Single-cell analysis of the aged ovarian immune system reveals a shift towards adaptive immunity and attenuated cell function

  1. Tal Ben Yaakov
  2. Tanya Wasserman
  3. Eliel Aknin
  4. Yonatan Savir  Is a corresponding author
  1. Department of Physiology, Biophysics and Systems Biology, Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Israel
  2. Rappaport Family Institute for Research in the Medical Sciences, Israel

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

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
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.

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 12). 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
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).

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.

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
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
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 T lymphocytes population that may be involved in improper immune regulation in the ovary and hence lead to infertility. In addition, ILC1 cells are the largest population in the young mouse ovaries, and the second largest in the old ovaries, after the DNT population. These cells, along with NK cells, constitute the group 1 innate lymphoid cells. As opposed to NK cells, ILC1 have weak-to-no cytotoxic capabilities, and are more similar to Th1 cells in their function, that is, inducing type 1 immune response against intracellular pathogens and inflammatory responses (Vivier et al., 2018). In addition, ILC1 cells take part in the process of tissue remodelling (Jowett et al., 2020). Interestingly, ILC1 distribution was also found to be elevated in the epididymal adipose of male mouse testis (Boulenouar et al., 2017). As a dynamic, constantly changing environment, the ovaries, which involve cycles of developing and regressing structures, may use ILC1 cells as orchestrators of other cell types participating in these processes. Our results also show a decline in the macrophages population with age, which may result in fertility deterioration because of impaired ovarian vasculature (Turner et al., 2011).

The effect of fertile female aging on the ovarian immune system.

As the female ages, most innate immunity cells, such as NTs, Mφs, ILCs, and NK cells exhibit a decrease in their fractions within the total immune population. Moreover, there is a substantial increase in the fraction of DNTs. The ovarian immune aging during the female fertile period is characterized by decreased inflammatory hallmarks, as opposed to the post-menopause inflammaging. (Created with BioRender.com).

Taking together the observed changes in global processes and expression of immune mediators such as chemokines and cytokines, a state of general attenuation and unresponsiveness is emerging. GO enrichment analysis of joint processes for several cell types present a decrease in general activity, in which immune cells exhibit lower levels of metabolism-related processes accompanied by decreased responsiveness to different stimuli and loss of regulation over various processes. Analysis of the chemokines and cytokines networks also revealed general attenuation and decreased expression of various inflammatory mediators. Chemoattractants, such as Cxcl2, Ccl2, Ccl3, Ccl4, and Ccl5, as well as pronounced inflammatory cytokine transcripts such as TNFα, IFNγ, IL1-α, and IL-1β were all decreased in different immune cell types. Moreover, decreased expression of several members of the TGFβ superfamily by macrophages at old age, such as Inhba and Acvr2a, might be connected to impaired or decreased follicles growth. While previous studies suggested an inflammatory state in the post-estropausal ovaries (Lliberos et al., 2021), our results indicate a decrease in the inflammatory characteristics of ovarian immune cells during pre-estropause aging. We have found an increase in genes involved in senescent cell recognition by several immune cell types, mostly Mφs but also NTs, NK, and NKT cells. Generally, as age progresses, senescent cells are accumulating within tissues (Childs et al., 2015; van Deursen, 2014; Velarde and Menon, 2016). In the ovaries, aging and senescence are accompanied by an increasing number of atretic follicles and cycle irregularities that end in estropause. Our results suggest that clearance of senescent ovarian cells by immune cells increases at old age to match the ovary needs.

There is a plethora of studies that suggest inflammaging - an age-related increase in the inflammatory environment that develops over time (Ferrucci and Fabbri, 2018; Franceschi et al., 2018; Giunta, 2006). Previous recently published papers who characterized the age effect in the single-cell level on the immune system in various fertility non-related tissues have demonstrated inflammaging (Almanzar et al., 2020; Kimmel et al., 2019; Mogilenko et al., 2021). There are several major differences between these papers to our research. First, these papers compared not just females, but also males. Moreover, they used very old, post-estropausal mice and analyzed mostly tissues that are not part of the reproductive system. Our results show that the most evident change in the ovaries of aging female mice before estropause is a shift from innate to adaptive immunity, which is associated with a dramatic increase of CD3+ lymphocytes, and a decrease in Mφs, NTs, and ILC1 fractions. Besides the changes in the composition of the immune milieu, most cell types exhibit a decrease in inflammatory hallmarks. Our results portray a novel intermediate state, in which prior to the elevation in inflammatory hallmarks and the development of inflammaging, there is an apparent decrease in inflammatory characteristics in the ovary (Figure 6). Our results suggest that ovarian immune aging is not linear, but a complicated process that exhibits alternations between anti- and pro-inflammatory environment.

Methods

Mice

All experiments involving mice conform to the relevant regulatory standards (Technion IACUC and national animal welfare laws, guidelines, and policies). Hsd:ICR female mice were purchased from Envigo RMS (Israel) and were housed in a 12 hr light / 12 hr dark cycle. Assessment of estrous cycle stage for the mice begun 3 days prior each experiment via vaginal smears (McLean et al., 2012). Briefly, mice’s vaginal canals were washed using 20 μL saline (PBS, 0.09% NaCl). The saline was then collected and mounted on a slide and observed under an inverted microscope (Olympus) equipped with X20 objective. The estrous cycle-stage was assessed according to epithelial cells morphology and the presence or absence of leukocytes. Mice that exhibited regular progress of their cycle for three consecutive days were eligible for further experiments.

Ovaries extraction and handling

Mice were anesthetized for 2 min using isoflurane and then euthanized by cervical dislocation. Each mouse’s ovaries were extracted and washed in RPMI-1640 media (Sigma-Aldrich) containing 10% FBS and transferred to 1.5 mL microcentrifuge tubes containing the same media. Next, the ovaries were cut using scissors and incubated for 30 min with ~7800 IU/mL (Lot dependent) of collagenase type IV (Sigma-Aldrich) at 37°C. After which, 2.5μ g/mL of DNase I (Sigma-Aldrich) was added, and tubes were incubated for additional 30 min. For proper tube content mixing, gentle tapping was performed every 5 min during incubation. Tissue homogenate was filtered through a 40 μm strainer (Greiner Bio-One) and washed with 0.5 mL RPMI media five times. Total cell count was then calculated using LUNA automated cell counter (Logos Biosystems). Cells were further processed for sorting (for single-cell RNA sequencing) or staining (for flow cytometry experiments).

Cell sorting

Cell samples went through a series of centrifugations (400g, 5 min, at 4°C) and were stained with PE anti-CD45 (30-F11, BioLegend) in staining buffer (see flow cytometry section) for 30 min at 4°C. CD45-positive cells were sorted and collected using FACSAria III Cell Sorter (BD Biosciences). Collected samples were centrifuged and brought to a final volume of ~50 μL, counted using LUNA automated cell counter (Logos Biosystems), and further processed for single-cell sample preparation.

