Multicellular state transitions and signaling rewiring during the proestrus-to-estrus switch in the porcine ovary

Multicellular state transitions and signaling rewiring during the proestrus-to-estrus switch in the porcine ovary | 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 Research Article Multicellular state transitions and signaling rewiring during the proestrus-to-estrus switch in the porcine ovary Zexin Zhu, Qinghui Wang, Mingjun Li, Fan Wu, Ting Wen, Yitao Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8972446/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background The porcine ovary undergoes rapid multicellular remodeling during the transition from proestrus to estrus, but the cell type–specific regulatory programs that drive this process remain incompletely defined. Here, we aimed to resolve stage-dependent cellular states and intercellular signaling events in the porcine ovary across proestrus and estrus at single-cell resolution. Results We generated a stage-resolved single-nucleus RNA sequencing atlas of porcine ovaries spanning proestrus and estrus. Integrative analyses of granulosa cell heterogeneity, metabolic pathway activity, and inferred metabolic flux highlighted ERBB4 and the glutamine transporter SLC1A5 as key regulatory factors associated with the proestrus-to-estrus transition. By combining cell type–specific differential expression profiling with intercellular communication network analysis across major ovarian somatic compartments, we further identified transforming growth factor beta (TGF-β) signaling as a central regulatory axis coordinating this developmental transition. Together, these data delineate coordinated transcriptional, metabolic, and microenvironmental remodeling programs during the proestrus-to-estrus switch. Conclusions This study provides a high-resolution cellular resource for the porcine ovary across proestrus and estrus and proposes ERBB4 , SLC1A5 , and TGF-β pathway components as candidate targets for modulating estrus-associated ovarian remodeling. These findings may support improved estrus detection, earlier recognition of reproductive disorders, and more precise reproductive management in swine production systems. Porcine ovary Estrous cycle snRNA-seq Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The ovary is the central organ of the female reproductive system and undergoes a series of tightly coordinated physiological changes across the estrous cycle, including follicular development, maturation, ovulation, and subsequent luteinization [ 1 , 2 ]. In pigs, the estrous cycle typically lasts 18–24 days, and the proestrus and estrus phases represent periods of particularly active follicular growth, accompanied by pronounced endocrine and local microenvironmental fluctuations; consequently, these phases are key determinants of ovulation rate and fertilization success [ 3 ]. Accordingly, elucidating the molecular mechanisms governing ovarian function across the estrous cycle is essential for improving reproductive efficiency, productivity, and fertility management in pigs. Nevertheless, our understanding of how diverse cell types within the porcine ovary collectively govern estrous transitions through cell-type-specific molecular programs remains limited. During the estrous cycle, the ovary functions as a highly heterogeneous, dynamically remodeling microenvironment composed of multiple somatic cell types, including granulosa cells, theca cells, fibroblasts, endothelial cells, smooth muscle cells, and diverse immune populations. Through tightly regulated paracrine signaling and dynamic remodeling of the extracellular matrix (ECM), these cell types collectively support follicular growth, selection, ovulation, and post-ovulatory remodeling [ 4 ]. Bulk ovarian tissue transcriptomics has provided important insights into stage-dependent gene expression programs across the porcine estrous cycle, revealing changes in steroidogenic pathways and in cytokine-cytokine receptor signaling, immune and inflammatory processes, ECM-receptor interactions, and cell-adhesion programs, thereby highlighting coordinated endocrine and microenvironmental remodeling at the tissue level [ 5 , 6 ]. However, bulk approaches inherently average signals across constituent cell types, thereby obscuring the distinct contributions of individual compartments and their cell-type-specific molecular responses. In recent years, advances in single-cell RNA sequencing (scRNA-seq) have transformed the study of complex tissues by enabling high-resolution profiling of cellular diversity, cell states, and cell–cell interactions. Although scRNA-seq has been widely applied to cell atlas construction and mechanistic studies in ovaries across multiple species [ 7 – 9 ], comprehensive cell-type–resolved analyses of the porcine ovary at key physiological stages of the estrous cycle remain limited. Consequently, the cellular composition, lineage trajectories, metabolic adaptations, and intercellular communication networks of the porcine ovary during the transition from proestrus to estrus remain poorly characterized. To elucidate the multicellular regulatory mechanisms underlying the proestrus-to-estrus transition in the porcine ovary, we performed single-nucleus RNA sequencing (snRNA-seq) on ovaries collected at the proestrus and estrus stages. We generated a single-cell–resolution transcriptomic atlas of the porcine ovary that captured major somatic cell types and their stage-dependent remodeling. We further resolved granulosa cell subtypes, reconstructed their developmental trajectory from proestrus to estrus, and inferred upstream regulators of cell-state transitions via transcription factor activity analysis. Based on this, we examined putative metabolic adaptations of granulosa cells between the proestrus and estrus stages. At the tissue-ecosystem level, we reconstructed global and cell-type–specific communication networks and prioritized ligand–target relationships, revealing coordinated remodeling of intercellular signaling across ovarian cell types. Collectively, these analyses delineate cell-type–specific regulatory programs operating during the proestrus-to-estrus transition in the porcine ovary. Materials and methods Animals The experimental animals included two proestrus and two estrus Landrace × Large White crossbred gilts, ~ 240 days of age and ~ 140 kg body weight. The animals used in this study were provided by Zhaoqing DaBeiNong Agriculture, Animal Husbandry & Food Co., Ltd. Estrous status was monitored twice daily (morning and evening) by assessing vulvar morphology and behavioral signs, combined with the standing reflex test. Only gilts with regular estrous cycles were included for subsequent experiments. Estrus onset was defined as the first time point at which the standing reflex test was positive. Ovaries were collected from the estrus group within 6 h of estrus onset, whereas ovaries from the proestrus group were collected 48 h before the anticipated onset of estrus. Following collection, ovaries were grossly examined to confirm physiological stage. Ovaries were rinsed with pre-chilled 1×PBS to remove residual blood and debris, immediately placed on ice, and transported to a sterile laboratory environment for downstream analyses. The animals were stunned by electrical shock prior to slaughter. Following stunning, they were rapidly exsanguinated to ensure loss of consciousness and death. No anaesthetic agents or injection procedures were administered specifically for this study. All procedures were carried out by certified personnel trained in humane slaughter methods, in strict accordance with relevant animal welfare regulations and animal euthanasia guidelines. Single nucleus suspension preparation and sequencing To prepare a single-nucleus suspension, the tissue was thawed, minced, and transferred to a Dounce homogenizer (TIANDZ) containing 1 mL of homogenization buffer consisting of 250 mM sucrose (Ambion), 10 mg/mL BSA (Ambion), 5 mM MgCl₂ (Ambion), 0.12 U/µL RNasin Plus (Promega, N2115), and 1× cOmplete Protease Inhibitor Cocktail (Roche, 11697498001). The tissue was then gently homogenized using a loose pestle with 25–50 strokes. The homogenate was filtered through a 40-µm cell strainer into a centrifuge tube and centrifuged at 4°C at 500 × g for 5 min to pellet nuclei. The pellet was resuspended in nuclei resuspension buffer (320 mM sucrose, 10 mg/mL BSA, 3 mM CaCl₂, 2 mM magnesium acetate, 0.1 mM EDTA, 10 mM Tris-HCl, 1 mM dithiothreitol (DTT), 1× cOmplete Protease Inhibitor Cocktail, and 0.12 U/µL RNasein) to obtain a single-nucleus suspension at 1,000 nuclei/µL. The single-nucleus suspension was processed using the DNBelab C Series High-throughput Single-Cell RNA Library Preparation Kit (V3.0) to generate barcoded libraries, following droplet generation, magnetic bead collection, reverse transcription, cDNA amplification, and purification. The DNBelab C workflow produces two libraries: a cDNA library and an oligonucleotide library, derived from mRNA-capture magnetic beads and droplet-identification microbeads, respectively. After library construction, both libraries were sequenced on a DNBSEQ-T7 platform using a paired-end 100-bp (PE100) strategy. snRNA-seq data analysis First, the reference genome index was built using the mkref function in DNBC4tools, using Sus_scrofa.Sscrofa11.1.dna.toplevel.fa as the reference genome and Sus_scrofa.Sscrofa11.1.115.gtf as the annotation file. Subsequently, DNBC4tools (v2.1.0) was used to preprocess raw sequencing data to generate a single-nucleus gene expression matrix and barcode information. To mitigate ambient RNA contamination, SoupX (v1.6.2) was applied to decontaminate the expression matrix using default parameters [ 10 ]. Potential doublets were identified and removed using DoubletFinder (v2.0.3) with default parameters [ 11 ]. Downstream analyses were performed in Seurat (v4.4.0). Quality control was applied to nuclei, retaining those with 200 < nFeature_RNA < 6,000 and 300 < nCount_RNA < 20,000 for subsequent analyses. Each sample was normalized using NormalizeData, and highly variable genes were identified with FindVariableFeatures (method = “vst”, nfeatures = 2,000). Batch effects were corrected and samples were integrated using Harmony (v1.2.3) [ 12 ]. Dimensionality reduction and clustering were performed on the integrated expression matrix. Clusters were identified using FindClusters (resolution = 0.8), and RunUMAP was used for visualization. Cluster-specific marker genes were identified using FindAllMarkers, and differentially expressed genes were defined as those with adjusted P 0.25. Cell cycle index estimation Cell-cycle status was assessed using high-confidence cell-cycle marker genes compiled by Stephen et al., together with AUCell (v1.22.0) to score cycling and non-cycling gene signatures for each cell type. The cycling index was calculated for each cell type as the log ratio of the number of cycling cells to the number of non-cycling cells [ 13 ]. Gene regulatory network inference The porcine cisTarget database was constructed following the standard SCENIC workflow ( https://github.com/aertslab/create_cisTarget_databases ). The “.tbl” mapping file required for motif-TF annotation was generated using scripts and guidance provided by JoGraesslin ( https://github.com/JoGraesslin/Zebrafish_SCENIC ). Using this database, we applied pySCENIC to construct the gene regulatory network (GRN) of porcine granulosa cells. Specifically, we used GRNBoost2 to infer co-expression links between transcription factors (TFs) and candidate target genes; we then performed cis-regulatory motif enrichment for each co-expression module using pySCENIC ctx; subsequently, we computed regulon activity in each cell using AUCell. Finally, calcRSS was used to identify key TFs/regulons specific to each granulosa cell subtype [ 14 ]. Identification of cell-type–specific TF modules was based on the connection specificity index (CSI) framework proposed by Suo et al. Briefly, Pearson correlation coefficients (PCC) between regulon pairs were computed using regulon activity scores across cells. For a given regulon pair A and B, CSI was defined as the proportion of regulons whose PCC with both A and B is lower than the PCC between A and B, thereby quantifying the specificity of the A–B association. Subsequently, Ward’s hierarchical clustering was used to construct TF/regulon modules based on the regulon-regulon similarity matrix (CSI > 0.9) [ 14 ]. Enrichment analysis GO and KEGG enrichment analyses were performed on the target gene set using Metascape with default parameters [ 15 ]. Transcriptional similarity analysis To systematically assess transcriptional similarity among different granulosa cell subtype, we used MetaNeighbor (v1.20.0) to compute AUROC scores for pairwise comparisons [ 16 ]. In addition, we calculated Spearman correlation coefficients between cell types based on the mean expression of highly variable genes [ 17 ]. Evaluation of metabolic activity Metabolic activity scores for granulosa cell subtypes were calculated using the Single-Cell-Metabolic-Landscape pipeline. First, the mean expression of each metabolic gene was calculated for each cell type. Next, gene-level expression in each cell type was normalized to the mean expression of the same gene across all cell types to obtain relative expression values. For each metabolic pathway, a weighted average of the relative expression values of pathway genes was computed. To reduce bias from lowly expressed genes and dropout, only genes with non-zero expression that were detected in ≥ 25% of cells were included. Finally, statistical significance of pathway activity in each cell type was evaluated using a permutation test [ 18 ]. In addition, scFEA was used to infer cell-level metabolic flux profiles from single-cell RNA sequencing data. After generating the granulosa cell