Single-cell atlas of reproductive endocrine organs reveals transcriptomic responses to type 1 diabetes mellitus in non-human primate

Single-cell atlas of reproductive endocrine organs reveals transcriptomic responses to type 1 diabetes mellitus in non-human primate | 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 Article Single-cell atlas of reproductive endocrine organs reveals transcriptomic responses to type 1 diabetes mellitus in non-human primate Qing-Yuan Sun, Zheng-Hui Zhao, Ning Xu, Xue-Ying Chen, Yong Lu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6903784/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Type 1 diabetes mellitus characterized by insulin deficiency and hyperglycemia is associated with female subfertility. However, how hyperglycemia affects the hypothalamic-pituitary-ovarian-uterine axis remains poorly understood. In this study, we performed single-cell transcriptomic profiling of the hypothalamus, pituitary, ovary and uterus during the proliferative phase of the menstrual cycle in type 1 diabetic macaques to systematically characterize changes in tissue-specific cellular heterogeneity, gene expression, and intercellular communication networks under diabetic conditions. Our analysis revealed significant downregulation of the SPP1 signaling pathway across multiple tissues, concomitant with marked activation of inflammation-related pathways, including TNF signaling. Notably, we observed that diabetes impairs the recruitment of perifollicular CHIT1 + macrophages and leads to reduced FSHR expression during granulosa cell differentiation. This process is further exacerbated by upregulation of SFRP4 , a known antagonist of follicle-stimulating hormone signaling molecule, resulting in diminished granulosa cell responsiveness to follicle-stimulating hormone. Consequently, this dysregulation correlates with increased FSHB expression in pituitary gonadotropes, likely due to disrupted ovarian feedback signaling. Collectively, our findings provide a comprehensive landscape of cellular and molecular alterations in immune and endocrine compartments in female reproductive system in diabetic states, advancing our understanding of immune-endocrine crosstalk in the context of metabolic disease. Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 1 diabetes Biological sciences/Computational biology and bioinformatics/Cellular signalling networks Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 5 Figure 6 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Female reproduction process is regulated by the coordinated actions of multiple neural and hormonal signaling pathways 1 . In the neurohormonal system, hypothalamic KISS1 neurons located in the arcuate nucleus and the anteroventral periventricular nucleus synthesize kisspeptins, which further stimulate gonadotropin-releasing hormone (GnRH) neurons to release the decapeptide GnRH, forming the central regulators of reproductive functions 2 . Subsequently, the pituitary gland, serving as a signal mediator between the hypothalamus and the ovary, is capable of producing and releasing hormones from the anterior lobe's endocrine cells, in response to the pulsatile secretion of GnRH 3 . Next, the anterior pituitary gonadotropes secrete two gonadotropins: follicle stimulating hormone (FSH) and luteinizing hormone (LH). These hormones promote the development of ovarian follicles during the follicular phase and the formation of the corpus luteum during the luteal phase, and also stimulate the production of ovarian hormones such as estradiol (E2) and progesterone (P4), respectively 4 . The follicular and luteal phases correspond to the proliferative and secretory phases of the endometrium that is the inner mucosal lining of the uterus. In response to ovarian hormones, the functional layer of the endometrium undergoes repeated cycles of shedding, scar-free repair, and regeneration with extensive growth and differentiation 5 . Moreover, ovarian hormones also exert feedback regulation on the release of LH and FSH, both directly and indirectly through GnRH signaling, to dynamically modulate the function of this neurohormonal axis 6, 7 . Additionally, numerous metabolic factors also play crucial roles in the coordinated regulation of reproduction processes. Among these, insulin serves as a key regulator of the hypothalamic-pituitary-ovarian-uterine (HPOU) axis. It can directly influence GnRH neurons to modulate their secretory activity, thereby affecting the functions of gonadotropic axis 8, 9 . Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease that typically develops in childhood or adolescence. T1DM is characterized by insulin deficiency and hyperglycemia, which further leads to disruptions in the reproductive endocrine system and immune responses at various levels of the HPOU axis 10, 11, 12 . Rodent models of T1DM have revealed that female animals with uncontrolled diabetes display a profound hypogonadotropic state, characterized by low basal levels of hormones 13, 14 . Additionally, immune-endocrine interactions are crucial for hormonal production and maintaining endocrine homeostasis. Within the HPOU axis, macrophages are the predominant type of immune cells, playing a key role in inflammatory responses and tissue homeostasis. These macrophages have varied origins and form diverse subpopulations within endocrine organs. Each adult organ contains its own unique pool of functionally distinct macrophages that control specific tissue and niche functions 15, 16 . Tissue-resident macrophages typically originate from embryonic progenitor cells in the yolk sac and/or fetal liver. In contrast, monocyte-derived macrophages primarily come from circulating monocytes that differentiate upon migrating into various organs 17 . Tissue-resident macrophages are maintained in tissues by local self-proliferation or monocyte recruitment from blood circulation 18 . However, monocytes are unable to enter the hypothalamus and pituitary, and therefore the macrophages in this unique immune environment are primarily maintained through self-proliferation 19, 20, 21 . In contrast, the macrophages in ovaries maintain themselves through both the self-proliferation of tissue-resident macrophages and the differentiation of circulating monocytes 22 . Although several research groups have provided detailed cell atlases of organs within the HPOU axis 22, 23, 24, 25 , the changes in reproductive endocrine functions and the inflammatory characteristics of the immune microenvironment in T1DM primateare still not known. This highlights the need for investigation into underlying mechanisms how T1DM affects immune-endocrine interactions in primates. Therefore, we utilized single-cell RNA sequencing to systematically profile the hypothalamus, pituitary, ovary, and uterus tissues and dissect T1DM-related changes in cellular composition and the immune-endocrine interaction mechanisms involved in regulating reproductive processes. We identified a substantial number of differentially expressed genes (DEGs) and biological pathways, providing a comprehensive view of the differences between control subjects and those with T1DM. Leveraging these high-resolution datasets, we found that diabetes significantly impairs the recruitment of perifollicular CHIT1 + macrophages and leads to a reduced responsiveness to follicle-stimulating hormone. Correspondingly, the disruption of follicular development is accompanied by increased FSHB expression in pituitary gonadotropes, highlighting systemic reproductive impairments in diabetic individuals. Our findings have clarified how T1DM impacts reproductive health at a cellular and molecular level, paving the way for more targeted therapeutic strategies. Results Transcriptome profiling of diabetic reproductive endocrine organs We utilized four female cynomolgus monkeys aging 5–6 years in our study, including two controls and two streptozotocin-induced type 1 diabetic macaques. For cynomolgus monkeys in T1DM group, intravenous administration of streptozotocin (STZ: 80 mg/kg/body weight) was performed to induce type 1 diabetes mellitus. Diabetic monkeys showed significantly increased fasting blood glucose, triglycerides, and cholesterol compared with T1DM control (T1DC) monkeys (Fig. 1 a, Figure S1a). C-peptide and body weight were significantly decreased in diabetic monkeys compared with T1DC (Fig. 1 a). Next, we used single-cell RNA sequencing (10x Genomics Chromium system) to profile single-cell suspensions from hypothalamic, pituitary, ovarian and uterine tissues (Fig. 1 b). After filtering and quality control (Figure S1b), a total of 41,548 cells were retained for downstream analysis. We integrated these cells into a normalized dataset and further subjected them to principal components analysis (PCA) for dimensional reduction, and 27 distinctive clusters were generated using unsupervised graph-based clustering (Fig. 1 c). Visualization on the UMAP plot illustrated the distribution of different cell types, tissues, and experimental groups (Fig. 1 c). Cluster analysis enabled the annotation of major cell types based on the expression of canonical markers. Hypothalamus mainly encompassed microglial cells ( P2RY12 + ) and myelinating oligodendrocyte ( MOG + ). Pituitary mainly encompassed pituitary stem cells ( SOX2 + ), neuroendocrine cells ( CHGB + ) and some less abundant cell types, including tanycyte ( CRYM + ) and folliculostellate ( FABP7 + ). Ovarian granulosa cells represented a heterogeneous mix of preantral granulosa cells (pGC, AMHR2 + ) and cumulus granulosa cells (cGC, CYP11A1 + ). Uterus mainly encompassed pericytes ( RGS5 + ) and SFRP4 + stromal cells ( SFRP4 + ) (Fig. 1 d). Furthermore, we identified a list of significantly DEGs for each cell type (p_val < 0.01, and avg_log2FC ≥ 0.25), followed by Gene Ontology (GO) enrichment analysis to characterize the associated biological functions. The enriched cellular and biological processes were highly consistent with the cell type identities, such as regulation of hormone secretion in neuroendocrine cells, and T cell activation in T cells (Figure S1c). Additionally, compared with T1DC, we observed an increased cellular proportion of neuroendocrine cells in T1DM group, while the proportion of cumulus granulosa cells in ovary and pericytes in uterus decreased significantly (Fig. 1 e). These results suggest that the impact of T1DM elicits heterogeneous responses across various cell types. Transcriptomic susceptibility of different cell types to diabetes To elucidate the cellular and molecular changes associated with diabetes, we first investigated the diverse cellular landscape of reproductive endocrine organs. Unbiased clustering showed tissue-specific cell types (Fig. 2 a; Figure S2a), and the number of MGC in hypothalamus, CHIT1 + MAC and B cells in pituitary, CHIT1 + MAC in ovary and pericytes in uterus exhibited significantly decreased (Figure S2b). Next, we sought to characterize diabetes-associated transcriptional alterations in individual organs within HPOU axis. We found that uterine tissues showed the largest number of DEGs, and the majority of DEGs are downregulated (Figure S2c). To elucidate the transcriptional responses of HPOU axis to diabetes, we identified DEGs in each reproductive endocrine organ (Figure S2d). Global analysis of DEGs and GO enrichment patterns revealed that upregulated genes overlapping across two or more tissues were predominantly enriched in pathways related to "oxidative phosphorylation" and "positive regulation of leukocyte activation", whereas commonly downregulated DEGs were enriched in "blood vessel development" and "activation of immune response " (Figure S2d). The heterogeneous cell populations within the HPOU axis have been previously associated with distinct regulatory roles in reproductive endocrine function. To further dissect the transcriptional alterations associated with diabetes, we conducted cell type-specific differential expression analysis between T1DM and T1DC macaques (Fig. 2 b). The cell types most affected during diabetes included MGC and MOC in hypothalamus; Gona, Lac&Som, PSC, Tany and HCN_MAC in pituitary; cGC, pGC, SMC, and CHIT1 + MAC in ovary; and pericyte, myo2, BEndo and SFRP4 + SC in uterus (Fig. 2 b). Furthermore, we identified diabetes-associated transcriptional changes across multiple cell types, and most DEGs were common induced (differentially expressed in at least one type) genes compared to the T1DC group (Fig. 2 c). Moreover, we performed GO analysis on the top 800 common induced genes, and found that upregulated DEGs are involved in ‘‘oxdative phosphorylation’’ and “inflammatory response”, which can be used as hallmarks of HPOU axis in diabetes (Fig. 2 c). Meanwhile, example genes GO terms are listed, including TXNIP , ATP6 , NCL and HIF1A representing the genes induced by diabetes (Fig. 2 d). Considering the disruption of DNA methylation from diabetic macaques, we next sought to determine the effects of diabetes on the ovarian and uterine methylation landscape. To obtain genome-wide DNA methylation profiles, we performed whole-genome bisulfite sequencing on ovarian and uterine tissues. Furthermore, the distribution of differentially methylated regions (DMRs) was examined, and the DNA methylation levels in the whole genome, promoter and exons were significantly decreased in ovary, but were not significant in uterus (Figure S2e,f). Moreover, DNA methylation profile of the example genes in ovary and uterus revealed that the DNA methylation level of TXNIP in the ovary was downregulated, whereas those of HIF1A were upregulated in both the ovary and uterus (Fig. 2 e). Altogether, these transcriptional responses suggest that the heterogeneity of the reproductive endocrine organs in response to diseases. Global alterations of intercellular signaling network within reproductive endocrine axis The intricate network of interactions among various cell types within the HPOU axis is essential for maintaining normal reproductive function. However, T1DM may disrupt these interactions, and understanding how these interactions lead to dysregulation on reproductive function is currently an area of significant interest. Therefore, we performed CellChat analysis to reconstruct ligand-receptor interaction networks and identify key signaling pathways that were significantly altered in T1DM compared to T1DC. Our analysis revealed a marked decrease in the overall number of intercellular interactions in the T1DM group. Notably, enhanced communication strength was observed between the hypothalamus and pituitary, while signaling interactions within the ovary and uterus were significantly downregulated, suggesting a compartment-specific disruption of neuroendocrine-reproductive networks under diabetic conditions (Fig. 3 a). Of particular interest, Tany cells in the hypothalamus and pituitary, as well as BEndo cells in the ovary and uterus, exhibited increased interaction numbers and enhanced communication strength with other cell types in the T1DM compared to the T1DC group (Fig. 3 b). Subsequent analysis of differentially activated signaling pathways revealed a marked upregulation of the TNF signaling cascade in the T1DM group, in contrast to a significant downregulation of the VISFATIN pathway within the reproductive endocrine axis, suggesting a potential imbalance in inflammatory and metabolic signaling pathways (Fig. 3 c). Furthermore, we conducted a detailed analysis of the TNF and VISFATIN signaling pathways across distinct cell types. Our results revealed that the upregulation of TNF signaling in the T1DM group was largely attributable to the participation of immune-associated cell populations, particularly T cells, in the ligand-receptor interactions underlying TNF-mediated communication (Fig. 3 d). Given the critical role of interactions between immune cells and endocrine cells in maintaining hormonal homeostasis, we next sought to dissect the specific interactions between macrophages and hormone-producing cells, with a focus on Gona and Lac&Som in the pituitary and theca cells and granulosa cells in the ovary. To characterize the distinct modes of immunomodulatory crosstalk within the pituitary microenvironment, we first investigated the differential