Sleep deprivation impairs follicular development attributable to granulosa cell pyroptosis mediated by S100A8/A9-driven macrophage M1 polarization.

The mice were randomly divided into control and experimental groups. To evaluate the efficiency of the CPW in SD, we conducted electroencephalogram (EEG)–/electromyogram (EMG)–based sleep-wake analysis in adult C57BL/6J mice (fig. S1A). On average, mice exposed to CPW remained awake for 1400 min per day, accounting for 94.3% of the total time ( Fig. 1A ). Hourly plots of wake/sleep states further illustrate the temporal distribution of wake, nonrapid eye movement sleep (NREMS), and rapid eye movement sleep (REMS) throughout the 24-hour period (fig. S1, E to G). SD was initiated 48 hours before the end of superovulation. To investigate the impact of SD on follicular development, we first assessed the estrous cycle in both groups. Following the completion of SD, the mice underwent continuous estrous cycle monitoring for 10 days ( Fig. 1B ). Mice in the SD group exhibited marked disturbances in their estrous cycle compared to the controls ( Fig. 1C ). Ovarian tissues were collected from two groups immediately following 48 hours of SD in the experimental group of mice ( Fig. 1D ). Subsequently, we performed hematoxylin and eosin (H&E) staining to quantitatively evaluate the number of follicles at various stages of development. To control for interindividual variability in estrous cycles, mice received an intraperitoneal injection of 10 IU of pregnant mare serum gonadotropin (PMSG) to synchronize follicular development before experimentation ( 35 ). The results revealed a significant reduction in the number of primordial, primary, secondary, and antral follicles in the SD group, with a corresponding increase in atretic follicles ( Fig. 1, E to G ). Masson staining of ovarian tissue showed that, compared with the control group, the SD group exhibited a significant increase in blue-stained collagen fibers ( Fig. 1E ). In addition, mice underwent superovulation, and oocytes were collected to evaluate both the number of oocytes retrieved and their morphology ( Fig. 1H ). The SD group exhibited a significantly lower oocytes retrieved rate, along with a markedly reduced maturation rate and increased fragmentation rate, when compared to the control group. Oocyte maturation was assessed by checking for the presence of the first polar body, indicating successful maturation ( Fig. 1, I to L ). ( A ) The amount of wakefulness, NREMS, and REMS over 24 hours for adult mice ( n  = 3) subjected to SD, Con, or eCon. ( B ) Schematic representation of estrous cycle monitoring procedure in mice. ( C ) Analysis of the estrous cycle in mice ( n  = 8). P, proestrus; E, estrus; M, metestrus; D, diestrus. The x axis indicates the days of monitoring the mouse estrous cycle, while the y axis represents the corresponding stage of the estrous cycle for each day. Black denotes the estrous cycle trajectories of the control group, and red denotes those of the SD group. The estrous cycle typically lasts 4 to 5 days, followed by proestrus, estrus, metestrus, and diestrus withdrawal cycles. ( D ) Schematic diagram of ovarian tissue collection procedure. ( E ) H&E and Masson’s trichrome staining of ovarian tissue. The following image is a magnified view of a section of the boxed image, with black arrows indicating the atretic follicles. Scale bars, 200 and 100 μm. ( F ) Quantification of primordial, primary and secondary follicles ( n  = 3). ( G ) Quantification of antral and atretic follicles ( n  = 3). ( H ) Schematic diagram of oocytes collection procedure. ( I ) Representative image of in vivo mature oocytes collected from mice. The lower inset shows a normal mature oocyte and an abnormally fragmented oocyte, with red arrows indicating abnormal oocytes. Scale bars, 100 and 50 μm. ( J ) Number of ovulated oocytes in two groups of mice ( n  = 6). ( K ) Percentage of oocyte fragmentation in two groups of mice ( n  = 6). ( L ) Percentage of MII oocytes in two groups of mice ( n  = 6). Data are presented as mean ± SEM. * P  < 0.05, ** P  < 0.01, and *** P  < 0.001. The P value was calculated using a one-tailed unpaired Student’s t test. Aneuploidy is a key indicator of low-quality oocytes. To assess this, we examined spindle structure using α-tubulin immunofluorescence staining followed by confocal imaging. Spindle morphology was assessed by immunofluorescence staining. Spindles were classified as abnormal if they exhibited irregular, misshapen, or disorganized structures, such as dispersed microtubule organization, loss of bipolarity, or misaligned chromosomes. In control mice, the spindle structure appeared normal, whereas in the SD group, a significant number of spindles exhibited abnormal morphology and irregular arrangement ( Fig. 2, A and C ). DNA damage in the oocytes was evaluated by immunofluorescence staining with γ-H2AX, which revealed significantly higher levels of DNA breaks in oocytes from SD mice compared to controls ( Fig. 2, B and D ). ( A ) Representative imaging of α-tubulin immunofluorescence staining in oocytes. Spindle morphology was assessed by immunofluorescence staining. Spindles were classified as abnormal if they exhibited irregular, misshapen, or disorganized structures, such as dispersed microtubule organization, loss of bipolarity, or misaligned chromosomes. Scale bar, 20 μm. ( B ) Representative image of γ-H2AX immunofluorescence staining in oocytes. Scale bar, 20 μm. ( C ) Percentage of oocytes with abnormal spindle, abnormal spindle refers to abnormal spindle morphology and irregular arrangement ( n  = 3). ( D ) Percentage of γ-H2AX-positive oocytes ( n  = 3). ( E ) Representative TEM image of oocytes and granulosa cells. Scale bars, 2 μm and 500 nm. Red rectangles indicated Microvilli. Black arrows indicate normal mitochondria, white arrows indicate abnormal mitochondria, and brown arrows indicate cell membrane rupture with expulsion of cellular contents. ( F ) Percentage of granulosa cells with membrane rupture ( n  = 3). ( G ) Representative images of two-cell embryos and blastocysts. Scale bar, 100 μm. ( H ) Normal fertilization rate in the two groups of mice ( n  = 6). ( I ) Two-cell embryo formation rate ( n  = 6). ( J ) Blastocyst formation rate ( n  = 6). Data are presented as mean ± SEM. ** P  < 0.01 and *** P  < 0.001. The P value was calculated using a one-tailed unpaired Student’s t test. We next exploited transmission electron microscopy (TEM) to evaluate the morphology of oocytes, the surrounding granulosa cells, and the structure of intracellular organelles. Oocytes in the control group displayed normal morphology, with intact cell membranes, normal intracellular mitochondria, and clear cristae. Most granulosa cells also exhibited normal morphology. However, in the SD group, the oocytes showed reduced microvilli and increased number of abnormal mitochondria. The morphology of granulosa cells was significantly altered, as characterized by cell membrane rupture, drainage of cellular contents, vacuolated changes, disrupted cristae, and mitochondrial crinkling ( Fig. 2, E and F ). We further assessed the fertilization potential of oocytes and their ability to support early embryo development through in vitro fertilization and early embryo culture. The results showed that the fertilization rate, two-cell embryo rate, and blastocyst formation rate of oocytes in the SD group were significantly reduced compared to the control group ( Fig. 2, G to J ). Collectively, these findings indicate that SD impairs the oocyte maturation, fertilization, and early embryonic development of mouse oocytes. To evaluate whether the CPW paradigm induces more stress than other SD methods, we compared mice subjected to CPW with those exposed to the commercially available standalone SD (SASD) paradigm, in which a sweeping bar continuously pushes the animals to disrupt their sleep. Serum corticosterone levels, a stress hormone, were comparable between the CPW and SASD groups (fig. S2A), indicating that mice exposed to the CPW paradigm did not experience higher levels of