Abstract
Age-dependent reproductive decline has become a significant global health concern as the average maternal age at first birth increases. Fertility loss associated with reproductive aging is driven in part by alterations to ovarian composition and function, dysregulation of folliculogenesis, and increased inflammatory signaling. Our understanding of the molecular changes underlying ovarian aging has been expanded by single-cell and spatial transcriptomic studies, which identified infiltration of immune cells as a feature of ovarian aging. However, the function of these age-associated immune cells and their potential contributions to the inflammaging phenotype remain unclear. In this study, we integrate single-cell and spatial transcriptomics to define changes in the composition and intercellular signaling in the aging mouse ovary. We identify specific macrophage and T cell subpopulations that increase with age and are key sources of pro-inflammatory signaling in old ovaries. Further, we predict bidirectional signaling between these pro-inflammatory cells and granulosa cell populations that may impair follicular growth and development while promoting immune cell recruitment. These findings provide insights into the mechanisms that drive ovarian inflammaging.
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Data availability
Original data underlying this manuscript can be accessed from the Stowers Original Data Repository at https://www.stowers.org/research/publications/libpb-2605. RNA seq data is available via GEO GSE317144 and the Single Cell Portal accession SCP3321.
References
Zabak, S., Varma, A., Bansod, S. & Pohane, M. R. Exploring the complex landscape of delayed childbearing: factors, history, and long-term implications. Cureus 15, e46291 (2023).
Frejka, T. & Sardon, J.-P. First birth trends in developed countries. Demogr. Res. 15, 147–180 (2006).
Gladys, M. & Martinez, K. D. Fertility of Men and Women Aged 15–49 in the United States: National Survey of Family Growth, 2015–2019. Natl. Health Stat. Rep. 179, 1–22 (2023).
Whiteley, J., DiBonaventura, M., Wagner, J. S., Alvir, J. & Shah, S. The impact of menopausal symptoms on quality of life, productivity, and economic outcomes. J. Women’s. Health 22, 983–990 (2013).
El Khoudary, S. R. et al. Patterns of menstrual cycle length over the menopause transition are associated with subclinical atherosclerosis after menopause. Menopause 29, 8–15 (2021).
Merone, L., Tsey, K., Russell, D. & Nagle, C. Sex Inequalities in Medical Research: A Systematic Scoping Review of the Literature. Women’s. Health Rep. 3, 49–59 (2022).
Richards, J. S. & Pangas, S. A. The ovary: basic biology and clinical implications. J. Clin. Invest. 120, 963–972 (2010).
Kinnear, H. M. et al. The ovarian stroma as a new frontier. Reproduction 160, R25–R39 (2020).
Field, S. L., Dasgupta, T., Cummings, M. & Orsi, N. M. Cytokines in ovarian folliculogenesis, oocyte maturation and luteinisation. Mol. Reprod. Dev. 81, 284–314 (2014).
Matsuura, T. et al. Anti-macrophage inhibitory factor antibody inhibits PMSG-hCG-induced follicular growth and ovulation in mice. J. Assist. Reprod. Genet 19, 591–595 (2002).
Kirsch, T. M., Friedman, A. C., Vogel, R. L. & Flickinger, G. L. Macrophages in corpora lutea of mice: characterization and effects on steroid secretion. Biol. Reprod. 25, 629–638 (1981).
Isola, J. V. V. et al. A single-cell atlas of the aging mouse ovary. Nat. Aging 4, 145–162 (2024).
Winkler, I. et al. The cycling and aging mouse female reproductive tract at single-cell resolution. Cell 187, 981–998.e925 (2024).
Ben Yaakov, T., Wasserman, T., Aknin, E. & Savir, Y. Single-cell analysis of the aged ovarian immune system reveals a shift towards adaptive immunity and attenuated cell function. Elife 12, e74915 (2023).
Shen, L., Liu, J., Luo, A. & Wang, S. The stromal microenvironment and ovarian aging: mechanisms and therapeutic opportunities. J. Ovar. Res 16, 237 (2023).
Amargant, F. et al. Ovarian stiffness increases with age in the mammalian ovary and depends on collagen and hyaluronan matrices. Aging Cell 19, e13259 (2020).
Foley, K. G., Pritchard, M. T. & Duncan, F. E. Macrophage-derived multinucleated giant cells: hallmarks of the aging ovary. Reproduction 161, V5–V9 (2021).
Huang, Y. et al. Inflamm-aging: a new mechanism affecting premature ovarian insufficiency. J. Immunol. Res 2019, 8069898 (2019).
Zhang, Z., Schlamp, F., Huang, L., Clark, H. & Brayboy, L. Inflammaging is associated with shifted macrophage ontogeny and polarization in the aging mouse ovary. Reproduction 159, 325–337 (2020).