Single-cell RNA sequencing

Samples were prepared as outlined by the 10x Genomics Single Cell 3′ v2 Reagent Kit user guide. Briefly, the samples were washed twice in PBS (Sigma-Aldrich) +0.04% BSA (Sigma-Aldrich) and re-suspended in PBS + 0.04% BSA. Sample viability was assessed with Trypan Blue (Biological Industries) using LUNA automated cell counter (Logos Biosystems) and validated using a hemocytometer. Following counting, the appropriate volume for each sample was calculated for a target capture of 10,000 and 7700 cells (old and young samples, respectively) and loaded onto the 10x Genomics single-cell-A chip. After droplet generation, samples were transferred onto a 96-well plate, and reverse transcription was performed using a Veriti 96-well thermal cycler (Thermo Fisher). After the reverse transcription, cDNA was recovered using Recovery Agent provided by 10x Genomics followed by a Silane DynaBead clean-up (Thermo Fisher) as outlined in the user guide. Purified cDNA was amplified before being cleaned up using SPRIselect beads (Beckman Coulter). Samples were quantified with Qubit Fluorometer (Invitrogen) and run on Agilent TapeStation for quality control. cDNA libraries were prepared as outlined by the Single Cell 3′ Reagent Kits v2 user guide with appropriate modifications to the PCR cycles based on the calculated cDNA concentration (as recommended by 10x Genomics). Post library construction quantification and QC were performed as mentioned above for the post cDNA amplification step. Sequencing was performed with NextSeq500 system (Illumina), with ~50,000 reads per cell and 75 cycles for each read.

Seurat R package (Butler et al., 2018) was used to read the 10x output data from Cell Ranger v3.0.1 according to the suggested protocol. Yield was 3693 and 5644 cells for young and old samples, respectively. Quality control tests were conducted to eliminate duplicates, and dead or low-quality cells – cells with less than 200 features, more than 2500 features, or more than 10% features of mitochondrial genes were excluded from further analysis. In total, we ended up with 3307 and 5468 cells for young and old samples (data available at Figure 1—source data 1 and Figure 2—source data 1).

Cell type annotations

The Seurat R package (version 4.1.0), along with the SingleR package (version 1.8.1) (Aran et al., 2019) were used to analyze single-cell RNA sequencing data. After using Seurat’s normalization tool (‘LogNormalize’, 10,000 scale) and clusters identification using Seurat’s graph-based clustering method, t-distributed stochastic neighbor embedding (tSNE) was used as a dimension reduction and visualization tool, with PCA as the latent space (15 PC dimensions). The log-normalized data was scaled prior to dimensionality reduction (using ‘scale.data’). Next, the SingleR algorithm was used to achieve the automatic annotation for each cell. Briefly, the algorithm compares each cell’s transcriptome to known transcriptomic ‘signatures’ from reference genomes taken from The Immunological Genome Project (ImmGen) (Heng et al., 2008). The algorithm calculates the correlation between the cell to different cell types, and based on the highest correlation suggests an annotation for the cell.

In addition, a set of literature-based gene markers was chosen to identify the different immune cell types. For each gene marker, a normalized score was calculated in every cell using the raw count matrix. Eg,i denotes the expression level of the gene g within the ith cell; Mg and mg denote the maximal and minimal expression of the gene g across all cells in the sample, respectively. Nc denotes the number of cells within cluster c, and Ng,c denotes the number of cells within cluster c that express the gene g. The normalized expression score, Sg,i,c, for the gene g in the ith cell within the cluster c is the multiplication of the gene relative expression and the relative fraction of cells within the cluster that express this gene, Sg,i,c=(Eg,imgMgmg)(Ng,cNc). Figure 1c illustrates the distribution of the normalization score for a particular gene g over all the cells in cluster c. Normalized scores are between 0 and 1.

The identity of each cluster was determined by taking into account the majority type as given by the SingleR and the type according to the literature markers. For example, according to the literature markers, cluster 10 is CD8+ enriched, and cluster 11 is CD4+ enriched. While SingleR cell type ID is consistent with this classification, several cells in cluster 10 were classified as CD4+ cells, while other cells in cluster 11 were classified as CD8+ cells. Therefore, the final assignment of these cells is different than the SingleR ID.

Flow cytometry analysis

Ovarian cell suspensions were stained for flow cytometry analysis as the commercial protocol suggested (BD Biosciences). Briefly, cells were plated in 96-well U-shaped plates and went through a series of centrifugations (400g for 5 min, at 4°C) for media and debris cleaning. Next, samples were stained as the commercial protocol suggested using the following antibodies (purchased from BioLegend) diluted in staining buffer (PBS containing 0.09% sodium azide (Sigma-Aldrich)): PE anti-CD45 (30-F11), BV421 anti-CD11b (M1/70), APC/Cy7 anti-F4/80 (BM8), APC anti-F4/80 (BM8), BV711 anti-CD11c (N418), Alexa Fluor 700 anti-Ly6G (1A8), APC anti-CD3 (17A2), Pacific Blue anti-CD3 (145–2 C11), APC/Cy7 anti-NK1.1 (PK136), PE/Cy7 anti-TCRβ (H57-597), Alexa Fluor 700 anti-CD8a (53–6.7), FITC anti-CD4 (GK1.5), PE/Cy7 anti-TCRγδ (GL3) and APC anti-CCR5 (HM-CCR5). Staining was performed at 4°C for 30 min. Flow cytometry analyses were made using the S100EXi (Stratedigm) flow cytometer. Viability test of CD45+ cells was conducted using Zombie-NIR (BioLegend).

Statistical analyses

All statistical analyses were calculated using MATLAB R2019b (MathWorks).

For the flow cytometry experiments, we used two-sample Kolmogorov-Smirnov test for identifying significant changes in cell types fractions at different ages.

To determine DEGs between old and young samples, genes were considered significant if they had twofold change in their mean expression and if their FDR ≤0.1, using Storey q-values approach (Storey, 2002) for a p-value ≤0.05 (two-tailed student’s t-test).

For analyzing significant changes in the fraction of each cell type, we used the MILO algorithm (Dann et al., 2022). Briefly, the algorithm constructs an undirected KNN graph of single cells based on the scRNA-seq, divides the data into neighborhoods of cells and compares how their size changes with different conditions. Therefore, we have used the batch-corrected data as input for it. To estimate the differential abundance in terms of cell types, the MILO algorithm assigns a cell type label to each neighborhood by finding the most abundant cell type within cells in each neighborhood (Figure 2—figure supplement 1). MILO uses a weighted version of the Benjamini-Hochberg method to regulate the spatial FDR in the KNN graph. In this method, p-values were weighted by the reciprocal of the neighborhood connectivity to regulate the graph.

