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. 2025 Oct 3;41(1):134. doi: 10.1007/s10565-025-10095-7

scRNA-seq deciphers molecular mechanisms of endocrine disruptor 4-nonylphenol impairing spermatogenesis in mice

Xue Zhao 1,2, Yanan Tian 1, Dan Zhou 1, Xiaojuan Tang 5, Xiaoyang Zhou 1, Xuelin Wang 1, Yan He 6, Pengxia Yu 1, Jiaolong Huang 1, Yan Tan 2,3,4,✉, Peng Duan 1,✉
PMCID: PMC12494639  PMID: 41042295

Abstract

4-Nonylphenol (NP) is an environmental endocrine disruptor widely used in consumer products. Previous studies have shown that NP can interfere with hormone synthesis and metabolism in humans and animals, leading to male reproductive dysfunction. This study utilized the scRNA-seq method to evaluate cell populations and their heterogeneity, aiming to elucidate the toxic mechanisms of NP exposure on testicular cells. We demonstrate, for the first time, the transcriptomic characteristics of testicular single cells in adolescent mice exposed to NP. Adolescent mice, initially exposed at 4 weeks of age, were subsequently analyzed at sexual maturity after a continuous exposure period of 3 months. The blank control and NP-exposed groups underwent scRNA-seq analysis, identifying ten cell populations. The results showed that after NP exposure, the number of germline cells was remarkably reduced compared to the control group. NP exposure significantly decreased the protein expression of the four common differentially expressed genes (DEGs) (Cmtm2b, Rpl28, Adam32, and Pgam2). The DEGs enriched in the GO functions of the four germline cell types were spermatogenesis and spermatid development. KEGG analysis showed that the DEGs were enriched in the oxidative phosphorylation, and ROS signaling pathways. Further analysis of intercellular interactions revealed that NP exposure altered intercellular communication between germ cells, with the NECTIN3-NECTIN2 receptor-ligand interactions activating between spermatogonia, Sertoli, and Leydig cells. Germ cells bind to Sertoli and Leydig cells via NECTIN3-NECTIN2 receptor ligands. Somatic cells bind to RS and ES through GRN-SORT1 receptor ligands. CADM1-CADM1 receptor-ligand interactions enhances between germ and Sertoli cells. Our study provides new insights into the potential impacts of NP on spermatogenesis and sperm function, emphasizing the importance of environmental hormones in male fertility issues.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10565-025-10095-7.

Keywords: 4-Nonylphenol, Spermatogenesis, Single-cell transcriptome, Intercellular communication, Male fertility

Highlights

  • NP exposure interfered with testicular development and spermatogenesis.

  • NP exposure affected the cell cycle of Spermatogonia and Spermatocyte.

  • NP exposure influenced the reactive oxygen species and oxidative phosphorylation of advanced Round Spermatid.

  • NP exposure impaired sperm motility by disrupting mitochondrial energy metabolism in Elongated Spermatid.

Graphical abstract

graphic file with name 10565_2025_10095_Figa_HTML.jpg

Supplementary Information

The online version contains supplementary material available at 10.1007/s10565-025-10095-7.

Introduction

With the rapid increase in the use of chemical products, large amounts of hazardous chemicals inevitably enter the environment and daily life, impacting human health and wildlife, even at very low levels of exposure (Ghosh, et al. 2022; Jasrotia, et al. 2021). Many of these environmental toxicants exhibit estrogenic activity, disrupt estrogenic signaling, and are considered as endocrine-disrupting chemicals (EDCs). 4-Nonylphenol (NP), a well-known EDC, is a major degradation product of alkylphenol ethoxylates. It is widely used in detergents, insecticides, plasticizers, textiles, cosmetics, and antioxidants (Cheng, et al. 2018; Crane 2021; Tao, et al. 2023; Wu, et al. 2021). EDCs like NP can negatively impact human health by disrupting the reproductive, endocrine, digestive, and circulatory systems (Duan, et al. 2016b, Lee, et al. 2017, Tao, et al. 2023, Zhang, et al. 2021). This disruption can lead to fetal developmental abnormalities (Chen, et al. 2020; Lopez Rodriguez, et al. 2020, Talia, et al. 2021), male pubertal developmental abnormalities (Cargnelutti, et al. 2020), reproductive diseases (Amir, et al. 2021, You, et al. 2021), endocrine disorders, thyroid dysfunction, cancer (Zahra, et al. 2020), and cardiovascular diseases (Zhang, et al. 2022). Notably, previous studies have reported that NP can interfere with hormone synthesis and metabolism in both humans and animals, posing a significant risk to male reproductive health (De Bruin, et al. 2019; Tao, et al. 2023). Current evidence suggests that NP predominantly induces endocrine disruption via estrogenic pathways (Duan, et al. 2019), which is the primary focus of this investigation.However, the potential androgenic effects of NP necessitate further systematic exploration in future research.

Male reproductive development relies heavily on the proper function of the reproductive system, with the testes being crucial organs susceptible to reproductive toxicity (Li, et al. 2021, Sikka, et al. 2008). The spermatogenic process advances sequentially through distinct stages, including spermatogonia (characterized by mitotic proliferation), spermatocytes (involved in meiotic division), and spermatids (undergoing post-meiotic differentiation), with each stage exhibiting susceptibility to environmental perturbations (Xu, et al. 2020). Testosterone, a key hormone in male reproductive health, is essential for maintaining spermatogenesis and developing secondary sexual characteristics in males. Existing research shows that NP exposure adversely affects male spermatogenesis, testosterone production, and testicular function (Jambor, et al. 2016, 2017). As demonstrated by Duan et al. in mouse models, NP damages spermatogenesis through oxidative stress, apoptosis, and autophagy, ultimately resulting in male reproductive dysfunction (Duan, et al., 2016a, Duan, et al. 2017a, Duan, et al. 2017b, Duan, et al. 2016b). Recent in vitro studies indicate that NP causes male reproductive toxicity by abnormally activating the PI3K/PDK1/AKT signaling pathway and promoting tyrosine phosphorylation (Hwang, et al. 2024). Additionally, multiple in vitro studies have also confirmed the negative effects of NP on sperm motility and viability (Jambor, et al. 2016, 2017). Although in vivo and in vitro study results are consistent, the exact mechanisms by which NP exposure influences male reproductive development, particularly at the cellular and molecular levels, remain largely unknown. There is a pressing need for toxicity assessments and mechanistic studies to elucidate the effects of NP on spermatogenesis.