count matrix, porcine gene symbols were mapped to their human orthologs. Subsequently, scFEA.py was used to estimate metabolic flux profiles and flux abundances for each granulosa cell [ 19 ]. Cell-cell interaction analysis To quantify the frequency and strength of cell–cell interactions among ovarian cell types, porcine gene symbols were first mapped to their human orthologs. CellChat (v1.6.1) was then applied using the computeCommunProb and aggregateNet functions. To account for potential effects of cell-type abundance, the population.size option was enabled [ 20 ]. In addition, NicheNet (v2.0.5) was used to infer ligand–target regulatory networks for granulosa cell subtypes in the porcine ovary [ 21 ]. Western blot analysis Porcine ovarian tissue was lysed in RIPA buffer (Beyotime, P00113B, Shanghai, China) and homogenized using a tissue homogenizer to extract total protein. Protein samples were separated by SDS-PAGE and transferred to a PVDF membrane (Millipore, ISEQ00010, USA). After transfer, membranes were blocked at RT for 2 h and incubated with primary antibodies overnight at 4 ℃, followed by incubation with the corresponding secondary antibodies at RT for 2 h (Table S1 ). Band signals were captured using a Tanon 5200 imaging system (Tanon, Shanghai, China), and band intensities were quantified using AlphaView SA software (ProteinSimple, California, USA). RT-qPCR Total RNA was extracted from ovarian tissue using the SPARKeasy RNA Extraction Kit (Sparkjade, AC0202, Qingdao, China). cDNA was synthesized by reverse transcription using the SPARKscript II RT Plus Kit (Sparkjade, AG0304). RT-qPCR was performed on a Bio-Rad CFX96 Real-Time PCR System using SYBR Premix Ex Taq™ (BioRad, CFX96, Hercules, CA, USA). Primer sequences are provided in Table S2 . Statistical analysis Statistical analyses were performed using GraphPad Prism8. Bioassay data are presented as means ± SD. Comparisons between two groups were performed using a two-tailed Student’s t-test. Results Construction of a single-nucleus transcriptomic atlas of porcine ovaries at proestrus and estrus To comprehensively characterize regulatory mechanisms underlying the proestrus-to-estrus transition in the porcine ovary, we collected ovarian samples at the proestrus and estrus stages for snRNA-seq (Fig. 1 A). After stringent quality control, 27,337 nuclei were retained for downstream analyses; specifically, Proestrus_1, Proestrus_2, Estrus_1, and Estrus_2 contained 8,328; 8,950; 5,152; and 4,907 nuclei, respectively (Figure S1 A-B). We next identified 19 clusters in the integrated porcine ovarian snRNA-seq dataset using uniform manifold approximation and projection (UMAP)-based clustering (Fig. 1 B). Cluster-specific marker expression supported the annotation of 10 major cell types, including endothelial cells ( VWF ), epithelial cells ( KRT7 ), fibroblasts ( PDGFRA ), glia ( NRXN1 ), granulosa cells ( FSHR , INHBA ), lymphatic endothelial cells ( FLT4 ), immune cells ( CSF1R ), pericytes ( NOTCH3 ), smooth muscle cells ( TAGLN ), and theca cells ( CYP17A1 ) (Fig. 1 C-D) [ 22 ]. Based on these annotations, we observed differences in the proportion of different cell types in the ovary during proestrus and estrus (Fig. 1 E). Furthermore, we performed cell-type–specific differential expression analysis to identify cell-type–specific gene expression profiles for each ovarian cell type (Fig. 1 F). GO enrichment analysis of cell-type marker genes identified cell-type–specific functional terms. For example, granulosa cell marker genes were enriched for terms related to metabolic regulation and hormone response, whereas theca cell marker genes were enriched for GO terms associated with steroid metabolic processes (Fig. 1 G). We further calculated cell-cycle scores for each ovarian somatic cell type and observed an overall decrease in cell-cycle activity during estrus relative to proestrus. Notably, granulosa cells exhibited significantly higher cell-cycle scores during proestrus, consistent with active follicular growth preceding the preovulatory transition (Fig. 1 H) [ 2 ]. Together, these results indicate extensive, stage-specific remodeling of cellular states in the ovary during the proestrus-to-estrus transition in pigs. Molecular characterization of porcine granulosa cell subtypes Granulosa cells play a pivotal role in follicular development, influencing ovarian function and oocyte maturation. In this study, we isolated granulosa cells from ovarian tissue and conducted cell clustering analysis using UMAP, which identified 11 distinct clusters. Further analysis revealed the presence of six granulosa cell subtypes, each characterized by unique marker gene expression profiles: atretic follicle granulosa cells (expressing GADD45A ), cumulus cells (expressing VCAN and HTRA1 ), cycling granulosa cells (expressing TOP2A ), early granulosa cells (expressing WT1 ), mural granulosa cells (expressing INHBA ), and steroidogenic granulosa cells (expressing LHCGR ) (Fig. 2 A-B) [ 23 , 24 ]. To elucidate the developmental trajectory of these subtypes, we mapped them onto a pseudotime trajectory. This analysis revealed a continuous differentiation path, where early granulosa cells and atretic follicle granulosa cells occupied the early developmental stages, progressing into two distinct branches. The first branch included cumulus cells and cycling granulosa cells, while the second branch gave rise to mural granulosa cells and steroidogenic granulosa cells (Fig. 2 C). This branching suggests a dynamic differentiation process, where atretic granulosa cells appear transcriptionally similar to early granulosa cells, but are suppressed in their developmental progression. As follicles mature, granulosa cells transition from an early developmental state into two divergent pathways: one leading to cumulus cells, which are intimately associated with the oocyte, and the other giving rise to mural granulosa cells, which form the follicle wall and prepare for luteinization. To further explore the molecular mechanisms underlying granulosa cell fate determination, we performed differential gene expression analysis along the pseudotime trajectory. We identified "granulosa cell fate-specific" genes that exhibited differential expression during early differentiation and at branch points. Pathway analysis of these fate-specific genes revealed distinct molecular signatures for each branch. Early granulosa cells were highly enriched in pathways related to “Tight junction”, “Ras signaling pathway”, “JAK−STAT signaling pathway”, “cAMP signaling pathway”, and “Apelin signaling pathway”. In contrast, fate-specific genes in the two branches highlighted distinct functional pathways. Cell fate 1, representing the cumulus and cycling granulosa cell lineage, was significantly enriched in the “TNF signaling pathway”, “PPAR signaling pathway”, “Notch signaling pathway”, “Glycerophospholipid metabolism”, and “Glutathione metabolism”. On the other hand, cell fate 2, which encompasses mural and steroidogenic granulosa cells, was associated with pathways such as “TGF − β signaling pathway”, “Pyrimidine metabolism”, “Purine metabolism”, “p53 signaling pathway”, and “AMPK signaling pathway” (Fig. 2 D). These findings indicate that granulosa cells undergo dynamic transcriptional and metabolic reprogramming during differentiation and possess unique molecular characteristics corresponding to lineage-specific functions during folliculogenesis. To further investigate the transcriptional regulation of granulosa cell differentiation, we applied the SCENIC approach to infer active TF networks in each granulosa cell subtype. SCENIC identified TFs with cell-type-specific activity, revealing distinct regulatory profiles for each granulosa cell subtype (Fig. 2 E). Specifically, atretic follicle granulosa cells were enriched in FOXO4 , ETS2 , and SOX4 ; cumulus cells exhibited high activity of NR4A3 , SMAD1 , and ELF4 ; cycling granulosa cells were enriched in E2F4 , MYBL2 , and ZFP42 ; early granulosa cells showed elevated activity of IRF6 , TFCP2 , and BHLHA15 ; mural granulosa cells showed activation of RELA , YY1 , and MAF ; and steroidogenic granulosa cells displayed high activity of JDP2 , NR5A2 , and SREBF2 . Additionally, it is well known that TFs often coordinate gene expression in a combinatorial manner. To systematically characterize these combinatorial patterns, we constructed a transcription factor co-expression network across the granulosa cell subtypes using the CSI. This analysis revealed distinct TF co-expression modules that were highly specific to each granulosa cell subtype (Fig. 2 F). We visualized the module activity patterns on the UMAP plot, confirming that each granulosa cell subtype possesses a unique combination of active regulatory modules (Fig. 2 G). Specifically, module 1 showed high activity primarily in mural granulosa cells and steroidogenic granulosa cells; module 2 exhibited high activity in early granulosa cells and atretic follicle granulosa cells; module 3 displayed high activity in cumulus cells and cycling granulosa cells; and module 4 showed significant activity in cumulus cells and mural granulosa cells. These module activity patterns align closely with the pseudotime developmental trajectory of granulosa cell subtypes, further supporting the idea that the transcriptional regulatory programs encoded by these modules are involved in the specific functional differentiation of granulosa cells. Estrous cycle–associated transcriptional changes in granulosa cell subtypes After identifying granulosa cell subtypes, we investigated subtype-specific transcriptional changes between the proestrus and estrus phases. We first quantified the relative abundance of each granulosa cell subtype across stages. Compared with the proestrus ovary, the proportions of cumulus cells, cycling granulosa cells, early granulosa cells, and mural granulosa cells were reduced during estrus, whereas steroidogenic granulosa cells constituted the predominant population (Fig. 3 A). These stage-dependent shifts in cellular composition underscore the dynamic remodeling of granulosa cell states during follicular maturation. We next performed differential expression analysis for the four granulosa cell subtypes that were well represented in both phases. Volcano plots summarized subtype-specific differentially expressed genes (DEGs), revealing 109 genes upregulated in estrus cumulus cells and 399 upregulated in proestrus; 51 upregulated in estrus cycling granulosa cells and 128 upregulated in proestrus; 30 upregulated in estrus early granulosa cells and 448 upregulated in proestrus; and 54 upregulated in estrus mural granulosa cells and 79 upregulated in proestrus (Fig. 3 B). KEGG enrichment analysis of subtype-specific DEGs indicated that, during proestrus, granulosa cell subtypes preferentially engaged developmental and growth-associated signaling pathways (e.g., Wnt and TGF-β signaling), whereas fewer pathways were consistently enriched across subtypes during estrus. Notably, focal adhesion was significantly enriched in estrus cumulus cells, while genes involved in mitochondrial energy metabolism were preferentially upregulated in estrus mural granulosa cells (Fig. 3 C-D). Collectively, these findings highlight pronounced stage-dependent functional reprogramming of granulosa cells during the transition from proestrus to estrus. Strikingly, cross-subtype comparison of DEGs identified ERBB4 as the only protein-coding gene significantly upregulated across all four granulosa cell subtypes during estrus (Fig. 3 E). To validate this observation, we examined ERBB4 expression in ovaries from both stages. Consistently, snRNA-seq data showed a significant increase in ERBB4 transcript abundance at the whole-ovary level during estrus relative to proestrus (Fig. 3 F), which was further corroborated by RT-qPCR (Fig. 3 G). Together, these orthogonal validations support ERBB4 as an estrus upregulated factor in pigs and implicate ERBB4 as a candidate regulator of estrus-associated follicular function. Metabolic plasticity and heterogeneity of granulosa cells during the proestrus-to-estrus transition Metabolic programs in the ovary are extensively reconfigured during the transition from proestrus to estrus [ 6 ]. To characterize metabolic heterogeneity among granulosa cell subtypes and their adaptive responses across the estrous cycle, we applied a single-cell metabolic landscape workflow to compare metabolic gene-expression signatures of each granulosa cell subtype between proestrus and estrus. We first quantified global metabolic pathway activity across granulosa cell subtypes at both stages. Overall metabolic activity was significantly reduced in all granulosa cell subtypes during estrus, reflecting a decreased metabolic demand of granulosa cells during this stage (Fig. 4 A). Notably, despite this global downshift, several pathways displayed subtype-specific increases during estrus. For example, fructose and mannose metabolism was elevated in cumulus cells during estrus relative to proestrus; steroid biosynthesis was markedly increased in mural granulosa cells and cycling granulosa cells; and pantothenate and CoA biosynthesis was enhanced in early granulosa cells and mural granulosa cells (Fig. 4 B). Collectively, these results indicate pronounced stage-dependent metabolic remodeling during the proestrus-to-estrus transition: while overall metabolic activity declines during estrus, discrete pathways are selectively augmented in a subtype-dependent manner, consistent with metabolic rerouting to support estrus functional programs [ 25 ]. We next compared estrus-versus-proestrus differences in metabolic module flux within each granulosa cell subtype, revealing widespread, significant flux alterations across stages. In particular, glutamine utilization flux was increased in cumulus cells and mural granulosa cells during estrus (Fig. 4 C). In parallel, analysis of the snRNA-seq dataset showed that expression of the glutamine transporter SLC1A5 was significantly higher in estrus ovaries than in proestrus ovaries (Fig. 4 D). This upregulation was independently validated by RT–qPCR and Western blotting, which confirmed significantly increased SLC1A5 expression at both the transcript and protein levels during estrus (Fig. 4 E-F). In summary, our findings demonstrate that granulosa cells during the estrous phase upregulate glutamine uptake, likely to support the bioenergetic and biosynthetic demands of specific cell lineages and developmental stages. Proestrus-to-estrus transcriptomic changes in non-granulosa ovarian somatic cells During the transition from the proestrus to the estrous phase in mammals, ovarian non-granulosa cell populations also undergo extensive transcriptional remodeling [ 3 , 26 , 27 ]. Accordingly, we performed differential expression analysis of the remaining nine major cell types identified in the pig ovarian cell transcriptome map—namely theca cells, epithelial cells, fibroblasts, smooth muscle cells, endothelial cells, lymphatic endothelial cells, pericytes, immune cells, and glia—between proestrus and estrus. Consistent with the patterns observed in granulosa cells, most somatic cell types exhibited a larger number of genes upregulated during proestrus than during estrus. Notably, endothelial cells, glia, theca cells, lymphatic endothelial cells, and smooth muscle cells displayed relatively high numbers of DEGs, whereas ovarian surface epithelial cells, fibroblasts, immune cells, and pericytes exhibited substantially fewer DEGs, suggesting cell-type-specific degrees of transcriptional responsiveness to the transition from the proestrus to the estrus phase (Fig. 5 A). To interpret the functional implications of these transcriptional changes, we conducted GO enrichment analysis for DEGs in each cell type. Across cell populations, enriched biological processes were broadly related to cell proliferation, cell motility, and migration, supporting the notion that the proestrus-to-estrus transition represents a coordinated, ovary-wide remodeling event. This convergence further suggests that diverse somatic compartments may be regulated by shared upstream cues that collectively govern population expansion, positional rearrangement, and tissue reorganization. In addition, glial cells showed enrichment of differentiation-related terms, implying that a transition in cell state may occur within the ovarian neuro-associated microenvironment during the proestrus-to-estrus transition (Fig. 5 B). We next performed KEGG pathway enrichment analysis for DEGs in each somatic cell type. Multiple cell populations consistently showed enrichment of focal adhesion, Rap1, PI3K–Akt, TGF-β, HIF-1, Hippo, and cAMP signaling pathways. This shared enrichment pattern points to widespread remodeling of ECM–cell adhesion and intercellular connectivity during the proestrus-to-estrus transition, accompanied by activation of growth factor, mechanotransduction, and hypoxia responsive programs that support proliferation and migration. Importantly, several cell-type-specific signatures were also evident. Endothelial cells were enriched for tight junction and Rap1/HIF-1 signaling, consistent with angiogenic activation and barrier remodeling during the follicular period. Theca cells were specifically enriched in steroid biosynthesis and glutathione metabolism, suggesting coordinated endocrine reprogramming and adaptation of redox homeostasis. Smooth muscle cells showed enrichment of calcium signaling, phospholipase D signaling, and sphingolipid signaling, implicating contractility- and membrane-dynamics-associated pathways potentially relevant to ovulation-associated mechanical events (Figure S2 A-B). We further quantified the activity of focal adhesion, Rap1, PI3K–Akt, TGF-β, HIF-1, Hippo, and cAMP signaling pathways across somatic cell types in proestrus and estrus. Consistently, pathway activity was higher during proestrus than during estrus across cell populations, in line with the global transcriptional activation observed in the proestrus stage (Fig. 5 C). Finally, we assessed DEG overlap across cell types and identified TGFBR3 and TSEN2 as the only genes differentially expressed across all examined cell populations (six cell types in the overlap analysis) (Fig. 5 D). TGFBR3 expression was significantly higher during proestrus than estrus, whereas TSEN2 showed the opposite pattern, with elevated expression during estrus (Fig. 5 E). RT-qPCR validation confirmed these stage-associated expression trends (Fig. 5 F). Collectively, these findings establish TGFBR3 and TSEN2 as reliable molecular markers of the transition from the proestrus to estrous phase in pigs, identifying them as potential regulatory nodes implicated in multicellular remodeling during this transition. Cell-cell communication networks during the proestrus-to-estrus transition After delineating specific transcriptional differences among porcine ovarian somatic cell populations across proestrus and estrus, we further explored how changes in the ovarian microenvironment during this transition influence cell state transitions and fate determination. To this end, we inferred the frequency and strength of intercellular communication among ovarian cell types at both stages. Overall, the proestrus ovary exhibited higher interaction frequency and greater communication strength than the estrus ovary, indicating a globally more active signaling milieu before ovulation (Fig. 6 A-B). We then systematically predicted ligand–target regulatory relationships influencing each granulosa cell subtype. Across subtypes, BMP2 emerged as the dominant predicted stimulatory ligand (Fig. 6 C). Consistent with this inference, BMP2 expression was significantly elevated in estrus ovaries relative to proestrus, and this stage-associated increase was further confirmed at the protein level by Western blotting, which showed significantly higher BMP2 abundance during estrus (Fig. 6 D-E). Finally, we performed KEGG enrichment analysis on the predicted target-gene sets for each granulosa cell subtype. The results revealed a highly concordant set of downstream functional modules shared across the granulosa cell subtypes, including TGF-β, MAPK, Wnt, and AMPK signaling pathways, as well as pathways related to intercellular connectivity and communication, such as focal adhesion, adherens junction, and gap junction (Fig. 6 F). These findings collectively indicate that despite transcriptional heterogeneity, granulosa cell subtypes converge on common intercellular signaling programs, which coordinate their functional responses during the proestrus‑to‑estrus transition. Discussion Sow reproductive performance is a primary determinant of production efficiency in the swine industry [ 28 ]. The timing and intensity of estrus critically influence reproductive outcomes [ 29 ]. Notably, the transition from proestrus to estrus represents a key physiological window for elucidating ovarian regulatory mechanisms that govern estrus expression [ 30 ]. In commercial production, delayed estrus and silent estrus remain persistent challenges that compromise reproductive efficiency, underscoring the need for improved mechanistic insight to enable early diagnosis and targeted intervention [ 31 ]. In this study, we generate the first snRNA atlas of porcine ovaries across proestrus and estrus. Focusing on granulosa cells, we resolve cellular heterogeneity and reconstruct differentiation trajectories to capture the gene-expression cascade accompanying granulosa cell maturation. In parallel, stage-specific differential-expression analyses across major ovarian somatic cell types identify cell type–specific regulatory programs driving the proestrus–to–estrus transition. Importantly, we further delineate features of ovarian microenvironment remodeling during this process, providing candidate molecular targets for modulating estrus expression and improving sow reproductive performance. During the proestrus–to–estrus transition, snRNA atlas resolved the granulosa compartment into six subtypes: atretic follicular granulosa cells, cumulus cells, cycling granulosa cells, early granulosa cells, mural granulosa cells, and steroidogenic granulosa cells. This is consistent with the granulosa cell subtypes described in adult mammalian ovaries (e.g., human, mouse, and sheep) [ 8 , 23 , 24 , 27 ]. Trajectory inference further reconstructed a granulosa-cell differentiation continuum across the pre-estrus–to–estrus transition, closely resembling trajectories described in adult sheep ovaries [ 23 ]. Specifically, early granulosa cells occupied an initial state and bifurcated into two major branches corresponding to mural granulosa cells and cumulus cells, indicating that granulosa-cell lineage progression and functional specialization are broadly conserved among mammals. Notably, we identified transcription factors associated with granulosa cell subtype specialization. In particular, FOXO4 and SOX4 showed elevated activity in atretic follicular granulosa cells. Prior studies indicate that FOXO4 and SOX4 can modulate granulosa cell proliferation and apoptosis [ 32 , 33 ], and granulosa cell apoptosis is a hallmark event initiating follicular atresia [ 34 ], suggesting that FOXO4 and SOX4 are crucial regulatory hubs driving granulosa cells towards an atretic fate. Furthermore, we identified ERBB4 as a robust estrus-associated gene: it was consistently upregulated across all granulosa-cell subtypes in estrus relative to proestrus, and we found that ERBB4 is mainly expressed in ovarian granulosa cells, consistent with a study by Veikkolainen et al [ 35 ]. Functionally, conditional disruption of Erbb4 in murine granulosa cells results in marked ovarian dysfunction, including an asynchronous estrous cycle, reduced ovulation, and subfertility [ 35 ]. In addition, studies in poultry have shown that precisely regulating ERBB4 activity can promote follicular development and maturation, potentially improving reproductive performance [ 36 ]. After characterizing the metabolic features of various granulosa cell subtypes, we observed a stage-dependent modulation of metabolic pathways during the transition from proestrus to estrus. Despite an overall decrease in metabolic activity during estrus, we identified specific enhancement of glutamine utilization, particularly in mural granulosa cells and cumulus cells. Glutamine, a critical energy source for cells, plays a central role in follicle and oocyte maturation [ 37 , 38 ]. Previous work by Zhang et al. highlighted that glutamine levels in follicular fluid act as a key metabolic signal in the regulation of ovulation [ 39 ]. A reduction in glutamine levels in follicular fluid is thought to represent a “signaling state” that promotes the transition of follicles to maturity. Consistent with this, our findings suggest that a glutamine-centered metabolic program orchestrates the transition from proestrus to estrus. Enhanced glutamine uptake during estrus likely serves dual functions: first, in mural granulosa cells, it provides TCA cycle replenishment and reducing power to support steroidogenesis and tissue remodeling; second, in cumulus cells, glutamine uptake contributes to cumulus expansion. Furthermore, the increased cellular uptake of glutamine may induce a local decrease in follicular fluid glutamine levels, thereby creating a microenvironmental gradient that is consistent with the mechanism by which follicular fluid glutamine regulates follicular maturation. Taken together, this indicates that ERBB4 and the glutamine transporter SLC1A5 represent key regulatory targets for modulating granulosa cell physiology during the proestrus-to-estrus transition. Targeting these molecules may offer strategies for improving estrus performance and enhancing reproductive efficiency in sows. The transition from the proestrus to estrus phase involves the coordinated remodeling of multiple ovarian somatic and granulosa cell populations [ 40 ]. Consistent with this concept, differential expression analysis of major ovarian somatic cell types revealed significant enrichment of the TGF-β signaling pathway, with TGFBR3 exhibiting stage-dependent expression changes across most somatic cell populations. Importantly, TGFBR3 is not a canonical signaling kinase receptor but rather a widely expressed co-receptor that modulates the activity of TGF-β superfamily ligands and facilitates receptor complex formation [ 41 ]. In agreement with transcriptome enrichment findings, our ligand-receptor network analysis further revealed extensive TGF-β signaling activity between somatic cell compartments and granulosa cell subtypes, indicating that this pathway serves as a conserved intercellular communication network during the proestrus-to-estrus transition. This pathway exhibits diverse functional roles in the mammalian ovary [ 42 ]. For instance, the oocyte-derived TGF-β superfamily member GDF9 is essential for early follicular development; oocytes from Gdf9-knockout mice fail to recruit surrounding ovarian somatic cells to form follicles [ 43 ]. Additionally, granulosa cell-derived TGF-β ligands inhibin and activin are critical components of the FSH feedback axis regulating pituitary-ovarian function; disruption of inhibin and activin signaling perturbs this endocrine feedback loop and results in severe ovarian defects [ 44 ]. Furthermore, AMH, functioning as an intraovarian growth factor, suppresses primordial follicle recruitment and attenuates FSH responsiveness in growing follicles, further exemplifying how TGF-β signaling integrates follicular dynamics with endocrine sensitivity [ 45 ]. Collectively, we hypothesize that the TGF-β signaling pathway serves as a central regulatory axis coordinating intra-ovarian signaling and granulosa cell state transitions during