The FDR of having six genes that have significantly modulated fraction change (p-value <0.01) out of a list of 24 random genes, for each cell type, was calculated analytically. The FDR in this case, where all hypotheses are considered null, is equal to the family-wise error rate (FWER), which can be calculated analytically using multinomial distribution (Korthauer et al., 2019).

GO enrichment analysis

Significant downregulated or upregulated genes for each cell type were taken for GO enrichment analysis of biological processes using the g:Profiler platform (Raudvere et al., 2019) (database versions: Ensembl 108, Ensembl Genomes 55, and Wormbase ParaSite 17, published on 2/23/2023). Next, all significant processes were further analyzed using the REVIGO platform for eliminating redundant GO terms (Supek et al., 2011). Then, cell-type specific and global (for at least two cell types) processes were found and analyzed once more using the REVIGO platform in order to cluster them according to their semantics distance.

Cytokines and chemokines networks

The Kyoto Encyclopedia of Genes and Genomes (KEGG) was used to construct the chemokines and cytokines ligand-receptor networks. These were built based on the Cytokine-cytokine receptor interaction - Mus musculus (mouse) map (pathway map mmu04060) (Kanehisa, 2000).

Data availability

All data used in this study are included in the manuscript, the supporting files and in GitHub:https://github.com/SavirLab/AgingOvarianImmuneMilieu (copy archived at SavirLab, 2023).

References

    1. Pal L
    2. Santoro N
    (2003) Age-Related decline in fertility
    Endocrinology and Metabolism Clinics of North America 32:669–688.
    https://doi.org/10.1016/s0889-8529(03)00046-x
    1. Storey JD
    (2002) A direct approach to false discovery rates
    Journal of the Royal Statistical Society Series B 64:479–498.
    https://doi.org/10.1111/1467-9868.00346

Decision letter

  1. Sara Hägg
    Reviewing Editor; Karolinska Institutet, Sweden
  2. Diane M Harper
    Senior Editor; University of Michigan, United States

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Single-cell analysis of the aged ovarian immune system reveals a shift towards adaptive immunity and attenuated cell function" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Ricardo Azziz as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1. Figure 1-2 begins to tell an interesting story of changes in macrophage and an ovary specific CD3+ CD4- CD8- double negative T-cell abundances in aging. While the flow cytometry here backs up their claims, the statistical rigor of the single-cell analysis itself is questionable. Rather than further probing gene expression differences in these specific subpopulations, the next sections proceeds to do (1) a global analysis of gene expression changes, (2) a CCC analysis (independent of existing tools) that claims decreased inflammation (see below points about doubts regarding this), and (3) an analysis of SASP recognition that is very limited.

2. Results for Figure 1-3 can be published with some changes. Figure 4 would need a lot of additional analyses and changes. The SASP section with no main figure associated shouldn't be included.

3. Code for computational analyses should at least be available on Github, preferably on CodeOcean for reproducible runs.

4. My concern regarding the single-cell results is that there does not seem to be a formal batch correction step. Figure 2A visually seems to show minimal batch effects, and a big positive is that flow cytometry results align with the single-cell observations. Unfortunately, it is impossible to rigorously claim from the scRNAseq that the cluster 12 frequency change is due to aging without a formal batch correction.

5. The problem with applying a batch correction now, even if there aren't major batch effects, would be that it can change downstream results at some resolution (e.g. p-values and effect sizes). A solution would be to add a supplemental section demonstrating that there are not batch effects. This may be done by applying a batch correction (e.g., Harmony or Seurat integration), and demonstrating that downstream clustering patterns remain similar (indicating that the informative transcriptional space of the cells are consistent).

6. A separate but complementary point regarding the question of frequency change in cluster 12 using scRNAseq: unlike microbiome, which has comprehensive compositional analysis methods, the question of cell abundance changes in single-cell is just recently beginning to be addressed. To more rigorously present these results, a few things would be useful:

6.1 Higher resolution in Figure 2C,D – specifically, gating on the CD3+ lymphocyte subpopulations, as well as some control cell types that do not show a change in single-cell (preferably, all cell type frequencies validated with flow).

6.2 More quantification of effect size or statistical assessment of Figure 1B using recently published tools. Some tools have been published on differential abundance testing in single-cell in the last couple years include scDC, MILO, and DA-seq.

7. As a side note, MELD does not give a differential abundance p-value, but does quantify the likelihood of observing a given cell in a given condition at single-cell resolution and allows you to further partition the data based on those values. This can allow for higher resolution differential expression testing and may be useful to you for future analyses.

8. There is a lack of multiple test correction (or stating of such correction) throughout statistical analyses which must be addressed.

9. It is difficult to trust the "skewed" DE patterns, especially for DNT cells (Figure 3A) -- a global downregulation of genes?

10. The claim regarding a decreased inflammatory state in aging is unconvincing. Currently, the results indicate a global downregulation of the transcriptome in aging, and so when you visualize just chemokines and cytokines, visually, it looks like inflammation is downregulated. The enrichment analysis would be better in supporting this claim -- the story could go: inflammatory response is a consistently enriched term in cell types X, Y, Z [the Results section regarding Figure 3], so we then focused on communicatory immune networks [Results section regarding Figure 4]. I had formatting issues in Supplementary Table 2, but it looks like the inflammatory response GO Term is only enriched in macrophages. Further discussion should be had to back up this claim.

11. Senescent cells section seems like an afterthought. It is not sufficient to make the claim of increased senescent cell recognition by immune cells via single-cell analysis of immune cells alone, and even if it was, these analyses are not rigorous enough.

12. There is no visualization of canonical SASP receptors expression changes across young vs old.

13. Analysis is not systematic: should start with a comprehensive list of canonical SASP receptors, rather than choosing some from literature.

14. Supplementary Figure S5 is an unorthodox analysis of gene expression changes. Were these genes differentially expressed in old macrophages?

15. Throughout the paper, it is important to show (by either experiments or using publicly available resources) that the changes observed are indeed specific to the ovaries.

16. The authors start the paper by discussing the reduced fertility as a function of age, so any of these results suggest a mechanisms for that? Some discussion of this point will be useful.

17. Because the scRNA-seq data presented by the authors show that the CD4- CD8- double-negative T cell subset co-express Trbc2 (TCRb) and Tcrgc2 (TCRg) genes, it would be important to test if these cells also co-express TCRb and TCRg/d at the protein levels. Pro-inflammatory CD4- CD8- double-negative T cells co-expressing TCRb and TCRg/d have been found in mice (Edwards et al., J Ex Med 2020), and it would be interesting to test whether the ovarian DNT cells show phenotypical or functional similarities with this cell type.