Single-cell RNA sequencing (scRNA-seq) technology has evolved into a sophisticated and robust method for analyzing gene expression within multicellular systems (Fu, et al. 2019). scRNA-seq has significantly enhanced our understanding of cell-specific gene expression, providing precise insights into cell types and their heterogeneity, as well as facilitating the analysis of intercellular interactions (Dong, et al. 2023; Lin, et al. 2022; Zhang, et al. 2023). Currently, there is a lack of research exploring the molecular mechanisms of NP exposure on reproductive developmental abnormalities using scRNA-seq technology. Therefore, we utilized the scRNA-seq method on the 10 × Genomics platform to assess cell populations and heterogeneity, aiming to elucidate the toxic mechanism of NP exposure on testicular cells, particularly its impact on sperm development. A comprehensive understanding of these molecular mechanisms could provide crucial insights into the risks that NP exposure poses to male fertility.

Materials and methods

Chemicals and reagents

NP (CAS no: 84852–15-3; mixture of isomers; empirical formula C15H24O; molecular weight 220.35) was purchased from Acros Organics (UK) with a purity of 99%. Corn oil without antioxidants was purchased from Sigma-Aldrich (CAS no: 8001–30-7).

Animals and treatments

The study was reviewed and approved by the Animal Care and Use Committee of Hubei University of Medicine (No. XYYYE20210002). C57BL/6 male mice (3 weeks of age) were obtained from the Hubei Provincial Center for Disease Control (Wuhan, China) and housed at the Animal Center of Xiangyang No.1 People's Hospital (Hubei, China) in a specific pathogen-free (SPF) environment.

All mice were housed in standard polypropylene cages (320 × 215 × 170 mm) under a 12-h light/dark period at a temperature of 21 ± 2 °C, with free access to water and standard laboratory food. Adolescent mice, aged 4 weeks at the onset of exposure, were evaluated for analysis at sexual maturity after a 3-month exposure duration. After one-week acclimation, mice were randomly divided into five groups (n = 6/group): (1) blank control (untreated), (2) solvent control (corn oil vehicle), (3) NP-treated group (5 μg/kg, 1 × human tolerable daily intake [TDI]), (4) NP-treated group (50 μg/kg, 10 × TDI), and (5) NP-treated group (100 μg/kg, 20 × TDI). The human TDI of NP is 5 μg/kg (Chang, et al. 2019; Li, et al. 2024; Niu, et al. 2015; Ringbeck, et al. 2021). We used 10 × and 20 × doses of the TDI as the medium- and high-concentration groups. NP was dissolved in corn oil to achieve required concentrations (5, 50, and 100 μg/kg), with fresh solutions prepared every 3 days and stored at 4 °C.

All treatments were administered via intragastric gavage using sterile 20-gauge ball-tipped needles between 9:00–11:00 AM every second day (Cary, et al. 2023; Xiong, et al. 2024). The dosing volume was strictly maintained at 0.1 mL per 10 g body weight, with weekly adjustments based on body weight measurements. This protocol was consistently followed throughout the 3-month experimental period to mimic chronic human exposure scenarios.

Collection of tissue samples

Following the final exposure, the mice were euthanized via cervical dislocation. The testes and epididymides were quickly removed and weighed. For each mouse, the left testis was used for biochemical analysis. After fixing the tissue with 10% formalin, half of the tissue was prepared for paraffin embedding for hematoxylin and eosin (H&E) staining and immunohistochemistry, while the other half was prepared for optimal cutting temperature (OCT) embedding for immunofluorescence. The right testis was used for scRNA-seq analysis. The remaining tissues were stored at –80 ℃ until biochemical analysis.

Determination of sperm quality

The mice were euthanized by decapitation, and the cauda epididymis was immediately rinsed with 100 μL of prewarmed sperm nutrient solution at room temperature. Ten deep cuts were made in each epididymis and incubated immediately in a CO2-humidified incubator (95% air, 5% CO2, 37 ℃) for one minute. The resulting sperm suspension was subsequently placed onto clean slides. Sperm analysis was performed using a sperm quality analyzer (BEION S3-3, Beion Medical Technology, Shanghai, China). Sperm counts, concentration, and motility were measured using a computer-aided sperm analysis (CASA) system (Beion Medical Technology).

H&E staining analysis

Fresh testicular tissue samples were immersed in 4% neutral-buffered formalin and fixed for 24 h at room temperature, then dehydrated with ethanol, embedded in paraffin, and sectioned into 5 µm slices. These sections were then stained with H&E and examined using a pathological section scanner (KFBio KF-PRO-020, Ningbo, China).

Tissue dissociation and single-cell sample preparation

To obtain samples for single-cell RNA sequencing, testicular tissue was used for cell capture. One testis from each of the blank control group and 100 μg/kg NP group was selected for analysis. The tissue was washed three times with PBS washing solution and transferred to a 1.5 mL EP tube, which was then then stored at –80 °C and shipped to Beijing SeekGene Biotechnology Co., Ltd. (Beijing, China) for tissue dissociation. The resulting cell suspension had a concentration between 400 to 1000 cells/μL, with a cell survival rate exceeding 90%. Barcode coding was performed using SeekOneChip, and scRNA-seq was conducted on the SeekOne ® MM system.

Quality control and cell annotation of single-cell RNA sequencing data

We utilized the 10 × Genomics platform for single-cell RNA sequencing of the sample. Using the Seurat package (version 4.3.1), we generated a single-cell expression matrix to calculate cell percentages, filter data, normalize expression values, determine principal components (PCs), perform dimensionality reduction using UMAP, cluster cells, annotate cell types, and analyze differentially expressed genes (DEGs). High-quality single-cell data were retained based on rigorous criteria: cells with a gene count between 200 and 5500 to exclude low-quality cells, empty droplets, and doublets; mitochondrial genes accounting for less than 40% of total gene expression; and genes expressed in at least 3 cells. This process resulted in 17,475 cells for further analysis.

