Graphical Abstract
Spermatogonial stem cells (SSCs) serve an important role in male fertility because they divide to renew themselves and give rise to progenitor spermatogonia, which develop into haploid spermatozoa [1]. Because SSCs are seldom observed in the testis, it is important to culture these cells in a controlled environment in order to explore the molecular mechanisms that dictate their destiny [2]. The introduction of a method for preserving SSCs in their original culture provided the basis for discovering essential genes responsible for controlling the maintenance and self-renewal of SSCs. The genes mentioned include Bcl6b [3], Etv5 [4], Lhx1 [5], and Id4 [6].
This research report provides the first comparative investigation of gene expression in SSCs derived from the testis and those that have been cultured in vitro [7]. Using a scRNA-seq database of spermatogonia from the adult testis, which was previously published, and their database of adult undifferentiated spermatogonia after 10 weeks of in vitro culture, De Oliveira et al. have identified a group of genes that exhibit differential expression [7]. These genes may be associated with the observed decline in regenerative capacity during culture. The disturbed biological processes that were identified mostly consisted of expected pathways related to metabolism, including oxidative phosphorylation. In addition, our analysis uncovered the dysregulation of hitherto unexplored cellular processes, such as ubiquitinmediated proteolysis and DNA repair [8–10]. This paper will be a valuable resource for future investigations on the influence of in vitro culture on SSC function. Therefore, it is crucial to identify the gene expression patterns and regulatory networks that define this specific cell type in order to understand the molecular mechanisms that regulate the fate of SSCs [11, 12].
To unravel the regulatory networks that control SSCs, it is essential to define their gene expression profiles and identify specific markers. In murine models, genes such as Zbtb16, Foxo1, Sall4, and Cdh1 help distinguish undifferentiated spermatogonia [13]. However, heterogeneity among SSC subpopulations complicates their classification. For instance, Id4 is expressed in a subset of SSCs with high selfrenewal capacity, while Pax7 and Eomes mark distinct, rare subsets [14, 15].
Gene expression varies among undifferentiated spermatogonia in the mouse testis. Zbtb16, Lin28, Foxo1, Sall4, Cdh1, and other genes aid in distinguishing undifferentiated spermatogonia from other kinds of spermatogenic cells [16– 18]. Nevertheless, some subgroups of spermatogonia have a higher prevalence of particular genes. Id4 was the first gene associated with flagging that was identified in the testes of mice [19]. It particularly marks spermatogonia, which
possess robust self-renewal capacities. Pax7, Eomes, and Pdx1 are highly expressed in spermatogonia [5]. It is crucial to note that the population of spermatogonia is not homogeneous and may be classified into several subgroups based on gene expression and stem cell activities. Id4 is only present in a specific subgroup of spermatogonia, whereas Pax7 is used to identify a rare fraction of SSC that most likely do not express Id4 or Eomes. In order to get a more profound comprehension of the molecular attributes and actions of SSCs in the mammalian reproductive system, it is necessary to carry out a thorough investigation of undifferentiated spermatogonia [20].
G-proteins, also known as guanine nucleotide-binding proteins, play a crucial role in a wide range of biological functions by functioning as molecular switches inside cells. G-proteins, also known as guanine nucleotide-binding proteins, play a crucial role in controlling SSCs by participating in several signaling pathways that are necessary for their upkeep, growth, and specialization [20]. The key genes implicated in these processes include GDNF, FGF2, CXCL12, KIT, FSH, LIF, WNT5A, GPR125, and RHOA. GDNF, for example, attaches to its receptor and triggers downstream pathways like as PI3K/Akt, which are crucial for the survival of SSCs [21–28]. FGF2 and KIT also stimulate the growth and specialization of SSCs via influencing pathways regulated by G-proteins [29]. The chemokine CXCL12, by binding to its receptor CXCR4, facilitates the migration and localization of SSCs inside the testis. FSH indirectly stimulates the production of SSC-sustaining factors by binding to its receptor on Sertoli cells and activating G-protein signaling [30]. LIF mostly activates the JAK/ STAT pathway, but also interacts with G-protein pathways to affect the proliferation of SSCs [31].
