Editor's evaluation: Single-cell analysis of the aged ovarian immune system reveals a shift towards adaptive immunity and attenuated cell function

Abstract

Full text Figures and data Side by side Abstract Editor's evaluation Introduction Results Discussion Methods Data availability References Decision letter Author response Article and author information Metrics Abstract The immune system plays a major role in maintaining many physiological processes in the reproductive system. However, a complete characterization of the immune milieu in the ovary, and particularly how it is affected by female aging, is still lacking. Here, we utilize single-cell RNA sequencing and flow cytometry to construct the complete description of the murine ovarian immune system. We show that the composition of the immune cells undergoes an extensive shift with age towards adaptive immunity. We analyze the effect of aging on gene expression and chemokine and cytokine networks and show an overall decreased expression of inflammatory mediators together with an increased expression of senescent cells recognition receptors. Our results suggest that the fertile female’s ovarian immune aging differs from the suggested female post-menopause inflammaging as it copes with the inflammatory stimulations during repeated cycles and the increasing need for clearance of accumulating atretic follicles. Editor's evaluation The study describes a single-cell analysis of the mammalian ovary in young, adult, and old mice, and is an important contribution to the field identifying clusters of immune cell populations across the different ages. The combination of single-cell RNA sequencing and flow cytometry used is a robust and unbiased approach that provides compelling evidence of immune cell alterations in aged ovaries. https://doi.org/10.7554/eLife.74915.sa0 Decision letter Reviews on Sciety eLife's review process Introduction One of the effects of aging in mammalians is a decline in fertility and hence a diminished capability to give birth to offspring (Pal and Santoro, 2003). In women from their early 30’s, there is a sharp decrease in fertility, accompanied by an exponentially increase in the odds of miscarriages and birth defects, alongside a drastically lower success rate of in-vitro fertilization (IVF) procedure (Madankumar et al., 2003; Nelson and Lawlor, 2011; Szamatowicz and Grochowski, 1998). Another well-documented effect of age is on the ability of the immune system to overcome illnesses and eliminate different pathogens (Kovacs et al., 2009; Plowden et al., 2004; Solana et al., 2006; Weiskopf et al., 2009). The presence of immune cells such as macrophages (Mφs), dendritic cells (DCs), granulocytes, T and B lymphocytes was identified through the entire female reproductive tract (Givan et al., 1997; Lee et al., 2015), and in the ovaries in particular (Best et al., 1996; Bukulmez and Arici, 2000; Carlock et al., 2013; Yang et al., 2019). These cells participate in many fertility-related processes in the ovaries – from follicle development up to ovulation and corpus luteum formation and regression (Cohen-Fredarow et al., 2014; Fair, 2015; Oakley et al., 2010; Wu et al., 2004; Yang et al., 2019). Ovulation, for example, is considered an inflammatory process that includes edema, vasodilation, pain, and heat (Duffy et al., 2019; Richards et al., 2008). Changes in the immune milieu, such as depletion of Mφs and DCs have been shown to result in a decreased number of ovulated oocytes, depletion of endothelial cells, increased follicular atresia, and lead to a delayed progression of the estrus cycle (Cohen-Fredarow et al., 2014; Turner et al., 2011; Wu et al., 2004). Characterizing the complete immune milieu in the ovary, and in particular how it is affected by female aging, is challenging mainly due to the small fraction of the immune cells compared to other cell types in the ovary (Lliberos et al., 2021; Wagner et al., 2020). For that reason, previous work using whole-ovary single-cell measurements did manage to portray the ovary’s main cell types (oocytes, granulosa, theca, immune, etc.) yet didn’t have the resolution to resolve the entire immune milieu (Fan et al., 2019; Lliberos et al., 2021; Wagner et al., 2020). Others have a priori focused on a limited set of cell types (Cohen-Fredarow et al., 2014; Wu et al., 2004), while additional studies have used bulk RNA sequencing experiments (Ma et al., 2020), hence did not capture the entire immune members in the ovaries. In this work, we were able to isolate the immune cells and perform consecutive single-cell analyses. Investigating the age effect on the immune system in the single-cell level have also been done in several recent studies (Almanzar et al., 2020; Kimmel et al., 2019; Mogilenko et al., 2021). However, their focus was on other non-fertility related tissues such as bladder, kidney, peritoneum and more. In addition, the aged group was very old, far past estropause. Attempts