psychological stress than those subjected to a conventional SD method. Further experiments revealed that mice in the SASD group also exhibited severe estrous cycle disruption (fig. S2B), abnormal follicular development (fig. S2, C to E), a reduced number of retrieved oocytes (fig. S2, F and G), impaired embryonic development (fig. S2, F and H to L), and decreased oocyte quality (fig. S3, A to D) after 48 hours of SD. TEM further observed a reduction in microvilli on oocytes, ruptured cell membranes of granulosa cells, and mitochondrial abnormalities in the SASD group (fig. S3, E and F). To exclude potential effects of the humid environment in the SD model, an environmental control (eCon) group was established. No significant differences were recorded between the eCon group and the control group in terms of oocyte number, maturation rate, or fragmentation rate (fig. S4, A to D). Similarly, no significant differences were observed in the fertilization rate, two-cell embryo rate, or blastocyst formation rate between the eCon and control groups (fig. S4, E to G), indicating that the humid environment did not exert additional effects on the mice and did not interfere with the study outcomes. To confirm the occurrence of a significant inflammatory response following 48 hours of SD, we measured 23 inflammatory cytokines and chemokines in the peripheral blood of mice. An additional group of mice was treated with the anti-inflammatory drug acetaminophen (APAP) during SD. APAP was dissolved in drinking water for oral administration in the SD + APAP and Con + APAP groups (1.6 mg/ml) ( Fig. 3A ). The results showed that most pro-inflammatory cytokines were significantly elevated after 48 hours of SD, with interleukin-6 (IL-6) and IL-17A exhibiting the most pronounced increases ( Fig. 3B ). These increases were significantly reduced by APAP treatment ( Fig. 3, C and D ). ( A ) Diagram of drug administration in mice. ( B ) Fold changes in peripheral blood cytokines and chemokines in the Con + APAP, SD, and SD + APAP groups compared to the control group ( n  = 3). ( C and D ) Concentrations of IL-6 and IL-17A in four groups of mice ( n  = 3). ( E ) Leukocyte analysis from differential blood count ( n  = 3). ( F ) Representative image of flow cytometry analysis of neutrophils in peripheral blood. The cell population positive for both CD11b and Ly6G represents mature neutrophils. ( G ) Flow cytometry analysis of the proportion of neutrophils ( n  = 3). Data are presented as mean ± SEM. ** P  < 0.01, *** P  < 0.001, and **** P  < 0.0001. Statistical analysis was performed using one-way ANOVA for multiple group comparisons, with Tukey’s post hoc test used for post hoc analyses. We further assessed peripheral blood leukocytes changes using a blood routine test. The results revealed a significant increase in the percentage of neutrophils and a decrease in the percentage of lymphocytes in the peripheral blood of the SD group, which was substantially reduced by APAP treatment ( Fig. 3E and table S3). This finding was confirmed by a peripheral blood immune cell flow cytometry assay, which showed a significantly higher proportion of neutrophils in the peripheral blood of the SD group compared to the control group. APAP treatment similarly reduced this proportion ( Fig. 3, F and G ). No significant differences were observed in these indices between Con and Con + APAP groups. These results indicate that SD induced a severe inflammatory response in mice, accompanied by a significant increase in neutrophils. To assess the impact of systemic inflammation induced by SD on the ovarian function, we first conducted transcriptome sequencing analysis of mouse ovarian tissues. Differential gene expression analysis revealed distinct transcriptomic profiles between the SD and control groups (fig. S5A). A volcano plots identified 407 up-regulated and 157 down-regulated genes in the SD group compared to the control group (fig. S5B). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis highlighted significant involvement of pathways such as the Wnt signaling pathway, IL-17 signaling pathway, cell adhesion molecules, and cytokine-receptor interactions (fig. S5D). To further assess the immune response in ovarian tissues, we performed clustering analysis of immune-related genes, which demonstrated substantial differences between the SD and the control groups ( Fig. 4A ). Given these findings, we hypothesized that SD induces inflammatory damage in ovarian tissues. To validate this, we examined markers of DNA damage, mitochondrial function, inflammatory damage, and oxidative stress, including γ-H2AX, ROS, adenosine triphosphate (ATP), malondialdehyde (MDA), superoxide dismutase (SOD), and glutathione (GSH). Immunofluorescence staining of ovarian granulosa cells showed a significant increase in γ-H2AX foci signals in the SD group, indicative of DNA damage. Notably, anti-inflammatory drug APAP treatment markedly reduced both the number and intensity of γ-H2AX foci ( Fig. 4B ). Flow cytometry analysis revealed a significant reduction in mitochondrial membrane potential in ovarian tissues of the SD, which was partially restored by APAP treatment ( Fig. 4, C and D ). Furthermore, ROS levels were significantly elevated in the SD group, while APAP treatment effectively reduced ROS accumulation ( Fig. 4, E and F ). Oxidative stress markers demonstrated that MDA levels were significantly increased in the SD group, whereas ATP, SOD, and GSH levels were markedly decreased. These alterations were significantly mitigated by APAP treatment ( Fig. 4, G to J ). No significant differences were observed in these indices between Con and Con + APAP groups. ( A ) Clustering analysis of immune-related differential expressed genes in ovarian tissue. ( B ) γ-H2AX immunofluorescence staining in ovarian tissue. Green fluorescence (FSHR) marks ovarian granulosa cells, while red fluorescence indicates the γ-H2AX. Scale bars, 50 μm. ( C ) Representative flow cytometry analysis of mitochondrial membrane potential in ovarian tissue. ( D ) Red fluorescence/green fluorescence ratio in ovarian tissue ( n  = 3). ( E ) Representative flow cytometry analysis of ROS in ovarian tissue. ( F ) Comparison of average ROS fluorescence intensity in ovarian tissue ( n  = 3). ( G to J ) Comparison of MDA, ATP, GSH, and SOD levels in ovarian tissue ( n  = 6). Data are presented as mean ± SEM. ns, not significant, * P  < 0.05, ** P  < 0.01, and *** P  < 0.001. Statistical analysis was performed using one-way ANOVA for multiple group comparisons, with Tukey’s post hoc test used for post hoc analyses. To further evaluate the impact of inflammation on reproductive outcome, we conducted superovulation, in vitro fertilization, and early embryo culture. The results demonstrated that APAP treatment significantly increased the number of oocytes retrieved, oocyte maturation rate, fragmentation rate, two-cell formation rate, and blastocyst formation rate in SD-exposed mice (fig. S6, A to G). No significant differences were observed in these indices between Con and Con + APAP groups. Collectively, these findings suggest that SD induces an inflammatory response that leads to ovarian tissue damage, thereby impairing oocyte development and reducing oocyte and embryo quality. To further investigate ovarian tissues damage, we assessed cell death in ovarian tissues using Annexin V/Propidium Iodide (PI) flow assay. Double positivity for Annexin V and PI is indicative of pyroptotic or necrotic apoptosis. The results showed a significantly higher proportion of Annexin V+/PI+ cells in the SD group compared to the controls, suggesting increased cell death due to SD ( Fig. 5, A and B ). ( A ) Representative flow cytometry analysis of Annexin V/PI in ovarian tissue. Pyroptosis was defined as Annexin V+/PI+. ( B ) Percentage of Annexin V+/PI+ cells in ovarian tissue ( n  = 3). ( C ) Representative image of GSDMD immunofluorescence staining in ovarian tissue. Scale bars, 100 μm. ( D ) Western blotting analysis of pyroptosis-related protein expression in ovarian tissue. ( E ) Quantitative calculation of the relative