the proestrus-to-estrus progression. Conclusion In summary, this study constructed a comprehensive ovarian cell atlas spanning the proestrus and estrus phases in sows, revealing coordinated multicellular remodeling during the proestrus-to-estrus transition. At the granulosa cell level, ERBB4 emerged as a key regulatory factor associated with the estrus phase. Concurrently, the stage-specific upregulation and metabolic flux changes of SLC1A5 suggest that a glutamine-centered metabolic program supports the energetic and biosynthetic demands surrounding ovulation. At the microenvironmental and intercellular communication level, TGF-β signaling was identified as a central regulatory axis governing the proestrus-to-estrus transition. These findings not only provide cell-type-resolved evidence for ERBB4 , SLC1A5 , and TGF-β pathway components as potential therapeutic targets but also establish a foundational resource for developing improved strategies for estrus detection, early diagnosis, and precision reproductive management in sows. Declarations Data availability The datasets generated during the current study are available in the Genome Sequence Archive (GSA, https://ngdc.cncb.ac.cn/gsa) repository under accession number CRA039587. Contributions The work presented here was carried out in collaboration among all authors. Z.Z., J.W., W.S., and Y.T. conceptualised the study, curated the data, and developed the methodology. Z.Z., Q.W., M.L., F.W., T.W., Y.Z., and J.C. performed data analyses, created visualisations, and wrote the original draft. Z.Z. and Y.T. were involved in data collection and analysis and in the preparation of figures. W.S. and Y.T. contributed to revising and editing the manuscript. All authors reviewed the manuscript. Conflict of Interest The authors declare no conflict of interest. Funding This work was supported by the National Key Research and Development Program of China (2023YFD1300504), the Key R&D Program of Shandong Province (2025LZGC007), and Shandong Modern Agricultural Industry Technology System - Swine Innovation Team (SDAIT-08-02). Acknowledgements Not applicable. Ethics declarations Clinical trial number: not applicable. All animal procedures were approved by the Ethics Committee of Qingdao Agricultural University (Approval No. SYXK-20220-021). 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Supplementary Files Supportinginformation.docx SupportinginformationUncroppedblots.docx SupportinginformationUncroppedblots3.9.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 May, 2026 Reviews received at journal 12 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers invited by journal 19 Mar, 2026 Editor assigned by journal 19 Mar, 2026 Editor invited by journal 10 Mar, 2026 Submission checks completed at journal 09 Mar, 2026 First submitted to journal 09 Mar, 2026 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. 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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-8972446","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610580594,"identity":"88cb79f6-fe65-4424-9519-89f84e0ba63a","order_by":0,"name":"Zexin Zhu","email":"","orcid":"","institution":"Zhaoqing DaBeiNong Agriculture, Animal Husbandry \u0026 Food Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Zexin","middleName":"","lastName":"Zhu","suffix":""},{"id":610580595,"identity":"74259ec9-a54a-4a2f-b97e-80e26328acc9","order_by":1,"name":"Qinghui Wang","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Qinghui","middleName":"","lastName":"Wang","suffix":""},{"id":610580596,"identity":"1ac3b676-5a1a-42ef-a9cf-5071dfc41201","order_by":2,"name":"Mingjun Li","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Mingjun","middleName":"","lastName":"Li","suffix":""},{"id":610580597,"identity":"d8f950b2-8cd6-4cca-93db-788dad280fb4","order_by":3,"name":"Fan Wu","email":"","orcid":"","institution":"Guangzhou Branch of Guangxi DaBeiNong Agriculture, Animal Husbandry \u0026 Food Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Wu","suffix":""},{"id":610580598,"identity":"f1114262-4fa2-4b9c-8eaf-dd08ca1e298b","order_by":4,"name":"Ting Wen","email":"","orcid":"","institution":"Guangzhou Branch of Guangxi DaBeiNong Agriculture, Animal Husbandry \u0026 Food Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Wen","suffix":""},{"id":610580599,"identity":"a1d9fc58-b196-4c23-a9f9-0aba2d030199","order_by":5,"name":"Yitao Zhang","email":"","orcid":"","institution":"Guangzhou Branch of Guangxi DaBeiNong Agriculture, Animal Husbandry \u0026 Food Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Yitao","middleName":"","lastName":"Zhang","suffix":""},{"id":610580600,"identity":"45129fb4-bea2-4373-af7f-50837c855b55","order_by":6,"name":"Jie Chen","email":"","orcid":"","institution":"Guangning County Animal Husbandry and Veterinary Bureau","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Chen","suffix":""},{"id":610580601,"identity":"399f4e74-f6ea-4a14-b678-c8bf4393b7eb","order_by":7,"name":"Junjie Wang","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Wang","suffix":""},{"id":610580602,"identity":"96b4db21-feec-4481-a762-2e2f269fd7d3","order_by":8,"name":"Wei Shen","email":"","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Shen","suffix":""},{"id":610580603,"identity":"eb6f39f2-a823-4c96-80c3-ff768acaa042","order_by":9,"name":"Yu Tian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYDACZjBpA+HwEKODB6IljRQtEOowCVrs2ZmfPeZtOy/bPyOB8cHbNgZ5c8IOYzM35m27bTzjRgKz4dw2BsOdDQS1MJhJA7UkNtxIYAMyGBIMDhDUwv4NqPJc4vwbCey/idTCA7LlQOIGoC3MxGk5zFMmOedcsvHGMw+bgQwJww2EtLD3H98m8abMTnbe8eSDH96U2cgTtAUEmIDRwdgAQgwMEkSoBwLGHwwQ9aNgFIyCUTAKsAIAL5s51pKMvakAAAAASUVORK5CYII=","orcid":"","institution":"Qingdao Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2026-02-26 02:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8972446/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8972446/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105564748,"identity":"ddf16310-cc22-4a4d-928c-6bd83fb6df82","added_by":"auto","created_at":"2026-03-27 12:50:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":356489,"visible":true,"origin":"","legend":"\u003cp\u003esnRNA-seq of porcine ovaries at proestrus and estrus. A: Overview of the experimental design yielding droplet-based snRNA-seq from porcine ovaries. B: UMAP plots showing unsupervised cell clusters. Cells are colored by 19 clusters. C: Expressions of 11 cell-specific genes in porcine ovaries cells. D: UMAP plot showing different cell clusters annotated by canonical marker gene expression. Endo, Endothelial cells; Epi, Epithelial cells; Fibro, fibroblasts; GC, Granulosa cells; Imm, Immune cells; LEC, Lymphatic endothelial cells; Peri, Pericytes; SMC, Smooth muscle cells; TC, Theca cells. E: Percentage of different cell types in the ovary during proestrus and estrus. F: Heatmap demonstrating cell-type specific marker gene expression across different cell populations. G: Representative GO terms. H: The cell cycle index for each cell type.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8972446/v1/425a542574ef48c77de8590e.png"},{"id":105293851,"identity":"ab088fb0-913a-4c5a-82b5-5cbcb351980a","added_by":"auto","created_at":"2026-03-24 12:46:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":463146,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular characterization of granulosa cell subtypes. A: Dot plot showing representative marker genes across different cell types. Dot size is proportional to the fraction of cells expressing each gene. Color intensity corresponds to the relative expression level of each gene. B: UMAP plot showing different cell clusters annotated by canonical marker gene expression. C: Pseudotime-based developmental trajectories of six granulosa cell subtypes. D: The heatmap illustrates changes in granulosa cell gene expression across two cell fate branches (left). The bar graph presents the results of KEGG enrichment analysis for three distinct gene sets (right). E: Heatmap showcasing the top three differentially active TFs per-cell type. F: Identification of four TF modules based on the regulon CSI of the cell landscape in the granulosa cell. G: UMAP illustrating the average AUCell score distribution for different regulon modules.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8972446/v1/0073a5e888d1432f71f48c01.png"},{"id":105564635,"identity":"e416c14c-e0ae-4b2a-91d1-8a8f933ebe62","added_by":"auto","created_at":"2026-03-27 12:50:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":256242,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential gene expression in granulosa cells between proestrus and estrus stages. A: Percentage of granulosa cell subtypes during the proestrus and estrus phases. B: The volcano plot illustrates the results of differential gene expression analysis of granulosa cell subtypes during estrus and proestrus. C: KEGG enrichment analysis of downregulated differentially expressed genes. D: KEGG enrichment analysis of upregulated differentially expressed genes. E: Venn diagram showing the overlap of differentially expressed genes among granulosa cell subtypes between proestrus and estrus. F: The box plot illustrates the expression levels of \u003cem\u003eERBB4\u003c/em\u003e in the ovaries during the proestrus and estrus phases. G: Quantification of \u003cem\u003eERBB4\u003c/em\u003e expression levels in porcine ovaries during the proestrus and estrus phases by RT‑qPCR (n = 5 biologically independent replicates).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8972446/v1/c175c33f0b7f53eec7d9c810.png"},{"id":105293857,"identity":"44f15193-8b6c-46ef-ac26-b39715576ce5","added_by":"auto","created_at":"2026-03-24 12:46:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":406499,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolic reprogramming in granulosa cell subtypes. A: The box plots illustrate the metabolic activity of granulosa cell subtypes during the proestrus and estrus phases. Each point represents the activity score for an individual metabolic pathway. The Wilcoxon rank-sum test was used to compare groups. B: The heatmap illustrates the activity scores of major metabolic pathways detected in granulosa cell subtypes. C: The volcano plot illustrates the differences in metabolic fluxes across granulosa cell subtypes during the proestrus and estrus phases. D: The box plot illustrates the expression levels of \u003cem\u003eSLC1A5\u003c/em\u003e in ovarian tissue during the proestrus and estrus phases. E: Quantification of \u003cem\u003eSLC1A5\u003c/em\u003eexpression levels in porcine ovaries during the proestrus and estrus phases by RT‑qPCR (n = 5 biologically independent replicates). F: Detection of SLC1A5 protein expression in ovarian tissue during the proestrus and estrus phases. GAPDH served as an internal control to calculate relative protein expression (n = 5 biologically independent replicates). Full-length blots are presented in Figure S3.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8972446/v1/cb86027e1f1abf0b63cdf3ef.png"},{"id":105564480,"identity":"95fd575e-e087-4ca4-b362-6c3b6fc09e6b","added_by":"auto","created_at":"2026-03-27 12:49:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":373037,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential transcriptomic profiling of non‑granulosa somatic cells in the porcine ovary. A: The volcano plot illustrates the results of differential expression analysis in nine ovarian somatic cell subtypes during the proestrus and estrus phases. B: GO enrichment analysis results for differentially expressed genes in various cell types. C: Pathway activity scores across different ovarian somatic cell types during proestrus and estrus. FA, Focal adhesion. D: Number of differentially expressed genes shared across ovarian somatic cell types. CT, cell type. E: The histogram illustrates the expression levels of \u003cem\u003eTGFBR3\u003c/em\u003e and \u003cem\u003eTSEN2\u003c/em\u003e in ovarian tissue during the proestrus and estrus stages. F: Quantification of \u003cem\u003eTGFBR3\u003c/em\u003eand \u003cem\u003eTSEN2\u003c/em\u003e expression levels in porcine ovaries during the proestrus and estrus phases was performed using RT‑qPCR (n = 5 biologically independent replicates).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8972446/v1/1afed4e7a4b626578a47f023.png"},{"id":105293855,"identity":"c6c2862b-5339-453b-91fc-415e030d7780","added_by":"auto","created_at":"2026-03-24 12:46:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":546511,"visible":true,"origin":"","legend":"\u003cp\u003eCell–cell interactions in the ovarian microenvironment. A: Cell–cell interaction networks among ovarian somatic cell subtypes during the proestrus and estrus phases. B: Cell‑cell interaction networks in ovarian somatic cell types during proestrus and estrus. C: The heatmap illustrates the regulatory potential of top-ranked ligands, as identified by NicheNet analysis, on characteristic genes of different granulosa cell subtypes. D: The box plot illustrates the expression levels of \u003cem\u003eBMP2\u003c/em\u003ein ovarian tissue during the proestrus and estrus phases. E: Detection of BMP2 protein expression in ovarian tissue during the proestrus and estrus phases. GAPDH served as an internal control to calculate relative protein expression (n = 5 biologically independent replicates). Full-length blots are presented in Figure S3. F: The bubble chart illustrates the results of KEGG enrichment analysis for ligand–target gene.