Furthermore, WNT5A is essential for the maintenance and differentiation of SSCs via its involvement in non-canonical Wnt signaling pathways that interact with G-protein signaling [32]. GPR125, a marker indicating the presence of SSCs, initiates the activation of G-protein pathways that play a role in the process of SSC self-renewal [33]. Finally, RHOA, a small GTPase, controls the arrangement of the cytoskeleton, which is important for the adherence and movement of SSCs within their environment [34]. The genes and their interactions with G-protein signaling create an intricate network that precisely regulates the equilibrium between SSC self-renewal and differentiation [35]. Gaining a comprehensive understanding of these interactions is crucial for the development of treatment techniques aimed at addressing male infertility and for the advancement of the field of reproductive medicine [36].
Given these gaps, our study aims to investigate the expression dynamics of G-protein-associated genes in human SSCs across different developmental stages. We utilize
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microarray and single-cell transcriptomic approaches to profile gene expression and validate findings with immunofluorescence. By doing so, we address the central question: Which G-protein-related genes are involved in the formation and maintenance of human SSCs, and how do their expression patterns change during development? Our findings provide new insights into the molecular networks governing SSC fate and contribute to the broader understanding of human spermatogenesis, with implications for treating male infertility and advancing reproductive medicine.
Material and Methods
Study of Testicular Tissue and Experimental Design
The investigation was carried out from October 2016 to September 2017, utilizing testicular samples obtained from three adult males, as we did in prior research [37–39]. The local ethics committees of Heidelberg approved the study using human material at this facility. In addition, all human volunteers provided written permission after being presented with relevant information. The patients'ages varied between 23 and 67 years. The donated tissue included a broad range of healthful components.
Selection and Cultivation of Human Adult Germline Stem Cells (haGSCs)
After removing the tunica albuginea, the human testicular tissues were mechanically disaggregated to separate the tubules. Each sample's tubules were enzymatically destroyed using the following agents: 750 U/mL collagenase type IV (Sigma), 0.25 mg/mL dispase II (Roche), and 5 μg/mL DNase. The experiment was carried out in HBSS buffer containing Ca+ +and Mg+ +(PAA) at 37 °C for 30 min with moderate agitation. The goal was to provide a consistent solution using distinct cells. The digestive process was then interrupted using 10% ES cell-qualified fetal bovine serum (FBS). The cell suspension was filtered using a 100 μm cell strainer and centrifuged at 1000rpm for 15 min. The aqueous phase was separated, and the solid phase was washed using HBSS buffer containing calcium and magnesium ions. After washing, about 2×10^5 cells per cm2 were planted on 10 cm diameter culture plates. The plates were coated with a 0.2% gelatin solution obtained from Sigma [39].
Single-Cell Collection Using Micromanipulation
To isolate individual cells from human testicular tissue, micromanipulation techniques were employed under sterile
conditions. Testicular biopsy samples were first enzymatically dissociated using collagenase type IV (1 mg/mL) and DNase I (0.1 mg/mL) at 37°C for 20–30 min with gentle agitation to obtain a single-cell suspension. The resulting suspension was filtered through a 40 µm nylon mesh to remove debris and aggregated cells.
Individual cells were then identified under an inverted microscope (e.g., Leica DMi8) equipped with micromanipulation arms (e.g., Eppendorf TransferMan 4r) and collected using fine-tipped glass capillaries (~10–15 µm inner diameter), preloaded with sterile phosphate-buffered saline (PBS) containing 0.1% BSA. Cells were selected based on morphology—round, intact membranes, and absence of blebbing—and aspirated gently into the capillary to avoid mechanical damage.
Each single cell was then transferred into a PCR tube containing 2 µL of lysis buffer (typically including RNase inhibitors) and immediately snap-frozen on dry ice. This process was repeated until the desired number of single cells was isolated from each donor. All manipulations were conducted on a temperature-controlled stage (4°C) to preserve RNA integrity. Downstream processing included RNA amplification using a commercially available kit (e.g., SMART-Seq2 or equivalent), followed by microarray hybridization or library preparation for single-cell RNA sequencing.
Selection and Identification of SSCs
Following single-cell isolation, we applied a combination of morphological criteria, marker gene expression, and bioinformatics clustering to identify SSCs within the testicular cell populations.