18. To better understand the function of double-negative T cell subset in aging ovaries, one possible way would be to purify these cells and measure which cytokines they produce after TCR activation in vitro and/or co-culture these cells with activated CD4/CD8 T cells in vitro to test if they are capable of suppressing T cell proliferation.

19. For the cluster annotation of scRNA-seq data, it would be interesting to perform additional gene expression analyses to test whether the two clusters of dendritic cells correspond to cDC1 and cDC2 populations.

20. Flow cytometry validation of scRNA-seq data in larger groups of mice presented in this study is chiefly limited by CD3+ T cells and CD11b+ cells. Additional flow cytometry experiments that validate alterations of central ovarian immune cell populations in old competed to adult mice would be helpful. Gating strategies for all flow cytometry experiments should be shown.

21. It would be interesting to compare the scRNA-seq data generated by the authors with published datasets on the immune aging in various mouse tissue (e.g., Almanzar et al., Nature 2019; Kimmel et al., Genome Res 2019; Mogilenko et al., Immunity 2021) to identify common and tissue-specific immune changes in aging ovaries.

22. Predicted changes in cytokine and chemokine expression levels and the crosstalk between immune and senescent cells presented in this study are based on scRNA-seq data but are lacking additional validation. For example, protein-level confirmation for some of these pathways would add important information about the mechanism of immune aging in the ovaries.

23. In Methods: antibody clone 17A2 is used for CD3 and CD4 detection (possible mistake).

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Single-cell analysis of the aged ovarian immune system reveals a shift towards adaptive immunity and attenuated cell function" for further consideration by eLife. Your revised article has been evaluated by Ricardo Azziz (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

Reviewer #1 (Recommendations for the authors):

In this study, the authors present an exciting work that lies at the intersection of immunology, aging, and single-cell RNA-sequencing analysis. It provides a valuable and well-annotated single-cell resource for other researchers in the community to use. It provides solid analyses to probe mechanistic changes in ovarian immune cell functions. More specifically, it leverages single-cell RNA sequencing to probe changes in various immune functions within the ovary in aging. The data provided is the most comprehensive of ovarian immune cells at the resolution of single-cell transcriptomics to date and will be valuable to other researchers. The authors explore four distinct immune functions:

1. Among other cell types, the authors identify macrophages and a unique CD3+ CD8- CD4- T-cell (DNT) subpopulation that changes in abundance with aging. The cell-type compositional analysis results are comprehensive and convincing.

2. The authors also analyze changes in global gene expression across cell types using an enrichment analysis; Figure 3B summarizes potential global and cell-type specific changes in gene expression programs during aging.

3. The authors infer differences in cell-cell communication mediated by various chemokines and cytokines, reasonably demonstrating a decreased inflammatory response.

4. The authors provide evidence that the fraction of macrophages and neutrophils recognizing secretory-associated senescence phenotype (SASP) molecules increases with age.

Both the data and biology presented are quite interesting. The distinction between an aging-associated decreased inflammatory response and cytokine/chemokine communication and an increase in SASP recognition in some cell types, particularly macrophages, demonstrates the complexities of the immune response that are amenable to further exploration. The role of the unique DNT population, which demonstrates substantial compositional changes with aging, in these systemic changes will also be interesting to further dissect.

Overall, while the authors have extensively addressed most of my concerns regarding the compositional analysis, the claims around a decreased inflammatory state with aging (particularly Figure 4B and the response regarding GO terms including both positive and negative regulators), and cell-cell communication analysis. I find the distinction between Figure 5 and Figure 5—figure supplement 1 to be interesting, with SASP recognition seemingly affecting a larger fraction of macrophages but not the average expression between the conditions. I also find it interesting that this same cell type decreases in abundance with age, possibly indicating that a subpopulation of macrophages that are retained with age are those exhibiting SASP recognition as an alternative explanation to the more natural conclusion that macrophages overall increase SASP recognition over time. While I am excited about this work, there are still some outstanding concerns, that I present below.

– Were the log-normalized data scaled prior to dimensionality reduction? PCA typically takes scaled data as input.

– Reading through the methods, it is unclear whether the p-values used in DE testing were multiple tests corrected. Line 364 of the Related Manuscript File "100652_1_related_ms_2716393_rmry4f.pdf" states "all other" statistical analyses applied an FDR correction, implying that this wasn't applied to the DE and other statistical tests discussed in the "Statistical analyses" subsection of the Methods. Furthermore, in the Supplementary Table reporting DE results, there is only the column "pVal" indicating that this is not a multiple test corrected significance value. If multiple test correction was not applied to differential expression output p-values, they must be. Similar concerns for the GO enrichment results and MILO results, which also include many tests. Furthermore, given the LFC threshold filter, I wouldn't expect results to change drastically. However, if there are similar concerns to my original comments regarding how batch correction will affect downstream effect sizes, a demonstration that applying the multiple test correction does not change the results is a necessary minimum. I would suggest demonstrating that the genes identified as significantly differentially expressed with multiple test correction (perhaps with FDR ≤ 0.1) are consistent with those in the current list. This could be done by showing that the gene list sizes are similar and have a high Jaccard index.

Reviewer #3 (Recommendations for the authors):

The authors made substantial improvements to the manuscript by cross-referencing the data and adding validation experiments. However, limitations of this revised manuscript still include not optimal validation strategies: e.g., in flow cytometry validation experiments, the authors defined dendritic cells as CD45+ CD11c+ subset, which might include a variety of CD11c+ macrophages; ILC1s were defined as CD45+ NK1.1+ cells, which might consist of NKT cells. These limitations prevent direct comparison of scRNA-seq data with the results of biological validation experiments.

https://doi.org/10.7554/eLife.74915.sa1

Author response

Essential revisions:

1. Figure 1-2 begins to tell an interesting story of changes in macrophage and an ovary specific CD3+ CD4- CD8- double negative T-cell abundances in aging. While the flow cytometry here backs up their claims, the statistical rigor of the single-cell analysis itself is questionable. Rather than further probing gene expression differences in these specific subpopulations, the next sections proceeds to do (1) a global analysis of gene expression changes, (2) a CCC analysis (independent of existing tools) that claims decreased inflammation (see below points about doubts regarding this), and (3) an analysis of SASP recognition that is very limited.

We thank the reviewers for their constructive remarks. In the following, we provide a point-to-point response to their comments. The revision includes performing additional experiments to validate the single-cell RNA-seq results and our analysis and performing additional computational analysis that strengthens our conclusion.