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8972446/v1/5e862fb40e3748e41110fd4d.png"},{"id":105569266,"identity":"06f5d45a-dda3-4b6a-9cdf-f4ba84481ee7","added_by":"auto","created_at":"2026-03-27 13:11:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4698770,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8972446/v1/c2b4fde7-65d1-4e45-bba0-568125a3b09e.pdf"},{"id":105565006,"identity":"7eac64d0-0172-43a1-977f-037742d3f6ba","added_by":"auto","created_at":"2026-03-27 12:51:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1209610,"visible":true,"origin":"","legend":"","description":"","filename":"Supportinginformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8972446/v1/d133408e2edc0073ab6f3bb4.docx"},{"id":105564221,"identity":"9d78084a-ea19-4b9f-ac62-b57d2b51bc4a","added_by":"auto","created_at":"2026-03-27 12:49:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12209050,"visible":true,"origin":"","legend":"","description":"","filename":"SupportinginformationUncroppedblots.docx","url":"https://assets-eu.researchsquare.com/files/rs-8972446/v1/08e4fa1c955605d052ade43b.docx"},{"id":105293858,"identity":"fc0603e9-db87-4215-a6ed-b759ed22d69f","added_by":"auto","created_at":"2026-03-24 12:46:09","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17360135,"visible":true,"origin":"","legend":"","description":"","filename":"SupportinginformationUncroppedblots3.9.docx","url":"https://assets-eu.researchsquare.com/files/rs-8972446/v1/239d2404fd2384eeb19b9365.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multicellular state transitions and signaling rewiring during the proestrus-to-estrus switch in the porcine ovary","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe ovary is the central organ of the female reproductive system and undergoes a series of tightly coordinated physiological changes across the estrous cycle, including follicular development, maturation, ovulation, and subsequent luteinization [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In pigs, the estrous cycle typically lasts 18\u0026ndash;24 days, and the proestrus and estrus phases represent periods of particularly active follicular growth, accompanied by pronounced endocrine and local microenvironmental fluctuations; consequently, these phases are key determinants of ovulation rate and fertilization success [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Accordingly, elucidating the molecular mechanisms governing ovarian function across the estrous cycle is essential for improving reproductive efficiency, productivity, and fertility management in pigs. Nevertheless, our understanding of how diverse cell types within the porcine ovary collectively govern estrous transitions through cell-type-specific molecular programs remains limited.\u003c/p\u003e \u003cp\u003eDuring the estrous cycle, the ovary functions as a highly heterogeneous, dynamically remodeling microenvironment composed of multiple somatic cell types, including granulosa cells, theca cells, fibroblasts, endothelial cells, smooth muscle cells, and diverse immune populations. Through tightly regulated paracrine signaling and dynamic remodeling of the extracellular matrix (ECM), these cell types collectively support follicular growth, selection, ovulation, and post-ovulatory remodeling [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Bulk ovarian tissue transcriptomics has provided important insights into stage-dependent gene expression programs across the porcine estrous cycle, revealing changes in steroidogenic pathways and in cytokine-cytokine receptor signaling, immune and inflammatory processes, ECM-receptor interactions, and cell-adhesion programs, thereby highlighting coordinated endocrine and microenvironmental remodeling at the tissue level [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, bulk approaches inherently average signals across constituent cell types, thereby obscuring the distinct contributions of individual compartments and their cell-type-specific molecular responses. In recent years, advances in single-cell RNA sequencing (scRNA-seq) have transformed the study of complex tissues by enabling high-resolution profiling of cellular diversity, cell states, and cell\u0026ndash;cell interactions. Although scRNA-seq has been widely applied to cell atlas construction and mechanistic studies in ovaries across multiple species [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], comprehensive cell-type\u0026ndash;resolved analyses of the porcine ovary at key physiological stages of the estrous cycle remain limited. Consequently, the cellular composition, lineage trajectories, metabolic adaptations, and intercellular communication networks of the porcine ovary during the transition from proestrus to estrus remain poorly characterized.\u003c/p\u003e \u003cp\u003eTo elucidate the multicellular regulatory mechanisms underlying the proestrus-to-estrus transition in the porcine ovary, we performed single-nucleus RNA sequencing (snRNA-seq) on ovaries collected at the proestrus and estrus stages. We generated a single-cell\u0026ndash;resolution transcriptomic atlas of the porcine ovary that captured major somatic cell types and their stage-dependent remodeling. We further resolved granulosa cell subtypes, reconstructed their developmental trajectory from proestrus to estrus, and inferred upstream regulators of cell-state transitions via transcription factor activity analysis. Based on this, we examined putative metabolic adaptations of granulosa cells between the proestrus and estrus stages. At the tissue-ecosystem level, we reconstructed global and cell-type\u0026ndash;specific communication networks and prioritized ligand\u0026ndash;target relationships, revealing coordinated remodeling of intercellular signaling across ovarian cell types. Collectively, these analyses delineate cell-type\u0026ndash;specific regulatory programs operating during the proestrus-to-estrus transition in the porcine ovary.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimals\u003c/h2\u003e \u003cp\u003eThe experimental animals included two proestrus and two estrus Landrace \u0026times; Large White crossbred gilts, ~\u0026thinsp;240 days of age and ~\u0026thinsp;140 kg body weight. The animals used in this study were provided by Zhaoqing DaBeiNong Agriculture, Animal Husbandry \u0026amp; Food Co., Ltd. Estrous status was monitored twice daily (morning and evening) by assessing vulvar morphology and behavioral signs, combined with the standing reflex test. Only gilts with regular estrous cycles were included for subsequent experiments. Estrus onset was defined as the first time point at which the standing reflex test was positive. Ovaries were collected from the estrus group within 6 h of estrus onset, whereas ovaries from the proestrus group were collected 48 h before the anticipated onset of estrus. Following collection, ovaries were grossly examined to confirm physiological stage. Ovaries were rinsed with pre-chilled 1\u0026times;PBS to remove residual blood and debris, immediately placed on ice, and transported to a sterile laboratory environment for downstream analyses. The animals were stunned by electrical shock prior to slaughter. Following stunning, they were rapidly exsanguinated to ensure loss of consciousness and death. No anaesthetic agents or injection procedures were administered specifically for this study. All procedures were carried out by certified personnel trained in humane slaughter methods, in strict accordance with relevant animal welfare regulations and animal euthanasia guidelines.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSingle nucleus suspension preparation and sequencing\u003c/h3\u003e\n\u003cp\u003eTo prepare a single-nucleus suspension, the tissue was thawed, minced, and transferred to a Dounce homogenizer (TIANDZ) containing 1 mL of homogenization buffer consisting of 250 mM sucrose (Ambion), 10 mg/mL BSA (Ambion), 5 mM MgCl₂ (Ambion), 0.12 U/\u0026micro;L RNasin Plus (Promega, N2115), and 1\u0026times; cOmplete Protease Inhibitor Cocktail (Roche, 11697498001). The tissue was then gently homogenized using a loose pestle with 25\u0026ndash;50 strokes. The homogenate was filtered through a 40-\u0026micro;m cell strainer into a centrifuge tube and centrifuged at 4\u0026deg;C at 500 \u0026times; g for 5 min to pellet nuclei. The pellet was resuspended in nuclei resuspension buffer (320 mM sucrose, 10 mg/mL BSA, 3 mM CaCl₂, 2 mM magnesium acetate, 0.1 mM EDTA, 10 mM Tris-HCl, 1 mM dithiothreitol (DTT), 1\u0026times; cOmplete Protease Inhibitor Cocktail, and 0.12 U/\u0026micro;L RNasein) to obtain a single-nucleus suspension at 1,000 nuclei/\u0026micro;L.\u003c/p\u003e \u003cp\u003eThe single-nucleus suspension was processed using the DNBelab C Series High-throughput Single-Cell RNA Library Preparation Kit (V3.0) to generate barcoded libraries, following droplet generation, magnetic bead collection, reverse transcription, cDNA amplification, and purification. The DNBelab C workflow produces two libraries: a cDNA library and an oligonucleotide library, derived from mRNA-capture magnetic beads and droplet-identification microbeads, respectively. After library construction, both libraries were sequenced on a DNBSEQ-T7 platform using a paired-end 100-bp (PE100) strategy.\u003c/p\u003e\n\u003ch3\u003esnRNA-seq data analysis\u003c/h3\u003e\n\u003cp\u003eFirst, the reference genome index was built using the mkref function in DNBC4tools, using Sus_scrofa.Sscrofa11.1.dna.toplevel.fa as the reference genome and Sus_scrofa.Sscrofa11.1.115.gtf as the annotation file. Subsequently, DNBC4tools (v2.1.0) was used to preprocess raw sequencing data to generate a single-nucleus gene expression matrix and barcode information. To mitigate ambient RNA contamination, SoupX (v1.6.2) was applied to decontaminate the expression matrix using default parameters [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Potential doublets were identified and removed using DoubletFinder (v2.0.3) with default parameters [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Downstream analyses were performed in Seurat (v4.4.0). Quality control was applied to nuclei, retaining those with 200\u0026thinsp;\u0026lt;\u0026thinsp;nFeature_RNA\u0026thinsp;\u0026lt;\u0026thinsp;6,000 and 300\u0026thinsp;\u0026lt;\u0026thinsp;nCount_RNA\u0026thinsp;\u0026lt;\u0026thinsp;20,000 for subsequent analyses. Each sample was normalized using NormalizeData, and highly variable genes were identified with FindVariableFeatures (method = \u0026ldquo;vst\u0026rdquo;, nfeatures\u0026thinsp;=\u0026thinsp;2,000). Batch effects were corrected and samples were integrated using Harmony (v1.2.3) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Dimensionality reduction and clustering were performed on the integrated expression matrix. Clusters were identified using FindClusters (resolution\u0026thinsp;=\u0026thinsp;0.8), and RunUMAP was used for visualization. Cluster-specific marker genes were identified using FindAllMarkers, and differentially expressed genes were defined as those with adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FC| \u0026gt; 0.25.\u003c/p\u003e\n\u003ch3\u003eCell cycle index estimation\u003c/h3\u003e\n\u003cp\u003eCell-cycle status was assessed using high-confidence cell-cycle marker genes compiled by Stephen et al., together with AUCell (v1.22.0) to score cycling and non-cycling gene signatures for each cell type. The cycling index was calculated for each cell type as the log ratio of the number of cycling cells to the number of non-cycling cells [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eGene regulatory network inference\u003c/h3\u003e\n\u003cp\u003eThe porcine cisTarget database was constructed following the standard SCENIC workflow (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/aertslab/create_cisTarget_databases\u003c/span\u003e\u003cspan address=\"https://github.com/aertslab/create_cisTarget_databases\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The \u0026ldquo;.tbl\u0026rdquo; mapping file required for motif-TF annotation was generated using scripts and guidance provided by JoGraesslin (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/JoGraesslin/Zebrafish_SCENIC\u003c/span\u003e\u003cspan address=\"https://github.com/JoGraesslin/Zebrafish_SCENIC\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Using this database, we applied pySCENIC to construct the gene regulatory network (GRN) of porcine granulosa cells. Specifically, we used GRNBoost2 to infer co-expression links between transcription factors (TFs) and candidate target genes; we then performed cis-regulatory motif enrichment for each co-expression module using pySCENIC ctx; subsequently, we computed regulon activity in each cell using AUCell. Finally, calcRSS was used to identify key TFs/regulons specific to each granulosa cell subtype [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIdentification of cell-type\u0026ndash;specific TF modules was based on the connection specificity index (CSI) framework proposed by Suo et al. Briefly, Pearson correlation coefficients (PCC) between regulon pairs were computed using regulon activity scores across cells. For a given regulon pair A and B, CSI was defined as the proportion of regulons whose PCC with both A and B is lower than the PCC between A and B, thereby quantifying the specificity of the A\u0026ndash;B association. Subsequently, Ward\u0026rsquo;s hierarchical clustering was used to construct TF/regulon modules based on the regulon-regulon similarity matrix (CSI\u0026thinsp;\u0026gt;\u0026thinsp;0.9) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment analysis\u003c/h2\u003e \u003cp\u003eGO and KEGG enrichment analyses were performed on the target gene set using Metascape with default parameters [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTranscriptional similarity analysis\u003c/h3\u003e\n\u003cp\u003eTo systematically assess transcriptional similarity among different granulosa cell subtype, we used MetaNeighbor (v1.20.0) to compute AUROC scores for pairwise comparisons [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In addition, we calculated Spearman correlation coefficients between cell types based on the mean expression of highly