expression level of each protein, normalized to the relative ratio compared to GAPDH levels ( n  = 3). Data are presented as mean ± SEM. ** P  < 0.01 and *** P  < 0.001. The P value was calculated using a one-tailed unpaired Student’s t test. Given the immune-related gene expression analysis, which revealed alterations in pyroptosis-associated genes, along with TEM observations indicating granulosa cell morphology consistent with pyroptotic features, we further validated pyroptosis in the ovarian tissues. First, immunofluorescence staining of Gasdermin D (GSDMD) protein showed a substantial increase in GSDMD-positive signals in the ovarian tissues of SD-exposed mice compared to controls ( Fig. 5C ). In addition, Western blot analysis further confirmed a significant up-regulation of pyroptosis-related proteins, including GSDMD, Caspase-1, NLRP3, IL-1β, and IL-18, in the ovarian tissues of the SD group ( Fig. 5, D and E ). These findings collectively indicate that SD induces pyroptotic cell death in ovarian tissues, contributing to tissue damage and dysfunction. Simultaneously, we used TEM to examine the morphology of oocytes and granulosa cells in the eCon group mice. No significant abnormalities or pyroptosis-like ultrastructural alterations were observed in either cell type (fig. S4H). Furthermore, immunofluorescence staining for the pyroptosis-related protein GSDMD in ovarian tissues revealed no detectable positive signals (fig. S4I). To further investigate the effect of SD on different cell types within ovarian tissues, we performed 10X single-cell transcriptome sequencing to generate single-cell profiles of ovarian tissues from both control and SD mice. After stringent cell filtration, a total of 68,720 cells were retained for further analysis. The overall ovarian cell population was visualized using uniform manifold approximation and projection (UMAP), which enabled the identification of six distinct cell types based on specific gene expression markers: granulosa cells, mesothelial cells, epithelial cells, oocytes, stromal cells, and immune cells ( Fig. 6A ). The relative proportions of these cell types captured by single-cell sequencing were comparable between control and SD mice, with granulosa cells constituting the largest proportion in ovarian tissue (fig. S7A). Gene ontology (GO) enrichment analysis of differentially expressed genes in oocytes revealed that SD predominantly affected pathways involved in DNA metabolic processes, DNA repair, chromatin organization, chromatin segregation, nuclear division, DNA replication, chromosomal regions, antioxidative stress, ATP metabolism, and mitochondrial functions (fig. S8). ( A ) UMAP plot showing six distinct cell types in ovarian tissue. ( B ) Comparison of pyroptosis-related gene scores among different cell types in ovarian tissue ( n  = 3). ( C ) Distribution of S100A8 and S100A9 gene expression in immune cells of ovarian tissue ( n  = 3). ( D ) Comparison of S100a8 and S100a9 gene expression in immune cells of ovarian tissue between two groups of mice ( n  = 3). ( E ) Measurement of S100A8/A9 levels in peripheral blood serum using ELISA ( n  = 3). ( F ) Measurement of S100A8/A9 levels in ovarian tissue using ELISA ( n  = 3). ( G ) Comparison of macrophage M1 polarization–related gene set scores in ovarian tissue between the two groups of mice. Larger dots represent a higher number of differential genes, and darker colors indicate larger gene expression differences. Data are presented as mean ± SEM. *** P  < 0.001. The P value was calculated using a one-tailed unpaired Student’s t test. One-way ANOVA for multiple group comparisons, with Tukey’s post hoc test used for post hoc analyses. Further analysis of cell-cell interactions demonstrated that ligand-receptor interactions between granulosa cells and oocytes were the most prevalent, suggesting a close functional association between granulosa cells and oocyte development (fig. S7D). Given our previous findings that ovarian tissues from SD mice underwent pyroptosis, we sought to identify the specific cell types involved. Pyroptosis-related gene set scoring across different ovarian cell types revealed that granulosa cells, immune cells, and mesothelial cells exhibited significantly elevated expression of pyroptosis-associated genes in the SD group compared to the control group ( Fig. 6B ). These findings suggest a potential link between granulosa cell pyroptosis and oocyte developmental abnormalities. Transcriptome analysis of ovarian tissue from SD mice also revealed significant alterations in immune-related genes and signaling pathways. To further characterize the immune cell landscape, we classified ovarian immune cells into distinct subpopulations based on established cell markers. These subpopulations included B cells, natural killer (NK) cells, neutrophils, macrophages, monocytes, conventional dendritic cell type 1 (cDC1), cDC2, and plasmacytoid dendritic cells (fig. S7C). Among the immune-related genes, S100a8 and S100a9 exhibited the most pronounced differential expression. Analysis of scRNA-seq data revealed that S100a8 and S100a9 were predominantly expressed in neutrophils within ovarian tissues. Their expression levels were significantly up-regulated in the SD group compared to the controls ( Fig. 6, C and D ). Given that S100A8 and S100A9 typically function as heterodimers (S100A8/A9) and exhibit structural stability, we quantified their levels in both peripheral blood serum and ovarian tissues using enzyme-linked immunosorbent assay (ELISA). The results confirmed a significant increase in S100A8/A9 in the SD group, which was effectively mitigated by anti-inflammatory treatment ( Fig. 6, E and F ). No significant difference was observed between the Con group and the Con + APAP group. These finding suggest that SD-induced systemic inflammation triggers the up-regulation and secretion of S100A8/A9 in neutrophils. Analysis of ligand-receptor interactions between neutrophils and other immune cells revealed neutrophils exhibited the strongest communication with macrophages. Further investigation into macrophage polarization indicated a significant shift toward M1 polarization in the SD group compared to controls ( Fig. 6G ). On the basis of these findings, we hypothesized that SD promotes the up-regulation and secretion of S100a8 and S100a9 in neutrophils, induced M1 polarization of macrophages, exacerbates local ovarian inflammatory, and ultimately leads to granulosa cell pyroptosis. To elucidate the role of S100A8/A9 in ovarian tissues, we administered the S100A8/A9 inhibitor Paquinimod (10 mg/kg) via intraperitoneally injection in SD mice. Paquinimod was administered concurrently with SD. Western blot and immunofluorescence staining results demonstrated that Paquinimod treatment significantly inhibited the expression of S100A8/A9 in ovarian tissues ( Fig. 7, B to D ). Immunofluorescence staining of frozen ovarian tissue sections revealed a significant increase in both the number and size of CD86-positive fluorescent foci, a marker of M1 polarization, in the SD group compared to controls. Paquinimod treatment markedly attenuated CD86 expression ( Fig. 7E ). Flow cytometry analysis demonstrated a significant increase in M1-polarized macrophage in SD mice, which was effectively reduced by Paquinimod treatment ( Fig. 7, F and G ). Western blot analysis further validated the expression of IL-1β and tumor necrosis factor–α (TNF-α), key proteins involved in macrophage M1 polarization. Their levels were significantly elevated in the SD group but were effectively down-regulated following Paquinimod treatment ( Fig. 7, H and I ). No significant differences were observed in these indices between Con and Con + Paquinimod groups. These findings suggest that S100A8/A9 promotes M1 polarization of macrophages in ovarian tissue. ( A ) Schematic diagram of drug administration in mice. ( B ) Western blotting detection of S100A8 and S100A9 protein expression. ( C ) Quantitative calculation of the relative expression level of S100A8 and S100A9, normalized to the relative ratio compared to GAPDH