intercellular communication patterns between gonadotropes (senders) and Lac&Som (senders) and various immune cell populations (receivers) (Figure S3a). Our analysis revealed that gonadotropes mainly interact with immune cells through the MK (Midkine) signaling pathway, while Lac&Som cells exhibit preferential engagement via the SPP1 (Secreted Phosphoprotein 1) pathway. Notably, both cell types exhibited significant crosstalk potential with immune cells through the MIF (Macrophage Migration Inhibitory Factor) signaling pathway (Fig. 3 e, Figure S3b). Strikingly, we observed a marked reduction in SPP1-mediated communication among pituitary cell populations in the T1DM group. To elucidate the molecular mechanisms underlying this disruption, we systematically assessed the expression profiles of both SPP1 and its canonical receptor complex components in the relevant cellular compartments. We found that SPP1 expression was significantly reduced in Lac&Som cells under diabetic conditions, while the expression of its cognate receptor subunits, such as CD44 , ITGB1 , and CD74 , was markedly elevated in immune cell populations. These findings suggest that the attenuation of SPP1 signaling in the diabetic pituitary is predominantly driven by the downregulation of the ligand at the source cell level, rather than by alterations in receptor availability (Fig. 3 f). We then extended our analysis to examine the intercellular communication between immune cells and ovarian endocrine cells, specifically theca and granulosa cells (Figure S3c). Our results indicated that these cells predominantly interacted with immune populations through the MK and MIF pathways (Figure S3d). Moreover, C1QC + macrophages were found to communicate with theca and granulosa cells primarily via the SPP1 pathway. Interestingly, we observed a substantial loss of SPP1-mediated signaling between C1QC + macrophages (sender) and both granulosa and theca cells (receiver) in the T1DM group (Fig. 3 e). To further dissect the molecular basis of this impaired signaling, we systematically assessed the expression levels of both SPP1 and its receptor components in corresponding cellular compartments. While SPP1 expression in C1QC + macrophages did not differ significantly between T1DC and T1DM groups, the expression of its key receptor subunit, ITGB1 , was notably reduced in both theca and granulosa cells (Fig. 3 f). This suggests that the impairment of SPP1 signaling in the diabetic ovary is mainly attributable to decreased receptor availability at the target cell level, rather than alterations in ligand production. Structural and molecular alterations in reproductive-related cells under diabetic conditions To further assess the impact of T1DM on reproductive function, we performed histopathological analysis of hypothalamic, ovarian and uterine sections using hematoxylin and eosin (H&E) staining. Pronounced morphological alterations, including disrupted follicular development and compromised endometrial proliferation, were observed in the T1DM group compared toT1DC, indicating structural impairments associated with diabetic conditions (Fig. 4 a, Figure S4a). To gain a better understanding of the reproductive-related cell-specific molecular characteristics in T1DM group, we isolated Gona and Lac&Som cells from pituitary, granulosa cells and theca cells from ovary, and subsequently categorized them into nine subtypes (Fig. 4 b). Given that the endometrium undergoes cyclic changes in response to ovarian hormones, we also focused on investigating the alterations in epithelial cells, stromal cells, and vascular-related cells of the uterus in the T1DM group (Fig. 4 b). To systematically compare the transcriptional responses across distinct reproductive-related cell populations in T1DM and T1DC conditions, we performed DEGs analysis of T1DM vs. T1DC groups among different cell clusters separately and generated an integrated volcano plot encompassing twelve key cell types from the pituitary, ovary, and uterus (Fig. 4 c). We found that the hormone related genes, such as FSHB and POMC in Gona and Som cells are upregulated in the T1DM group. Moreover, the PRL in Gona and CGA in Lac and Thyro cells are also upregulated in the T1DM group. Notably, BPIFA1 , a secreted biomarker, was downregulated across these four pituitary subtypes (Fig. 4 c), which may correlate with the reduction of secretory cells and anti-inflammatory functions 26 , 27 . In ovarian subtypes, the SLC39A4 and CST3 are upregulated in pGC and cGC cells, while CHIT1 and GPNMB are downregulated in pGC, cGC and theca_S cells. Meanwhile, the DEG analysis in uterus reveals that collagen family-related genes, such as COL1A1 , COL1A2 and COL3A1 , were downregulated in uterine cells except for BEndo, while the genes related to energy metabolism, such as ND1 , ND2 and ND3 , were upregulated in these uterine cells (Fig. 4 c). The diverse transcriptional responses to diabetes in these reproductive related cells provide a valuable resource for identifying candidate regulators and pathways that may drive cell-specific dysfunction in the diabetic reproductive system. We subsequently performed an overlap analysis between the identified DEGs and a transcription factor database derived from macaques, revealing that the majority of differentially expressed transcription factors were downregulated in the T1DM group (Figure S4b). Furthermore, we performed GO analysis across these subtypes, and found that the ‘cellular response to corticosteroid stimulus’ and ‘regulation of steroid hormone secretion’ in Gona cells; and the ‘steroid hormone receptor signaling pathway’ in pGC and the ‘response to steroid hormone’ in cGC cells; the ‘response to estradiol’ and ‘response to peptide hormone’ in BEndo and the ‘response to progesterone’, ‘response to corticosteroid’ and ‘response to steroid hormone’ in Pericyte are markedly upregulated (Fig. 4 d, Figure S4c). These results suggest that T1DM induces widespread alterations in steroid and peptide hormone signaling pathways in a cell-specific manner, potentially contributing to impaired endocrine regulation in the reproductive endocrine axis. Immune microenvironment disorders associated with diabetes The immune microenvironment plays a crucial role in the regulation of homeostasis within the reproductive endocrine system. To gain a deeper understanding of the molecular changes of immune cells between T1DC and T1DM samples, we isolated the immune cells and categorized them into eleven subtypes based on marker genes for different immune cell types (Fig. 5 a,b). We observed a significant reduction in the number of CHIT1 + MAC1 in the ovary and CD79B + B cells and JCHAIN + B cells in the pituitary in the T1DM group (Fig. 5 a,b). Next, we performed DEGs analysis of T1DM vs. T1DC groups among different immune clusters separately (Fig. 5 c). We found the downregulation of immune-associated genes such as BPIFA1 , BPIFB1 , and CHIT1 in the majority of immune cell subtypes. In contrast, we also noticed the increased expression level of HOXA11 in MAC1, SPP1 in SCT_B cells and CXCL9 in Pro_Immune cells. Furthermore, GO analysis in the subtypes of immune cells revealed upregulated DEGs enriched in categories for ‘regulation of leukocyte activation’, ‘cellular response to lipid’ and ‘activation of immune response’, whereas downregulated DEGs enriched categories for ‘hemopoiesis’, ‘regulation of mitotic cell cycle’ and ‘myeloid cell differentiation’. Specifically, the ‘B cell proliferation’, ‘purine nucleotide metabolic process’ and ‘oxidative phosphorylation’ were upregulated in MAC cells, while the ‘cellular response to glucose starvation’, ‘response to steroid hormone’ and ‘fatty acid metabolic process’ were downregulated in MAC cells (Fig. 5 d). To further explore the effect of T1DM on the different immune cells, we estimated the effect of T1DM on the chemokine and cytokines interactions between the immune cells (Fig. 5 e). The changes in the cytokines network were found mainly in the IL1 superfamily, such as IL1A and IL1B . Moreover, TNF-receptor TNFRSF1B and its ligand TNF also showed a significant increase in T1DM group (Fig. 5 f). As IL1 and TNF superfamily members are considered inflammatory, along with the evident increase in various inflammatory chemokines and their receptors, our results suggest T1DM shifts the phenotype of HPOU axis towards a more inflammatory state. To further explore macrophages alterations in T1DM, we isolated MAC1 cells from immune cells, and subsequently categorized them into 11 subtypes (Figure S5a). The subtypes were annotated mainly based on the expression of established cell markers and DEGs. Hypothalamic macrophages mainly encompassed ENPEP high _ P2RY12 high MAC, SPP1 low _ P2RY12 low MAC, SPP1 high _ P2RY12 low MAC1 and SPP1 high _ P2RY12 low MAC2. Pituitary immune niche represented a heterogeneous mix of macrophages ( HLA-DQA1 high ), cluster 5 (C5: CDKN1A high and NR4A1 high ), cluster 6 (C6: CDKN1A high and NR4A1 low ) and cluster 7 (C7: CDKN1A low and NR4A1 low ). Notably, the C5 and C7 macrophages are also prominently present in the hypothalamus. Additionally, the ovarian macrophage subpopulations are predominantly composed of two subsets: CHIT1 + and VCAN + macrophages. (Figure S5a,b). After annotation, we found that the relative proportions and actual cell numbers of macrophage subtypes between T1DC and T1DM were significant different (Figure S5c). Moreover, we noticed a significant decreased proportion of ENPEP high _ P2RY12 high MAC in hypothalamus and CHIT1 + MAC in ovary (Figure S5c). To further understand the alterations of different macrophage subtypes between T1DC and T1DM groups, we performed correlation analysis. We observed that in the T1DC group, VCAN + _ CST7 + MAC exhibited the lowest correlation with other macrophage subtypes. In contrast, in the T1DM group, it was the VCAN + _ CST7 − MAC that showed the lowest correlation with other macrophage types. This suggests that VCAN + _ CST7 − MAC in the ovary are relatively less affected by T1DM, whereas CHIT1 + MAC undergo significant alterations in the T1DM group (Figure S5d). Additionally, we examined key genes involved in carbohydrate, lipid, and protein metabolism. We found that SLC2A1 (also known as GLUT1) and Lipoprotein Lipase (LPL), a key enzyme in lipid metabolism, were significantly downregulated in ovarian macrophage clusters. This indicates the presence of profound disturbances in glucose and lipid metabolism within ovarian macrophages (Figure S5e). Reduced follicle-stimulating hormone responsiveness in granulosa cells under diabetic conditions Given the observed follicular developmental abnormalities and the marked reduction in CHIT1 + macrophages in the diabetic ovary, we next sought to dissect the cellular and molecular alterations underlying granulosa cell differentiation and interactions between macrophages and granulosa cells in T1DM. To this end, we re-clustered granulosa cells and CHIT1-expressing macrophages to identify their subtypes based on marker genes (Fig. 6 a, Figure S6a). To further characterize the impact of diabetes on granulosa cell differentiation, we constructed pseudotime trajectory using monocle2 in both T1DC and T1DM groups (Fig. 6 b). Striking differences were observed in the inferred trajectories of granulosa cells, and the diabetic granulosa cells exhibited an incomplete or a delayed maturation process (Fig. 6 b). Specifically, INHBB , a gene associated with fully differentiated granulosa cell states was significantly upregulated in the terminal trajectories (Figure S6b). Moreover, we identified significant differences in gene expression profiles along the pseudotime trajectories. Notably, the expression of FSHR , a central mediator of FSH signaling, was significantly downregulated in both cGC and ProGC from the diabetic group, whereas the expression of SFRP4 , an extracellular inhibitor of WNT signaling implicated in antagonizing FSH action 28 , was markedly upregulated in these populations (Fig. 6 c,d). Furthermore, a significant downregulation of AR (androgen receptor) expression was observed specifically in ProGC, suggesting impaired androgen signaling that may disrupt the delicate balance between androgen and estrogen biosynthesis during folliculogenesis (Fig. 6 c,d). Additionally, the upregulation of AMHR2 in diabetic pGC may represent a compensatory mechanism to counteract aberrant follicular hyperactivation and preserve follicular homeostasis under metabolic stress conditions (Figure S6b,c). Collectively, these findings reveal a profound impact of diabetes on the hormonal regulatory landscape of ovarian granulosa cells. Given the critical role of immune-endocrine crosstalk in regulating ovarian hormonal homeostasis, we further explored the intercellular communication between CHIT1 + macrophages and granulosa cells using cellchat to uncover potential mechanisms linking immune dysregulation to hormonal imbalance in diabetic ovaries. We observed a significant decline in the number and strength of cell-cell communication networks in the T1DM ovaries, suggesting impaired intercellular coordination under diabetic conditions (Fig. 6 e, Figure S6d). Subsequently, we analyzed the differential patterns of intercellular communication between granulosa cell subtypes (senders) and CHIT1 ⁺ macrophages (receivers). Notably, several key ligand-receptor pairs, including PTN-SDC2, PTN-NCL, and GRN-SORT1, were significantly downregulated in the T1DM group, suggesting impaired supportive signaling from granulosa cells to macrophages (Fig. 6 f, Figure S6e). In contrast, ligand-receptor interactions such as ANGPTL4-SDC3, ANGPTL4-ITGA5, and ANGPTL4-ITGB1 were markedly upregulated, potentially reflecting a compensatory or stress-associated signaling response under diabetic conditions. Conversely, when examining communication from CHIT1 ⁺ macrophages (senders) to granulosa cell subtypes (receivers), we observed a significant reduction in the expression of NAMPT-INSR and HBEGF-EGFR pairs, which are implicated in metabolic regulation and follicular development 29 , 30 . Meanwhile, EREG-EGFR and AREG-EGFR signaling axes were significantly enhanced in the T1DM group, indicating a potential shift toward aberrant EGFR activation that may disrupt granulosa cell function (Fig. 6 f, Figure S6e). Moreover, immunofluorescence staining of ovarian tissue sections revealed a distinct spatial and developmental-stage-specific distribution of CHIT1 + macrophages. In control ovaries, CHIT1 + cells were predominantly localized around follicles at the antral stage and exhibited a progressive increase in recruitment with follicular development (Fig. 6 g). These macrophages formed a dense perifollicular layer surrounding the follicles, suggesting a potential role in supporting advanced follicular maturation. In contrast, ovaries from diabetic group showed a marked reduction in the accumulation of CHIT1 + macrophages around antral follicles (Fig. 6 g), which may provide a potential link between altered immune cell localization and compromised ovarian function in diabetes. Discussion The utilization of scRNA-seq technology has revolutionized our ability to dissect the molecular identity and functional states of diverse cell populations at a high resolution. In this study, we systematically investigated the cellular and molecular changes associated with their vulnerability to diabetic perturbations across HPOU axis. Overall, we uncovered critical abnormalities including cell proportion and communication networks (Fig. 1 e, Fig. 3 ), hormone synthesis, secretion and responsiveness (Fig. 4 c,d), immune microenvironment and granulosa cell differentiation (Fig. 5 , Fig. 6 b-d). Moreover, we also observed the persistent upregulation of “oxidative phosphorylation” and “inflammatory response” in the majority of cell types from diabetic group (Fig. 2 c). These alterations may further induce reproductive endocrine dysfunction. The immune microenvironment is not only essential for the development and normal functioning of endocrine tissues under physiological conditions, but also plays pivotal roles in the pathogenesis of various endocrine-related disorders. In this study, we dissected the cellular heterogeneity and molecular signatures of