variable genes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eEvaluation of metabolic activity\u003c/h3\u003e\n\u003cp\u003eMetabolic activity scores for granulosa cell subtypes were calculated using the Single-Cell-Metabolic-Landscape pipeline. First, the mean expression of each metabolic gene was calculated for each cell type. Next, gene-level expression in each cell type was normalized to the mean expression of the same gene across all cell types to obtain relative expression values. For each metabolic pathway, a weighted average of the relative expression values of pathway genes was computed. To reduce bias from lowly expressed genes and dropout, only genes with non-zero expression that were detected in \u0026ge;\u0026thinsp;25% of cells were included. Finally, statistical significance of pathway activity in each cell type was evaluated using a permutation test [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In addition, scFEA was used to infer cell-level metabolic flux profiles from single-cell RNA sequencing data. After generating the granulosa cell count matrix, porcine gene symbols were mapped to their human orthologs. Subsequently, scFEA.py was used to estimate metabolic flux profiles and flux abundances for each granulosa cell [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCell-cell interaction analysis\u003c/h2\u003e \u003cp\u003eTo quantify the frequency and strength of cell\u0026ndash;cell interactions among ovarian cell types, porcine gene symbols were first mapped to their human orthologs. CellChat (v1.6.1) was then applied using the computeCommunProb and aggregateNet functions. To account for potential effects of cell-type abundance, the population.size option was enabled [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In addition, NicheNet (v2.0.5) was used to infer ligand\u0026ndash;target regulatory networks for granulosa cell subtypes in the porcine ovary [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot analysis\u003c/h2\u003e \u003cp\u003ePorcine ovarian tissue was lysed in RIPA buffer (Beyotime, P00113B, Shanghai, China) and homogenized using a tissue homogenizer to extract total protein. Protein samples were separated by SDS-PAGE and transferred to a PVDF membrane (Millipore, ISEQ00010, USA). After transfer, membranes were blocked at RT for 2 h and incubated with primary antibodies overnight at 4 ℃, followed by incubation with the corresponding secondary antibodies at RT for 2 h (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Band signals were captured using a Tanon 5200 imaging system (Tanon, Shanghai, China), and band intensities were quantified using AlphaView SA software (ProteinSimple, California, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRT-qPCR\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from ovarian tissue using the SPARKeasy RNA Extraction Kit (Sparkjade, AC0202, Qingdao, China). cDNA was synthesized by reverse transcription using the SPARKscript II RT Plus Kit (Sparkjade, AG0304). RT-qPCR was performed on a Bio-Rad CFX96 Real-Time PCR System using SYBR Premix Ex Taq\u0026trade; (BioRad, CFX96, Hercules, CA, USA). Primer sequences are provided in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using GraphPad Prism8. Bioassay data are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Comparisons between two groups were performed using a two-tailed Student\u0026rsquo;s t-test.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of a single-nucleus transcriptomic atlas of porcine ovaries at proestrus and estrus\u003c/h2\u003e \u003cp\u003eTo comprehensively characterize regulatory mechanisms underlying the proestrus-to-estrus transition in the porcine ovary, we collected ovarian samples at the proestrus and estrus stages for snRNA-seq (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). After stringent quality control, 27,337 nuclei were retained for downstream analyses; specifically, Proestrus_1, Proestrus_2, Estrus_1, and Estrus_2 contained 8,328; 8,950; 5,152; and 4,907 nuclei, respectively (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-B). We next identified 19 clusters in the integrated porcine ovarian snRNA-seq dataset using uniform manifold approximation and projection (UMAP)-based clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Cluster-specific marker expression supported the annotation of 10 major cell types, including endothelial cells (\u003cem\u003eVWF\u003c/em\u003e), epithelial cells (\u003cem\u003eKRT7\u003c/em\u003e), fibroblasts (\u003cem\u003ePDGFRA\u003c/em\u003e), glia (\u003cem\u003eNRXN1\u003c/em\u003e), granulosa cells (\u003cem\u003eFSHR\u003c/em\u003e, \u003cem\u003eINHBA\u003c/em\u003e), lymphatic endothelial cells (\u003cem\u003eFLT4\u003c/em\u003e), immune cells (\u003cem\u003eCSF1R\u003c/em\u003e), pericytes (\u003cem\u003eNOTCH3\u003c/em\u003e), smooth muscle cells (\u003cem\u003eTAGLN\u003c/em\u003e), and theca cells (\u003cem\u003eCYP17A1\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-D) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Based on these annotations, we observed differences in the proportion of different cell types in the ovary during proestrus and estrus (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Furthermore, we performed cell-type\u0026ndash;specific differential expression analysis to identify cell-type\u0026ndash;specific gene expression profiles for each ovarian cell type (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). GO enrichment analysis of cell-type marker genes identified cell-type\u0026ndash;specific functional terms. For example, granulosa cell marker genes were enriched for terms related to metabolic regulation and hormone response, whereas theca cell marker genes were enriched for GO terms associated with steroid metabolic processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). We further calculated cell-cycle scores for each ovarian somatic cell type and observed an overall decrease in cell-cycle activity during estrus relative to proestrus. Notably, granulosa cells exhibited significantly higher cell-cycle scores during proestrus, consistent with active follicular growth preceding the preovulatory transition (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Together, these results indicate extensive, stage-specific remodeling of cellular states in the ovary during the proestrus-to-estrus transition in pigs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMolecular characterization of porcine granulosa cell subtypes\u003c/h2\u003e \u003cp\u003eGranulosa cells play a pivotal role in follicular development, influencing ovarian function and oocyte maturation. In this study, we isolated granulosa cells from ovarian tissue and conducted cell clustering analysis using UMAP, which identified 11 distinct clusters. Further analysis revealed the presence of six granulosa cell subtypes, each characterized by unique marker gene expression profiles: atretic follicle granulosa cells (expressing \u003cem\u003eGADD45A\u003c/em\u003e), cumulus cells (expressing \u003cem\u003eVCAN\u003c/em\u003e and \u003cem\u003eHTRA1\u003c/em\u003e), cycling granulosa cells (expressing \u003cem\u003eTOP2A\u003c/em\u003e), early granulosa cells (expressing \u003cem\u003eWT1\u003c/em\u003e), mural granulosa cells (expressing \u003cem\u003eINHBA\u003c/em\u003e), and steroidogenic granulosa cells (expressing \u003cem\u003eLHCGR\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. To elucidate the developmental trajectory of these subtypes, we mapped them onto a pseudotime trajectory. This analysis revealed a continuous differentiation path, where early granulosa cells and atretic follicle granulosa cells occupied the early developmental stages, progressing into two distinct branches. The first branch included cumulus cells and cycling granulosa cells, while the second branch gave rise to mural granulosa cells and steroidogenic granulosa cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). This branching suggests a dynamic differentiation process, where atretic granulosa cells appear transcriptionally similar to early granulosa cells, but are suppressed in their developmental progression. As follicles mature, granulosa cells transition from an early developmental state into two divergent pathways: one leading to cumulus cells, which are intimately associated with the oocyte, and the other giving rise to mural granulosa cells, which form the follicle wall and prepare for luteinization. To further explore the molecular mechanisms underlying granulosa cell fate determination, we performed differential gene expression analysis along the pseudotime trajectory. We identified \"granulosa cell fate-specific\" genes that exhibited differential expression during early differentiation and at branch points. Pathway analysis of these fate-specific genes revealed distinct molecular signatures for each branch. Early granulosa cells were highly enriched in pathways related to \u0026ldquo;Tight junction\u0026rdquo;, \u0026ldquo;Ras signaling pathway\u0026rdquo;, \u0026ldquo;JAK\u0026minus;STAT signaling pathway\u0026rdquo;, \u0026ldquo;cAMP signaling pathway\u0026rdquo;, and \u0026ldquo;Apelin signaling pathway\u0026rdquo;. In contrast, fate-specific genes in the two branches highlighted distinct functional pathways. Cell fate 1, representing the cumulus and cycling granulosa cell lineage, was significantly enriched in the \u0026ldquo;TNF signaling pathway\u0026rdquo;, \u0026ldquo;PPAR signaling pathway\u0026rdquo;, \u0026ldquo;Notch signaling pathway\u0026rdquo;, \u0026ldquo;Glycerophospholipid metabolism\u0026rdquo;, and \u0026ldquo;Glutathione metabolism\u0026rdquo;. On the other hand, cell fate 2, which encompasses mural and steroidogenic granulosa cells, was associated with pathways such as \u0026ldquo;TGF\u0026thinsp;\u0026minus;\u0026thinsp;β signaling pathway\u0026rdquo;, \u0026ldquo;Pyrimidine metabolism\u0026rdquo;, \u0026ldquo;Purine metabolism\u0026rdquo;, \u0026ldquo;p53 signaling pathway\u0026rdquo;, and \u0026ldquo;AMPK signaling pathway\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). These findings indicate that granulosa cells undergo dynamic transcriptional and metabolic reprogramming during differentiation and possess unique molecular characteristics corresponding to lineage-specific functions during folliculogenesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further investigate the transcriptional regulation of granulosa cell differentiation, we applied the SCENIC approach to infer active TF networks in each granulosa cell subtype. SCENIC identified TFs with cell-type-specific activity, revealing distinct regulatory profiles for each granulosa cell subtype (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Specifically, atretic follicle granulosa cells were enriched in \u003cem\u003eFOXO4\u003c/em\u003e, \u003cem\u003eETS2\u003c/em\u003e, and \u003cem\u003eSOX4\u003c/em\u003e; cumulus cells exhibited high activity of \u003cem\u003eNR4A3\u003c/em\u003e, \u003cem\u003eSMAD1\u003c/em\u003e, and \u003cem\u003eELF4\u003c/em\u003e; cycling granulosa cells were enriched in \u003cem\u003eE2F4\u003c/em\u003e, \u003cem\u003eMYBL2\u003c/em\u003e, and \u003cem\u003eZFP42\u003c/em\u003e; early granulosa cells showed elevated activity of \u003cem\u003eIRF6\u003c/em\u003e, \u003cem\u003eTFCP2\u003c/em\u003e, and \u003cem\u003eBHLHA15\u003c/em\u003e; mural granulosa cells showed activation of \u003cem\u003eRELA\u003c/em\u003e, \u003cem\u003eYY1\u003c/em\u003e, and \u003cem\u003eMAF\u003c/em\u003e; and steroidogenic granulosa cells displayed high activity of \u003cem\u003eJDP2\u003c/em\u003e, \u003cem\u003eNR5A2\u003c/em\u003e, and \u003cem\u003eSREBF2\u003c/em\u003e. Additionally, it is well known that TFs often coordinate gene expression in a combinatorial manner. To systematically characterize these combinatorial patterns, we constructed a transcription factor co-expression network across the granulosa cell subtypes using the CSI. This analysis revealed distinct TF co-expression modules that were highly specific to each granulosa cell subtype (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). We visualized the module activity patterns on the UMAP plot, confirming that each granulosa cell subtype possesses a unique combination of active regulatory modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). Specifically, module 1 showed high activity primarily in mural granulosa cells and steroidogenic granulosa cells; module 2 exhibited high activity in early granulosa cells and atretic follicle granulosa cells; module 3 displayed high activity in cumulus cells and cycling granulosa cells; and module 4 showed significant activity in cumulus cells and mural granulosa cells. These module activity patterns align closely with the pseudotime developmental trajectory of granulosa cell subtypes, further supporting the idea that the transcriptional regulatory programs encoded by these modules are involved in the specific functional differentiation of granulosa cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEstrous cycle\u0026ndash;associated transcriptional changes in granulosa cell subtypes\u003c/h2\u003e \u003cp\u003eAfter identifying granulosa cell subtypes, we investigated subtype-specific transcriptional changes between the proestrus and estrus phases. We first quantified the relative abundance of each granulosa cell subtype across stages. Compared with the proestrus ovary, the proportions of cumulus cells, cycling granulosa cells, early granulosa cells, and mural granulosa cells were reduced during estrus, whereas steroidogenic granulosa cells constituted the predominant population (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). These stage-dependent shifts in cellular composition underscore the dynamic remodeling of granulosa cell states during follicular maturation. We next performed differential