levels ( n  = 3). ( D ) Representative image of S100A9/A9 immunofluorescence staining in ovarian tissue. Scale bars, 50 μm. ( E ) Immunofluorescence images of macrophage M1 and M2 polarization in ovarian tissue. Red fluorescence (CD86) represents the M1 polarization marker, green fluorescence (CD163) represents the M2 polarization marker, and blue fluorescence indicates nuclear staining. Scale bars, 50 μm. ( F ) Representative flow cytometry analysis of macrophage M1 polarization in ovarian tissue. CD11b and CD86 double-positive cells indicate macrophages undergoing M1 polarization. ( G ) Proportion of macrophages undergoing M1 polarization in ovarian tissue ( n  = 3). ( H ) Western blotting detection of IL-1β and TNF-α protein expression. ( I ) Quantitative calculation of the relative expression level of IL-1β and TNF-α, normalized to the relative ratio compared to GAPDH levels ( n  = 3). ( J ) Western blotting detection of TLR4, MyD88, and NF-κB protein expression. ( K ) Quantitative calculation of the relative expression level of TLR4, MyD88, and NF-κB, normalized to the relative ratio compared to GAPDH levels ( n  = 3). ( L ) Western blotting detection of pyroptosis-related protein expression. ( M ) Quantitative calculation of the relative expression level of pyroptosis-related, normalized to the relative ratio compared to GAPDH levels ( n  = 3). ( N ) Immunofluorescence staining of GSDMD in ovarian tissue. Scale bar, 100 μm. Data are presented as mean ± SEM. ** P  < 0.01 and *** P  < 0.001. Statistical analysis was performed using one-way ANOVA for multiple group comparisons, with Tukey’s post hoc test used for post hoc analyses. S100A8/A9, a member of the S100 protein family, functions as a TLR4 ligand and specifically binds to this receptor on macrophage surfaces. In this study, the protein expression levels of TLR4, MyD88, and nuclear factor κB (NF-κB) were significantly up-regulated in the ovarian tissues of SD mice, where Paquinimod effectively suppressed their expression ( Fig. 7, J and K ). Given that the TLR4–NF-κB pathway induces NLRP3 activation, we further examined pyroptosis-related indicators. Western blot analysis confirmed that pyroptosis-associated proteins, including NLRP3, Caspase-1, GSDMD-N, IL-1β, and IL-18, were significantly up-regulated in in the SD group, but their expression was markedly reduced by Paquinimod treatment ( Fig. 7, L and M ). The SD group exhibited significantly increased GSDMD fluorescence signals compared to the control group ( Fig. 7N ). To assess the functional impact of Paquinimod on oocyte and embryo quality, we subjected control, SD and SD + Paquinimod mice to superovulation, in vitro fertilization, and embryo culture. Paquinimod treatment significantly increased the number of oocytes retrieved, oocyte maturation rate, fertilization rate, two-cell embryo rate, and blastocyst rate, while reducing the oocyte fragmentation in SD mice (fig. S9). No significant differences were observed in these indices between Con and Con + Paquinimod groups. These results suggest that inhibition of S100A8/A9 expression mitigates inflammatory damage in ovarian tissues by suppressing the NLRP3-induced pyroptosis pathway. Collectively, our findings indicate that SD enhances the expression and secretion of S100A8/A9 in mice, which in turn activates the TLR4/MyD88/NF-κB signaling pathway, inducing M1 polarization of macrophages. This exacerbates local ovarian inflammation, promotes granulosa cell pyroptosis, and ultimately impairs oocyte developmental and embryo quality. To further investigate the role of S100A8/A9 in macrophage M1 polarization and granulosa cell pyroptosis, we established an in vitro model using the Transwell coculture system. Macrophages were divided into three groups: M0, M0 + S100A8/A9, and M0 + S100A8/A9 + Paquinimod. S100A8/A9 recombinant protein (5 μg/ml) and Paquinimod (2 μg/ml) were used for treatment ( Fig. 8A ). After 24 hours of exposure to S100A8/A9, fresh cells from each group were collected and analyzed via flow cytometry to assess macrophage M1/M2 polarization. The results showed a significant increase in M1 polarization in the M0 + S100A8/A9 group, which was effectively suppressed by Paquinimod treatment ( Fig. 8, B and C ). Immunofluorescence staining of macrophage revealed a significant increase in both the number and size of CD86-positive fluorescent foci, a marker of M1 polarization, in the M0 + S100A8/A9 group compared to M0 group. Paquinimod treatment markedly attenuated CD86 expression ( Fig. 8D ). To further confirm these findings, we measured TNF-α and IL-1β expression levels by quantitative polymerase chain reaction (qPCR) and Western blot. Both mRNA and protein expression levels of TNF-α and IL-1β were significantly up-regulated in the M0 + S100A8/A9 group, whereas Paquinimod treatment effectively down-regulated their expression ( Fig. 8, E, F, and G ). In addition, we examined the TLR4/MyD88/NF-κB signaling pathway and found that Paquinimod effectively suppressed the expression of key signaling pathway components ( Fig. 8, H, I and J ). ( A ) Schematic diagram of macrophage and granulosa cell coculture using the Transwell system. ( B ) Flow cytometry analysis of macrophage M1 and M2 polarization. ( C ) Proportion of macrophages undergoing M1 polarization in three groups ( n  = 3). ( D ) Immunofluorescence images of macrophage M1 and M2 polarization. Red fluorescence (CD86) represents the M1 polarization marker, green fluorescence (CD163) represents the M2 polarization marker, and blue fluorescence indicates nuclear staining. Scale bars, 50 μm. ( E ) mRNA expression of Tnf- α and Il-1 β genes in macrophages ( n  = 6). ( F ) Western blotting detection of IL-1β and TNF-α protein expression in macrophages. ( G ) Quantification of the relative expression levels of IL-1β and TNF-α protein, normalized to GAPDH levels ( n  = 3). ( H ) mRNA expression of Tlr4 , Myd88 , and Nf κ b genes in macrophages ( n  = 6). ( I ) Western blotting detection of TLR, MyD88, and NF-κB protein expression in macrophages. ( J ) Quantification of the relative expression levels of TLR, MyD88, and NF-κB protein, normalized to GAPDH levels ( n  = 3). Data are presented as mean ± SEM. * P  < 0.05, ** P  < 0.01, and *** P  < 0.001. Statistical analysis was performed using one-way ANOVA for multiple group comparisons, with Tukey’s post hoc test used for post hoc analyses. Since both macrophages and granulosa cells are adherent, we used the Transwell system for coculture. Granulosa cells were seeded in the lower chamber, while macrophages were seeded in the upper Transwell insert. The number of cells was adjusted on the basis of the well plate size. After 48 hours of PMSG injection in mice, ovarian granulosa cells were isolated for subsequent coculture experiments ( Fig. 9A ). The administration of PMSG promotes follicular development and increases the number of granulosa cells in the ovaries, thereby improving the extraction efficiency of granulosa cells ( 36 ). RAW264.7 macrophages were first plated into the Transwell inserts and allowed to adhere for 4 to 6 hours before being divided into four groups: M0 + GCs, M0 + GCs + Paquinimod, M0 + S100A8/A9 + GCs, and M0 + S100A8/A9 + Paquinimod + GCs. After 24 hours of treatment, the medium was replaced, and the Transwell inserts containing macrophages were transferred onto fresh granulosa cell cultures for an additional 72 hours of coculture ( Fig. 8A ). ( A ) Schematic diagram of granulosa cell extraction from mouse ovarian tissue. ( B ) Immunofluorescence staining of three groups of cells for GSDMD. Green fluorescence (FSHR) marks ovarian granulosa cells, while red fluorescence indicates the pyroptosis-related protein GSDMD. Scale bars, 50 μm. ( C ) qPCR was used to detect the mRNA expression of Nlrp3 , Il-1 β, Il-18 , Caspase-1 , and Gsdmd genes ( n  = 6). ( D ) Western blotting detection of pyroptosis-related protein expression. ( E ) Quantitative calculation of the relative expression level of each protein, normalized to the relative ratio compared to GAPDH levels ( n  = 3). ( F ) Immunofluorescence staining