Technical batch effects, defined by experimental groups (control vs. NP groups) and sequencing runs, were corrected using Harmony (v1.0). Successful integration was validated by pre-/post-correction visualization, a high mixing efficiency (distance ratio = 0.971), and preserved biological clustering (silhouette score > 0.8) (Fig. S2). After integrating the data, we applied the Harmony package to mitigate batch effects. We determined the optimal number of PCs and selected those with a significance level (P < 0.05) for visualization using dimensionality reduction with uniform manifold approximation and projection (UMAP). After multiple iterations, we identified and visually represented 33 distinct clusters using UMAP. To accurately annotate cell types, we utilized specific markers. Finally, we visualized the annotated cell populations using UMAP and clustered them at an appropriate resolution.

Searching for DEGs in ten different cell populations

DEGs analysis was performed for ten cell populations among the control and NP groups using the Wilcoxon Rank-Sum test. A scatter plot of DEGs was generated using ggplot2, with selection based on a P < 0.01 considered as upregulated genes and a P ≥ 0.01 considered downregulated. A RANK waterfall plot of DEGs was also created using ggplot2, with genes arranged according to avg_log2FC. A avg_log2FC > 0 indicates an increase, while avg_log2FC ≤ 0 indicates a decrease. Red denotes the 5 genes with the most significant upregulation, while blue represents the 5 genes with the most significant downregulation.

GO and KEGG analysis

GO and KEGG enrichment analyses were performed using the online Metascape tool (https://metascape.org). The KEGG enrichment analysis of DEGs in RS was conducted using the online DAVID tool (https://david.ncifcrf.gov/). Additionally, GO and KEGG analysis of DEGs were conducted using the R software clusterProfiler package, and the results were visualized using the ggplot2 package. The GO terms and KEGG pathways for four germ cell populations, including spermatogonium (SPG), spermatocyte (SPC), round spermatid (RS), and elongated spermatid (ES), as well as three individual cell populations (Sertoli, Leydig, and macrophage), revealed the top six rankings based on logP values, except for the RS KEGG pathway, which was visualized with P values. Four representative terms selected from the top six GO terms and KEGG pathways (P < 0.05) were displayed for germ cell staging.

Protein–protein interaction (PPI) network analysis

The STRING online database (https://string-db.org) was used to map DEGs and construct a PPI network. Cytoscape (version 3.9.1) was then utilized to visualize and analyze the interactions within the PPI network of the DEGs.

Trajectory inference

The R software package Monocle (version 2.28) was utilized to infer the trajectories of RS and ES subtypes. First, data from RS or ES subtypes was extracted to construct CellDataSet (CDS) objects, followed by filtering out low-quality cells based on criteria such as removing genes expressed in fewer than 10 cells. Clustering was performed using highly variable genes identified by Seurat. DEGs were identified using differentialGeneTest function, with genes having a q-value < 0.01 selected for further analysis. Dimensionality reduction was conducted using the DDRTree reverse graph embedding method. Finally, the results were visualized using plot_cell_trajectory function.

Intercellular communication analysis

The Cellchat software package (version 1.6.1) in the R package was applied to construct Cellchat objects, preprocess data from the control and NP groups, and infer cell communication networks. The number and intensity of ligand-receptor pairs between cells were calculated and visualized using a Circle plot. Conservative and specific signaling pathways between the control and NP groups were identified, with differences in all ligand-receptor pairs displayed using bubble plots.

Western blotting

Western blot analysis included testicular tissues from 6 biological replicates per group. The total proteins of the testicular tissues were extracted with RIPA lysis buffer (Servicebio, Wuhan, China) supplemented with 1 × protease inhibitor cocktail (Roche, 04693132001). Protein concentrations were determined by BCA assay, with 30 μg of total protein loaded per sample. Equal amounts of protein extracts were electrophoresed on SDS-PAGE gels and subsequently transferred onto polyvinylidene fluoride (PVDF) membranes (0.45 μm, Servicebio, Wuhan, China). The membranes were incubated with rabbit anti-CMTM2 polyclonal antibody (1:1000, C15132, Delaware, USA), rabbit anti-RPL28 polyclonal antibody (1:1000, abs146380, Absin, China), rabbit anti-PGAM2 polyclonal antibody (1:1000, FNab06347, FineTest, China), rabbit anti-ADAM32 polyclonal antibody (1:1000, PA5-90161, Invitrogen, USA), and mouse anti-β-actin monoclonal antibody (1:5000, A5316, Sigma-Aldrich, USA). After primary antibody incubation, the membranes were rinsed and incubated with an HRP-labeled secondary antibody (1:5000, GB23301, Servicebio, Wuhan, China).

Immunoreactive proteins were detected using ECL detection reagent (G2161, Servicebio, Wuhan, China). The protein bands were visualized using the enhanced chemiluminescence plus Western blot detection system (SCG-W5000, Servicebio, Wuhan, China). Densitometric analysis of the protein bands was performed using AIWBwell™ analysis software (Servicebio, Wuhan, China).

Statistical analysis

Data are presented as mean ± standard error of the mean (SEM). Data normality was verified using the Shapiro–Wilk test. For normally distributed data, we conducted one-way analysis of variance (ANOVA) followed by the least significant difference (LSD) post-hoc test. Single-cell data analysis and computations were conducted using R software (version 4.3.1). A P-value less than 0.05 was considered statistically significant.

Results

The effects of NP exposure on sperm quality and testicular tissue

As shown in Figs. 1A and B, the 50 and 100 μg/kg NP exposure groups exhibited reduced sperm motility and morphological changes in the seminiferous tubules (Figs. 1A and B). Compared to the control group of mice, the total sperm count, density, and motility were markedly reduced in mice exposed to NP at concentrations of 50 and 100 μg/kg (Fig. 1C). Only the 100 μg/kg NP group showed a significant decrease in the testicular organ coefficient (Fig. 1C). No significant difference in the diameter of the seminiferous tubule was found among the five groups (Fig. S3A). However, the seminiferous epithelium thickness was significantly diminished in both the 50 and 100 μg/kg NP groups compared to the control group (Figs. 1B and S3B). Additionally, the lumen diameter of the seminiferous tubules was significantly larger in the 50 μg/kg and 100 μg/kg NP groups compared to the control group (Fig. S3C).

