Single-oocyte proteome-transcriptome co-profiling reveals a role of dysregulated lactate metabolism in oocyte aging

Single-oocyte proteome-transcriptome co-profiling reveals a role of dysregulated lactate metabolism in oocyte aging | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Resource Single-oocyte proteome-transcriptome co-profiling reveals a role of dysregulated lactate metabolism in oocyte aging Xudong Fu, Cao Lanrui, Yirong Jiang, Zhuo Yang, Peipei Ren, Panpan Zhao, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6309099/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The declined oocyte quality with age is a major risk factor for female infertility. Although transcriptomic changes have been examined in aged oocytes, the proteomic landscape, which reflects the primary functional executors of genes, and factors shaping the landscape, remains largely unexplored. This gap limits our understanding of molecular features driving oocyte aging. To address this, we performed single-cell proteome/transcriptome co-profiling in GV and MII-aged oocytes from mice and humans, revealing species- and stage-specific proteomic/transcriptomic changes during oocyte aging. Strikingly, we observed uncoupled proteomic and transcriptomic alterations, indicating that proteomic changes in aged oocytes are not primarily driven by RNA alterations. Leveraging our single-cell profiling, we captured the molecular heterogeneity in aged oocytes and revealed MCT4 as a conserved oocyte aging biomarker. Functional studies suggested that MCT4 mediated oocyte aging via lactate export, and its inhibition improved aged oocyte quality. These findings indicated altered lactate metabolism as a driver and intervention target of oocyte aging and underscored the value of our profiling in dissecting oocyte aging. Biological sciences/Developmental biology/Ageing Biological sciences/Stem cells/Embryonic germ cells single-cell multi-omics profiling aged oocytes MCT4 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Human female fertility declines significantly after the age of 35, primarily due to reproductive aging 1 – 4 . Beyond its impact on fertility, female reproductive aging is associated with an increased risk of obstetric difficulties and perinatal hazards, presenting significant challenges for older women seeking to conceive 5 , 6 . A key aspect of reproductive aging is oocyte aging, which is primarily characterized by a progressive decline in oocyte quality 7 – 9 . This decline manifests in several ways, including irregular meiotic resumption, defects in meiotic maturation, and reduced fertilization capacity 7 – 9 . Aged oocytes with declining quality exhibit systemic cellular impairments such as spindle checkpoint integrity disruptions, epigenetic erosion, and chromosomal anomalies 10 – 16 . Intriguingly, not all aged oocytes exhibit quality defects, and even oocytes from the same-aged female can vary in maturation and fertilization competence 17 . However, the molecular basis of oocyte aging and the heterogeneity of aged oocytes remains largely unknown. To investigate the molecular mechanisms underlying oocyte aging, researchers have employed omics profiling techniques. For example, single-cell transcriptomic profiling has revealed alterations in ubiquitination, epigenetic regulation, and mitochondrial genes upon oocyte aging in humans and mice 18 – 21 . However, transcriptomic data may not reliably infer proteins, the primary executors of gene functions, due to the weak correlation between RNA and protein levels in oocytes 22 . This limitation restricts transcriptomics from capturing the key features of oocyte aging. Indeed, despite the clear phenotypic abnormalities observed in aged oocytes, transcriptomic data alone fails to distinguish young oocytes from aged ones in humans 20 . Compared to transcriptional sequencing, proteomic profiling can directly provide the protein abundance of genes to infer functional abnormalities in aged oocytes 23 . For instance, mass spectrometry-based proteomics identified around 4,000 proteins in mouse-aged oocytes, revealing altered RNA splicing events in DNA damage repair genes 24 . However, this approach requires multiple oocytes, limiting its application to human samples, and cannot capture heterogeneity features within aged oocytes. Single-cell proteomics, while promising, is limited by profiling depth; for example, human single-oocyte proteomics profiling using plexDIA identified only ~ 1,300 proteins in total 25 . Furthermore, proteomic profiling alone cannot reveal the coordination between proteomic and transcriptomic alterations during oocyte aging, limiting its ability to infer the molecular driver—such as RNA or translational alterations—that induces protein changes in aged oocytes. These technical limitations restrict the profiling of protein features in aged oocytes. Recent advances in high-sensitivity and single-cell proteomic profiling techniques offer a powerful approach to uncovering the proteomic features of oocyte aging and have proven valuable in studying oocyte development 26 – 46 . We previously developed a single-cell simultaneous transcriptome and proteome (scSTAP) profiling platform, integrating microfluidic technology with high-sensitivity proteomics, specifically optimized for oocyte analysis 22 . This platform enables the detection of ~ 3,000 proteins and ~ 22,000 transcripts from a single mouse oocyte, providing a unique opportunity to characterize single-cell proteomic features and the coordination between proteomic and transcriptomic alterations during oocyte aging. Additionally, the single-cell multi-omics resource generated by scSTAP enables the exploration of multiple molecular aspects of oocyte aging, including protein expression variability, RNA-protein correlation, and quality heterogeneity, thereby facilitating oocyte aging study. To this end, we set out to exploit scSTAP to dissect oocyte aging. We performed single-cell profiling on young and aged oocytes from mice and humans, analyzing a total of 79 oocytes (38 aged, 41 young), and identified approximately 3,000 and 6,000 proteins, as well as 20,000 and 30,000 transcripts, in mouse and human oocytes, respectively. Our profiling resource unveiled the single-cell proteomic/transcriptomic landscape of aged oocytes in mice and humans and identified uncoupled proteomic and transcriptomic changes during oocyte aging. Additionally, our resource captured the molecular heterogeneity in aged oocytes and pinpointed MCT4, along with its lactate export function, as a mediator, biomarker, and potential intervention target for oocyte aging. These findings underscore the value of our profiling resource in studying oocyte aging. Results Deep single-cell co-profiling of proteome and transcriptome in mouse-aged oocytes by scSTAP To profile the proteome and its correlation with transcriptome in aged oocytes, we utilized the scSTAP platform we developed (Fig. 1 a). Specifically, a single oocyte was lysed using an enzyme-assisted lysis buffer within a small reaction volume to ensure effective lysis. The sequential operation droplet array (SODA) technique was then applied to precisely split the lysate into two equal portions while minimizing lysate loss 47 , 48 . One portion was used for high sensitivity and resolution nano-liquid chromatography-mass spectrometry (nanoLC-MS) to achieve deep proteomic profiling. The split portion was used for SMART-seq-based transcriptomic profiling, enabling the acquisition of transcriptomic data directly comparable to previously reported datasets. To minimize protein loss from surface absorption and multi-step treatment, a single insert tube with a tapered bottom and hydrophobic surface was used for oocyte lysis, sample spiting, and nanoLC-MS. This single-cell multi-omics profiling method allowed us to acquire a total quantification of ~ 24000 genes and ~ 3300 proteins in germinal vesicle (GV) oocytes isolated from young mice at the single-cell level (Fig. 1 b-c), with strong correlation coefficients across all samples tested (Fig. 1 d, Supplementary Table 1 ), supporting the depth and reliability of scSTAP profiling. Based on the literature, we selected 12-month-old mice to isolate aged oocytes for profiling 49 . We first confirmed that the GV and metaphase II (MII) oocytes isolated from 12-month-old mice exhibited significant aging-induced quality defects. These were characterized by a notable decline in ovulatory capacity ( Extended Data Fig. 1 a), maturation quality ( Extended Data Fig. 1 b-c), and fertilization competence of these oocytes compared to those isolated from younger mice (6-8-week-old; Extended Data Fig. 1 d). Interestingly, after successful fertilization, we did not observe significant differences in the developmental potential between embryos derived from young and aged oocytes, which is consistent with previous reports ( Extended Data Fig. 1 e) 50 . These findings suggest that age-related oocyte defects manifest primarily at the pre-fertilization stage. Based on these results, we selected GV and MII oocytes from 12-month-old mice to profile oocyte aging. In total, 21 GV (9 aged, 12 young) and 20 MII stages (8 aged, 12 young) oocytes were isolated and analyzed by the scSTAP, achieving single-oocyte resolution resources with a total quantification depth of ~ 20000 genes and ~ 3000 protein and strong correlation coefficients across all samples tested (Fig. 1 b-d, Extended Data Fig. 1 f, and Supplementary Table 1–2 ). Single-cell transcriptomic landscape during mouse oocyte aging To validate our profiling results, we first performed separate analyses of the transcriptomic and proteomic data. For the transcriptomic data, we conducted the following analyses to confirm the validity. Firstly, we confirmed that our single-cell transcriptomic data effectively captured stage-specific gene expression profiles in GV and MII oocytes ( Extended Data Fig. 1 g). For example, GV-stage marker genes, such as Zar1l , exhibited a specific expression pattern to the GV stage, whereas MII-stage marker genes, including H1foo and Ndc80 , were predominantly expressed in MII oocytes ( Extended Data Fig. 1 g) 51 – 53 . Secondly, we integrated the single-cell transcriptomic data to generate a bulk transcriptome, and it was highly correlated with previously published bulk RNA sequencing data from mouse oocytes ( Extended Data Fig. 1 h) 9 , 54 , 55 . Furthermore, we confirmed the aging-induced RNA expression changes of selected genes identified by the scSTAP platform using quantitative PCR (qPCR) ( Extended Data Fig. 1 i). Collectively, these findings support the reliability of our single-cell transcriptomic data. After validating the accuracy of our profiling, we explored aging-induced transcriptional changes in GV and MII oocytes. In GV oocytes, we identified 537 differentially expressed genes (DEGs) associated with oocyte aging, which were enriched in the unsaturated fatty acid metabolic processes and regulation of endocytosis pathways ( Extended Data Fig. 1 j-k). In MII oocytes, 1,420 genes exhibited significantly altered RNA expression levels, with these changes being linked to the regulation of autophagy and chromatin remodeling pathways ( Extended Data Fig. 1 j-k). Notably, the aging-induced transcriptional changes in aged GV oocytes showed very little overlap with those in MII oocytes ( Extended Data Fig. 1 l), suggesting that aging impacts the oocyte transcriptome in a stage-specific manner. Aging impairs the meiotic maturation of oocytes from the GV stage to the MII stage ( Extended Data Fig. 1 b-c). To explore the underlying transcriptional signatures, we compared the RNA dynamics of stage-specific genes between young and aged oocytes and identified six clusters of genes (see Methods, Extended Data Fig. 1 m). The RNA dynamics of selected genes were validated by qPCR, supporting the validity of our analysis ( Extended Data Fig. 1 n). Our results showed that aging impaired the upregulation of a substantial set of genes that normally increased in expression during meiotic maturation (Cluster 1 genes) ( Extended Data Fig. 1 m). These genes are enriched in chromosome segregation and spindle organization pathways, which are critical for meiotic maturation 56 . Similarly, aging disrupts the downregulation of a large set of genes that normally decrease in expression during meiotic maturation (Cluster 3 genes) ( Extended Data Fig. 1 m). Intriguingly, aged oocytes also exhibited a unique group of genes that got upregulated (Cluster 5 genes) or downregulated (Cluster 6 genes) during meiotic maturation ( Extended Data Fig. 1 m). The abnormal dynamics of these genes may contribute to the maturation defects in aged oocytes. Single-cell proteomic landscape during mouse oocyte aging We next analyzed the single-cell proteomic data acquired by scSTAP and first evaluated its validity. Our analysis confirmed that the scSTAP proteomic profiling successfully separated GV and MII oocytes by capturing stage-specific protein markers in oocytes, such as CPEB1 for the GV stage and BUB1B for the MII stage (Fig. 1 e-f) 57 , 58 . In addition, the bulk proteome merged from single-cell data highly correlated with previously published proteomic data from mouse oocytes (Fig. 1 g) 58 . We further validated the selected aging-induced protein changes identified by our profiling, including MCT4, PDCD4, and NTMT1, through western-blot analysis (Fig. 1 h). Collectively, these results support the validity of our proteomic profiling. To investigate the proteomic changes associated with oocyte aging, we compared the protein profiles of young and aged oocytes. We observed that proteins associated with mRNA processing and protein secretion pathways were altered in aged GV oocytes (Fig. 1 i-j). In MII oocytes, our data revealed alterations in proteins associated with the regulation of autophagy and cellular senescence pathways (Fig. 1 i-j). Interestingly, the aging-induced proteomic changes in GV oocytes showed very little overlap with those in MII oocytes (Fig. 1 k), suggesting that aging impacts the oocyte proteome in a stage-specific manner. To examine the proteomic features underlying maturation defects in aged oocytes, we classified the proteins based on their expression dynamics during oocyte maturation in young and aged oocytes (see Methods). A large set of proteins that normally increased during meiotic maturation (Cluster 1 proteins) exhibited an impaired upregulation with oocyte aging, and they were enriched in the regulation of the chromosome organization pathway (Fig. 1 l-m), which is crucial for meiotic maturation 56 . On the other hand, Clusters 3 proteins, which normally decreased during meiotic maturation, exhibited an impaired downregulation in aged oocytes (Fig. 1 l-m). In addition, we also identified proteins that went through upregulation (Cluster 5) or downregulation (Cluster 6) during meiotic maturation only in aged oocytes (Fig. 1 l-m), and the altered dynamics of these proteins may be involved in the effect of aging on oocytes. Intriguingly, the dynamics of Cluster 5/6 proteins did not appear to be solely driven by RNA-level alterations (Fig. 1 m), suggesting that post-transcriptional changes, such as translation or protein stability changes, may contribute to these dynamics. Single-cell proteome revealed a more uniform expression of proteins in aged oocytes An intriguing feature associated with aging is the increased cell-to-cell transcriptional variability 59 . However, whether aging similarly induces protein expression variability has not been fully examined. Leveraging our single-cell resolution proteomic data, we set out to investigate this question in oocytes. Unexpectedly, we observed a significant decrease in protein expression variability in aged oocytes ( Extended Data Fig. 2 a-b). Examining individual proteins, we found that while a small subset of genes showed increased protein expression variability, a larger set of proteins exhibited a decreased expression variability in aged GV and MII oocytes ( Extended Data Fig. 2 c). This contradicts the fact that more RNA showed increased expression variability during oocyte aging ( Extended Data Fig. 2 d). The unexpected reduction in protein expression variability suggests a potential role for these proteins and their associated pathways, such as mitochondrial and glycolysis-related pathways ( Extended Data Fig. 2 e), in preserving the viability or function of aged oocytes. Notably, other studies have also pointed out the importance of mitochondrial function in aged oocytes 19 , 60 , 61 . We further explored the molecular mechanisms driving this uniform expression of proteins in aged oocytes. We found that the proteins with decreased expression variability did not show significant changes in overall expression levels upon oocyte aging ( Extended Data Fig. 2 f-g), suggesting that expression levels alone do not explain this pattern. Additionally, most of these proteins did not display reduced expression variability at the RNA level ( Extended Data Fig. 2 h-i), suggesting that the RNA alterations were not the primary driver of this protein-level uniformity. These findings point to post-translational mechanisms as potential contributors to the decreased protein expression variability in aged oocytes. Multi-omic analysis revealed uncoupled proteomic and transcriptomic changes during mouse oocyte aging Our profiling not only provides single-cell proteome data but also captures the coordination between proteomic and transcriptomic alterations during oocyte aging. This information can be exploited to reveal how transcriptomic features affect protein alterations and to infer molecular factors, such as translation efficiency, that shape the proteome landscape in aged oocytes. Using our co-profiling data, we first examined the correlation between RNA and protein in aged oocytes. We identified a low correlation between RNA and protein expression levels in aged oocytes, with correlation values below 0.35 for GV oocytes and below 0.2 for MII oocytes (Fig. 2 a). This low correlation between RNA and protein is also valid for each profiled oocyte, indicating this is a general feature of aged oocytes (Fig. 2 b). We also calculated the RNA-protein correlation for each detected gene and confirmed a generally low RNA-protein pair correlation for genes, with more than 85% of genes exhibiting a pair correlation below 0.5 (Fig. 2 c, Supplementary Table 3 ). Taken together, these results suggest that protein abundance in aged oocytes is not primarily determined by RNA levels, indicating the involvement of additional regulatory factors. Interestingly, we found that genes with the highest translational efficiency tend to exhibit a higher RNA-protein correlation than those with the lowest translational efficiency in aged oocytes (Fig. 2 d), suggesting a role of translation efficiency in contributing to the low RNA-protein correlation in aged oocytes 15 . Next, we examined the coordination between transcriptomic and proteomic alterations upon oocyte aging. To assess this coordination, we grouped genes into four clusters based on their RNA and protein changes during oocyte aging (Fig. 2 e-f, Supplementary Table 4 ). Among these groups, Class 1 genes exhibited positively correlated RNA and protein changes, Class 2/3 genes exhibited uncorrelated RNA-protein alterations, and Class 4 genes represented genes with negatively correlated RNA-protein changes. Surprisingly, in GV oocytes, of all genes with RNA or protein changes, only a small proportion (3.68%, Class 1/Class [1–4]) exhibited positive correlations between RNA and protein changes during aging (Fig. 2 e). In contrast, a larger subset (92.43%, Class [2–3]/Class [1–4]) exhibited non-correlated alterations, and 19 genes (3.89%, Class 4/Class [1–4]) showed negatively correlated changes (Fig. 2 e). Notably, single-cell analysis suggested that uncoupled RNA-protein changes in representative genes reflected a general trend among oocytes (Fig. 2 g). A similar pattern was also observed in MII oocytes (Fig. 2 f-g). We further confirmed the uncoupled RNA and protein changes in selected genes ( Ntmt1 and Pdcd4 , where protein levels changed without corresponding RNA changes) upon oocyte aging by western blot and qPCR (Fig. 1 h and 2 h). These results suggest that proteomic changes during oocyte aging are not primarily driven by RNA alterations, and other molecular factors, such as translational efficiency 62 , may contribute to these changes. To investigate this possibility, we integrated the translatome landscape of oocytes with our profiling to assess the impact of translation on uncoupled RNA-protein alterations during oocyte aging 15 . We focused on aging-affected genes with translational efficiency alterations during oocyte aging. The results showed that among genes with aging-induced translational efficiency defects, 32.85% (Group 1/Group [1–2]) exhibited decreased protein levels without corresponding reductions in RNA levels (Fig. 2 i, Supplementary Table 4 ). Conversely, among genes with increased translational efficiency during oocyte aging, 49.42% (Group 1/Group [1–2]) showed elevated protein levels without a corresponding RNA increase (Fig. 2 j, Supplementary Table 4 ). These results suggest a potential role of translational alterations in shaping the uncoupled RNA-protein changes of these Group 1 genes during oocyte aging. Single-cell co-profiling identified two groups of mouse-aged oocytes with distinct molecular features It has been reported that aging may affect oocyte quality with heterogeneity, with oocytes from the same-aged female displaying varying fertilization efficiencies 63 , 64 . However, the molecular features to confirm or evaluate this heterogeneous impact of aging on oocytes are limited. Our profiling resource, which provides RNA and protein data at single-cell resolution for aged oocytes, offers a unique opportunity to explore the heterogeneity of aging impacts in oocytes and to identify potential assessing biomarkers. We first assessed whether transcriptomics or proteomics is more effective in evaluating the aging impact on oocytes. Using aging-associated gene sets from the literature as markers 65 – 67 , we compared the performance of transcriptome and proteome in identifying the aging features in oocytes (Fig. 3 a). Overall, proteomic data outperformed transcriptomic data (Fig. 3 a), indicating that proteomics better captures the aging impact on oocytes. In addition, we calculated the centroid distance between young and aged oocytes in the Principal Component Analysis (PCA) plot based on the proteome or transcriptome. The centroid distance between young and aged oocytes in the proteome PCA was greater in the proteome-based PCA than in the transcriptome-based PCA, suggesting that the proteome distinguishes aged oocytes from young oocytes more effectively than the transcriptome (Fig. 3 b). These results suggest that proteomics is more suitable for analyzing the impact of aging on oocytes. Based on this information, we applied proteomics to investigate oocyte aging heterogeneity. To do so, we first tried to identify a suitable index for evaluating aging features in oocytes. Interestingly, signaling entropy, a verified aging index 67 , can effectively differentiate aged oocytes from the young controls (Fig. 3 c). We, therefore, used signaling entropy to analyze heterogeneity in aged GV oocytes. To achieve this, we clustered aged oocytes into two groups based on their median signaling entropy values (see Methods, Fig. 3 d). Group I oocytes exhibited signaling entropy comparable to that of young oocytes, suggesting that this group of oocytes exhibits mild aging features. In contrast, Group II oocytes showed a significant decrease in signaling entropy compared to young oocytes, indicating that these oocytes exhibit severe aging characteristics (Fig. 3 e). To confirm that our clustering method captured the heterogeneity in aged GV oocytes, we performed the following analysis. Firstly, we found that these two groups of oocytes were clearly separated in the PCA plot, supporting the validity of our clustering strategy (Fig. 3 d). Secondly, we analyzed the aging-associated proteomic changes in Group I and II oocytes. The results showed that Group II oocytes exhibited more proteomic changes than Group I oocytes during aging (Fig. 3 f). In addition, several aging-associated proteins, including ribosomal proteins ( e.g. , RPS24, RPL7) and factors linked to aging-induced oocyte quality defects ( e.g. , KDM1B, SFPQ), exhibited exclusive alterations in Group II oocytes (Fig. 3 g) 15 , 68 , 69 . These results suggest that Group II oocytes exhibited more aging-associated proteomic changes than Group I oocytes. In addition to proteomics, we also examined transcriptomics features in these two groups of aged oocytes. During aging, Group II GV oocytes exhibited more pronounced transcriptional changes and a more significant decline in RNA-protein correlation than Group I GV oocytes (Fig. 3 h-i). These findings suggest that Group II oocytes undergo more extensive transcriptomic alterations during aging compared to Group I oocytes. Collectively, our results suggest that the analytical strategy based on signaling entropy effectively identified two groups of aged GV oocytes with distinct molecular features. After identifying two groups of aged oocytes with distinct molecular features, we investigated potential biomarkers to differentiate them. To this end, we clustered proteins based on their expression patterns in young, Group I, and Group II oocytes. This analysis revealed four distinct protein clusters exhibiting unique dynamics during oocyte aging (Fig. 3 j, Supplementary Table 5 ). Clusters 1/3 contained proteins that were upregulated or downregulated in both Group I and Group II oocytes compared to young control, suggesting that they represent general aging features in aged oocytes. In contrast, Clusters 2/4 included proteins that were exclusively upregulated or downregulated in Group II oocytes compared to young control, respectively, and they were enriched in pathways related to the spliceosome, chromatin remodeling, and carboxylic acid transmembrane transport (Fig. 3 j-l, Supplementary Table 5 ). Given that Group II samples likely represent oocytes experiencing more severe molecular changes during aging, these Cluster 2/4 proteins and their associated pathways may serve as biomarkers of the GV oocyte aging state and contribute to aging-induced molecular changes in oocytes. Notably, these clusters included known oocyte aging regulators ( e.g. , KDM1B and SFPQ, Fig. 3 l) 15 , 68 , supporting the validity of our identified aging biomarkers. Clusters 2/4 proteins also contained previously unreported factors associated with oocyte aging ( e.g. , monocarboxylate transporter 4 [MCT4] and CD47, Fig. 3 l), and their role in oocyte aging merit further investigation. Next, we exploited the same analysis strategy to examine the heterogeneity in MII-aged oocytes. Two groups of MII oocytes were clustered based on the median signaling entropy of oocytes ( Extended Data Fig. 3 a). Compared to Group I oocytes, Group II oocytes exhibited decreased signaling entropy ( Extended Data Fig. 3 b), downregulation of oocyte functional proteins ( e.g. , TRIP13 and SALL4, Extended Data Fig. 3 c), and more proteomic/transcriptomic alterations during aging ( Extended Data Fig. 3 d-e) 70 , 71 . These findings suggest that Group II MII oocytes undergo more molecular alterations during aging compared to Group I oocytes. We further compared the proteomic features between these two groups of aged oocytes. The results identified a subset of proteins (Clusters 2 and 4) specifically altered in Group II MII oocytes during oocyte aging ( Extended Data Fig. 3 f-g, Supplementary Table 5 ), and they are enriched in mRNA processing and cellular respiration pathways ( Extended Data Fig. 3 h). The expression patterns of these proteins in aged MII oocytes suggest they may serve as biomarkers for assessing the aging state of MII oocytes and mediate the aging-associated molecular alterations in MII oocytes. Interestingly, we noticed that the aged oocytes isolated from the same mice may belong to different groups of aged oocytes (Fig. 3 m and Extended Data Fig. 3 i). These results raised the possibility that oocytes within the same reservoir may be variably impacted by aging. MCT4 mediated the aging-induced defects in mouse oocytes Using our single-cell data, we identified candidate genes that may serve as biomarkers and mediators for oocyte aging. To provide validation for this analysis, we decided to perform functional validation on selected candidate genes. Among these candidates, MCT4 emerged as a prime candidate for several reasons. First, MCT4 and its associated pathway, carboxylic acid transmembrane transport, were one of the biomarkers for GV oocyte aging (Fig. 3 j-k and Extended Data Fig. 4 a). Secondly, MCT4 modulates lactate transport and lactate metabolism plays a critical role in cellular function, suggesting that MCT4 may serve as both marker and mediator for GV oocyte aging 72 , 73 . Finally, the availability of VB124, a highly specific MCT4 inhibitor, provides a unique pharmacological tool to investigate MCT4-targeted therapeutic strategies for mitigating oocyte aging 72 . MCT4 expression is elevated in aged mouse GV oocytes (Fig. 1 h). To investigate its role in oocyte aging, we used siRNA to suppress MCT4 expression in aged GV oocytes (Fig. 4 a, Extended Data Fig. 4 b). This intervention increased the polar body extrusion (PBE) rate of aged GV oocytes ( Extended Data Fig. 4 c) and caused the aged MII oocytes to exhibit reduced spindle abnormalities (Fig. 4 b-d), increased chromosome alignment (Fig. 4 c and 4 e), improved ability to bind sperm (Fig. 4 f), and increased fertilization rate (Fig. 4 g). These results indicated Mct4 knockdown improved meiotic maturation and fertilization capacity of aged oocytes. Beyond functional improvements, Mct4 knockdown also mitigated the cellular epigenetic hallmark associated with aging—H3K9me3 erosion in aged GV oocytes (Fig. 4 h), supporting an anti-aging effect of Mct4 knockdown in oocytes 74 . The anti-aging effect of Mct4 knockdown on aged oocytes