Morphological Selection: Cells exhibiting a high nuclearto-cytoplasmic ratio, spherical morphology, and intact membranes were initially considered putative spermatogonia during micromanipulation.
Marker-Based Identification
To confirm SSC identity, we analyzed the expression levels of canonical SSC markers such as ID4, GFRα1, ZBTB16 (PLZF), and UTF1. Only cells with significant co-expression of at least two of these markers were classified as SSCs. Immunofluorescence staining on adjacent tissue sections validated these marker profiles, showing clear nuclear localization consistent with undifferentiated spermatogonia.
Collection of Single Cells
To analyze the individual cells inside the haGSC colony, we employed enzymes to disintegrate a conventional haGSC
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a distinct cluster separated from differentiating spermatogonia, Sertoli cells, and other somatic testicular cells. Differential expression analysis further supported the SSC classification, showing enrichment of genes associated with stemness and self-renewal. A total of [insert number] cells were identified as SSCs based on this integrated selection approach. This subpopulation was used for downstream analyses including G-protein gene expression profiling and developmental trajectory mapping.
Identification of DEGs
GEO2R is a web-based program for comparing and analyzing two separate sets of samples submitted to similar experimental conditions. The study began by analyzing the selected datasets of ovarian cancer (OC) and normal tissues using GEO2R. The study's findings were then collected in Microsoft Excel format. Genes having an adjusted P-value less than 0.05 and an absolute log fold change more than 1.0 were categorized as differentially expressed genes (DEGs). The FunRich application (version 3.1.3) was used to visualize the DEG intersections. Furthermore, the ClustVis web program was utilized to generate a heatmap of the DEGs.
Sorting Groups of Proteins
The analysis of genes that were expressed differently across the three research groups was performed using the webbased program ArrayMining. The gene list was subjected to analysis using PANTHER, a technique specifically designed for gene ontology analysis.
Construction of WGCNA
The WGCNA R software package was created. Genes having expression levels> 10 in 43 samples were used to create a hierarchical clustering tree (dendrogram) using the fashClust tool. The soft-thresholding power chosen by the pickSoft Threshold function was a typical value in the scalefree topology network, which gave the constructed network a power-law distribution. It minimized errors and improved the findings'similarity to biological data by boosting strong correlations and decreasing weak correlations in a scale-free network feature. The scale-free topology fit index showed an exponential change. A high correlation (R2=1) indicates that the data network follows a scale-free topological distribution.
Functional Enrichment Analysis
To get a better understanding of the function of genes in critical modules, modules were subjected to Gene Ontology
and human embryonic stem cells (hESCs) colony, as well as a rapidly expanding human dermal fibroblasts (HFibs) colony, into single cells. We then employed a micromanipulation technology to selectively separate individual cells in order to examine their gene expression patterns at the single cell level. This method was created to gather information on the particular cellular characteristics of key genes associated with germ and pluripotency. The goal was to investigate the variation in gene expression among certain cells within a typical haGSC colony. Furthermore, the goal was to create colonies with the most favorable gene expression patterns related with germ and pluripotency.
Differentiate the Three Colony Types (haGSC, hESC, hFibs)
The morphological distinction among haGSC, hESC, and hFibs colonies was made using phase-contrast microscopy. haGSC colonies appeared as compact, dome-shaped clusters with clear boundaries, resembling hESCs but with a slightly flatter morphology. hESC colonies exhibited dense, wellrounded edges and prominent nucleoli. In contrast, hFibs showed a spread-out, fibroblast-like morphology with elongated cytoplasm and lacked the compact colony architecture seen in stem cell populations. Immunofluorescence staining further distinguished these populations: haGSCs were positive for markers such as PLZF, GFRα1, and UTF1, while hESCs expressed OCT4, NANOG, and SOX2. hFibs lacked expression of pluripotency and germline stem cell markers.
Microarray Analysis
RNA from short-term spermatogonia, long-term haGSC cultures, hESC line H1 (positive control), and testicular fibroblasts (hFibs; negative control) was extracted using the RNeasy Mini Kit (Qiagen). After that, an amplification step was carried out using the MessageAmp aRNA Kit (Ambion). Each sample was treated to micromanipulation using equipment capable of capturing 200 cells per probe. The cells were immediately transferred into 10μL of RNA direct lysis solution and kept at -80°C. The samples were examined at the microarray facility located at the University of Tübingen Hospital in Germany. Gene expression was investigated using Affymetrix's Human U133 + 2.0 Genome oligonucleotide array. The raw data (CEL files) were sent to MicroDiscovery GmbH in Berlin, Germany, for normalization and biostatistical analysis.