Fig. 1.

Fig. 1

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Semen quality and morphological changes in testicular tissue after exposure to NP (n = 6 mice/group). (A) Sperm activity trajectory map (20X). (B) H&E staining images of testicular tissue. (C) The effect of NP on total count, total density, motile sperm, immotile sperm, and testicular organ coefficient (n = 6). Significant differences were evaluated using ANOVA, with different letters indicating statistically significant differences (P < 0.05) among groups

Identification of testicular cell types through scRNA transcriptome analysis

In this study, to elucidate the greater toxic effects and probable mechanisms underlying the NP toxicity on spermatogenesis, we selected the control and 100 μg/kg NP groups (100 μg/kg) for preprocessing of scRNA sequencing data. A total of 17,475 cells were successfully classified into 33 independent clusters, comprising 9,756 cells from the control group and 7,719 cells from the NP group (Fig. 2A). Based on the identified marker genes for germ and somatic cell types, 33 cell clusters were annotated into ten cell subtypes, including SPG, SPC, RS, ES, Sertoli, Leydig, macrophagic, fibrocyte innate lymphoid, and endothelial cells. The corresponding marker gene expression of the ten cell subtypes were Anp32b, AA467197, Spaca3, 1700027A15Rik, Cst12, Fabp3, C1qb, Col1a2, Ccl5, and Abcb1a, respectively (Figs. 2B and S4). Among these, the number of SPC subtype was the most decrease, followed by SPG and RS (Figs. 2C and S5).

Fig. 2.

Fig. 2

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Results of single-cell RNA sequencing data analysis. (A) UMAP plot comparing the control and the NP groups. (B) Violin plots of selected marker genes for ten cell subtypes. (C) Number and proportion of ten cell subtypes in the control group and NP groups. (D) Scatter plot of DEGs in ten cell subtypes. The red dots represent upregulated genes, while the green dots represent downregulated genes. (E) GO enrichment analysis of germ cells (SPG, SPC, RS, and ES)

Figure 2D illustrates the number of DEGs across ten cell populations. The total number of DEGs in germ cells was 321. Specifically, SPG had 42 DEGs, SPC had 46 DEGs, RS had 203 DEGs, and ES had 30 DEGs (Fig. 2D). To gain further insight, we performed GO and KEGG enrichment analyses on the DEGs from the four germ cell subtypes (Figs. 2E and S6). GO analysis of the germ cells revealed high enrichment in biological processes related to spermatogenesis, sperm development, sperm differentiation, and sperm flagellum characteristics (Fig. 2E). Moreover, KEGG analysis revealed that DEGs were significantly enriched in pathways related to the FoxO signaling pathway and glycolysis/gluconeogenesis (Fig. S6).

Effects of NP exposure on SPG

We analyzed the SPG population and categorized it into two stages: undifferentiated SPG and differentiated SPG (Figs. 3A and S7B). Id4 and Uchl1 were identified as marker genes for undifferentiated SPG, while Mki67, Stra8, and Kit served as marker genes for differentiated SPG (Green, et al., 2018, Hermann, et al. 2018, Sohni, et al. 2019) (Fig. S7A). Compared to the control group, the differentiation of SPG was remarkably reduced in the NP group (Fig. 3B). In undifferentiated SPG, GO analysis focused on responses to translation and prostaglandin metabolic processes, while KEGG analysis highlighted pathways related to oxidative phosphorylation. In differentiated SPG, GO analysis showed significant responses related to cellular response to DNA damage stimulus and regulation of cell cycle, while KEGG analysis identified pathways related to the Ras signaling pathway and the cell cycle (Fig. 3C).

Fig. 3.

Fig. 3

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Analysis of SPG subpopulations. (A) UMAP plot of SPG subpopulations in the control and NP groups. (B) The number of cells and percentage plots of undifferentiated and differentiated SPG. (C) GO and KEGG enrichment analysis. (D) Venn diagram of DEGs. (E) GO enrichment analysis of common DEGs in undifferentiated and differentiated SPG

We identified DEGs from undifferentiated and differentiated SPG to construct RANK waterfall and PPI plots. In undifferentiated SPG, upregulated genes included Cmtm2b, Rpl28, and Cct7, while downregulated genes comprised Ptgds, Pum2, and Gstm1 (Figs. S8 and S9). In differentiated SPG, upregulated genes included Cct7, Pgam2, and Rpl28, while the downregulated genes were Odf1, Nfat5, and Gstm1 (Figs. S8 and S9). Venn diagram analysis of DEGs in undifferentiated and differentiated SPG revealed common genes. GO analysis of these 10 genes mainly focused on the response to the prostaglandin metabolic process and glycogen production (Figs. 3D and E). As shown in Fig. S10, the expression levels of Cmtm2b, Cct7, Rpl28, Rps14, Pgam2, Bsg, and Uqcc2 were remarkably lower in both undifferentiated and differentiated SPG groups compared to the control group. Additionally, expression levels were lower in differentiated SPG than in undifferentiated SPG. Notably, Cmtm2b, and Pgam2 were only expressed in undifferentiated SPG. Pum2, Kdm2a, and Ppp4r3b were only expressed in differentiated SPG (Fig. S10).

Effects of NP exposure on SPC

We classified SPC into four stages: leptonema, zygonema, pachynema, and diplonema (Figs. 4A, S11A and S11B). Among these, Sycp3 serves as the marker gene for leptonema (Green, et al., 2018), Piwil1 and Tdrd5 are marker genes for zygonema (Salehi et al., 2023)), Rbakdn is the marker gene for pachynema, and Gapdhs and Ggn are marker genes for diplonema (Green, et al., 2018) (Figs. S11A and B). Compared to the control group, the diplonema stage of SPC in the NP group was significantly inhibited (Fig. 4B). For the leptonema stage, GO term enrichment involved spermatogenesis, cell cycle, and cell division, with the main enriched KEGG pathways being ribosome and autophagy. In the zygonema stage, GO term enrichment included the male germ cell nucleus, while the primary KEGG pathways was Huntington disease. The pachynema stage showed enrichment in the GO term related to spermatogenesis and spermatid development, while the main, enriched KEGG pathway was glycolysis/gluconeogenesis. In the diplonema stage, the GO term enrichment focused on spermatogenesis and mitotic cell cycle, while the main KEGG pathway involved protein processing in the endoplasmic reticulum (Figs. 4C and S12).