immune cells to understand the changes of immune microenvironment across HPOU axis under diabetic conditions. We observed that the diabetic condition was associated with a marked decrease in the cellular abundance of hypothalamic microglia, pituitary B cells and CHIT1 + macrophages, and ovarian CHIT1 + macrophages within the reproductive endocrine axis (Fig. 1 e, Figure S2b). Furthermore, we also examined hormone-producing and secretory cell populations in the pituitary and ovary, including gonadotropes and granulosa cells. Notably, GO enrichment analysis revealed that upregulated DEGs in these cells were predominantly associated with hormone-responsive pathways, suggesting a potential adaptive or compensatory mechanism in the HPOU axis under diabetic conditions (Fig. 4 d, Figure S4c). These findings provide novel insights into the cellular and molecular alterations associated with T1DM and highlight the importance of investigating how interactions between hormonal and immune networks are altered in T1DM. Macrophages, as key components of the innate immune system, play diverse and critical roles in maintaining homeostasis throughout the body. In particular, they are known to reside within endocrine glands, where emerging evidence highlights their close interactions with endocrine cells 31 . Notably, macrophages contribute significantly to the regulation of the reproductive endocrine system, influencing hormone production, tissue remodeling, and local immune tolerance. Here, we used scRNA-seq to profile the macrophages and hormone related cells comprehensively and examined the communication networks between them within the pituitary and ovary. We observed a significant downregulation of the SPP1 signaling pathway in both the pituitary and ovary of the T1DM group (Fig. 3 e). Notably, this reduction exhibited cell type-specific mechanisms: in the pituitary, it was primarily attributed to decreased expression of SPP1 ligand in lactotrophs and somatotrophs, whereas in the ovary, the diminished signaling was largely due to reduced expression of its receptor ITGB1 in cGC, ProGC, and theca cells (Fig. 3 f). CHIT1 + macrophages represent a distinct subset of macrophages that were markedly reduced in both the pituitary and ovarian compartments of the T1DM group. Specifically, we observed a dynamic spatial redistribution of CHIT1 ⁺ macrophages during folliculogenesis: these cells were scarcely present around primordial or primary follicles but progressively accumulated around antral follicles as follicular development advanced, ultimately forming a perifollicular distribution in mature follicles (Fig. 6 f). This spatial recruitment pattern suggests that CHIT1 ⁺ macrophages may be functionally engaged in later stages of follicle maturation and ovulatory preparation 32 . Importantly, this process was profoundly disrupted in the T1DM model, where CHIT1 ⁺ macrophages failed to accumulate around developing follicles (Fig. 6 f), indicating a potential defect in follicle-macrophage cross-talk under diabetic conditions. Furthermore, the reciprocal communication from CHIT1 ⁺ macrophages to granulosa cells was significantly perturbed in the diabetic ovary. Notably, the expression of key ligand-receptor pairs, including NAMPT-INSR and HBEGF-EGFR (Fig. 6 f), was markedly reduced in T1DM. These findings suggest a loss of critical metabolic and trophic support from macrophages to granulosa cells, which may compromise follicular survival, steroidogenesis, and overall ovarian function. Of particular interest is the downregulation of the HBEGF–EGFR signaling axis, which has been well-established as a central regulator of follicular development and oocyte maturation 29 . HBEGF, primarily secreted by immune cells, binds to EGFR on granulosa and cumulus cells to drive processes such as cumulus expansion, meiotic resumption, and oocyte competence. The observed reduction in HBEGF–EGFR interactions in T1DM likely reflects the impaired recruitment and functional crosstalk of CHIT1 ⁺ macrophages around developing follicles, potentially contributing to disrupted folliculogenesis and suboptimal oocyte quality. Collectively, these alterations in macrophage-granulosa cell communication underscore the importance of immune-endocrine crosstalk in maintaining ovarian function, with HBEGF–EGFR signaling emerging as a potential hub affected by metabolic dysregulation in T1DM. The ovary plays a central role in the synthesis and regulation of steroid hormones, including E2 and P4, which are critical for follicular development, endometrial proliferation, and the neuroendocrine feedback that governs the HPOU axis. In the ovary, granulosa cells represent a key cellular hub for hormone production and response, with FSH signaling via FSHR being essential for aromatase activation, estrogen biosynthesis, and follicular maturation. In this study, a key finding is the marked downregulation of FSHR , the primary receptor for follicle-stimulating hormone, in both cGC and ProGC in the T1DM group (Fig. 6 c,d). Given that FSH signaling via FSHR is indispensable for granulosa cell proliferation, differentiation, and steroidogenesis, this reduction in receptor expression likely contributes to impaired responsiveness to gonadotropic stimulation and disrupted follicular development under diabetic conditions. Importantly, this decline in FSHR expression was accompanied by a significant upregulation of SFRP4 (Fig. 6 c,d), an extracellular antagonist of WNT signaling that has been previously implicated in antagonizing FSH action through GSK3β-AMPK-AKT signaling pathway 28 . Collectively, these molecular alterations-reduced FSHR and elevated SFRP4 -point to a state of diminished hormonal sensitivity in granulosa cells from diabetic ovaries and also suggest that targeting SFRP4 or restoring WNT/FSH crosstalk could represent novel therapeutic strategies for improving follicular health in metabolic disorders. Single-cell RNA sequencing represents a robust methodology for systematically identifying distinct cellular populations within a specified tissue. Nonetheless, this technique inherently fails to preserve spatial information, which can lead to suboptimal characterization of cell types and their physiological functions. Additionally, although our single-cell atlas of the non-human primate HPOU axis constitutes an indispensable resource for investigating transcriptional dynamics between T1DC and T1DM conditions, it does present certain limitations. Specifically, critical longitudinal data-such as the precise timing of T1DM onset were not captured in our dataset. Future research endeavors, particularly those incorporating time-course single-cell multi-omics analyses, could significantly mitigate these deficiencies. Such approaches would enable a more nuanced understanding of the molecular signatures underlying T1DM, thereby advancing our comprehension of this condition with enhanced resolution and depth. To conclude, our profiling of single-cell atlases reveals the molecular changes of HPOU axis exposed to diabetes and provides potential candidate therapeutic targets or biomarkers for the evaluation of diabetic effects. Our study also sheds light on how macrophages may impact HPOU axis functionality at the single cell resolution, which may be helpful for better understanding diabetic effects on the functions of reproductive endocrine system. Notably, any window of exposure to T1DM may, to some extent, lead to decreased fertility, it is therefore essential to establish stage-specific T1DM models to systematically evaluate the impact of T1DM onset timing on the functional integrity of the HPOU axis. Materials and Methods Ethics statement The usage of cynomolgus monkeys (Macaca fascicularis) and the experimental procedures in this study were evaluated and approved by Primate Life Sciences Ethics Committee of the Centre for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences (CEBSIT-2022019) in accordance with the guidelines of Association for Assessment and Accreditation of Laboratory Animal Care. Induction of diabetes To induce type 1 diabetes, four young female cynomolgus monkeys (Macaca fascicularis) were 5–6 years of age and weighted 3.1–3.4 kg and were fasted overnight. The next morning the 80 mg/kg of STZ was dissolved in saline and administered via intravenous injection into the jugular vein of anesthetized cynomolgus monkeys over a period of 5 minute. After STZ administration, blood glucose levels were monitored every 4 hours within the 48-hour period. During the first week post-injection, measurements were taken twice daily. Subsequently, from the second week onward, blood glucose was assessed twice weekly. The monkeys were treated by insulin administration, if necessary, to avoid metabolic dysfunction. A fasting blood glucose level of greater than 200 mg/dL in combination with a stimulated C-peptide level of less than 0.5 ng/mL was considered indicative of diabetes. Tissue digestion and library construction Hypothalamus, pituitary gland, ovary, and uterus were harvested from anesthetized monkeys perfused with physiological saline, which were used for each scRNA-seq experiment, respectively. Briefly, single-cell suspensions were prepared from four tissues using a standardized enzymatic dissociation protocol with the Tumor Dissociation Kit (#130-095-929, Miltenyi Biotec), according to the manufacturer's instructions. Following tissue digestion, cell viability and concentration were assessed to ensure optimal conditions for droplet-based encapsulation. The cell suspensions with a viability of over 85% and a density ranging from 300 to 600 cells per microliter were subsequently loaded onto the 10x Genomics Chromium platform for generation of gel bead-in-emulsion (GEM) partitions. Complementary cDNA synthesis was performed within each GEM through reverse transcription, incorporating unique molecular identifiers (UMIs) for subsequent transcript quantification. After emulsion breakage, the cDNA was amplified via PCR with a total of 12 amplification cycles. Library preparation was then carried out following the Chromium Single-Cell 3′ Reagent Version 3 Chemistry protocol (10x Genomics). Resulting libraries were evaluated for fragment size distribution using a Fragment Analyzer with the High Sensitivity NGS Analysis Kit (Advanced Analytical Technologies), and library concentration was determined by qPCR using the KAPA Library Quantification Kit for Illumina (Kapa Biosystems). Paired-end sequencing (2 × 150 bp) was performed on an Illumina NovaSeq 6000 system, generating high-throughput transcriptomic profiles suitable for downstream single-cell RNA-seq analysis. Processing of single cell RNA-seq data Sequencing data generated on the Illumina NovaSeq 6000 platform were demultiplexed using bcl2fastq (version 2.20) to produce de-multiplexed FASTQ files. A custom reference genome for Macaca fascicularis (version Macaca_fascicularis_6.0) was constructed in accordance with the Cell Ranger (version 4.0.0) pipeline specifications. Subsequently, FASTQ files were processed using the count function within Cell Ranger under default parameters, which included alignment to the Macaca fascicularis reference genome using STAR, quality filtering, and UMI-based transcript counting. The resulting gene expression matrices were imported into R using the Read10X function from the Seurat package (version 4.1.0) 33 . Low-quality cells were filtered based on predefined criteria: those expressing fewer than 200 unique genes or exhibiting a mitochondrial gene content exceeding 5% of total transcripts were excluded from further analysis. To correct for technical variability across samples, batch effect removal was performed using the Harmony algorithm 34 . Following batch correction, data were normalized using the LogNormalize method, and principal component analysis (PCA) was conducted on the filtered and normalized dataset to reduce dimensionality. The top principal components were selected for downstream clustering and visualization. Cells were clustered using a resolution parameter optimized for biological heterogeneity, and the results were visualized in two dimensions via uniform manifold approximation and projection (UMAP) 35 . Cluster annotation was achieved by identifying differentially expressed marker genes using the FindAllMarkers function in Seurat with default settings, allowing for robust classification of cell populations based on transcriptional signatures. Cell-cell interaction analysis Cell–cell communication analysis was performed using the CellChat 36 R package (v1; available at: https://github.com/sqjin/CellChat ). Briefly, a CellChat object was constructed based on the processed single-cell RNA-seq data. The analysis was conducted using the built-in ‘CellChatDB.human’ database as a reference for ligand-receptor interactions. Default parameters were applied to infer potential cellular communication networks and signaling strengths across cell populations. Pseudotime analysis Pseudotime analysis of granulosa cells was conducted using the Monocle 2 R package to reconstruct putative developmental trajectories 37 . A gene expression count matrix was used as input to generate a new Monocle object. Genes exhibiting significant differential expression across distinct cell clusters were selected as ordering genes, which served to infer the progression path of cellular states during differentiation. These ordering genes were subsequently employed to model lineage trajectories and infer pseudotime trajectory. To assess the reliability of the reconstructed trajectories, differentially expressed genes were clustered and visualized according to their expression trends across pseudotime trajectory. GO analysis Gene Ontology (GO) enrichment analysis was carried out using the MetaScape web-based platform ( http://metascape.org/gp/index.html ; version 3.5). Only GO terms with a p -value ≤ 0.05 were considered statistically significant. Hematoxylin and Eosin (H&E) staining Tissue sections were prepared from paraffin-embedded blocks by cutting 5 µm thick sections using a rotary microtome (Leica RM2235, Leica Biosystems). Prior to staining, slides were deparaffinized in xylene followed by rehydration through graded ethanol solutions. Subsequently, sections were washed with phosphate-buffered saline (PBS). For H&E staining, sections were first stained with Harris hematoxylin solution (Sigma-Aldrich) followed by differentiation in 1% acid alcohol to remove excess stain and improve nuclear contrast. The sections were then briefly rinsed under running tap water for 1 minute to develop the hematoxylin stain. Subsequently, sections were counterstained with eosin Y solution (Sigma-Aldrich) to visualize cytoplasmic structures. After staining, sections were dehydrated through an ascending series of ethanol concentrations, cleared in xylene, and mounted with coverslips. Immunofluorescence analysis For immunofluorescence staining, paraffin-embedded tissue sections (5 µm thick) were deparaffinized in xylene and rehydrated through a graded ethanol series into phosphate-buffered saline (PBS). Antigen retrieval was performed by heating the sections in citrate buffer (pH 6.0) at 98°C for 20 minutes, followed by cooling to room temperature. To block nonspecific binding and permeabilize cell membranes, sections were incubated in a blocking solution containing 5% bovine serum albumin (BSA) and 0.3% Triton X-100 in PBS for 1 hour at room temperature. Sections were then incubated overnight at 4°C with primary antibodies diluted in blocking buffer. The following primary antibodies were used: anti-CD68 (ABclonal, A20555, 1:100), anti-CHIT1 (Santa Cruz, sc-271460, 1:100). Following three washes with PBS, sections were incubated with species-appropriate fluorescently labeled secondary antibodies for 1 hour at room temperature. Nuclei were counterstained with 4′,6-diamidino-2-phenylindole (DAPI; Life Technologies). Finally, sections were mounted under coverslips and examined using a confocal laser scanning microscope. Declarations Data availability The sequencing data generated in this study will be made publicly available upon publication. Acknowledgments We are grateful to Yue Shi from easygenebio Technology for help with scRNA-seq. This work was supported by National R&D program of China, Grant/Award Number:2022YFC2703501; Guangdong Basic and Applied Basic Research Foundation, Grant/Award Number: 2023B1515120027; Science and Technology Program of Guangzhou, China, Grant/Award Number 202201020292. National Key Research and Development Program of China (2022YFF0710901). National Natural Science Foundation of China, Grant/Award Number:82401895. Author contributions Zheng-Hui Zhao, Xiang-Hong Ou, Qiang Sun and Qing-Yuan Sun conceived and supervised the project, designed the experiments and wrote our manuscript. Ning Xu collected and processed tissue samples. Zheng-Hui Zhao and Xue-Ying Chen conducted computational analysis and validation experiments. Yong Lu and Ang Li provided technical assistance. Competing interests The authors declare no competing interests. 