Comparative analysis of human and mouse ovaries across age

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Gaylord , View ORCID Profile Mariko H. Foecke , View ORCID Profile Ryan M. Samuel , View ORCID Profile Bikem Soygur , View ORCID Profile Angela M. Detweiler , View ORCID Profile Leah Dorman , View ORCID Profile Michael Borja , View ORCID Profile Amy E. Laird , View ORCID Profile Ritwicq Arjyal , Juan Du , James M. Gardner , View ORCID Profile Norma Neff , View ORCID Profile Faranak Fattahi , View ORCID Profile Diana J. Laird doi: https://doi.org/10.1101/2025.02.27.640481 Eliza A. Gaylord 1 Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California , San Francisco; San Francisco, 94143, USA 2 Center for Reproductive Sciences, Department of Obstetrics, Gynecology and Reproductive Science, University of California , San Francisco; San Francisco, 94143, USA 3 Bakar Aging Research Institute, University of California , San Francisco; San Francisco, 94143, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Eliza A. Gaylord Mariko H. Foecke 1 Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California , San Francisco; San Francisco, 94143, USA 2 Center for Reproductive Sciences, Department of Obstetrics, Gynecology and Reproductive Science, University of California , San Francisco; San Francisco, 94143, USA 3 Bakar Aging Research Institute, University of California , San Francisco; San Francisco, 94143, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mariko H. Foecke Ryan M. Samuel 1 Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California , San Francisco; San Francisco, 94143, USA 4 Department of Cellular and Molecular Pharmacology, University of California , San Francisco; San Francisco, 94143, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ryan M. Samuel Bikem Soygur 1 Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California , San Francisco; San Francisco, 94143, USA 2 Center for Reproductive Sciences, Department of Obstetrics, Gynecology and Reproductive Science, University of California , San Francisco; San Francisco, 94143, USA 5 Reproductive Biology Hub, Center for Healthy Aging in Women, Buck Institute for Research on Aging ; Novato, 94945, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bikem Soygur Angela M. Detweiler 6 Genomics and Computational Biology Platforms, Chan Zuckerberg Biohub San Francisco ; San Francisco, 94158, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Angela M. Detweiler Leah Dorman 6 Genomics and Computational Biology Platforms, Chan Zuckerberg Biohub San Francisco ; San Francisco, 94158, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Leah Dorman Michael Borja 6 Genomics and Computational Biology Platforms, Chan Zuckerberg Biohub San Francisco ; San Francisco, 94158, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michael Borja Amy E. Laird 7 School of Public Health, Oregon Health and Science University – Portland State University ; Portland, 97201, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Amy E. Laird Ritwicq Arjyal 6 Genomics and Computational Biology Platforms, Chan Zuckerberg Biohub San Francisco ; San Francisco, 94158, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ritwicq Arjyal Juan Du 8 Diabetes Center, University of California , San Francisco; San Francisco, 94153, USA 9 Department of Surgery, University of California , San Francisco; San Francisco, 94153, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site James M. Gardner 8 Diabetes Center, University of California , San Francisco; San Francisco, 94153, USA 9 Department of Surgery, University of California , San Francisco; San Francisco, 94153, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Norma Neff 6 Genomics and Computational Biology Platforms, Chan Zuckerberg Biohub San Francisco ; San Francisco, 94158, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Norma Neff Faranak Fattahi 1 Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California , San Francisco; San Francisco, 94143, USA 4 Department of Cellular and Molecular Pharmacology, University of California , San Francisco; San Francisco, 94143, USA 10 Program in Craniofacial Biology, University of California , San Francisco; San Francisco, 94143, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Faranak Fattahi Diana J. Laird 1 Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California , San Francisco; San Francisco, 94143, USA 2 Center for Reproductive Sciences, Department of Obstetrics, Gynecology and Reproductive Science, University of California , San Francisco; San Francisco, 94143, USA 3 Bakar Aging Research Institute, University of California , San Francisco; San Francisco, 94143, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Diana J. Laird For correspondence: diana.laird{at}ucsf.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Mouse is a tractable model for human ovarian biology, however its utility is limited by incomplete understanding of how transcription and signaling differ interspecifically and with age. We compared ovaries between species using 3D-imaging, single-cell transcriptomics, and functional studies. In mice, we mapped declining follicle numbers and oocyte competence during aging; in human ovaries, we identified cortical follicle pockets and density changes. Oocytes had species-specific gene expression patterns during growth that converged toward maturity. Age-related transcriptional changes were greater in oocytes than granulosa cells across species, although mature oocytes change more in humans. We identified ovarian sympathetic nerves and glia; nerve density increased in aged human ovaries and, when ablated in mice, perturbed folliculogenesis. This comparative atlas defines shared and species-specific hallmarks of ovarian biology. Introduction The ovary orchestrates and supports dynamic communication between the germline and the somatic cells ( 1 – 3 ) to generate mature oocytes and produce hormones that signal to multiple organs to coordinate fertility, pregnancy, and aging. In both humans and mice, the finite and non-renewable oocyte pool forms before birth when somatic cells encapsulate non-dividing oocytes, forming primordial follicles ( 4 ). During the conserved process of folliculogenesis, cohorts of quiescent primordial follicles synchronously ‘activate’ ( 5 ) and progress asynchronously through four distinct stages of growth: primary, secondary, tertiary/antral, and ovulatory ( 6 ). Within the growing follicle, granulosa and theca cells concurrently differentiate, secreting estrogen and androgen, respectively ( 7 , 8 ). After puberty, folliculogenesis is hormonally regulated and culminates in ovulation and corpus luteum (CL) formation ( 9 ). Despite broad similarities in cellular composition and function, folliculogenesis has diverged in scale between species; humans are largely mono-ovulatory, with typically singleton pregnancies, while mice are multi-ovulatory, bearing litters of 2-12 pups depending on strain ( 10 ). In both species, the decline in fertility precedes the age-related degeneration of all other organs ( 11 ); clinically, a woman or birthing person 35 years of age (Y) and older is considered to be of advanced reproductive age (ARA) ( 12 ) and menopause occurs around 51Y ( 13 , 14 ), marking the cessation of cycling. Although they do not undergo menopause, mice recapitulate key aspects of human reproductive aging, including ceasing hormonal cycling, and ovulation ( 15 ). There is a need for direct mouse-human ovarian comparisons to leverage strengths of mouse models toward translating findings into therapies for fertility and ovarian aging. Many existing single-species single-cell RNA sequencing (scRNAseq) datasets identify mechanisms of ovarian aging, including inflammation ( 16 – 18 ), senescence ( 19 , 20 ), fibrosis ( 17 , 21 ), and altered transcriptional signatures in the follicle ( 19 , 