Fig. 4.

Fig. 4

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Analysis of SPC subpopulations. (A) UMAP plot of SPC subpopulations in the control and NP groups. (B) The number of cells and percentage plots in leptonema, zygonema, pachynema, and diplonema of SPC. (C) GO enrichment analysis. (D) Venn diagram of DEGs. (E) GO enrichment analysis of common DEGs in leptonema, zygonema, pachynema, and diplonema of SPC. (F) Violin plots comparison of common DEG expression levels in leptonema, zygonema, pachynema, and diplonema between the control and NP groups (*P < 0.05, **P < 0.01,****P < 0.0001)

Using the RANK waterfall diagram and PPI diagram, we identified specific gene expression patterns across different stages of SPC (Figs. S13 and S14). In the leptonema stage, upregulated genes included Ddx51, Trim33, and Tubd1, while downregulated genes included Rpl28, Pla2g6, and Cmtm2b. In zygonema, upregulated genes included Clic4, Trappc2l, and Odf1, while downregulated genes were Pgam2, Cmtm2b, and Cct7. In pachynema, upregulated genes included Prm1, Prm2, and Psmc3, with downregulated genes being Adam32, Rpl28, and Cmtm2b. Finally, in the diplonema stage, upregulated genes included mt-Nd1, mt-Co1, and Gstm1, while downregulated genes included Adam32, Rpl28, and Cct7 (Figs. S13 and S14). A total of 10 genes were identified as common DEGs (Fig. 4D). GO analysis of these 10 common DEGs primarily revealed enrichment in translation and binding of sperm to zona pellucida (Fig. 4E). Additionally, the violin plot illustrated that the expression levels of common DEGs across all four stages were remarkably lower in the NP group compared to the control group (Fig. 4F).

Effects of NP exposure on RS

We used Monocle2 to analyze RS development, dividing RS into early and advanced stages (Salehi et al., 2023, Wang, et al. 2021) (Fig. 5A). Compared to the control group, the advanced RS in the NP group was significantly decreased (Fig. 5B). GO analysis for early RS revealed involvement in processes such as spermatogenesis and spermatid development, while the main KEGG pathways included reactive oxygen species and necroptosis. In advanced RS, the main enriched GO terms were related to oxidative phosphorylation, with primary enrichment in KEGG pathways being reactive oxygen species and oxidative phosphorylation (Fig. 5C). A total of 503 DEGs were identified in early RS, while 24 DEGs were identified in advanced RS. Among these, 5 DEGs (mt-Co1, mt-Co2, Prm1, Cox6b2, and Morf4l1) were shared between early and advanced RS (Figs. 5D and S15). GO analysis of these shared DEGs revealed enrichment in processes such as oxidative phosphorylation, cytochrome c oxidase activity, and respiratory chain complex IV (Fig. 5E).

Fig. 5.

Fig. 5

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Analysis of round spermatid subpopulations. (A) Pseudo-temporal analysis map of RS in the control and NP groups (Pseudo-time progression diagram, with darker colors indicating the beginning and lighter colors indicating the end). (B) The number of cells and percentage plots of early and advanced RS. (C) GO and KEGG enrichment analysis. (D) Venn diagram of DEGs. (E) GO enrichment analysis of common DEGs in early and advanced RS

Effects of NP exposure on ES

We used Monocle2 to analyze ES development, dividing ES into early and advanced stages (Salehi, et al., 2023) (Fig. 6A). Compared to the control group, the NP group showed comparable numbers of early ES and advanced ES, with a minor decrease in the proportion of advanced ES (Fig. 6B). In early ES, GO terms were predominantly related to spermatogenesis and cell differentiation, while KEGG pathways were mainly enriched in ribosome and the glycolysis/gluconeogenesis. For advanced ES, GO terms were related to spermatogenesis and flagellated sperm motility, and KEGG pathways were enriched in ribosome and protein processing in endoplasmic reticulum (Fig. 6C). From the analysis of early and advanced ES, 222 DEGs were identified in early ES and 167 in advanced ES. Among these, 13 DEGs were shared between early and advanced ES stages (Figs. 6D and S16). GO enrichment analysis of these 13 common DEGs revealed terms related to the sperm flagellum and spermatogenesis (Fig. 6E).

Fig. 6.

Fig. 6

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Analysis of elongated spermatid subpopulations. (A) Pseudo-temporal analysis map of ES in the control and the 100 μg/kg NP groups (Pseudo-time progression diagram, with darker colors indicating the beginning and lighter colors indicating the end). (B) The number of cells and percentage plots of early and advanced ES. (C) GO and KEGG enrichment analysis. (D) Venn diagram of DEGs. (E) GO enrichment analysis of common DEGs in early and advanced ES

The DEGs common to four types of germ cells

We conducted Venn and PPI analyses on the DEGs in germ cells, identifying four common DEGs (Cmtm2b, Rpl28, Adam32, and Pgam2) (Figs. 7A and S17). The mRNA expression levels of Cmtm2b, Rpl28, Adam32, and Pgam2 were significantly lower in the NP group compared to control group (Fig. 7B). In this study, we further validated the protein expression changes of these genes through Western blot analysis. As shown in Figs. 7C and D, the protein expression of CMTM2, RPL28, PGAM2, and ADAM32 were significantly reduced in the NP group compared to the control group (Figs. 7C and D). The Western blot results of these four common DEGs are consistent with the scRNA-seq findings, confirming the reliability of the scRNA-seq data.

Fig. 7.