of three groups of cells for GSDMD. Green fluorescence (FSHR) marks ovarian granulosa cells, while red fluorescence indicates the pyroptosis-related protein GSDMD. Scale bars, 50 μm. ( G ) Western blotting detection of GSDMD-N protein expression. ( H ) Quantitative calculation of the relative expression level of the protein, normalized to the relative ratio compared to GAPDH levels ( n  = 3). Data are presented as mean ± SEM. ** P  < 0.01 and *** P  < 0.001. Statistical analysis was performed using one-way ANOVA for multiple group comparisons, with Tukey’s post hoc test used for post hoc analyses. Immunofluorescence staining revealed significantly increased GSDMD signals in granulosa cells of the M0 + S100A8/A9 group compared to the M0 + GCs and M0 + GCs + Paquinimod groups ( Fig. 9B ). Consistently, qPCR and Western blot analyses showed a marked up-regulation of pyroptosis-related genes and proteins, while Paquinimod treatment significantly reduced granulosa cell pyroptosis ( Fig. 9, C to E ). No significant differences were observed in these indices between M0 + GCs and M0 + GCs + Paquinimod groups. Given the possibility that S100A8/A9 may directly affect granulosa cells, we further evaluated GSDMD expression in untreated granulosa cells (control), GCs + S100A8/A9, and GCs + M0 + S100A8/A9 groups. Immunofluorescence staining revealed significantly increased GSDMD signals in granulosa cells of the GCs + M0 + S100A8/A9 group compared to the GCs + S100A8/A9 group ( Fig. 9F ). Western blot analysis showed significantly increased GSDMD proteins expression in both experimental groups compared to the control, with the highest expression observed in the GCs + M0 + S100A8/A9 group ( Fig. 9, G and H ). These findings indicate that S100A8/A9-induced macrophage M1 polarization exacerbates the inflammatory response, leading to enhanced granulosa cell pyroptosis.

In this study, we reported that ovarian dysfunction is induced by SD. Our findings demonstrate that SD triggers a robust inflammatory response characterized by increased neutrophil infiltration and excessive release of S100A8/A9. This inflammatory milieu activates the TLR4/MyD88/NF-κB signaling pathway, promoting macrophage M1 polarization, exacerbating local ovarian inflammation, and inducing granulosa cell pyroptosis, ultimately leading to follicular development disorders and compromised oocyte quality ( Fig. 10 ). SD enhances neutrophil-driven S100A8/A9 release, activating the TLR4/MyD88/NF-κB signaling pathway, and promoting macrophage M1 polarization. This cascade amplifies ovarian inflammation and induces granulosa cell pyroptosis, ultimately compromising follicular development and embryonic quality in female mice. Altered sleep duration has been widely used in mechanistic studies of sleep in both human and animal models ( 37 , 38 ). Following the methodology of Sang et al. ( 16 ), we implemented the CPW paradigm for SD, which effectively induced sustained sleep loss in mice. Initial observations revealed that SD disrupted the estrous cycle, and histological analysis (H&E staining) showed a significant reduction in follicles across all developmental stages, with a concomitant increase in atretic follicles. These findings align with previous studies demonstrating the adverse effects of chronic SD on folliculogenesis ( 39 ). Previous studies have shown that chronic SD causes impaired oocyte maturation in vitro ( 40 ). In our study, SD impaired oocyte maturation, as evidenced by a significant decrease in oocyte retrieval numbers, oocyte maturation rate, two-cell embryo formation rate, and blastocyst development rate, alongside an increase in oocyte fragmentation. Furthermore, SD disrupted meiotic spindle morphology and increased DNA damage in oocytes. Ultrastructural analysis via TEM revealed compromised cellular integrity in both oocytes and granulosa cells, indicating extensive cytoplasmic and membrane damage. SD is well established to modulate innate and adaptive immune responses, predisposing individuals to chronic inflammatory states and increasing susceptibility to cardiometabolic, autoimmune, neurodegenerative, and oncological diseases ( 13 , 14 ). Acute SD has been reported to significantly amplify systemic inflammation ( 41 ). Wang et al. ( 42 ) observed elevated IL-6 and TNF-α levels in individuals with sleep durations shorter than 6 hours, suggesting a strong correlation between sleep restriction and inflammatory marker up-regulation. An association between neutrophil inflammation and reduced sleep duration has been observed in animal models and human studies, including healthy controls ( 43 ). Consistently, our analysis of peripheral blood chemokines and cytokines in sleep-deprived mice revealed a pronounced inflammatory response and a significant surge in neutrophil populations. Most studies report that serum IL-6 level in normal control mice are typically below 20 pg/ml ( 16 , 44 , 45 ). Most studies indicate that the level of IL-17A in peripheral blood of normal wild-type mice is relatively low ( 16 , 46 ). Consistent with previous literature, SD triggers excessive release of inflammatory cytokines, leading to an inflammatory storm ( 16 ). Compared to the immune response triggered by mild infections, such as in a mouse model of upper respiratory tract infection where serum IL-6 levels remained below 100 pg/ml ( 47 ), SD provoked a more severe immune reaction. This finding confirms that the severe inflammatory response in mice was caused by SD per se, rather than an immune response triggered by wet body parts and endogenous infection. The results of the differential blood count also revealed lymphocytopenia. The concurrent observations of neutrophilia and lymphocytopenia are consistent with a classic acute inflammatory response ( 48 , 49 ). The underlying mechanism for this phenomenon may involve significantly increased lymphocyte apoptosis during acute inflammation ( 50 , 51 ). Inflammation can impair the regenerative capacity of parenchymal cells while stimulating the accumulation of stromal cells and extracellular matrix, ultimately leading to tissue fibrosis ( 52 ). Our results confirm that ovarian tissues in the SD group exhibited marked fibrotic changes. Further investigations demonstrated that ovarian inflammation was markedly attenuated following anti-inflammatory intervention. The ovarian microenvironment is highly dynamic, comprising resident immune cells that regulate tissue homeostasis through cytokine and growth factor secretion ( 53 , 54 ). Notably, scRNA-seq profiling of peripheral blood in patient with ovulatory disorders, including polycystic ovary syndrome, primary ovarian insufficiency, and menopause, has revealed substantial immune dysregulation, characterized by heightened inflammatory responses and oxidative stress ( 55 ). Pharmacological inhibition of CD38, which increases ovarian nicotinamide adenine dinucleotide (NAD⁺) levels and reduces inflammation, has been shown to improve oocyte quality ( 56 ). Excessive ROS generation triggers apoptosis in developing oocytes and granulosa cells, leading to genomic instability and lipid peroxidation, ultimately impairing follicular function ( 57 ). In endometriosis, oxidative stress has been implicated in granulosa cell senescence via endoplasmic reticulum stress, contributing to infertility ( 33 ). Given the critical role of redox homeostasis in oocyte competence, inflammation-driven oxidative stress may represent a key pathogenic mechanism linking SD to reproductive dysfunction ( 58 – 60 ). Despite the established association between SD and systemic inflammation, the precise mechanisms underlying ovarian dysfunction remain incompletely understood. Advances in single-cell technologies have provided deeper insights into ovarian cellular heterogeneity and immune interactions. Integrating bulk RNA-seq and scRNA-seq, we identified significant immune-related transcriptomic alterations in ovarian tissues of sleep-deprived mice, with notable up-regulation of S100a8 , S100a9 , and macrophage M1 polarization–related genes. Furthermore, granulosa cells, immune cells, and