpopulation did not diffuse uniformly but specifically aggregated towards perivascular regions highly enriched with pericytes, forming a tight spatial co-localization between the two \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Concurrently, the spatial distribution of the pro-fibrotic Fibroblasts_ACTA2 subpopulation was relatively contracted \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. This dynamic spatial repositioning\u0026mdash;\"selective aggregation and concomitant contraction\"\u0026mdash;strongly indicates that estrogen actively reconfigures cellular spatial distribution, constructing a specialized \"perivascular repair niche\" in situ.\u003c/p\u003e\u003cp\u003eTo dissect the cellular and molecular basis of this niche, we performed in situ validation at the protein level. Immunofluorescence results demonstrated that the cells specifically enriched around vasculature post-estrogen treatment were a population concurrently expressing high levels of HAS1 and the collagen receptor DDR2, but lacking expression of α-SMA. This unique HAS1\u0026thinsp;+\u0026thinsp;DDR2\u0026thinsp;+\u0026thinsp;α-SMA- phenotype represents a novel class of repair cells functionally equipped with dual capabilities\u0026mdash;\"matrix synthesis\" and \"environmental sensing\"\u0026mdash;while successfully avoiding the \"fibrotic\" terminal fate. Specifically, HAS1 empowers these cells to synthesize hyaluronic acid, constructing a hydrated, elastic reparative matrix to improve tissue tension. DDR2, as a key collagen receptor, enables them to continuously sense mechanical and chemical signals from the surrounding collagen microenvironment, facilitating dynamic dialogue between the cell and the ECM. The α-SMA- phenotype clearly distinguishes them from terminally differentiated, contractile, tissue-hardening-driving myofibroblasts \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The combination of these three characteristics allows this cell type to achieve a complete in situ \"sensing-response\" functional loop at the perivascular hub, a nexus for nutrient and signal exchange.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Remodeling of the Cellular Communication Network and Coordinated Regulation\u003c/h2\u003e\u003cp\u003eWhile physical proximity between cells is a necessary condition for functional collaboration, it is not sufficient. We therefore investigated whether the estrogen-guided spatial co-localization translates into functional molecular crosstalk that coordinates the execution of the repair program. Previous research into the molecular mechanisms of POP, including extensive reports on the aberrant activation of classic signaling pathways such as transforming growth factor-β (TGF-β) \u003csup\u003e26,40,41\u003c/sup\u003e, has largely operated under an implicit assumption: that activity changes in these pathways are primarily cell-autonomous behaviors. For instance, studies have predominantly focused on expression changes of TGF-β receptors or downstream SMAD proteins within fibroblasts themselves \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. This \"cell-centric\" perspective largely overlooks the crucial role of intercellular communication as an upstream regulator of signaling pathways.\u003c/p\u003e\u003cp\u003eOur cell-cell communication analysis broke through this limitation, shifting the mechanistic understanding from the intracellular to the intercellular realm. The results demonstrated that fibroblasts act as the central hub of the signaling network within the vaginal wall tissue \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Most importantly, we found that estrogen's regulation of key signaling pathways is fundamentally achieved by remodeling the communication relationships between specific cell pairs. Taking the canonical TGF-β pathway as an example, although the overall proportion of pericytes decreased after estrogen treatment, the number and strength of interactions between the reparative Fibroblasts_HAS1 subpopulation and pericytes increased significantly by approximately 25%. This implies that estrogen does not simply increase or decrease the overall \"volume\" of TGF-β signaling across the tissue. Instead, akin to adjusting the \"bandwidth\" of a communication network, it specifically enhances the signal flux through the particular \"communication channel\" of the \"fibroblast-pericyte\" pair.\u003c/p\u003e\u003cp\u003eFurther molecular mechanism dissection through ligand-receptor pair analysis not only confirmed the specific regulation of known pathways, such as TGFB1-TGFBR1, within specific cell pairs but also identified a series of novel ligand-receptor pairs, like FTH1-SCARA5, previously underappreciated in the POP field. The interaction between FTH1, a key protein for iron storage and antioxidant stress, and the scavenger receptor SCARA5 suggests that iron metabolism homeostasis and cytoprotective mechanisms may be as integral to maintaining the function of the repair niche as the ECM remodeling we previously focused on \u003csup\u003e45\u003c/sup\u003e. The discovery of these non-canonical pathways indicates that the stability of the estrogen-constructed repair niche is supported by a diversified signaling network.