Fig. 7

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Expression of DEGs (Cmtm2, Rpl28, Pgam2, and Adam32) common to four types of germ cells. (A) Venn diagrams showing the common DEGs shared among the four types of germ cells. (B) Violin plots comparing the expression levels of DEGs between the control and the NP groups in four types of germ cells (****P < 0.0001). (C) Western blot analysis of germ cell-associated proteins Cmtm2, Rpl28, Pgam2, and Adam32 after NP exposure. (D) Bar graphs showing the relative protein levels of Cmtm2, Rpl28, Pgam2, and Adam32 in the control and NP groups. Data are presented as mean ± standard deviation (n = 6), (**P < 0.01)

Global alterations in intercellular signaling networks after NP exposure

We applied the CellChat software package to analyze the intercellular signaling patterns between the control and NP groups. Visual representation of these findings highlighted ten cell types (Figs. S18 and S19), revealing a notable increase in both the number and strength of interactions within the NP group compared to the control group (Figs. 8A and B). Several pathways, such as VEGF, TNF, IGF, KIT, NOTCH and CSF, were activated in the NP group compared to the control group. Additionally, the activity of several key pathways, such as MHC-I, CADM, COLLAGEN, ANGPTL, GRN, LAMININ, and GALECTIN, increased in the NP group, while the activity of other important pathways, such as PDGF, PECAM1, JAM and PTN, significantly decreased. The PDGF pathway was only present in the control group (Fig. S20). Compared to the control group, the strength of intercellular signaling networks in the NP group increased in the NECTIN, GRN, and CADM signaling pathways, with NECTIN and GRN signaling pathways enhancing communication between germ cells and somatic cells (Figs. 8C, D and S21). Moreover, the Nectin3-Nectin2, Cadm1-Cadm1, and Grn-Sort1 ligand-receptor pairs involved in the signaling pathway were examined. In the NP group, germ cells interacted with the Sertoli and Leydig through increased NECTIN and CADM signaling (Fig. S22A). Additionally, Leydig, Sertoli, and macrophage interacted with SPG, RS, and ES by increasing GRN and CADM signaling (Figs. S22B and C). However, when examining the Grn-Sort1 and Cadm1-Cadm1 ligand-receptor pairs, Sertoli and Leydig in the NP group were found to interact with RS by decreasing GRN and CADM signaling (Fig. S22D).

Fig. 8.

Fig. 8

Open in a new tab

Cell-to-cell communication alterations mediated by interaction signaling pathways after NP exposure. (A) The differences in the number and intensity (B) of interactions between germ and somatic cells in the control and NP groups. The line color denotes cell types, while the line thickness indicates the number of interactions. (C) Differences in intercellular communication mediated by NECTIN and (D) CADM signaling pathways between the control and NP groups

Discussion

Male infertility has become a global issue in medicine, culture, and society. Previous studies have shown that genetic mutations, endocrine and metabolic disturbances, and lifestyle changes can affect semen quality and even lead to male infertility (Thacharodi, et al. 2023, Virant, et al., 2022). Increasingly, environmental pollutants with endocrine-disrupting activity are recognized as contributors to male reproductive dysfunction (Thacharodi, et al. 2023, Virant Klun, et al., 2022). Long-term exposure to EDCs negatively impacts male reproductive function (Thacharodi, et al. 2023), disrupting the production of male steroids, altering hormone levels, and ultimately affecting sperm production (Thacharodi, et al. 2023). NP is an endocrine-active environmental pollutant found in cosmetics and paints, etc., closely linked to daily life (Ghosh, et al. 2019). While there is extensive literature on the effects of NP on the male reproductive system (Jambor, et al. 2016, Li, et al. 2017, Liu, et al. 2021, Malmir, et al. 2020, Zhang, et al. 2021), the variability in NP exposure and its impact on germ cell damage remains underexplored. In this study, we established a chronic NP exposure model in mice from adolescence to adulthood and used scRNA-seq to create a single-cell atlas of mouse testicular cells. We evaluated the effects of NP exposure on various cell types, with a particular focus on germ cells. Utilizing single-cell gene expression profiling, we conducted a systematic evaluation of the effects of NP exposure. This methodological approach elucidated its distinct impacts on germ cell development and cellular communication networks. Our findings revealed the toxic mechanisms of NP exposure on spermatogenesis and identified potential biomarkers for the development of abnormal spermatogenesis.

Spermatogenesis is initiated at puberty and continues throughout life. The present study focused on the impact of NP on reproductive cells during the adolescent-to-adult period in mice. Our studies revealed that NP exposure during this period adversely affected semen quality, compromising male fertility. These results align with findings from Aly et al.(Aly, et al. 2012) and Hwang et al. (Hwang, et al. 2024). Analysis of scRNA-seq data indicated that NP exposure during sexual maturity (puberty) markedly diminished the number of germ cells, consistent with results from previous studies on NP analogs (Kim, et al. 2019). Thus, we suggested that NP exposure exhibited substantial toxicity to germ cells in the testes. Notably, the number of SPC subpopulations decreased remarkably after NP exposure, indicating that SPC may be a primary target cell population for NP toxicity. DEGs in four types of germ cells were enriched in similar GO terms, including spermatogenesis, spermatid development, spermatid differentiation, and sperm flagellum. These changes may contribute to the observed reduction in sperm count and motility. The FoxO signaling pathway plays a crucial role in cell proliferation, regulation, differentiation, and apoptosis (Chen, et al. 2022, Huang, et al., 2016b). The expression of FoxO3 in germ cells is crucial for spermatogenesis and plays a key role in maintaining male fertility (Ni, et al. 2024). Our research identified DEGs related to RS affecting the FoxO pathway, which inhibited cell proliferation and differentiation, possibly contributing to the decline in RS cell numbers. Shen et al. (Shen, et al. 2022) demonstrated that the inactivation of FoxO1 in SPG after birth could lead to abnormal germ cells and male infertility. In vitro studies also showed that the proliferation of FoxO1-deficient SPG was substantially impaired (Shen, et al. 2022). Additionally, the glycolysis/gluconeogenesis pathway provides essential energy for sperm motility and vitality (Tourmente, et al. 2015, Zhang, et al., 2020a). Our research suggested that RS affected sperm motility and vitality by regulating glycolysis/glycogenesis. Overall, our findings indicated that NP exposure during spermatogenesis disrupted various stages of germ cell development, ultimately having a negative impact on male fertility.