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Nature biotechnology , (2018). Jin S , et al. Inference and analysis of cell-cell communication using CellChat. Nature communications 12 , 1088 (2021). Qiu X , et al. Reversed graph embedding resolves complex single-cell trajectories. Nature methods 14 , 979-982 (2017). Additional Declarations There is NO Competing Interest. Supplementary Files Supplementaryfigurelegends.docx FigureS1.pdf Figure S1 FigureS2.pdf Figure S2 FigureS3.pdf Figure S3 FigureS4.pdf Figure S4 FigureS5.pdf Figure S5 FigureS6.pdf Figure S6 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6903784","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":475779162,"identity":"cb9a0ba1-ecd8-4f8a-a3b6-fee89dca9221","order_by":0,"name":"Qing-Yuan Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYNCCCgbGfgnStJxhYJw5gyQdjG0MjBtuEKtafkbuwdu882plG243MD6u+MUgb05Ii8GNvGRr3m3HjRvnHGA2PNvHYLizgZAWiRwzad5txxKbJRLYJBt7GBIMDhB0GEjLnGOJbURrYbgB0tJQk9gD0tLwgwgtBmfeGFvOOXbAeIZEYrNhY4OE4QaCDmvPMbzxpqZOdv+N5IMPG/7YyBN2GBBI8TAcBlKMDcAIIjINSP5gqIMy/xCnYxSMglEwCkYWAABAS0KtwzWTjQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-0148-2414","institution":"Fertility Preservation Lab, Reproductive Medicine Center, Guangdong Second Provincial General Hospital, Guangzhou","correspondingAuthor":true,"prefix":"","firstName":"Qing-Yuan","middleName":"","lastName":"Sun","suffix":""},{"id":475779163,"identity":"fda11dcc-6fa2-45eb-9338-01ea81591c94","order_by":1,"name":"Zheng-Hui Zhao","email":"","orcid":"https://orcid.org/0000-0002-4549-8565","institution":"Fertility Preservation Lab, Guangzhou Key Laboratory of Metabolic Diseases and Reproductive Health, Guangdong-Hong Kong Metabolism \u0026 Reproduction Joint Laboratory, Reproductive Medicine Center, Guan","correspondingAuthor":false,"prefix":"","firstName":"Zheng-Hui","middleName":"","lastName":"Zhao","suffix":""},{"id":475779164,"identity":"b6e74a50-53e3-4be8-9789-89048350e523","order_by":2,"name":"Ning Xu","email":"","orcid":"","institution":"Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Xu","suffix":""},{"id":475779165,"identity":"85ed6984-fe57-40ba-9c78-db64a3e93df8","order_by":3,"name":"Xue-Ying Chen","email":"","orcid":"","institution":"Affiliated Guangdong Second Provincial General Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Xue-Ying","middleName":"","lastName":"Chen","suffix":""},{"id":475779166,"identity":"e7987088-3609-4ec8-becd-b4126bb35ec7","order_by":4,"name":"Yong Lu","email":"","orcid":"","institution":"Institute of Neuroscience","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Lu","suffix":""},{"id":475779167,"identity":"04d21145-7fc4-41db-894e-49ead994623d","order_by":5,"name":"Ang Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ang","middleName":"","lastName":"Li","suffix":""},{"id":475779168,"identity":"d3b719ad-9852-4a97-bc5a-efafec778ae7","order_by":6,"name":"Qiang Sun","email":"","orcid":"https://orcid.org/0000-0002-3359-9465","institution":"Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Sun","suffix":""},{"id":475779169,"identity":"5733dab9-2d35-4d6e-a191-6e9180d65648","order_by":7,"name":"Xiang-Hong Ou","email":"","orcid":"https://orcid.org/0000-0001-9047-1612","institution":"Guangdong Second Provincial General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiang-Hong","middleName":"","lastName":"Ou","suffix":""}],"badges":[],"createdAt":"2025-06-16 09:11:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6903784/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6903784/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85377330,"identity":"12279ab0-7284-45d3-817f-948c9e8ce18b","added_by":"auto","created_at":"2025-06-25 08:39:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1059480,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell transcriptomic atlas of the HPOU axis in diabetic macaques.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e Violin plots showing fasting blood glucose, C-peptide, Cholesterol, triglyceride, and weight in T1DC and T1DM monkeys. \u003cstrong\u003eb\u003c/strong\u003e Schematic diagram of the experimental design and workflow for single-cell RNA-seq. \u003cstrong\u003ec\u003c/strong\u003e Unsupervised clustering analysis of 41,548 cells (divided into 27 clusters) from pooled samples of HPOU axis, colored by cell type. The spherical icon in the middle shows the distribution of cells in each organ, and group. \u003cstrong\u003ed\u003c/strong\u003e Heatmap of cell type-specific marker genes. \u003cstrong\u003ee\u003c/strong\u003e The percentage of cells classified by experimental groups.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/a105dd093d1e65d3e4cb5f7b.png"},{"id":85377889,"identity":"49ecd445-391d-492e-8808-fad387d57717","added_by":"auto","created_at":"2025-06-25 08:47:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1841797,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiabetes-associated transcriptional alterations in various cell types. a\u003c/strong\u003e UMAP plots showing the cellular heterogeneity of each reproductive endocrine organ, with cells color-coded by identified main cell types. MGC, Microglial cells; MOC, Myelinating oligodendrocyte cells; PSC, Pituitary stem cells; HCN_MAC, HLA-DQA1\u003csup\u003ehigh\u003c/sup\u003e_CDKN1A\u003csup\u003ehigh\u003c/sup\u003e_NR4A1\u003csup\u003ehigh\u003c/sup\u003e macrophages; HC_MAC, HLA-DQA1\u003csup\u003ehigh\u003c/sup\u003e_CDKN1A\u003csup\u003ehigh\u003c/sup\u003e macrophages; \u003cem\u003eSFRP4\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e SC, \u003cem\u003eSFRP4\u003c/em\u003e\u003csup\u003e+ \u003c/sup\u003estromal cells\u003cstrong\u003e b\u003c/strong\u003e Number of upregulated (red bar) and downregulated (blue bar) genes in each cell types. \u003cstrong\u003ec\u003c/strong\u003e Distribution of differentially expressed genes in a heatmap indicating whether a gene (row) is a DEG in a given cluster (column) (left). Gene ontology terms associated with top 800 common induced DEGs (right). \u003cstrong\u003ed\u003c/strong\u003e Violin plots showing upregulated and downregulated DEGs of HPOU axis. \u003cstrong\u003ee\u003c/strong\u003e Methylation profile of the TXNIP and HIF1A genes in ovary and uterus.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/21c84128d339926be48786e3.png"},{"id":85377327,"identity":"6b711b86-c4c3-4f35-8d17-fa62894dfbc9","added_by":"auto","created_at":"2025-06-25 08:39:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1889808,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal cell-cell interactions in diabetic HPOU axis.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e Bar plots showing the number of inferred interactions on the top panel and interaction strength at the bottom panel in the cell-cell communication network analyzed by CellChat. \u003cstrong\u003eb\u003c/strong\u003e Heatmaps show the differential interaction numbers and strength between T1DM vs. T1DC groups \u003cstrong\u003ec\u003c/strong\u003e The representative information flow for typical signaling pathways in T1DC (blue) and T1DM (red). \u003cstrong\u003ed\u003c/strong\u003e Circle plots showing selected inferred differential signaling networks. The edge width represents the communication probability. \u003cstrong\u003ee\u003c/strong\u003e The changes of SPP1 signaling pathway in the pituitary and ovary between diabetic and control group. \u003cstrong\u003ef\u003c/strong\u003e The expression changes of SPP1 signaling pathway ligands and receptors in the pituitary and ovary between control and diabetic groups.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/528ee0d1fd9f33266158a49a.png"},{"id":85377351,"identity":"0527cf3b-1692-4b71-9c91-d56adc9d0bd5","added_by":"auto","created_at":"2025-06-25 08:39:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4534421,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptional alterations in reproductive related cells. a\u003c/strong\u003e Hematoxylin and Eosin (H\u0026amp;E) staining of the hypothalamus, ovary, and uterus in control and diabetic groups. \u003cstrong\u003eb\u003c/strong\u003e Highlight the cells in the pituitary, ovary and uterus from Figure 2A, showing the cells selected for focused analysis on the top panel; UMAP visualization of subtypes of these cell populations at the bottom panel. \u003cstrong\u003ec V\u003c/strong\u003eolcano plots show the DEGs of T1DM vs. T1DC in each subtype. The representative upregulated and down-regulated genes were labeled. \u003cstrong\u003ed\u003c/strong\u003e Gene ontology terms associated with DEGs in pituitary and ovarian given clusters.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/58f3d9dfced3f0249e6543b9.png"},{"id":85379124,"identity":"70385032-6bec-4a40-847a-97c19899ad24","added_by":"auto","created_at":"2025-06-25 08:55:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2969232,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiversity of immune cells at the molecular and functional levels.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e Highlight of immune cells in the UMAP from Figure 1C, showing the cells selected for focused analysis on the top panel; UMAP visualization of immune cell derivation and subtypes in the middle panel; UMAP visualization the group of immune cells at the bottom panel. \u003cstrong\u003eb\u003c/strong\u003e Expression of selected markers identifying immune cell subtypes cast on the UMAP plot. Purple (or gray) represents a high (or low) expression level as shown on the color key at the right bottom. \u003cstrong\u003ec\u003c/strong\u003e Volcano plots show the DEGs of T1DM vs. T1DC in each immune cell subtypes. \u003cstrong\u003ed\u003c/strong\u003e Gene ontology terms associated with DEGs in immune cell given clusters. \u003cstrong\u003ee\u003c/strong\u003e Heatmap showing the differentially expressed chemokines, cytokines, and their receptors in different immune cell types.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/37e1950f4e08918917229619.png"},{"id":85377892,"identity":"6e2595e7-04b2-478c-9027-3e6daa83020b","added_by":"auto","created_at":"2025-06-25 08:47:32","extension":"pdf","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5839950,"visible":true,"origin":"","legend":" Bubble plot indicates the differential signaling pathways in pituitary between T1DC and T1DM groups. Circle plots showing the MIF and MK signaling pathways in pituitary. The edge width represents the communication probability. Bubble plot indicates the differential signaling pathways in ovary between T1DC and T1DM groups. Circle plots showing the MIF and MK signaling pathways in ovary. The edge width represents the communication probability.","description":"","filename":"Figure5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/08d2a2cdd2e139e9868f5f2c.pdf"},{"id":85377894,"identity":"93e9be6d-e57a-4702-991d-fef5b46b838e","added_by":"auto","created_at":"2025-06-25 08:47:32","extension":"pdf","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":11780174,"visible":true,"origin":"","legend":" Highlight of macrophages in the UMAP from Fig.\u0026nbsp;A, showing the cells selected for focused analysis on the top panel; UMAP visualization of macrophages derivation and subtypes in the middle panel; UMAP visualization the group of macrophages at the bottom panel. Expression of selected markers identifying macrophages subtypes cast on the UMAP plot. Purple (or gray) represents a high (or low) expression level as shown on the color key at the right bottom. The bar plot shows the relative proportions of macrophages subtypes of different groups on the top panel; The bar plot shows the numbers of macrophages subtypes of different groups at the bottom panel. Correlation analysis of macrophage subtypes between T1DC and T1DM groups. Heatmap showing the differentially expressed metabolic-related genes in different macrophages subtypes.","description":"","filename":"Figure6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/da1173b26e402fd622777d9a.pdf"},{"id":85377362,"identity":"b6ac4029-f23a-476c-9b8c-d48d9ef9ce23","added_by":"auto","created_at":"2025-06-25 08:39:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":6054884,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular changes of granulosa cells and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCHIT1\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e macrophages in diabetes.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e Visualization the subclusters of granulosa cells and \u003cem\u003eCHIT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e macrophages using UMAP. \u003cstrong\u003eb\u003c/strong\u003e Pseudotime trajectory of granulosa cells analyzed by Monocle. \u003cstrong\u003ec\u003c/strong\u003e Expression of hormone-responsive genes along pseudotime trajectory. \u003cstrong\u003ed \u003c/strong\u003eViolin plots showing hormone-responsive genes between T1DC and T1DM groups. \u003cstrong\u003ee\u003c/strong\u003e The number of inferred interactions (left) and the interaction strength (right) in the cellcell communication network analyzed by CellChat. \u003cstrong\u003ef\u003c/strong\u003e The decreased signaling pathways between granulosa cells and \u003cem\u003eCHIT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e macrophages. \u003cstrong\u003eg\u003c/strong\u003e Immunostaining for CD68 and CHIT1 in ovary sections. DNA was counterstained with DAPI. Scale bar, 100 µm.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/c04a977b0404f79245e3dc91.png"},{"id":85377857,"identity":"0a602e17-0b7a-4d7a-8608-251d330b65f3","added_by":"auto","created_at":"2025-06-25 08:47:31","extension":"pdf","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2435827,"visible":true,"origin":"","legend":" UMAP plots showing the cellular heterogeneity of each reproductive endocrine organ, with cells color-coded by identified main cell types. MGC, Microglial cells; MOC, Myelinating oligodendrocyte cells; PSC, Pituitary stem cells; HCN_MAC, HLA-DQA1_CDKN1A_NR4A1 macrophages; HC_MAC, HLA-DQA1_CDKN1A macrophages; SC, stromal cells Number of upregulated (red bar) and downregulated (blue bar) genes in each cell types. Distribution of differentially expressed genes in a heatmap indicating whether a gene (row) is a DEG in a given cluster (column) (left). Gene ontology terms associated with top 800 common induced DEGs (right). Violin plots showing upregulated and downregulated DEGs of HPOU axis. Methylation profile of the TXNIP and HIF1A genes in ovary and uterus.","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/18c8431a03a41a44261bb3f4.pdf"},{"id":85377329,"identity":"04f16c04-b363-4b25-9f47-fe6428feb809","added_by":"auto","created_at":"2025-06-25 08:39:30","extension":"pdf","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2312147,"visible":true,"origin":"","legend":" Hematoxylin and Eosin (H\u0026amp;E) staining of the hypothalamus, ovary, and uterus in control and diabetic groups. Highlight the cells in the pituitary, ovary and uterus from Fig.