Machlin J. H. et al. Fibroinflammatory signatures increase with age in the human ovary and follicular fluid. Int. J. Mol. Sci. 22, 4902 (2021).
Rowley J. E. et al. Low molecular weight hyaluronan induces an inflammatory response in ovarian stromal cells and impairs gamete development in vitro. Int. J. Mol. Sci. 21, 1036 (2020).
Zhang, J. et al. A multi-omic single-cell landscape of the aging mouse ovary. Geroscience 47, 4485–4498 (2025).
Babayev, E. et al. Cumulus expansion is impaired with advanced reproductive age due to loss of matrix integrity and reduced hyaluronan. Aging Cell 22, e14004 (2023).
Mara, J. N. et al. Ovulation and ovarian wound healing are impaired with advanced reproductive age. Aging 12, 9686–9713 (2020).
Wang, S. et al. Single-cell transcriptomic atlas of primate ovarian aging. Cell 180, 585–600.e519 (2020).
Lan, T. C. T. et al. Aging disrupts spatiotemporal coordination in the cycling ovary. BioRxiv. (2024).
Morris M. E. et al. A single-cell atlas of the cycling murine ovary. Elife 11, e77227 (2022).
Han, X. et al. Mapping the Mouse Cell Atlas by Microwell-Seq. Cell 172, 1091–1107.e1017 (2018).
Walchli, T. et al. Single-cell atlas of the human brain vasculature across development, adulthood and disease. Nature 632, 603–613 (2024).
Wu, J. et al. Aging conundrum: A perspective for ovarian aging. Front Endocrinol. 13, 952471 (2022).
Lliberos, C. et al. Evaluation of inflammation and follicle depletion during ovarian ageing in mice. Sci. Rep. 11, 278 (2021).
Jin, C. et al. Molecular and genetic insights into human ovarian aging from single-nuclei multi-omics analyses. Nat. Aging 5, 275–290 (2025).
Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).
Liang, L. F., Chamow, S. M. & Dean, J. Oocyte-specific expression of mouse Zp-2: developmental regulation of the zona pellucida genes. Mol. Cell Biol. 10, 1507–1515 (1990).
Roller, R. J., Kinloch, R. A., Hiraoka, B. Y., Li, S. S. & Wassarman, P. M. Gene expression during mammalian oogenesis and early embryogenesis: quantification of three messenger RNAs abundant in fully grown mouse oocytes. Development 106, 251–261 (1989).
Berkel, C. Hormonally-regulated KCTD14 Expression is higher in oocytes from antral follicles compared to those from preantral follicles in mice, with implications in fertility. Reprod. Sci. 32, 1374–1378 (2025).
Duncan F. E., Confino R., Pavone M. E. Female Reproductive Aging: From Consequences to Mechanisms, Markers, and Treatments, 2nd edn. Elsevier (2019).
Parkening, T. A., Collins, T. J. & Elder, F. F. Orthotopic ovarian transplantations in young and aged C57BL/6J mice. Biol. Reprod. 32, 989–997 (1985).
Broekmans, F. J., Soules, M. R. & Fauser, B. C. Ovarian aging: mechanisms and clinical consequences. Endocr. Rev. 30, 465–493 (2009).
Mantri, M., Zhang, H. H., Spanos, E., Ren, Y. A. & De Vlaminck, I. A spatiotemporal molecular atlas of the ovulating mouse ovary. Proc. Natl. Acad. Sci. USA 121, e2317418121 (2024).
Richards, J. S., Ren, Y. A., Candelaria, N., Adams, J. E. & Rajkovic, A. Ovarian follicular theca cell recruitment, differentiation, and impact on fertility: 2017 update. Endocr. Rev. 39, 1–20 (2018).
Martin, P. C. N., Kim, H., Lovkvist, C., Hong, B. W. & Won, K. J. Vesalius: high-resolution in silico anatomization of spatial transcriptomic data using image analysis. Mol. Syst. Biol. 18, e11080 (2022).
DeJarnette, J. B. et al. Specific requirement for CD3epsilon in T cell development. Proc. Natl. Acad. Sci. USA 95, 14909–14914 (1998).
Converse, A. et al. Multinucleated giant cells are hallmarks of ovarian aging with unique immune and degradation-associated molecular signatures. PLoS Biol. 23, e3003204 (2025).
Ribot, J. C. et al. CD27 is a thymic determinant of the balance between interferon-gamma- and interleukin 17-producing gammadelta T cell subsets. Nat. Immunol. 10, 427–436 (2009).
McKenzie, D. R., Comerford, I., Silva-Santos, B. & McColl, S. R. The emerging complexity of gammadeltaT17 cells. Front Immunol. 9, 796 (2018).