In mammals, the process of spermatogenesis undergoes three main stages: the formation of primary SPG, meiosis, and spermatogenesis (Xie, et al. 2021). The formation of primary SPG establishes the foundation for further differentiation. This process occurs in two stages: the transition from undifferentiated SPG to differentiated SPG, followed by the formation of Pre-SPC (Xie, et al. 2021). Previous studies have shown that exposure to NP adversely affects germ cell differentiation (Park, et al. 2020a, 2020b). After NP exposure, we observed changes in DEGs in both undifferentiated SPG and differentiated SPG, including Cmtm2b, Cct7, Rpl28, and Rps14. The Cmtm2b gene promotes cell proliferation and regulates the fertilization ability of sperm (Xiong, et al. 2019, Yoshitaka Fujihara, et al., 2018, Zhang, et al. 2020b). Cct7 is involved in protein folding and affects cell cycle regulation (Huang, et al. 2022, Wang, et al., 2020). Rpl28 and Rps14, integral ribosomal proteins participating in ribosome biogenesis and translational regulation, facilitate protein synthesis, stimulate cellular proliferation, and impact developmental processes (Sara Hounguè, 2019, Shi, et al. 2021, Xu, et al. 2023). Collectively, these findings suggested that Cmtm2b, Cct7, Rpl28, and Rps14 in SPG play important roles in spermatogenesis in mice.

Oxidative phosphorylation plays a key role in metabolic processes, providing energy to sperm and supporting their metabolic activities by producing ATP (Foutouhi, et al., 2022, Kong, et al., 2024, Kyrgiafini, et al. 2024). Previous studies have shown that the expression levels of proteins related to oxidative phosphorylation are abnormal in the sperm of male infertility patients, indicating that oxidative phosphorylation is crucial for male reproduction (Nowicka Bauer, et al. 2018). Additionally, a decrease in mitochondrial oxidative phosphorylation activity can affect ATP supply and may lead to male infertility (Meng, et al. 2024), which is consistent with our research findings. Our research showed that NP exposure affected the oxidative phosphorylation of undifferentiated SPG, thereby reducing ATP production and impairing cell proliferation and normal development. The cell cycle is essential for cell proliferation and differentiation. Our results indicate that NP exposure affects the regulation of the cell cycle in differentiated SPG, particularly in the regulation of the G1/S transition of the mitotic cell cycle. Therefore, we speculate that NP exposure interferes with the regulation of the cell cycle in differentiated SPG, leading to abnormal mitosis, ultimately resulting in male infertility. NP exposure may interfere with both the oxidative phosphorylation of undifferentiated SPG and the cell cycle of differentiated SPG. These effects collectively result in a decreased SPG population and impairment of its development and mitosis.

Our research indicated that, in addition to SPG, SPC is the germ cell type most affected by NP exposure. Previous literature has defined the four stages of SPC as leptonema, zygonema, pachynema, and diplonema (Lin, et al., 2019, Luo, et al. 2024). We observed that NP exposure impaired spermatogenesis and disrupted the development of male germ cell nucleus. Bioinformatic analyses indicate that the DEGs co-expressed throughout all stages of SPC development may be involved in biological processes such as sperm binding to the zona pellucida and protein translation. Adam32 is primarily expressed in the testes of mice (Khan, et al. 2021; Rajan, et al. 2020) and is closely associated with the activation of fertilization-related genes in pachynema SPC, playing a crucial role in spermatogenesis (Khan, et al. 2021; Rajan, et al. 2020). Similarly, the Cmtm2b gene plays a critical role in meiosis and in the expression of germ cells following meiosis (Kang, et al. 2019, Kumar, et al. 2018, Xiong, et al. 2019, Yoshitaka Fujihara, et al., 2018), which is consistent with our results. Meiosis is a specific cell division process regulated by the cell cycle. Abnormal cell division may lead to impaired spermatogenesis, thereby affecting fertility and potentially causing infertility (Dai, et al. 2021; Lee, et al. 2022). Additionally, Pgam2, as a key enzyme in glycolysis and gluconeogenesis, is closely related to the fibrous sheath structure and ATP supply in sperm flagella (Zheng, et al. 2019). Pgam2 maintains sperm vitality and morphology by providing ATP, thereby promoting sperm motility (Zheng, et al. 2019), consistent with our findings. Based on GO analysis, we propose that NP exposure may affect the cell cycle and division in leptonema, impair the male germ cell nucleus in zygonema, and adversely affect spermatogenesis and cell development in pachynema and the cell cycle of diplonema. These changes may be explained by the dysregulation of the expression of specific gene such as Adam32, Cmtm2b, and Pgam2 in SPC.

The development of RS depends on energy metabolism, with ATP being generated by the mitochondria in the center of the germ cell through oxidative phosphorylation Lehtiniemi, and Kotaja, 2017). The mitochondrial membrane plays a vital role in sperm motility, and changes in respiratory chain activity can also affect motility (Akbarinejad, et al. 2020; Gallo, et al. 2021). Cytochrome c is a key component of the electron transfer chain. ROS produced by mitochondria are crucial for maintaining male fertility. However, excessive ROS-induced oxidative stress can damage the structure and Function of germ cells, ultimately resulting in abnormal spermatogenesis and spermatid development Chianese, et al., 2021, Gallo, et al. 2021, Park, et al., 2021). Previous studies have shown that factors affecting mitochondrial ATP synthesis can damage spermatogenesis and may lead to idiopathic asthenozoospermia (Durairajanayagam, et al. 2021; Vertika, et al. 2020). These findings are consistent with our research, which revealed that NP exposure affects spermatogenesis and germ cell development in early RS, as well as oxidative phosphorylation and ROS in advanced RS. Our results also showed that NP exposure affects cytochrome c oxidase activity and respiratory chain complex IV. Therefore, we speculate that NP exposure negatively impacts oxidative phosphorylation and ROS, leading to abnormal RS development, ultimately affecting semen quantity.