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Therapeutic Paradigm Shift and Translational Prospects\u003c/h2\u003e\u003cp\u003eBuilding upon these discoveries, we face a critical translational question: how can these mechanistic insights be converted into actionable therapeutic strategies? Traditional POP drug development has long focused on the single dimension of \"pathology suppression,\" for instance, by striving to develop TGF-β signaling inhibitors or matrix metalloproteinase inhibitors to block excessive ECM degradation or abnormal deposition \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. However, such \"anti-fibrotic\" or \"anti-catabolic\" strategies have repeatedly encountered setbacks in clinical development. The fundamental reason lies in their sole aim to \"block the bad\" while failing to \"promote the good\" \u003csup\u003e20,46\u003c/sup\u003e\u0026mdash;that is, they do not actively activate and support the endogenous, orderly repair programs within the tissue.\u003c/p\u003e\u003cp\u003eOur BeyondCell computational pharmacology analysis offers a new perspective on this challenge. The analysis revealed that the cell microenvironment remodeled by estrogen exhibits a distribution in the global drug sensitivity space that is markedly distinct from the untreated group, implying it has acquired a unique \"pharmacological identity.\" At single-cell resolution, different natural estrogen subtypes showed differential predicted sensitivities towards the distinct cell types constituting the repair unit: fibroblasts, which play a central role in driving repair, exhibited low sensitivity to estradiol, estrone, and estriol, consistent with a mechanism where estrogen primarily induces cell state reprogramming rather than directly and potently agonizing their proliferation \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In contrast, pericytes, acting as \"collaborative partners\" within the repair niche, displayed clear high sensitivity to estrone.\u003c/p\u003e\u003cp\u003eThis highly instructive discovery suggests that the overall therapeutic effect of estrogen may stem from the synergistic action of different subtypes on different cell populations: estradiol likely serves as the primary \"instructional signal\" initiating fibroblast reprogramming \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, whereas estrone, by acting directly on pericytes, indirectly consolidates and supports the cooperative interactions and niche stability of the \"fibroblast-pericyte axis\". This cell type-specific pharmacological profile not only provides a fresh perspective for understanding the complex actions of estrogen but also theoretically demonstrates that the discovered \"HAS1\u0026thinsp;+\u0026thinsp;fibroblast-pericyte\" repair unit is itself a biological entity with tangible potential for precise targeting \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBased on this new paradigm, future therapeutic strategies need no longer be confined to broad-spectrum hormone replacement or single-pathway inhibition. We can envision more precise interventions, including: developing small-molecule drugs capable of specifically activating POLR3G or its downstream network to directly drive fibroblast differentiation towards the reparative HAS1\u0026thinsp;+\u0026thinsp;phenotype; utilizing biomaterial or chemokine strategies to mimic or enhance the \"homing\" of HAS1\u0026thinsp;+\u0026thinsp;DDR2\u0026thinsp;+\u0026thinsp;reparative cells to the perivascular niche \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e; and designing combination regimens incorporating different estrogen subtypes or specific receptor modulators based on cell-specific pharmacological profiles \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusion and Perspectives","content":"\u003cp\u003eThis study, through the integration of single-cell and spatial multi-omics, has for the first time systematically delineated a holistic picture of estrogen-mediated remodeling of the vaginal wall microenvironment in postmenopausal POP patients. Based on these findings, we have constructed a novel and coherent mechanistic model. This model clearly delineates the sequential chain of estrogen's therapeutic action: it is initiated by the precise reprogramming of cell states, where estrogen activates a specific transcriptional program centered on POLR3G to direct fibroblast differentiation from a pro-fibrotic fate towards the HAS1 + reparative phenotype \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Subsequently, in the spatial dimension, it actively organizes these reparative cells, guiding their specific homing to the perivascular region to co-construct a structured \"repair niche\" with pericytes \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Finally, it enhances cooperative cellular communication, specifically strengthening interactions at key signaling hubs like the \"fibroblast-pericyte axis,\" thereby converting spatial proximity into functional synergy to efficiently execute the tissue repair program \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis multi-level integrated model not only provides a new theoretical explanation for understanding the individual variation in the clinical efficacy of estrogen from cellular and spatial perspectives but, more importantly, represents a fundamental paradigm shift in concept. Future research should focus on: (1) utilizing tools such as gene editing and organoids in cellular and animal models to validate the causal roles of core regulatory factors in driving reparative differentiation and spatial homing \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e; (2) developing precision strategies capable of specifically targeting the repair niche, including designing small-molecule agonists targeting POLR3G, developing biomaterial scaffolds that mimic cellular homing behavior \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, and designing optimal estrogen subtype combination therapies based on cell-specific pharmacological profiles \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e; and (3) validating the multi-omics signature established in this study across larger cohorts \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e to construct a biomarker system capable of predicting patient responsiveness to estrogen therapy \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eUltimately, the value of this study lies in completing a full scientific cycle from mechanistic dissection to theoretical innovation, and further to pathway planning. It not only provides a new spatial biological framework for understanding estrogen's action but, more significantly, lays a solid theoretical foundation and points towards promising clinical translation directions for developing next-generation pelvic floor regenerative medicine strategies that move beyond traditional hormone replacement towards truly etiology-targeted therapies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eEthics statement and sample collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003e This study was approved by the Institutional Review Board of Shanxi Bethune Hospital, and written informed consent was obtained from all participants. A total of 11 postmenopausal women with pelvic organ prolapse (POP) were enrolled, including 5 untreated patients (Prolapse group) and 6 patients treated with topical promestriene (Prolapse + Estrogen group) once daily for 6 weeks. Vaginal anterior wall tissues were collected during surgery and immediately processed for single-cell and spatial transcriptomic analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTissue dissociation and single-cell suspension preparation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFresh tissue samples were stored in sCelLiveTM Tissue Preservation Solution (Singleron) on ice within 30 minutes after surgery. Tissues were washed three times with Hanks Balanced Salt Solution (HBSS), minced into small pieces, and digested with 3 mL sCelLiveTM Tissue Dissociation Solution (Singleron) using the Singleron PythoNTM Tissue Dissociation System at 37°C for 15 minutes. The cell suspension was filtered through a 40-µm sterile strainer. Red blood cells were lysed using GEXSCOPE® Red Blood Cell Lysis Buffer (RCLB, Singleron) at a volume ratio of 1:2 (cell pellet:RCLB) for 5–8 minutes at room temperature. The mixture was centrifuged at 300 × g at 4°C for 5 minutes, and the pellet was resuspended in PBS. Cell viability was assessed using Trypan Blue staining and microscopic evaluation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSingle-cell RNA sequencing library preparation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSingle-cell suspensions were adjusted to a concentration of 2 × 10^5 cells/mL in PBS. Cells were loaded onto a microwell chip using the Singleron Matrix® Single Cell Processing System. Barcoding beads were collected from the chip, and reverse transcription was performed to synthesize cDNA from mRNA captured by the beads. The cDNA was amplified by PCR, fragmented, and ligated with sequencing adapters using the GEXSCOPE® Single Cell RNA Library Kit (Singleron). Libraries were quantified, diluted to 4 nM, pooled, and sequenced on an Illumina NovaSeq 6000 platform with 150 bp paired-end reads.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpatial transcriptomics library preparation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFresh frozen vaginal tissue sections (10 µm thickness) were mounted on Visium HD slides (10x Genomics). Tissue permeabilization was optimized to release RNA, which was captured by spatially barcoded oligonucleotides on the slide. cDNA synthesis, amplification, and library construction were performed according to the Visium HD Spatial Gene Expression protocol (10x Genomics). Libraries were quantified and sequenced on an Illumina NovaSeq 6000 platform.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSequencing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBoth scRNA-seq and spatial transcriptomics libraries were sequenced on the Illumina NovaSeq 6000 system using 150 bp paired-end reads. Sequencing depth aimed for at least 50,000 reads per cell for scRNA-seq and 50,000 reads per spot for spatial transcriptomics.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSingle-cell RNA-seq data processing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRaw sequencing data were processed using the CeleScope pipeline (v1.9.0, Singleron). Briefly, low-quality reads and adapter sequences were trimmed using Cutadapt (v1.17). Cell barcodes and UMIs were extracted and corrected. Clean reads were aligned to the GRCh38 reference genome (Ensembl version 92) using STAR (v2.6.1a). Gene expression counts were generated using featureCounts (v2.0.1). The output was a gene-by-cell expression matrix for downstream analysis.\u003c/p\u003e\u003cp\u003eQuality control and clustering of scRNA-seq data\u003c/p\u003e\u003cp\u003eThe Seurat R package (v5.0) was used for quality control and clustering. Cells with fewer than 200 genes or more than the top 2% of gene counts were filtered out. Mitochondrial gene content exceeding 10% was also excluded. After filtering, 116,775 high-quality cells were retained. Gene expression was normalized using the NormalizeData function, and variable genes were identified with FindVariableFeatures (top 2000 genes). Principal component analysis (PCA) was performed, and the top 20 principal components were used for clustering with the FindClusters function (resolution = 0.5). Cell clusters were visualized using UMAP.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCell type annotation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCell types were annotated based on the expression of canonical marker genes from the SynEcoSys™ database (Singleron) and published references. Major cell types identified included epithelial cells, endothelial cells, fibroblasts, Pericytes, T cells, mast cells, and mononuclear phagocytes. Fibroblast subclusters were further annotated using subsetting and reclustering.