20 , 22 – 24 ). However, discrepancies between ovarian cell type annotations within and across species, particularly in the cell types that comprise the ovarian microenvironment, present challenges for comparing observations interspecifically ( 21 , 22 , 25 ). To assess diversity of ovarian cell types and the capacity of the mouse to model human ovarian biology, we compared mouse and human ovaries across the reproductive window at the organ and single-cell levels. Using 3D quantitative imaging, we identified species differences in oocyte spatial distribution, but conserved peripheral nervous system patterning. Our scRNAseq pipeline captured all cell types in the ovary, including a novel glia population, from young and aged mouse and human ovaries. This work reveals species-shared and -specific ovarian programs spanning development to aging. Results 3D imaging reveals species-specific follicle geographies The goal of each mouse estrous or human menstrual cycle is to ovulate a mature, competent oocyte. Unlike mono-ovulatory humans, mice sustain a large pool of growing follicles to support multiple ovulations per cycle and generate large litters ( 26 ). The asynchronous process of folliculogenesis is challenging to quantify histologically given the vast discrepancies in size and morphology of co-existing follicles ( 16 , 27 , 28 ). To map the large-scale dynamics of folliculogenesis in three-dimensions (3D), we developed a protocol to immunostain and clear whole mouse ovaries ( 29 ) and large human ovary pieces ( Fig. 1A ). Download figure Open in new tab Fig. 1. Mapping of C57BL/6 mouse and human ovaries in 3D reveals species-specific oocyte distribution. (A) Schematic of whole-mount processing for 3D imaging and analysis of intact mouse ovaries and human ovary pieces. Murine ovaries were collected and analyzed at 2M, 4M, 6M, 9M, and 12M. Human ovaries from 23Y, 30Y, and 37Y human donors were cut into ∼1cm 3 pieces before processing. (B) Representative whole-mount IF imaging of C57BL/6 ovaries at 2M, 4M, 6M, 9M, and 12M. Oocytes marked by NOBOX (green) and growing follicles and corpora lutea marked by AMH and StAR, respectively (magenta). Scale bar, 200μm. (C) Quantification of primordial oocytes, (D) growing oocytes, and (E) total oocytes from the whole-mount IF images at 2M (n = 13), 4M (n = 7), 6M (n = 6), 9M (n = 8), and 12M (n = 6). All data represented as Mean + SEM; * p < 0.05; ** p < 0.01, *** p < 0.001, **** p < 0.0001, ANOVA test. (F) Density plot showing the relationship of primordial to growing follicles with age where the slope represents the rate of decline in oocyte number with age. (G) Visualization of the age-related decrease of oocytes within different follicle stages compared with 2M in C57BL/6 ovaries. (H) Low and ( H’ ) high magnification whole-mount IF images of an ERA human ovary piece. Oocytes marked by VASA (green) and small growing follicles marked by laminin (magenta). Scale bar, 100μm and 50μm, respectively. (I) Low and (I’) high magnification whole-mount IF images of an ERA human ovary piece. Oocytes marked by VASA (green) and large growing follicles marked by AMH (magenta). Scale bar, 200μm. (J) Quantification of total oocytes normalized to volume (mm 3 ) in mouse ovaries and human ovary pieces. We imaged ovary geography of estrous-staged C57BL/6 mice ( 30 ) from 2 to 12 months (M; n ≥ 4 per group) ( Fig. 1B , table S1 ). We devised a classification of growing follicles using NOBOX (marking all oocytes) localization, AMH/StAR expression, and morphology ( fig. S1A-B, movie S1 ) allowing us to quantify primordial, growing (primary, secondary, tertiary+), atretic (dying), and total follicles ( Fig. 1C-E , fig. S1A-G ). Importantly, AMH and StAR expression was specific to growing follicles or corpora lutea, respectively ( fig. S1C ). Primordial follicle numbers declined gradually until 6M, then steeply until 12M ( Fig. 1C ). Growing follicle numbers remained stable until 6M and declined at 9M ( Fig. 1D , fig. S1D, F ) despite secondary follicle loss as early as 4M ( fig. S1E ), revealing more complex age-related dynamics than previously recognized. Atretic follicles followed a similar trajectory to the growing pool ( fig. S1G ). Total follicle numbers trended downward between 2M and 6M ( p = 0.0559 ) and declined significantly by 9M ( Fig. 1E ). The rate of decline between growing and primordial follicles remained stable between 2M and 9M ( Fig. 1F ), after which the altered slope indicates accelerated depletion of growing follicles relative to the primordial pool, alongside a decrease in total oocyte numbers ( Fig. 1E ), suggests a loss of follicular homeostasis and identifies 9M as a critical transition in ovarian aging ( 27 , 31 ) ( Fig. 1G ). Given the scarcity and large size (∼3-4 cm) of human ovaries, we analyzed ≥ 1 cm 3 ovary pieces collected from Early Reproductive Aged (ERA, 23 and 30 years old) and Advanced Reproductive Aged (ARA, 37 years old) donors in whole mount ( fig. S2A, table S1 ). Donor ovaries were from individuals without hormonal interventions, fertility preservation procedures, or reproductive tract cancer. Total oocytes were quantified using VASA/DDX4 ( movie S2) , with newly growing follicles identified by laminin+ granulosa cells ( Fig. 1H-I ’, fig. S2B ) and larger follicles, which could exceed ∼800μm, by AMH+ granulosa cells ( Fig. 1I-I ’ ). 3D visualization revealed species geographic differences: oocytes were homogeneously distributed throughout the mouse ovary, whereas human ovaries featured cortical “pockets” of oocytes separated by large follicle-free deserts ( fig. S2C ). Total oocyte number within a mouse ovary or human oocyte pocket was normalized to volume (mm 3 ) ( fig. S1H-J, fig. S2D-F ), revealing an age-associated decrease in oocyte density in both species ( Fig. 1J , fig. S1J, 2F ). While a single human ovary pocket exceeded the volume of a mouse ovary by 10-fold ( fig. S1I, fig. S2E ), mouse oocytes were more densely packed ( Fig. 1J ). Taken together, our data show human ovaries have oocyte “pockets”, unlike mice, that are more pronounced with age, and both species show age-related declines in oocyte density. Conserved cellular composition between mouse and human ovaries We performed scRNAseq at ERA (mouse, 2M; human, 26 and 30Y) and ARA (mouse, 9M; human, 55 and 56Y; table S1 ), combining 10X Genomics Chromium Single Cell 3’ GEX (henceforward referred to as 10X 3’ GEX) with the Smart-seq2 workflow to maximize capture of somatic cells and oocytes, respectively ( Fig. 2A ). In the mouse, unbiased clustering and uniform manifold approximation and projection (UMAP) revealed the 43,316 cells captured belonged to 11 distinct broad clusters annotated as oocyte (170), granulosa (4,594), luteal (1,502), theca (10,547), stroma (14,620), smooth muscle (6,283), pericyte (1,701), epithelia (395), endothelia (1,317), immune (2,157), and glia ( 30 ) using established cell type markers ( Fig. 2B-C ). We confirmed each animal was represented across clusters ( fig. S3A ) and that estrous stage did not alter cell type composition of the ovary ( fig. S3B ). Module scoring using the Isola et. al 2024 ( 16 ) dataset as a reference revealed that corresponding cell types scored highly across data sets ( fig. S3C ). Download figure Open in new tab Fig. 2. Cellular composition is conserved between mouse and human ovaries. (A) Schematic of ovary processing for scRNAseq. ERA samples included 2M mice and 26 and 30Y humans; ARA samples included 9M mice and 55 and 56Y humans. (B) UMAP plot of the eleven cell types captured in the mouse with cell type numbers captured and (C) dot plot of the scaled average expression of cell type marker genes used for annotation. (D) UMAP plot of the ten cell types captured in the human with cell type numbers captured and (E) dot plot of the scaled average expression of cell type marker genes used for annotation . (F) Heatmap of the average scaled module score of the mouse cell type transcriptional signatures (top 100 differentially expressed genes) in the human cell types. Parallel annotation of the human dataset revealed 47,791 cells belonging to 10 distinct broad clusters annotated as oocyte ( 97 ), granulosa (180), theca (577), stroma (36,358), smooth muscle (594), pericyte (4,976), epithelia (651), endothelia (3,739), immune (570), and glia ( 49 ) using established cell type markers ( Fig. 2D-E ). Module scoring using the Wu et. al 2024 dataset as a reference ( 19 ) showed high correspondence between matching human cell types ( fig. S3E ). Cross-species analysis confirmed all broad cell types were most transcriptionally similar to their counterpart in the other species ( Fig. 2F ). Luteal cells, which produce progesterone to support early embryogenesis ( 32 ), were not detected in the human dataset likely due to the donor’s menstrual cycle stage or use of contraceptives, both which affect luteal cell differentiation. We identified robust markers for annotating species-shared cell-type populations across available datasets ( fig. S3G ). Follicular cell types ( fig. S3F ), which were contributed predominantly by human ERA donors ( fig. S3D ) were underrepresented in the human dataset. All follicle-associated cell types, including smooth muscle cells which are essential for ovulation, were more highly represented in mouse ovaries ( fig. S3F ) ( 33 ), likely due to the higher follicle density observed in mice ( Fig. 1J ). Overall, both species shared conserved cell types at different proportions, including a novel population of ovarian glia. Greater conservation of maturation gene patterns in more mature oocytes We suspected oocyte maturity was biased by the sequencing method: Smart-seq2 selects for mature oocytes (≥40μm) while 10X 3’ GEX selects for immature oocytes (≤30μm). To maximize representation of oocyte maturation stages, we integrated 10X oocyte subsets from Isola et al. 2024 (mouse) and Wu et al. 2024 (human) ( Fig. 3A-A ’ ) isolated from young (mouse: 2M, human: 18-30Y) and aged donors (mouse: 9M, human: 47-49Y). After harmonizing by age to isolate transcriptional variation due to maturation, 6 mouse and 5 human oocyte sub-clusters were aligned along a maturation trajectory using Partition Based Graph Abstraction (PAGA) and visualized with Forced Atlas (FA; Fig. 3B-B ’, fig. S4A-A’ ). Oocyte sub-clusters of each species formed a linear trajectory, separating by sequencing technology ( fig. S4A-B’ ). Growing oocyte markers, including Gdf9 / GDF9 ( 34 ), were enriched in Smart-seq2 oocytes ( fig. S4C-C’ ), confirming the direction of maturation and trajectory tree inference. Thus, the 10X end of the tree was selected as the root node (*) ( Fig. 3B-B ’ ). Download figure Open in new tab Fig. 3. Greater conservation of maturation gene patterns in more mature oocytes. (A) Schematic illustrating integration of the oocytes from the Isola et al. 2024 and (A’) Wu et al. 2024 datasets with our subsetted mouse and human oocytes, respectively. (B) FA dimensionality reduction of the oocytes from mouse and (B’) human colored by pseudotime value with the root node indicated (*). (C) Average fitted expression over pseudotime of genes in 15 mouse and (C’) 18 human unique expression patterns. (D) Heatmap displaying the percentage of shared human homologous genes in mouse broad pattern categories of oocyte maturation. The displayed values indicate the percentage of genes in the mouse gene category (column) for which a human ortholog was found in the corresponding human gene category (row). (E) Dot plots of ontology pathways enriched in mouse and (E’) human gene patterns. Select GSEA pathway names were abbreviated, see scRNAseq Methods Table for the abbreviated pathways and the full name. (F) The fitted expression of mouse homologous genes for Late patterns along the pseudotime trajectory of human oocyte maturation. We identified 15 mouse and 18 human distinct patterns of gene expression along the pseudotime trajectory of oocyte maturation ( Fig. 3C-C ’) which were broadly grouped into five stages: Early, Mid-Early, Mid, Mid-Late and Late. Pathway enrichment revealed greater cross-species similarity in mature oocytes (>24%) than in immature oocytes (<14%) ( Fig. 3D , fig S4D) . Late-stage oocytes showed conserved enrichment in chromatin and chromosome organization ( Fig. 3E-E ’ ), with shared expression of the meiosis regulator Aurka / AURKA ( 35 ) ( fig. S4E-E’ ). Mouse Late-stage patterns were distinguished by nuclear pore proteins Nup37 ( 36 ), Nup85 , and Nup98 ( fig. S4E ), echoing the accumulation of nuclear pore proteins in late Drosophila oocytes to support embryonic division ( 37 ). Human Late-stage patterns included meiosis-related genes TUBB8 ( 38 ), ZAR1L ( 39 ), and SIRT7 ( 40 ), alongside TRAPPC11 , linked to vesicle trafficking ( 41 ) ( fig. S4E’ ). To visualize gene-level conservation, we plotted the expression of homologous genes for each broad pattern along the pseudotime trajectory of the other species ( Fig. 3F , fig. S4F) . Several homologs peaked at similar pseudotime points, suggesting their conserved timing and function oocyte maturation. However, many genes peaked outside the pseudotime expression range of the corresponding broad oocyte stage, indicating divergence in expression pattern. Notably, roughly half of the human Early-pattern homologs in mice peaked at the Mid stage ( fig. S4F ), while expression timing of most Late-pattern genes aligned between species ( Fig. 3F ) . Consistent with limited transcript overlap between mouse and human oocytes found previously ( 25 ), our data suggest greater divergence in early oocyte gene expression programs, with increasing convergence during oocyte maturation. Identification of species-specific sub-populations of theca, pericytes, and epithelia To identify cell subtypes, each broad somatic cluster was sub-clustered to the highest resolution with distinct marker genes, producing mouse (m) and human (h) granulosa, immune, theca, stroma, pericyte, and endothelial subclusters ( Fig. 4A ). Granulosa and immune subtypes were annotated using reported markers ( fig. S5A-E ), while other subtypes were characterized by unique markers and inferred function using gene set enrichment analysis (GSEA). Species were compared by hierarchical clustering of GSEA pathways, with two levels of granularity indicating broadly shared (Coarse) and subtype-specific (Fine) gene ontology (GO) pathways. However, module scoring showed no consistent one-to-one subtype relationships across species, suggesting interspecific subtype heterogeneity ( Fig. 4A ). Notably, granulosa, stroma, and endothelial subtypes were largely conserved, while species-specific subtypes emerged in the theca, pericyte, and epithelial compartments. Download figure Open in new tab Fig. 4. Pathway analysis reveals species-specific theca, pericyte, and epithelia subtypes. (A) Heatmap of the average scaled module score of the mouse subtype transcriptional signatures (top 100 differentially expressed genes) in the human subtypes. (B) UMAP plots showing the three theca subtypes identified in the mouse and the human. (C) Heatmap of normalized enrichment scores for pathways enriched in differentially expressed genes for mouse and human theca subtypes. Pathways displayed are unique to or more highly enriched in hierarchical clusters which were found by clustering based on NES of all pathways. (D) UMAP plots showing the two pericyte subtypes in the mouse and the human. (E) Heatmap of normalized enrichment scores for pathways enriched in differentially expressed genes for mouse and human pericyte subtypes. Pathways displayed are unique to or more highly enriched in hierarchical clusters which were found by clustering based on NES of all pathways. (F) UMAP plot showing the three epithelia subtypes in the