to characterize the effects of female age on the immune system in the ovary were limited to a small subset of cells (Lliberos et al., 2021; Zhang et al., 2020), addressing mainly changes in the macrophages fraction that decreases with age, and accumulation of inflammatory mediators such as cytokines and reactive oxygen species within the tissue (Lliberos et al., 2021; Zhang et al., 2020). In this work, we provide the first complete detailed characterization of the murine ovarian immune system composition at the single-cell level. We show the presence of various immune cell populations, such as Mφs, DC’s, neutrophils (NTs), NK cells, NKT cells, innate lymphoid cells (ILCs), B cells, and several T cell types – including an ovary specific CD3+ CD4- CD8- double-negative T (DNT) cells. Moreover, we show an extensive tissue-specific effect of female age on the ovarian immune milieu, resulting in a shift towards adaptive immunity, mainly by a significant increase in the DNT population. In addition, we analyzed the changes in gene expression of the cells and discovered a global attenuation in their general function and responsiveness. We also found a decrease in the expression of inflammatory mediators such as cytokines and chemokines. Moreover, we identified some evidence for an increase in senescent cells recognition activity. Our results serve as an opening for a much more comprehensive understanding of the interaction between female aging and the immune system in fertile female mammals. Results The ovarian immune milieu is altered with age To characterize the ovarian immune milieu, we have isolated immune cells (CD45+ cells) from the ovaries of young (11–15 weeks), adult (20–37 weeks), and old (40–47 weeks) virgin mice, and utilized flow cytometry and single-cell RNA sequencing (scRNA-seq) to characterize the ovarian immune cells and how they are affected by female age (Figure 1A). First, we performed scRNA-seq on isolated immune cells from the ovaries of 13 weeks old mice (n=2; 3307 cells). To cluster the cells and identify their type, we used a combination of both literature-based annotation and automatic annotation methods (Seurat R package and SingleR algorithm, “Methods” and Figure 1—figure supplement 1). In addition, we performed a batch correction analysis to validate the clusters that emerged from the tSNE analysis (Figure 1—figure supplement 2). The combination of these methods allowed us to identify within the ovaries the following cell types: Mφs, DCs, NTs, B cells, NK cells, NKT cells, ILC1, ILC2, ILC3, and several clusters of T lymphocytes: CD8+ (CD8 T), CD4+ (CD4 T), and CD4- CD8- double-negative T cells (DNT cells) (Figure 1B and C and Figure 1—figure supplement 3). Most of the cells were innate immune cells, mostly ILC1, Mφs, NTs, and NK cells. Figure 1 with 8 supplements see all Download asset Open asset The ovarian immune milieu is consisted of various cell types. (A) Schematic illustration of the experimental pipeline (created with BioRender.com). Ovaries of female mice at different ages were extracted. Then, cells were gated for CD45 expression and further analyzed using single-cell RNA sequencing or flow cytometry. (B) tSNE plot of joint data from both samples (young and old), divided into clusters. (C) Violin plot of normalized expression for cluster-specific markers. Each row represents the normalized expression of a single marker across all immune clusters. Normalized expression values are between 0–1. Figure 1—source data 1 Young raw data. https://cdn.elifesciences.org/articles/74915/elife-74915-fig1-data1-v2.zip Download elife-74915-fig1-data1-v2.zip To validate the presence of the immune populations that emerged from our scRNA-seq experiments, we used flow cytometry (the gating strategy is shown in Figure 1—figure supplement 4). First, we measured the fractions of group 1 of innate lymphoid cells (G1-ILC), CD45+ NK1.1+ CD3- cells (i.e. NK and ILC1) in the mouse ovaries and spleen (Figure 1—figure supplement 5). The average fraction of G1-ILC in the spleen was 11.81% and in the ovary was 42.71%. These results are consistent with the high G1-ILC fraction resulting from the ovary scRNA-seq analysis (35.4% ILC1 and 9.8% NK), and previous results, which demonstrated that G1-ILC proportion in mice spleen is relatively low (Boulenouar et al., 2017). In addition, further characterization of the DCs clusters (3 and 4) revealed that their transcriptomic signature corresponds to conventional dendritic cells type 2 (cDC2) and type 1 (cDC1), respectively (Figure 1—figure supplement 6). Among other cell types that were found, DNT cells are unique, somewhat less well-defined cell population. To confirm the presence of CD4- CD8- T cells in the ovaries, we conducted a flow cytometry experiment comparing the fractions of CD4+, CD8+, and CD4- CD8- T cells in the mouse ovaries, spleen, and peritoneum (Figure 1—figure supplement 7). Using additional flow cytometry experiments we validated