2. Results for Figure 1-3 can be published with some changes. Figure 4 would need a lot of additional analyses and changes. The SASP section with no main figure associated shouldn't be included.

Following these important comments, we have performed additional analysis of the results presented in Figure 4 that includes demonstrating that the inferred interactions are indeed significant (see detailed description in the relevant points below 9-10). We added a figure that illustrates the increase of SASP in macrophages to the main text together with a detailed analysis of its significance (see detailed description in the relevant points below 11-14)

3. Code for computational analyses should at least be available on Github, preferably on CodeOcean for reproducible runs.

The code is available at GitHub at https://github.com/SavirLab/AgingOvarianImmuneMilieu

4. My concern regarding the single-cell results is that there does not seem to be a formal batch correction step. Figure 2A visually seems to show minimal batch effects, and a big positive is that flow cytometry results align with the single-cell observations. Unfortunately, it is impossible to rigorously claim from the scRNAseq that the cluster 12 frequency change is due to aging without a formal batch correction.

5. The problem with applying a batch correction now, even if there aren't major batch effects, would be that it can change downstream results at some resolution (e.g. p-values and effect sizes). A solution would be to add a supplemental section demonstrating that there are not batch effects. This may be done by applying a batch correction (e.g., Harmony or Seurat integration), and demonstrating that downstream clustering patterns remain similar (indicating that the informative transcriptional space of the cells are consistent).

We thank the reviewer for these important comments (4+5). These comments raise two main questions that are related but different. The first is how one can determine the cell types within each condition. The second, given the cell types, how can one have some confidence in the estimation of the fraction of each cell type out of the population within each condition and confidence in the change in fraction between the two conditions. In our response to points 4,5 and 6, we address these two main issues.

Indeed, if inferring the cell types rely solely on clustering the two conditions together, batch correction is vital. In our case, we determined the cell types not only by using clustering the two conditions together but also curated the cell types by examining each condition by itself, using literature markers, and automated algorithms (SingleR) that infer the immune cell types for each condition by itself. That is, the tSNE plot that shows the overlay of the population aims to demonstrate that even without batch correction our identification of the cell types is consistent between the two conditions.

Following, the reviewer’s comment, we performed formal batch correction using Seurat and show that the dimension reduction and cluster distribution remain almost identical, and that our cell type identification holds. We added a figure that demonstrates that, to the supplementary information (Figure 1—figure supplement 2), and addressed it in the relevant parts of the revised manuscript.

There are two complementary approaches for having confidence in the estimation of the changes in cell fraction as a function of age. The first is using classical simultaneous confidence estimations for the cell type fractions for each condition by itself. The second is to perform differential analysis using tools such as MILO that accounts also for the cell’s space topology.

As the reviewer mentioned, when using the latter approach, it is crucial to use batch correction. When we performed this analysis, we used it on the batch-corrected data. (We elaborate on it in point 6.2 below).

6. A separate but complementary point regarding the question of frequency change in cluster 12 using scRNAseq: unlike microbiome, which has comprehensive compositional analysis methods, the question of cell abundance changes in single-cell is just recently beginning to be addressed. To more rigorously present these results, a few things would be useful:

6.1 Higher resolution in Figure 2C,D – specifically, gating on the CD3+ lymphocyte subpopulations, as well as some control cell types that do not show a change in single-cell (preferably, all cell type frequencies validated with flow).

Following the reviewer’s comments, we performed additional flow cytometry experiments in which we characterized the CD3+ subpopulation at a finer resolution – we measured the change in NKT cells, CD4, CD8, and double negative T-cells. These results are shown in the revised Figure 2D and are consistent with the changes in cluster 12 as measured using the scRNAseq. In addition, we also validated the changes in the fractions of several innate immune populations such as dendritic cells, neutrophils, and group 1 ILC cells (ILC1 and NK cells). These results are shown in Figure 2—figure supplement 2 in the revised supplementary information.

6.2 More quantification of effect size or statistical assessment of Figure 1B using recently published tools. Some tools have been published on differential abundance testing in single-cell in the last couple years include scDC, MILO, and DA-seq.

7. As a side note, MELD does not give a differential abundance p-value, but does quantify the likelihood of observing a given cell in a given condition at single-cell resolution and allows you to further partition the data based on those values. This can allow for higher resolution differential expression testing and may be useful to you for future analyses.

Following the reviewer’s remarks, we took a dual approach for estimating the confidence levels in the change of cell types’ abundance with age.

First, recall that Figure 2B illustrates the estimator for the fraction of each cell type, which is simply the ratio between the number of cells for a given type divided by the total number of cells, within each condition (which is the maximum likelihood estimator of a multinomial distribution). The simultaneous confidence level on this estimator can be calculated by using bootstrapping or by using approximated analytic expression. We used bootstrapping to calculate the 95% simultaneous confidence levels of the fraction and revised Figure 2B accordingly. This provides one type of confidence level in the changes between conditions.

Second, following the reviewer’s comment, we applied MILO to estimate the differentiation abundance between conditions. As we mentioned in our response to point 4, it is critical to use batch-corrected data (using Seurat in our case) because this method identifies local neighborhoods in the transcriptome space and evaluates the change in abundance locally. The results shown in Figure S9 in the SI. This analysis shows that indeed the change in cluster 12 is significant.

8. There is a lack of multiple test correction (or stating of such correction) throughout statistical analyses which must be addressed.

We thank the reviewer for this important comment. All the relevant statistical tests were adjusted to multiple tests. We mentioned it in the relevant places in the revised manuscript. The approach we took throughout the entire work was to use FDR.

9. It is difficult to trust the "skewed" DE patterns, especially for DNT cells (Figure 3A) -- a global downregulation of genes?

10. The claim regarding a decreased inflammatory state in aging is unconvincing. Currently, the results indicate a global downregulation of the transcriptome in aging, and so when you visualize just chemokines and cytokines, visually, it looks like inflammation is downregulated. The enrichment analysis would be better in supporting this claim -- the story could go: inflammatory response is a consistently enriched term in cell types X, Y, Z [the Results section regarding Figure 3], so we then focused on communicatory immune networks [Results section regarding Figure 4]. I had formatting issues in Supplementary Table 2, but it looks like the inflammatory response GO Term is only enriched in macrophages. Further discussion should be had to back up this claim.

We thank the reviewer for these important comments (9+10). Following these comments, we carefully revised and clarified the argument regarding the decrease of the inflammatory state.