mesothelial cells exhibited increased expression of pyroptosis-related genes. Cell-cell interaction analysis highlighted the strongest communication between granulosa cells and oocytes, whereas immune cells subgroup analysis revealed robust cross-talk between neutrophils and macrophages. Granulosa cells constitute the predominant somatic cell population within ovarian follicles and play a crucial role in follicular development and hormonal regulation ( 61 ). Excessive oxidative stress in granulosa cells detrimentally affects folliculogenesis, ovulation, and ovarian reserve, contributing to infertility ( 33 ). Macrophages, the most abundant immune cells in the ovary, orchestrate tissue homeostasis and are pivotal regulators of the ovarian immune microenvironment ( 62 , 63 ). Dysregulation of the macrophage M1/M2 balance has been implicated in ovarian pathologic, including aging ( 20 ), polycystic ovary syndrome, and ovarian cancer ( 21 , 22 ). S100A8 and S100A9, primarily secreted by neutrophils, form heterodimers that serve as endogenous ligands for TLR4, playing central roles in inflammatory pathophysiology. Notably, S100A8/A9 constitute approximately 40% of neutrophilic cytoplasmic proteins and mediate leukocyte recruitment and cytokine secretion during inflammatory responses ( 23 , 24 ). Previous studies have demonstrated that pharmacological inhibition of S100A8/A9 with Paquinimod can effectively attenuate neutrophil extracellular trap–mediated neuroinflammation ( 64 ). S100A8/A9 is closely associated with macrophage migration, infiltration, and differentiation ( 65 ). During inflammatory and stress responses, neutrophils rapidly release S100A8/A9, which activates the TLR4/MyD88/NF-κB signaling pathway. This cascade induces the activation of immune cells (including macrophages) and promotes the release of pro-inflammatory cytokines such as TNF-α, IL-1β, and IL-6 ( 24 ). S100A8/A9 promotes macrophage M1 polarization and glycolysis via the TLR4/MyD88/NF-κB signaling pathway, playing a pivotal role in the pathogenesis of allergic asthma ( 66 ). Our in vivo and in vitro experiments demonstrated that SD-induced neutrophilic inflammation led to the excessive secretion of S100A8/A9, which bound to TLR4 on ovarian macrophages, triggering M1 polarization and amplifying ovarian inflammatory damage. Moreover, NLRP3 inflammasome plays an important role in female fertility ( 67 ). Ovarian inflammation has been shown to activate NLRP3 inflammasomes, driving granulosa cell pyroptosis and follicular dysfunction, ultimately leading to ovarian fibrosis ( 34 ). In our study, S100A8/A9-induced macrophage polarization was associated with exacerbation of ovarian inflammation, activation of the NLRP3 inflammasome, and pyroptotic cell death in granulosa cells. These observations suggest that targeting S100A8/A9-mediated inflammatory signaling may represent a therapeutic strategy for mitigating SD-induced ovarian dysfunction. Estrogen receptors, progesterone receptors, and androgen receptors are expressed by various immune cells, such as T cells, B cells, dendritic cells, monocytes and macrophages, NK cells, and others ( 68 ). Estrogens can exert either anti-inflammatory (at high concentrations) or pro-inflammatory (at low concentrations) effects. During pregnancy or ovulation periods when estrogen concentrations are elevated, estrogen exerts inhibitory effects on T cells, M1-polarized macrophages, dendritic cells, neutrophils, and microglia through NF-κB suppression ( 69 ). However, the interaction between SD-induced inflammatory responses and sex hormones remains unclear and warrants further investigation. It remains unclear whether this method results in confounding effects, such as elevated stress levels that may increase cortisol and consequently contribute to inflammation. An optimized active SD model may help address this issue. In addition to pharmacological inhibition with Paquinimod, incorporating genetic knockdown or knockout approaches would significantly strengthen this study. However, these experiments cannot be completed within the current study due to constraints in both timeline and research funding. We plan to pursue this as a key focus in subsequent research. In conclusion, our study identifies an immunological mechanism underlying SD-induced follicular developmental disorders. SD promotes neutrophil-driven S100A8/A9 release, which activates the TLR4/MyD88/NF-κB signaling pathway, induces macrophage M1 polarization, exacerbates ovarian inflammation, and triggers granulosa cell pyroptosis. These processes collectively impair follicular development and embryo quality in female mice. Our findings highlight the critical interplay between sleep, immunity, and reproductive health, and provide potential targets for therapeutic intervention in SD-associated ovarian dysfunction.

Sleep is a fundamental physiological process essential for maintaining overall health in most animals ( 1 , 2 ). However, because of increasing societal and occupational pressures, the average sleep duration among humans has significantly declined, with approximately less than 6 hours of sleeping per day in one-thirds of adults ( 3 ). Consequently, sleep deprivation (SD) is now recognized as a major public health concern, contributing to numerous physiological and psychological disorders ( 4 – 8 ). Emerging evidence suggests that sleep plays a crucial role in reproductive health. Clinical studies have implicated that sleep disorders are associated with impaired female fertility, including decreased oocyte retrieval, hormonal imbalances (such as abnormal luteinizing hormone levels), and reduced pregnancy success rates ( 9 , 10 ). Notably, sleep disturbances are particularly prevalent among patients undergoing in vitro fertilization and persist throughout the entire treatment cycle, potentially affecting treatment outcomes ( 11 , 12 ). However, the underlying biological mechanisms linking SD to reproductive dysfunction remain unclear. SD alters inflammatory immune processes through multiple pathways, thereby increasing susceptibility to immune-related diseases ( 13 , 14 ). Recent studies have demonstrated that prolonged SD leads to an accumulation of reactive oxygen species (ROS) in the gut, possibly resulting in lethality outcomes ( 15 ). In addition, extended SD in mice has been reported to trigger a systemic inflammatory storm characterized by excessive pro-inflammatory cytokine release, neutrophil accumulation, and multiorgan dysfunction ( 16 ). Data from large-scale epidemiological studies (e.g., the National Health and Nutrition Examination Survey database) further confirm that insufficient sleep (less than 6 hours per night) is strongly correlated with elevated systemic inflammation ( 17 ). Despite this growing body of evidence, the specific effects of SD on the ovarian immune environment remain largely unexplored. The ovary harbors a unique immune microenvironment composed of diverse immune cell populations, among which macrophages are predominant ( 18 , 19 ). The balance between pro-inflammatory M1 and anti-inflammatory M2 macrophages is critical for ovarian homeostasis. Disruption of this balance has been implicated in various ovarian pathologies, including aging ( 20 ), polycystic ovary syndrome, and ovarian cancer ( 21 , 22 ). Neutrophil-derived S100A8/A9 proteins play a pivotal role in immune regulation by binding to Toll-like receptor 4 (TLR4) on macrophages, thereby promoting inflammatory signaling ( 23 ). During inflammation, neutrophils release S100A8/A9, playing a key role in regulating the inflammatory response by stimulating leukocyte recruitment and inducing cytokine secretion ( 24 ). Studies have shown that excessive S100A9 expression inhibits macrophage M2 polarization, leading to chronic inflammation and impaired tissue repair ( 25 ). Pyroptosis, a form of programmed inflammatory cell death, is another key player in immune-mediated ovarian dysfunction. Prior research suggests that macrophage M1 polarization exacerbates the inflammatory response and may induce pyroptosis by activating NACHT, LRR and PYD