that ovarian DNT cells are TCRγδ- (Figure 1—figure supplement 8). These measurements validate the scRNA-seq results and show that although present in other tissues at small fractions, CD3+ TCRβ+ CD4- CD8- cells are tissue-specific cells to the ovaries. Next, we examined the changes in the ovarian immune milieu at older ages. Using cells isolated from old, near estropause mouse (43 weeks; the rodent equivalent of the human menopause; 5468 cells), we characterized the old ovarian immune system (using the same annotation methods) and compared it to its younger counterpart (Figure 2A and B). The results demonstrated a shift at older age towards a lymphocytes-rich environment that was accompanied by decreased fractions of several immune populations such as ILC1 cells, Mφs, NTs, and NK cells (Figure 2B, Figure 2—figure supplements 1–2). To both validate the scRNA-seq results and to check whether this effect is cycle-stage dependent, we conducted several flow cytometry experiments. Figure 2 with 5 supplements see all Download asset Open asset The effect of female age on the ovarian immune milieu. (A) 3-D tSNE plot (left) and an overlay (right) of all ovarian CD45+ cells found in scRNA-seq, divided by age group. (B) The effect of female age on the fractions of each cell type, with a confidence interval of 95% at the top of each bar. The green and yellow rectangles mark the macrophages and CD3+ populations, respectively. (C) Violin plot of the changes in fraction distributions of macrophages and CD3+ lymphocytes as a function of age as measured by flow cytometry (Kolmogorov-Smirnov test, ** p-value <10–2, *** p-value <10–3). (D) Change in the fraction of different CD3+ population comparing old (for CD4 and CD8 T cells – 42.6–49.6 weeks, n=4; for DNT and NKT cells – 49.6 weeks, n=2) and young (for CD4 and CD8 T cells – 10.1–14.5 weeks, n=5; for DNT and NKT cells – 10.1 weeks, n=3) mice as measured using flow cytometry. Error bars denote standard deviation. (E) Comparison between transcriptome and protein level of immune populations within the ovaries at different female ages. Each spider plot shows the distribution of different immune cell types measured using scRNA-seq (left panel) and flow cytometry (right). Figure 2—source data 1 Old raw data. https://cdn.elifesciences.org/articles/74915/elife-74915-fig2-data1-v2.zip Download elife-74915-fig2-data1-v2.zip To measure the effect of female age on the fractions of T-lymphocytes (CD3+) and Mφs (CD11b+ F4/80+) from total ovarian CD45+ cells, mice were divided into three groups of age: young (11–15 weeks, n=4), adult (20–37 weeks, n=15) and old (40–47 weeks, n=12). The results show a significant increase with age of CD3+ cells’ fraction (young vs. adult and adult vs. old, Kolmogorov-Smirnov test, p-value <10–2), while the fraction of Mφs was significantly decreased (young vs. adult and young vs. old, Kolmogorov-Smirnov test, p-value <10–3 and p-value <10–2, respectively) at older ages. Moreover, flow cytometry experiments validated that, as with the scRNA-seq results, most of the change in CD3+ lymphocytes’ fraction is due to a substantial increase in DNT cells at old age (Figure 2C, D and E and Figure 2—figure supplement 3). Furthermore, analysis of splenic CD3+ lymphocytes show that in contrast to the ovaries, the fraction of these cells decreases at old age, while the fraction of DNT cells doesn’t change (Figure 2—figure supplement 4). These age-dependent results are cycle-stage independent (Figure 2—figure supplement 5). Taking the results both from the scRNA-seq and the flow cytometry (Figure 2E), there is a consistent shift towards adaptive immunity (an increase in the CD3+ lymphocytes fraction), while most innate immune cells’ fraction (Mφs, NKs, ILC1, and NTs) decreases. The female aging effect on the ovarian immune cells’ transcriptome After identifying the ovarian immune milieu and the changes it undergoes at older age, we characterized the changes in gene expression within each immune cluster (Supplementary file 1). Figure 3A depicts the differentially expressed genes patterns across all clusters. Several clusters, such as DNT cells, ILC1, NKT cells, and CD4 T cells exhibited an extremely skewed pattern, in which most of their differentially expressed genes (DEGs) were downregulated, compared to upregulated DEGs. This may imply that these cell types are more susceptible to female aging. After defining DEGs for each cell type, we explored changes in biological processes via GO enrichment analysis (Figure 3B, ‘Methods’). Most cell types showed an enriched set of processes that were downregulated with female age. Using the REVIGO platform (‘Methods’), we eliminated redundant GO terms and counted the appearances of each GO term across all cell types (Supplementary file 2). Terms that were found in more than one cell type were classified as ‘global’, and cell type-unique terms as ‘specific’. For further analysis, we took all the global terms and used the