suggests that MCT4 contributes to aging-induced defects in oocytes. To further explore the hypothesis, we overexpress MCT4 in young GV oocytes (Fig. 4 i and Extended Data Fig. 4 d). This overexpression recapitulated the majority of the defects observed in aged oocytes, including decreased PBE rate ( Extended Data Fig. 4 e), increased spindle assembly abnormalities (Fig. 4 j-l), chromosome misalignment (Fig. 4 k and 4 m), and diminished sperm-binding and fertilization capacity (Fig. 4 n-o). Interestingly, MCT4 overexpression alone did not reduce H3K9me3 levels in oocytes (Fig. 4 p), suggesting that while MCT4 is necessary, it is not sufficient to drive the aging-induced decrease in H3K9me3 in GV oocytes. Altogether, these findings suggest that elevated MCT4 contributes, at least in part, to aging-induced defects in aged GV oocytes. MCT4 inhibition by VB124 improved the oocyte quality of aged mice Developing anti-aging interventions for aged oocytes is in high demand, given the challenges associated with declining oocyte quality and fertility in aging females 75 . Recognizing MCT4's contribution to aging-induced defects in oocytes, we investigated whether MCT4 inhibition could be an effective anti-aging strategy for oocytes. To explore the possibility, we utilized VB124, a highly effective, orally bioavailable MCT4 inhibitor 72 . We first test the anti-aging effect of VB124 on oocytes in vitro . Aged GV oocytes were isolated from 12-month-old mice and cultured in a medium supplemented with VB124 and milrinone for a 20-hour pre-treatment. Following milrinone washout and resumption of meiosis, functional and molecular characterization of aged oocytes were conducted (Fig. 5 a). VB124 treatment significantly improved the meiotic maturation in aged GV oocytes, as evidenced by the increased PBE rate ( Extended Data Fig. 4 f), decreased spindle abnormal rate (Fig. 5 b-d ) , and improved chromosome alignment in aged oocytes (Fig. 5 c and 5 e ) . In addition, VB124 improved the sperm binding rate and fertilization rate of aged oocytes (Fig. 5 f-g), indicating that VB124 could improve the fertilization capacity of aged oocytes. Aside from functional analysis, VB124 also ameliorated aging-associated epigenetic erosion of H3K9me3 and transcriptomic alterations in aged oocytes (Fig. 5 h and Extended Data Fig. 4 g-h). These data collectively suggest that VB124 improves the quality of aged oocytes. We next explored the effect of VB124 on aged oocytes in vivo . We conducted the in vivo study by involving gastric lavage with VB124 (30 mg/kg, once daily for 15 consecutive days) in 12-month-old mice (Fig. 5 i). The oocytes isolated from VB124-treated aged mice exhibited a significant quality improvement, as evidenced by decreased spindle abnormal rate (Fig. 5 j-l), improved chromosome alignment (Fig. 5 k and 5 m ) , decreased H3K9me3 (Fig. 5 n), and increased fertilization competence (Fig. 5 o). Intriguingly, VB124 also reversed the aging-induced litter size decrease and follicular apoptosis in aged mice (Fig. 5 p-q, Extended Data Fig. 4 i), indicating that VB124 improves the general fertility of aged mice. Collectively, these findings reveal that MCT4 inhibition by VB124 enhances oocyte quality in aged animals, highlighting its potential as a therapeutic intervention for reproductive aging. MCT4 regulated oocyte aging by modulating lactate export MCT4 is a proton-coupled lactate symporter that plays an important role in maintaining cellular lactate homeostasis through regulated lactate efflux 72 , 76 . Pathological upregulation of MCT4 has been implicated in excessive lactate efflux, leading to impaired oxidative phosphorylation and subsequent cellular dysfunction 72 . We thus hypothesized that MCT4-mediated lactate dysregulation underlies age-related quality decline in oocytes. To test the hypothesis, we first examined the role of MCT4 in lactate transport in oocytes. We found that MCT4 overexpression in young oocytes exhibited decreased cytosolic lactate ( Extended Data Fig. 5 a-b ) and elevated lactate in the culture medium ( Extended Data Fig. 5 c), indicating that MCT4 mediates lactate export in oocytes. The decrease in cytosolic lactate (Fig. 6 a-b) and increase in lactate levels in the culture medium (Fig. 6 c) was also observed in aged GV oocytes, which exhibited increased MCT4 expression, and VB124 treatment effectively reversed these phenotypes (Fig. 6 a-c), suggesting that MCT4 induced an increased lactate export in aged GV oocytes. Importantly, the restored lactate export by VB124 in aged oocytes was accompanied by improved oocyte quality (Fig. 5 b-g), suggesting that MCT4-mediated lactate export modulates the quality defects of aged oocytes. MCT4 upregulation mediates excessive lactate export and depletes cytosolic lactate. Consequentially, it may divert pyruvate from mitochondrial aerobic respiration toward lactate production to compensate for the lactate loss 72 , 77 . In support of this hypothesis, we observed that MCT4 overexpression in young oocytes led to reduced mitochondrial pyruvate levels ( Extended Data Fig. 5 d-e). Similarly, aged GV oocytes exhibited decreased mitochondrial pyruvate levels, and these defects were partially reversed by treatment with VB124 (Fig. 6 d-e). These findings suggest that elevated MCT4 impairs mitochondrial pyruvate influx in aged oocytes. Mitochondrial pyruvate is essential for ATP generation via aerobic respiration, and ATP depletion has been linked to functional decline in aged oocytes 78 . We, therefore, hypothesized that MCT4-induced mitochondrial pyruvate depletion impairs oocyte quality by reducing ATP production. Consistent with our hypothesis, GV oocytes with MCT4 overexpression or aging exhibited decreased ATP levels ( Extended Data Fig. 5 f, Fig. 6 f). Additionally, supplementation with VB124 or pyruvate in aged GV oocytes, which increased mitochondrial pyruvate levels (Fig. 6 e and 6 g-h), restored ATP levels (Fig. 6 f and 6 i) and improved oocyte quality (Fig. 5 b-g and 6 j-k). These findings suggest that MCT4-induced pyruvate depletion contributes to ATP loss and the decline in oocyte quality. In summary, our results suggest that the increased MCT4 in aged GV oocytes drives excessive lactate export, leading to decreased mitochondrial pyruvate influx and ATP depletion. These metabolic disruptions may contribute to the aging-induced defects in GV oocytes (Fig. 6 l). Lactate export measurement served as a potential non-invasive approach to assessing aged GV oocyte quality in mice Our analysis revealed that MCT4 was upregulated in aged GV oocytes with severe aging impairments ( Extended Data Fig. 4 a ) . Functional studies further demonstrated that MCT4 regulated oocyte quality by mediating lactate export (Fig. 6 a-c). Based on these findings, we proposed that lactate export could serve as an indicator of GV oocyte aging. Specifically, GV oocytes with greater aging-induced damage are expected to show higher MCT4 expression and lactate export, whereas less affected oocytes should exhibit levels comparable to young controls. To test this, we measured the lactate export of single GV oocytes isolated from aged mice. Each oocyte was cultured in a lactate-free medium for 16 hours, and the lactate concentration of oocyte medium was measured as an indicator of lactate export levels. The oocytes then underwent in vitro maturation, and spindle assembly in MII oocytes was assessed as a quality marker. Young GV oocytes with normal maturation served as controls. Our results showed that aged GV oocytes with abnormal maturation showed significantly higher lactate export than young GV oocytes and aged GV oocytes with normal maturation (Fig. 6 m). These findings support lactate export measurement as a potential noninvasive assessment of aged GV oocyte quality in mice. Taken together, our functional analysis suggests that MCT4 and its lactate export function as a mediator, biomarker, and potential intervention target for oocyte aging, supporting the validity of our analysis in identifying biomarkers and mediators of oocyte aging. scSTAP profiling captured the transcriptomic and proteomic landscape in human-aged oocytes at the single-cell level Our scSTAP platform effectively profiled proteomic and transcriptomic landscapes in mouse-aged oocytes. This resource allowed us to uncover the molecular features underlying mouse oocyte aging, identify potential mediators, and develop intervention and assessment strategies for oocyte aging. Motivated by the capability of scSTAP profiling in dissecting oocyte aging, we exploited the scSTAP profiling to investigate oocyte aging in humans. Studies have reported a significant decline in oocyte quality in females aged 35 years and older. Based on this, we collected 22 oocytes (13 GV and 9 MII) from females aged 35 to 42 (mean age: 40) as the aged group 17 , 61 . As the young group control, 16 oocytes (8 GV and 8 MII) were obtained from females aged 21 to 32 (mean age: 27). Through scSTAP profiling, we acquired single-cell transcriptomic and proteomic data for each oocyte, achieving total profiling depth of ~ 30000 genes and ~ 6000 proteins (Fig. 7 a-b, Extended Data Fig. 6 a-b), with strong correlation coefficients across all samples tested (Fig. 7 c, Extended Data Fig. 6 c, and Supplementary Table 1–2 ). Both our single-cell transcriptomic and proteomic data successfully captured stage-specific markers for GV and MII oocytes, such as ZAR1 and PATL2 for GV oocytes and MED30 and TPRXL for MII oocytes (Fig. 7 d, Extended Data Fig. 6 d) 79 – 81 . Furthermore, our integrated transcriptome and proteome datasets show a high correlation with previously published bulk data, validating the robustness of our profiling data (Fig. 7 e, Extended Data Fig. 6 e) 15 . Using this dataset, we identified DEGs and differentially expressed proteins (DEPs) in GV and MII oocytes and their enriched biological pathways (Fig. 7 f-g, Extended Data Fig. 6 f-g, and Supplementary Table 2 ). For instance, proteins associated with PI3K-AKT signaling, oxidative phosphorylation, and RNA splicing were significantly altered in human oocytes upon aging (Fig. 7 g). Interestingly, the aging-induced transcriptomic and proteomic alterations in GV and MII oocytes showed very little overlap, indicating a stage-specific effect of aging on oocytes (Fig. 7 h, Extended Data Fig. 6 h). In mice, we observed altered transcriptomic and proteomic transitions from GV to MII oocytes as a molecular feature of oocyte aging. Similarly, we also detected molecular dynamic alterations in human-aged oocytes during meiotic maturation (Fig. 7 i-j, Extended Data Fig. 6 i-j). The dynamics of a significant portion of protein or RNA during the meiotic maturation underwent impairment upon aging (Clusters 1 and 3), while some novel RNA and protein dynamics were induced (Clusters 5 and 6, Fig. 7 i-j and Extended Data Fig. 6 i-j). These altered dynamics may underlie the maturation defects in human-aged oocytes. Intriguingly, the aging-induced protein dynamic alterations did not appear to be driven by RNA-level alterations (Fig. 7 j), suggesting that translation or protein stability changes may play a role in the observed dynamics. Exploiting our single-cell resolution data, we examined whether aging affected protein expression verifiability in human oocytes. Similar to findings in mice, aging unexpectedly decreased protein expression variability in human oocytes ( Extended Data Fig. 7 a). Specifically, it led to a more uniform expression in a subset of mitochondrial-associated proteins ( Extended Data Fig. 7 b-c) through a mechanism largely independent of protein abundance or RNA expression variability ( Extended Data Fig. 7 d-e). The uniform expression of these proteins in human-aged oocytes raises the possibility that these proteins contribute to maintaining human-aged oocyte viability or function. Multi-omic analysis revealed uncoupled dynamics in transcriptome and proteome during human oocyte aging In mice, single-cell multiomics analysis revealed a low correlation between RNA and protein in aged oocytes and uncoupled alterations in proteomics and transcriptomics during oocyte aging. This observation raises the question of whether similar RNA-protein uncoupling occurs during human oocyte aging. We first examined RNA-protein correlation in human oocytes. Consistent with observations in mice, the overall correlation between RNA and protein levels was low (< 0.1) in young and aged human oocytes ( Extended Data Fig. 8 a). This low correlation was also evident in each oocyte profiled (Fig. 7 k), indicating this is a general feature for human oocytes. Furthermore, we examined the RNA-protein correlation for each detected gene and confirmed a generally low RNA-protein correlation across all genes detected, with more than 75% of genes exhibiting a correlation below 0.5 ( Extended Data Fig. 8 b, Supplementary Table 3 ). These results suggest that RNA levels do not primarily determine protein abundance in human oocytes. Interestingly, genes with the highest translational efficiency exhibited higher RNA-protein correlation than those with the lowest translational efficiency in human-aged GV oocytes, suggesting that translational efficiency may influence RNA-protein correlation in human-aged oocytes ( Extended Data Fig. 8 c). To investigate the coordination between aging-induced proteomic and transcriptomic alterations in human oocytes, we clustered genes based on the correlation between their RNA and protein changes during oocyte aging (Fig. 7 l-m, Supplementary Table 4 ). In GV oocytes, Class 1 genes (36 genes) showed a positive correlation between RNA and protein changes, while Class 2–4 genes (818 genes) displayed uncorrelated or negatively correlated RNA and protein changes (Fig. 7 l). Based on these results, we proposed that the majority of genes exhibited uncoupled proteomic and transcriptomic alterations upon GV oocyte aging in humans. Similar uncoupled RNA-protein alterations were also observed in MII oocytes (Fig. 7 m). Furthermore, single-cell analysis revealed that uncoupled RNA-protein changes in representative genes reflect a general trend in human oocyte aging (Fig. 7 n-o). Taken together, our profiling data indicated uncoupled protein and RNA alterations during human oocyte aging, which suggests that proteomic changes in human-aged oocytes are not primarily driven by RNA alterations. In mice, our analysis revealed a potential role of translational defects in the uncoupled RNA and protein changes during oocyte aging. To investigate whether translational efficiency contributes to this phenomenon in human oocyte aging, we integrated human oocyte translational data with our profiling data, focusing on aging-affected genes with translational efficiency alterations. Among genes with aging-induced translational defects, 38.84% (Group 1/Group [1–2]) exhibited decreased protein levels without a corresponding RNA decline ( Extended Data Fig. 8 d, Supplementary Table 4 ). Conversely, among genes with increased translational efficiency during aging, 10.3% (Group 1/Group [1–2]) showed elevated protein levels without a corresponding RNA increase ( Extended Data Fig. 8 e, Supplementary Table 4 ). These findings suggest that translational alterations may contribute to the uncoupled RNA-protein changes observed in these Group 1 genes during human oocyte aging. Single-cell co-profiling identified two groups of human-aged GV oocytes with distinct aging-associated molecular features Using single-cell proteomics in mouse oocytes, we captured the molecular heterogeneity in mouse-aged oocytes and identified biomarkers for assessing mouse oocyte aging state. In humans, aged oocytes are also reported to exhibit quality variation, suggesting potential heterogeneity in the impact of aging 64 . However, there are currently no molecular features to confirm or assess this heterogeneity in human-aged oocytes. Our single-cell profiling resource provides a unique opportunity to address this gap. In mice, we used the signaling entropy to identify heterogeneity in aged oocytes. Applying the same strategy to aged human GV oocytes, we clustered aged GV oocytes into two groups based on the median signaling entropy (Fig. 8 a). Group I oocytes, compared to Group II, exhibited higher signaling entropy (Fig. 8 a), maintained the expression of oocyte functional proteins ( e.g. , SRSF7 and PABPN1, Fig. 8 b) 82 , 83 , and experienced fewer alterations in proteome (Fig. 8 c), transcriptome (Fig. 8 d), and RNA-protein correlation (Fig. 8 e) during aging. Furthermore, the median donor ages of Group I oocytes are lower than that of Group II oocytes (Fig. 8 f). These features together suggest that Group I samples represent oocytes with fewer molecular changes during aging, whereas Group II samples represent oocytes with more molecular changes during aging. Next, we sought to identify biomarkers distinguishing the two groups of aged GV oocytes. Through a proteomic comparison among young, Group I, and Group II oocytes, we identified four distinct protein clusters based on their expression patterns (Fig. 8 g, Supplementary Table 5 ). Among these, Clusters 2 and 4 proteins were specifically upregulated and downregulated in Group II oocytes and enriched in pathways related to RNA splicing and carboxylic acid transmembrane transport (Fig. 8 g-h). Given that Group II oocytes exhibited more pronounced molecular changes upon aging, these clusters of proteins may contribute to aging-induced defects and serve as potential biomarkers for assessing the aging state in human GV oocytes. Notably, several key functional proteins for oocytes, such as SRSF7 and PABPN1 82, 83 , were classified in Cluster 4, supporting the validity of our identified aging biomarkers (Fig. 8 g-h). We also observed that MCT4, a biomarker of mouse oocyte aging, along with its associated carboxylic acid transmembrane transport pathway, was identified in Cluster 2 (Fig. 8 g-h). These findings suggest that MCT4 and its lactate export function may represent a conserved feature of oocyte aging in both humans and mice. Lastly, we examined whether molecular heterogeneity exists within human-aged MII oocytes. Using the same analysis strategy, we clustered the oocytes into two groups based on their median signaling entropy ( Extended Data Fig. 9a ). Unexpectedly, Group I oocytes did not exhibit milder molecular changes during aging than Group II oocytes, including proteomic, transcriptomic, and RNA-protein correlation changes ( Extended Data Fig. 9b-d ). These findings suggest that human-aged MII oocytes may exhibit lower molecular heterogeneity compared to GV oocytes or that such heterogeneity cannot be fully captured by our proteomic analysis alone. Cross-species analysis of oocyte aging in mice and humans Mouse models have served as one of the primary experimental systems for investigating the molecular mechanisms underlying human oocyte aging 78 . Our profiling has identified several conserved aging features between mouse and human oocytes, such as more conformed protein expression and uncoupled RNA-protein alterations. To further evaluate the translational relevance of murine models to human oocyte aging, we conducted a detailed comparative analysis of aging phenotypes between these two species. We first compare the genes and proteins affected by aging in mice and humans. Specifically, aging-induced DEGs in GV and MII oocytes showed minimal overlap between humans and mice (Fig. 8 i, Supplementary Table 2 ). Similarly, aging-induced proteomic changes also showed low overlap between humans and mice in both GV and MII oocytes (Fig. 8 j, Supplementary Table 2 ). The RNA and proteins that are uniquely changed upon oocyte aging in humans are enriched in sister chromatid segregation and carboxylic acid/lipid-associated pathways, while the RNA and proteins that are uniquely changed upon oocyte aging in mice are enriched in mitochondria- and actin-filament-associated pathways (Fig. 8 k). The overlapping changes include multiple ribosomal genes (e.g., RPL11 , RPL35 , RPS9 ), and translation-associated proteins (e.g., EEF1E1) (Fig. 8 i-j). These shared DEPs/DEGs suggest that translational alterations may be a conserved feature of oocyte aging. Notably, previous studies have also highlighted the role of translational defects in oocyte aging 15 , 84 , 85 . We further compared the candidate biomarkers assessing the impact of aging on human and mouse oocytes. The results showed that the biomarkers for humans and mice also show minimal overlap (Fig. 8 m). Nevertheless, several conserved biomarkers were identified, including MCT4, a validated marker in mice (Fig. 8 m). Altogether, these comparisons indicate that while mouse oocyte aging exhibits similar aging characteristics to humans, the specific genes and proteins affected differ. This highlights the need for caution when using mouse models to study human reproductive aging. Discussion Oocyte quality decline due to aging has become a significant challenge for female reproductive health 7 , 8 . However, the limited availability of aged oocytes, particularly human-aged oocytes, has constrained comprehensive molecular profiling on oocyte aging, challenging the elucidation of the mechanisms underlying this decline. In this study, we employed the scSTAP to perform integrated single-cell proteomic and transcriptomic profiling of both mouse- and human-aged oocytes 22 . Our profiling established the proteomic and transcriptomic landscape of oocyte aging in humans and mice, providing a valuable resource for uncovering molecular features and mechanisms associated with oocyte aging. In addition to the proteomic/transcriptomic landscape, our integrated multi-omics data also uncovered the RNA-protein correlation during oocyte aging. Using our data, we revealed that both humans and mice exhibited a low RNA-protein correlation in aged oocytes. In addition, we showed that aging-induced alterations in RNA and protein levels were largely uncoupled in mouse and human oocytes, and translational defects might partially contribute to these uncoupled changes. These findings align with previous studies showing the role of translational efficiency in oocyte aging 84 , 85 . Taken together, our results suggest that scSTAP profiling can dissect how the proteomic landscape is shaped by transcriptomic features during oocyte aging and facilitate the identification of mediators modulating proteomic changes in aged oocytes. In addition, our results highlight a critical methodological consideration: the limited predictive value of RNA-based inferences for protein expression in aged oocytes, emphasizing the necessity of direct proteomic characterization when studying molecular changes associated with oocyte aging. Aged oocytes are reported to exhibit quality heterogeneity. Even oocytes from same-aged donors show varying competence for fertilization and development 63 , 64 . However, the molecular features and mechanisms underlying this variability remain unclear. Our single-cell profiling provided a valuable resource for examining the molecular characteristics associated with oocyte heterogeneity. We utilized signaling entropy, an index that effectively captures aging features in oocytes, to cluster aged oocytes into two groups. These groups exhibited significantly different extents of molecular changes with aging. By analyzing these groups, we identified proteins that may serve as biomarkers for assessing the impact of aging on oocytes. Interestingly, this analytical strategy failed to capture the heterogeneity of aged human MII oocytes. One possible explanation is that human MII oocytes have undergone a quality selection during harvest, resulting in fewer molecular differences. Alternatively, this may suggest that our proteomic analysis alone is insufficient to reveal heterogeneity in human MII oocytes, and additional layers of cellular information are required. MCT4 is one of the candidate markers of GV oocyte aging identified in both mice and humans. Our study suggests that MCT4-mediated lactate export functions as both a mediator and a potential intervention target for oocyte aging. Metabolic alterations have been implicated in oocyte aging 78 , and our findings reveal a previously unrecognized metabolic dysfunction—lactate metabolism—associated with this process. Moreover, our results suggest that this metabolic disorder may underlie the molecular heterogeneity observed in aged oocytes. Leveraging this feature, we provide evidence suggesting that oocyte lactate export could be a non-invasive biomarker for assessing oocyte aging. These findings underscore the translational potential of MCT4 and lactate metabolism in the diagnosis and treatment of oocyte aging. Oocytes reside within the ovary, and their function and quality are highly influenced by the ovarian environment 86 . Studies have shown that the ovary undergoes significant changes during aging and that a healthier follicular microenvironment can markedly enhance the quality of aged oocytes 87 – 92 . These findings emphasize the critical role of the ovarian niche in maintaining oocyte quality. Leveraging our profiling resource of aged oocytes, future investigations could explore the interplay between oocyte and ovarian aging and identify key cellular communication pathways contributing to oocyte aging. Methods Mice and VB124 in vivo treatment C57BL/6J mice were acquired from the Laboratory Animal Center of Zhejiang Academy of Medical Sciences (China). Both young (6–8 weeks old) and aged (12 months old) mice were maintained under pathogen-free conditions with ad libitum access to standard chow and autoclaved water. The vivarium maintained controlled environmental parameters: 12-hour photoperiod cycling, ambient temperature regulated at 20 ± 1°C, and relative humidity between 50–70%. All experimental protocols received prior approval from the Institutional Animal Care and Use Committee of Zhejiang University (IACUC approval: ZJU20220303). For pharmacological intervention, aged subjects were randomly allocated into two treatment groups. The experimental group received daily intraperitoneal injections of VB124 (MedChemExpress, HY-139665; 30 mg/kg body weight) dissolved in vehicle solution (40% PEG300, 5% Tween-80, 45% physiological saline). Control animals were administered equivalent volumes of vehicle solution alone. This treatment regimen persisted for 15 days, with daily animal welfare monitoring. Mouse oocyte collection and in vitro culture The experimental protocol involved intraperitoneal administration of 10 IU pregnant mare serum gonadotropin (PMSG; Ningbo Sansheng Pharmaceutical, veterinary approval 110044564) to sexually mature female mice. Following a 44-hour pharmacological stimulation period, the animals were humanely sacrificed according to institutional animal care guidelines. Oocytes at the GV stage were harvested from ovarian follicles using the M2 collection medium (Sigma-Aldrich, M7167) and transferred to maturation culture conditions. The in vitro maturation process was conducted in an M16 culture medium (Sigma-Aldrich, M7292) layered with mineral oil (Sigma-Aldrich, M8410), maintained at 37°C in a humidified incubator with 5% CO2 tension. After 16 hours of controlled culture conditions, oocytes successfully progressed to the MII stage. Superovulation Female mice were given a 10 IU injection of hCG roughly 48 hours after receiving a 10 IU PMSG injection. After an additional 16 hours, cumulus-oocyte complexes (COCs) were surgically harvested from the fallopian tubes. In vitro fertilization (IVF) Mature oocytes arrested at the MII stage were selected for subsequent fertilization procedures. Following a 60-minute incubation period in a pre-equilibrated IVF medium to achieve optimal dilution, freshly prepared capacitated sperm samples were used. Concurrently, collected COCs from five donor mice or denuded oocytes at the MII stage were transferred into the fertilization medium microdroplets (200 µL volume). Precise aliquots (3–5 µL) of the optimized sperm suspension were then added to the microdroplets containing the COCs to achieve a final concentration of 4 × 10 6 motile spermatozoa per milliliter in the droplets. Gamete co-culture was maintained under controlled conditions (37°C, 5% CO₂) for 4 to 6 hours to allow successful fertilization. Sperm binding assay Spermatozoa collected from sexually mature mice underwent capacitation through 60-minute incubation in the IVF medium. Following this preparatory phase, the capacitated gametes were subsequently exposed to either ovulated oocytes or two-cell stage embryos during a 30-minute coincubation. In the experimental design, two-cell stage embryo interactions served as the negative control, and immature oocyte interactions provided the baseline. Mouse litter size analysis Each female mouse was mated with a young male mouse of proven fertility. Mating was confirmed by the presence of a copulatory plug, and the day of plug detection was designated as embryonic day 0.5 (E0.5). Litters were collected at birth (postnatal day 0, P0), and the number of pups per litter was recorded. Pups were counted and weighed within 24 hours of birth to ensure accurate litter size and pup viability measurement. Quantification of oocyte ATP levels Oocyte ATP concentrations were measured in batches of five oocytes using a commercially available ATP bioluminescence detection kit (Sigma-Aldrich, FLASC) according to the manufacturer's protocol. For accurate quantification, calibration curves were systematically generated using seven ATP reference concentrations (0, 0.01, 0.03, 0.1, 0.3, 1, and 3 µM) in parallel with the experimental samples. The ATP quantities in biological samples were subsequently determined through regression analysis, utilizing the mathematical relationship established from the linear portion of the standard curve. FiLa-based medium lactate analysis For the FiLa-sensor-based medium lactate analysis, 2 µL self-prepared lactate-free M2 culture medium samples were collected after a 16-hour single-cell culture. To ensure consistency in sample volume, 48 µL of the detection solution was added to the collected culture medium samples. This step was crucial to match the sample volume with that of the standard solutions used in