human. (G) Heatmap of normalized enrichment scores for pathways enriched in differentially expressed genes for human epithelia subtypes. Pathways displayed are unique to or more highly enriched in hierarchical clusters which were found by clustering based on NES of all pathways. Granulosa cells resolved into four shared subtypes, augmented by data from Wu et al. 2024 : Preantral-Cumulus, Antral-Cumulus, Mural, and Mitotic ( fig. S5A-D, O ( 16 , 42 – 61 )). Module scoring confirmed each mouse subtype most resembled the corresponding human subtype ( fig. S5B ). A fifth granulosa subtype, Ghr+ (growth hormone receptor, Ghr ), was identified only in mice; sparsely detected GHR transcripts in human ( fig. S5D ) suggest this subtype may be shared but was undetected due to sampling limitations. While previously used to annotate atretic granulosa cells ( 16 , 48 ), GHR was also implicated in follicle growth and protection from atresia ( 62 , 63 ). Given ( 1 ) markers of this subtype ( Itih5 , Pid1 ) similarly regulate growth in other organs ( 64 , 65 ) ( fig. S5A ) and ( 2 ) the transcriptional similarity of the Ghr+ subtype to the mPreantral-Cumulus and mMural subtypes ( fig. S5A, C ), we postulate the Ghr+ subtype represents a transitory state in follicle maturation. Immune cells in the ovary support follicular development and post-ovulation tissue remodeling ( 18 ) in addition to surveillance. We identified species-shared Macrophage, Tissue Resident (TR) Macrophage, Neutrophil, Activated T Cell, and Conventional Dendritic (cDC) subtypes ( fig. S5E ). While human cDCs were homogenous, mice had distinct cDC1 (Type 1) and cDC2 (Type 2) subtypes. Four additional immune subtypes – Natural Killer (NK), Th17 Helper T Cell, Th2 Helper T Cell, and B cell – were uniquely captured in mouse ( fig. S5E ). Differences in lymphocytes-to-neutrophil ratios between mouse and human blood may explain the increased lymphocyte-derived T cell subtypes captured in the mouse ovary ( 66 ). Theca and stroma cells surround the follicles, producing androgen and providing structural support, respectively ( 67 ). We identified three theca and four stroma subtypes in both species ( Fig. 4B-C , fig. S5F-H ). Module scoring confirmed Cyp17a1 / CYP17A1 + theca subtypes (mTheca3, hTheca1) were steroidogenic (fig. S5F) , yet they did not cluster together at Coarse resolution, indicating species-specific steroidogenesis signatures ( Fig. 4C ). Theca Group1 (mTheca2, mTheca3, and hTheca3) was enriched for BMP signaling and axon guidance, and may therefore play a role in recruitment of innervation to the follicle. Fine resolution revealed species-specific functions: mGroup1.1 (mTheca2, mTheca3) was linked to immune infiltration pathways ( 68 ), which can regulate steroidogenesis and proliferation ( 69 ), while hGroup2.2 (hTheca2) was enriched for organic compound metabolism, namely hydroxy groups essential for androstenedione and testosterone synthesis. Despite shared steroidogenic roles, we identified theca subtypes with inferred species-specific functions. Endothelial cells and pericytes form the ovarian vasculature, essential for systemic hormone signaling ( 70 ). We identified four endothelia, one lymphatic endothelium, and two pericyte subtypes per species ( Fig. 4D , fig. S5I-J ). Endothelial Group1 subtypes were enriched for ovary-specific pathways, including female gamete generation and blastocyst development ( fig. S5J ); Group1.1 was uniquely enriched in nerve growth pathways, suggesting a role in vascular innervation ( 71 ). Coarse clustering showed Group2 pericytes, present in both species, were enriched for reproductive structure development, consistent with their role in follicle neovascularization ( 72 ). mPericyte1, an outlier by Coarse resolution, was uniquely enriched for fibroblast growth factor signaling, upstream of PDGFRβ signaling ( 73 ), indicating both species-shared and specific pericyte functions in ovarian vasculature. We identified three novel human epithelia and three mouse luteal subtypes ( Fig. 4F , fig. S5K-L ). Cross-species subtype analysis was limited as mouse epithelia formed a homogenous cluster and human luteal cells were not captured. Group1 (hEpithelia1) was uniquely enriched for BMP signaling and reproduction-associated pathways, while Group2.1 (hEpithelia2) and Group2.2 (hEpithelia3) were enriched for glandular development and immune activation pathways, respectively ( Fig. 4G ); the greater heterogeneity in human epithelia likely reflects the thicker ovarian epithelial layer ( 74 ). Luteal Group1 (mLuteal1, mLuteal2) was enriched for vasculogenesis and follicle development pathways ( fig. S5L ). Fine resolution revealed mLuteal1 was associated with morphogenesis of progesterone-metabolizing organs ( 75 ), while mLuteal2 was enriched for cholesterol biosynthesis pathways, the precursor for progesterone synthesis. mLuteal2 ( Parm1 / Lhcgr ) and mLuteal1 ( Akr1c18 ) align with the early and late luteal cells identified by Slide-Seq ( 76 ), with mLuteal3 as a transitional state expressing both early ( Lhcgr ) and late ( Sfrp4 ) markers but lacking Akr1c18 ( Fig. S5M ). CLs develop and regress with the estrous cycle ( 77 , 78 ) and luteal subtype distribution aligned with cycle stage: diestrus-stage mice primarily contributed to mLuteal1 and partially to mLuteal3, while mLuteal2 derived solely from estrus/metestrus-stage mice ( fig. S5N ). This correspondence supports a developmental progression of the three mLuteal subtypes. Our analysis reveals species-shared roles of granulosa, stroma, and endothelial subtypes, but species-specific subtypes in the theca, pericyte, and epithelial compartments. The observed species-specific subtypes may reflect differences in organ architecture and scale. Sympathetic nerve loss perturbs first wave folliculogenesis While peripheral nerves regulate organ-specific processes, their role in the ovary remains understudied. The absence of sympathetic and sensory neurons in this and prior scRNAseq studies is expected, as their cell bodies reside in the celiac ganglion ( 79 ). However, we identified a novel, interspecific population of glia, the supporting cells of neurons, using canonical markers S100b / S100β ( 80 ) and Sox10/SOX10 ( 81 ). Peripheral nerves regulate organ function by releasing neurotransmitters that signal through target cell receptors ( 82 ), and reciprocally, local organ-derived cues maintain axons ( 83 ). Examining the expression of innervation cues and receptors in the ovary ( Fig. 5A-B , fig. S6A-B ), we found Gabbr1 / GABBR1 (a GABA receptor) was lowly expressed in theca cells of both species, while only human theca cells expressed ADRA2A (an adrenergic receptor) ( Fig. 5A ). The neuroprotective neuropeptide Nampt / NAMPT was broadly expressed, but more highly in human ( fig. S6A ). Theca cells interspecifically expressed Robo1 / ROBO1 , an axon guidance cue that may facilitate follicle innervation ( Fig. 5B ). Glia from both species highly expressed the axon guidance factor Sema3b / SEMA3B ( Fig.5B ), while only human glia expressed NRXN3 , a synaptic adhesion molecule ( fig. S6B ). Both mouse and human pericytes expressed Ngf / NGF , which is essential for axon maintenance ( Fig. 5B ). IF confirmed NGF+ pericytes (PDGFRβ+) were closely associated with sympathetic neurons (TH+) and endothelial cells (CD31+) in mouse and human ovaries, highlighting their role in ovarian neurovascular interactions ( Fig. 5C ). Download figure Open in new tab Fig. 5. Sympathetic nerve loss perturbs first wave folliculogenesis. (A) Heatmaps showing the average expression of neurotransmitter receptors and (B) axon guidance genes across the broad cell types in the mouse and human ovary. (C) IF staining of mouse and human ovary sections for sympathetic nerves marked by TH (red), endothelial cells marked by CD31 (cyan), pericytes marked by PDGFRβ (yellow), and nerve growth