\u0026nbsp;A, showing the cells selected for focused analysis on the top panel; UMAP visualization of subtypes of these cell populations at the bottom panel. olcano plots show the DEGs of T1DM vs. T1DC in each subtype. The representative upregulated and down-regulated genes were labeled. Gene ontology terms associated with DEGs in pituitary and ovarian given clusters.","description":"","filename":"FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/e1cb9c9004371a73e570b96e.pdf"},{"id":85377344,"identity":"d70d1444-689d-4ff0-bfb7-d07d395dec0e","added_by":"auto","created_at":"2025-06-25 08:39:31","extension":"pdf","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3142070,"visible":true,"origin":"","legend":" Visualization the subclusters of granulosa cells and macrophages using UMAP. Pseudotime trajectory of granulosa cells analyzed by Monocle. Expression of hormone-responsive genes along pseudotime trajectory. Violin plots showing hormone-responsive genes between T1DC and T1DM groups. The number of inferred interactions (left) and the interaction strength (right) in the cellcell communication network analyzed by CellChat. The decreased signaling pathways between granulosa cells and macrophages. Immunostaining for CD68 and CHIT1 in ovary sections. DNA was counterstained with DAPI. Scale bar, 100 \u0026micro;m.","description":"","filename":"FigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/b35a5ee282bd3369430d69d5.pdf"},{"id":87988856,"identity":"c35df1f7-79a8-49d1-85cc-9c8ceb4fb1c0","added_by":"auto","created_at":"2025-07-31 07:58:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":17798442,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/c2ff4fbe-21cd-459c-ae43-4be6dfc23692.pdf"},{"id":85377333,"identity":"358ef7e8-ac57-4d98-874b-44d34d16f1ed","added_by":"auto","created_at":"2025-06-25 08:39:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16565,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigurelegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/378c859383973ef656f2fc86.docx"},{"id":85377367,"identity":"7910c66e-56c8-4a3c-a1b1-6a5e7ee6cc86","added_by":"auto","created_at":"2025-06-25 08:39:33","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2435827,"visible":true,"origin":"","legend":"Figure S1","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/3cb1abc46a2c033e1552b94c.pdf"},{"id":85377826,"identity":"d50957ee-98e8-45f4-a6d7-1f2a6e4e72c2","added_by":"auto","created_at":"2025-06-25 08:47:30","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2312147,"visible":true,"origin":"","legend":"Figure S2","description":"","filename":"FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/f4af7665683847b5c93b3f8c.pdf"},{"id":85377369,"identity":"58e09a9d-be01-4c8e-990c-6ca467247665","added_by":"auto","created_at":"2025-06-25 08:39:33","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3142070,"visible":true,"origin":"","legend":"Figure S3","description":"","filename":"FigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/bc6698c4049cf05349dda2c4.pdf"},{"id":85377337,"identity":"ee6f5465-c832-41f0-a57f-a03484502fd4","added_by":"auto","created_at":"2025-06-25 08:39:30","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":6842710,"visible":true,"origin":"","legend":"Figure S4","description":"","filename":"FigureS4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/9a07918a82184c873dd55e47.pdf"},{"id":85377353,"identity":"03fb9e89-2353-4fee-8fc3-897b3633e71d","added_by":"auto","created_at":"2025-06-25 08:39:32","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":6685016,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S5\u003c/p\u003e","description":"","filename":"FigureS5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/0a30ce012e41624c9b5c3e35.pdf"},{"id":85377358,"identity":"ba7ab7c8-0a98-4bd8-893c-cffe4645d50d","added_by":"auto","created_at":"2025-06-25 08:39:32","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":2768710,"visible":true,"origin":"","legend":"Figure S6","description":"","filename":"FigureS6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6903784/v1/b14b0b046dae5d3d9ee6c9c4.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Single-cell atlas of reproductive endocrine organs reveals transcriptomic responses to type 1 diabetes mellitus in non-human primate","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFemale reproduction process is regulated by the coordinated actions of multiple neural and hormonal signaling pathways\u003csup\u003e1\u003c/sup\u003e. In the neurohormonal system, hypothalamic KISS1 neurons located in the arcuate nucleus and the anteroventral periventricular nucleus synthesize kisspeptins, which further stimulate gonadotropin-releasing hormone (GnRH) neurons to release the decapeptide GnRH, forming the central regulators of reproductive functions\u003csup\u003e2\u003c/sup\u003e. Subsequently, the pituitary gland, serving as a signal mediator between the hypothalamus and the ovary, is capable of producing and releasing hormones from the anterior lobe\u0026apos;s endocrine cells, in response to the pulsatile secretion of GnRH\u003csup\u003e3\u003c/sup\u003e. Next, the anterior pituitary gonadotropes secrete two gonadotropins: follicle stimulating hormone (FSH) and luteinizing hormone (LH). These hormones promote the development of ovarian follicles during the follicular phase and the formation of the corpus luteum during the luteal phase, and also stimulate the production of ovarian hormones such as estradiol (E2) and progesterone (P4), respectively\u003csup\u003e4\u003c/sup\u003e. The follicular and luteal phases correspond to the proliferative and secretory phases of the endometrium that is the inner mucosal lining of the uterus. In response to ovarian hormones, the functional layer of the endometrium undergoes repeated cycles of shedding, scar-free repair, and regeneration with extensive growth and differentiation\u003csup\u003e5\u003c/sup\u003e. Moreover, ovarian hormones also exert feedback regulation on the release of LH and FSH, both directly and indirectly through GnRH signaling, to dynamically modulate the function of this neurohormonal axis\u003csup\u003e6, 7\u003c/sup\u003e. Additionally, numerous metabolic factors also play crucial roles in the coordinated regulation of reproduction processes. Among these, insulin serves as a key regulator of the hypothalamic-pituitary-ovarian-uterine (HPOU) axis. It can directly influence GnRH neurons to modulate their secretory activity, thereby affecting the functions of gonadotropic axis\u003csup\u003e8, 9\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eType 1 diabetes mellitus (T1DM) is a chronic autoimmune disease that typically develops in childhood or adolescence. T1DM is characterized by insulin deficiency and hyperglycemia, which further leads to disruptions in the reproductive endocrine system and immune responses at various levels of the HPOU axis\u003csup\u003e10, 11, 12\u003c/sup\u003e. Rodent models of T1DM have revealed that female animals with uncontrolled diabetes display a profound hypogonadotropic state, characterized by low basal levels of hormones\u003csup\u003e13, 14\u003c/sup\u003e. Additionally, immune-endocrine interactions are crucial for hormonal production and maintaining endocrine homeostasis. Within the HPOU axis, macrophages are the predominant type of immune cells, playing a key role in inflammatory responses and tissue homeostasis. These macrophages have varied origins and form diverse subpopulations within endocrine organs. Each adult organ contains its own unique pool of functionally distinct macrophages that control specific tissue and niche functions\u003csup\u003e15, 16\u003c/sup\u003e. Tissue-resident macrophages typically originate from embryonic progenitor cells in the yolk sac and/or fetal liver. In contrast, monocyte-derived macrophages primarily come from circulating monocytes that differentiate upon migrating into various organs\u003csup\u003e17\u003c/sup\u003e. Tissue-resident macrophages are maintained in tissues by local self-proliferation or monocyte recruitment from blood circulation\u003csup\u003e18\u003c/sup\u003e. However, monocytes are unable to enter the hypothalamus and pituitary, and therefore the macrophages in this unique immune environment are primarily maintained through self-proliferation\u003csup\u003e19, 20, 21\u003c/sup\u003e. In contrast, the macrophages in ovaries maintain themselves through both the self-proliferation of tissue-resident macrophages and the differentiation of circulating monocytes\u003csup\u003e22\u003c/sup\u003e. Although several research groups have provided detailed cell atlases of organs within the HPOU axis\u003csup\u003e22, 23, 24, 25\u003c/sup\u003e, the changes in reproductive endocrine functions and the inflammatory characteristics of the immune microenvironment in T1DM primateare still not known. This highlights the need for investigation into underlying mechanisms how T1DM affects immune-endocrine interactions in primates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTherefore, we utilized single-cell RNA sequencing to systematically profile the hypothalamus, pituitary, ovary, and uterus tissues and dissect T1DM-related changes in cellular composition and the immune-endocrine interaction mechanisms involved in regulating reproductive processes. We identified a substantial number of differentially expressed genes (DEGs) and biological pathways, providing a comprehensive view of the differences between control subjects and those with T1DM. Leveraging these high-resolution datasets, we found that diabetes significantly impairs the recruitment of perifollicular \u003cem\u003eCHIT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e macrophages and leads to a reduced responsiveness to follicle-stimulating hormone. Correspondingly, the disruption of follicular development is accompanied by increased \u003cem\u003eFSHB\u003c/em\u003e expression in pituitary gonadotropes, highlighting systemic reproductive impairments in diabetic individuals. Our findings have clarified how T1DM impacts reproductive health at a cellular and molecular level, paving the way for more targeted therapeutic strategies.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome profiling of diabetic reproductive endocrine organs\u003c/h2\u003e \u003cp\u003eWe utilized four female cynomolgus monkeys aging 5\u0026ndash;6 years in our study, including two controls and two streptozotocin-induced type 1 diabetic macaques. For cynomolgus monkeys in T1DM group, intravenous administration of streptozotocin (STZ: 80 mg/kg/body weight) was performed to induce type 1 diabetes mellitus. Diabetic monkeys showed significantly increased fasting blood glucose, triglycerides, and cholesterol compared with T1DM control (T1DC) monkeys (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, Figure S1a). C-peptide and body weight were significantly decreased in diabetic monkeys compared with T1DC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Next, we used single-cell RNA sequencing (10x Genomics Chromium system) to profile single-cell suspensions from hypothalamic, pituitary, ovarian and uterine tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). After filtering and quality control (Figure S1b), a total of 41,548 cells were retained for downstream analysis. We integrated these cells into a normalized dataset and further subjected them to principal components analysis (PCA) for dimensional reduction, and 27 distinctive clusters were generated using unsupervised graph-based clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Visualization on the UMAP plot illustrated the distribution of different cell types, tissues, and experimental groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Cluster analysis enabled the annotation of major cell types based on the expression of canonical markers. Hypothalamus mainly encompassed microglial cells (\u003cem\u003eP2RY12\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) and myelinating oligodendrocyte (\u003cem\u003eMOG\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e). Pituitary mainly encompassed pituitary stem cells (\u003cem\u003eSOX2\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e), neuroendocrine cells (\u003cem\u003eCHGB\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) and some less abundant cell types, including tanycyte (\u003cem\u003eCRYM\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) and folliculostellate (\u003cem\u003eFABP7\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e). Ovarian granulosa cells represented a heterogeneous mix of preantral granulosa cells (pGC, \u003cem\u003eAMHR2\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) and cumulus granulosa cells (cGC, \u003cem\u003eCYP11A1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e). Uterus mainly encompassed pericytes (\u003cem\u003eRGS5\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) and \u003cem\u003eSFRP4\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e stromal cells (\u003cem\u003eSFRP4\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Furthermore, we identified a list of significantly DEGs for each cell type (p_val\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and avg_log2FC\u0026thinsp;\u0026ge;\u0026thinsp;0.25), followed by Gene Ontology (GO) enrichment analysis to characterize the associated biological functions. The enriched cellular and biological processes were highly consistent with the cell type identities, such as regulation of hormone secretion in neuroendocrine cells, and T cell activation in T cells (Figure S1c). Additionally, compared with T1DC, we observed an increased cellular proportion of neuroendocrine cells in T1DM group, while the proportion of cumulus granulosa cells in ovary and pericytes in uterus decreased significantly (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). These results suggest that the impact of T1DM elicits heterogeneous responses across various cell types.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptomic susceptibility of different cell types to diabetes\u003c/h2\u003e \u003cp\u003eTo elucidate the cellular and molecular changes associated with diabetes, we first investigated the diverse cellular landscape of reproductive endocrine organs. Unbiased clustering showed tissue-specific cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea; Figure S2a), and the number of MGC in hypothalamus, \u003cem\u003eCHIT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e MAC and B cells in pituitary, \u003cem\u003eCHIT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e MAC in ovary and pericytes in uterus exhibited significantly decreased (Figure S2b). Next, we sought to characterize diabetes-associated transcriptional alterations in individual organs within HPOU axis. We found that uterine tissues showed the largest number of DEGs, and the majority of DEGs are downregulated (Figure S2c). To elucidate the transcriptional responses of HPOU axis to diabetes, we identified DEGs in each reproductive endocrine organ (Figure S2d). Global analysis of DEGs and GO enrichment patterns revealed that upregulated genes overlapping across two or more tissues were predominantly enriched in pathways related to \"oxidative phosphorylation\" and \"positive regulation of leukocyte activation\", whereas commonly downregulated DEGs were enriched in \"blood vessel development\" and \"activation of immune response \" (Figure S2d).