the assay. Then FiLa protein probe (Provoson, PFLA100S) was gently mixed and diluted 20-fold with the detection solution to prepare the working solution. Each assay was performed in a 96-well black bottom plate using 50 µL diluted samples or 50 µL lactate protein with 50 µL working solution. Fluorescence intensity was measured immediately by a multifunctional microplate reader (Tecan, SPARK) using 485 BP 20 nm or 420 BP 10 nm excitation and 528 BP 20 nm emission bandpass filters. A 5-point standard curve (0, 0.256, 1.28, 6.4, 32 µM of lactate) was generated in each assay, and the lactate content was calculated using the formula derived from the linear regression of the standard curve. Plasmid construction Mouse Mct4 cDNAs were PCR amplified from a mouse GV oocytes cDNA pool and ligated into pcDNA-based eukaryote expression vectors. Fila-cyto plasmid was acquired from Provoson (Provoson, #FLA1001). PyronicSF/pcDNA3.1 was acquired from Addgene (Addgene, #124812). To achieve mitochondrial expression of PyronicSF, a mitochondrial-targeting sequence of COX8 ( Supplementary Table 6 ) was fused at the N terminus of PyronicSF/pcDNA3.1, generating the PyronicSF-mito plasmid. In vitro transcription and microinjection Expression vectors, FiLa-cyto and PyronicSF-Mito plasmids were linearized using specific restriction enzymes to prepare mRNAs for microinjection. The synthesis of 5′-capped mRNA transcripts was accomplished through in vitro transcription employing T7/SP6 RNA polymerase systems (message mMACHINE™ Kits, Invitrogen, AM1344/AM1340) under standard reaction conditions (37°C, 4 hours). Post-transcriptional processing included polyadenylation using the Poly(A) Tailing Kit (Invitrogen, AM1350), followed by purification via LiCl precipitation and subsequent resuspension in nuclease-free aqueous solution. Gene-specific silencing molecules targeting Mct4 were generated through in vitro transcription with the T7 RNAi system (Vazyme, TR102) per manufacturer specifications, with corresponding oligonucleotide sequences detailed in Supplementary Table 6 . For microinjection, a Nikon microscope-integrated micromanipulation system was employed to administer precise 5–10 pL injections into oocytes. Injection solutions contained synthetic mRNA (1 mg/mL working concentration), proteins (2 mg/mL), or siRNA duplexes (20 µM final concentration), appropriately diluted in a sterile injection buffer. Following microinjection, the oocytes were cultured in a milrinone-supplemented medium for a defined period (as illustrated in the schematic) to maintain meiotic arrest and allow sufficient time for perturbation. Subsequently, the oocytes are thoroughly washed to remove milrinone, thereby relieving meiotic inhibition and enabling the resumption of the maturation process. The treated oocytes are then transferred to a fresh maturation medium for subsequent functional studies. Oocyte lactate and pyruvate detection by live-cell fluorescence imaging Cytosolic lactate and mitochondrial pyruvate levels in oocytes were measured using the Fila-cyto and PyronicSF-mito sensors, respectively, as previously described 93 , 94 . Specifically, the oocytes were microinjected with fluorescence sensors (Fila-cyto or PyronicSF-mito) along with mCherry as an injection control. The detailed microinjection process is described above. Subsequently, these oocytes were plated on a 35 mm glass-bottom dish. The injected FiLa and PyronicSF sensors were expressed in different subcellular compartments (cytosol or mitochondrial) by tagging with organelle-specific signal peptides ( Supplementary Table 6 ). Live-cell imaging was performed using a Zeiss LSM900 system, with sequential scanning using 488 nm and 594 nm laser lines to prevent spectral bleed-through. The GFP-conjugated probes were detected through 493–556 nm excitation/emission filters, while mCherry fluorescence was recorded using 587–628 nm bandpass settings. All acquisition parameters were maintained at 12-bit depth with identical gain/pixel dwell time configurations across experimental replicates. Captured images were processed and exported as uncompressed TIFF sequences using Zen Blue software before quantitative analysis in ImageJ. Fluorescence quantification involved background subtraction and compartment-specific ROI selection based on morphology thresholds. Signal normalization was performed against mCherry reference values using integrated measurement tools (ImageJ). Immunofluorescence Oocytes were subjected to a fixing process involving a 4% paraformaldehyde solution (PBS-buffered) for a period of 30 minutes, followed by permeabilization in PBS with 0.3% Triton X-100 for 30 minutes. Subsequently, samples were blocked in PBS containing 1% bovine serum albumin for 30 minutes, followed by incubation with primary antibodies at 25°C for 1 hour. Nuclei were stained with 4′,6-diamidino-2-phenylindole (DAPI), and secondary antibodies were co-incubated with DAPI for 30 minutes. Images were acquired using a Zeiss LSM900 confocal microscope. Quantitative analysis of fluorescence intensity and length measurement were performed by ImageJ. The information of antibodies used were included in Supplemental Table 6 . Histological analysis Ovarian tissues were collected and immediately fixed in 4% paraformaldehyde-phosphate-buffered saline (PBS, pH 7.4) at 4°C for 16 hours. Following standard histological procedures, the fixed specimens underwent sequential ethanol dehydration (70%-100% gradient), xylene clearing, and paraffin infiltration. Tissue blocks were sectioned coronally at 5 µm using a rotary microtome (Leica RM2235). Apoptotic cells were detected using the TUNEL BrightRed Apoptosis Detection Kit (Vazyme, A113-01) through fluorometric labeling of DNA fragmentation sites, with experimental conditions strictly adhered to according to the manufacturer’s instructions. Extraction of minute quantities of RNA, reverse transcription (RT), RT-PCR Following oocyte collection (n = 10), cellular lysis was carried out using a 2 µL lysis buffer (1% Triton X-100, 40 U/µl RNase inhibitor). RNA purification was subsequently performed, and around 10–15 ng of mRNA could be obtained. First-strand cDNA synthesis was achieved with SuperScript II Reverse Transcriptase following manufacturer guidelines, and cDNA products underwent 6-9-fold dilution in sterile nuclease-free water before serving as template DNA in subsequent analyses. Quantitative RT-PCR was conducted using Taq Pro Universal SYBR qPCR Master Mix (Vazyme, cat. no. Q712) and the ABI 7500 Real-Time PCR system with primers specified in Supplemental Table 6 . Data normalization was performed against Gapdh housekeeping gene expression levels. To ensure reproducibility, each qPCR assay included at least three technical replicates per biological sample. Western-blot analysis Protein homogenization was performed using a cracking buffer (Absin, abs953) supplemented with β-mercaptoethanol, followed by thermal denaturation at 95°C for 10 min. The denatured samples were loaded into SDS-PAGE (13% separation gel) and subsequently electroblotted onto PVDF membranes (0.45 µm pore size; Millipore). Membranes underwent blocking with 5% (w/v) non-fat dry milk in TBST for 30 min at ambient temperature before overnight incubation with primary antibodies at 4°C. Following three 5-minute TBST washes, immunodetection was conducted using horseradish peroxidase (HRP)-conjugated secondary antibodies (1:5000; Jackson ImmunoResearch) with 1-hour incubation at room temperature. After three subsequent TBST rinses (10 min each), chemiluminescent signals were developed with SuperSignal West Femto substrate (Thermo Fisher Scientific) and captured using a digital imaging system (Amersham Imager 680). Detailed antibody specifications, including clone identifiers, target epitopes, and working dilutions, are provided in Supplementary Table 6 . Human oocyte collection and culture The human-associated experiment of this study was approved by the Medical Ethics Committee of Sir Run Shaw Hospital, School of Medicine, Zhejiang University (No.2022 − 0461) and was conducted in accordance with the criteria set by the Declaration of Helsinki. Human oocytes were donated from women who underwent intracytoplasmic sperm injection (ICSI) treatment due to male factors. The woman had no family history of genetic diseases. Ovarian stimulation was carried out following a progestin-primed ovarian stimulation protocol. Once two follicles grew to a diameter of 18 mm or three follicles reached 17 mm, we triggered the final oocyte maturation by injection of 5000-10,000 IU of human chorionic gonadotropin (hCG; Lizon Pharma Pharmaceutical Trading Co., Ltd., Zhuhai, China) or addition of 0.1 mg of triptorelin (Decapeptyl; Ferring Pharmaceuticals, Hoofddorp, Netherlands) as a GnRH agonist. Approximately 36 hours later, the oocytes were retrieved through transvaginal ultrasound. From all collected oocytes, only those at the immature GV stage were used in this study because they were not routinely utilized for clinical purposes, given that these patients had an adequate number of MII oocytes for their treatment. The in vitro matured MII oocytes were obtained from GV oocytes, which were cultured in IVM medium (RC-1060, ARSCI Biomedical Inc., Zhejiang, China) at 37°C in an atmosphere with 6% CO 2 for 24–28 hours. The aged group consisted of women (n = 16) with a mean age of 40 (age range 35–46). 22 oocytes (13 GV and 9 MII) were collected from these women. In the young group, 16 oocytes (8 GV and 8 MII) were obtained from women (n = 13) with a mean age of 27 years (age range 21–32). All women who participated in this study provided their written informed consent. Single-cell multi-omics sequencing A schematic workflow of the single-cell multi-omics sequencing is shown in Fig. 1 a. The previously developed SODA technique was used for single-cell capture and pretreatment 47 , 48 , 95 . Briefly, a capillary probe with a tapered tip was connected to a high-precision syringe pump for nanoliter-scale liquid manipulation, and an x-y-z translation stage was controlled for the positioning and moving of insert tubes and 24-well plates. First, single oocyte cells were picked up and washed using the capillary probe under the observation of a microscope and were deposited into the insert tubes individually, forming a 200 nL single-cell droplet in each insert tube. Next, 400 nL of 50 ng/µL Lys-C solution was added into each insert tube and incubated at 37°C for 15 min, and then 400 nL of 0.3% (w/v) RapiGest was added with an incubation condition of 60°C for 15 min for cell lysis. After the cell lysis, the droplets containing cell lyses were split into two aliquots using the capillary probe. One aliquot was used to analyze transcriptome, and the other aliquot left in the same insert tube was used for proteomic analysis. In the transcriptomic analysis, the droplet was analyzed using the Smart-seq2 technique 96 . The lysis was used for library generation using the V2 model library preparation kit (TruePrep DNA, Vazyme Cat# TD503). In the proteomic analysis, the droplet was analyzed using an improved deep single-cell proteomic technique developed by us 22 . The bottom-up strategy was used with an improved workflow. Briefly, 500 nL of 10 mM tris (2-carboxyethyl)-phosphine (TCEP) was added into the insert tube containing the aliquot droplet of a lysed single cell with an incubation time of 20 min at 25°C. Then, 50 mM iodoacetamide (IAA) was added to the droplet with an incubation time of 20 min at 25°C in the dark. The proteins in the droplet were then digested by adding 500 nL of 50 ng/µL enzyme solution (trypsin: lys-C = 1:1) and incubating the droplet at 37°C for 4 h. Finally, 1 µL of 50% (v/v) formic acid was added to the droplet to terminate the reactions. After the pretreatment, the insert tubes containing the droplets for scProteomics were transferred into the autosampler of an EASY-nLC 1200 LC system (Thermo, USA) coupled with a timsTOF Pro mass spectrometer (Bruker, USA). The injection needle and valve of the autosampler were used to sequentially complete the loading and injection of the droplet samples into a homemade capillary LC column (50 µm i.d., 6 cm length, 1.7 µm C18 particles with 120 Å pore size) installed in the LC system. The peptides in the droplet samples were separated in the LC column and detected by the mass spectrometer. The flow rate of the mobile phase in LC separations was 150 nL/min with a 70 min gradient (mobile phase A = 0.1% FA in water; mobile phase B = 0.1% FA, 80% ACN in water; 0–40 min, 0–40% mobile phase B; 40–41 min, 40–100% B; and 41–70 min, 100% B). The spray voltage of the ion source was set at 1.75 kV under the positive mode. The acquisition mode of diaPASEF was used with a mass range of 350–1150 m/z, an isolation width of 25 m/z, a mobility 1/k0 range of 0.6–1.3, and a collision energy range of 20–47.3 eV in collision-induced dissociation (CID). Smart-seq2 analysis The hg38 human genome, the mm10 mouse genome, and the genomic annotation file were downloaded from the Ensemble database. The raw RNA-seq reads were trimmed by trim-galore (v 0.6.7). The clean data were mapped using Hisat2 (v 2.10) 97 against the corresponding genome index with the following parameters:’-x /Path/To/Genomeindex -p 5 − 1 Sampe_fq1.gz -2 Sampe_fq2.gz -S Sample.sam’. Then, the sam files were converted to bam format using Samtools (v 1.9) 98 . Read counts were calculated by Featurecount (v 1.6.5) with the following parameters:’-T 5 -p -B -t exon -g gene_name -a genome.gtf -o Sample_count.txt Sample.sort.bam’. The DEGs were calculated using the DESeq2 package (v 1.32.0) 99 . The genes satisfying |Log 2 FoldChange| > 1 and P values < 0.05 were identified as DEGs. using P values were obtained using the Wald test within a negative binomial generalized linear model. Clusterprofiler package (v 4.0.5) 100 was used to perform GO function enrichment and KEGG pathway enrichment. The enriched gene count and P value for specific terms were used to generate the dot plot. ComplexHeatmap package (v 1.0.12) was used to plot the heatmap. The ‘prcomp’ function in R (v 4.1.1) was used to perform the PCA. We averaged the PC1 and PC2 of young or aged oocytes to generate the centroid. The centroid distance was defined as the euclidean distance between the centroid of each group in low-dimensional space and was calculated by R (v 4.1.1). Single-cell proteome analysis The raw data of single-cell proteomics were analyzed using Spectronaut software (v 15.6.211220.50606 for mice samples & 18.2.230802.50606 for human samples) in library-based mode. The library was established using 20 quality control samples containing 2 ng oocyte proteins. The UniProt database (UP000000589.fasta, Mus musculus: 21,985 entries; UP000005640.fasta, Homo sapiens: 20,422 entries) was used in software analysis with the default settings. The false discovery rates (FDR) for both precursors and proteins were 0.01. The DEPs were identified based on previously reported methods as a reference 101 . Specifically, the protein expression matrix was first increased by 1 to avoid the error during the fold change calculation. Then, each group's average protein expression level was used to calculate the fold change of detected protein. Statistical significance was calculated by the Wilcoxon rank-sum test using the ‘Wilcox.test’ function in R (v 4.1.1). The proteins satisfying |Log 2 FoldChange| > 1 and P values < 0.05 were identified as DEPs. Clusterprofiler package (v 4.0.5) was used to perform GO function enrichment and KEGG pathway enrichment. The ‘prcomp’ function in R (v 4.1.1) was used to perform the Principal Component Analysis (PCA). We averaged the PC1 and PC2 of young or aged oocytes to generate the centroid. The centroid distance was defined as the Euclidean distance between the centroid of each group in low-dimensional space and was calculated by R (v 4.1.1). Signaling entropy analysis using single-cell proteome The signaling entropy value was calculated by the ‘CompSRana’ function in the LandSCENT package (v 0.99.5) 102 . Data is normalized Log 2 (expression matrix + 1.1) as suggested 102 . The protein-protein interaction ‘net13Jun12.m’ was used to integrate the single-cell proteome data and pre-defined protein connectome. Given the lack of knowledge on the heterogeneity distribution in aged oocytes, we decided to use the median signaling entropy within the aged oocyte group as a criterion for clustering. This strategy has been previously employed to dissect heterogeneity within samples 103 , 104 . Specifically, oocytes with signaling entropy above the median were designated as Group I, while those with signaling entropy below the median were classified as Group II. Oocytes with signaling entropy equal to the median were excluded from clustering. Correlation between mRNA and Protein Expression The mRNA expression level was represented by fragments per kilobase of mapped reads (FPKM). The protein expression level was represented using the automatic protein LFQ method in the Spectronaut software. The genes detected by both Smart-seq2 and single-cell proteome were then used to perform correlation analysis. The ‘cor’ function in R (v 4.1.1) was used to calculate Pearson’s correlation coefficient between the mRNA/protein expression level and between our profiling data and public data. The 'cor.test’ function in R (v 4.1.1) was used to calculate the statistical significance of correlation through the parametric t-test. Analysis of gene variation score To avoid the effect of the expression level, we first normalized the RNA expression matrix and protein expression matrix of genes. The expression level of each gene was normalized by dividing by the difference between the maximum value and minimum value. Then, the gene variation score (RNA or protein) was calculated as: $$\:range=\frac{\text{max}\left({Gene}_{i}\right)-\text{min}\left({Gene}_{i}\right)}{\text{max}\left({Gene}_{i}\right)+\text{min}\left({Gene}_{i}\right)}$$ $$\:mad=\frac{1}{N}\left({\sum\:}_{i=1}^{N}\left(\text{a}\text{b}\text{s}\right(\text{m}\text{e}\text{d}\text{i}\text{a}\text{n}\left(\left\{{Gene}_{i}\right\}\right)\:-\:{Gene}_{i})\right)\:$$ Where \(\:{Gene}_{i}\) donated the expression level of gene i . Max, min, median, and abs denoted the maximum gene expression value, minimum gene expression value, median gene expression value, and absolute value of gene expression value. In the following analysis, we removed the gene whose range is equal to 1. We calculate the fold change by dividing the gene variation score of the aged group by the gene variation score of the young group. The genes satisfying |Log 2 FoldChange| > 1 were identified as changed genes. Analysis of stage-specific genes We first generated the list of DEGs/DEPs between GV and MII oocytes at young and aged states based on scSTAP profiling data. The DEGs/DEPs were identified as described above. We then split these stage-specific genes/proteins into six clusters, including: 1) The gene was activated during young oocyte maturation, while the activation trend decreased during aged oocyte maturation; 2) The gene was activated during young oocyte maturation, while the activation trend increased during aged oocyte maturation; 3) The gene was repressed during young oocyte maturation, while the repression trend decreased during aged oocyte maturation; 4) The gene was repressed during young oocyte maturation; while the repression trend increased during aged oocyte maturation; 5) The gene was stable during young oocyte maturation but was repressed during aged oocyte maturation; 6) The gene was stable during young oocyte maturation but was activated during aged oocyte maturation. For groups 1–4, whether the gene/protein dynamics are changed upon aging was determined by the difference between the Log 2 (Foldchange [YMII vs YGV]) and Log 2 (Foldchange [AMII vs AGV]). We set different threshold values for the above two data: 0.25 for single-cell proteome data and 0.5 for Smart-seq2. The ‘geom_smooth’ function in gglot2 (V 3.4.4) was used to fit the temporal expression patterns of the protein levels and the corresponding RNA levels. Aging features determined by public gene set The FPKM matrix for Smart-seq2 and the scaled protein expression matrix were used for this analysis. We collect eight aging-associated gene sets, including CSgene 65 , SenMayo 66 , and SID gene sets (SID1-6) 67 . For CSgene and SenMyao gene sets, the aging scores were calculated as below: $$\:aging\:score={\sum\:}_{i=1}^{Z}{Gene}_{i}$$ For the SID gene set, the aging score was calculated as below: $$\:aging\:score={\sum\:}_{i=1}^{Z}{UpGene}_{i}-{\sum\:}_{i=1}^{Z}{DownGene}_{i}$$ where Z denoted the gene number of the corresponding geneset, \(\:{Gene}_{i}\) donated the expression level of gene i . \(\:{UpGene}_{i}\) and \(\:{DownGene}_{j}\) donated the expression level of up-regulated gene i and down-regulated gene j in the SID gene set. We treat the aging score as a signature of aging and evaluate the performance of the aging score to distinguish aged oocytes from young oocytes. The area under the receiver operating characteristic (ROC) curve (AUC) was used as a metric. The AUC analysis was performed by the ‘roc’ function in the pROC package (v 1.18.5). Translational efficiency analysis The translatome and transcriptome data of mouse GV oocytes were downloaded from CRA008819 15 . We use the same method of Smart-seq2 to map and count the translatome data. The translatome and transcriptome data were first transferred into Transcripts Per Million (TPM) format. Then, for each gene, the average TPM value of translatome data was divided by the average TPM value of transcriptome data to calculate the translational efficiency. The genes satisfying |Log2FoldChange (AGV vs YGV) | > 1 were identified as significantly changed genes. Statistics and reproducibility Statistical significance was determined using Student’s t-test (two-tailed), two-tailed Mann-Whitney U-tests for datasets with non-normal distribution, hypergeometric test for gene set enrichment analysis, or parametric t-test for correlation analysis as indicated in the corresponding figure legends. P value for protein expression level was obtained using the Wilcoxon rank-sum test, and the P value for RNA expression level was obtained using the Wald test within a negative binomial generalized linear model. Boxes in all box plots extend from the 25th to the 75th percentiles, with a line at the median. Statistical tests were performed using Prism8 (GraphPad Software) or R. Each experimental procedure was repeated a minimum of three times to ensure reproducibility. The outcomes are reported as the arithmetic mean accompanied by its standard error of measurement (SEM). Declarations Data and code availability All transcriptomic generated have been deposited in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA023659, GSA-Human: HRA010683), and all proteomics data have been deposited in the ProteomeXchange Consortium under accession number PXD061896. All the codes used are available online (see Methods). Any additional information required to reanalyze the data of this study is available from the corresponding author upon reasonable request. Acknowledgment We thank the core facility of Liangzhu Laboratory for the technical support. This research was funded by grants from the National Key R&D Program of China (2022YFC2702300, 2021YFA1301601), the Youth, General, and Key Program of the National Natural Science Foundation of China (82201837, 32470840, 32270852, 22234007), Zhejiang Provincial Natural Science Foundation of China (LZ24C120001), Key R&D Program of Zhejiang (2024C03144), and “Pioneer” R&D programs of Zhejiang Province (No. 2024C03005). Author information Lanrui Cao, Hao Wu, and Yirong Jiang contributed equally to this work. Lanrui Cao, Hao Wu, Yirong Jiang, Zhuo Yang, Peipei Ren, Panpan Zhao, Yinli Zhang Songying Zhang, Hengyu Fan, Yongcheng Wang, Xiaomei Tong, Qun Fang, Xudong Fu Authors and Affiliations First Affiliated Hospital, Zhejiang University School of Medicine, and Liangzhu Laboratory of Zhejiang University, Hangzhou, 310000, China Lanrui Cao, Hao Wu, Yirong Jiang, Yongcheng Wang, and Xudong Fu Institute of Hematology, Zhejiang University, Hangzhou, Zhejiang, 310000, China Xudong Fu Institute of Microanalytical Systems, Department of Chemistry, Key Laboratory of Excited-State Materials of Zhejiang Province, Zhejiang University, Hangzhou, 310058, China Yirong Jiang and Qun Fang Assisted Reproduction Unit, Department of Obstetrics and Gynecology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China Peipei Ren, Panpan Zhao, Yinli Zhang, Songying Zhang, Heng-yu Fan, and Xiaomei Tong School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, 110016, China Zhuo Yang Department of Geriatrics, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China. Xudong Fu Single-cell Proteomics Research Center, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 311200, China Qun Fang Key Laboratory for Biomedical Engineering of Ministry of Education, Cancer Center, Zhejiang University, Hangzhou, 310007, China Qun Fang Author Contributions L.R.C., Y.R.J, Z.Y., Y.C.W, and Q.F. generated the single-cell proteomic and transcriptomic resources. L.R.C. performed oocyte experiments and manuscript editing. H.W. performed bioinformatics analysis and manuscript editing. P.P.R, P.P.Z, and X.M.T collected the human oocytes. Y.L.Z, H.Y.F, and S.Y.Z provided reagents and expertise. Y.C.W., Q.F., and X.D.F. conceived the project. X.D.F. performed data interpretation and wrote the manuscript. All authors discussed the results and contributed to the manuscript preparation. Corresponding author Correspondence to Yongcheng Wang, Xiaomei Tong, Qun Fang, and Xudong Fu. 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university","correspondingAuthor":false,"prefix":"","firstName":"Cao","middleName":"","lastName":"Lanrui","suffix":""},{"id":440716883,"identity":"24475114-4dd4-4604-bcfa-d9c765b85d65","order_by":2,"name":"Yirong Jiang","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Yirong","middleName":"","lastName":"Jiang","suffix":""},{"id":440716884,"identity":"914be709-6e42-4a8b-9eb9-0c050d8c99ef","order_by":3,"name":"Zhuo Yang","email":"","orcid":"","institution":"Shenyang Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Zhuo","middleName":"","lastName":"Yang","suffix":""},{"id":440716885,"identity":"aed74a43-945d-451d-9026-904230096cb9","order_by":4,"name":"Peipei Ren","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Peipei","middleName":"","lastName":"Ren","suffix":""},{"id":440716886,"identity":"b5d21f4d-89b5-459e-a87a-6462a97f3f01","order_by":5,"name":"Panpan Zhao","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Panpan","middleName":"","lastName":"Zhao","suffix":""},{"id":440716887,"identity":"b37ad21d-3f5b-4674-90f1-52c24b3b363f","order_by":6,"name":"Yin-Li Zhang","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Yin-Li","middleName":"","lastName":"Zhang","suffix":""},{"id":440716888,"identity":"8b9c4c3b-278f-4cb4-bc29-5176aad2f08f","order_by":7,"name":"Songying Zhang","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Songying","middleName":"","lastName":"Zhang","suffix":""},{"id":440716889,"identity":"123313a2-f978-48e1-a1f4-7014471a0500","order_by":8,"name":"Heng-Yu Fan","email":"","orcid":"https://orcid.org/0000-0003-4544-4724","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Heng-Yu","middleName":"","lastName":"Fan","suffix":""},{"id":440716890,"identity":"638e3124-c832-46d3-bd98-15897a037375","order_by":9,"name":"Yongcheng Wang","email":"","orcid":"https://orcid.org/0000-0003-2820-4243","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Yongcheng","middleName":"","lastName":"Wang","suffix":""},{"id":440716891,"identity":"c3df8b23-048f-4783-8599-87ef48ea01d6","order_by":10,"name":"Xiaomei Tong","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Xiaomei","middleName":"","lastName":"Tong","suffix":""},{"id":440716892,"identity":"1eab33b6-7ede-4cbd-bcac-5c64df267136","order_by":11,"name":"Qun Fang","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Qun","middleName":"","lastName":"Fang","suffix":""},{"id":440716893,"identity":"e7cb08d1-6056-41de-9b5f-ee36cf470d8f","order_by":12,"name":"Hao Wu","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-03-26 06:21:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6309099/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6309099/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82325202,"identity":"1e97bfd9-e22d-4812-8d0e-bfc5df86135f","added_by":"auto","created_at":"2025-05-09 06:06:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4303629,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003escSTAP captures the single-cell proteomic and transcriptomic landscape in mouse-aged oocytes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e Schematic graph of the scSTAP workflow. Created by Figdraw.com. nanoUHPLC, nano ultra-high-performance liquid chromatography.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb, \u003c/strong\u003eThe number of total identified genes (\u003cstrong\u003eleft\u003c/strong\u003e) and proteins (\u003cstrong\u003eright\u003c/strong\u003e) in indicated mouse oocytes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec, \u003c/strong\u003eThe box plot showing the range of identified gene numbers (\u003cstrong\u003eleft\u003c/strong\u003e) and protein numbers (\u003cstrong\u003eright\u003c/strong\u003e) in profiled mouse oocytes. The numbers in the figure refer to the average identified genes or proteins for indicated oocytes. n = 12 (YGV), 12 (YMII), 9 (AGV), 9 (AMII).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed,\u003c/strong\u003e The correlation coefficient of the proteome in the aged GV (n = 9) and MII (n=8) oocytes. The detailed correlation coefficient for all sample groups is listed in \u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee, \u003c/strong\u003ePCA of proteomes for young and aged mouse oocytes profiled.