factor (NGF, magenta). Arrows denote vasculature-associated sympathetic axons. Scale bars, 5μm. (D) IF staining of nuclei marked by Hoechst (gray) and glia marked by S100β (magenta) during development at 8 and 20wpc in the human ovary and P6 and P28 in the mouse ovary. Scale bars, 50μm. (E) Whole-mount IF staining of sympathetic nerves marked by TH (red) and of (F) glia marked by S100β (magenta) in the 2M mouse and 23Y human ovary. Scale bars, 200μm in the mouse and 300μm in the human. (G) IF staining of nuclei marked by Hoechst (gray), sympathetic nerves marked by TH (red), and glia marked by S100β (magenta) showing the presence of nerve associating glia in the mouse and human ovary. (H) Whole-mount IF staining of Th Cre/+ control and Th Cre/+ TrkA fl/fl cKO ovaries at P21 where NOBOX (green) marks all oocytes and TH (red) marks sympathetic nerves. Single channel images of TH (gray) and NOBOX (gray) to show nerve deletion and primordial follicle oocyte increase, respectively. Scale bars, 100μm. ov = ovary; ovi = oviduct. (I) Quantification of primordial, (J) total, (K) growing, and (L) antral oocytes in Th Cre/+ and Th Cre/+ TrkA fl/fl ovaries at P21. All data represented as Mean + SEM; * p < 0.05; ** p < 0.01, *** p < 0.001, Student’s t-test. Peripheral nerves and glia emerge early in ovarian development, detected in mice by embryonic day (E) 16.5 ( 84 ) and in humans by 8 weeks post conception (wpc), expanding further by 20wpc ( Fig. 5D , fig. S6D ). As E15 (Theiler stage 23) and 8wpc (Carnegie stage 19-22) represent comparable developmental milestones ( 85 ), our findings suggest ovarian innervation occurs within a conserved developmental window, albeit slightly earlier in humans. TH+ sympathetic nerves and S100B+ glia localized to the ovarian medulla during postnatal development ( Fig. 5D , fig. S6C-E ), later expanding into the theca and stroma compartments in adulthood ( fig. S6F ). 3D imaging revealed extensive innervation in both species, with large neuronal projections spanning the medullary and cortical regions ( Fig. 5E , movies S2-3 ). Glia were abundant and largely nerve-associated ( Fig. 5F-G ). Co-staining with TUBB3 (pan-neuronal) and TH (sympathetic-specific) showed that most neurons in adult mouse and human ovaries are sympathetic ( fig. S6G ). Sympathetic and sensory nerves regulate facets of folliculogenesis ( 71 , 86 ), particularly in polycystic ovarian syndrome (PCOS), which is characterized by hyper-sympathetic activity and increased antral follicles ( 87 – 89 ). However, disentangling the role of sympathetic regulation of folliculogenesis is complicated by the non-specific or lethal nature of many in vivo innervation-modulating tools. Like in other organs, ovarian peripheral innervation relies on nerve growth factor (NGF) signaling to tropomyosin-related kinase A (TRKA) receptors on sympathetic nerves ( 90 , 91 ). Accordingly, we ablated sympathetic nerves by crossing Th Cre ( 92 ) to TrkA flox mice ( 93 – 95 ) ( Th Cre/+ ;TrkA fl/fl , cKO) and analyzed ovarian follicle dynamics after completion of the first wave of folliculogenesis at postnatal day (P) 21 using whole-mount 3D imaging ( Fig. 5H ). cKO ovaries lacked sympathetic nerves in the ovary and oviduct ( Fig. 5H ). Interestingly, cKO ovaries had increased primordial follicles ( Fig. 5I ) and a higher total follicle count ( Fig. 5J ), but significantly fewer growing follicles, particularly antral/pre-ovulatory ( Fig. 5K-L ) compared to Th Cre controls. These findings demonstrate the conserved emergence of sympathetic nerves and glia in ovarian development, and reveal that, in mice, sympathetic innervation regulates follicle recruitment and maturation during the first wave of folliculogenesis. Increases in innervation and fibrosis underlie conserved and species-specific transcriptional changes in ovarian aging Age-related ovarian decline in both mice and humans is driven by the microenvironmental changes, particularly increases in fibrosis and inflammation ( 96 ). Several ovarian cell subtypes showed age bias in mice, with distinct young and aged contributions ( fig. S7A-A’ ). In humans, follicular cells — including oocytes, granulosa, and theca cells — came primarily from ERA donors ( fig. S7B-B’ ). Conversely hEpithelia3, exclusive to ARA donors, was enriched for inflammatory pathways ( Fig. 4G , fig. S7B’ ). Mouse and human Endothelia2 subtypes expressed genes linked to accelerated endothelial aging ( Fabp4 ( 97 ), Edil3 ( 98 ), and ACKR1 ( 99 )) and clustered together in GSEA at Fine resolution, and both were almost entirely derived from aged ovaries ( fig. S5I, 7A’-B’ ); the identification of this conserved endothelial subpopulation specific to the aged ovary aligns with recent findings that decline in vascular integrity contributes to murine reproductive aging ( 100 ). To identify the cellular compartments within the ovarian microenvironment with the greatest transcriptional changes across species, we calculated transcriptional variance explained by age for each cell type. Using 50 principal components (PCs) derived from all detected genes, we fit a linear regression model to quantify age influence. Total variance was computed as the weighted sum of age-associated variance across PCs. In mice, theca cells exhibited the greatest transcriptional changes with age, followed by stroma and smooth muscle ( Fig. 6A ). In humans, stromal and endothelial cells showed the greatest age-related shifts ( Fig. 6A ’ ). Immune cells in both species remained largely transcriptionally stable with age. Download figure Open in new tab Fig. 6. Down-regulation of collagen gene expression precedes age-related fibrosis across species. (A) Rose plots illustrating the magnitude of transcriptional variance explained by age across the broad cell types in the mouse and (A’) human ovary. (B) Mouse versus human homologous gene expression fold change between young and aged theca and (C) epithelia with genes colored by similar or diverging changes in expression with age between species. (D) Pathway enrichment analysis of the genes decreased and (E) increased with age in both mouse and human. Select GSEA pathway names were abbreviated in (D) and (E), see scRNAseq Methods Table for the abbreviated pathways and the full name. (F) Representative images of thresholded PSR staining in the young and aged mouse and (F’) human ovaries with high magnification IF staining panels of fibrillar collagen marked by CNA35 and COLIII (gray). Scale bars, 100μm. (G) Quantification of the percent of fibrotic tissue in the young and aged mouse and (G’) human ovaries. (H) IF staining of mouse and (H’) human ovary for sympathetic nerves with TH (gray) at young and aged timepoints. Scale bars, 50μm. (I) Quantification of the percent of TH+ area within the stroma in young and aged mouse ovary sections. (I’) Quantification of the percent of TH+ area in ERA and ARA human ovary sections. All data represented as Mean + SEM; * p < 0.05, Student’s t-test. To compare age-dependent transcriptional changes ( Fig. 6B-C ), homologous genes were grouped by expression trends based on fold change associated with the aging condition ( fig. S7C-F ). In theca cells, neuro-repulsive genes Sema5A/SEMA5A and Nexn/NEXN increased with age in mice but decreased in ARA humans, while steroidogenic genes Cyp11a1 / CYP11A1 declined in both species ( Fig. 6B ). Androgen receptor ( Ar / AR ) increased with age in epithelia from both species, while estrogen receptor ( ESR1 ) increased in ARA human epithelia, genes both previously linked to fibrosis regulation in mice ( 21 ) ( Fig. 6C ). Aging-associated genes Lmna / LMNA ( 101 ) and Insr / INSR ( 102 , 103 ) increased with age in both mouse and human stroma ( fig. S7C ). Ovarian aging-associated genes Foxp1 / FOXP1 ( 19 ) and Igf1r / IGF1R ( 104 ) increased in mouse but decreased in human smooth muscle with age ( fig. S7D ). To identify conserved