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe heterogeneous cell populations within the HPOU axis have been previously associated with distinct regulatory roles in reproductive endocrine function. To further dissect the transcriptional alterations associated with diabetes, we conducted cell type-specific differential expression analysis between T1DM and T1DC macaques (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The cell types most affected during diabetes included MGC and MOC in hypothalamus; Gona, Lac\u0026amp;Som, PSC, Tany and HCN_MAC in pituitary; cGC, pGC, SMC, and \u003cem\u003eCHIT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e MAC in ovary; and pericyte, myo2, BEndo and \u003cem\u003eSFRP4\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e SC in uterus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Furthermore, we identified diabetes-associated transcriptional changes across multiple cell types, and most DEGs were common induced (differentially expressed in at least one type) genes compared to the T1DC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Moreover, we performed GO analysis on the top 800 common induced genes, and found that upregulated DEGs are involved in \u0026lsquo;\u0026lsquo;oxdative phosphorylation\u0026rsquo;\u0026rsquo; and \u0026ldquo;inflammatory response\u0026rdquo;, which can be used as hallmarks of HPOU axis in diabetes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Meanwhile, example genes GO terms are listed, including \u003cem\u003eTXNIP\u003c/em\u003e, \u003cem\u003eATP6\u003c/em\u003e, \u003cem\u003eNCL\u003c/em\u003e and \u003cem\u003eHIF1A\u003c/em\u003e representing the genes induced by diabetes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Considering the disruption of DNA methylation from diabetic macaques, we next sought to determine the effects of diabetes on the ovarian and uterine methylation landscape. To obtain genome-wide DNA methylation profiles, we performed whole-genome bisulfite sequencing on ovarian and uterine tissues. Furthermore, the distribution of differentially methylated regions (DMRs) was examined, and the DNA methylation levels in the whole genome, promoter and exons were significantly decreased in ovary, but were not significant in uterus (Figure S2e,f). Moreover, DNA methylation profile of the example genes in ovary and uterus revealed that the DNA methylation level of \u003cem\u003eTXNIP\u003c/em\u003e in the ovary was downregulated, whereas those of \u003cem\u003eHIF1A\u003c/em\u003e were upregulated in both the ovary and uterus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Altogether, these transcriptional responses suggest that the heterogeneity of the reproductive endocrine organs in response to diseases.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGlobal alterations of intercellular signaling network within reproductive endocrine axis\u003c/h3\u003e\n\u003cp\u003eThe intricate network of interactions among various cell types within the HPOU axis is essential for maintaining normal reproductive function. However, T1DM may disrupt these interactions, and understanding how these interactions lead to dysregulation on reproductive function is currently an area of significant interest. Therefore, we performed CellChat analysis to reconstruct ligand-receptor interaction networks and identify key signaling pathways that were significantly altered in T1DM compared to T1DC. Our analysis revealed a marked decrease in the overall number of intercellular interactions in the T1DM group. Notably, enhanced communication strength was observed between the hypothalamus and pituitary, while signaling interactions within the ovary and uterus were significantly downregulated, suggesting a compartment-specific disruption of neuroendocrine-reproductive networks under diabetic conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Of particular interest, Tany cells in the hypothalamus and pituitary, as well as BEndo cells in the ovary and uterus, exhibited increased interaction numbers and enhanced communication strength with other cell types in the T1DM compared to the T1DC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Subsequent analysis of differentially activated signaling pathways revealed a marked upregulation of the TNF signaling cascade in the T1DM group, in contrast to a significant downregulation of the VISFATIN pathway within the reproductive endocrine axis, suggesting a potential imbalance in inflammatory and metabolic signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Furthermore, we conducted a detailed analysis of the TNF and VISFATIN signaling pathways across distinct cell types. Our results revealed that the upregulation of TNF signaling in the T1DM group was largely attributable to the participation of immune-associated cell populations, particularly T cells, in the ligand-receptor interactions underlying TNF-mediated communication (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGiven the critical role of interactions between immune cells and endocrine cells in maintaining hormonal homeostasis, we next sought to dissect the specific interactions between macrophages and hormone-producing cells, with a focus on Gona and Lac\u0026amp;Som in the pituitary and theca cells and granulosa cells in the ovary. To characterize the distinct modes of immunomodulatory crosstalk within the pituitary microenvironment, we first investigated the differential intercellular communication patterns between gonadotropes (senders) and Lac\u0026amp;Som (senders) and various immune cell populations (receivers) (Figure S3a). Our analysis revealed that gonadotropes mainly interact with immune cells through the MK (Midkine) signaling pathway, while Lac\u0026amp;Som cells exhibit preferential engagement via the SPP1 (Secreted Phosphoprotein 1) pathway. Notably, both cell types exhibited significant crosstalk potential with immune cells through the MIF (Macrophage Migration Inhibitory Factor) signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ee, Figure S3b). Strikingly, we observed a marked reduction in SPP1-mediated communication among pituitary cell populations in the T1DM group. To elucidate the molecular mechanisms underlying this disruption, we systematically assessed the expression profiles of both SPP1 and its canonical receptor complex components in the relevant cellular compartments. We found that \u003cem\u003eSPP1\u003c/em\u003e expression was significantly reduced in Lac\u0026amp;Som cells under diabetic conditions, while the expression of its cognate receptor subunits, such as \u003cem\u003eCD44\u003c/em\u003e, \u003cem\u003eITGB1\u003c/em\u003e, and \u003cem\u003eCD74\u003c/em\u003e, was markedly elevated in immune cell populations. These findings suggest that the attenuation of SPP1 signaling in the diabetic pituitary is predominantly driven by the downregulation of the ligand at the source cell level, rather than by alterations in receptor availability (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). We then extended our analysis to examine the intercellular communication between immune cells and ovarian endocrine cells, specifically theca and granulosa cells (Figure S3c). Our results indicated that these cells predominantly interacted with immune populations through the MK and MIF pathways (Figure S3d). Moreover, \u003cem\u003eC1QC\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e macrophages were found to communicate with theca and granulosa cells primarily via the SPP1 pathway. Interestingly, we observed a substantial loss of SPP1-mediated signaling between \u003cem\u003eC1QC\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e macrophages (sender) and both granulosa and theca cells (receiver) in the T1DM group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). To further dissect the molecular basis of this impaired signaling, we systematically assessed the expression levels of both SPP1 and its receptor components in corresponding cellular compartments. While \u003cem\u003eSPP1\u003c/em\u003e expression in \u003cem\u003eC1QC\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e macrophages did not differ significantly between T1DC and T1DM groups, the expression of its key receptor subunit, \u003cem\u003eITGB1\u003c/em\u003e, was notably reduced in both theca and granulosa cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). This suggests that the impairment of SPP1 signaling in the diabetic ovary is mainly attributable to decreased receptor availability at the target cell level, rather than alterations in ligand production.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eStructural and molecular alterations in reproductive-related cells under diabetic conditions\u003c/h3\u003e\n\u003cp\u003eTo further assess the impact of T1DM on reproductive function, we performed histopathological analysis of hypothalamic, ovarian and uterine sections using hematoxylin and eosin (H\u0026amp;E) staining. Pronounced morphological alterations, including disrupted follicular development and compromised endometrial proliferation, were observed in the T1DM group compared toT1DC, indicating structural impairments associated with diabetic conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, Figure S4a). To gain a better understanding of the reproductive-related cell-specific molecular characteristics in T1DM group, we isolated Gona and Lac\u0026amp;Som cells from pituitary, granulosa cells and theca cells from ovary, and subsequently categorized them into nine subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Given that the endometrium undergoes cyclic changes in response to ovarian hormones, we also focused on investigating the alterations in epithelial cells, stromal cells, and vascular-related cells of the uterus in the T1DM group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo systematically compare the transcriptional responses across distinct reproductive-related cell populations in T1DM and T1DC conditions, we performed DEGs analysis of T1DM vs. T1DC groups among different cell clusters separately and generated an integrated volcano plot encompassing twelve key cell types from the pituitary, ovary, and uterus (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). We found that the hormone related genes, such as \u003cem\u003eFSHB\u003c/em\u003e and \u003cem\u003ePOMC\u003c/em\u003e in Gona and Som cells are upregulated in the T1DM group. Moreover, the \u003cem\u003ePRL\u003c/em\u003e in Gona and \u003cem\u003eCGA\u003c/em\u003e in Lac and Thyro cells are also upregulated in the T1DM group. Notably, \u003cem\u003eBPIFA1\u003c/em\u003e, a secreted biomarker, was downregulated across these four pituitary subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003ec), which may correlate with the reduction of secretory cells and anti-inflammatory functions\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In ovarian subtypes, the \u003cem\u003eSLC39A4\u003c/em\u003e and \u003cem\u003eCST3\u003c/em\u003e are upregulated in pGC and cGC cells, while \u003cem\u003eCHIT1\u003c/em\u003e and \u003cem\u003eGPNMB\u003c/em\u003e are downregulated in pGC, cGC and theca_S cells. Meanwhile, the DEG analysis in uterus reveals that collagen family-related genes, such as \u003cem\u003eCOL1A1\u003c/em\u003e, \u003cem\u003eCOL1A2\u003c/em\u003e and \u003cem\u003eCOL3A1\u003c/em\u003e, were downregulated in uterine cells except for BEndo, while the genes related to energy metabolism, such as \u003cem\u003eND1\u003c/em\u003e, \u003cem\u003eND2\u003c/em\u003e and \u003cem\u003eND3\u003c/em\u003e, were upregulated in these uterine cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eThe diverse transcriptional responses to diabetes in these reproductive related cells provide a valuable resource for identifying candidate regulators and pathways that may drive cell-specific dysfunction in the diabetic reproductive system. We subsequently performed an overlap analysis between the identified DEGs and a transcription factor database derived from macaques, revealing that the majority of differentially expressed transcription factors were downregulated in the T1DM group (Figure S4b). Furthermore, we performed GO analysis across these subtypes, and found that the \u0026lsquo;cellular response to corticosteroid stimulus\u0026rsquo; and \u0026lsquo;regulation of steroid hormone secretion\u0026rsquo; in Gona cells; and the \u0026lsquo;steroid hormone receptor signaling pathway\u0026rsquo; in pGC and the \u0026lsquo;response to steroid hormone\u0026rsquo; in cGC cells; the \u0026lsquo;response to estradiol\u0026rsquo; and \u0026lsquo;response to peptide hormone\u0026rsquo; in BEndo and the \u0026lsquo;response to progesterone\u0026rsquo;, \u0026lsquo;response to corticosteroid\u0026rsquo; and \u0026lsquo;response to steroid hormone\u0026rsquo; in Pericyte are markedly upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003ed, Figure S4c). These results suggest that T1DM induces widespread alterations in steroid and peptide hormone signaling pathways in a cell-specific manner, potentially contributing to impaired endocrine regulation in the reproductive endocrine axis.\u003c/p\u003e\n\u003ch3\u003eImmune microenvironment disorders associated with diabetes\u003c/h3\u003e\n\u003cp\u003eThe immune microenvironment plays a crucial role in the regulation of homeostasis within the reproductive endocrine system. To gain a deeper understanding of the molecular changes of immune cells between T1DC and T1DM samples, we isolated the immune cells and categorized them into eleven subtypes based on marker genes for different immune cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003ea,b). We observed a significant reduction in the number of \u003cem\u003eCHIT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e MAC1 in the ovary and \u003cem\u003eCD79B\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e B cells and \u003cem\u003eJCHAIN\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e B cells in the pituitary in the T1DM group (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003ea,b). Next, we performed DEGs analysis of T1DM vs. T1DC groups among different immune clusters separately (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). We found the downregulation of immune-associated genes such as \u003cem\u003eBPIFA1\u003c/em\u003e, \u003cem\u003eBPIFB1\u003c/em\u003e, and \u003cem\u003eCHIT1\u003c/em\u003e in the majority of immune cell subtypes. In contrast, we also noticed the increased expression level of \u003cem\u003eHOXA11\u003c/em\u003e in MAC1, \u003cem\u003eSPP1\u003c/em\u003e in SCT_B cells and \u003cem\u003eCXCL9\u003c/em\u003e in Pro_Immune cells. Furthermore, GO analysis in the subtypes of immune cells revealed upregulated DEGs enriched in categories for \u0026lsquo;regulation of leukocyte activation\u0026rsquo;, \u0026lsquo;cellular response to lipid\u0026rsquo; and \u0026lsquo;activation of immune response\u0026rsquo;, whereas downregulated DEGs enriched categories for \u0026lsquo;hemopoiesis\u0026rsquo;, \u0026lsquo;regulation of mitotic cell cycle\u0026rsquo; and \u0026lsquo;myeloid cell differentiation\u0026rsquo;. Specifically, the \u0026lsquo;B cell proliferation\u0026rsquo;, \u0026lsquo;purine nucleotide metabolic process\u0026rsquo; and \u0026lsquo;oxidative phosphorylation\u0026rsquo; were upregulated in MAC cells, while the \u0026lsquo;cellular response to glucose starvation\u0026rsquo;, \u0026lsquo;response to steroid hormone\u0026rsquo; and \u0026lsquo;fatty acid metabolic process\u0026rsquo; were downregulated in MAC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). To further explore the effect of T1DM on the different immune cells, we estimated the effect of T1DM on the chemokine and cytokines interactions between the immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). The changes in the cytokines network were found mainly in the \u003cem\u003eIL1\u003c/em\u003e superfamily, such as \u003cem\u003eIL1A\u003c/em\u003e and \u003cem\u003eIL1B\u003c/em\u003e. Moreover, TNF-receptor \u003cem\u003eTNFRSF1B\u003c/em\u003e and its ligand \u003cem\u003eTNF\u003c/em\u003e also showed a significant increase in T1DM group (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). As \u003cem\u003eIL1\u003c/em\u003e and \u003cem\u003eTNF\u003c/em\u003e superfamily members are considered inflammatory, along with the evident increase in various inflammatory chemokines and their receptors, our results suggest T1DM shifts the phenotype of HPOU axis towards a more inflammatory state.