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef,\u003c/strong\u003e The heatmap showing the protein expression levels of representative stage-specific genes in the young oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eg,\u003c/strong\u003e The correlation coefficient (\u003cem\u003eR\u003c/em\u003e) between our profiling data and previously published studies of proteomics data in young mouse oocytes (PRIDE: PXD018777)\u003csup\u003e105\u003c/sup\u003e. \u003cem\u003eP\u003c/em\u003e value was obtained using the parametric t-test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eh, \u003c/strong\u003eWestern blotting validation on the aging-induced expression changes of selected proteins (MCT4, PDCD4, and NTMT1) in oocytes at GV and MII stages. Actin and Histone H3 (H3) serve as control proteins.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ei, \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003evolcano plot displaying differently expressed proteins (DEPs, upregulated, red; downregulated, blue) with statistical significance (|Log\u003csub\u003e2\u003c/sub\u003eFoldchange| \u0026gt; 1 and \u003cem\u003eP\u003c/em\u003e values \u0026lt; 0.05) in aged oocytes compared to those from young ones. Total proteins analyzed (n) = 3,447. \u003cem\u003eP\u003c/em\u003e value was obtained using the Wilcoxon rank-sum test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ej,\u003c/strong\u003e Gene ontology (GO) term enrichment analysis of DEPs identified in\u003cstrong\u003e Fig. 1i\u003c/strong\u003e. \u003cem\u003eP\u003c/em\u003e value was obtained using the hypergeometric test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ek,\u003c/strong\u003e The overlap of aging-induced DEPs in GV and MII phases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003el,\u003c/strong\u003e The heatmap (left) showing the expression patterns of six clusters of proteins (the protein numbers of each cluster [n] were indicated in \u003cstrong\u003eFig. 1m\u003c/strong\u003e), which exhibited distinct expression dynamics during oocyte maturation between young and aged oocytes (see Methods), and the corresponding GO terms for each cluster of proteins (\u003cstrong\u003eright\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003em, \u003c/strong\u003eThe temporal expression patterns of the protein levels and the corresponding RNA levels of the six clusters of proteins identified in \u003cstrong\u003eFig. 1l\u003c/strong\u003e. The log\u003csub\u003e2\u003c/sub\u003e(Foldchange [GV vs MII]) in young and aged oocytes of indicated genes was used to perform statistical analysis. \u003cem\u003eP\u003c/em\u003e value was obtained using two-tailed Mann-Whitney U-tests; the protein numbers of each cluster (n) were indicated in the figure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb-h,\u003c/strong\u003e YGV, Young GV oocytes; AGV, Aged GV oocytes; YMII. Young MII oocytes; AMII, Aged MII oocytes.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6309099/v1/a27c4707f8e243eca0ea83d7.png"},{"id":82326389,"identity":"9b40f3b1-ef15-41df-8cc3-cac42e8839cb","added_by":"auto","created_at":"2025-05-09 06:22:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3063504,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUncoupled RNA and protein alterations during mouse oocyte aging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e The correlation coefficient (\u003cem\u003eR\u003c/em\u003e) between RNA and protein expression levels across all detected genes in the indicated oocytes. The average RNA and protein levels of all detected genes in indicated oocyte groups were used to calculate RNA-protein Pearson’s correlation coefficient. The numbers of genes analyzed in each oocyte group (n):\u0026nbsp; 3,091 (YGV), 3,142 (OGV), 2,699 (YMII), 2,653 (OGV).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb, \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003ebox plot showing the correlation between RNA and protein expression levels computed individually for each single oocyte in the indicated oocyte groups. n = 12 (YGV), 12 (YMII), 9 (AGV), 9 (AMII). The RNA-protein Pearson’s correlation coefficient for each oocyte was calculated using the average RNA and protein levels of all detected genes in that oocyte.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec, \u003c/strong\u003eThe distribution of correlation coefficients for RNA-protein gene pairs detected in the indicated mouse oocytes. The number of genes (n) exhibiting an RNA-protein pair correlation below 0.5, between -0.5 and 0.5, and above 0.5, along with their respective percentages among all analyzed genes, were indicated in the figure. Pearson’s correlation coefficient of each gene was calculated using the average RNA and protein expression level of that gene in indicated oocytes. Pearson’s correlation coefficient of all genes was used to draw the bar plot.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed, \u003c/strong\u003eThe box plot showing the RNA-protein correlation for genes in the top 5% (n = 156) and bottom 5% (n = 156) of translational efficiency (TE). The translational efficiency was analyzed from public data (GSA: CRA008819)\u003csup\u003e15\u003c/sup\u003e. \u003cem\u003eP\u003c/em\u003e values were obtained using two-tailed Mann-Whitney U-tests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee, \u003c/strong\u003eThe two-dimensional volcano plot illustrating protein and RNA expression changes for genes with both protein and RNA identified (n = 3,188) in aged GV oocytes (\u003cstrong\u003eleft\u003c/strong\u003e), alongside the bar plot showing the number of genes in each category (\u003cstrong\u003eright\u003c/strong\u003e). Gene classification is performed following previously reported methods and is based on the fold change in RNA or protein expression\u003csup\u003e80\u003c/sup\u003e. Class I (n = 18): these genes exhibit more than a 2-fold change in both RNA and protein levels, and the RNA and protein changes are positively correlated. Class II (n =397): these genes exhibited more than a 2-fold change in protein but not RNA. Class III (n = 55): these genes exhibited more than a 2-fold change in RNA but not protein. Class IV (n =19): these genes exhibit more than a 2-fold change in both RNA and protein levels, and the RNA and protein changes are negatively correlated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef, \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003etwo-dimensional volcano plot illustrating the protein and RNA expression changes for genes with protein/RNA co-identified (n = 2,824) in aged MII oocytes\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003eleft\u003c/strong\u003e),\u003cstrong\u003e \u003c/strong\u003ealongside the bar plot showing the number of genes of each class (\u003cstrong\u003eright\u003c/strong\u003e). The class definition is the same as \u003cstrong\u003eFig. 2e\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eg, \u003c/strong\u003eThe violin plot showing the RNA and protein expression of representative genes exhibiting aging-induced uncoupled RNA and protein changes. \u003cem\u003eP\u003c/em\u003e value for protein expression level was obtained using the Wilcoxon rank-sum test, while the \u003cem\u003eP\u003c/em\u003e value for RNA expression level was obtained using the Wald test within a negative binomial generalized linear model. n = 12 (YGV), 12 (YMII), 9 (AGV), 9 (AMII).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eh,\u003c/strong\u003e qPCR of \u003cem\u003eNtmt1\u003c/em\u003e (\u003cstrong\u003etop\u003c/strong\u003e) and \u003cem\u003ePdcd4\u003c/em\u003e (\u003cstrong\u003ebottom\u003c/strong\u003e) in indicated oocytes. Results are represented as mean ± s.e.m., n = 3, and statistical analysis was performed using an unpaired two-tailed t-test. \u003cem\u003eP\u003c/em\u003e values were indicated in the figures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ei, \u003c/strong\u003eThe two-dimensional volcano plot illustrating the protein and RNA expression changes for genes exhibiting decreased TE\u003cstrong\u003e \u003c/strong\u003ein aged GV oocytes (n = 892, \u003cstrong\u003eleft\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eand the number of genes of each group (\u003cstrong\u003eright\u003c/strong\u003e). Genes with decreased TE (log\u003csub\u003e2\u003c/sub\u003e(Foldchange[AGV/YGV]) of TE \u0026lt; -1) were identified from published datasets (GSA: CRA008819)\u003csup\u003e15\u003c/sup\u003e. Group 1: Genes that exhibit more than a 2-fold decrease in protein levels but not in RNA levels. Group 2: Genes that undergo aging-induced changes in protein or RNA levels but do not belong to Group 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ej, \u003c/strong\u003eTwo-dimensional volcano plot illustrating the protein and RNA expression changes for genes exhibiting increased TE in aged GV oocytes (n = 552, \u003cstrong\u003eleft\u003c/strong\u003e) and the number of genes of each group (\u003cstrong\u003eright\u003c/strong\u003e). Genes with increased TE (log\u003csub\u003e2\u003c/sub\u003e(Foldchange[AGV/YGV]) of TE \u0026gt; 1) were identified from published datasets (GSA: CRA008819)\u003csup\u003e15\u003c/sup\u003e. Group 1: Genes that exhibit more than a 2-fold increase in protein levels but not in RNA levels. Group 2: Genes that undergo aging-induced changes in protein or RNA levels but do not belong to Group 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea-j,\u003c/strong\u003e YGV, Young GV oocytes; AGV, Aged GV oocytes; YMII. Young MII oocytes; AMII, Aged MII oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee-f\u003c/strong\u003e, \u003cstrong\u003ei-j\u003c/strong\u003e, FC, fold change\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6309099/v1/43650f690b9701982b349f9b.png"},{"id":82326002,"identity":"8580ddeb-9a6d-4af4-b263-b65d0e211203","added_by":"auto","created_at":"2025-05-09 06:14:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2011634,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003escSTAP profiling captured the molecular heterogeneity in mouse-aged GV oocytes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e AUC (Area under the curve) score analysis of published cellular senescence markers using the transcriptome and proteome data of aged GV oocytes. A detailed description of the selection of aging marker genes can be found in the Methods section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb, \u003c/strong\u003eThe centroid distance between young and aged oocytes in the PCA plot, based on transcriptome or proteome, quantifies the overall molecular divergence between these two groups. A higher centroid distance indicated a clearer separation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec,\u003c/strong\u003e The signaling entropy in indicated GV oocytes calculated using proteomic data. \u003cem\u003eP\u003c/em\u003e value was obtained using two-tailed Mann-Whitney U-tests. n = 12 (YGV), 9 (AGV).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed,\u003c/strong\u003e The PCA plot based on proteomic data of profiled GV oocytes. Individual oocytes are depicted as dots, with variations in shape and color that indicate the distinct features of oocytes. Aged oocytes were clustered into Group I and Group II subgroups. Aged Oocytes represented by colorless boxes refer to the oocytes excluded from clustering (see Methods).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee. \u003c/strong\u003eThe signaling entropy calculated using proteomic data of indicted groups of oocytes. \u003cem\u003eP\u003c/em\u003e value was obtained using two-tailed Mann-Whitney U-tests. n = 12 (YGV), 4 (Group I), 4 (Group II).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef, \u003c/strong\u003eNumbers of DEPs across the indicated oocytes compared to young GV oocytes. The proteins satisfying |Log\u003csub\u003e2\u003c/sub\u003eFoldchange| \u0026gt; 1 and \u003cem\u003eP\u003c/em\u003e values \u0026lt; 0.05 were identified as DEPs. Red bars indicated upregulated proteins and blue bars indicated downregulated proteins. \u003cem\u003eP\u003c/em\u003e values were obtained using the Wilcoxon rank-sum test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eg,\u003c/strong\u003e The heatmap showing the protein expression levels of selected genes that exhibit differential expression between Group II and young oocytes, but no significant difference between Group I and young oocytes. The proteins satisfying |Log\u003csub\u003e2\u003c/sub\u003eFoldchange| \u0026gt; 0.5 and \u003cem\u003eP\u003c/em\u003e values \u0026lt; 0.05 were selected. \u003cem\u003eP\u003c/em\u003e values were obtained using the Wilcoxon rank-sum test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eh, \u003c/strong\u003eNumbers of DEGs across the indicated oocytes compared to young GV oocytes. The genes satisfying |Log\u003csub\u003e2\u003c/sub\u003eFoldchange| \u0026gt; 1 and \u003cem\u003eP\u003c/em\u003e values \u0026lt; 0.05 were identified as DEGs. Red bars indicated upregulated genes and blue bars indicated downregulated genes. \u003cem\u003eP\u003c/em\u003e values were obtained using the Wald test within a negative binomial generalized linear model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ei, \u003c/strong\u003eThe RNA-protein correlation of indicated groups of oocytes. \u003cem\u003eP\u003c/em\u003e value was obtained using two-tailed Mann-Whitney U-tests. n = 12 (YGV), 4 (Group I), 4 (Group II). The RNA-protein Pearson’s correlation coefficient for oocytes was calculated using the average RNA and protein levels of all detected genes in indicated oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ej, \u003c/strong\u003eThe heatmap showing the expression level of four clusters of proteins with different expression patterns between young, Group I, and Group II oocytes. Proteins in Clusters 1 and 3 are upregulated and downregulated, respectively, in both Group I and Group II oocytes compared to young oocytes. In contrast, proteins in Clusters 2 and 4 are exclusively upregulated and downregulated in Group II oocytes. The proteins satisfying |Log\u003csub\u003e2\u003c/sub\u003eFoldchange| \u0026gt; 1 and \u003cem\u003eP\u003c/em\u003e values \u0026lt; 0.05 were identified as DEPs. n = 125 (cluster 1), 92 (cluster 2), 64 (cluster 3), 76 (cluster 4). \u003cem\u003eP\u003c/em\u003e values were obtained using the Wilcoxon rank-sum test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ek, \u003c/strong\u003eGO term enrichment analysis of protein clusters identified in \u003cstrong\u003eFig. 3j\u003c/strong\u003e. \u003cem\u003eP\u003c/em\u003e value was obtained using the hypergeometric test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003el, \u003c/strong\u003ePCA plots showing the expression level of representative Clusters 2 and 4 proteins identified in \u003cstrong\u003eFig. 3j\u003c/strong\u003e in individual GV oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003em,\u003c/strong\u003e The PCA plot showing the aged GV oocytes isolated from the same mouse. \u003cstrong\u003eb-j, \u003c/strong\u003eYGV, young GV oocytes; AGV, aged GV oocytes.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6309099/v1/9017ff66935d788c6f23e056.png"},{"id":82325203,"identity":"582153dd-38cb-4d10-a14d-235d7e841bae","added_by":"auto","created_at":"2025-05-09 06:06:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4550277,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMCT4 mediated the aging-induced defects in mouse oocytes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e A schematic timeline illustrating the process of \u003cem\u003eMct4\u003c/em\u003eknockdown in mouse aged oocytes and the subsequent functional analyses. GVBD: germinal vesicle breakdown; PBE: polar body extrusion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb, \u003c/strong\u003eRepresentative images showing the spindle structure and chromosome positioning in indicated MII oocytes. Scale bars, 50 µm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec, \u003c/strong\u003eEnlarged images showing the thickness of the spindle midzone in oocytes of \u003cstrong\u003eFig. 4b\u003c/strong\u003e. Scale bars, 10 µm. CD refers to the maximum span of chromosomes.SL refers to the maximum length of spindles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed, \u003c/strong\u003eThe spindle abnormal rate in young (n = 82), aged (n = 26), and \u003cem\u003eMct4\u003c/em\u003e-knockdown aged (n = 17) mouse MII oocytes. Results are depicted with three replicates per group. n, the overall count of oocytes examined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee, \u003c/strong\u003eThe violin plot showing the relative CD/SL ratio in indicated oocytes shown in \u003cstrong\u003eFig. 5b-c\u003c/strong\u003e. n, the overall count of oocytes examined. CD refers to the maximum span of chromosomes.SL refers to the maximum length of spindles. Results were normalized to the young oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef, \u003c/strong\u003eRepresentative images (\u003cstrong\u003eleft\u003c/strong\u003e) and quantification (\u003cstrong\u003eright\u003c/strong\u003e) of sperm bound to the indicated zona pellucida enveloping mouse MII oocytes. The 2-cell stage samples are used as the negative control as the hardening of the zona pellucida in the 2-cell embryos prevents sperm from binding. n, the overall count of oocytes examined. Scale bar, 50 µm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eg, \u003c/strong\u003eThe fertilization rate in young (n = 26), aged (n = 18), and \u003cem\u003eMct4\u003c/em\u003e-knockdown aged (n = 13) oocytes. Results are depicted with three replicates per group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eh, \u003c/strong\u003eRepresentative images (\u003cstrong\u003eleft\u003c/strong\u003e) and relative expression of H3K9me3 (\u003cstrong\u003eright\u003c/strong\u003e) in indicated mouse GV-stage oocytes. n, the overall count of oocytes examined. Scale bar, 50 µm. Results were normalized to the young oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ei. \u003c/strong\u003eA schematic timeline illustrating the process of \u003cem\u003eMct4\u003c/em\u003e overexpression in young mouse oocytes and the subsequent functional analyses. GVBD: germinal vesicle breakdown; PBE: polar body extrusion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ej, \u003c/strong\u003eRepresentative images showing the spindle structure and chromosome positioning in indicated mouse MII young oocytes. Scale bars, 50 µm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ek, \u003c/strong\u003eEnlarged images showing the thickness of the spindle midzone in oocytes of \u003cstrong\u003eFig. 4j\u003c/strong\u003e. Scale bars, 10 µm. CD refers to the maximum span of chromosomes.SL refers to the maximum length of spindles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003el, \u003c/strong\u003eThe spindle abnormal rate of control (n = 98) and \u003cem\u003eMct4\u003c/em\u003e-overexpressed (n = 54) mouse MII young oocytes. Results are depicted with three replicates per group. n, the overall count of oocytes examined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003em, \u003c/strong\u003eThe violin plot displaying the relative CD/SL ratio in indicated oocytes shown in \u003cstrong\u003eFig. 4j-k\u003c/strong\u003e. n, the overall count of oocytes examined. CD refers to the maximum span of chromosomes.SL refers to the maximum length of spindles. Results were normalized to the control oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003en, \u003c/strong\u003eRepresentative images (\u003cstrong\u003eleft\u003c/strong\u003e) and quantification (\u003cstrong\u003eright\u003c/strong\u003e) of sperm bound to the indicated zona pellucida enveloping mouse MII oocytes. The 2-cell stage samples are used as the negative control as the hardening of the zona pellucida in the 2-cell embryos prevents sperm from binding. n, the overall count of oocytes examined. Scale bar, 50 µm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eo, \u003c/strong\u003eThe fertilization rate of control (n = 34) and \u003cem\u003eMct4\u003c/em\u003e-overexpressed (n = 37) mouse young oocytes. Results are depicted with three replicates per group. n, the overall count of oocytes examined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ep, \u003c/strong\u003eRepresentative images (\u003cstrong\u003eleft\u003c/strong\u003e) and relative expression of H3K9me3 (\u003cstrong\u003eright\u003c/strong\u003e) in indicated mouse GV-stage oocytes. The fact that MCT4 overexpression did not decrease H3K9me3 indicated that MCT4 was required but insufficient to decrease H3K9me3 in GV oocytes. n, the overall count of oocytes examined. Scale bar, 50 µm. Results were normalized to the control oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed-h, l-p,\u003c/strong\u003e Results are represented as mean ± s.e.m., and statistical analysis was performed using an unpaired two-tailed \u003cem\u003et\u003c/em\u003e-test. \u003cem\u003eP\u003c/em\u003evalues were indicated in the figures.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6309099/v1/31296d8eae28e25b2a671ab7.png"},{"id":82325209,"identity":"8b572244-f33e-4e36-9119-7ea4bd07f731","added_by":"auto","created_at":"2025-05-09 06:06:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5569733,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVB124, a selective MCT4 inhibitor, improved the quality of mouse-aged oocytes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e A schematic timeline illustrating the process of \u003cem\u003ein vitro\u003c/em\u003e treatment of VB124 (20 μM) to mouse-aged oocytes and the subsequent functional analyses. GVBD: germinal vesicle breakdown; PBE: polar body extrusion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb, \u003c/strong\u003eRepresentative images of spindle structure and chromosome positioning in indicated mouse MII-stage oocytes. Scale bars, 50 µm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec, \u003c/strong\u003eEnlarged representative images showing the thickness of the spindle midzone in indicated oocytes of \u003cstrong\u003eFig. 5b\u003c/strong\u003e. Scale bars, 10 µm. CD refers to the maximum span of chromosomes.SL refers to the maximum length of spindles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed, \u003c/strong\u003eThe spindle abnormal rate of indicated mouse MII oocytes. Results are depicted with four replicates per group. The overall count of oocytes examined (n): young (n = 74), aged (n = 39), and aged + VB124 (n = 36).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee, \u003c/strong\u003eThe violin plot showing the relative CD/SL ratios of the indicated oocytes shown in \u003cstrong\u003eFig. 5b-c\u003c/strong\u003e. n, the overall count of oocytes examined. CD refers to the maximum span of chromosomes.SL refers to the maximum length of spindles. Results were normalized to the young oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef, \u003c/strong\u003eRepresentative images (\u003cstrong\u003eleft\u003c/strong\u003e) and quantification (\u003cstrong\u003eright\u003c/strong\u003e) of sperm bound to the indicated zona pellucida enveloping mouse MII oocytes. n, the overall count of oocytes examined. Scale bar, 50 µm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eg,\u003c/strong\u003e The fertilization rate of mouse young (n = 41), aged (n = 23), and aged + VB124 (n = 22) oocytes. Results are depicted with three replicates per group. n, the overall count of oocytes examined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eh, \u003c/strong\u003eRepresentative images (\u003cstrong\u003eleft\u003c/strong\u003e) and relative expression of H3K9me3 (\u003cstrong\u003eright\u003c/strong\u003e) in indicated mouse GV-stage oocytes. n, the overall count of oocytes examined. Scale bar, 50 µm. Results were normalized to the young oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ei,\u003c/strong\u003e A schematic timeline detailing the process of \u003cem\u003ein vivo \u003c/em\u003etreatment of VB124 to mouse and oocyte functional analyses at different stages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ej, \u003c/strong\u003eRepresentative images of spindle structure and chromosome positioning in indicated mouse MII oocytes. Scale bars, 50 µm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ek, \u003c/strong\u003eEnlarged representative images showing the thickness of the spindle midzone in indicated oocytes of \u003cstrong\u003eFig. 5j\u003c/strong\u003e. Scale bars, 10 µm. CD refers to the maximum span of chromosomes.SL refers to the maximum length of spindles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003el, \u003c/strong\u003eThe spindle abnormal rate in MII oocytes isolated from young (n = 49), aged (n = 33), and aged + VB124 (n = 20) mice. Results are depicted with three replicates per group. n, the overall count of oocytes examined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003em, \u003c/strong\u003eThe violin plot displaying the relative CD/SL ratios of the oocytes shown in \u003cstrong\u003eFig. 5j-k\u003c/strong\u003e. n, the overall count of oocytes examined. CD refers to the maximum span of chromosomes.SL refers to the maximum length of spindles. Results were normalized to the young oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003en, \u003c/strong\u003eRepresentative images (\u003cstrong\u003eleft\u003c/strong\u003e) and relative expression of H3K9me3 (\u003cstrong\u003eright\u003c/strong\u003e) in GV oocytes isolated from young, aged, and aged + VB124 mice. n, the overall count of oocytes examined. Scale bar, 50 µm. Results were normalized to the young oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eo, \u003c/strong\u003eThe fertilization rate of oocytes isolated from young (n = 37), aged (n = 27), and aged + VB124 (n = 37) mice. Results are depicted with three replicates per group. n, the overall count of oocytes examined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ep,\u003c/strong\u003e Representative images of pups born of indicated mice. Scale bar, 2 cm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eq,\u003c/strong\u003e The number of offspring produced by young (n = 5), aged (n = 5), and aged + VB124 (n = 5) female mice following mating with young male mice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed-h, l-p,\u003c/strong\u003e Results are represented as mean ± s.e.m., and statistical analysis was performed using an unpaired two-tailed \u003cem\u003et\u003c/em\u003e-test. \u003cem\u003eP\u003c/em\u003evalues were indicated in the figures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb-h\u003c/strong\u003e, 20 μM VB124 was used; \u003cstrong\u003ej-q\u003c/strong\u003e, VB124 was administered daily at a dose of 30 mg/kg body weight.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6309099/v1/c396f33ba97686d5de3a5bbc.png"},{"id":82326007,"identity":"25064eea-7e24-41c4-9079-762ba51a8f42","added_by":"auto","created_at":"2025-05-09 06:14:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7065048,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMCT4 modulated the aging effect on mouse oocytes by\u003c/strong\u003e \u003cstrong\u003eaffecting the lactate-pyruvate metabolic axis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003eRepresentative sensor FiLa-cyto fluorescence microscopy and DIC (Differential interference contrast) images of indicated mouse GV oocytes with sensor FiLa-cyto expressed in the oocyte cytosol. Scale bars are indicated within the figure. FiLa-cyto and mCherry were co-injected into the oocyte, and mCherry served as a control for injection quantity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb,\u003c/strong\u003e Normalized FiLa-cyto intensity in the cytosol of indicated GV oocytes. The quantity of injected FiLa-cyto is standardized relative to the signal from mCherry, which is co-injected into the oocyte. n, the overall count of oocytes examined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec,\u003c/strong\u003e Relative lactate efflux of a single GV oocyte from the indicated group compared to the young GV oocyte group was shown. n, the overall count of oocytes examined. Results were normalized to the young oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed, \u003c/strong\u003eRepresentative\u003cstrong\u003e \u003c/strong\u003esensor PyronicSF fluorescence microscopy and DIC images of indicated mouse GV oocytes with sensor PyronicSF expressed in the oocyte mitochondria. Scale bars are indicated within the figure. PyronicSF and mCherry were co-injected into the oocyte, and mCherry served as a control for injection quantity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee,\u003c/strong\u003e Normalized PyronicSF intensity in the mitochondria of indicated GV oocytes. The quantity of injected PyronicSF is standardized relative to the signal from mCherry, which is co-injected into the oocyte. n, the overall count of oocytes examined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef,\u003c/strong\u003e Relative ATP levels of GV oocytes from young (n = 20), aged (n = 20), and aged + VB124 (n = 20) groups. Five oocytes were pooled as one biological replicate, with four replicates per group included. Results were normalized to the young oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eg,\u003c/strong\u003e Representative fluorescence microscopy and DIC images of indicated mouse GV oocytes, with sensor PyronicSF expressed in the oocyte mitochondria. Scale bars are indicated within the figure. PyronicSF-mito and mCherry were co-injected into the oocyte, and mCherry served as a control for injection quantity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eh,\u003c/strong\u003e Normalized PyronicSF intensity in the mitochondria of indicated GV oocytes. The quantity of injected PyronicSF is standardized relative to the signal from mCherry, which is co-injected into the oocyte. n, the overall count of oocytes examined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ei,\u003c/strong\u003e Relative ATP levels of oocytes from aged (n = 20) and aged + Pyruvate (n = 20) groups. Five oocytes were pooled as one biological replicate, with four replicates per group included. Results were normalized to the aged oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ej,\u003c/strong\u003e The GVBD and PBE rate of indicated mouse-aged GV oocytes. Results are depicted with n = 3 replicates per group. The overall count of oocytes examined (n) is noted as the number within each bar. GVBD: germinal vesicle breakdown; PBE: polar body extrusion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ek, \u003c/strong\u003eRepresentative images of spindle structure and chromosome positioning in indicated mouse MII oocytes (\u003cstrong\u003eleft\u003c/strong\u003e) and the spindle abnormal rate in indicated MII oocytes (\u003cstrong\u003eright\u003c/strong\u003e). n, the overall count of oocytes examined. Scale bars, 50 µm. Results are depicted with three replicates per group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003el, \u003c/strong\u003eA diagram illustrating the mechanism by which MCT4 mediates the aging effects on oocytes. In aged GV oocytes, abnormal upregulation of MCT4 enhances lactate efflux from the cytosol into the extracellular space. This depletion of cytosolic lactate competitively diverts pyruvate away from mitochondrial transport, leading to reduced mitochondrial pyruvate levels and diminished ATP production. Consequently, these metabolic disruptions contribute to the quality decline observed in aged oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003em,\u003c/strong\u003e Relative lactate efflux of a single GV oocyte from the indicated group compared to the young group was shown. The aged oocytes are categorized based on spindle assembly during subsequent maturation as normal (N) or abnormal (A). n, the overall count of oocytes examined. Results were normalized to the young oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb-c, e-f, and h-m,\u003c/strong\u003e Results are represented as mean ± s.e.m., and statistical analysis was performed using an unpaired two-tailed \u003cem\u003et\u003c/em\u003e-test. \u003cem\u003eP\u003c/em\u003evalues were indicated in the figures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eg-k\u003c/strong\u003e, 0.66 mM pyruvate was used.