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDifferential expression and pathway enrichment analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDifferentially expressed genes (DEGs) between groups were identified using the FindMarkers function in Seurat with the Wilcoxon rank-sum test. Genes expressed in more than 10% of cells in both groups and with an average log2 fold change \u0026gt; 0.25 were considered significant. Adjusted p-values were calculated using Bonferroni correction. Pathway enrichment analysis was performed using the clusterProfiler R package (v3.16.1) for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. Pathways with adjusted p-value \u0026lt; 0.05 were considered significantly enriched.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrajectory inference analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCell differentiation trajectories were inferred using CytoTRACE (v0.3.3) to estimate differentiation potential. Pseudotime analysis was performed with Monocle2 (v2.10.0) or Monocle3 (v1.0.0) on fibroblast subpopulations. Highly variable genes were used to order cells along trajectories, and branch points were visualized using UMAP.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCell-cell interaction analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCellChat (v0.0.2) was used to infer intercellular communication networks. Ligand-receptor interactions were evaluated based on a curated database. Interaction strength was calculated and visualized using circle plots or heatmaps. Specific interactions between fibroblast subpopulations and Pericytes were highlighted.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpatial transcriptomics data processing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSpatial gene expression data were processed using Space Ranger (10x Genomics) to generate count matrices. Data were analyzed in Seurat (v5.0) using the Load10X_Spatial function. Spots with fewer than 10 UMI counts were filtered out. Gene expression was normalized and scaled. Dimensionality reduction was performed using PCA, and clustering was done with FindClusters. Spatial gene expression was visualized using SpatialFeaturePlot.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIntegration of scRNA-seq and spatial transcriptomics data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIntegration was performed using the Seurat integration pipeline. Anchor points between scRNA-seq and spatial data were identified with FindTransferAnchors. Cell type labels from scRNA-seq were transferred to spatial spots using TransferData. The proportion of cell types per spot was calculated and visualized with SpatialDimPlot. Spatial deconvolution was also validated using RCTD (Robust Cell Type Decomposition) in doublet mode.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll statistical analyses were performed in R (v4.1.0). Group comparisons were conducted using non-parametric tests (Wilcoxon test for two groups). P-values \u0026lt; 0.05 were considered statistically significant. Visualization was done using ggplot2, pheatmap, and Seurat plotting functions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSingle-cell drug susceptibility assessment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo assess drug sensitivity profiles at single-cell resolution, we performed computational analysis using the R package Beyondcell (version 1.2.1). Drug perturbation signatures from the built-in Beyondcell database were applied to our scRNA-seq dataset. Data preprocessing, including normalization and correction for the number of detected genes per cell, was conducted following the developer's recommendations. The analysis generated a Beyondcell score for each cell-drug pair, which quantifies the predicted sensitivity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHistological Analysis (H\u0026amp;E Staining)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor histological evaluation, portions of fresh vaginal anterior wall tissues were fixed in 4% paraformaldehyde for 24 hours at room temperature, followed by dehydration, clearing in xylene, and embedding in paraffin. Tissue sections were cut at 5 µm thickness using a microtome (Leica, Germany). After deparaffinization and rehydration, sections were stained with Harris Hematoxylin (Sigma-Aldrich) for 5 minutes, rinsed, differentiated in 1% acid ethanol, and blued in Scott's tap water. Eosin Y (Sigma-Aldrich) was applied for 2 minutes as a counterstain. Sections were then dehydrated, cleared, and mounted with neutral balsam (Sigma-Aldrich). Slides were scanned using a digital slide scanner (e.g., Nikon Eclipse Ci-L) or imaged with a bright-field microscope (e.g., Olympus BX53). H\u0026amp;E staining was used to verify the integrity of full-thickness vaginal wall architecture, including the epithelium, lamina propria, and muscularis layers.