expression analysis for the four granulosa cell subtypes that were well represented in both phases. Volcano plots summarized subtype-specific differentially expressed genes (DEGs), revealing 109 genes upregulated in estrus cumulus cells and 399 upregulated in proestrus; 51 upregulated in estrus cycling granulosa cells and 128 upregulated in proestrus; 30 upregulated in estrus early granulosa cells and 448 upregulated in proestrus; and 54 upregulated in estrus mural granulosa cells and 79 upregulated in proestrus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). KEGG enrichment analysis of subtype-specific DEGs indicated that, during proestrus, granulosa cell subtypes preferentially engaged developmental and growth-associated signaling pathways (e.g., Wnt and TGF-β signaling), whereas fewer pathways were consistently enriched across subtypes during estrus. Notably, focal adhesion was significantly enriched in estrus cumulus cells, while genes involved in mitochondrial energy metabolism were preferentially upregulated in estrus mural granulosa cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D). Collectively, these findings highlight pronounced stage-dependent functional reprogramming of granulosa cells during the transition from proestrus to estrus. Strikingly, cross-subtype comparison of DEGs identified \u003cem\u003eERBB4\u003c/em\u003e as the only protein-coding gene significantly upregulated across all four granulosa cell subtypes during estrus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). To validate this observation, we examined \u003cem\u003eERBB4\u003c/em\u003e expression in ovaries from both stages. Consistently, snRNA-seq data showed a significant increase in \u003cem\u003eERBB4\u003c/em\u003e transcript abundance at the whole-ovary level during estrus relative to proestrus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF), which was further corroborated by RT-qPCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Together, these orthogonal validations support \u003cem\u003eERBB4\u003c/em\u003e as an estrus upregulated factor in pigs and implicate \u003cem\u003eERBB4\u003c/em\u003e as a candidate regulator of estrus-associated follicular function.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic plasticity and heterogeneity of granulosa cells during the proestrus-to-estrus transition\u003c/h2\u003e \u003cp\u003eMetabolic programs in the ovary are extensively reconfigured during the transition from proestrus to estrus [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. To characterize metabolic heterogeneity among granulosa cell subtypes and their adaptive responses across the estrous cycle, we applied a single-cell metabolic landscape workflow to compare metabolic gene-expression signatures of each granulosa cell subtype between proestrus and estrus. We first quantified global metabolic pathway activity across granulosa cell subtypes at both stages. Overall metabolic activity was significantly reduced in all granulosa cell subtypes during estrus, reflecting a decreased metabolic demand of granulosa cells during this stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Notably, despite this global downshift, several pathways displayed subtype-specific increases during estrus. For example, fructose and mannose metabolism was elevated in cumulus cells during estrus relative to proestrus; steroid biosynthesis was markedly increased in mural granulosa cells and cycling granulosa cells; and pantothenate and CoA biosynthesis was enhanced in early granulosa cells and mural granulosa cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Collectively, these results indicate pronounced stage-dependent metabolic remodeling during the proestrus-to-estrus transition: while overall metabolic activity declines during estrus, discrete pathways are selectively augmented in a subtype-dependent manner, consistent with metabolic rerouting to support estrus functional programs [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next compared estrus-versus-proestrus differences in metabolic module flux within each granulosa cell subtype, revealing widespread, significant flux alterations across stages. In particular, glutamine utilization flux was increased in cumulus cells and mural granulosa cells during estrus (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). In parallel, analysis of the snRNA-seq dataset showed that expression of the glutamine transporter \u003cem\u003eSLC1A5\u003c/em\u003e was significantly higher in estrus ovaries than in proestrus ovaries (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). This upregulation was independently validated by RT\u0026ndash;qPCR and Western blotting, which confirmed significantly increased SLC1A5 expression at both the transcript and protein levels during estrus (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-F). In summary, our findings demonstrate that granulosa cells during the estrous phase upregulate glutamine uptake, likely to support the bioenergetic and biosynthetic demands of specific cell lineages and developmental stages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eProestrus-to-estrus transcriptomic changes in non-granulosa ovarian somatic cells\u003c/h2\u003e \u003cp\u003eDuring the transition from the proestrus to the estrous phase in mammals, ovarian non-granulosa cell populations also undergo extensive transcriptional remodeling [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Accordingly, we performed differential expression analysis of the remaining nine major cell types identified in the pig ovarian cell transcriptome map\u0026mdash;namely theca cells, epithelial cells, fibroblasts, smooth muscle cells, endothelial cells, lymphatic endothelial cells, pericytes, immune cells, and glia\u0026mdash;between proestrus and estrus. Consistent with the patterns observed in granulosa cells, most somatic cell types exhibited a larger number of genes upregulated during proestrus than during estrus. Notably, endothelial cells, glia, theca cells, lymphatic endothelial cells, and smooth muscle cells displayed relatively high numbers of DEGs, whereas ovarian surface epithelial cells, fibroblasts, immune cells, and pericytes exhibited substantially fewer DEGs, suggesting cell-type-specific degrees of transcriptional responsiveness to the transition from the proestrus to the estrus phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). To interpret the functional implications of these transcriptional changes, we conducted GO enrichment analysis for DEGs in each cell type. Across cell populations, enriched biological processes were broadly related to cell proliferation, cell motility, and migration, supporting the notion that the proestrus-to-estrus transition represents a coordinated, ovary-wide remodeling event. This convergence further suggests that diverse somatic compartments may be regulated by shared upstream cues that collectively govern population expansion, positional rearrangement, and tissue reorganization. In addition, glial cells showed enrichment of differentiation-related terms, implying that a transition in cell state may occur within the ovarian neuro-associated microenvironment during the proestrus-to-estrus transition (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). We next performed KEGG pathway enrichment analysis for DEGs in each somatic cell type. Multiple cell populations consistently showed enrichment of focal adhesion, Rap1, PI3K\u0026ndash;Akt, TGF-β, HIF-1, Hippo, and cAMP signaling pathways. This shared enrichment pattern points to widespread remodeling of ECM\u0026ndash;cell adhesion and intercellular connectivity during the proestrus-to-estrus transition, accompanied by activation of growth factor, mechanotransduction, and hypoxia responsive programs that support proliferation and migration. Importantly, several cell-type-specific signatures were also evident. Endothelial cells were enriched for tight junction and Rap1/HIF-1 signaling, consistent with angiogenic activation and barrier remodeling during the follicular period. Theca cells were specifically enriched in steroid biosynthesis and glutathione metabolism, suggesting coordinated endocrine reprogramming and adaptation of redox homeostasis. Smooth muscle cells showed enrichment of calcium signaling, phospholipase D signaling, and sphingolipid signaling, implicating contractility- and membrane-dynamics-associated pathways potentially relevant to ovulation-associated mechanical events (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA-B). We further quantified the activity of focal adhesion, Rap1, PI3K\u0026ndash;Akt, TGF-β, HIF-1, Hippo, and cAMP signaling pathways across somatic cell types in proestrus and estrus. Consistently, pathway activity was higher during proestrus than during estrus across cell populations, in line with the global transcriptional activation observed in the proestrus stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Finally, we assessed DEG overlap across cell types and identified \u003cem\u003eTGFBR3\u003c/em\u003e and \u003cem\u003eTSEN2\u003c/em\u003e as the only genes differentially expressed across all examined cell populations (six cell types in the overlap analysis) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). \u003cem\u003eTGFBR3\u003c/em\u003e expression was significantly higher during proestrus than estrus, whereas \u003cem\u003eTSEN2\u003c/em\u003e showed the opposite pattern, with elevated expression during estrus (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). RT-qPCR validation confirmed these stage-associated expression trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Collectively, these findings establish \u003cem\u003eTGFBR3\u003c/em\u003e and \u003cem\u003eTSEN2\u003c/em\u003e as reliable molecular markers of the transition from the proestrus to estrous phase in pigs, identifying them as potential regulatory nodes implicated in multicellular remodeling during this transition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eCell-cell communication networks during the proestrus-to-estrus transition\u003c/h2\u003e \u003cp\u003eAfter delineating specific transcriptional differences among porcine ovarian somatic cell populations across proestrus and estrus, we further explored how changes in the ovarian microenvironment during this transition influence cell state transitions and fate determination. To this end, we inferred the frequency and strength of intercellular communication among ovarian cell types at both stages. Overall, the proestrus ovary exhibited higher interaction frequency and greater communication strength than the estrus ovary, indicating a globally more active signaling milieu before ovulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). We then systematically predicted ligand\u0026ndash;target regulatory relationships influencing each granulosa cell subtype. Across subtypes, \u003cem\u003eBMP2\u003c/em\u003e emerged as the dominant predicted stimulatory ligand (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Consistent with this inference, \u003cem\u003eBMP2\u003c/em\u003e expression was significantly elevated in estrus ovaries relative to proestrus, and this stage-associated increase was further confirmed at the protein level by Western blotting, which showed significantly higher BMP2 abundance during estrus (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-E). Finally, we performed KEGG enrichment analysis on the predicted target-gene sets for each granulosa cell subtype. The results revealed a highly concordant set of downstream functional modules shared across the granulosa cell subtypes, including TGF-β, MAPK, Wnt, and AMPK signaling pathways, as well as pathways related to intercellular connectivity and communication, such as focal adhesion, adherens junction, and gap junction (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). These findings collectively indicate that despite transcriptional heterogeneity, granulosa cell subtypes converge on common intercellular signaling programs, which coordinate their functional responses during the proestrus‑to‑estrus transition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSow reproductive performance is a primary determinant of production efficiency in the swine industry [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The timing and intensity of estrus critically influence reproductive outcomes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Notably, the transition from proestrus to estrus represents a key physiological window for elucidating ovarian regulatory mechanisms that govern estrus expression [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In commercial production, delayed estrus and silent estrus remain persistent challenges that compromise reproductive efficiency, underscoring the need for improved mechanistic insight to enable early diagnosis and targeted intervention [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In this study, we generate the first snRNA atlas of porcine ovaries across proestrus and estrus. Focusing on granulosa cells, we resolve cellular heterogeneity and reconstruct differentiation trajectories to capture the gene-expression cascade accompanying granulosa cell maturation. In parallel, stage-specific differential-expression analyses across major ovarian somatic cell types identify cell type\u0026ndash;specific regulatory programs driving the proestrus\u0026ndash;to\u0026ndash;estrus transition. Importantly, we further delineate features of ovarian microenvironment remodeling during this process, providing candidate molecular targets for modulating estrus expression and improving sow reproductive performance.