As the reviewer correctly mentioned, a general downregulation can lead to an artifact in which inflammation-related genes appear to be decreased although it is not significant under the global downregulation. To account for this global downregulation, we’ve performed another differentially expressed genes (DEGs) analysis for each cluster, considering only the top down/up regulated genes with a p-value of 0.025 (which were defined by CDF analysis). The GO enrichment analysis using the g:Profiler platform, “Inflammatory response” (GO:0006954) downregulated in four different clusters (Neutrophils, Macrophages, Dendritic cells and ILC3 cells). In addition, the process “negative regulation of neuroinflammatory response” (GO:0150079) was upregulated in NK cells (see Supplementary File 5).

Yet, it is important to consider that enrichment of the GO term “Inflammatory response” (GO:0006954) is not necessarily an indicator of an inflammatory state. This term includes genes that are positive regulators of inflammation as well as negative ones. That is, one cannot infer whether inflammation goes up or down just because the GO term went up or down. This is true for other GO terms that involve immune response processes.

Therefore, in order to determine whether inflammation-related processes are decreased, one has to carefully examine the mediators of immune response – chemokines and cytokines.

Following the reviewer’s comment, we added the analysis that shows that even on top of the global downregulation, chemokines and cytokines are significantly downregulated in the cells from old ovary. This analysis was added as Figure 4B in the revised manuscript. The question following this step is whether the significant decrease in chemokines and cytokines indeed translates to a decrease in inflammation.

To answer this question, one must consider both the identity of the significantly downregulated chemokines and cytokines and their interaction across different cell types.

It is important to note that we did not cherry-pick the interactions that appear in Figure 4, but included all known chemokines and cytokine interactions. Most of the significant chemokines and cytokines changes were inflammatory interactions that were downregulated. Following the reviewer’s comments, we revised Figure 4 so it now contains the entire chemokine and cytokine networks.

To validate our results further, we applied cell2cell (Armingol et al., 2022) to our data to infer the age-dependent interactions. Consistently with our analysis, it yielded CCR5 as the main receptor that is modulated by age (Figure 4—figure supplement 1A).

Finally, to experimentally validate our results, we measured the fraction of cells that express CCR5 – and showed that indeed it decreases significantly (Figure 4—figure supplement 1B).

11. Senescent cells section seems like an afterthought. It is not sufficient to make the claim of increased senescent cell recognition by immune cells via single-cell analysis of immune cells alone, and even if it was, these analyses are not rigorous enough.

12. There is no visualization of canonical SASP receptors expression changes across young vs old.

13. Analysis is not systematic: should start with a comprehensive list of canonical SASP receptors, rather than choosing some from literature.

14. Supplementary Figure S5 is an unorthodox analysis of gene expression changes. Were these genes differentially expressed in old macrophages?

We thank the reviewers for these important comments (11-14) regarding the SASP analysis. Following these comments, we have thoroughly revised this section and added a new figure to the manuscript (Figure 5 in the revised manuscript and Figure 5—figure supplement 1 in the revised SI).

First, following the reviewers’ comments, we have compiled a curated list of SASP receptors. While there is a wide milieu of SASP-related ligands and receptors in the literature, there is no single canonical list of SASP receptors. After an extensive literature review, we have compiled a list that contains 24 SASP-related receptors based on the available databases (such as KEGG) and literature. (Supplementary File 4).

Besides analyzing the change in gene expression (for example, by compiling the volcano plots that appear in Figure 3A in the revised manuscript) it is also crucial to consider the change in the fraction of cells that express a particular gene. In many immune-related processes, the fraction of cells that express a particular receptor determines the response. This is the reason that in many immune studies, the determination of whether an immune cell-type population was changed is based on the change in the fraction that expresses a particular receptor rather than expression fold change.

We found that although there are no significant changes in the fold change expression of the SASP receptors (Figure 5—figure supplement 1 in the revised SI), there is a very significant increase in the fraction of the macrophages and neutrophils that express SASP receptors. These cell types are among the main mediators of the removal of senescent cells. Again, we would like to stress that we did not cherry-pick the genes and took into account the global change. The false discovery rate of macrophages changing the fraction of their SASP milieu is slim.

Finally, following the reviewer’s comments, we also fine-tuned our statements regarding the SASP and revised Figure 6, accordingly.

15. Throughout the paper, it is important to show (by either experiments or using publicly available resources) that the changes observed are indeed specific to the ovaries.

In the original manuscript, we have shown that the abundance of the most frequent ovarian cell types (DNT in the old females and ILC1 in the young ones) is ovary specific. We have shown that the abundance of the double negative T cell population of old females is ovary-specific (Figure 1—figure supplement 7) compared with the spleen and the peritoneum. We have also shown that the abundance of the ILC1 population in young females is ovary specific compared with the spleen (Figure 1—figure supplement 5).

Following the reviewer’s comment, we performed additional experiments that also show that the change in CD3+ lymphocytes (and its subpopulations, DNT+NKT, CD4, and CD8) composition is ovary specific (Figure 2—figure supplement 4).

We also revisited the current literature (see our response to comment 21 below), there is no evidence for the presence of DNT cells in other tissues such as the bladder, lung, and kidney.

16. The authors start the paper by discussing the reduced fertility as a function of age, so any of these results suggest a mechanisms for that? Some discussion of this point will be useful.

Following the reviewers’ comment, we added the following addition to the discussion of the revised manuscript.

Age-related disruption of tightly controlled immune functions may lead to fertility decline. Turner et al., show that macrophages are pivotal in maintaining ovarian vascular integrity as their progressive ablation results in ovarian hemorrhage and tissue structural damage (Turner et al., Reproduction, 2011). Our results indicate a decline in the macrophage population with age which may result in fertility deterioration because of impaired ovarian vasculature. In addition, enrichment in CD3+ lymphocytes in the ovary was found to be associated with poor follicular reserve (Jasti et al., Biology of Reproduction, 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., Scientific Reports, 2019). Similarly, our results indicate an age-dependent change in the T lymphocyte population that may be involved in improper immune regulation in the ovary and hence lead to infertility. More specifically, our results show a decreased expression of several members of the TGF superfamily, such as Inhba and Acvr2a, by macrophages at old age. A previous study has shown that Activin A (which is composed of two Inhibin-ab, the translation product of Inhba), is related to LH secretion, plays a significant role in expanding the primordial follicle pool, and contributes to the early stages of follicular growth.

17. Because the scRNA-seq data presented by the authors show that the CD4- CD8- double-negative T cell subset co-express Trbc2 (TCRb) and Tcrgc2 (TCRg) genes, it would be important to test if these cells also co-express TCRb and TCRg/d at the protein levels. Pro-inflammatory CD4- CD8- double-negative T cells co-expressing TCRb and TCRg/d have been found in mice (Edwards et al., J Ex Med 2020), and it would be interesting to test whether the ovarian DNT cells show phenotypical or functional similarities with this cell type.