REVIGO platform to cluster them according to their semantics distance (Figure 3B). The results show a global decrease in several clusters of processes. The clusters with the processes that were mostly shared among cell types included decreased biosynthesis and metabolism-related processes. Another distinct cluster includes a general loss of regulation over various processes. The third well-defined cluster includes a decrease in the cellular response to different stimuli. Figure 3 Download asset Open asset Changes in gene expression along with female age. (A) Volcano plots of the scRNA-seq analysis for the different immune cell types. The vertical dashed lines mark twofold upregulation and downregulation, the blue horizontal dashed line marks p-value = 0.05, and the red horizontal dashed line marks FDR = 0.1. Genes in the top left section of each graph are significantly downregulated at old age, while genes in the top right section are upregulated at old age. Each grey dot represents the change in the expression of different genes. Red dots denote significantly changed chemokines and cytokines. (B) Each circle represents a downregulated biological process in the old mice, which appeared in at least two types of immune cells. The axes represent semantic similarities distance as calculated by REVIGO (‘Methods’). The color of each term represents the number of immune cell types in which the process is downregulated with age. The size of each term represents the hierarchy of the biological process; the bigger the circle, the higher the hierarchy of the process is. Among the cell-type-specific downregulated processes, Mφs exhibit attenuation in immune and inflammatory responses, along with decreased tissue remodeling and wound healing processes. DCs show a decrease in cell activation and regulation of immune response. Several immune cell types showed a limited set of enriched upregulated processes, in which the most prominent ones were exhibited by Mφs and DNT cells and included T cell activation and differentiation processes (Supplementary file 3). Aging affects cytokines and chemokines connectome of ovarian immune cells To get a better notion of the effect of aging on the different immune cell types, we estimated the effect of aging on the chemokine and cytokines interactions between the immune cells (Figure 4). For both chemokines and cytokines, we used the KEGG database to extract the network of ligands and receptors and their interactions (‘Methods’). Within each network, we focused on significantly changed connections, which we defined as edges that both of their nodes (i.e. both the ligand and receptor) have been significantly decreased or increased in their expression at old age. Significant nodes can be in the same cell type, or each one in different cell type, and in at least one cell type. We found that almost all significant nodes and edges in both networks were downregulated (Figure 4C and D). Figure 4 with 1 supplement see all Download asset Open asset The effect of female age on the chemokines and cytokines networks of the ovarian immune cells. (A) A heat map of a significant (p-value <0.05; FDR ≤0.1) twofold decrease (red) or increase (green) in the expression levels of chemokines, cytokines, and their receptors in different immune cell types. (B) Cumulative probability distribution (CDF) of the fold change (FC) of chemokines (upper panel) and cytokines (lower panel) (red line). The gray lines are the CDFs of FC for random groups of genes at the same size as the chemokines/cytokines genes. The blue line is the CDF of the FC of all the genes. There is a significant decrease in the expression of chemokines and cytokines with age (Kolmogorov-Smirnov test p-value <0.01). (C) Downregulation of the chemokines network due to age. Upper panel - Edges within the chemokine network in which both the ligand and the receptor were significantly downregulated in at least one cell type are colored in red. Edges that only the ligand/receptor, or none of them, were significantly downregulated are colored in grey. The sub-graph that contains the affected interactions is magnified at the right-hand side of the figure. Bottom panel - Chord diagram that illustrate the decrease in chemokine ligand-receptor interactions between the different cell types. The color of each chord denotes the color of the cell type that underwent a reduction in ligand expression. In the upper semi-circles, the colors indicate the cell type that showed a decrease in receptor expression. In the lower semi-circle, the outer and inner colors denote the cell types in which ligands and target receptors were downregulated, respectively. (D) Downregulation in the cytokines network due to age. Same color-coding as in (C) for the cytokines interaction network. The most prominent affected edges in the chemokines network involved Ccr5 expressed by DCs and ILC1 cells, Ccr2 expressed by ILC2 cells, and their ligands Ccl2, Ccl3, Ccl4, Ccl5, Ccl7, Ccl8, and