and species-specific processes in ovarian aging, we performed pathway enrichment analysis on DEGs for each cell type ( Fig. 6D-E , fig. S7G-H ). In theca cells for example, steroidogenic functions and their upstream cholesterol metabolism pathways commonly decreased with age ( Fig. 6D ), while p38 MAP kinase pathways, which regulate proinflammatory cytokine production, were increased ( Fig. 6E ). hTheca cells uniquely upregulated mitochondrial inner membrane processes ( 105 ) ( fig. S7G ), whereas aged mTheca cells increased genes associated with negative-regulation of axon extension ( fig. S7H ). In the stroma, smooth muscle, and pericytes, both species showed reduced expression of Col1a2 / COL1A2 , Col3a1 / COL3A1 , Col4A1 / COL4A1 , and Col18a1 / COL18A1 ( fig. S7C-E ), refining previous results in unfractionated murine ovaries ( 106 ). Despite known accumulation of fibrotic collagen in mouse ovary and human ovarian cortex with age, collagen-binding and integrin-related gene sets were decreased in aged stroma of both species ( Fig. 6D ). To quantify collagen deposition, we performed Picrosirius red (PSR) staining on cortical and medullary regions ( Fig. 6F-F ’ ). Collagen levels remained stable until 9M in mice as expected ( 107 ), while ARA ovaries showed significant fibrosis ( 108 ) ( Fig. 6G-G ’ ). Unlike young ovaries, aged ovaries from both species showed positivity for the fibrillar collagen-specific marker collagen-binding adhesion protein 35 (CNA35 ( 109 , 110 )), which largely colocalized with COLIII deposition ( Fig. 6F-F ’ ). This discrepancy between transcript and protein suggests a conserved compensatory response to slow fibrosis. Given the identified role of sympathetic nerves in follicle recruitment, we examined age-related shifts in sympathetic signaling to ovarian cells. We analyzed changes in neurotransmitter receptors and axon guidance gene expression between young and aged cell types ( fig. S7I-J ), where positive values indicate increased expression with age. Mouse and human theca cells showed increased Gabbr1 / GABBR1 , a receptor for hormone-sensitive GABA ( 111 ) ( fig. S7I ), suggesting compensation for reduced GABAergic signaling during perimenopause ( 112 ). Across species, aging increased Fgf2 / FGF2 expression in glia, Ngf / NGF in pericytes, and Robo1 / ROBO1 and ROBO2 in theca cells, with a stronger effect in humans ( fig. S7J ). Since FGF2 ( 113 ), NGF ( 114 ), and ROBO1/2 ( 115 ) regulate axonal growth, we interspecifically quantified nerve density in young and aged ovaries by TH+ area in sections ( Fig. 7H-I ’ ). Sympathetic innervation remained stable in mouse ovaries through 9M ( Fig. 7H, I ), but significantly increased in ARA human ovaries ( Fig. 6H ’, I’ ). As high estrogen levels reduce sympathetic innervation in other reproductive organs ( 116 ), this increase is likely a consequence of declining estrogen levels at menopause. Download figure Open in new tab Fig. 7. Human and C57BL/6 oocytes have a larger magnitude of transcriptional change with age than granulosa cells. (A) Total number of oocytes retrieved at 2M (n = 5), 4M (n = 7), 6M (n = 6), 9M (n = 8), and 12M (n = 5) from superovulation. Data represented as Mean + SEM; * p < 0.05; ** p < 0.01, *** p < 0.001, **** p < 0.0001, ANOVA test. (B) Percentage of superovulated females that responded to hormone priming. The numbers above each graph bar indicate the number of females that responded out of the total injected. (C) Percentage of superovulated oocytes that successfully proceeded to the 2C and blastocyst stages following in vitro fertilization at 2M (n = 5), 4M (n = 5), 6M (n = 5), 9M (n = 8), and 12M (n = 5). Data represented as Kaplan-Meier estimate + SE; Mantel-Cox test. (D) Visualization of loss of measures of fertility and oocyte quality in C57BL6/J ovaries compared with 2M of age. (E) Rose plots illustrating the magnitude of transcriptional variance explained by age across the cell types of the follicle in the mouse and (E’) human ovaries. (F) Pathways enriched in genes differentially expressed with age in the Early and (G) Late oocytes in mouse (Increased = red, Decreased = blue) and human (Increased = magenta, Decreased = cyan). Select GSEA pathway names were abbreviated in (F) and (G), see scRNAseq Methods Table for the abbreviated pathways and the full name. (H) CellChat analysis between aged and young mPreantral-Cumulus and hCumulus subtypes as the sender with the Early oocytes as the receiver and (I) the mAntral-Cumulus, mMitotic, and mMural and hCumulus and hMural subtypes as the sender with the Late oocytes as the receiver. Pathways shared between species are indicated in purple, species-specific pathways are denoted in green. Aging alters the transcriptome of oocytes more than that of granulosa cells Despite widespread age-related transcriptional changes in the ovarian soma, fertility decline is largely attributed to oocyte deterioration ( 117 ). To assess oocyte competence, we performed in vitro fertilization (IVF) on C57BL/6 mice at 2-, 4-, 6-, 9- and 12M (n ≥ 6 per group). Oocyte yield after gonadotropin stimulation declined at 9M, consistent with the shrinking follicle pool ( Fig. 7A ), while superovulation response remained stable until 12M ( Fig. 7B ), suggesting a loss of available oocytes precedes reduced capacity for hormone responsiveness. Kaplan-Meier survival analysis, which weighs blastocyst formation probability per oocyte, revealed developmental competence remained stable between 2- and 4M but declined by 6M, with the 2-Cell (2C)-to-blastocyst transition being the most vulnerable to age ( Fig. 7C ). By 9-12M, survival to both the 2C and blastocyst stages significantly declined compared to 2M. Statistical modeling confirmed sperm quality did not impact developmental success across female age ( table S2 ). The observed decline in oocyte quantity and competence by 9M in mice ( Fig. 7D ) echoes the reduced follicle numbers and embryo development rates in ARA humans (> 35 years) ( 118 ). Since follicles support oocyte maturation, we examined age-related transcriptional changes in granulosa cells and oocytes. Mouse (m) and human (h) oocyte and granulosa clusters were consolidated to maximize young versus aged comparisons. hPreantral- and hAntral-Cumulus clusters were merged into a single Cumulus cluster, and hMitotic granulosa cells, most similar to hMural cells, were combined into a single Mural cluster ( fig. S8B’) . Using scFates, oocyte clusters ( Fig. 4A-A ’ ) were divided into Early and Late milestones along the pseudotime trajectory ( fig. S8A-A’) . Variance explained calculations revealed greater age-dependent transcriptional changes in oocytes than granulosa cells. Among granulosa subtypes, Cumulus cells showed the most age-related transcriptional changes. Interestingly, the most affected oocyte group differed by species: mEarly and hLate oocytes exhibited the largest transcriptomic shifts with age ( Fig. 7E-E ’ ). Age-associated DEGs were identified for each follicular cell type in both species. To enable cross-species comparison, mCumulus and mMural/Mitotic granulosa clusters were merged to match the human granulosa groups ( fig. S8B ), while mGhr+ granulosa remained a species-specific cluster. Differential expression (DE) was visualized using minus versus average (MA) plots, displaying gene expression levels versus fold change with age, where a positive fold change denotes increased age-related expression. Cross-species similarity was assessed by mapping significant DEGs from one species onto the MA plots of the other (mouse: increased (red)/decreased (blue), human: increased (magenta)/decreased (cyan); source species (inlay)) ( fig. S8C-G ). Only ARA hLate oocytes captured with 10X were used for DE analysis, as Smart-seq2 did not capture this population. Consistent with the variance explained calculations, mEarly and hLate oocytes had the most age-related