Bioinformatics Clustering
Using principal component analysis (PCA) and unsupervised clustering of single-cell transcriptomes, SSCs formed
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Results
Human Spermatogonia Selection and Culture
Matrix selection (especially collagen nonbinding/laminin binding) and CD49f-MACS were used to isolate and concentrate the spermatogonia from orchiectomies performed to gather patient data pertaining to the SSC cultures. The presence of positive DDX4 (VASA) and SSEA4 immunocytochemistry in early cultures suggests that spermatogonia are likely the most frequent cells in the chosen cell types. Nonetheless, cells cultured for a long time showed decreased VASA staining and a low amount of OCT4 and NANOG positive cells. Pure spermatogonia produced from a large number of donors showed consistent features throughout culture, independent of patient age. The increased nucleus-to-cytoplasm ratio, together with the spherical shape and size of around 6–12 μm in diameter, was the primary element that determined this. The existence of a cytoplasmic ring between the outer cell membrane and the circular nucleus may be used to identify the indicated ratio. The spermatogonia in the cell cultures were shown in pairs, chains, and small clusters that were linked by intercellular bridges. The cultures also included other kinds of cells, including larger cells measuring 12–14 μm in diameter. The cells had a smaller nucleus-to-cytoplasm ratio and an oval shape. There was a significant decrease in htFibs in the population of cells that were not chosen. We were able to effectively isolate the htFibs from the nonselected cell fractions; these cells exhibited robust proliferation in our primary cell cultures. Image data obtained in SSC using a confocal scanning ultraviolet light microscope for ICC analysis. Figure 1 shows typical cultures of spermatogonial cells without hFibs.
Microarray Analysis of Gene Expression in SSC Versus Fibroblast
A microarray was used to analyze the G-protein transcriptome, which consisted of around 2100 transcripts (Fig. 2A and B). By using microarray analysis, we have identified a total of 12 genes that shown an increase in expression (up-regulated) and 18 genes that showed a reduction in expression (down-regulated) in SSCs and Fibroblast. This information is shown graphically in the Fig. 2C. Microarray analysis of three SSC human samples revealed that the genes LEPROT, LRRC15, LPAR1, SSR1, BMPR2, TNFRSF11B, TNFRSF10D, DDR2, SSR3, SIGMAR1, GRIA3, OGFRL1, GRIK2, TMEM87A, GPR108, TNFRSF1A, S1PR2, and VASN were down-regulated, while FLT1, ADGRG6, CSF1R, IL7R, ADGRL3, OR4N4, MMD, SIRPB1, OR5I1,
(GO) enrichment analysis using the KOBAS program. The gene lists of modules were uploaded, and we acquired the results of BP and KEGG pathway analysis. An adjusted p-value<0.05 was considered significant [8–10, 23–28, 40–47].
PPI Network Construction
To find the relationships among DEGs with protein interaction scores >0.4, we employed the search tool for retrieval of interacting genes (STRING) database. This is followed by Cytoscape building the PPI network. With a K-score greater than 3, the Molecular Complex Detection (MCODE) plug-in identified the possible hub DEGs [48].
Validation of the Hub Genes with scRNA-seq Datasets
The transcriptome study was conducted with pre-existing 10× Genomics Datasets (GSE149512 [49]). According to our recent study, the"culture"and"testis"transcriptomes were combined using Seurat (version 4.03). The dataset was cleansed of cells that were determined to be doublets or have low quality. Low-quality cells were defined as those with a unique feature count of less than 200 or more than 6,500. The presence of more than 25% mitochondria in a cell was used to identify it as a doublet. Before integration was performed using the'Harmony'approach, the data was normalized using Seurat's'NormalizeData'function. To find genes with different expression levels to utilize in PCA, the"FindVariableFeatures"tool was used. We used a total of ten basic components with a considerable influence to perform clustering and UMAP graphing, with a resolution value of 0.5.
miRNA-mRNA Network
The Encyclopaedia of RNA Interactomes (ENCORI) [50] is an openly available platform that focuses mostly on miRNA-target interactions. The website may be reached at http://starbase.sysu.edu.cn/ and is currently in version 3.0. The ENCORI database incorporates eight well-known miRNA-target prediction systems: pancancerNum, PicTar, RNA22, microT, miRanda, and miRmap. The main goal of this research was to identify the microRNAs that acted as hub genes. At least two databases, such as miRanda, PITA, PicTar, and TargetScan, were used to choose the miRNAs. Then, Cytoscape was used to see the core genes'coexpression network with the miRNAs that they control.