Ccl12 (Figure 4C). Both of these receptors have been shown to take part in chemoattraction of immune cells in the context of various inflammatory processes (D’Ambrosio et al., 2003; Mencarelli et al., 2016; Proudfoot, 2002). For further validation, we applied the cell2cell algorithm (Armingol et al., 2022), which also pointed out Ccr5 as the main chemokine receptor modulated by age (Figure 4—figure supplement 1A). Thus, we measured experimentally the fraction of cells that express CCR5 using flow cytometry experiments, and show it is indeed decreases significantly (Figure 4—figure supplement 1B). Moreover, Cxcl2, an inflammatory chemokine that mediates neutrophils trafficking (Lentini et al., 2020; Li et al., 2016), was significantly decreased in almost all immune cell types (Figure 4A). CCRL2 is an atypical chemokine receptor that was found to be upregulated in activated immune cells after induction of inflammatory signals (Del Prete et al., 2013). Ccrl2 was downregulated in several cell types such as ILC1 and NKT, although it is mainly expressed by myeloid cells such as NTs, DCs, and Mφs (Del Prete et al., 2013). The changes in the cytokines network were found mainly in the IL-1 superfamily (Il1r1 and Il1r2, along with Il1a, Il1b and Il1rn). Moreover, TNF-receptor Tnfsfr1b and its ligand Tnf (TNFα) also showed a significant decrease at old age (Figure 4A). As IL-1 and TNF superfamily members are considered inflammatory, along with the evident decrease in various inflammatory chemokines and their receptors, our results suggest that beyond overall inhibition in the ovarian immune function, aging also shifts its phenotype towards a less inflammatory state. To account for possible global downregulation, as emerges from Figure 3A, that may lead to an artifact in which DEGs that were found, are not significant under the global downregulation, we’ve performed another analysis. In this analysis, we considered only the top down/up-regulated genes with a p-value≤0.025, defined by cumulative probability distribution (CDF) analysis. GO enrichment analysis using the new DEGs show that ‘Inflammatory response’ (GO:0006954) was downregulated in four different clusters (NTs, Mφs, DCs and ILC3 cells). In addition, the process ‘Negative regulation of neuroinflammatory response’ (GO:0150079) was upregulated in NK cells (see Supplementary file 4). Another evident downregulated edges in the cytokine network were in the TGFβ superfamily (Gdf11 and Inhba along with their receptors Acvr2a and Tgfbr1). Activin A, a dimer composed of two Inhibin-βA subunits (the translation product of Inhba), is produced among others by the gonads and promotes LH secretion from the pituitary. It plays an important role in expanding the primordial follicle pool and contributes to the early stages of follicular growth by increasing FSH receptor expression on granulosa cells (Namwanje and Brown, 2016). In addition, Activin A was found to activate resting macrophages – yet there are contradictory findings as to rather its effect is pro or anti-inflammatory (Morianos et al., 2019). Decreased expression of both Inhba and Acvr2a by ovarian macrophages at older age might suggest a specific role of macrophages in supporting follicular growth (via Activin secretion) during the estrous cycle, which decays as age progresses. Moreover, these results may present a mechanism in which macrophages are participating in inducing an inflammatory environment as part of the ovulation process as a response to Activin. Aging affects recognition of senescent cells by ovarian immune cells Inducing cell senescence, which is an irreversible state of growth arrest, is a mechanism the body uses to handle cell stress which can accumulate during aging (Campisi and d’Adda di Fagagna, 2007) and may result in chronic diseases and tissue dysfunction (Muñoz-Espín and Serrano, 2014; Ovadya and Krizhanovsky, 2014). One of the main molecular features of senescent cells is the senescence-associated secretory phenotype (SASP), in which the senescent cells create an inflammatory environment by secreting inflammatory cytokines, chemokines, growth factors, extracellular remodeling factors, and more (Prata et al., 2018; Song et al., 2020). Immune cells respond to these factors, detect specific markers expression or their absence on the senescent cells’ membrane and clear them either by phagocytosis (by Mφs, for example) or by killing (by NTs or NK cells for example) (Song et al., 2020). Ovarian senescence was already studied in the past (Velarde and Menon, 2016); however, the specific mechanism of senescent cells clearance within the ovaries is still unclear. We compiled a list of SASP receptors based on the literature containing 24 receptors (Supplementary file 5) and examined how their expression in different cell types depends on the female age. We found that the fraction of cells that express Ccr2, Csf2ra, and Csf1r, which are all receptors for known SASP