\u003c/p\u003e \u003cp\u003eDuring the proestrus\u0026ndash;to\u0026ndash;estrus transition, snRNA atlas resolved the granulosa compartment into six subtypes: atretic follicular granulosa cells, cumulus cells, cycling granulosa cells, early granulosa cells, mural granulosa cells, and steroidogenic granulosa cells. This is consistent with the granulosa cell subtypes described in adult mammalian ovaries (e.g., human, mouse, and sheep) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Trajectory inference further reconstructed a granulosa-cell differentiation continuum across the pre-estrus\u0026ndash;to\u0026ndash;estrus transition, closely resembling trajectories described in adult sheep ovaries [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Specifically, early granulosa cells occupied an initial state and bifurcated into two major branches corresponding to mural granulosa cells and cumulus cells, indicating that granulosa-cell lineage progression and functional specialization are broadly conserved among mammals. Notably, we identified transcription factors associated with granulosa cell subtype specialization. In particular, \u003cem\u003eFOXO4\u003c/em\u003e and \u003cem\u003eSOX4\u003c/em\u003e showed elevated activity in atretic follicular granulosa cells. Prior studies indicate that \u003cem\u003eFOXO4\u003c/em\u003e and \u003cem\u003eSOX4\u003c/em\u003e can modulate granulosa cell proliferation and apoptosis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and granulosa cell apoptosis is a hallmark event initiating follicular atresia [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], suggesting that \u003cem\u003eFOXO4\u003c/em\u003e and \u003cem\u003eSOX4\u003c/em\u003e are crucial regulatory hubs driving granulosa cells towards an atretic fate. Furthermore, we identified \u003cem\u003eERBB4\u003c/em\u003e as a robust estrus-associated gene: it was consistently upregulated across all granulosa-cell subtypes in estrus relative to proestrus, and we found that \u003cem\u003eERBB4\u003c/em\u003e is mainly expressed in ovarian granulosa cells, consistent with a study by Veikkolainen et al [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Functionally, conditional disruption of \u003cem\u003eErbb4\u003c/em\u003e in murine granulosa cells results in marked ovarian dysfunction, including an asynchronous estrous cycle, reduced ovulation, and subfertility [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In addition, studies in poultry have shown that precisely regulating \u003cem\u003eERBB4\u003c/em\u003e activity can promote follicular development and maturation, potentially improving reproductive performance [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. After characterizing the metabolic features of various granulosa cell subtypes, we observed a stage-dependent modulation of metabolic pathways during the transition from proestrus to estrus. Despite an overall decrease in metabolic activity during estrus, we identified specific enhancement of glutamine utilization, particularly in mural granulosa cells and cumulus cells. Glutamine, a critical energy source for cells, plays a central role in follicle and oocyte maturation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Previous work by Zhang et al. highlighted that glutamine levels in follicular fluid act as a key metabolic signal in the regulation of ovulation [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. A reduction in glutamine levels in follicular fluid is thought to represent a \u0026ldquo;signaling state\u0026rdquo; that promotes the transition of follicles to maturity. Consistent with this, our findings suggest that a glutamine-centered metabolic program orchestrates the transition from proestrus to estrus. Enhanced glutamine uptake during estrus likely serves dual functions: first, in mural granulosa cells, it provides TCA cycle replenishment and reducing power to support steroidogenesis and tissue remodeling; second, in cumulus cells, glutamine uptake contributes to cumulus expansion. Furthermore, the increased cellular uptake of glutamine may induce a local decrease in follicular fluid glutamine levels, thereby creating a microenvironmental gradient that is consistent with the mechanism by which follicular fluid glutamine regulates follicular maturation. Taken together, this indicates that \u003cem\u003eERBB4\u003c/em\u003e and the glutamine transporter \u003cem\u003eSLC1A5\u003c/em\u003e represent key regulatory targets for modulating granulosa cell physiology during the proestrus-to-estrus transition. Targeting these molecules may offer strategies for improving estrus performance and enhancing reproductive efficiency in sows.\u003c/p\u003e \u003cp\u003eThe transition from the proestrus to estrus phase involves the coordinated remodeling of multiple ovarian somatic and granulosa cell populations [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Consistent with this concept, differential expression analysis of major ovarian somatic cell types revealed significant enrichment of the TGF-β signaling pathway, with \u003cem\u003eTGFBR3\u003c/em\u003e exhibiting stage-dependent expression changes across most somatic cell populations. Importantly, TGFBR3 is not a canonical signaling kinase receptor but rather a widely expressed co-receptor that modulates the activity of TGF-β superfamily ligands and facilitates receptor complex formation [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In agreement with transcriptome enrichment findings, our ligand-receptor network analysis further revealed extensive TGF-β signaling activity between somatic cell compartments and granulosa cell subtypes, indicating that this pathway serves as a conserved intercellular communication network during the proestrus-to-estrus transition. This pathway exhibits diverse functional roles in the mammalian ovary [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. For instance, the oocyte-derived TGF-β superfamily member GDF9 is essential for early follicular development; oocytes from Gdf9-knockout mice fail to recruit surrounding ovarian somatic cells to form follicles [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Additionally, granulosa cell-derived TGF-β ligands inhibin and activin are critical components of the FSH feedback axis regulating pituitary-ovarian function; disruption of inhibin and activin signaling perturbs this endocrine feedback loop and results in severe ovarian defects [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Furthermore, AMH, functioning as an intraovarian growth factor, suppresses primordial follicle recruitment and attenuates FSH responsiveness in growing follicles, further exemplifying how TGF-β signaling integrates follicular dynamics with endocrine sensitivity [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Collectively, we hypothesize that the TGF-β signaling pathway serves as a central regulatory axis coordinating intra-ovarian signaling and granulosa cell state transitions during the proestrus-to-estrus progression.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study constructed a comprehensive ovarian cell atlas spanning the proestrus and estrus phases in sows, revealing coordinated multicellular remodeling during the proestrus-to-estrus transition. At the granulosa cell level, \u003cem\u003eERBB4\u003c/em\u003e emerged as a key regulatory factor associated with the estrus phase. Concurrently, the stage-specific upregulation and metabolic flux changes of \u003cem\u003eSLC1A5\u003c/em\u003e suggest that a glutamine-centered metabolic program supports the energetic and biosynthetic demands surrounding ovulation. At the microenvironmental and intercellular communication level, TGF-β signaling was identified as a central regulatory axis governing the proestrus-to-estrus transition. These findings not only provide cell-type-resolved evidence for \u003cem\u003eERBB4\u003c/em\u003e, \u003cem\u003eSLC1A5\u003c/em\u003e, and TGF-β pathway components as potential therapeutic targets but also establish a foundational resource for developing improved strategies for estrus detection, early diagnosis, and precision reproductive management in sows.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available in the Genome Sequence Archive (GSA, https://ngdc.cncb.ac.cn/gsa) repository under accession number CRA039587.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work presented here was carried out in collaboration among all authors. Z.Z., J.W., W.S., and Y.T. conceptualised the study, curated the data, and developed the methodology. Z.Z., Q.W., M.L., F.W., T.W., Y.Z., and J.C. performed data analyses, created visualisations, and wrote the original draft. Z.Z. and Y.T. were involved in data collection and analysis and in the preparation of figures. W.S. and Y.T. contributed to revising and editing the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key Research and Development Program of China (2023YFD1300504), the Key R\u0026amp;D Program of Shandong Province (2025LZGC007), and Shandong Modern Agricultural Industry Technology System - Swine Innovation Team (SDAIT-08-02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003eAll animal procedures were approved by the Ethics Committee of Qingdao Agricultural University (Approval No. SYXK-20220-021).\u003c/p\u003e\n\u003cp\u003eThe pigs used in this study were provided by Zhaoqing DaBeiNong Agriculture, Animal Husbandry \u0026amp; Food Co., Ltd., and informed consent for the use of these animals in the study was obtained from the owner.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eOverland MR, Li Y, Derpinghaus A, Aksel S, Cao M, Ladwig N, Cunha GR, Himelreich-Perić M, Baskin LS: \u003cstrong\u003eDevelopment of the human ovary: Fetal through pubertal ovarian morphology, folliculogenesis and expression of cellular differentiation markers\u003c/strong\u003e. \u003cem\u003eDifferentiation; research in biological diversity\u0026nbsp;\u003c/em\u003e2023, \u003cstrong\u003e129\u003c/strong\u003e:37-59.\u003c/li\u003e\n \u003cli\u003eStocco C, Telleria C, Gibori G: \u003cstrong\u003eThe molecular control of corpus luteum formation, function, and regression\u003c/strong\u003e. \u003cem\u003eEndocr 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\u003cstrong\u003e71\u003c/strong\u003e(3):191-197.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Porcine ovary, Estrous cycle, snRNA-seq","lastPublishedDoi":"10.21203/rs.3.rs-8972446/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8972446/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe porcine ovary undergoes rapid multicellular remodeling during the transition from proestrus to estrus, but the cell type\u0026ndash;specific regulatory programs that drive this process remain incompletely defined. Here, we aimed to resolve stage-dependent cellular states and intercellular signaling events in the porcine ovary across proestrus and estrus at single-cell resolution.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe generated a stage-resolved single-nucleus RNA sequencing atlas of porcine ovaries spanning proestrus and estrus. Integrative analyses of granulosa cell heterogeneity, metabolic pathway activity, and inferred metabolic flux highlighted \u003cem\u003eERBB4\u003c/em\u003e and the glutamine transporter \u003cem\u003eSLC1A5\u003c/em\u003e as key regulatory factors associated with the proestrus-to-estrus transition. By combining cell type\u0026ndash;specific differential expression profiling with intercellular communication network analysis across major ovarian somatic compartments, we further identified transforming growth factor beta (TGF-β) signaling as a central regulatory axis coordinating this developmental transition. Together, these data delineate coordinated transcriptional, metabolic, and microenvironmental remodeling programs during the proestrus-to-estrus switch.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study provides a high-resolution cellular resource for the porcine ovary across proestrus and estrus and proposes \u003cem\u003eERBB4\u003c/em\u003e, \u003cem\u003eSLC1A5\u003c/em\u003e, and TGF-β pathway components as candidate targets for modulating estrus-associated ovarian remodeling. These findings may support improved estrus detection, earlier recognition of reproductive disorders, and more precise reproductive management in swine production systems.\u003c/p\u003e","manuscriptTitle":"Multicellular state transitions and signaling rewiring during the proestrus-to-estrus switch in the porcine ovary","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 12:46:04","doi":"10.21203/rs.3.rs-8972446/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-18T08:40:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T20:21:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252574302363615406623519890561467841337","date":"2026-05-07T19:18:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T10:19:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182316080733160458703659620812802449937","date":"2026-03-20T07:43:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-19T18:36:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-19T18:25:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-10T06:43:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-09T11:37:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2026-03-09T10:20:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fd828653-cc07-47d2-a7a7-fee05ab95d1e","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-18T08:40:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T20:21:49+00:00","index":85,"fulltext":""},{"type":"reviewerAgreed","content":"252574302363615406623519890561467841337","date":"2026-05-07T19:18:11+00:00","index":84,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T08:55:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 12:46:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8972446","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8972446","identity":"rs-8972446","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}