Velikkakam, T., Gollob, K. J. & Dutra, W. O. Double-negative T cells: Setting the stage for disease control or progression. Immunology 165, 371–385 (2022).
Olguin-Martinez, E., Ruiz-Medina, B. E. & Licona-Limon, P. Tissue-specific molecular markers and heterogeneity in Type 2 innate lymphoid cells. Front. Immunol. 12, 757967 (2021).
Sadeghalvad, M., Khijakadze, D., Orangi, M. & Takei, F. Flow cytometric analysis of innate lymphoid cells: challenges and solutions. Front. Immunol. 14, 1198310 (2023).
Qin, M. et al. Tissue microenvironment induces tissue specificity of ILC2. Cell Death Discov. 10, 324 (2024).
Jonsson, A. H. et al. Granzyme K(+) CD8 T cells form a core population in inflamed human tissue. Sci. Transl. Med. 14, eabo0686 (2022).
Louisa, F. Alim 1 CK, Fernando Souza-Fonseca-Guimarães 3. Molecular mechanisms of tumour necrosis factor signalling via TNF receptor 1 and TNF receptor 2 in the tumour microenvironment. Curr. Opin. Immunol. 86, 102409 (2023).
Wittner, J. & Schuh, W. Kruppel-like factor 2: a central regulator of B cell differentiation and plasma cell homing. Front. Immunol. 14, 1172641 (2023).
Korholz, J. et al. Novel mutation and expanding phenotype in IRF2BP2 deficiency. Rheumatology 62, 1699–1705 (2023).
Tseng, W. Y., Stacey, M. & Lin, H. H. Role of Adhesion G Protein-Coupled Receptors in Immune Dysfunction and Disorder. Int. J. Mol. Sci. 24, 15141 (2023).
Kaur, S. & Roberts, D. D. Emerging functions of thrombospondin-1 in immunity. Semin Cell Dev. Biol. 155, 22–31 (2024).
Sun, S. & Wang, W. Mechanosensitive adhesion G protein-coupled receptors: Insights from health and disease. Genes Dis. 12, 101267 (2025).
Fei, L. et al. Systematic identification of cell-fate regulatory programs using a single-cell atlas of mouse development. Nat. Genet 54, 1051–1061 (2022).
Wang, R. et al. Construction of a cross-species cell landscape at single-cell level. Nucleic Acids Res. 51, 501–516 (2023).
Li, N. et al. Two distinct resident macrophage populations coexist in the ovary. Front. Immunol. 13, 1007711 (2022).
Ma, R. Y., Black, A. & Qian, B. Z. Macrophage diversity in cancer revisited in the era of single-cell omics. Trends Immunol. 43, 546–563 (2022).
Martinez, F. O. et al. Genetic programs expressed in resting and IL-4 alternatively activated mouse and human macrophages: similarities and differences. Blood 121, e57–e69 (2013).
Scuruchi, M. et al. Serglycin as part of IL-1beta induced inflammation in human chondrocytes. Arch. Biochem. Biophys. 669, 80–86 (2019).
Lecker, L. S. M. et al. TGFBI production by macrophages contributes to an immunosuppressive microenvironment in ovarian cancer. Cancer Res. 81, 5706–5719 (2021).
Shen, Z. et al. Expansion of macrophage and liver sinusoidal endothelial cell subpopulations during non-alcoholic steatohepatitis progression. iScience 26, 106572 (2023).
Coletta, S. et al. The immune receptor CD300e negatively regulates T cell activation by impairing the STAT1-dependent antigen presentation. Sci. Rep. 10, 16501 (2020).
Pagliari, M. et al. Helicobacter pylori affects the antigen presentation activity of macrophages modulating the expression of the immune receptor CD300E through miR-4270. Front. Immunol. 8, 1288 (2017).
Isobe, M. et al. The CD300e molecule in mice is an immune-activating receptor. J. Biol. Chem. 293, 3793–3805 (2018).
Carnazzo, V. et al. Calprotectin: two sides of the same coin. Rheumatology 63, 26–33 (2024).
Frangogiannis, N. Transforming growth factor-beta in tissue fibrosis. J. Exp. Med. 217, e20190103 (2020).
Sugiura, K., Pendola, F. L. & Eppig, J. J. Oocyte control of metabolic cooperativity between oocytes and companion granulosa cells: energy metabolism. Dev. Biol. 279, 20–30 (2005).
Hornick, J. E., Duncan, F. E., Shea, L. D. & Woodruff, T. K. Multiple follicle culture supports primary follicle growth through paracrine-acting signals. Reproduction 145, 19–32 (2013).
Zhou, H. et al. Synergy of Paracrine signaling during early-stage mouse ovarian follicle development in vitro. Cell Mol. Bioeng. 11, 435–450 (2018).