domains-containing protein 3 (NLRP3) inflammasomes through excess ROS, inflammatory cytokines, etc. ( 26 , 27 ). However, because of the low abundance of immune cells in ovarian tissues, investigating their role in ovarian function has been challenging. Advances in single-cell RNA sequencing (scRNA-seq) have provided new opportunities to explore these immune cell populations at unprecedented resolution, shedding light on their roles in follicular development and ovarian homeostasis ( 28 – 30 ). Granulosa cells, which communicate with oocytes via intercellular gap junctions to deliver nutrients and support oocyte growth and maturation, have been the focus of high-throughput transcriptomic analyses that reveal their diverse roles in follicular development ( 31 , 32 ). Excessive oxidative stress adversely affects follicular development, oocyte maturation, and ovulation, impairing ovarian reserve, ultimately impairing ovarian reserve and contributing to female infertility ( 33 ). Animal studies demonstrated that hyperandrogenism triggers chronic low-grade inflammation in the ovary, activating NLRP3 inflammasomes, promoting granulosa cell death, and leading to follicular dysfunction and fibrosis of the ovarian mesenchymal stromal cells ( 34 ). Nevertheless, whether SD induces similar inflammatory cascades in the ovary and its precise underlying regulatory mechanisms remain to be elucidated. To investigate the effects of SD on follicular development, we used a well-established entire SD model as depicted by the “curling prevention by water (CPW)” method ( 16 ). Female mice were subjected to 48 hours of continuous SD, and subsequent analyses revealed that SD induced ovarian inflammatory damage and impaired follicular development. Notably, these effects were alleviated by anti-inflammatory treatment and inhibition of S100A8/A9 signaling. This study elucidated the inflammatory pathways through which SD impairs follicular development, identifying the role of S100A8/A9-mediated macrophage polarization and pyroptosis. Our findings would likely provide a scientific foundation for fertility preservation strategies in individuals affected by SD, including those with occupational circadian rhythm disruptions.

In this study, 6- to 8-week-old C57BL/6J female mice were obtained from the Animal Experiment Center of the First Affiliated Hospital of the University of Science and Technology of China (USTC). All animal experiments were approved by USTC’s Animal Ethics Committee [ethics no. 2023-N(A)-0164]. The mouse SD model was implemented using the CPW method ( 16 ). Mice in the SD group were sleep deprived for 48 hours by adding distilled water at room temperature to a depth of 8 mm in the rat cage. The water was replaced every 24 hours to maintain a clean environment for the sleep-deprived mice. Replacement cages and troughs were cleaned and disinfected with a disinfectant solution and ultraviolet light before reuse. All mice were housed in a temperature-controlled room with a 12-hour light-dark cycle and ad libitum access to food and water. For the eCon group ( 16 ), the same system setup as described above was used. A 10-cm petri dish was filled with distilled water to stabilize it, and the lid was wrapped with wet gauze. The gauze-covered petri dish was placed in the mouse cage opposite to the trough for mice to sleep on the petri dish to create a moist survival environment for the mice, and to assess whether the moist survival environment had an effect on the mice. The SASD paradigm was conducted using a continuous rotation apparatus (model XR-XS108, Xinruan Information Technology Co. Ltd., Shanghai, China). This cage-based system uses an adjustable-frequency deprivation bar that sweeps back and forth along the cage floor to provide an unobstructed environment for SD experiments. Animals had ad libitum access to food and water throughout the procedure. The bar was programmed to rotate 360° clockwise at a speed of 10 rpm, followed by a 6-s pause, and then to rotate 360° counterclockwise. EEG and EMG were used for sleep/wake analysis in mice, following previously reported experimental protocols ( 16 ). The estrous cycle of the mice was determined by vaginal smears to observe the major cell types ( 70 ). Vaginal cytology samples were collected daily from mice between 9:00 a.m. and 11:00 a.m., starting the day after the completion of the 48-hour SD period. Sampling was performed using a cotton swab moistened with physiological saline. The collected cells were then smeared onto slides, stained with Giemsa, and examined under a light microscope for cytological classification. This process was repeated continuously for 10 days. The estrous cycle typically lasts 4 to 5 days, followed by proestrus, estrus, metestrus, and diestrus withdrawal cycles. An estrous cycle was identified as abnormal if it was prolonged (>5 days) or remained in a single stage for an extended duration (>3 days) ( 71 ). Ovaries from 6- to 8-week-old female mice were collected and fixed in 4% paraformaldehyde at 4°C for 24 hours. Gradient dehydration was used, and serial sections were made after paraffin embedding, the slices were 5 μm thick. The slices were deparaffinized and then stained with H&E. Morphological changes in ovarian tissue were observed under a microscope, and follicles were classified, counted, and the mean value was calculated. Follicles were categorized into two groups according to their growth and development characteristics: primordial follicles and growing follicles, among which growing follicles included primary growing follicles, secondary growing follicles, sinusoidal follicles, and preovulatory follicles ( 72 , 73 ). To examine the effects of SD on follicular development and oocyte maturation in mice while eliminating potential confounding effects from sleep recovery, we initiated a 48-hour SD protocol immediately before oocyte retrieval (i.e., starting 48 hours before euthanasia) to ensure mice were processed immediately following the SD period. Female mice aged 6 to 8 weeks were selected for superovulation, first receiving an intraperitoneal injection of 10 IU of PMSG, followed by an injection of 10 IU of human chorionic gonadotropin (HCG) 48 hours later. Superovulation refers to the hormonal stimulation of a female (often in animal research or fertility treatments) to produce a larger-than-normal number of mature eggs in a single reproductive cycle ( 74 ). Superovulation provides sufficient oocyte yields per animal while controlling for individual differences ( 75 , 76 ). In this study, all experiments involving mouse oocyte collection and in vitro embryo culture were performed using superovulation protocols. Mice were euthanized 12 to 16 hours after the injection of HCG. Oocyte collection and in vitro fertilization procedure was performed according to the previously described protocol ( 77 ). Mice were anesthetized with pentobarbital (i.p., 80 mg/kg) for retro-orbital blood collection. Blood was deposited into 1.5-ml EP tubes and allowed to stand at room temperature for 10 min before centrifugation at 4°C 2500 g for 10 min. Serum was collected and stored at −80°C for subsequent assays. ELISA was performed using S100A8/A9, MDA, SOD, ATP, and GSH kits according to the manufacturer’s instructions. Inflammatory cytokines and chemokines in mouse peripheral blood serum were detected using the Luminex Liquid Suspension Microarray assay with the Bio-Plex Pro Mouse Cytokine Group I Panel 23-plex kit (Bio-Rad), and values were read with the Bio-Plex MAGPIX System (Bio-Rad). Mice were anesthetized with pentobarbital (i.p., 80 mg/kg) for orbital blood collection. EDTA anticoagulation tubes (BD Biosciences) were used. A blood cell analyzer was used for analysis (BC-5000vet). Cumulus oophorus complex collected from the mouse fallopian tubes were immediately fixed into 2.5% glutaraldehyde for 24 hours. The fixative was removed, and the samples were placed in phosphate-buffered saline (PBS) buffer for 6 hours, followed by fixation in 1% osmium tetroxide for 2 hours. The samples were successively dehydrated using different concentrations of