DEGs, though few were shared interspecifically, indicating species-specific oocyte aging mechanisms ( fig. S8C-D, H ). In mEarly oocytes, all zona pellucida glycoproteins ( Zp1, Zp2, and Zp3 ) uniquely declined with age, suggesting compromised structure and ability to bind sperm. In hLate oocytes, chromosome segregation genes ( FMN2 ( 119 ), RANGAP1 ( 120 ), and SMC1B ) decreased, while DNA damage response genes ( RAD50 and MDC1 ( 121 , 122 )) increased. Among granulosa cells, Cumulus subtypes had the most DEGs, showing, similar to oocytes, largely species-specific age-related changes. To compare follicle aging processes, we performed pathway enrichment on age-related DEGs for each follicular subtype in both species ( Fig. 7F-G , fig. S9A-C ). Pathways upregulated in aged subtypes were denoted as increased (mouse: red, human: magenta), while pathways enriched in young subtypes were denoted as decreased (mouse: blue, human: cyan). Young mCumulus granulosa DEGs were enriched for hormone secretion pathways, while ERA hCumulus and hMural granulosa DEGs were enriched for vesicle trafficking, a potential oocyte-granulosa communication mechanism ( 123 ) ( fig. S9A-B ). Young mEarly oocytes were enriched for cytoplasmic translation pathways, whereas ERA hEarly oocytes were enriched for platelet alpha granule pathways, which include oocyte-relevant genes IGF1 ( 124 , 125 ) , HSPA1A ( 126 ), and SMAD7 ( 127 ) ( Fig. 7F ). With aging, mCumulus and hCumulus granulosa were enriched for inflammatory and reactive oxygen species pathways ( fig. S9B ), while aged Ghr+ granulosa showed enrichment for extracellular matrix binding ( fig. S9C ). Aged mEarly oocytes were enriched for protein ubiquitination and TOR signaling pathways, while ARA hEarly oocytes were enriched for DNA damage, aligning with the known decline in oocyte quality and emerging effectiveness of TOR inhibition via rapamycin ( 128 , 129 ). Given the largely unchanged age-related transcriptomic landscape of mLate oocytes, unique enriched pathways were identified only in hLate oocytes: ERA and ARA hLate oocytes were, respectively, enriched for spindle organization and chromatin condensation pathways, both of which contribute to oocyte aneuploidy when dysregulated with age ( 130 ) ( Fig. 7G ). For successful growth and maturation, follicles rely on tightly coordinated cell-cell communication. Using CellChat, we identified species-shared (purple) and species-specific (green) age-related signaling changes from granulosa cells to oocytes. Granulosa subtypes were assigned as senders (ligand-expressing) and oocytes as receivers (receptor-expressing). Less mature granulosa subtypes (mPreantral-Cumulus, hCumulus) were paired with Early oocytes ( Fig. 7H ), while more mature subtypes (mAntral-Cumulus, mMitotic, mMural, hCumulus, hMural) were paired with Late oocytes ( Fig. 7I ). Given its putative transitory state, mGhr+ was included as a sender for both mEarly and mLate oocytes. KIT signaling ( 131 ) was detected between mPreantral-Cumulus granulosa and mEarly oocytes, while BMP signaling was present between mGhr+ granulosa and mEarly oocytes ( Fig. 7H ). ERA hCumulus granulosa communicated with hEarly oocytes via MIF ( 132 ) and MK ( 133 ) signaling, both pathways implicated in oocyte dynamics. CypA signaling, which regulates extracellular matrix proteins via TGFβ/Smad3 ( 134 ), was conserved between granulosa and Early oocytes in both species. In aging mice, ANGPTL and IGF signaling from granulosa to mLate oocytes decreased ( Fig. 7I ). In humans, CD99 and 27HC signaling increased between ARA hCumulus granulosa and hLate oocytes, with implications in ovarian cancer ( 135 , 136 ). CDH signaling, essential for follicle integrity, declined between ARA hMural granulosa and hLate oocytes, consistent with the role of N-cadherin in follicular structure and ovulatory capacity ( 137 ). Theca cells support follicle growth, provide structural integrity, and drive granulosa estrogen synthesis via androgen secretion ( 138 ). Using CellChat, we identified age-related changes in theca-to-granulosa signaling, with theca subtypes as senders and Cumulus/Mural granulosa subtypes as receivers ( fig. S9D-E ). In both species, testosterone signaling was present across all theca subtypes, while androstenedione signaling, a precursor in testosterone biosynthesis, was restricted to mTheca1, mTheca3, and hTheca2. In mice, testosterone and androstenedione signaling remained stable with age, whereas in humans, they were specific to ERA hTheca. CypA signaling was present in all theca subtypes, but restricted to ERA human and Aged mouse follicles ( fig. S9D ). IGF signaling, which stimulates sex steroid production ( 139 ), was unique to ERA human theca-granulosa crosstalk, while age-related decreases in cholesterol signaling were conserved between species, reiterating our earlier findings ( Fig. 6D , fig. S9D-E ). These findings reveal species-specific shifts in androgen signaling with age; in light of the observed decline in testosterone during perimenopause ( 140 ), which is linked to aging hallmarks ranging from cognitive decline to bone loss ( 141 , 142 ), this divergence highlights key differences in how ovarian aging influences follicular endocrine function across species. Discussion We comprehensively compared mouse and human ovaries across the reproductive window at functional, tissue, cellular, and transcriptional levels. Using whole-organ imaging, we quantified and 3D-mapped follicle growth stages, identifying a decline in follicle density with age in both species and secondary follicle loss by 4M in C57BL/6 mice. We revealed interspecific spatiotemporal similarities in ovarian peripheral nervous system development and performed transcriptomic analysis across all ovarian cell types, identifying a novel glia population. Our dataset provides the most detailed transcriptomic profile of oocytes across stages and species, uncovering divergent oocyte maturation gene patterns and species-specific age-related intrafollicular and microenvironmental changes. Collectively, this study spans ovarian development, homeostasis, and aging, distinguishing ovarian signatures that are species-shared or -specific between mouse and human. The number of non-renewing, primordial oocytes is a key determinant of fertility and reproductive lifespan, but remains difficult to quantify. Clinically, antral follicle counts detected via ultrasound and circulating AMH serve as proxies for the ovarian reserve, though their relationship to the number of primordial follicles remains unclear. Our quantifications in C57BL/6 mice reveal a linear relationship between primordial and growing follicles from 2 to 9M, followed by a sharp decline ( Fig 1F ), suggesting steady follicle recruitment maintains the activated pool until 9M ( 16 , 143 ). Secondary follicles were the most vulnerable with age, decreasing as early as 4M, accompanied by an overall drop in follicle density. Since the secondary stage is transient, this depletion may reflect reduced recruitment from the primordial pool or accelerated progression of paused secondary follicles ( 144 ). Extending our whole-ovary imaging at cellular resolution to human ovaries, we identified key species differences: mouse oocytes were evenly distributed, whereas human oocytes were clustered in “pockets”. The shift in the growing-to-primordial follicle ratio at 9M in mice likely models folliculogenesis dysregulation in humans during the 4th decade ( 145 ), and highlights the need for improved ovarian reserve biomarkers. The gargantuan size of oocytes, which increase in diameter during folliculogenesis by ∼60μm in mice ( 146 ) and ∼90μm ( 147 ) in humans, limited their capture in previous droplet-based studies ( 48 , 148 ). By coupling 10X Genomics and well-based Smart-seq2, we captured all somatic cell types, except luteal cells and neurons, and oocytes at all