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMultiplex Immunofluorescence Staining and Imaging\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo validate transcriptomic findings, multiplex immunofluorescence (mIF) was performed on formalin-fixed, paraffin-embedded vaginal wall sections using tyramide signal amplification (TSA). Sections underwent deparaffinization, rehydration, and antigen retrieval. After blocking, a sequential staining protocol was applied for three targets: HAS1, α-SMA, and DDR2. Each staining cycle included incubation with a primary antibody (1:5000) at 4°C overnight, followed by an HRP-conjugated secondary antibody and a fluorophore-conjugated TSA reagent. Antibodies were eluted between cycles to prevent cross-reactivity. Nuclei were counterstained with DAPI, autofluorescence was quenched, and sections were mounted with anti-fade medium. Slides were scanned using a digital slide scanner, and high-resolution multispectral images were acquired for co-localization analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLin Wang and Xiaochun Liu conceived and designed the study. Lin Wang, Shuyu Wang, Lingyun Wei, and Mengyu Geng were responsible for sample collection and processing. Lin Wang, Shuyu Wang, and Wenzhen Wang performed the experimental work and data acquisition. Lin Wang, Shuyu Wang, Mengyu Geng, and Xiaochun Liu contributed to data analysis and interpretation. The manuscript was drafted by Lin Wang and critically reviewed and revised by all authors. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Shanxi Provincial Science and Technology Innovation Talent Team Project (No. 202204051002031), the 2025 Central Guidance Fund for Local Science and Technology Development Projects (No. YDZJSX2025D076), the National Natural Science Foundation of China (Grant No. 81971365), the Shanxi Province Selective Funding Program for Scientific and Technological Activities of Returned Scholars (Grant No. 2025058), and the 2024 Traditional Chinese Medicine Research Project (Approval No.: 2024ZYY2C037).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the patients who participated in this study and the clinical staff involved in sample collection. We also thank our colleagues from the Third Hospital of Shanxi Medical University and Shanxi Bethune Hospital for their insightful discussions and technical support. Special thanks are extended to the staff at the Singleron and 10x Genomics sequencing platforms for their professional assistance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequencing data (single-cell RNA-seq and spatial transcriptomics) generated in this study have been deposited in the NCBI BioProject database under the accession number PRJNA1347381. All other data supporting the findings of this paper are available within the article and its supplementary materials, or from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI-Generated Content\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no AI-generated content was used in the preparation of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWeintraub, A. Y., Glinter, H. \u0026amp; Marcus-Braun, N. Narrative review of the epidemiology, diagnosis and pathophysiology of pelvic organ prolapse. \u003cem\u003eInt. Braz. J. Urol.\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 5\u0026ndash;14 (2020).\u003c/li\u003e\n\u003cli\u003eJeon, M. J. Surgical decision making for symptomatic pelvic organ prolapse: evidence-based approach. \u003cem\u003eObstet. Gynecol. 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Ther.\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, S13 (2013).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":false,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pelvic organ prolapse, Estrogen therapy, Single-cell RNA-seq, Spatial transcriptomics, Fibroblast reprogramming, Perivascular niche, Tissue remodeling, Cell-cell communication","lastPublishedDoi":"10.21203/rs.3.rs-7983273/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7983273/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe clinical paradox of local estrogen therapy in pelvic organ prolapse\u0026mdash;its widespread application juxtaposed with inconsistent therapeutic outcomes\u0026mdash;highlights a critical gap in our understanding of its actions within the native tissue architecture. To resolve this, we employed high-definition spatial transcriptomics, achieving a near-cellular resolution map of the postmenopausal vaginal wall. This spatial atlas directly visualized estrogen\u0026rsquo;s core mechanism: the orchestration of a structured HAS1\u0026thinsp;+\u0026thinsp;fibroblast-pericyte reparative niche around vasculature. Estrogen directs the recruitment of POLR3G-driven, HAS1\u0026thinsp;+\u0026thinsp;fibroblasts into this precise micro-anatomical location, enabling their functional coupling with pericytes. This co-localization facilitates a rewired fibroblast-pericyte signaling axis, enhancing pro-repair communication. Computational pharmacology further affirms this niche as a druggable functional unit. Our findings establish a new paradigm: estrogen's efficacy is not mediated by broad tissue stimulation, but through the precise spatial engineering of a multicellular repair unit, a mechanism unveiled only through high-definition spatial mapping and one that redefines the future of targeted therapeutic strategies.\u003c/p\u003e","manuscriptTitle":"Single-Cell and Spatial Multi-Omics Unravel Estrogen-Driven Remodeling of the Prolapsed Uuterine Microenvironment via Fibroblast Reprogramming and Intercellular Communication","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 08:57:51","doi":"10.21203/rs.3.rs-7983273/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c4017ce2-addb-4268-b84f-6b7355924530","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57180480,"name":"Biological sciences/Molecular biology/Transcriptomics"},{"id":57180481,"name":"Health sciences/Diseases/Urogenital diseases/Urinary incontinence"},{"id":57180482,"name":"Health sciences/Signs and symptoms/Reproductive signs and symptoms"}],"tags":[],"updatedAt":"2025-12-08T09:54:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-31 08:57:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7983273","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7983273","identity":"rs-7983273","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}