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6309099/v1/86d95c2156aacfc818157bb3.png"},{"id":82326009,"identity":"b8f52b95-ddb6-4cb8-b3bf-8e7d6fd0f7fc","added_by":"auto","created_at":"2025-05-09 06:14:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3446944,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell proteomic and transcriptomic co-profiling revealed uncoupled RNA and protein alterations during human oocyte aging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e The number of total identified proteins in indicated human oocyte samples.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb,\u003c/strong\u003e The box plot showing the range of identified proteins in profiled human oocytes. The numbers in the figure refer to the average identified proteins for indicated oocytes. n = 9 (YGV), 8 (YMII), 13 (AGV), 8 (AMII).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec,\u003c/strong\u003e The correlation coefficient of the proteome in the human aged oocytes. The detailed correlation coefficient for all sample groups is listed in \u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e. n = 13 (AGV), 8 (AMII).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed, \u003c/strong\u003eThe heatmap showing the protein expression levels of representative stage-specific proteins in young oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee,\u003c/strong\u003e The correlation coefficient (\u003cem\u003eR\u003c/em\u003e) between our profiling data and previously published studies of proteomics data (PRIDE: PXD024267) in human MII oocytes. \u003cem\u003eP\u003c/em\u003e value was obtained using the parametric t-test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef,\u003c/strong\u003e The volcano plot displaying DEPs (upregulated, red; downregulated, blue) with statistical significance in oocytes from aged humans compared to those from young ones. The proteins satisfying |Log2Foldchange| \u0026gt; 1 and \u003cem\u003eP\u003c/em\u003e values \u0026lt; 0.05 were identified as DEPs. Total proteins analyzed (n) = 6,299. \u003cem\u003eP\u003c/em\u003e values were obtained using the Wilcoxon rank-sum test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eg,\u003c/strong\u003e GO term enrichment analysis of DEPs identified in \u003cstrong\u003eFig. 7f\u003c/strong\u003e. \u003cem\u003eP\u003c/em\u003e value was obtained using the hypergeometric test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eh,\u003c/strong\u003e The overlap of aging-induced DEPs in GV and MII phases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ei, \u003c/strong\u003eThe heatmap (\u003cstrong\u003eleft\u003c/strong\u003e) showing the expression patterns of six clusters of proteins, which exhibited distinct expression dynamics during oocyte maturation between young and aged human oocytes (see Methods), and the corresponding GO terms for each cluster of proteins (\u003cstrong\u003eright\u003c/strong\u003e). The protein numbers for each cluster (n) were indicated in \u003cstrong\u003eFig. 7j\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ej,\u003c/strong\u003e The temporal expression patterns of the protein and RNA levels of the six clusters of proteins identified in \u003cstrong\u003eFig. 7i\u003c/strong\u003e. The log\u003csub\u003e2\u003c/sub\u003e(Foldchange [GV vs MII]) in young and aged human oocytes of the indicated gene/protein was used to perform statistical analysis. \u003cem\u003eP \u003c/em\u003evalue was obtained using two-tailed Mann-Whitney U-tests. The protein numbers for each cluster (n) were indicated in the figure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ek, \u003c/strong\u003eThe box plot showing the correlation between RNA and protein expression levels computed individually for each single oocyte in the indicated oocyte groups. n = 9 (YGV), 8 (YMII), 13 (AGV), 8 (AMII). The RNA-protein Pearson’s correlation coefficient for each oocyte was calculated using the average RNA and protein levels of all detected genes in that oocyte.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003el, \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003etwo-dimensional volcano plot illustrating the protein and RNA expression changes for genes with protein/RNA co-identified (n = 6,265) in aged human GV oocytes (\u003cstrong\u003eleft\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eand the number of genes of each class (\u003cstrong\u003eright\u003c/strong\u003e). The class definition is the same as \u003cstrong\u003eFig. 2e\u003c/strong\u003e. FC, fold change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003em, \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003etwo-dimensional volcano plot illustrating the protein and RNA expression changes for genes with protein/RNA co-identified (n = 6,265) in aged human MII oocytes (\u003cstrong\u003eleft\u003c/strong\u003e) and the number of genes of each class (\u003cstrong\u003eright\u003c/strong\u003e). The class definition is the same as \u003cstrong\u003eFig. 2e\u003c/strong\u003e. FC, fold change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003en, \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003eviolin plot showing the RNA and protein expression of representative genes exhibiting aging-induced uncoupled RNA and protein changes in human GV oocytes. \u003cem\u003eP\u003c/em\u003e value for protein expression level was obtained using the Wilcoxon rank-sum test, while the \u003cem\u003eP\u003c/em\u003e value for RNA expression level was obtained using the Wald test within a negative binomial generalized linear model. n = 9 (YGV), 13 (AGV).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eo, \u003c/strong\u003eThe\u003cstrong\u003e \u003c/strong\u003eviolin plot showing the RNA and protein expression on representative genes with aging-induced uncoupled RNA and protein changes in human MII oocytes. \u003cem\u003eP\u003c/em\u003e value for protein expression level was obtained using the Wilcoxon rank-sum test, while the \u003cem\u003eP\u003c/em\u003e value for RNA expression level was obtained using the Wald test within a negative binomial generalized linear model. n = 8 (YMII), 8 (AMII).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea-e, k-m, \u003c/strong\u003eYGV, Young GV oocytes; AGV, Aged GV oocytes; YMII. Young MII oocytes; AMII, Aged MII oocytes.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6309099/v1/f6fff75450524846dde8238b.png"},{"id":82325215,"identity":"e7fc15d9-eff8-4e92-800e-bcd4e275dcbf","added_by":"auto","created_at":"2025-05-09 06:06:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2446080,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular heterogeneity in human-aged GV oocytes and comparative analysis of human and mouse oocyte aging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e The signaling entropy calculated using proteomic data in indicated human-aged GV oocytes. \u003cem\u003eP\u003c/em\u003e value was obtained using two-tailed Mann-Whitney U-tests. n= 9 (YGV), 6 (Group I), 6 (Group II).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb, \u003c/strong\u003eThe box plot showing the expression level of SRSF7 and PABPN1 in indicated oocytes. \u003cem\u003eP\u003c/em\u003e value was obtained using two-tailed Mann-Whitney U-tests. n= 9 (YGV), 6 (Group I), 6 (Group II).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec, \u003c/strong\u003eNumber of DEPs in the indicated oocytes compared to young GV oocytes. The proteins satisfying |Log2Foldchange| \u0026gt; 1 and \u003cem\u003eP\u003c/em\u003e values \u0026lt; 0.05 were identified as DEPs. Red bars indicated upregulated proteins and blue bars indicated downregulated proteins. \u003cem\u003eP\u003c/em\u003e values were obtained using the Wilcoxon rank-sum test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed, \u003c/strong\u003eNumber of DEGs in the indicated oocytes compared to young GV oocytes. The genes satisfying |Log\u003csub\u003e2\u003c/sub\u003eFoldchange| \u0026gt; 1 and \u003cem\u003eP\u003c/em\u003e values \u0026lt; 0.05 were identified as DEGs. Red bars indicated upregulated genes and blue bars indicated downregulated genes. \u003cem\u003eP\u003c/em\u003e values were obtained using the Wald test within a negative binomial generalized linear model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee, \u003c/strong\u003eThe RNA-protein correlation in indicated oocytes. \u003cem\u003eP\u003c/em\u003e value was obtained using two-tailed Mann-Whitney U-tests. n= 9 (YGV), 6 (Group I), 6 (Group II). The RNA-protein Pearson’s correlation coefficient for oocytes was calculated using the average RNA and protein levels of all detected genes in indicated oocytes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef, \u003c/strong\u003eThe donor age distribution of indicated oocytes. \u003cem\u003eP\u003c/em\u003e value was obtained using two-tailed Mann-Whitney U-tests. n= 9 (YGV), 6 (Group I), 6 (Group II).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eg, \u003c/strong\u003eThe heatmap showing the expression level of four clusters of proteins with different expression patterns between young, Group I, and Group II oocytes. Proteins in Clusters 1 and 3 are upregulated and downregulated, respectively, in both Group I and Group II oocytes compared to young oocytes. In contrast, proteins in Clusters 2 and 4 are exclusively upregulated and downregulated in Group II oocytes. The proteins satisfying |Log\u003csub\u003e2\u003c/sub\u003eFoldchange| \u0026gt; 1 and \u003cem\u003eP\u003c/em\u003e values \u0026lt; 0.05 were identified as differently expressed proteins. n = 23 (cluster 1), 31 (cluster 2), 26 (cluster 3), 99 (cluster 4). \u003cem\u003eP\u003c/em\u003e values were obtained using the Wilcoxon rank-sum test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eh, \u003c/strong\u003eGO term enrichment analysis of different clusters of proteins identified in \u003cstrong\u003eFig. 8g\u003c/strong\u003e. \u003cem\u003eP\u003c/em\u003e value was obtained using the hypergeometric test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ei, \u003c/strong\u003eThe overlap of aging-induced DEGs between humans and mice in GV or MII oocytes. \u003cem\u003eP\u003c/em\u003e value was obtained using the hypergeometric test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ej, \u003c/strong\u003eThe overlap of aging-induced DEPs between human and mouse in GV or MII oocytes. \u003cem\u003eP\u003c/em\u003e value was obtained using the hypergeometric test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ek, \u003c/strong\u003eGO-term enrichment analysis of aging-induced DEPs and DEGs that are specifically present in human or mouse oocytes, including both GV and MII stages. \u003cem\u003eP\u003c/em\u003e value was obtained using a hypergeometric test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003el,\u003c/strong\u003e The overlap of aging biomarkers for GV oocyte between human (identified in \u003cstrong\u003eFig. 8g\u003c/strong\u003e) and mouse (identified in \u003cstrong\u003eFig. 3j\u003c/strong\u003e). \u003cem\u003eP\u003c/em\u003e value was obtained using the hypergeometric test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea-g,\u003c/strong\u003e YGV, young GV oocytes;\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6309099/v1/5206bab4649f9a139da88ad6.png"},{"id":82327122,"identity":"bca8ec0d-eb66-450d-8fcf-63147c6685da","added_by":"auto","created_at":"2025-05-09 06:31:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":35328147,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6309099/v1/4f78b5ff-e051-4012-a91f-7821cb9292c4.pdf"},{"id":82325206,"identity":"b32b70a1-ba94-42c6-aaa8-be26cf4f6b16","added_by":"auto","created_at":"2025-05-09 06:06:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6896902,"visible":true,"origin":"","legend":"Extended Data Figures","description":"","filename":"ExtendedDataFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-6309099/v1/6cf3022df65601263a426513.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Single-oocyte proteome-transcriptome co-profiling reveals a role of dysregulated lactate metabolism in oocyte aging","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHuman female fertility declines significantly after the age of 35, primarily due to reproductive aging\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Beyond its impact on fertility, female reproductive aging is associated with an increased risk of obstetric difficulties and perinatal hazards, presenting significant challenges for older women seeking to conceive\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. A key aspect of reproductive aging is oocyte aging, which is primarily characterized by a progressive decline in oocyte quality\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This decline manifests in several ways, including irregular meiotic resumption, defects in meiotic maturation, and reduced fertilization capacity\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAged oocytes with declining quality exhibit systemic cellular impairments such as spindle checkpoint integrity disruptions, epigenetic erosion, and chromosomal anomalies\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Intriguingly, not all aged oocytes exhibit quality defects, and even oocytes from the same-aged female can vary in maturation and fertilization competence\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. However, the molecular basis of oocyte aging and the heterogeneity of aged oocytes remains largely unknown. To investigate the molecular mechanisms underlying oocyte aging, researchers have employed omics profiling techniques. For example, single-cell transcriptomic profiling has revealed alterations in ubiquitination, epigenetic regulation, and mitochondrial genes upon oocyte aging in humans and mice\u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. However, transcriptomic data may not reliably infer proteins, the primary executors of gene functions, due to the weak correlation between RNA and protein levels in oocytes\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This limitation restricts transcriptomics from capturing the key features of oocyte aging. Indeed, despite the clear phenotypic abnormalities observed in aged oocytes, transcriptomic data alone fails to distinguish young oocytes from aged ones in humans\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCompared to transcriptional sequencing, proteomic profiling can directly provide the protein abundance of genes to infer functional abnormalities in aged oocytes\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. For instance, mass spectrometry-based proteomics identified around 4,000 proteins in mouse-aged oocytes, revealing altered RNA splicing events in DNA damage repair genes\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. However, this approach requires multiple oocytes, limiting its application to human samples, and cannot capture heterogeneity features within aged oocytes. Single-cell proteomics, while promising, is limited by profiling depth; for example, human single-oocyte proteomics profiling using plexDIA identified only\u0026thinsp;~\u0026thinsp;1,300 proteins in total\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Furthermore, proteomic profiling alone cannot reveal the coordination between proteomic and transcriptomic alterations during oocyte aging, limiting its ability to infer the molecular driver\u0026mdash;such as RNA or translational alterations\u0026mdash;that induces protein changes in aged oocytes. These technical limitations restrict the profiling of protein features in aged oocytes.\u003c/p\u003e \u003cp\u003eRecent advances in high-sensitivity and single-cell proteomic profiling techniques offer a powerful approach to uncovering the proteomic features of oocyte aging and have proven valuable in studying oocyte development\u003csup\u003e\u003cspan additionalcitationids=\"CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43 CR44 CR45\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. We previously developed a single-cell simultaneous transcriptome and proteome (scSTAP) profiling platform, integrating microfluidic technology with high-sensitivity proteomics, specifically optimized for oocyte analysis\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This platform enables the detection of ~\u0026thinsp;3,000 proteins and ~\u0026thinsp;22,000 transcripts from a single mouse oocyte, providing a unique opportunity to characterize single-cell proteomic features and the coordination between proteomic and transcriptomic alterations during oocyte aging. Additionally, the single-cell multi-omics resource generated by scSTAP enables the exploration of multiple molecular aspects of oocyte aging, including protein expression variability, RNA-protein correlation, and quality heterogeneity, thereby facilitating oocyte aging study.\u003c/p\u003e \u003cp\u003eTo this end, we set out to exploit scSTAP to dissect oocyte aging. We performed single-cell profiling on young and aged oocytes from mice and humans, analyzing a total of 79 oocytes (38 aged, 41 young), and identified approximately 3,000 and 6,000 proteins, as well as 20,000 and 30,000 transcripts, in mouse and human oocytes, respectively. Our profiling resource unveiled the single-cell proteomic/transcriptomic landscape of aged oocytes in mice and humans and identified uncoupled proteomic and transcriptomic changes during oocyte aging. Additionally, our resource captured the molecular heterogeneity in aged oocytes and pinpointed MCT4, along with its lactate export function, as a mediator, biomarker, and potential intervention target for oocyte aging. These findings underscore the value of our profiling resource in studying oocyte aging.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDeep single-cell co-profiling of proteome and transcriptome in mouse-aged oocytes by scSTAP\u003c/h2\u003e \u003cp\u003eTo profile the proteome and its correlation with transcriptome in aged oocytes, we utilized the scSTAP platform we developed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Specifically, a single oocyte was lysed using an enzyme-assisted lysis buffer within a small reaction volume to ensure effective lysis. The sequential operation droplet array (SODA) technique was then applied to precisely split the lysate into two equal portions while minimizing lysate loss\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. One portion was used for high sensitivity and resolution nano-liquid chromatography-mass spectrometry (nanoLC-MS) to achieve deep proteomic profiling. The split portion was used for SMART-seq-based transcriptomic profiling, enabling the acquisition of transcriptomic data directly comparable to previously reported datasets. To minimize protein loss from surface absorption and multi-step treatment, a single insert tube with a tapered bottom and hydrophobic surface was used for oocyte lysis, sample spiting, and nanoLC-MS. This single-cell multi-omics profiling method allowed us to acquire a total quantification of ~\u0026thinsp;24000 genes and ~\u0026thinsp;3300 proteins in germinal vesicle (GV) oocytes isolated from young mice at the single-cell level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb-c), with strong correlation coefficients across all samples tested (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e), supporting the depth and reliability of scSTAP profiling.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the literature, we selected 12-month-old mice to isolate aged oocytes for profiling\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. We first confirmed that the GV and metaphase II (MII) oocytes isolated from 12-month-old mice exhibited significant aging-induced quality defects. These were characterized by a notable decline in ovulatory capacity (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), maturation quality (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb-c), and fertilization competence of these oocytes compared to those isolated from younger mice (6-8-week-old; \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Interestingly, after successful fertilization, we did not observe significant differences in the developmental potential between embryos derived from young and aged oocytes, which is consistent with previous reports (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee)\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. These findings suggest that age-related oocyte defects manifest primarily at the pre-fertilization stage. Based on these results, we selected GV and MII oocytes from 12-month-old mice to profile oocyte aging. In total, 21 GV (9 aged, 12 young) and 20 MII stages (8 aged, 12 young) oocytes were isolated and analyzed by the scSTAP, achieving single-oocyte resolution resources with a total quantification depth of ~\u0026thinsp;20000 genes and ~\u0026thinsp;3000 protein and strong correlation coefficients across all samples tested (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb-d, \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef, and \u003cb\u003eSupplementary Table\u0026nbsp;1\u0026ndash;2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSingle-cell transcriptomic landscape during mouse oocyte aging\u003c/h3\u003e\n\u003cp\u003eTo validate our profiling results, we first performed separate analyses of the transcriptomic and proteomic data. For the transcriptomic data, we conducted the following analyses to confirm the validity. Firstly, we confirmed that our single-cell transcriptomic data effectively captured stage-specific gene expression profiles in GV and MII oocytes (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg). For example, GV-stage marker genes, such as \u003cem\u003eZar1l\u003c/em\u003e, exhibited a specific expression pattern to the GV stage, whereas MII-stage marker genes, including \u003cem\u003eH1foo\u003c/em\u003e and \u003cem\u003eNdc80\u003c/em\u003e, were predominantly expressed in MII oocytes (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg)\u003csup\u003e\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Secondly, we integrated the single-cell transcriptomic data to generate a bulk transcriptome, and it was highly correlated with previously published bulk RNA sequencing data from mouse oocytes (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Furthermore, we confirmed the aging-induced RNA expression changes of selected genes identified by the scSTAP platform using quantitative PCR (qPCR) (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ei). Collectively, these findings support the reliability of our single-cell transcriptomic data.\u003c/p\u003e \u003cp\u003eAfter validating the accuracy of our profiling, we explored aging-induced transcriptional changes in GV and MII oocytes. In GV oocytes, we identified 537 differentially expressed genes (DEGs) associated with oocyte aging, which were enriched in the unsaturated fatty acid metabolic processes and regulation of endocytosis pathways (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ej-k). In MII oocytes, 1,420 genes exhibited significantly altered RNA expression levels, with these changes being linked to the regulation of autophagy and chromatin remodeling pathways (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ej-k). Notably, the aging-induced transcriptional changes in aged GV oocytes showed very little overlap with those in MII oocytes (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003el), suggesting that aging impacts the oocyte transcriptome in a stage-specific manner.\u003c/p\u003e \u003cp\u003eAging impairs the meiotic maturation of oocytes from the GV stage to the MII stage (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb-c). To explore the underlying transcriptional signatures, we compared the RNA dynamics of stage-specific genes between young and aged oocytes and identified six clusters of genes (see Methods, \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003em). The RNA dynamics of selected genes were validated by qPCR, supporting the validity of our analysis (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003en). Our results showed that aging impaired the upregulation of a substantial set of genes that normally increased in expression during meiotic maturation (Cluster 1 genes) (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003em). These genes are enriched in chromosome segregation and spindle organization pathways, which are critical for meiotic maturation\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Similarly, aging disrupts the downregulation of a large set of genes that normally decrease in expression during meiotic maturation (Cluster 3 genes) (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003em). Intriguingly, aged oocytes also exhibited a unique group of genes that got upregulated (Cluster 5 genes) or downregulated (Cluster 6 genes) during meiotic maturation (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003em). The abnormal dynamics of these genes may contribute to the maturation defects in aged oocytes.\u003c/p\u003e\n\u003ch3\u003eSingle-cell proteomic landscape during mouse oocyte aging\u003c/h3\u003e\n\u003cp\u003eWe next analyzed the single-cell proteomic data acquired by scSTAP and first evaluated its validity. Our analysis confirmed that the scSTAP proteomic profiling successfully separated GV and MII oocytes by capturing stage-specific protein markers in oocytes, such as CPEB1 for the GV stage and BUB1B for the MII stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee-f)\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. In addition, the bulk proteome merged from single-cell data highly correlated with previously published proteomic data from mouse oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg)\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. We further validated the selected aging-induced protein changes identified by our profiling, including MCT4, PDCD4, and NTMT1, through western-blot analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh). Collectively, these results support the validity of our proteomic profiling.\u003c/p\u003e \u003cp\u003eTo investigate the proteomic changes associated with oocyte aging, we compared the protein profiles of young and aged oocytes. We observed that proteins associated with mRNA processing and protein secretion pathways were altered in aged GV oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ei-j). In MII oocytes, our data revealed alterations in proteins associated with the regulation of autophagy and cellular senescence pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ei-j). Interestingly, the aging-induced proteomic changes in GV oocytes showed very little overlap with those in MII oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ek), suggesting that aging impacts the oocyte proteome in a stage-specific manner.\u003c/p\u003e \u003cp\u003eTo examine the proteomic features underlying maturation defects in aged oocytes, we classified the proteins based on their expression dynamics during oocyte maturation in young and aged oocytes (see Methods). A large set of proteins that normally increased during meiotic maturation (Cluster 1 proteins) exhibited an impaired upregulation with oocyte aging, and they were enriched in the regulation of the chromosome organization pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003el-m), which is crucial for meiotic maturation\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. On the other hand, Clusters 3 proteins, which normally decreased during meiotic maturation, exhibited an impaired downregulation in aged oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003el-m). In addition, we also identified proteins that went through upregulation (Cluster 5) or downregulation (Cluster 6) during meiotic maturation only in aged oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003el-m), and the altered dynamics of these proteins may be involved in the effect of aging on oocytes. Intriguingly, the dynamics of Cluster 5/6 proteins did not appear to be solely driven by RNA-level alterations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003em), suggesting that post-transcriptional changes, such as translation or protein stability changes, may contribute to these dynamics.