As the reviewer mentioned, Edwards et al., identified a unique population of hybrid αβ-γδ T cells in human PBMC and mouse spleen and LNs.

Following the reviewer’s comment, we performed additional experiments to test this point. First, we have verified our ability to observe γδ T-cells in our model. FACS measurements of mouse spleen samples show that the TCRγδ DNT cells (CD3+ CD4- CD8-) constitute 48% and 23% out of the total DNT population for young and old mice, respectively. However, in the ovaries, the fraction of γδ DNT out of the overall DNT cells is low ~2.5% and age-independent (Figure 1—figure supplement 8). These results suggest that while a subset of the T-cells co-express Trbc2 and Tcrgc2, at the protein level the population that is TCRγδ DNT (which is the upper limit for the TCRγδ TCRαβ DNT) is only a small fraction (~2.5%, an order of magnitude smaller than the spleen) out of the total DNT.

18. To better understand the function of double-negative T cell subset in aging ovaries, one possible way would be to purify these cells and measure which cytokines they produce after TCR activation in vitro and/or co-culture these cells with activated CD4/CD8 T cells in vitro to test if they are capable of suppressing T cell proliferation.

We thank the reviewer for the suggestion. The goal of this manuscript is to reveal the immune milieu in the ovaries and their modulation due to maternal aging and analyze them. Performing such an experiment can be an excellent first step in exploring the possible direct mechanistic interaction between ovarian DNT and CD4/CD8 T cells. Yet, the number of immune cells that can be recovered from the ovaries is small (~20,000-50,000 CD45+ cells per mouse) and the number of DNT cells is even smaller (~1,000 cells per young mouse and ~15,000 cells per old mouse). It will require tens of mice to perform such a co-culture or to harness methods such as microfluidic chambers or wells (to perform co-culturing in smaller volumes). While deciphering the mechanics of the DNT population is an interesting follow-up, these types of in-vitro experiments are outside the scope of this paper.

19. For the cluster annotation of scRNA-seq data, it would be interesting to perform additional gene expression analyses to test whether the two clusters of dendritic cells correspond to cDC1 and cDC2 populations.

We thank the reviewer for this insightful comment – this is indeed the case. We tested the expression levels of several literature-based markers for cDC1 and cDC2 and show that cluster 3 corresponds to an expression pattern of cDC2, while cluster 4 corresponds to an expression pattern of cDC1. Figure 1—figure supplement 6 in the revised supplementary information illustrates this analysis.

20. Flow cytometry validation of scRNA-seq data in larger groups of mice presented in this study is chiefly limited by CD3+ T cells and CD11b+ cells. Additional flow cytometry experiments that validate alterations of central ovarian immune cell populations in old competed to adult mice would be helpful. Gating strategies for all flow cytometry experiments should be shown.

Following the reviewer’s comments, we have performed additional flow cytometry experiments. We were able to break down the CD3+ population into its subpopulations and measured the change in NKT cells, CD4, CD8, and double negative T-cells (Figure 2D). In addition, we also validated the changes in the fractions of several innate immune populations such as dendritic cells, neutrophils, and group-1 ILC (ILC1 and NK cells). These results are shown in Figure 2—figure supplement 2 in the revised supplementary information. Our gating strategies are shown in the SI (Figure 1—figure supplement 4).

21. It would be interesting to compare the scRNA-seq data generated by the authors with published datasets on the immune aging in various mouse tissue (e.g., Almanzar et al., Nature 2019; Kimmel et al., Genome Res 2019; Mogilenko et al., Immunity 2021) to identify common and tissue-specific immune changes in aging ovaries.

We thank the author for this important comment. These recent studies reveal age-specific changes in cell populations in different mouse tissues, independently of female fertility-related aging. Our data compare between young and aged female mice with an emphasis on their fertility window. Aged mice at that age do not necessarily exhibit an aging phenotype in other tissues, which are fertility non-related. As our study’s most aged group is 40-47 weeks old (~9-11 months) we can deduce that female mice of much older age such as 18m or 21m old (Almanzar et al.), 22-23m old (Kimmel at al. – males only) and 17-24m old (Mogilenko et al.) are post-estropausal.

Interestingly, we reveal that the ovarian immune milieu is characterized by age-related decrease in inflammatory hallmarks confined to the female fertile window period, while Almanzar et al., Kimmel at al. and Mogilenko et al., report increased expression of inflammatory markers at older ages. For example, bladder cells from both male and virgin female mice exhibit a shift towards a pro-inflammatory microenvironment when comparing mice at 1, 3, 18 and 24 months. This shift is reflected by an increase in tissue leukocytes and pro-inflammatory markers expression, together with a decrease in anti-inflammatory markers expression (Almanzar et al.). Age-related upregulation of pro-inflammatory environment was observed in kidney, lung, and spleen tissues of male mice in both immune and non-immune cell types (Kimmel et al.). In addition, pro-inflammatory cytokines and chemokines were elevated in the serum of old male mice, along with an increase in the fraction of inflammatory GzmK+ CD8+ T cells in all tested tissues (Mogilenko et al.).

While Almanzar et al., observed an age-related decrease in T cell population together with a B cell increase in both spleen and mammary tissues, our study of younger aged mice shows an opposite trend in the ovary (i.e., an increase in T-cell population in parallel to a decrease in B cells). Moreover, T cells in our study (most notably DNT cells) are the most prominent cell type in the aged ovaries.

Kimmel et al. compared cells harvested from old mice (22-23 months) with aged adult mice (7-8 months), the latter are age-equivalent to our old mice. The authors describe an increase in the prevalence of immune cells in old tissues, a phenomenon we also witnessed in the old ovaries. In addition, lymphocytes were found to be more abundant in lungs and kidneys at old age – consistent with our results in the ovaries. On the other hand, Kimmel et al., also report an increase in inflammatory properties, as reflected in GO enrichment analysis, which reveals an up-regulation of inflammatory processes in more than five cell types. This result, as already mentioned, is opposed to our findings, in which the inflammatory properties of the ovaries decrease, as the female mice approach the end of the fertile period.

In the study of Mogilenko et al., the most prominent effect found is the elevation in GzmK+ CD8+ T cells in the spleen, lungs, liver, and peritoneum. In our data, the fraction of these cells is insignificant (less than 1%) and decreases 1.6-fold in older mice.

The findings observed by these papers are consistent with the previously defined inflammaging, in which aging is accompanied by the development of a pro-inflammatory environment. It is important to note that our study compares female mice during their fertile period, which could explain the absence of inflammaging in the ovaries at older age. Furthermore, our study suggests that ovarian immune aging is not linear, but a complicated process that exhibits alternations between anti- and pro-inflammatory environment.