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further explore macrophages alterations in T1DM, we isolated MAC1 cells from immune cells, and subsequently categorized them into 11 subtypes (Figure S5a). The subtypes were annotated mainly based on the expression of established cell markers and DEGs. Hypothalamic macrophages mainly encompassed \u003cem\u003eENPEP\u003c/em\u003e\u003csup\u003ehigh\u003c/sup\u003e_\u003cem\u003eP2RY12\u003c/em\u003e\u003csup\u003ehigh\u003c/sup\u003e MAC, \u003cem\u003eSPP1\u003c/em\u003e\u003csup\u003elow\u003c/sup\u003e_\u003cem\u003eP2RY12\u003c/em\u003e\u003csup\u003elow\u003c/sup\u003e MAC, \u003cem\u003eSPP1\u003c/em\u003e\u003csup\u003ehigh\u003c/sup\u003e_\u003cem\u003eP2RY12\u003c/em\u003e\u003csup\u003elow\u003c/sup\u003e MAC1 and \u003cem\u003eSPP1\u003c/em\u003e\u003csup\u003ehigh\u003c/sup\u003e_\u003cem\u003eP2RY12\u003c/em\u003e\u003csup\u003elow\u003c/sup\u003e MAC2. Pituitary immune niche represented a heterogeneous mix of macrophages (\u003cem\u003eHLA-DQA1\u003c/em\u003e\u003csup\u003ehigh\u003c/sup\u003e), cluster 5 (C5: \u003cem\u003eCDKN1A\u003c/em\u003e\u003csup\u003ehigh\u003c/sup\u003e and \u003cem\u003eNR4A1\u003c/em\u003e\u003csup\u003ehigh\u003c/sup\u003e), cluster 6 (C6: \u003cem\u003eCDKN1A\u003c/em\u003e\u003csup\u003ehigh\u003c/sup\u003e and \u003cem\u003eNR4A1\u003c/em\u003e\u003csup\u003elow\u003c/sup\u003e) and cluster 7 (C7: \u003cem\u003eCDKN1A\u003c/em\u003e\u003csup\u003elow\u003c/sup\u003e and \u003cem\u003eNR4A1\u003c/em\u003e\u003csup\u003elow\u003c/sup\u003e). Notably, the C5 and C7 macrophages are also prominently present in the hypothalamus. Additionally, the ovarian macrophage subpopulations are predominantly composed of two subsets: \u003cem\u003eCHIT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e and \u003cem\u003eVCAN\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e macrophages. (Figure S5a,b).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter annotation, we found that the relative proportions and actual cell numbers of macrophage subtypes between T1DC and T1DM were significant different (Figure S5c). Moreover, we noticed a significant decreased proportion of \u003cem\u003eENPEP\u003c/em\u003e\u003csup\u003ehigh\u003c/sup\u003e_\u003cem\u003eP2RY12\u003c/em\u003e\u003csup\u003ehigh\u003c/sup\u003e MAC in hypothalamus and \u003cem\u003eCHIT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e MAC in ovary (Figure S5c). To further understand the alterations of different macrophage subtypes between T1DC and T1DM groups, we performed correlation analysis. We observed that in the T1DC group, \u003cem\u003eVCAN\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e_\u003cem\u003eCST7\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e MAC exhibited the lowest correlation with other macrophage subtypes. In contrast, in the T1DM group, it was the \u003cem\u003eVCAN\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e_\u003cem\u003eCST7\u003c/em\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e MAC that showed the lowest correlation with other macrophage types. This suggests that \u003cem\u003eVCAN\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e_\u003cem\u003eCST7\u003c/em\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e MAC in the ovary are relatively less affected by T1DM, whereas \u003cem\u003eCHIT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e MAC undergo significant alterations in the T1DM group (Figure S5d). Additionally, we examined key genes involved in carbohydrate, lipid, and protein metabolism. We found that \u003cem\u003eSLC2A1\u003c/em\u003e (also known as GLUT1) and Lipoprotein Lipase (LPL), a key enzyme in lipid metabolism, were significantly downregulated in ovarian macrophage clusters. This indicates the presence of profound disturbances in glucose and lipid metabolism within ovarian macrophages (Figure S5e).\u003c/p\u003e\n\u003ch3\u003eReduced follicle-stimulating hormone responsiveness in granulosa cells under diabetic conditions\u003c/h3\u003e\n\u003cp\u003eGiven the observed follicular developmental abnormalities and the marked reduction in \u003cem\u003eCHIT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e macrophages in the diabetic ovary, we next sought to dissect the cellular and molecular alterations underlying granulosa cell differentiation and interactions between macrophages and granulosa cells in T1DM. To this end, we re-clustered granulosa cells and CHIT1-expressing macrophages to identify their subtypes based on marker genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, Figure S6a). To further characterize the impact of diabetes on granulosa cell differentiation, we constructed pseudotime trajectory using monocle2 in both T1DC and T1DM groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Striking differences were observed in the inferred trajectories of granulosa cells, and the diabetic granulosa cells exhibited an incomplete or a delayed maturation process (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Specifically, \u003cem\u003eINHBB\u003c/em\u003e, a gene associated with fully differentiated granulosa cell states was significantly upregulated in the terminal trajectories (Figure S6b). Moreover, we identified significant differences in gene expression profiles along the pseudotime trajectories. Notably, the expression of \u003cem\u003eFSHR\u003c/em\u003e, a central mediator of FSH signaling, was significantly downregulated in both cGC and ProGC from the diabetic group, whereas the expression of \u003cem\u003eSFRP4\u003c/em\u003e, an extracellular inhibitor of WNT signaling implicated in antagonizing FSH action\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, was markedly upregulated in these populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003ec,d). Furthermore, a significant downregulation of AR (androgen receptor) expression was observed specifically in ProGC, suggesting impaired androgen signaling that may disrupt the delicate balance between androgen and estrogen biosynthesis during folliculogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003ec,d). Additionally, the upregulation of \u003cem\u003eAMHR2\u003c/em\u003e in diabetic pGC may represent a compensatory mechanism to counteract aberrant follicular hyperactivation and preserve follicular homeostasis under metabolic stress conditions (Figure S6b,c). Collectively, these findings reveal a profound impact of diabetes on the hormonal regulatory landscape of ovarian granulosa cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGiven the critical role of immune-endocrine crosstalk in regulating ovarian hormonal homeostasis, we further explored the intercellular communication between \u003cem\u003eCHIT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e macrophages and granulosa cells using cellchat to uncover potential mechanisms linking immune dysregulation to hormonal imbalance in diabetic ovaries. We observed a significant decline in the number and strength of cell-cell communication networks in the T1DM ovaries, suggesting impaired intercellular coordination under diabetic conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003ee, Figure S6d). Subsequently, we analyzed the differential patterns of intercellular communication between granulosa cell subtypes (senders) and \u003cem\u003eCHIT1\u003c/em\u003e⁺ macrophages (receivers). Notably, several key ligand-receptor pairs, including PTN-SDC2, PTN-NCL, and GRN-SORT1, were significantly downregulated in the T1DM group, suggesting impaired supportive signaling from granulosa cells to macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003ef, Figure S6e). In contrast, ligand-receptor interactions such as ANGPTL4-SDC3, ANGPTL4-ITGA5, and ANGPTL4-ITGB1 were markedly upregulated, potentially reflecting a compensatory or stress-associated signaling response under diabetic conditions. Conversely, when examining communication from \u003cem\u003eCHIT1\u003c/em\u003e⁺ macrophages (senders) to granulosa cell subtypes (receivers), we observed a significant reduction in the expression of NAMPT-INSR and HBEGF-EGFR pairs, which are implicated in metabolic regulation and follicular development\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Meanwhile, EREG-EGFR and AREG-EGFR signaling axes were significantly enhanced in the T1DM group, indicating a potential shift toward aberrant EGFR activation that may disrupt granulosa cell function (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003ef, Figure S6e). Moreover, immunofluorescence staining of ovarian tissue sections revealed a distinct spatial and developmental-stage-specific distribution of \u003cem\u003eCHIT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e macrophages. In control ovaries, \u003cem\u003eCHIT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e cells were predominantly localized around follicles at the antral stage and exhibited a progressive increase in recruitment with follicular development (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eg). These macrophages formed a dense perifollicular layer surrounding the follicles, suggesting a potential role in supporting advanced follicular maturation. In contrast, ovaries from diabetic group showed a marked reduction in the accumulation of \u003cem\u003eCHIT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e macrophages around antral follicles (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eg), which may provide a potential link between altered immune cell localization and compromised ovarian function in diabetes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe utilization of scRNA-seq technology has revolutionized our ability to dissect the molecular identity and functional states of diverse cell populations at a high resolution. In this study, we systematically investigated the cellular and molecular changes associated with their vulnerability to diabetic perturbations across HPOU axis. Overall, we uncovered critical abnormalities including cell proportion and communication networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e), hormone synthesis, secretion and responsiveness (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003ec,d), immune microenvironment and granulosa cell differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eb-d). Moreover, we also observed the persistent upregulation of \u0026ldquo;oxidative phosphorylation\u0026rdquo; and \u0026ldquo;inflammatory response\u0026rdquo; in the majority of cell types from diabetic group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). These alterations may further induce reproductive endocrine dysfunction.\u003c/p\u003e \u003cp\u003eThe immune microenvironment is not only essential for the development and normal functioning of endocrine tissues under physiological conditions, but also plays pivotal roles in the pathogenesis of various endocrine-related disorders. In this study, we dissected the cellular heterogeneity and molecular signatures of immune cells to understand the changes of immune microenvironment across HPOU axis under diabetic conditions. We observed that the diabetic condition was associated with a marked decrease in the cellular abundance of hypothalamic microglia, pituitary B cells and \u003cem\u003eCHIT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e macrophages, and ovarian \u003cem\u003eCHIT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e macrophages within the reproductive endocrine axis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee, Figure S2b). Furthermore, we also examined hormone-producing and secretory cell populations in the pituitary and ovary, including gonadotropes and granulosa cells. Notably, GO enrichment analysis revealed that upregulated DEGs in these cells were predominantly associated with hormone-responsive pathways, suggesting a potential adaptive or compensatory mechanism in the HPOU axis under diabetic conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003ed, Figure S4c). These findings provide novel insights into the cellular and molecular alterations associated with T1DM and highlight the importance of investigating how interactions between hormonal and immune networks are altered in T1DM.\u003c/p\u003e \u003cp\u003eMacrophages, as key components of the innate immune system, play diverse and critical roles in maintaining homeostasis throughout the body. In particular, they are known to reside within endocrine glands, where emerging evidence highlights their close interactions with endocrine cells\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Notably, macrophages contribute significantly to the regulation of the reproductive endocrine system, influencing hormone production, tissue remodeling, and local immune tolerance. Here, we used scRNA-seq to profile the macrophages and hormone related cells comprehensively and examined the communication networks between them within the pituitary and ovary. We observed a significant downregulation of the SPP1 signaling pathway in both the pituitary and ovary of the T1DM group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Notably, this reduction exhibited cell type-specific mechanisms: in the pituitary, it was primarily attributed to decreased expression of \u003cem\u003eSPP1\u003c/em\u003e ligand in lactotrophs and somatotrophs, whereas in the ovary, the diminished signaling was largely due to reduced expression of its receptor \u003cem\u003eITGB1\u003c/em\u003e in cGC, ProGC, and theca cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003cem\u003eCHIT1\u003c/em\u003e \u003csup\u003e+\u003c/sup\u003e macrophages represent a distinct subset of macrophages that were markedly reduced in both the pituitary and ovarian compartments of the T1DM group. Specifically, we observed a dynamic spatial redistribution of \u003cem\u003eCHIT1\u003c/em\u003e⁺ macrophages during folliculogenesis: these cells were scarcely present around primordial or primary follicles but progressively accumulated around antral follicles as follicular development advanced, ultimately forming a perifollicular distribution in mature follicles (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003ef). This spatial recruitment pattern suggests that \u003cem\u003eCHIT1\u003c/em\u003e⁺ macrophages may be functionally engaged in later stages of follicle maturation and ovulatory preparation\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Importantly, this process was profoundly disrupted in the T1DM model, where \u003cem\u003eCHIT1\u003c/em\u003e⁺ macrophages failed to accumulate around developing follicles (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003ef), indicating a potential defect in follicle-macrophage cross-talk under diabetic conditions. Furthermore, the reciprocal communication from \u003cem\u003eCHIT1\u003c/em\u003e⁺ macrophages to granulosa cells was significantly perturbed in the diabetic ovary. Notably, the expression of key ligand-receptor pairs, including NAMPT-INSR and HBEGF-EGFR (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003ef), was markedly reduced in T1DM. These findings suggest a loss of critical metabolic and trophic support from macrophages to granulosa cells, which may compromise follicular survival, steroidogenesis, and overall ovarian function. Of particular interest is the downregulation of the HBEGF\u0026ndash;EGFR signaling axis, which has been well-established as a central regulator of follicular development and oocyte maturation\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. HBEGF, primarily secreted by immune cells, binds to EGFR on granulosa and cumulus cells to drive processes such as cumulus expansion, meiotic resumption, and oocyte competence. The observed reduction in HBEGF\u0026ndash;EGFR interactions in T1DM likely reflects the impaired recruitment and functional crosstalk of \u003cem\u003eCHIT1\u003c/em\u003e⁺ macrophages around developing follicles, potentially contributing to disrupted folliculogenesis and suboptimal oocyte quality. Collectively, these alterations in macrophage-granulosa cell communication underscore the importance of immune-endocrine crosstalk in maintaining ovarian function, with HBEGF\u0026ndash;EGFR signaling emerging as a potential hub affected by metabolic dysregulation in T1DM.