\u003c/p\u003e\n\u003ch3\u003eSingle-cell proteome revealed a more uniform expression of proteins in aged oocytes\u003c/h3\u003e\n\u003cp\u003eAn intriguing feature associated with aging is the increased cell-to-cell transcriptional variability\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. However, whether aging similarly induces protein expression variability has not been fully examined. Leveraging our single-cell resolution proteomic data, we set out to investigate this question in oocytes.\u003c/p\u003e \u003cp\u003eUnexpectedly, we observed a significant decrease in protein expression variability in aged oocytes (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-b). Examining individual proteins, we found that while a small subset of genes showed increased protein expression variability, a larger set of proteins exhibited a decreased expression variability in aged GV and MII oocytes (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). This contradicts the fact that more RNA showed increased expression variability during oocyte aging (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). The unexpected reduction in protein expression variability suggests a potential role for these proteins and their associated pathways, such as mitochondrial and glycolysis-related pathways (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee), in preserving the viability or function of aged oocytes. Notably, other studies have also pointed out the importance of mitochondrial function in aged oocytes\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further explored the molecular mechanisms driving this uniform expression of proteins in aged oocytes. We found that the proteins with decreased expression variability did not show significant changes in overall expression levels upon oocyte aging (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef-g), suggesting that expression levels alone do not explain this pattern. Additionally, most of these proteins did not display reduced expression variability at the RNA level (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh-i), suggesting that the RNA alterations were not the primary driver of this protein-level uniformity. These findings point to post-translational mechanisms as potential contributors to the decreased protein expression variability in aged oocytes.\u003c/p\u003e\n\u003ch3\u003eMulti-omic analysis revealed uncoupled proteomic and transcriptomic changes during mouse oocyte aging\u003c/h3\u003e\n\u003cp\u003eOur profiling not only provides single-cell proteome data but also captures the coordination between proteomic and transcriptomic alterations during oocyte aging. This information can be exploited to reveal how transcriptomic features affect protein alterations and to infer molecular factors, such as translation efficiency, that shape the proteome landscape in aged oocytes.\u003c/p\u003e \u003cp\u003eUsing our co-profiling data, we first examined the correlation between RNA and protein in aged oocytes. We identified a low correlation between RNA and protein expression levels in aged oocytes, with correlation values below 0.35 for GV oocytes and below 0.2 for MII oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). This low correlation between RNA and protein is also valid for each profiled oocyte, indicating this is a general feature of aged oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). We also calculated the RNA-protein correlation for each detected gene and confirmed a generally low RNA-protein pair correlation for genes, with more than 85% of genes exhibiting a pair correlation below 0.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). Taken together, these results suggest that protein abundance in aged oocytes is not primarily determined by RNA levels, indicating the involvement of additional regulatory factors. Interestingly, we found that genes with the highest translational efficiency tend to exhibit a higher RNA-protein correlation than those with the lowest translational efficiency in aged oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed), suggesting a role of translation efficiency in contributing to the low RNA-protein correlation in aged oocytes\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNext, we examined the coordination between transcriptomic and proteomic alterations upon oocyte aging. To assess this coordination, we grouped genes into four clusters based on their RNA and protein changes during oocyte aging (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee-f, \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). Among these groups, Class 1 genes exhibited positively correlated RNA and protein changes, Class 2/3 genes exhibited uncorrelated RNA-protein alterations, and Class 4 genes represented genes with negatively correlated RNA-protein changes. Surprisingly, in GV oocytes, of all genes with RNA or protein changes, only a small proportion (3.68%, Class 1/Class [1\u0026ndash;4]) exhibited positive correlations between RNA and protein changes during aging (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). In contrast, a larger subset (92.43%, Class [2\u0026ndash;3]/Class [1\u0026ndash;4]) exhibited non-correlated alterations, and 19 genes (3.89%, Class 4/Class [1\u0026ndash;4]) showed negatively correlated changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Notably, single-cell analysis suggested that uncoupled RNA-protein changes in representative genes reflected a general trend among oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg). A similar pattern was also observed in MII oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef-g). We further confirmed the uncoupled RNA and protein changes in selected genes (\u003cem\u003eNtmt1\u003c/em\u003e and \u003cem\u003ePdcd4\u003c/em\u003e, where protein levels changed without corresponding RNA changes) upon oocyte aging by western blot and qPCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh). These results suggest that proteomic changes during oocyte aging are not primarily driven by RNA alterations, and other molecular factors, such as translational efficiency\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, may contribute to these changes.\u003c/p\u003e \u003cp\u003eTo investigate this possibility, we integrated the translatome landscape of oocytes with our profiling to assess the impact of translation on uncoupled RNA-protein alterations during oocyte aging\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. We focused on aging-affected genes with translational efficiency alterations during oocyte aging. The results showed that among genes with aging-induced translational efficiency defects, 32.85% (Group 1/Group [1\u0026ndash;2]) exhibited decreased protein levels without corresponding reductions in RNA levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ei, \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). Conversely, among genes with increased translational efficiency during oocyte aging, 49.42% (Group 1/Group [1\u0026ndash;2]) showed elevated protein levels without a corresponding RNA increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ej, \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). These results suggest a potential role of translational alterations in shaping the uncoupled RNA-protein changes of these Group 1 genes during oocyte aging.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell co-profiling identified two groups of mouse-aged oocytes with distinct molecular features\u003c/h2\u003e \u003cp\u003eIt has been reported that aging may affect oocyte quality with heterogeneity, with oocytes from the same-aged female displaying varying fertilization efficiencies\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. However, the molecular features to confirm or evaluate this heterogeneous impact of aging on oocytes are limited. Our profiling resource, which provides RNA and protein data at single-cell resolution for aged oocytes, offers a unique opportunity to explore the heterogeneity of aging impacts in oocytes and to identify potential assessing biomarkers.\u003c/p\u003e \u003cp\u003eWe first assessed whether transcriptomics or proteomics is more effective in evaluating the aging impact on oocytes. Using aging-associated gene sets from the literature as markers\u003csup\u003e\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, we compared the performance of transcriptome and proteome in identifying the aging features in oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Overall, proteomic data outperformed transcriptomic data (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), indicating that proteomics better captures the aging impact on oocytes. In addition, we calculated the centroid distance between young and aged oocytes in the Principal Component Analysis (PCA) plot based on the proteome or transcriptome. The centroid distance between young and aged oocytes in the proteome PCA was greater in the proteome-based PCA than in the transcriptome-based PCA, suggesting that the proteome distinguishes aged oocytes from young oocytes more effectively than the transcriptome (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). These results suggest that proteomics is more suitable for analyzing the impact of aging on oocytes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on this information, we applied proteomics to investigate oocyte aging heterogeneity. To do so, we first tried to identify a suitable index for evaluating aging features in oocytes. Interestingly, signaling entropy, a verified aging index\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, can effectively differentiate aged oocytes from the young controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). We, therefore, used signaling entropy to analyze heterogeneity in aged GV oocytes. To achieve this, we clustered aged oocytes into two groups based on their median signaling entropy values (see Methods, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Group I oocytes exhibited signaling entropy comparable to that of young oocytes, suggesting that this group of oocytes exhibits mild aging features. In contrast, Group II oocytes showed a significant decrease in signaling entropy compared to young oocytes, indicating that these oocytes exhibit severe aging characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003eTo confirm that our clustering method captured the heterogeneity in aged GV oocytes, we performed the following analysis. Firstly, we found that these two groups of oocytes were clearly separated in the PCA plot, supporting the validity of our clustering strategy (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Secondly, we analyzed the aging-associated proteomic changes in Group I and II oocytes. The results showed that Group II oocytes exhibited more proteomic changes than Group I oocytes during aging (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). In addition, several aging-associated proteins, including ribosomal proteins (\u003cem\u003ee.g.\u003c/em\u003e, RPS24, RPL7) and factors linked to aging-induced oocyte quality defects (\u003cem\u003ee.g.\u003c/em\u003e, KDM1B, SFPQ), exhibited exclusive alterations in Group II oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. These results suggest that Group II oocytes exhibited more aging-associated proteomic changes than Group I oocytes. In addition to proteomics, we also examined transcriptomics features in these two groups of aged oocytes. During aging, Group II GV oocytes exhibited more pronounced transcriptional changes and a more significant decline in RNA-protein correlation than Group I GV oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh-i). These findings suggest that Group II oocytes undergo more extensive transcriptomic alterations during aging compared to Group I oocytes. Collectively, our results suggest that the analytical strategy based on signaling entropy effectively identified two groups of aged GV oocytes with distinct molecular features.\u003c/p\u003e \u003cp\u003eAfter identifying two groups of aged oocytes with distinct molecular features, we investigated potential biomarkers to differentiate them. To this end, we clustered proteins based on their expression patterns in young, Group I, and Group II oocytes. This analysis revealed four distinct protein clusters exhibiting unique dynamics during oocyte aging (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ej, \u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e). Clusters 1/3 contained proteins that were upregulated or downregulated in both Group I and Group II oocytes compared to young control, suggesting that they represent general aging features in aged oocytes. In contrast, Clusters 2/4 included proteins that were exclusively upregulated or downregulated in Group II oocytes compared to young control, respectively, and they were enriched in pathways related to the spliceosome, chromatin remodeling, and carboxylic acid transmembrane transport (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ej-l, \u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e). Given that Group II samples likely represent oocytes experiencing more severe molecular changes during aging, these Cluster 2/4 proteins and their associated pathways may serve as biomarkers of the GV oocyte aging state and contribute to aging-induced molecular changes in oocytes. Notably, these clusters included known oocyte aging regulators (\u003cem\u003ee.g.\u003c/em\u003e, KDM1B and SFPQ, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003el)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, supporting the validity of our identified aging biomarkers. Clusters 2/4 proteins also contained previously unreported factors associated with oocyte aging (\u003cem\u003ee.g.\u003c/em\u003e, monocarboxylate transporter 4 [MCT4] and CD47, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003el), and their role in oocyte aging merit further investigation.\u003c/p\u003e \u003cp\u003eNext, we exploited the same analysis strategy to examine the heterogeneity in MII-aged oocytes. Two groups of MII oocytes were clustered based on the median signaling entropy of oocytes (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Compared to Group I oocytes, Group II oocytes exhibited decreased signaling entropy (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), downregulation of oocyte functional proteins (\u003cem\u003ee.g.\u003c/em\u003e, TRIP13 and SALL4, \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), and more proteomic/transcriptomic alterations during aging (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed-e)\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. These findings suggest that Group II MII oocytes undergo more molecular alterations during aging compared to Group I oocytes. We further compared the proteomic features between these two groups of aged oocytes. The results identified a subset of proteins (Clusters 2 and 4) specifically altered in Group II MII oocytes during oocyte aging (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef-g, \u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e), and they are enriched in mRNA processing and cellular respiration pathways (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh). The expression patterns of these proteins in aged MII oocytes suggest they may serve as biomarkers for assessing the aging state of MII oocytes and mediate the aging-associated molecular alterations in MII oocytes.\u003c/p\u003e \u003cp\u003eInterestingly, we noticed that the aged oocytes isolated from the same mice may belong to different groups of aged oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003em and \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei). These results raised the possibility that oocytes within the same reservoir may be variably impacted by aging.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMCT4 mediated the aging-induced defects in mouse oocytes\u003c/h3\u003e\n\u003cp\u003eUsing our single-cell data, we identified candidate genes that may serve as biomarkers and mediators for oocyte aging. To provide validation for this analysis, we decided to perform functional validation on selected candidate genes. Among these candidates, MCT4 emerged as a prime candidate for several reasons. First, MCT4 and its associated pathway, carboxylic acid transmembrane transport, were one of the biomarkers for GV oocyte aging (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ej-k and \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Secondly, MCT4 modulates lactate transport and lactate metabolism plays a critical role in cellular function, suggesting that MCT4 may serve as both marker and mediator for GV oocyte aging\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Finally, the availability of VB124, a highly specific MCT4 inhibitor, provides a unique pharmacological tool to investigate MCT4-targeted therapeutic strategies for mitigating oocyte aging\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMCT4 expression is elevated in aged mouse GV oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh). To investigate its role in oocyte aging, we used siRNA to suppress MCT4 expression in aged GV oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). This intervention increased the polar body extrusion (PBE) rate of aged GV oocytes (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec) and caused the aged MII oocytes to exhibit reduced spindle abnormalities (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb-d), increased chromosome alignment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee), improved ability to bind sperm (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef), and increased fertilization rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg). These results indicated \u003cem\u003eMct4\u003c/em\u003e knockdown improved meiotic maturation and fertilization capacity of aged oocytes. Beyond functional improvements, \u003cem\u003eMct4\u003c/em\u003e knockdown also mitigated the cellular epigenetic hallmark associated with aging\u0026mdash;H3K9me3 erosion in aged GV oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh), supporting an anti-aging effect of \u003cem\u003eMct4\u003c/em\u003e knockdown in oocytes\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe anti-aging effect of \u003cem\u003eMct4\u003c/em\u003e knockdown on aged oocytes suggests that MCT4 contributes to aging-induced defects in oocytes. To further explore the hypothesis, we overexpress MCT4 in young GV oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei and \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). This overexpression recapitulated the majority of the defects observed in aged oocytes, including decreased PBE rate (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee), increased spindle assembly abnormalities (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ej-l), chromosome misalignment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ek and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003em), and diminished sperm-binding and fertilization capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003en-o). Interestingly, MCT4 overexpression alone did not reduce H3K9me3 levels in oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ep), suggesting that while MCT4 is necessary, it is not sufficient to drive the aging-induced decrease in H3K9me3 in GV oocytes. Altogether, these findings suggest that elevated MCT4 contributes, at least in part, to aging-induced defects in aged GV oocytes.\u003c/p\u003e\n\u003ch3\u003eMCT4 inhibition by VB124 improved the oocyte quality of aged mice\u003c/h3\u003e\n\u003cp\u003eDeveloping anti-aging interventions for aged oocytes is in high demand, given the challenges associated with declining oocyte quality and fertility in aging females\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Recognizing MCT4's contribution to aging-induced defects in oocytes, we investigated whether MCT4 inhibition could be an effective anti-aging strategy for oocytes.\u003c/p\u003e \u003cp\u003eTo explore the possibility, we utilized VB124, a highly effective, orally bioavailable MCT4 inhibitor\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. We first test the anti-aging effect of VB124 on oocytes \u003cem\u003ein vitro\u003c/em\u003e. Aged GV oocytes were isolated from 12-month-old mice and cultured in a medium supplemented with VB124 and milrinone for a 20-hour pre-treatment. Following milrinone washout and resumption of meiosis, functional and molecular characterization of aged oocytes were conducted (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). VB124 treatment significantly improved the meiotic maturation in aged GV oocytes, as evidenced by the increased PBE rate (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef), decreased spindle abnormal rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb-d\u003cb\u003e)\u003c/b\u003e, and improved chromosome alignment in aged oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee\u003cb\u003e)\u003c/b\u003e. In addition, VB124 improved the sperm binding rate and fertilization rate of aged oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef-g), indicating that VB124 could improve the fertilization capacity of aged oocytes. Aside from functional analysis, VB124 also ameliorated aging-associated epigenetic erosion of H3K9me3 and transcriptomic alterations in aged oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eh and \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg-h). These data collectively suggest that VB124 improves the quality of aged oocytes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next explored the effect of VB124 on aged oocytes \u003cem\u003ein vivo\u003c/em\u003e. We conducted the \u003cem\u003ein vivo\u003c/em\u003e study by involving gastric lavage with VB124 (30 mg/kg, once daily for 15 consecutive days) in 12-month-old mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ei). The oocytes isolated from VB124-treated aged mice exhibited a significant quality improvement, as evidenced by decreased spindle abnormal rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ej-l), improved chromosome alignment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ek and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003em\u003cb\u003e)\u003c/b\u003e, decreased H3K9me3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003en), and increased fertilization competence (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eo). Intriguingly, VB124 also reversed the aging-induced litter size decrease and follicular apoptosis in aged mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ep-q, \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei), indicating that VB124 improves the general fertility of aged mice.\u003c/p\u003e \u003cp\u003eCollectively, these findings reveal that MCT4 inhibition by VB124 enhances oocyte quality in aged animals, highlighting its potential as a therapeutic intervention for reproductive aging.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMCT4 regulated oocyte aging by modulating lactate export\u003c/h2\u003e \u003cp\u003eMCT4 is a proton-coupled lactate symporter that plays an important role in maintaining cellular lactate homeostasis through regulated lactate efflux\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Pathological upregulation of MCT4 has been implicated in excessive lactate efflux, leading to impaired oxidative phosphorylation and subsequent cellular dysfunction\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. We thus hypothesized that MCT4-mediated lactate dysregulation underlies age-related quality decline in oocytes.\u003c/p\u003e \u003cp\u003eTo test the hypothesis, we first examined the role of MCT4 in lactate transport in oocytes. We found that MCT4 overexpression in young oocytes exhibited decreased cytosolic lactate (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-b\u003cb\u003e)\u003c/b\u003e and elevated lactate in the culture medium (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec), indicating that MCT4 mediates lactate export in oocytes. The decrease in cytosolic lactate (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-b) and increase in lactate levels in the culture medium (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec) was also observed in aged GV oocytes, which exhibited increased MCT4 expression, and VB124 treatment effectively reversed these phenotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-c), suggesting that MCT4 induced an increased lactate export in aged GV oocytes. Importantly, the restored lactate export by VB124 in aged oocytes was accompanied by improved oocyte quality (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb-g), suggesting that MCT4-mediated lactate export modulates the quality defects of aged oocytes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMCT4 upregulation mediates excessive lactate export and depletes cytosolic lactate. Consequentially, it may divert pyruvate from mitochondrial aerobic respiration toward lactate production to compensate for the lactate loss\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. In support of this hypothesis, we observed that MCT4 overexpression in young oocytes led to reduced mitochondrial pyruvate levels (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed-e). Similarly, aged GV oocytes exhibited decreased mitochondrial pyruvate levels, and these defects were partially reversed by treatment with VB124 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed-e). These findings suggest that elevated MCT4 impairs mitochondrial pyruvate influx in aged oocytes.\u003c/p\u003e \u003cp\u003eMitochondrial pyruvate is essential for ATP generation via aerobic respiration, and ATP depletion has been linked to functional decline in aged oocytes\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. We, therefore, hypothesized that MCT4-induced mitochondrial pyruvate depletion impairs oocyte quality by reducing ATP production. Consistent with our hypothesis, GV oocytes with MCT4 overexpression or aging exhibited decreased ATP levels (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef). Additionally, supplementation with VB124 or pyruvate in aged GV oocytes, which increased mitochondrial pyruvate levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eg-h), restored ATP levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ei) and improved oocyte quality (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb-g and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ej-k). These findings suggest that MCT4-induced pyruvate depletion contributes to ATP loss and the decline in oocyte quality.\u003c/p\u003e \u003cp\u003eIn summary, our results suggest that the increased MCT4 in aged GV oocytes drives excessive lactate export, leading to decreased mitochondrial pyruvate influx and ATP depletion. These metabolic disruptions may contribute to the aging-induced defects in GV oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003el).\u003c/p\u003e \u003cp\u003e \u003cb\u003eLactate export measurement served as a potential non-invasive approach to assessing aged GV oocyte quality in mice\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOur analysis revealed that MCT4 was upregulated in aged GV oocytes with severe aging impairments (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. Functional studies further demonstrated that MCT4 regulated oocyte quality by mediating lactate export (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-c). Based on these findings, we proposed that lactate export could serve as an indicator of GV oocyte aging. Specifically, GV oocytes with greater aging-induced damage are expected to show higher MCT4 expression and lactate export, whereas less affected oocytes should exhibit levels comparable to young controls.\u003c/p\u003e \u003cp\u003eTo test this, we measured the lactate export of single GV oocytes isolated from aged mice. Each oocyte was cultured in a lactate-free medium for 16 hours, and the lactate concentration of oocyte medium was measured as an indicator of lactate export levels. The oocytes then underwent \u003cem\u003ein vitro\u003c/em\u003e maturation, and spindle assembly in MII oocytes was assessed as a quality marker. Young GV oocytes with normal maturation served as controls. Our results showed that aged GV oocytes with abnormal maturation showed significantly higher lactate export than young GV oocytes and aged GV oocytes with normal maturation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003em). These findings support lactate export measurement as a potential noninvasive assessment of aged GV oocyte quality in mice.\u003c/p\u003e \u003cp\u003eTaken together, our functional analysis suggests that MCT4 and its lactate export function as a mediator, biomarker, and potential intervention target for oocyte aging, supporting the validity of our analysis in identifying biomarkers and mediators of oocyte aging.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003escSTAP profiling captured the transcriptomic and proteomic landscape in human-aged oocytes at the single-cell level\u003c/h2\u003e \u003cp\u003eOur scSTAP platform effectively profiled proteomic and transcriptomic landscapes in mouse-aged oocytes. This resource allowed us to uncover the molecular features underlying mouse oocyte aging, identify potential mediators, and develop intervention and assessment strategies for oocyte aging. Motivated by the capability of scSTAP profiling in dissecting oocyte aging, we exploited the scSTAP profiling to investigate oocyte aging in humans.