proteins (Rhinn et al., 2019; Song et al., 2020), was significantly higher in old Mφs. In addition, the fraction of old Mφs that express cell surface markers that were previously reported to take part in the recognition of senescent cells, such as membrane IgM’s (Ighm) and C-type lectin receptors (Clec4a2-3) (Burton and Stolzing, 2018) was also elevated. Moreover, old NTs and Mφs showed upregulated expression of Ifngr1 (Figure 4A), a part of the IFNγ receptor, while the cytokine itself is overexpressed by senescent cells (Lujambio et al., 2013; Pan et al., 2021). In addition, both NTs and Mφs, as well as NK cells showed a higher expression fraction of this receptor. In total, we found six SASP receptors that were significantly overexpressed by old Mφs. The probability that six genes would be significantly modulated (p<0.01) out of a list of 24 random genes is low (FDR = 10–12, Figure 5). Moreover, across all cell types, only old Mφs and NTs presented a significant elevated fractions of cells expressing SASP receptors (Figure 5B). As a complementary analysis, we checked the expression levels of all SASP receptors. Results show that SASP receptors expression in ovarian Mφs is not altered at old age (Figure 5—figure supplement 1). NTs also exhibited elevated fractions of Ccr1, a receptor for several SASP chemokines (Coppé et al., 2010), while old NKT cells had higher levels of Cd74, a receptor for MIF, another SASP member (Coppé et al., 2010; Kim et al., 2018). CXCR6 is another novel mediator of senescence control which was recently discovered as part of senescence surveillance in the liver by CD4 T and NKT cells (Mossanen et al., 2019). Cxcr6 expression was also increased in old Mφs. Figure 5 with 1 supplement see all Download asset Open asset The fraction of Macrophages and Neutrophils expressing SASP receptors is elevated in old age. (A) Cumulative probability distribution (CDF) of the difference of a fraction of old and young macrophages that express 24 SASP genes (red line). The gray lines are the fraction difference CDFs of groups of 24 random genes (10,000 samples). The blue line is the CDF of the fraction difference of all the macrophage genes. There is a significant increase with age in the fraction of cells that express members of the SASP genes (Kolmogorov-Smirnov test p-value <0.01). (B) The false discovery rate (FDR) for significant change (p-value <0.01) in the fraction of different cell types that express SASP genes. Macrophages and neutrophils exhibit FDR which is much smaller than 0.001. Discussion In this work, we characterized the first complete mouse ovarian immune milieu and its changes with female age up to near-estropause state, and discovered major changes in the fractions of different immune cell types (Figure 6). Our results present an elevation in CD3+ TCRβ+ lymphocytes along with age, indicating a dramatic change in the fraction of DNT cells (from ~5% to ~35% at old age). Other identified DNT cells, in the blood or lymph nodes, for example, consist of 1–5% out of all the lymphocytes (Hillhouse and Lesage, 2013; Juvet and Zhang, 2012). These results are consistent with a previous study that showed an increase in the TCRβ+ lymphocytes that is not due to CD4 or CD8 T cells change in mice before estropause (Lliberos et al., 2021). Double-negative T cells were found to be the most common lymphocyte across the female mice uterus and cervix (Johansson and Lycke, 2003). These cells were shown to have a regulatory function, showed no proliferation, inhibited the proliferation of splenic T cells when co-cultured together in vitro, and their origin was suggested to be extrathymic. The DNT population we observed in the ovary, showed almost no expression of classical thymocytes development markers at the double-negative stages such as Notch1, Socs3, Dtx1, Hes1, and more (Puthier et al., 2004). Other studies showed that DNT cells in peripheral blood or lymphoid organs have a suppressive role and assist in preventing allograft rejection and autoimmune responses (Hillhouse et al., 2013; Juvet and Zhang, 2012). Some studies suggest that DNT cells are the result of down-regulation of CD4 and CD8 due to chronic stimulation (Rodríguez-Rodríguez et al., 2015; Grishkan et al., 2013). The ovaries present ongoing cycles of inflammation processes in each ovulation, which may result in chronic stimulation of ovarian T cells and may be consistent with an increase of CD4 T cells after estropause (Lliberos et al., 2021). Furthermore, enrichment in CD3+ lymphocytes in the ovary was found to be associated with poor follicular reserve (Jasti et al., 2012) and thus may have an impact on ovarian autoimmune diseases. Moreover, one of the characteristics of polycystic ovary syndrome (PCOS) is an abnormal T lymphocyte milieu that is suspected to be involved in disease pathogenesis and ovarian dysfunction that leads to infertility (Li et al., 2019). Similarly, our results indicate an age-dependent change in the