Our results indicate that the DEGs common to both early and advanced ES include Prm1. The transcription of the Prm1 gene begins during the primary stage of spermatogenesis in haploid sperm cells after meiosis (Hamad 2019). Cell differentiation plays a pivotal role in spermatogenesis, determining the quality and function of the final sperm. Sperm flagella are essential for sperm motility, and proteins related to sperm flagella are synthesized during spermatogenesis, making them necessary for the assembly of mature sperm (Gu, et al. 2019; Shen, et al. 2019). Abnormalities in sperm flagella may adversely affect spermatogenesis and lead to male infertility (Yu, et al. 2021). These finding supports our research. We speculate that NP exposure may disrupt cell differentiation and flagellated sperm motility in ES by affecting genes like Prm1, ultimately inducing damage to sperm motility.

We found that NP exposure induced a notable increase in intercellular interactions in the testicular microenvironment, reflecting cellular heterogeneity in response to NP exposure. Cellular communication is crucial for spermatogenesis (Zhang, et al. 2022). The microenvironment of testicular somatic cells provides essential support for germ cells during spermatogenesis. Nectin is a Ca2+-independent intercellular adhesion molecule belonging to the immunoglobulin superfamily, playing a vital role in various cellular functions, including proliferation, development, adhesion, and polarization (Huang, et al., 2016a, Mandai, et al. 2015). Nectin not only contributes to cellular functions but also to the development of various organs, such as in spermatogenesis and synapse formation (Mandai, et al. 2015). Our results showed that NECTIN3-NECTIN2 receptor-ligand interactions activated SPG-Sertoli and SPG-Leydig cell crosstalk following NP exposure. Our data confirm the essential role of NECTIN3-NECTIN2 interactions in establishing and maintaining SPG-Sertoli cell connections (Wanta, et al. 2023, Wu, et al. 2023). Moreover, we found that CADM1-CADM1 receptor-ligand interactions enhanced the germ-Sertoli cell communication following NP exposure. CADM1 is involved in the germ-Sertoli cell crosstalk during spermatogenesis (Gewaily, et al. 2020). Our findings reaffirm the importance of CADM1-CADM1 receptor-ligand interactions in maintaining testicular integrity and regulating spermatogenesis. Additionally, we discovered that macrophages bind to RS and ES through GRN-SORT1 receptor-ligand. GRN participates in regulating the macrophage function (Zhang, et al. 2024a, 2024b). In this perspective, we propose that the GRN-mediated signaling pathway is a potential mechanism by which NP exposure impairs testicular immune privilege and spermatogenesis. This study emphasizes the critical roles of NECTIN, CADM, and GRN in spermatogenesis. Further research is needed to investigate how these three pathways interfere with spermatogenesis under NP exposure.

Conclusion

Findings from this study have shown that exposure to NP during adolescence to adulthood interferes with testicular development and spermatogenesis in mice, leading to a decrease in semen quality in adulthood. Using scRNA-seq, we found that NP exposure affected the oxidative phosphorylation and cell cycle of SPG, negatively impacting multiple stages of SPC development by interfering with the cell cycle and cell division and causing deterioration of intracellular oxidative phosphorylation and ROS homeostasis in the differentiation of RS into ES. The developmental issues in ES may result from disruptions in cell differentiation and flagellated sperm motility. Moreover, NP exposure alters cellular crosstalk between germ and somatic cells by affecting signaling pathways such as NECTIN, CADM, and GRN.

Overall, this study provides new insights into the potential impact of NP on male reproductive health, emphasizing the significance of environmental hormones in fertility issues. However, our research has some limitations. Although scRNA-seq revealed differences in gene expression among cell types, batch effects may have impacted the results. Furthermore, we were unable to fully investigate the effects of NP exposure on cell development at different stages, and we lacked a more in-depth analysis of its specific roles in reproductive health. Further research into these mechanisms is needed to identify new therapeutic targets to mitigate reproductive toxicity associated with environmental pollutants such as NP.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOC 38495 KB) (37.6MB, doc)

Abbreviations

ANOVA

Analysis of variance

ADAM

A member of the disintegrin and metalloproteinase

ATP

Adenosine triphosphate

BW

Body weight

CASA

Computer-aided sperm analysis

CDS

CellDataSet

DEGs

Differentially expressed genes

ECL

Enhanced chemiluminescence

EDCs

Endocrine-disrupting chemicals

ES

Elongated Spermatid

scRNA-seq

Single-cell sequencing

GO

Gene ontology

H&E

Hematoxylin and eosin

KEGG

Kyoto encyclopedia of genes and genomes

LSD

Least significant difference

NP

4-Nonylphenol

OCT

Optimal cutting temperature

PBS

Phosphate-buffered saline

PCs

Principal components

PPI

Protein-protein interaction

ROS

Reactive oxygen species

RS

Round Spermatid

SEM

Standard error of the mean

SPC

Spermatocyte

SPF

Specific pathogen-free

SPG

Spermatogonia

TBS

Tris-buffered saline

TDI

Tolerable daily intake

UMAP

Uniform manifold approximation and projection

Authors contribution

X.Z.: Investigation, Conceptualization, Data curation, Formal analysis, Validation, Methodology, Writing-original draft. Y.T.: Investigation, Validation, Data curation. D.Z.: Investigation. X.T.: Validation, Investigation. X.Z.: Investigation. X.W.: Validation; Y.H.: Validation; P.Y: Validation, Data curation, Funding acquisition. J.H: Validation, Data curation, Funding acquisition. Y.T.: Writing-review & editing, Conceptualization, Supervision, Formal analysis, Funding acquisition, Writing-original draft. P.D.: Writing-review & editing, Conceptualization, Supervision, Formal analysis, Funding acquisition, Project administration, Writing-original draft.

Funding

This work, in part, was supported by grants from the National Natural Science Foundation of China (82301938), the Natural Science Foundation of Hubei Province (2022CFD010, 2025AFD054), Innovative Research Program of Xiangyang No.1 People’s Hospital (XYY2025LJ02), Innovative Research Program for Graduates of Hubei University of Medicine (YC2023034), and Key Projects of Scientific Research Program of Hubei Provincial Department of Education (D20242102).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Competing interest

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Yan Tan, Email: tanyan-1@163.com.

Peng Duan, Email: meduanpeng@163.com.

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