\u003c/p\u003e \u003cp\u003eStudies have reported a significant decline in oocyte quality in females aged 35 years and older. Based on this, we collected 22 oocytes (13 GV and 9 MII) from females aged 35 to 42 (mean age: 40) as the aged group\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. As the young group control, 16 oocytes (8 GV and 8 MII) were obtained from females aged 21 to 32 (mean age: 27). Through scSTAP profiling, we acquired single-cell transcriptomic and proteomic data for each oocyte, achieving total profiling depth of ~\u0026thinsp;30000 genes and ~\u0026thinsp;6000 proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea-b, \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-b), with strong correlation coefficients across all samples tested (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec, \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec, and \u003cb\u003eSupplementary Table\u0026nbsp;1\u0026ndash;2\u003c/b\u003e). Both our single-cell transcriptomic and proteomic data successfully captured stage-specific markers for GV and MII oocytes, such as \u003cem\u003eZAR1\u003c/em\u003e and PATL2 for GV oocytes and \u003cem\u003eMED30\u003c/em\u003e and TPRXL for MII oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed, \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed)\u003csup\u003e\u003cspan additionalcitationids=\"CR80\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Furthermore, our integrated transcriptome and proteome datasets show a high correlation with previously published bulk data, validating the robustness of our profiling data (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee, \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing this dataset, we identified DEGs and differentially expressed proteins (DEPs) in GV and MII oocytes and their enriched biological pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef-g, \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef-g, and \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). For instance, proteins associated with PI3K-AKT signaling, oxidative phosphorylation, and RNA splicing were significantly altered in human oocytes upon aging (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eg). Interestingly, the aging-induced transcriptomic and proteomic alterations in GV and MII oocytes showed very little overlap, indicating a stage-specific effect of aging on oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eh, \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eh). In mice, we observed altered transcriptomic and proteomic transitions from GV to MII oocytes as a molecular feature of oocyte aging. Similarly, we also detected molecular dynamic alterations in human-aged oocytes during meiotic maturation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ei-j, \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ei-j). The dynamics of a significant portion of protein or RNA during the meiotic maturation underwent impairment upon aging (Clusters 1 and 3), while some novel RNA and protein dynamics were induced (Clusters 5 and 6, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ei-j and \u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ei-j). These altered dynamics may underlie the maturation defects in human-aged oocytes. Intriguingly, the aging-induced protein dynamic alterations did not appear to be driven by RNA-level alterations (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ej), suggesting that translation or protein stability changes may play a role in the observed dynamics.\u003c/p\u003e \u003cp\u003eExploiting our single-cell resolution data, we examined whether aging affected protein expression verifiability in human oocytes. Similar to findings in mice, aging unexpectedly decreased protein expression variability in human oocytes (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). Specifically, it led to a more uniform expression in a subset of mitochondrial-associated proteins (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb-c) through a mechanism largely independent of protein abundance or RNA expression variability (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed-e). The uniform expression of these proteins in human-aged oocytes raises the possibility that these proteins contribute to maintaining human-aged oocyte viability or function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMulti-omic analysis revealed uncoupled dynamics in transcriptome and proteome during human oocyte aging\u003c/h2\u003e \u003cp\u003eIn mice, single-cell multiomics analysis revealed a low correlation between RNA and protein in aged oocytes and uncoupled alterations in proteomics and transcriptomics during oocyte aging. This observation raises the question of whether similar RNA-protein uncoupling occurs during human oocyte aging.\u003c/p\u003e \u003cp\u003eWe first examined RNA-protein correlation in human oocytes. Consistent with observations in mice, the overall correlation between RNA and protein levels was low (\u0026lt;\u0026thinsp;0.1) in young and aged human oocytes (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). This low correlation was also evident in each oocyte profiled (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ek), indicating this is a general feature for human oocytes. Furthermore, we examined the RNA-protein correlation for each detected gene and confirmed a generally low RNA-protein correlation across all genes detected, with more than 75% of genes exhibiting a correlation below 0.5 (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb, \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). These results suggest that RNA levels do not primarily determine protein abundance in human oocytes. Interestingly, genes with the highest translational efficiency exhibited higher RNA-protein correlation than those with the lowest translational efficiency in human-aged GV oocytes, suggesting that translational efficiency may influence RNA-protein correlation in human-aged oocytes (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo investigate the coordination between aging-induced proteomic and transcriptomic alterations in human oocytes, we clustered genes based on the correlation between their RNA and protein changes during oocyte aging (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003el-m, \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). In GV oocytes, Class 1 genes (36 genes) showed a positive correlation between RNA and protein changes, while Class 2\u0026ndash;4 genes (818 genes) displayed uncorrelated or negatively correlated RNA and protein changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003el). Based on these results, we proposed that the majority of genes exhibited uncoupled proteomic and transcriptomic alterations upon GV oocyte aging in humans. Similar uncoupled RNA-protein alterations were also observed in MII oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003em). Furthermore, single-cell analysis revealed that uncoupled RNA-protein changes in representative genes reflect a general trend in human oocyte aging (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003en-o). Taken together, our profiling data indicated uncoupled protein and RNA alterations during human oocyte aging, which suggests that proteomic changes in human-aged oocytes are not primarily driven by RNA alterations.\u003c/p\u003e \u003cp\u003eIn mice, our analysis revealed a potential role of translational defects in the uncoupled RNA and protein changes during oocyte aging. To investigate whether translational efficiency contributes to this phenomenon in human oocyte aging, we integrated human oocyte translational data with our profiling data, focusing on aging-affected genes with translational efficiency alterations. Among genes with aging-induced translational defects, 38.84% (Group 1/Group [1\u0026ndash;2]) exhibited decreased protein levels without a corresponding RNA decline (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed, \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). Conversely, among genes with increased translational efficiency during aging, 10.3% (Group 1/Group [1\u0026ndash;2]) showed elevated protein levels without a corresponding RNA increase (\u003cb\u003eExtended Data\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ee, \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). These findings suggest that translational alterations may contribute to the uncoupled RNA-protein changes observed in these Group 1 genes during human oocyte aging.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell co-profiling identified two groups of human-aged GV oocytes with distinct aging-associated molecular features\u003c/h2\u003e \u003cp\u003eUsing single-cell proteomics in mouse oocytes, we captured the molecular heterogeneity in mouse-aged oocytes and identified biomarkers for assessing mouse oocyte aging state. In humans, aged oocytes are also reported to exhibit quality variation, suggesting potential heterogeneity in the impact of aging\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. However, there are currently no molecular features to confirm or assess this heterogeneity in human-aged oocytes. Our single-cell profiling resource provides a unique opportunity to address this gap.\u003c/p\u003e \u003cp\u003eIn mice, we used the signaling entropy to identify heterogeneity in aged oocytes. Applying the same strategy to aged human GV oocytes, we clustered aged GV oocytes into two groups based on the median signaling entropy (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). Group I oocytes, compared to Group II, exhibited higher signaling entropy (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea), maintained the expression of oocyte functional proteins (\u003cem\u003ee.g.\u003c/em\u003e, SRSF7 and PABPN1, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb)\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e, and experienced fewer alterations in proteome (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec), transcriptome (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed), and RNA-protein correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ee) during aging. Furthermore, the median donor ages of Group I oocytes are lower than that of Group II oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ef). These features together suggest that Group I samples represent oocytes with fewer molecular changes during aging, whereas Group II samples represent oocytes with more molecular changes during aging.\u003c/p\u003e \u003cp\u003eNext, we sought to identify biomarkers distinguishing the two groups of aged GV oocytes. Through a proteomic comparison among young, Group I, and Group II oocytes, we identified four distinct protein clusters based on their expression patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eg, \u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e). Among these, Clusters 2 and 4 proteins were specifically upregulated and downregulated in Group II oocytes and enriched in pathways related to RNA splicing and carboxylic acid transmembrane transport (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eg-h). Given that Group II oocytes exhibited more pronounced molecular changes upon aging, these clusters of proteins may contribute to aging-induced defects and serve as potential biomarkers for assessing the aging state in human GV oocytes. Notably, several key functional proteins for oocytes, such as SRSF7 and PABPN1\u003csup\u003e82, 83\u003c/sup\u003e, were classified in Cluster 4, supporting the validity of our identified aging biomarkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eg-h). We also observed that MCT4, a biomarker of mouse oocyte aging, along with its associated carboxylic acid transmembrane transport pathway, was identified in Cluster 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eg-h). These findings suggest that MCT4 and its lactate export function may represent a conserved feature of oocyte aging in both humans and mice.\u003c/p\u003e \u003cp\u003eLastly, we examined whether molecular heterogeneity exists within human-aged MII oocytes. Using the same analysis strategy, we clustered the oocytes into two groups based on their median signaling entropy (\u003cb\u003eExtended Data Fig.\u0026nbsp;9a\u003c/b\u003e). Unexpectedly, Group I oocytes did not exhibit milder molecular changes during aging than Group II oocytes, including proteomic, transcriptomic, and RNA-protein correlation changes (\u003cb\u003eExtended Data Fig.\u0026nbsp;9b-d\u003c/b\u003e). These findings suggest that human-aged MII oocytes may exhibit lower molecular heterogeneity compared to GV oocytes or that such heterogeneity cannot be fully captured by our proteomic analysis alone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCross-species analysis of oocyte aging in mice and humans\u003c/h2\u003e \u003cp\u003eMouse models have served as one of the primary experimental systems for investigating the molecular mechanisms underlying human oocyte aging\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Our profiling has identified several conserved aging features between mouse and human oocytes, such as more conformed protein expression and uncoupled RNA-protein alterations. To further evaluate the translational relevance of murine models to human oocyte aging, we conducted a detailed comparative analysis of aging phenotypes between these two species.\u003c/p\u003e \u003cp\u003eWe first compare the genes and proteins affected by aging in mice and humans. Specifically, aging-induced DEGs in GV and MII oocytes showed minimal overlap between humans and mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ei, \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). Similarly, aging-induced proteomic changes also showed low overlap between humans and mice in both GV and MII oocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ej, \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). The RNA and proteins that are uniquely changed upon oocyte aging in humans are enriched in sister chromatid segregation and carboxylic acid/lipid-associated pathways, while the RNA and proteins that are uniquely changed upon oocyte aging in mice are enriched in mitochondria- and actin-filament-associated pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ek). The overlapping changes include multiple ribosomal genes (e.g., \u003cem\u003eRPL11\u003c/em\u003e, \u003cem\u003eRPL35\u003c/em\u003e, \u003cem\u003eRPS9\u003c/em\u003e), and translation-associated proteins (e.g., EEF1E1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ei-j). These shared DEPs/DEGs suggest that translational alterations may be a conserved feature of oocyte aging. Notably, previous studies have also highlighted the role of translational defects in oocyte aging\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe further compared the candidate biomarkers assessing the impact of aging on human and mouse oocytes. The results showed that the biomarkers for humans and mice also show minimal overlap (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003em). Nevertheless, several conserved biomarkers were identified, including MCT4, a validated marker in mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003em).\u003c/p\u003e \u003cp\u003eAltogether, these comparisons indicate that while mouse oocyte aging exhibits similar aging characteristics to humans, the specific genes and proteins affected differ. This highlights the need for caution when using mouse models to study human reproductive aging.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOocyte quality decline due to aging has become a significant challenge for female reproductive health\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, the limited availability of aged oocytes, particularly human-aged oocytes, has constrained comprehensive molecular profiling on oocyte aging, challenging the elucidation of the mechanisms underlying this decline. In this study, we employed the scSTAP to perform integrated single-cell proteomic and transcriptomic profiling of both mouse- and human-aged oocytes\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Our profiling established the proteomic and transcriptomic landscape of oocyte aging in humans and mice, providing a valuable resource for uncovering molecular features and mechanisms associated with oocyte aging.\u003c/p\u003e \u003cp\u003eIn addition to the proteomic/transcriptomic landscape, our integrated multi-omics data also uncovered the RNA-protein correlation during oocyte aging. Using our data, we revealed that both humans and mice exhibited a low RNA-protein correlation in aged oocytes. In addition, we showed that aging-induced alterations in RNA and protein levels were largely uncoupled in mouse and human oocytes, and translational defects might partially contribute to these uncoupled changes. These findings align with previous studies showing the role of translational efficiency in oocyte aging\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. Taken together, our results suggest that scSTAP profiling can dissect how the proteomic landscape is shaped by transcriptomic features during oocyte aging and facilitate the identification of mediators modulating proteomic changes in aged oocytes. In addition, our results highlight a critical methodological consideration: the limited predictive value of RNA-based inferences for protein expression in aged oocytes, emphasizing the necessity of direct proteomic characterization when studying molecular changes associated with oocyte aging.\u003c/p\u003e \u003cp\u003eAged oocytes are reported to exhibit quality heterogeneity. Even oocytes from same-aged donors show varying competence for fertilization and development\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. However, the molecular features and mechanisms underlying this variability remain unclear. Our single-cell profiling provided a valuable resource for examining the molecular characteristics associated with oocyte heterogeneity. We utilized signaling entropy, an index that effectively captures aging features in oocytes, to cluster aged oocytes into two groups. These groups exhibited significantly different extents of molecular changes with aging. By analyzing these groups, we identified proteins that may serve as biomarkers for assessing the impact of aging on oocytes. Interestingly, this analytical strategy failed to capture the heterogeneity of aged human MII oocytes. One possible explanation is that human MII oocytes have undergone a quality selection during harvest, resulting in fewer molecular differences. Alternatively, this may suggest that our proteomic analysis alone is insufficient to reveal heterogeneity in human MII oocytes, and additional layers of cellular information are required.\u003c/p\u003e \u003cp\u003eMCT4 is one of the candidate markers of GV oocyte aging identified in both mice and humans. Our study suggests that MCT4-mediated lactate export functions as both a mediator and a potential intervention target for oocyte aging. Metabolic alterations have been implicated in oocyte aging\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e, and our findings reveal a previously unrecognized metabolic dysfunction—lactate metabolism—associated with this process. Moreover, our results suggest that this metabolic disorder may underlie the molecular heterogeneity observed in aged oocytes. Leveraging this feature, we provide evidence suggesting that oocyte lactate export could be a non-invasive biomarker for assessing oocyte aging. These findings underscore the translational potential of MCT4 and lactate metabolism in the diagnosis and treatment of oocyte aging.\u003c/p\u003e \u003cp\u003eOocytes reside within the ovary, and their function and quality are highly influenced by the ovarian environment\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. Studies have shown that the ovary undergoes significant changes during aging and that a healthier follicular microenvironment can markedly enhance the quality of aged oocytes\u003csup\u003e\u003cspan additionalcitationids=\"CR88 CR89 CR90 CR91\" citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e–\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. These findings emphasize the critical role of the ovarian niche in maintaining oocyte quality. Leveraging our profiling resource of aged oocytes, future investigations could explore the interplay between oocyte and ovarian aging and identify key cellular communication pathways contributing to oocyte aging.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eMice and VB124\u003c/strong\u003e \u003cstrong\u003ein vivo\u003c/strong\u003e \u003cstrong\u003etreatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC57BL/6J mice were acquired from the Laboratory Animal Center of Zhejiang Academy of Medical Sciences (China). Both young (6\u0026ndash;8 weeks old) and aged (12 months old) mice were maintained under pathogen-free conditions with \u003cem\u003ead libitum\u003c/em\u003e access to standard chow and autoclaved water. The vivarium maintained controlled environmental parameters: 12-hour photoperiod cycling, ambient temperature regulated at 20\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C, and relative humidity between 50\u0026ndash;70%. All experimental protocols received prior approval from the Institutional Animal Care and Use Committee of Zhejiang University (IACUC approval: ZJU20220303). For pharmacological intervention, aged subjects were randomly allocated into two treatment groups. The experimental group received daily intraperitoneal injections of VB124 (MedChemExpress, HY-139665; 30 mg/kg body weight) dissolved in vehicle solution (40% PEG300, 5% Tween-80, 45% physiological saline). Control animals were administered equivalent volumes of vehicle solution alone. This treatment regimen persisted for 15 days, with daily animal welfare monitoring.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMouse oocyte collection and\u003c/strong\u003e \u003cstrong\u003ein vitro\u003c/strong\u003e \u003cstrong\u003eculture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental protocol involved intraperitoneal administration of 10 IU pregnant mare serum gonadotropin (PMSG; Ningbo Sansheng Pharmaceutical, veterinary approval 110044564) to sexually mature female mice. Following a 44-hour pharmacological stimulation period, the animals were humanely sacrificed according to institutional animal care guidelines. Oocytes at the GV stage were harvested from ovarian follicles using the M2 collection medium (Sigma-Aldrich, M7167) and transferred to maturation culture conditions. The \u003cem\u003ein vitro\u003c/em\u003e maturation process was conducted in an M16 culture medium (Sigma-Aldrich, M7292) layered with mineral oil (Sigma-Aldrich, M8410), maintained at 37\u0026deg;C in a humidified incubator with 5% CO2 tension. After 16 hours of controlled culture conditions, oocytes successfully progressed to the MII stage.\u003c/p\u003e\n\u003ch2\u003eSuperovulation\u003c/h2\u003e\n\u003cp\u003eFemale mice were given a 10 IU injection of hCG roughly 48 hours after receiving a 10 IU PMSG injection. After an additional 16 hours, cumulus-oocyte complexes (COCs) were surgically harvested from the fallopian tubes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn vitro\u003c/strong\u003e \u003cstrong\u003efertilization (IVF)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMature oocytes arrested at the MII stage were selected for subsequent fertilization procedures. Following a 60-minute incubation period in a pre-equilibrated IVF medium to achieve optimal dilution, freshly prepared capacitated sperm samples were used. Concurrently, collected COCs from five donor mice or denuded oocytes at the MII stage were transferred into the fertilization medium microdroplets (200 \u0026micro;L volume). Precise aliquots (3\u0026ndash;5 \u0026micro;L) of the optimized sperm suspension were then added to the microdroplets containing the COCs to achieve a final concentration of 4 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e motile spermatozoa per milliliter in the droplets. Gamete co-culture was maintained under controlled conditions (37\u0026deg;C, 5% CO₂) for 4 to 6 hours to allow successful fertilization.\u003c/p\u003e\n\u003ch2\u003eSperm binding assay\u003c/h2\u003e\n\u003cp\u003eSpermatozoa collected from sexually mature mice underwent capacitation through 60-minute incubation in the IVF medium. Following this preparatory phase, the capacitated gametes were subsequently exposed to either ovulated oocytes or two-cell stage embryos during a 30-minute coincubation. In the experimental design, two-cell stage embryo interactions served as the negative control, and immature oocyte interactions provided the baseline.\u003c/p\u003e\n\u003ch2\u003eMouse litter size analysis\u003c/h2\u003e\n\u003cp\u003eEach female mouse was mated with a young male mouse of proven fertility. Mating was confirmed by the presence of a copulatory plug, and the day of plug detection was designated as embryonic day 0.5 (E0.5). Litters were collected at birth (postnatal day 0, P0), and the number of pups per litter was recorded. Pups were counted and weighed within 24 hours of birth to ensure accurate litter size and pup viability measurement.\u003c/p\u003e\n\u003ch2\u003eQuantification of oocyte ATP levels\u003c/h2\u003e\n\u003cp\u003eOocyte ATP concentrations were measured in batches of five oocytes using a commercially available ATP bioluminescence detection kit (Sigma-Aldrich, FLASC) according to the manufacturer\u0026apos;s protocol. For accurate quantification, calibration curves were systematically generated using seven ATP reference concentrations (0, 0.01, 0.03, 0.1, 0.3, 1, and 3 \u0026micro;M) in parallel with the experimental samples. The ATP quantities in biological samples were subsequently determined through regression analysis, utilizing the mathematical relationship established from the linear portion of the standard curve.\u003c/p\u003e\n\u003ch2\u003eFiLa-based medium lactate analysis\u003c/h2\u003e\n\u003cp\u003eFor the FiLa-sensor-based medium lactate analysis, 2 \u0026micro;L self-prepared lactate-free M2 culture medium samples were collected after a 16-hour single-cell culture. To ensure consistency in sample volume, 48 \u0026micro;L of the detection solution was added to the collected culture medium samples. This step was crucial to match the sample volume with that of the standard solutions used in the assay. Then FiLa protein probe (Provoson, PFLA100S) was gently mixed and diluted 20-fold with the detection solution to prepare the working solution. Each assay was performed in a 96-well black bottom plate using 50 \u0026micro;L diluted samples or 50 \u0026micro;L lactate protein with 50 \u0026micro;L working solution. Fluorescence intensity was measured immediately by a multifunctional microplate reader (Tecan, SPARK) using 485 BP 20 nm or 420 BP 10 nm excitation and 528 BP 20 nm emission bandpass filters. A 5-point standard curve (0, 0.256, 1.28, 6.4, 32 \u0026micro;M of lactate) was generated in each assay, and the lactate content was calculated using the formula derived from the linear regression of the standard curve.\u003c/p\u003e\n\u003ch2\u003ePlasmid construction\u003c/h2\u003e\n\u003cp\u003eMouse \u003cem\u003eMct4\u003c/em\u003e cDNAs were PCR amplified from a mouse GV oocytes cDNA pool and ligated into pcDNA-based eukaryote expression vectors. Fila-cyto plasmid was acquired from Provoson (Provoson, #FLA1001). PyronicSF/pcDNA3.1 was acquired from Addgene (Addgene, #124812). To achieve mitochondrial expression of PyronicSF, a mitochondrial-targeting sequence of COX8 (\u003cstrong\u003eSupplementary Table\u0026nbsp;6\u003c/strong\u003e) was fused at the N terminus of PyronicSF/pcDNA3.1, generating the PyronicSF-mito plasmid.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn vitro\u003c/strong\u003e \u003cstrong\u003etranscription and microinjection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExpression vectors, FiLa-cyto and PyronicSF-Mito plasmids were linearized using specific restriction enzymes to prepare mRNAs for microinjection. The synthesis of 5\u0026prime;-capped mRNA transcripts was accomplished through \u003cem\u003ein vitro\u003c/em\u003e transcription employing T7/SP6 RNA polymerase systems (message mMACHINE\u0026trade; Kits, Invitrogen, AM1344/AM1340) under standard reaction conditions (37\u0026deg;C, 4 hours). Post-transcriptional processing included polyadenylation using the Poly(A) Tailing Kit (Invitrogen, AM1350), followed by purification via LiCl precipitation and subsequent resuspension in nuclease-free aqueous solution. Gene-specific silencing molecules targeting \u003cem\u003eMct4\u003c/em\u003e were generated through \u003cem\u003ein vitro\u003c/em\u003e transcription with the T7 RNAi system (Vazyme, TR102) per manufacturer specifications, with corresponding oligonucleotide sequences detailed in \u003cstrong\u003eSupplementary Table\u0026nbsp;6\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eFor microinjection, a Nikon microscope-integrated micromanipulation system was employed to administer precise 5\u0026ndash;10 pL injections into oocytes. Injection solutions contained synthetic mRNA (1 mg/mL working concentration), proteins (2 mg/mL), or siRNA duplexes (20 \u0026micro;M final concentration), appropriately diluted in a sterile injection buffer. Following microinjection, the oocytes were cultured in a milrinone-supplemented medium for a defined period (as illustrated in the schematic) to maintain meiotic arrest and allow sufficient time for perturbation. Subsequently, the oocytes are thoroughly washed to remove milrinone, thereby relieving meiotic inhibition and enabling the resumption of the maturation process. The treated oocytes are then transferred to a fresh maturation medium for subsequent functional studies.