maturation stages from unstimulated mouse and human ovaries. Cross-dataset comparison revealed shared roles of granulosa, stroma, and endothelial subtypes, while theca, pericyte, and epithelial compartments exhibited species-specific differences. We speculate the greater size of the human ovary and expanses between follicle pockets contributed to the increased interspecific heterogeneity in gene expression and subtype composition. scRNAseq identified a novel population of ovarian glial cells extensively present in both developing and adult mouse and human ovaries. Human ovarian glia and sympathetic nerves follow the same developmental patterning observed in embryonic mouse ovaries ( 149 ). Using a sympathetic neuron-specific conditional deletion model ( 94 ), we showed a functional role for sympathetic nerves in the first wave of follicle recruitment and maturation. Absence of sympathetic nerves led to more primordial but fewer antral/pre-ovulatory follicles, reinforcing their role in follicle maturation and coinciding with PCOS, where increased sympathetic activity is linked to higher antral follicle counts ( 87 – 89 ); thus, sympathetic nerves may be potential targets for controlling precocious follicle maturation. Notably, the increase we observed in sympathetic innervation in ARA ovaries, along with the known age-related rise in adrenergic activity ( 150 – 153 ), suggests a complex regulatory shift beyond neuronal dysregulation. Further investigation is needed to determine if these neurons remain functional and whether their growth compensates for a declining follicle reserve. Age-related fertility decline is linked to decreased oocyte quality. Although we likely did not capture the terminal stage(s) of human oocyte maturation given the monoovulatory system, hLate and mLate oocytes were similarly enriched for chromosome segregation pathways, a feature previously associated only with human preovulatory oocytes ( 154 ). We found mEarly oocytes exhibited greater transcriptomic shifts with age than mLate oocytes, whereas hLate oocytes showed the opposite trend. Similarly, macaque scRNAseq data reported increased DEGs in mature oocytes with age, suggesting primates experience more pronounced age-associated transcriptional changes in late-stage oocytes. Understanding the age-related decline in oocyte quality is crucial for improving ART, as identifying key signaling mediators may reveal therapeutic targets. Analysis of the most variable age-associated genes across oocyte maturation stages highlighted increased TOR signaling in mEarly oocytes, reinforcing previous findings that rapamycin delays murine ovarian aging and suggesting global mTOR inhibition with age may positively impact oocyte quality ( 128 , 155 ). Recent studies identified aging hallmarks in the ovarian microenvironment ( 16 – 19 , 21 , 26 – 28 , 156 ); we found the stroma, smooth muscle, and epithelial compartments exhibited the greatest transcriptional changes with age across species. Previous work in mice detected fibrotic foci between 7-9M and significant ovarian fibrosis by 14-17M ( 21 , 107 ). Accordingly, we observed no fibrosis increase in mice by 12M, but report a significant increase in ARA human ovaries. Surprisingly, both species showed age-decreased collagen transcript levels in the stroma, suggesting a conserved compensatory downregulation to delay fibrosis. These findings highlight the potential benefit of earlier anti-fibrotic intervention to enhance ovulation rescue at advanced ages ( 156 , 157 ). This study provides a comprehensive comparison of species-shared and species-specific facets of mammalian ovarian biology from development through aging. We generated robust scRNAseq datasets of the mouse and human ovary, capturing a novel, interspecific ovarian glia population and overcoming previous difficulties in capturing oocytes by integrating well-based and microfluidics technologies with published datasets. We mapped the ovarian reserve in 3D, uncovered transcriptional signatures of oocyte maturation, and identified species-specific vulnerabilities in aging oocytes. This work enhances our understanding of the mouse as a model for human ovarian biology and serves as a valuable resource for future interspecific analyses. Funding National Institutes of Health grant 1F31HD108875 (EAG) National Institutes of Health grant 1F31HD110208 (MHF) UCSF Discovery Fellowship (EAG) Hillblom/BARI Graduate Student Fellowship Award (EAG, MHF) National Institutes of Health grant 1R01GM122902 (DJL) National Institutes of Health grant 1R01ES023297 (DJL) Biohub Investigator grant (DJL) The Global Consortium for Reproductive Health through the Bia-Echo Foundation GCRLE-0123 (DJL) The W.M. Keck Foundation (DJL) The Juno Fund (DJL) Author contributions Conceptualization: EAG, MHF, RMS, DJL Methodology: EAG, MHF, RMS, BS, AMD, LD, MB, AEL, RA Investigation: EAG, MHF, RMS Visualization: EAG, MHF, RMS Funding acquisition: EAG, MHF, DJL Project administration: DJL Supervision: DJL, FF, NN Writing – original draft: EAG, MHF, RMS Writing – review & editing: EAG, MHF, RMS, DJL, BS, AMD, LD, FF Competing interests DJL is on the SAB of Vitra, Inc. Data and materials availability All sequencing data (scRNA-seq) and expression count matrices (scRNA-seq) will be deposited in Gene Expression Omnibus and the cellxgene portal prior to publication. Acknowledgments Most importantly, we thank the families who consented to donate human ovaries for this study. In addition, we acknowledge the staff within the UCSF Genomics CoLab for their assistance with 10X library preparation, A. Edwards and the staff within the Biological Imaging Development CoLab (BIDC) at UCSF Parnassus Heights for their training and support in using the white-light Leica TCS SP8 inverted confocal microscope, and R. Blandino at the Gladstone Institutes Histology and Light Microscopy Core (HLMC) for her training and support in using the Miltenyi Blaze Ultra-Imaging 3D Light Sheet Microscope. S. Paul, H. Mekonen, and A. Zhou from the Genomics group at CZ Biohub for their contributions. A. Rajkovic for kindly gifting the NOBOX antibody and K. Tharp and C. Minor for kindly gifting the CNA35. M. Stout, J. Isola, C. Hubbart, and the Stout Lab for sharing their data and insights. B. Jones, S. Crasta, and the Tabula Sapiens Consortium at Stanford University, as well as J. Gardner and J. Du at the UCSF VITAL core for facilitating access to human donor tissue. G. Zaza and the Reproductive Biology Hub at the Buck Institute for Research on Aging for performing the PSR analysis. L. La Follette for providing clinical insights on ovary morphology and N. Wolcott for assistance with EstrousNet. Members of the Laird lab (J. Zussman, E. Rojas, S. Cincotta, R. Dhada) for their thoughtful feedback on the manuscript. Our thesis committee members (S. Villeda, T. Nystul, M. Conti, L. Jones) for their invaluable guidance throughout this work. Schematics were created in BioRender (Publication License E. Gaylord (2025 https://BioRender.com/f09s767 ). References 1. ↵ C. A. Doherty , F. Amargant , S. Y. Shvartsman , F. E. Duncan , E. R. 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Share Comparative analysis of human and mouse ovaries across age Eliza A. Gaylord , Mariko H. Foecke , Ryan M. Samuel , Bikem Soygur , Angela M. Detweiler , Leah Dorman , Michael Borja , Amy E. Laird , Ritwicq Arjyal , Juan Du , James M. Gardner , Norma Neff , Faranak Fattahi , Diana J. Laird bioRxiv 2025.02.27.640481; doi: https://doi.org/10.1101/2025.02.27.640481 Share This Article: Copy Citation Tools Comparative analysis of human and mouse ovaries across age Eliza A. Gaylord , Mariko H. Foecke , Ryan M. Samuel , Bikem Soygur , Angela M. Detweiler , Leah Dorman , Michael Borja , Amy E. Laird , Ritwicq Arjyal , Juan Du , James M. Gardner , Norma Neff , Faranak Fattahi , Diana J. 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