ethanol. The samples were then embedded in Epon 812 epoxy resin, sectioned into ultrathin slices, stained, and observed and photographed under a TEM (JEOL JEM-1400 Plus, USA). Mouse peripheral blood was collected into EDTA anticoagulation tubes, appropriate amount of lysate was added, incubated at room temperature for 10 to 15 min, centrifuged at 500 g for 5 min, and washed twice with PBS buffer. The cells were then incubated with fluorescein isothiocyanate (FITC) anti-mouse CD45 (BioLegend, 30-F11), APC anti-mouse CD11b (BioLegend, M1/70), and PE anti-mouse Ly6G (BioLegend, 1A8) for 30 min at 4°C, protected from light. The cells were washed twice with PBS buffer and centrifuged at 500 g for 5 min at 4°C. Data were collected in real-time using a flow cytometer (BECKMAN, CytoFLEX) and analyzed. Cells were collected. Five microliter of Annexin V–FITC and 10 μl of PI were added to each tube. After gently vortexing and mixing the cell suspension, it was incubated at room temperature for 5 min away from light. The gating strategy was validated using both unstained and fluorescence minus one (FMO) controls to ensure accurate discrimination of Annexin V/PI subpopulations. Annexin V − /PI − represents viable cells, Annexin V + /PI − represents early apoptotic cells, Annexin V + /PI + represents pyroptotic or necroptotic cells, and Annexin V − /PI + represents mechanically damaged cells. Flow cytometry analysis was performed according to the experimental protocol. Cells were collected, and 100 μl of cell suspension was taken. Antibodies against CD11b (BioLegend, 101206) and F4/80 (BioLegend, 123110) were added to each tube, and protected from light for 15 min at room temperature. Cell fixative and membrane-breaking solution were added, and CD86 (BioLegend, 105012) and CD206 (BioLegend, 141716), and protected from light at room temperature for 20 min. Cells were washed with PBS buffer and resuspended, and then detected by flow cytometry. Antibody information is provided in table S2. Cells were collected, 10 μmol/liter of DCFH-DA probe was added to the cells and incubated at 37°C for 20 min. Cells without staining treatment were set as a negative control group. ROS were detected on flow cytometry and analyzed with FlowJo10.8.1 software. Cells were collected. The cells were then resuspended in 500 μl of JC-1 staining buffer and incubated for 20 min at 37°C in 5% CO 2 . Cells were collected by centrifugation at 600 g for 4 min. After washing, the cells were resuspended using an appropriate amount of JC-1 staining buffer. Cells were examined by flow cytometry and data were analyzed using FlowJo10.8 software. Extraction of total RNA from ovarian tissue was done for quality control. Cellular RNA was extracted according to the operating instructions of the TRIzol kit, and the concentration, purity and integrity of the extracted total RNA were determined by ultraviolet spectrophotometry and gel electrophoresis. Extracted total RNA samples were used for cDNA generation and sequencing. Raw sequencing data contained low-quality and linker-contaminated reads, which were removed using CPSS software to obtain clean reads. The obtained pure sequences were aligned with the reference genome or transcriptome sequences, and the distribution information of the read-length fragments was obtained using RPKM (reads per kilobases per million reads, per billion bases per kilobase length of a gene) to calculate the expression of the gene. scRNA-seq library construction and sequencing were performed. Ovaries were washed in ice-cold RPMI 1640 and dissociated using collagenase IV, neutral protease, and deoxyribonuclease Ι. After cell counting, fresh cells were washed twice in RPMI 1640 and then resuspended at 1 × 106 cells/ml in PBS containing 0.04% bovine serum albumin (BSA). scRNA-seq libraries were then prepared using the Seek One Digital Droplet Single Cell 3’ Library Preparation Kit (Seek Gene), and the libraries were sequenced on an Illumina Nova Seq 6000 with a read length of PE150. Follow the reagent instructions. Reverse transcription to cDNA was performed using PrimeScript RT Master Mix and then stored at −20°C until use. Real-time quantitative PCR was performed using SYBR Green PCR Master Mix and Quant-Studio7 Flex real-time PCR system. Data were normalized with Gapdh and quantification of fold change was determined by comparing CT methods. Experiments were repeated at least three times. Primer information is provided in table S1. Samples were sonicated in RIPA lysis buffer, and the cellular lysates were separated by SDS–polyacrylamide gel electrophoresis. The separated proteins were transferred to polyvinylidene difluoride membrane and then sequentially incubated with specific primary and secondary antibodies. Protein bands were detected using an enhanced chemiluminescence detection system. Protein expression levels were quantified by measuring the intensity of the protein bands using ImageJ software. Antibody information is provided in table S2. Ovarian tissue sections were deparaffinized and rehydrated through a gradient of xylene and ethanol. Antigen repair was performed using citrate buffer. 0.1% Triton X-100 permeabilized the tissues for 30 min, the tissues were sealed with 1% BSA for 1 hour, and then incubated overnight at 4°C with primary antibody. After three washes, the tissue was incubated for 1 hour at room temperature with the appropriate secondary antibody of choice, washed three times, and finally sealed with an antifluorescence quenching sealer [containing 4′,6-diamidino-2-phenylindole (DAPI)] and scanned by a digital section scanner (Pannoramic MIDI). Antibody information is provided in table S2. For oocyte immunofluorescence staining, the samples were fixed with 4% paraformaldehyde at room temperature for 30 min, then permeabilized with 0.5% Triton X-100 at room temperature for 20 min, washed with PBS three times, each for 5 min, block with 1% BSA at room temperature for 1 hour, incubated with the primary antibody at 4°C overnight, washed with PBS three times, each for 10 min, and incubated with the secondary antibody at room temperature in the dark for 1 hour ( 78 ). Last, the tablets were sealed with antifluorescence quenching tablets (including DAPI). Confocal microscopy (Carl Zeiss) was used as soon as possible. Antibody information is provided in table S2. A noncontact Transwell system (0.4 μm, LABSELECT) was used to coculture RAW264.7 macrophages, and mouse ovarian granulosa cells, which can be used for subsequent isolation of different cell types for experiments. RAW264.7 macrophages were seeded into the upper Transwell chamber and cultured in the incubator for 4 to 6 hours. After observing the normal wall attachment of the cells, S100A8/A9 recombinant protein (5 μg/ml; R&D Systems) was added, and the cells were cultured in the incubator for 24 hours. The medium was changed, and the macrophages inoculated in the Transwell chamber were placed into the upper layer of the inoculated granulosa cells culture plate and cultured for a total of 72 hours. The cell supernatant, RAW264.7 macrophages, and granulosa cells were collected and used for subsequent experiments. The sample sizes were determined on the basis of previously published studies and widely accepted standards in the field. All experiments were performed with at least three biological replicates and were independently repeated at least three times to ensure robustness and reproducibility. In addition, experiments were conducted in a randomized and blinded manner to minimize bias. Data were analyzed using SPSS 25.0 software, and information on continuous variables that conformed to normal distribution was expressed as mean ± SEM. Comparisons of differences between groups were performed using t tests or nonparametric tests. Count data were expressed as n (%), χ2 test was used for one-way categorical variable analysis, and Fisher’s exact test was used for ordered multicategorical variables. Comparisons of means among the three groups were analyzed by analysis of variance (ANOVA), and multiple comparisons were performed by Tukey’s post hoc test.