Following the reviewer’s comment, we added this discussion to the ‘Introduction’ and ‘Discussion’ sections of the revised manuscript.

22. Predicted changes in cytokine and chemokine expression levels and the crosstalk between immune and senescent cells presented in this study are based on scRNA-seq data but are lacking additional validation. For example, protein-level confirmation for some of these pathways would add important information about the mechanism of immune aging in the ovaries.

We thank the reviewer for this important comment. To validate our connectome results even further, we first performed additional computational analysis using a tool (Cell2Cell) developed by another group (Figure 4—figure supplement 1A). The results of this analysis were consistent with our own analysis, pointing CCR5 as the central hub of interaction that is decreased due to maternal age.

We used flow cytometry to test this result directly and show that, in agreement with our analysis, the fraction of cells that express CCR5 indeed decreases significantly (Figure 4—figure supplement 1B).

23. In Methods: antibody clone 17A2 is used for CD3 and CD4 detection (possible mistake).

We thank the reviewer for this correction. We have corrected the typo for CD4 clone that should be GK1.5.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

Reviewer #1 (Recommendations for the authors):

Overall, while the authors have extensively addressed most of my concerns regarding the compositional analysis, the claims around a decreased inflammatory state with aging (particularly Figure 4B and the response regarding GO terms including both positive and negative regulators), and cell-cell communication analysis. I find the distinction between Figure 5 and Figure 5—figure supplement 1 to be interesting, with SASP recognition seemingly affecting a larger fraction of macrophages but not the average expression between the conditions. I also find it interesting that this same cell type decreases in abundance with age, possibly indicating that a subpopulation of macrophages that are retained with age are those exhibiting SASP recognition as an alternative explanation to the more natural conclusion that macrophages overall increase SASP recognition over time. While I am excited about this work, there are still some outstanding concerns, that I present below.

– Were the log-normalized data scaled prior to dimensionality reduction? PCA typically takes scaled data as input.

Yes, the log-normalized data was scaled prior to dimensionality reduction (using Seurat scale.data). We have clarified it in the Cell type annotation section of the Methods in the revised MS.

– Reading through the methods, it is unclear whether the p-values used in DE testing were multiple tests corrected. Line 364 of the Related Manuscript File "100652_1_related_ms_2716393_rmry4f.pdf" states "all other" statistical analyses applied an FDR correction, implying that this wasn't applied to the DE and other statistical tests discussed in the "Statistical analyses" subsection of the Methods. Furthermore, in the Supplementary Table reporting DE results, there is only the column "pVal" indicating that this is not a multiple test corrected significance value. If multiple test correction was not applied to differential expression output p-values, they must be. Similar concerns for the GO enrichment results and MILO results, which also include many tests. Furthermore, given the LFC threshold filter, I wouldn't expect results to change drastically. However, if there are similar concerns to my original comments regarding how batch correction will affect downstream effect sizes, a demonstration that applying the multiple test correction does not change the results is a necessary minimum. I would suggest demonstrating that the genes identified as significantly differentially expressed with multiple test correction (perhaps with FDR ≤ 0.1) are consistent with those in the current list. This could be done by showing that the gene list sizes are similar and have a high Jaccard index.

We thank the reviewer for this very important comment. To remove any concerns regarding the rigor of our results, we have redefined what significant DEGs are. Following the reviewer’s comments, we took into account multiple hypothesis testing by applying the Storey q-values approach. As a cutoff, we used FDR≤0.1 (together with previous fold-change conditions). We have revised all the downstream analyses and used the new definition for significant DEGs for the GO analysis (Figure 3) and the connectome analysis (Figure 4).

While, as expected, fewer genes are now considered significant, our conclusions still hold both in terms of biological processes and in terms of chemokine and cytokine interactions. Most importantly, our conclusion regarding the chemokine and cytokine changes due to age does not change. The chemokine network exhibits the same downregulation profile, with a downregulation of CCR2 and CCR5 (which are consistent with our experimental validation of the decreased expression of CCR5 in dendritic cells). In the cytokines network, only three receptors that were marginally significant, do not pass the FDR. Yet there is a significant downregulation in the IL-1 superfamily and in Tnfsfr1b and its ligand Tnf (TNFα).

Overall, the reviewer comments have strengthened the manuscript demonstrating the significance of our results.

https://doi.org/10.7554/eLife.74915.sa2

Article and author information

Author details

  1. Tal Ben Yaakov

    Department of Physiology, Biophysics and Systems Biology, Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0005-0550-2901
  2. Tanya Wasserman

    Department of Physiology, Biophysics and Systems Biology, Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel
    Contribution
    Conceptualization, Data curation, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0221-1891
  3. Eliel Aknin

    Department of Physiology, Biophysics and Systems Biology, Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel
    Contribution
    Software, Validation, Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  4. Yonatan Savir

    1. Department of Physiology, Biophysics and Systems Biology, Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel
    2. Rappaport Family Institute for Research in the Medical Sciences, Haifa, Israel
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    For correspondence
    yoni.savir@technion.ac.il
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5345-8491

Funding

Israel Science Foundation (1619/20)

  • Tal Ben Yaakov
  • Tanya Wasserman
  • Eliel Aknin
  • Yonatan Savir

Rappaport Family Institute for Research in the Medical Sciences

  • Tal Ben Yaakov
  • Eliel Aknin
  • Yonatan Savir

Wolfson Foundation

  • Yonatan Savir

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Nati Karin, Keren Yitzhak, Noam Kaplan, and the Savir lab members for fruitful discussions. This work was supported by the Israel Science Foundation (ISF) grant #1619/20, the Rappaport Family Institute for Research in the Medical Sciences Thematic Grant, and the Wolfson Foundation.

Ethics

All mouse experiments performed in this study were approved by the Animal Care and Use Committee of the Technion, Israel Institute of Technology, and found to confirm with the regulations of this Institution for work with laboratory animals, protocol No: IL-069-05-2021.

Senior Editor

  1. Diane M Harper, University of Michigan, United States

Reviewing Editor

  1. Sara Hägg, Karolinska Institutet, Sweden

Version history

  1. Preprint posted: August 13, 2021 (view preprint)
  2. Received: October 21, 2021
  3. Accepted: April 19, 2023
  4. Accepted Manuscript published: April 25, 2023 (version 1)
  5. Version of Record published: May 16, 2023 (version 2)

Copyright

© 2023, Ben Yaakov et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Tal Ben Yaakov
  2. Tanya Wasserman
  3. Eliel Aknin
  4. Yonatan Savir
(2023)
Single-cell analysis of the aged ovarian immune system reveals a shift towards adaptive immunity and attenuated cell function
eLife 12:e74915.
https://doi.org/10.7554/eLife.74915

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https://doi.org/10.7554/eLife.74915

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