\u003c/p\u003e \u003cp\u003eThe ovary plays a central role in the synthesis and regulation of steroid hormones, including E2 and P4, which are critical for follicular development, endometrial proliferation, and the neuroendocrine feedback that governs the HPOU axis. In the ovary, granulosa cells represent a key cellular hub for hormone production and response, with FSH signaling via FSHR being essential for aromatase activation, estrogen biosynthesis, and follicular maturation. In this study, a key finding is the marked downregulation of \u003cem\u003eFSHR\u003c/em\u003e, the primary receptor for follicle-stimulating hormone, in both cGC and ProGC in the T1DM group (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003ec,d). Given that FSH signaling via FSHR is indispensable for granulosa cell proliferation, differentiation, and steroidogenesis, this reduction in receptor expression likely contributes to impaired responsiveness to gonadotropic stimulation and disrupted follicular development under diabetic conditions. Importantly, this decline in \u003cem\u003eFSHR\u003c/em\u003e expression was accompanied by a significant upregulation of \u003cem\u003eSFRP4\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003ec,d), an extracellular antagonist of WNT signaling that has been previously implicated in antagonizing FSH action through GSK3β-AMPK-AKT signaling pathway\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Collectively, these molecular alterations-reduced \u003cem\u003eFSHR\u003c/em\u003e and elevated \u003cem\u003eSFRP4\u003c/em\u003e-point to a state of diminished hormonal sensitivity in granulosa cells from diabetic ovaries and also suggest that targeting \u003cem\u003eSFRP4\u003c/em\u003e or restoring WNT/FSH crosstalk could represent novel therapeutic strategies for improving follicular health in metabolic disorders.\u003c/p\u003e \u003cp\u003eSingle-cell RNA sequencing represents a robust methodology for systematically identifying distinct cellular populations within a specified tissue. Nonetheless, this technique inherently fails to preserve spatial information, which can lead to suboptimal characterization of cell types and their physiological functions. Additionally, although our single-cell atlas of the non-human primate HPOU axis constitutes an indispensable resource for investigating transcriptional dynamics between T1DC and T1DM conditions, it does present certain limitations. Specifically, critical longitudinal data-such as the precise timing of T1DM onset were not captured in our dataset. Future research endeavors, particularly those incorporating time-course single-cell multi-omics analyses, could significantly mitigate these deficiencies. Such approaches would enable a more nuanced understanding of the molecular signatures underlying T1DM, thereby advancing our comprehension of this condition with enhanced resolution and depth.\u003c/p\u003e \u003cp\u003eTo conclude, our profiling of single-cell atlases reveals the molecular changes of HPOU axis exposed to diabetes and provides potential candidate therapeutic targets or biomarkers for the evaluation of diabetic effects. Our study also sheds light on how macrophages may impact HPOU axis functionality at the single cell resolution, which may be helpful for better understanding diabetic effects on the functions of reproductive endocrine system. Notably, any window of exposure to T1DM may, to some extent, lead to decreased fertility, it is therefore essential to establish stage-specific T1DM models to systematically evaluate the impact of T1DM onset timing on the functional integrity of the HPOU axis.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003e The usage of cynomolgus monkeys (Macaca fascicularis) and the experimental procedures in this study were evaluated and approved by Primate Life Sciences Ethics Committee of the Centre for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences (CEBSIT-2022019) in accordance with the guidelines of Association for Assessment and Accreditation of Laboratory Animal Care.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eInduction of diabetes\u003c/h2\u003e \u003cp\u003eTo induce type 1 diabetes, four young female cynomolgus monkeys (Macaca fascicularis) were 5\u0026ndash;6 years of age and weighted 3.1\u0026ndash;3.4 kg and were fasted overnight. The next morning the 80 mg/kg of STZ was dissolved in saline and administered via intravenous injection into the jugular vein of anesthetized cynomolgus monkeys over a period of 5 minute. After STZ administration, blood glucose levels were monitored every 4 hours within the 48-hour period. During the first week post-injection, measurements were taken twice daily. Subsequently, from the second week onward, blood glucose was assessed twice weekly. The monkeys were treated by insulin administration, if necessary, to avoid metabolic dysfunction. A fasting blood glucose level of greater than 200 mg/dL in combination with a stimulated C-peptide level of less than 0.5 ng/mL was considered indicative of diabetes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTissue digestion and library construction\u003c/h2\u003e \u003cp\u003eHypothalamus, pituitary gland, ovary, and uterus were harvested from anesthetized monkeys perfused with physiological saline, which were used for each scRNA-seq experiment, respectively. Briefly, single-cell suspensions were prepared from four tissues using a standardized enzymatic dissociation protocol with the Tumor Dissociation Kit (#130-095-929, Miltenyi Biotec), according to the manufacturer's instructions. Following tissue digestion, cell viability and concentration were assessed to ensure optimal conditions for droplet-based encapsulation. The cell suspensions with a viability of over 85% and a density ranging from 300 to 600 cells per microliter were subsequently loaded onto the 10x Genomics Chromium platform for generation of gel bead-in-emulsion (GEM) partitions. Complementary cDNA synthesis was performed within each GEM through reverse transcription, incorporating unique molecular identifiers (UMIs) for subsequent transcript quantification. After emulsion breakage, the cDNA was amplified via PCR with a total of 12 amplification cycles. Library preparation was then carried out following the Chromium Single-Cell 3\u0026prime; Reagent Version 3 Chemistry protocol (10x Genomics). Resulting libraries were evaluated for fragment size distribution using a Fragment Analyzer with the High Sensitivity NGS Analysis Kit (Advanced Analytical Technologies), and library concentration was determined by qPCR using the KAPA Library Quantification Kit for Illumina (Kapa Biosystems). Paired-end sequencing (2 \u0026times; 150 bp) was performed on an Illumina NovaSeq 6000 system, generating high-throughput transcriptomic profiles suitable for downstream single-cell RNA-seq analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eProcessing of single cell RNA-seq data\u003c/h2\u003e \u003cp\u003eSequencing data generated on the Illumina NovaSeq 6000 platform were demultiplexed using bcl2fastq (version 2.20) to produce de-multiplexed FASTQ files. A custom reference genome for Macaca fascicularis (version Macaca_fascicularis_6.0) was constructed in accordance with the Cell Ranger (version 4.0.0) pipeline specifications. Subsequently, FASTQ files were processed using the count function within Cell Ranger under default parameters, which included alignment to the Macaca fascicularis reference genome using STAR, quality filtering, and UMI-based transcript counting. The resulting gene expression matrices were imported into R using the Read10X function from the Seurat package (version 4.1.0)\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Low-quality cells were filtered based on predefined criteria: those expressing fewer than 200 unique genes or exhibiting a mitochondrial gene content exceeding 5% of total transcripts were excluded from further analysis. To correct for technical variability across samples, batch effect removal was performed using the Harmony algorithm\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Following batch correction, data were normalized using the LogNormalize method, and principal component analysis (PCA) was conducted on the filtered and normalized dataset to reduce dimensionality. The top principal components were selected for downstream clustering and visualization. Cells were clustered using a resolution parameter optimized for biological heterogeneity, and the results were visualized in two dimensions via uniform manifold approximation and projection (UMAP)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Cluster annotation was achieved by identifying differentially expressed marker genes using the FindAllMarkers function in Seurat with default settings, allowing for robust classification of cell populations based on transcriptional signatures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCell-cell interaction analysis\u003c/h2\u003e \u003cp\u003eCell\u0026ndash;cell communication analysis was performed using the CellChat\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e R package (v1; available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/sqjin/CellChat\u003c/span\u003e\u003cspan address=\"https://github.com/sqjin/CellChat\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Briefly, a CellChat object was constructed based on the processed single-cell RNA-seq data. The analysis was conducted using the built-in \u0026lsquo;CellChatDB.human\u0026rsquo; database as a reference for ligand-receptor interactions. Default parameters were applied to infer potential cellular communication networks and signaling strengths across cell populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePseudotime analysis\u003c/h2\u003e \u003cp\u003ePseudotime analysis of granulosa cells was conducted using the Monocle 2 R package to reconstruct putative developmental trajectories \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. A gene expression count matrix was used as input to generate a new Monocle object. Genes exhibiting significant differential expression across distinct cell clusters were selected as ordering genes, which served to infer the progression path of cellular states during differentiation. These ordering genes were subsequently employed to model lineage trajectories and infer pseudotime trajectory. To assess the reliability of the reconstructed trajectories, differentially expressed genes were clustered and visualized according to their expression trends across pseudotime trajectory.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGO analysis\u003c/h2\u003e \u003cp\u003eGene Ontology (GO) enrichment analysis was carried out using the MetaScape web-based platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://metascape.org/gp/index.html\u003c/span\u003e\u003cspan address=\"http://metascape.org/gp/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; version 3.5). Only GO terms with a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eHematoxylin and Eosin (H\u0026amp;E) staining\u003c/h2\u003e \u003cp\u003eTissue sections were prepared from paraffin-embedded blocks by cutting 5 \u0026micro;m thick sections using a rotary microtome (Leica RM2235, Leica Biosystems). Prior to staining, slides were deparaffinized in xylene followed by rehydration through graded ethanol solutions. Subsequently, sections were washed with phosphate-buffered saline (PBS). For H\u0026amp;E staining, sections were first stained with Harris hematoxylin solution (Sigma-Aldrich) followed by differentiation in 1% acid alcohol to remove excess stain and improve nuclear contrast. The sections were then briefly rinsed under running tap water for 1 minute to develop the hematoxylin stain. Subsequently, sections were counterstained with eosin Y solution (Sigma-Aldrich) to visualize cytoplasmic structures. After staining, sections were dehydrated through an ascending series of ethanol concentrations, cleared in xylene, and mounted with coverslips.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eImmunofluorescence analysis\u003c/h2\u003e \u003cp\u003eFor immunofluorescence staining, paraffin-embedded tissue sections (5 \u0026micro;m thick) were deparaffinized in xylene and rehydrated through a graded ethanol series into phosphate-buffered saline (PBS). Antigen retrieval was performed by heating the sections in citrate buffer (pH 6.0) at 98\u0026deg;C for 20 minutes, followed by cooling to room temperature. To block nonspecific binding and permeabilize cell membranes, sections were incubated in a blocking solution containing 5% bovine serum albumin (BSA) and 0.3% Triton X-100 in PBS for 1 hour at room temperature. Sections were then incubated overnight at 4\u0026deg;C with primary antibodies diluted in blocking buffer. The following primary antibodies were used: anti-CD68 (ABclonal, A20555, 1:100), anti-CHIT1 (Santa Cruz, sc-271460, 1:100). Following three washes with PBS, sections were incubated with species-appropriate fluorescently labeled secondary antibodies for 1 hour at room temperature. Nuclei were counterstained with 4\u0026prime;,6-diamidino-2-phenylindole (DAPI; Life Technologies). Finally, sections were mounted under coverslips and examined using a confocal laser scanning microscope.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sequencing data generated in this study will be made publicly available upon publication. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to Yue Shi from easygenebio Technology for help with scRNA-seq. This work was supported by National R\u0026amp;D program of China, Grant/Award Number:2022YFC2703501; Guangdong Basic and Applied Basic Research Foundation, Grant/Award Number: 2023B1515120027; Science and Technology Program of Guangzhou, China, Grant/Award Number 202201020292. National Key Research and Development Program of China (2022YFF0710901). National Natural Science Foundation of China, Grant/Award Number:82401895.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZheng-Hui Zhao, Xiang-Hong Ou, Qiang Sun and Qing-Yuan Sun conceived and supervised the project, designed the experiments and wrote our manuscript. Ning Xu collected and processed tissue samples. Zheng-Hui Zhao and Xue-Ying Chen conducted computational analysis and validation experiments. Yong Lu and Ang Li provided technical assistance. \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\u003eRoa J, Aguilar E, Dieguez C, Pinilla L, Tena-Sempere M. 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However, how hyperglycemia affects the hypothalamic-pituitary-ovarian-uterine axis remains poorly understood. In this study, we performed single-cell transcriptomic profiling of the hypothalamus, pituitary, ovary and uterus during the proliferative phase of the menstrual cycle in type 1 diabetic macaques to systematically characterize changes in tissue-specific cellular heterogeneity, gene expression, and intercellular communication networks under diabetic conditions. Our analysis revealed significant downregulation of the SPP1 signaling pathway across multiple tissues, concomitant with marked activation of inflammation-related pathways, including TNF signaling. Notably, we observed that diabetes impairs the recruitment of perifollicular \u003cem\u003eCHIT1\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e macrophages and leads to reduced \u003cem\u003eFSHR\u003c/em\u003e expression during granulosa cell differentiation. This process is further exacerbated by upregulation of \u003cem\u003eSFRP4\u003c/em\u003e, a known antagonist of follicle-stimulating hormone signaling molecule, resulting in diminished granulosa cell responsiveness to follicle-stimulating hormone. Consequently, this dysregulation correlates with increased \u003cem\u003eFSHB\u003c/em\u003e expression in pituitary gonadotropes, likely due to disrupted ovarian feedback signaling. Collectively, our findings provide a comprehensive landscape of cellular and molecular alterations in immune and endocrine compartments in female reproductive system in diabetic states, advancing our understanding of immune-endocrine crosstalk in the context of metabolic disease.\u003c/p\u003e","manuscriptTitle":"Single-cell atlas of reproductive endocrine organs reveals transcriptomic responses to type 1 diabetes mellitus in non-human primate","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 08:39:14","doi":"10.21203/rs.3.rs-6903784/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f47bb2ae-5fb8-4458-99d6-356e38aa29b4","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50521139,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 1 diabetes"},{"id":50521140,"name":"Biological sciences/Computational biology and bioinformatics/Cellular signalling networks"}],"tags":[],"updatedAt":"2025-07-31T07:50:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 08:39:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6903784","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6903784","identity":"rs-6903784","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}