\u003c/p\u003e\n\u003ch2\u003eOocyte lactate and pyruvate detection by live-cell fluorescence imaging\u003c/h2\u003e\n\u003cp\u003eCytosolic lactate and mitochondrial pyruvate levels in oocytes were measured using the Fila-cyto and PyronicSF-mito sensors, respectively, as previously described\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e. Specifically, the oocytes were microinjected with fluorescence sensors (Fila-cyto or PyronicSF-mito) along with mCherry as an injection control. The detailed microinjection process is described above. Subsequently, these oocytes were plated on a 35 mm glass-bottom dish. The injected FiLa and PyronicSF sensors were expressed in different subcellular compartments (cytosol or mitochondrial) by tagging with organelle-specific signal peptides (\u003cstrong\u003eSupplementary Table\u0026nbsp;6\u003c/strong\u003e). Live-cell imaging was performed using a Zeiss LSM900 system, with sequential scanning using 488 nm and 594 nm laser lines to prevent spectral bleed-through. The GFP-conjugated probes were detected through 493\u0026ndash;556 nm excitation/emission filters, while mCherry fluorescence was recorded using 587\u0026ndash;628 nm bandpass settings. All acquisition parameters were maintained at 12-bit depth with identical gain/pixel dwell time configurations across experimental replicates. Captured images were processed and exported as uncompressed TIFF sequences using Zen Blue software before quantitative analysis in ImageJ. Fluorescence quantification involved background subtraction and compartment-specific ROI selection based on morphology thresholds. Signal normalization was performed against mCherry reference values using integrated measurement tools (ImageJ).\u003c/p\u003e\n\u003ch2\u003eImmunofluorescence\u003c/h2\u003e\n\u003cp\u003eOocytes were subjected to a fixing process involving a 4% paraformaldehyde solution (PBS-buffered) for a period of 30 minutes, followed by permeabilization in PBS with 0.3% Triton X-100 for 30 minutes. Subsequently, samples were blocked in PBS containing 1% bovine serum albumin for 30 minutes, followed by incubation with primary antibodies at 25\u0026deg;C for 1 hour. Nuclei were stained with 4\u0026prime;,6-diamidino-2-phenylindole (DAPI), and secondary antibodies were co-incubated with DAPI for 30 minutes. Images were acquired using a Zeiss LSM900 confocal microscope. Quantitative analysis of fluorescence intensity and length measurement were performed by ImageJ. The information of antibodies used were included in \u003cstrong\u003eSupplemental Table\u0026nbsp;6\u003c/strong\u003e.\u003c/p\u003e\n\u003ch2\u003eHistological analysis\u003c/h2\u003e\n\u003cp\u003eOvarian tissues were collected and immediately fixed in 4% paraformaldehyde-phosphate-buffered saline (PBS, pH 7.4) at 4\u0026deg;C for 16 hours. Following standard histological procedures, the fixed specimens underwent sequential ethanol dehydration (70%-100% gradient), xylene clearing, and paraffin infiltration. Tissue blocks were sectioned coronally at 5 \u0026micro;m using a rotary microtome (Leica RM2235). Apoptotic cells were detected using the TUNEL BrightRed Apoptosis Detection Kit (Vazyme, A113-01) through fluorometric labeling of DNA fragmentation sites, with experimental conditions strictly adhered to according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003ch2\u003eExtraction of minute quantities of RNA, reverse transcription (RT), RT-PCR\u003c/h2\u003e\n\u003cp\u003eFollowing oocyte collection (n\u0026thinsp;=\u0026thinsp;10), cellular lysis was carried out using a 2 \u0026micro;L lysis buffer (1% Triton X-100, 40 U/\u0026micro;l RNase inhibitor). RNA purification was subsequently performed, and around 10\u0026ndash;15 ng of mRNA could be obtained. First-strand cDNA synthesis was achieved with SuperScript II Reverse Transcriptase following manufacturer guidelines, and cDNA products underwent 6-9-fold dilution in sterile nuclease-free water before serving as template DNA in subsequent analyses. Quantitative RT-PCR was conducted using Taq Pro Universal SYBR qPCR Master Mix (Vazyme, cat. no. Q712) and the ABI 7500 Real-Time PCR system with primers specified in \u003cstrong\u003eSupplemental Table\u0026nbsp;6\u003c/strong\u003e. Data normalization was performed against \u003cem\u003eGapdh\u003c/em\u003e housekeeping gene expression levels. To ensure reproducibility, each qPCR assay included at least three technical replicates per biological sample.\u003c/p\u003e\n\u003ch2\u003eWestern-blot analysis\u003c/h2\u003e\n\u003cp\u003eProtein homogenization was performed using a cracking buffer (Absin, abs953) supplemented with \u0026beta;-mercaptoethanol, followed by thermal denaturation at 95\u0026deg;C for 10 min. The denatured samples were loaded into SDS-PAGE (13% separation gel) and subsequently electroblotted onto PVDF membranes (0.45 \u0026micro;m pore size; Millipore). Membranes underwent blocking with 5% (w/v) non-fat dry milk in TBST for 30 min at ambient temperature before overnight incubation with primary antibodies at 4\u0026deg;C. Following three 5-minute TBST washes, immunodetection was conducted using horseradish peroxidase (HRP)-conjugated secondary antibodies (1:5000; Jackson ImmunoResearch) with 1-hour incubation at room temperature. After three subsequent TBST rinses (10 min each), chemiluminescent signals were developed with SuperSignal West Femto substrate (Thermo Fisher Scientific) and captured using a digital imaging system (Amersham Imager 680). Detailed antibody specifications, including clone identifiers, target epitopes, and working dilutions, are provided in \u003cstrong\u003eSupplementary Table\u0026nbsp;6\u003c/strong\u003e.\u003c/p\u003e\n\u003ch2\u003eHuman oocyte collection and culture\u003c/h2\u003e\n\u003cp\u003eThe human-associated experiment of this study was approved by the Medical Ethics Committee of Sir Run Shaw Hospital, School of Medicine, Zhejiang University (No.2022\u0026thinsp;\u0026minus;\u0026thinsp;0461) and was conducted in accordance with the criteria set by the Declaration of Helsinki. Human oocytes were donated from women who underwent intracytoplasmic sperm injection (ICSI) treatment due to male factors. The woman had no family history of genetic diseases.\u003c/p\u003e\n\u003cp\u003eOvarian stimulation was carried out following a progestin-primed ovarian stimulation protocol. Once two follicles grew to a diameter of 18 mm or three follicles reached 17 mm, we triggered the final oocyte maturation by injection of 5000-10,000 IU of human chorionic gonadotropin (hCG; Lizon Pharma Pharmaceutical Trading Co., Ltd., Zhuhai, China) or addition of 0.1 mg of triptorelin (Decapeptyl; Ferring Pharmaceuticals, Hoofddorp, Netherlands) as a GnRH agonist. Approximately 36 hours later, the oocytes were retrieved through transvaginal ultrasound. From all collected oocytes, only those at the immature GV stage were used in this study because they were not routinely utilized for clinical purposes, given that these patients had an adequate number of MII oocytes for their treatment. The \u003cem\u003ein vitro\u003c/em\u003e matured MII oocytes were obtained from GV oocytes, which were cultured in IVM medium (RC-1060, ARSCI Biomedical Inc., Zhejiang, China) at 37\u0026deg;C in an atmosphere with 6% CO\u003csub\u003e2\u003c/sub\u003e for 24\u0026ndash;28 hours.\u003c/p\u003e\n\u003cp\u003eThe aged group consisted of women (n\u0026thinsp;=\u0026thinsp;16) with a mean age of 40 (age range 35\u0026ndash;46). 22 oocytes (13 GV and 9 MII) were collected from these women. In the young group, 16 oocytes (8 GV and 8 MII) were obtained from women (n\u0026thinsp;=\u0026thinsp;13) with a mean age of 27 years (age range 21\u0026ndash;32). All women who participated in this study provided their written informed consent.\u003c/p\u003e\n\u003ch3\u003eSingle-cell multi-omics sequencing\u003c/h3\u003e\n\u003cp\u003eA schematic workflow of the single-cell multi-omics sequencing is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea. The previously developed SODA technique was used for single-cell capture and pretreatment\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. Briefly, a capillary probe with a tapered tip was connected to a high-precision syringe pump for nanoliter-scale liquid manipulation, and an x-y-z translation stage was controlled for the positioning and moving of insert tubes and 24-well plates. First, single oocyte cells were picked up and washed using the capillary probe under the observation of a microscope and were deposited into the insert tubes individually, forming a 200 nL single-cell droplet in each insert tube. Next, 400 nL of 50 ng/\u0026micro;L Lys-C solution was added into each insert tube and incubated at 37\u0026deg;C for 15 min, and then 400 nL of 0.3% (w/v) RapiGest was added with an incubation condition of 60\u0026deg;C for 15 min for cell lysis. After the cell lysis, the droplets containing cell lyses were split into two aliquots using the capillary probe. One aliquot was used to analyze transcriptome, and the other aliquot left in the same insert tube was used for proteomic analysis. In the transcriptomic analysis, the droplet was analyzed using the Smart-seq2 technique\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e. The lysis was used for library generation using the V2 model library preparation kit (TruePrep DNA, Vazyme Cat# TD503).\u003c/p\u003e\n\u003cp\u003eIn the proteomic analysis, the droplet was analyzed using an improved deep single-cell proteomic technique developed by us\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The bottom-up strategy was used with an improved workflow. Briefly, 500 nL of 10 mM tris (2-carboxyethyl)-phosphine (TCEP) was added into the insert tube containing the aliquot droplet of a lysed single cell with an incubation time of 20 min at 25\u0026deg;C. Then, 50 mM iodoacetamide (IAA) was added to the droplet with an incubation time of 20 min at 25\u0026deg;C in the dark. The proteins in the droplet were then digested by adding 500 nL of 50 ng/\u0026micro;L enzyme solution (trypsin: lys-C\u0026thinsp;=\u0026thinsp;1:1) and incubating the droplet at 37\u0026deg;C for 4 h. Finally, 1 \u0026micro;L of 50% (v/v) formic acid was added to the droplet to terminate the reactions. After the pretreatment, the insert tubes containing the droplets for scProteomics were transferred into the autosampler of an EASY-nLC 1200 LC system (Thermo, USA) coupled with a timsTOF Pro mass spectrometer (Bruker, USA). The injection needle and valve of the autosampler were used to sequentially complete the loading and injection of the droplet samples into a homemade capillary LC column (50 \u0026micro;m i.d., 6 cm length, 1.7 \u0026micro;m C18 particles with 120 \u0026Aring; pore size) installed in the LC system. The peptides in the droplet samples were separated in the LC column and detected by the mass spectrometer. The flow rate of the mobile phase in LC separations was 150 nL/min with a 70 min gradient (mobile phase A\u0026thinsp;=\u0026thinsp;0.1% FA in water; mobile phase B\u0026thinsp;=\u0026thinsp;0.1% FA, 80% ACN in water; 0\u0026ndash;40 min, 0\u0026ndash;40% mobile phase B; 40\u0026ndash;41 min, 40\u0026ndash;100% B; and 41\u0026ndash;70 min, 100% B). The spray voltage of the ion source was set at 1.75 kV under the positive mode. The acquisition mode of diaPASEF was used with a mass range of 350\u0026ndash;1150 m/z, an isolation width of 25 m/z, a mobility 1/k0 range of 0.6\u0026ndash;1.3, and a collision energy range of 20\u0026ndash;47.3 eV in collision-induced dissociation (CID).\u003c/p\u003e\n\u003ch2\u003eSmart-seq2 analysis\u003c/h2\u003e\n\u003cp\u003eThe hg38 human genome, the mm10 mouse genome, and the genomic annotation file were downloaded from the Ensemble database. The raw RNA-seq reads were trimmed by trim-galore (v 0.6.7). The clean data were mapped using Hisat2 (v 2.10)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e against the corresponding genome index with the following parameters:\u0026rsquo;-x /Path/To/Genomeindex -p 5\u0026thinsp;\u0026minus;\u0026thinsp;1 Sampe_fq1.gz -2 Sampe_fq2.gz -S Sample.sam\u0026rsquo;. Then, the sam files were converted to bam format using Samtools (v 1.9)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e. Read counts were calculated by Featurecount (v 1.6.5) with the following parameters:\u0026rsquo;-T 5 -p -B -t exon -g gene_name -a genome.gtf -o Sample_count.txt Sample.sort.bam\u0026rsquo;.\u003c/p\u003e\n\u003cp\u003eThe DEGs were calculated using the DESeq2 package (v 1.32.0)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e. The genes satisfying |Log\u003csub\u003e2\u003c/sub\u003eFoldChange| \u0026gt; 1 and \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were identified as DEGs. using \u003cem\u003eP\u003c/em\u003e values were obtained using the Wald test within a negative binomial generalized linear model. Clusterprofiler package (v 4.0.5)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e was used to perform GO function enrichment and KEGG pathway enrichment. The enriched gene count and \u003cem\u003eP\u003c/em\u003e value for specific terms were used to generate the dot plot. ComplexHeatmap package (v 1.0.12) was used to plot the heatmap. The \u0026lsquo;prcomp\u0026rsquo; function in R (v 4.1.1) was used to perform the PCA. We averaged the PC1 and PC2 of young or aged oocytes to generate the centroid. The centroid distance was defined as the euclidean distance between the centroid of each group in low-dimensional space and was calculated by R (v 4.1.1).\u003c/p\u003e\n\u003ch2\u003eSingle-cell proteome analysis\u003c/h2\u003e\n\u003cp\u003eThe raw data of single-cell proteomics were analyzed using Spectronaut software (v 15.6.211220.50606 for mice samples \u0026amp; 18.2.230802.50606 for human samples) in library-based mode. The library was established using 20 quality control samples containing 2 ng oocyte proteins. The UniProt database (UP000000589.fasta, Mus musculus: 21,985 entries; UP000005640.fasta, Homo sapiens: 20,422 entries) was used in software analysis with the default settings. The false discovery rates (FDR) for both precursors and proteins were 0.01. The DEPs were identified based on previously reported methods as a reference\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e. Specifically, the protein expression matrix was first increased by 1 to avoid the error during the fold change calculation. Then, each group\u0026apos;s average protein expression level was used to calculate the fold change of detected protein. Statistical significance was calculated by the Wilcoxon rank-sum test using the \u0026lsquo;Wilcox.test\u0026rsquo; function in R (v 4.1.1). The proteins satisfying |Log\u003csub\u003e2\u003c/sub\u003eFoldChange| \u0026gt; 1 and \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were identified as DEPs. Clusterprofiler package (v 4.0.5) was used to perform GO function enrichment and KEGG pathway enrichment. The \u0026lsquo;prcomp\u0026rsquo; function in R (v 4.1.1) was used to perform the Principal Component Analysis (PCA). We averaged the PC1 and PC2 of young or aged oocytes to generate the centroid. The centroid distance was defined as the Euclidean distance between the centroid of each group in low-dimensional space and was calculated by R (v 4.1.1).\u003c/p\u003e\n\u003ch2\u003eSignaling entropy analysis using single-cell proteome\u003c/h2\u003e\n\u003cp\u003eThe signaling entropy value was calculated by the \u0026lsquo;CompSRana\u0026rsquo; function in the LandSCENT package (v 0.99.5)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e. Data is normalized Log\u003csub\u003e2\u003c/sub\u003e(expression matrix\u0026thinsp;+\u0026thinsp;1.1) as suggested\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e. The protein-protein interaction \u0026lsquo;net13Jun12.m\u0026rsquo; was used to integrate the single-cell proteome data and pre-defined protein connectome. Given the lack of knowledge on the heterogeneity distribution in aged oocytes, we decided to use the median signaling entropy within the aged oocyte group as a criterion for clustering. This strategy has been previously employed to dissect heterogeneity within samples\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e103\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e. Specifically, oocytes with signaling entropy above the median were designated as Group I, while those with signaling entropy below the median were classified as Group II. Oocytes with signaling entropy equal to the median were excluded from clustering.\u003c/p\u003e\n\u003ch2\u003eCorrelation between mRNA and Protein Expression\u003c/h2\u003e\n\u003cp\u003eThe mRNA expression level was represented by fragments per kilobase of mapped reads (FPKM). The protein expression level was represented using the automatic protein LFQ method in the Spectronaut software. The genes detected by both Smart-seq2 and single-cell proteome were then used to perform correlation analysis. The \u0026lsquo;cor\u0026rsquo; function in R (v 4.1.1) was used to calculate Pearson\u0026rsquo;s correlation coefficient between the mRNA/protein expression level and between our profiling data and public data. The \u0026apos;cor.test\u0026rsquo; function in R (v 4.1.1) was used to calculate the statistical significance of correlation through the parametric t-test.\u003c/p\u003e\n\u003ch3\u003eAnalysis of gene variation score\u003c/h3\u003e\n\u003cp\u003eTo avoid the effect of the expression level, we first normalized the RNA expression matrix and protein expression matrix of genes. The expression level of each gene was normalized by dividing by the difference between the maximum value and minimum value. Then, the gene variation score (RNA or protein) was calculated as:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:range=\\frac{\\text{max}\\left({Gene}_{i}\\right)-\\text{min}\\left({Gene}_{i}\\right)}{\\text{max}\\left({Gene}_{i}\\right)+\\text{min}\\left({Gene}_{i}\\right)}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:mad=\\frac{1}{N}\\left({\\sum\\:}_{i=1}^{N}\\left(\\text{a}\\text{b}\\text{s}\\right(\\text{m}\\text{e}\\text{d}\\text{i}\\text{a}\\text{n}\\left(\\left\\{{Gene}_{i}\\right\\}\\right)\\:-\\:{Gene}_{i})\\right)\\:$$\u003c/div\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"text-align: start; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255); font-size: medium; font-family: \u0026quot;\u0026quot;;\"\u003e\u003cbr\u003e\u003c/p\u003e\u003c/div\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Gene}_{i}\\)\u0026nbsp;\u003c/span\u003e\u003c/span\u003edonated the expression level of gene \u003cem\u003ei\u003c/em\u003e. Max, min, median, and abs denoted the maximum gene expression value, minimum gene expression value, median gene expression value, and absolute value of gene expression value.\u003c/p\u003e\u003cp\u003eIn the following analysis, we removed the gene whose range is equal to 1. We calculate the fold change by dividing the gene variation score of the aged group by the gene variation score of the young group. The genes satisfying |Log\u003csub\u003e2\u003c/sub\u003eFoldChange| \u0026gt; 1 were identified as changed genes.\u003c/p\u003e\u003ch3\u003eAnalysis of stage-specific genes\u003c/h3\u003e\u003cp\u003eWe first generated the list of DEGs/DEPs between GV and MII oocytes at young and aged states based on scSTAP profiling data. The DEGs/DEPs were identified as described above. We then split these stage-specific genes/proteins into six clusters, including: 1) The gene was activated during young oocyte maturation, while the activation trend decreased during aged oocyte maturation; 2) The gene was activated during young oocyte maturation, while the activation trend increased during aged oocyte maturation; 3) The gene was repressed during young oocyte maturation, while the repression trend decreased during aged oocyte maturation; 4) The gene was repressed during young oocyte maturation; while the repression trend increased during aged oocyte maturation; 5) The gene was stable during young oocyte maturation but was repressed during aged oocyte maturation; 6) The gene was stable during young oocyte maturation but was activated during aged oocyte maturation. For groups 1\u0026ndash;4, whether the gene/protein dynamics are changed upon aging was determined by the difference between the Log\u003csub\u003e2\u003c/sub\u003e(Foldchange [YMII vs YGV]) and Log\u003csub\u003e2\u003c/sub\u003e(Foldchange [AMII vs AGV]). We set different threshold values for the above two data: 0.25 for single-cell proteome data and 0.5 for Smart-seq2. The \u0026lsquo;geom_smooth\u0026rsquo; function in gglot2 (V 3.4.4) was used to fit the temporal expression patterns of the protein levels and the corresponding RNA levels.\u003c/p\u003e\u003ch2\u003eAging features determined by public gene set\u003c/h2\u003e\u003cp\u003eThe FPKM matrix for Smart-seq2 and the scaled protein expression matrix were used for this analysis. We collect eight aging-associated gene sets, including CSgene\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e, SenMayo\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, and SID gene sets (SID1-6)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. For CSgene and SenMyao gene sets, the aging scores were calculated as below:\u003c/p\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e$$\\:aging\\:score={\\sum\\:}_{i=1}^{Z}{Gene}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eFor the SID gene set, the aging score was calculated as below:\u003c/p\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e$$\\:aging\\:score={\\sum\\:}_{i=1}^{Z}{UpGene}_{i}-{\\sum\\:}_{i=1}^{Z}{DownGene}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere Z denoted the gene number of the corresponding geneset, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Gene}_{i}\\)\u003c/span\u003e\u003c/span\u003e donated the expression level of gene \u003cem\u003ei\u003c/em\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{UpGene}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{DownGene}_{j}\\)\u003c/span\u003e\u003c/span\u003e donated the expression level of up-regulated gene \u003cem\u003ei\u003c/em\u003e and down-regulated gene \u003cem\u003ej\u003c/em\u003e in the SID gene set.\u003c/p\u003e\u003cp\u003eWe treat the aging score as a signature of aging and evaluate the performance of the aging score to distinguish aged oocytes from young oocytes. The area under the receiver operating characteristic (ROC) curve (AUC) was used as a metric. The AUC analysis was performed by the \u0026lsquo;roc\u0026rsquo; function in the pROC package (v 1.18.5).\u003c/p\u003e\u003ch2\u003eTranslational efficiency analysis\u003c/h2\u003e\u003cp\u003eThe translatome and transcriptome data of mouse GV oocytes were downloaded from CRA008819\u003csup\u003e15\u003c/sup\u003e. We use the same method of Smart-seq2 to map and count the translatome data. The translatome and transcriptome data were first transferred into Transcripts Per Million (TPM) format. Then, for each gene, the average TPM value of translatome data was divided by the average TPM value of transcriptome data to calculate the translational efficiency. The genes satisfying |Log2FoldChange (AGV vs YGV) | \u0026gt; 1 were identified as significantly changed genes.\u003c/p\u003e\u003ch2\u003eStatistics and reproducibility\u003c/h2\u003e\u003cp\u003eStatistical significance was determined using Student\u0026rsquo;s t-test (two-tailed), two-tailed Mann-Whitney U-tests for datasets with non-normal distribution, hypergeometric test for gene set enrichment analysis, or parametric t-test for correlation analysis as indicated in the corresponding figure legends. \u003cem\u003eP\u003c/em\u003e value for protein expression level was obtained using the Wilcoxon rank-sum test, and the \u003cem\u003eP\u003c/em\u003e value for RNA expression level was obtained using the Wald test within a negative binomial generalized linear model. Boxes in all box plots extend from the 25th to the 75th percentiles, with a line at the median. Statistical tests were performed using Prism8 (GraphPad Software) or R. Each experimental procedure was repeated a minimum of three times to ensure reproducibility. The outcomes are reported as the arithmetic mean accompanied by its standard error of measurement (SEM).\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData and code availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll transcriptomic generated have been deposited in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA023659, GSA-Human: HRA010683), and all proteomics data have been deposited in the ProteomeXchange Consortium under accession number PXD061896. All the codes used are available online (see Methods). Any additional information required to reanalyze the data of this study is available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the core facility of Liangzhu Laboratory for the technical support. This research was funded by grants from the National Key R\u0026amp;D Program of China (2022YFC2702300, 2021YFA1301601), the Youth, General, and Key Program of the National Natural Science Foundation of China (82201837, 32470840, 32270852, 22234007), Zhejiang Provincial Natural Science Foundation of China (LZ24C120001), Key R\u0026amp;D Program of Zhejiang (2024C03144), and “Pioneer” R\u0026amp;D programs of Zhejiang Province (No. 2024C03005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLanrui Cao, Hao Wu, and Yirong Jiang contributed equally to this work.\u003c/p\u003e\n\u003cp\u003eLanrui Cao, Hao Wu, Yirong Jiang, Zhuo Yang, Peipei Ren, Panpan Zhao, Yinli Zhang Songying Zhang, Hengyu Fan, Yongcheng Wang, Xiaomei Tong, Qun Fang, Xudong Fu\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFirst Affiliated Hospital, Zhejiang University School of Medicine, and Liangzhu Laboratory of Zhejiang University, Hangzhou, 310000, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLanrui Cao, Hao Wu, Yirong Jiang, Yongcheng Wang, and Xudong Fu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitute of Hematology, Zhejiang University, Hangzhou, Zhejiang, 310000, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXudong Fu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitute of Microanalytical Systems, Department of Chemistry, Key Laboratory of Excited-State Materials of Zhejiang Province, Zhejiang University, Hangzhou, 310058, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYirong Jiang and Qun Fang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssisted Reproduction Unit, Department of Obstetrics and Gynecology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeipei Ren, Panpan Zhao, Yinli Zhang, Songying Zhang, Heng-yu Fan, and Xiaomei Tong\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSchool of Pharmacy, Shenyang Pharmaceutical University, Shenyang, 110016, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhuo Yang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Geriatrics, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, China.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXudong Fu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-cell Proteomics Research Center, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, 311200, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQun Fang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKey Laboratory for Biomedical Engineering of Ministry of Education, Cancer Center, Zhejiang University, Hangzhou, 310007, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQun Fang\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eL.R.C., Y.R.J, Z.Y., Y.C.W, and Q.F. generated the single-cell proteomic and transcriptomic resources. L.R.C. performed oocyte experiments and manuscript editing. H.W. performed bioinformatics analysis and manuscript editing. P.P.R, P.P.Z, and X.M.T collected the human oocytes. Y.L.Z, H.Y.F, and S.Y.Z provided reagents and expertise. Y.C.W., Q.F., and X.D.F. conceived the project. X.D.F. performed data interpretation and wrote the manuscript. All authors discussed the results and contributed to the manuscript preparation.\u003c/p\u003e\n\u003cp\u003eCorresponding author\u003c/p\u003e\n\u003cp\u003eCorrespondence to Yongcheng Wang, Xiaomei Tong, Qun Fang, and Xudong Fu.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWasielak-Politowska, M. \u0026amp; Kordowitzki, P. 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Although transcriptomic changes have been examined in aged oocytes, the proteomic landscape, which reflects the primary functional executors of genes, and factors shaping the landscape, remains largely unexplored. This gap limits our understanding of molecular features driving oocyte aging. To address this, we performed single-cell proteome/transcriptome co-profiling in GV and MII-aged oocytes from mice and humans, revealing species- and stage-specific proteomic/transcriptomic changes during oocyte aging. Strikingly, we observed uncoupled proteomic and transcriptomic alterations, indicating that proteomic changes in aged oocytes are not primarily driven by RNA alterations. Leveraging our single-cell profiling, we captured the molecular heterogeneity in aged oocytes and revealed MCT4 as a conserved oocyte aging biomarker. Functional studies suggested that MCT4 mediated oocyte aging via lactate export, and its inhibition improved aged oocyte quality. These findings indicated altered lactate metabolism as a driver and intervention target of oocyte aging and underscored the value of our profiling in dissecting oocyte aging.\u003c/p\u003e","manuscriptTitle":"Single-oocyte proteome-transcriptome co-profiling reveals a role of dysregulated lactate metabolism in oocyte aging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 06:06:53","doi":"10.21203/rs.3.rs-6309099/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-aging","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nataging","sideBox":"Learn more about [Nature Aging](https://www.nature.com/nataging/)","snPcode":"","submissionUrl":"","title":"Nature Aging","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cb523885-1fe9-456c-af13-3cac12b43d35","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":46923467,"name":"Biological sciences/Developmental biology/Ageing"},{"id":46923468,"name":"Biological sciences/Stem cells/Embryonic germ cells"}],"tags":[],"updatedAt":"2025-05-09T06:06:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 06:06:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6309099","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6309099","identity":"rs-6309099","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}