Akitsu, A. & Iwakura, Y. Interleukin-17-producing gammadelta T (gammadelta17) cells in inflammatory diseases. Immunology 155, 418–426 (2018).
D’Acquisto, F. et al. Annexin-1 modulates T-cell activation and differentiation. Blood 109, 1095–1102 (2007).
Hayden, M. S. & Ghosh, S. Regulation of NF-kappaB by TNF family cytokines. Semin. Immunol. 26, 253–266 (2014).
Richardson, S. J., Senikas, V. & Nelson, J. F. Follicular depletion during the menopausal transition: evidence for accelerated loss and ultimate exhaustion. J. Clin. Endocrinol. Metab. 65, 1231–1237 (1987).
Faddy, M. J., Gosden, R. G., Gougeon, A., Richardson, S. J. & Nelson, J. F. Accelerated disappearance of ovarian follicles in mid-life: implications for forecasting menopause. Hum. Reprod. 7, 1342–1346 (1992).
Gougeon, A., Ecochard, R. & Thalabard, J. C. Age-related changes of the population of human ovarian follicles: increase in the disappearance rate of non-growing and early-growing follicles in aging women. Biol. Reprod. 50, 653–663 (1994).
Grieb, G., Merk, M., Bernhagen, J. & Bucala, R. Macrophage migration inhibitory factor (MIF): a promising biomarker. Drug N. Perspect. 23, 257–264 (2010).
Sondag, C. M. & Combs, C. K. Amyloid precursor protein mediates proinflammatory activation of monocytic lineage cells. J. Biol. Chem. 279, 14456–14463 (2004).
Saitoh, O., Olson, E. N. & Periasamy, M. Muscle-specific RNA processing continues in the absence of myogenin expression. J. Biol. Chem. 265, 19381–19384 (1990).
Umehara, T. et al. Female reproductive life span is extended by targeted removal of fibrotic collagen from the mouse ovary. Sci. Adv. 8, eabn4564 (2022).
Isola, J. V. V. et al. Reproductive Ageing: Inflammation, immune cells, and cellular senescence in the aging ovary. Reproduction 168, R25–R39 (2024).
Briley, S. M. et al. Reproductive age-associated fibrosis in the stroma of the mammalian ovary. Reproduction 152, 245–260 (2016).
Duncan, F. E. et al. Age-associated dysregulation of protein metabolism in the mammalian oocyte. Aging Cell 16, 1381–1393 (2017).
Losslein, A. K. et al. Monocyte progenitors give rise to multinucleated giant cells. Nat. Commun. 12, 2027 (2021).
Young, M. D. & Behjati, S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience 9, giaa151 (2020).
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e3529 (2021).
McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell Syst. 8, 329–337.e324 (2019).
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).
Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).
Liao, Y., Wang, J., Jaehnig, E. J., Shi, Z. & Zhang, B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res 47, W199–W205 (2019).
Jin, S. et al. Inference and analysis of cell-cell communication using CellChat. Nat. Commun. 12, 1088 (2021).
Brewster, C. E., Mann, F. G., Benham-Pyle, B. W. & Sánchez Alvarado A. Syrah, a Slide-seqV2 pipeline augmentation. BioRxiv. (2022).
Acknowledgements
We are grateful to the Stowers Institute for Medical Research (SIMR) Sequencing and Discovery Genomics Technology Center, specifically Anoja Perera and Amanda Lawlor, for their technical expertise. We also appreciate the computational support from the SIMR Computational Biology Technology Center, particularly Jay Unruh and Madelaine Gogol. Special thanks to Dr. Fei Chen’s group at the Broad Institute for providing the Slide-seq pucks used to capture our spatial transcriptomics data. We are also indebted to Carolyn Brewster, who wrote the Syrah pipeline, which we used to process our spatial transcriptomics data. Special thanks to the SIMR Rodent Department for their support. We thank Mark Miller for the contribution of original illustrations. Finally, we extend our gratitude to Prianka Hashim for many insightful discussions throughout this project. This work was supported in part by R01HD105752-01 from NICHD to FED and JLG, F31HD116553-01 from the NICHD to AMG, and the Stowers Institute for Medical Research.
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Anna Galligos and Joseph Varberg led the analysis of data, interpretation of data, and wrote the manuscript. Wei-Ting Yueh led data acquisition. Aubrey Converse and Seth Malloy contributed to data acquisition. Fatimah Aljubran contributed to data interpretation. Francesca Duncan and Jennifer Gerton supervised the work.
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Galligos, A., Varberg, J.M., Yueh, WT. et al. Multicellular origins of murine ovarian inflammaging. Commun Biol (2026). https://doi.org/10.1038/s42003-026-09826-1
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DOI: https://doi.org/10.1038/s42003-026-09826-1