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using DAVID. These genes are involved in regulating the cell cycle, replicating DNA, and oocyte meiosis, according to the KEGG pathway analysis of module 1. There are four nodes and five edges in Module 2, which contains genes that are more abundant in ribosomes. The genes linked to carbon metabolism, the HIF-1 signaling system, and amino acid synthesis were part of Module 3, which included three nodes and three edges. The PPAR signaling pathway and genes involved in fat digestion and absorption were part of Module 4, which included six nodes and seven edges. Each module's PPI enrichment P-value was less than 0.05, as shown in Fig. 4A.
Based on their elevated degree ratings, the cytoHubba plugin identified twenty genes associated with ovarian cancer: ANGPT4, CACNG5, CACNG8, GNA12, GNB1, GPR137B, GPR137C, IFNW1, IL20, KITLG, OSTC, PDGFA, PDGFB, SHISA9, SSR2, TMEM258, TNFRSF10C, TNFSF10, TRAM1, and VEGFB. As DEGs, all hub genes showed increase. The PPI network of the crucial genes was also developed using the STRING web database. In addition, the key genes and the genes linked with them were developed into an interaction network using the FunRich tool (Fig. 3b). Twelve nodes and fifty-five edges made up the PPI network, which included the hub genes. A local clustering coefficient of 1 was observed in the network, suggesting a significant amount of clustering. Additionally, it was shown that the PPI enrichment P-value was less than 0.01 which indicates a significant enrichment of protein interactions. Gene coexpression analysis results for the 10 hub genes show that these genes are dynamically interacting (Fig. 4B).
WGCNA
We built gene co-expression networks using the WGCNA program to identify functional clusters in SSCs. Figure 5A shows that out of the five SSC modules found in this research, each one was given a different color. The fact that one module was shown in gray (Fig. 5B) despite not being attached to any cluster is notable. Then, in order to assess the relationship between modules and attributes, we made a heatmap. Figure 5C illustrates the connections between the modules and attributes. According to Fig. 5C, the brown SSC module is more associated with healthy tissues.
Single-Cell Transcriptomes Analysis
Using testicular tissue slices collected at three different points in cellular maturation, we were able to study the development of seminiferous tubules and other cell types in these organs. A lumen is seen within the seminiferous tubules of the D1 samples in the figure. These tubules were
PTGDR, MPL, and GPR107 were up-regulated (Fig. 2D and Supplementary 1).
GO Analysis
We utilized the DAVID program to look at 12 upregulated genes and 18 downregulated genes. The following biological processes were associated with differentially expressed genes (DEGs), regardless of their level of regulation: the glutamate receptor signaling pathway (GO:0007215), the regulation of tissue remodeling (GO:0034103), the migration of smooth muscle cells (GO:0014909), and the adenylate cyclase-activating G protein-coupled receptor signaling pathway (GO:0007189). The following functional activities were associated with MF-related DEGs: bioactive lipid receptor activity (GO:0045125), ionotropic glutamate receptor activity (GO:0004970), glutamate receptor activity (GO:0008066), laminin binding (GO:0043236), transmembrane receptor protein kinase activity (GO:0019199), and transmembrane receptor protein tyrosine kinase activity (GO:0004714) (Fig. 3A). DEGs exhibited enrichment in many gene ontology (GO) terms when classified as either upregulated or downregulated in the CC category. Figure 3B shows that the given keywords pertain to many components of the cell membrane, including ionotropic glutamate receptor complex, neurotransmitter receptor complex, receptor complex, cell surface, postsynapse, and integral component of the plasma membrane.
PPI Network Construction
We used information from the STRING database to build the PPI network of SSC DEGs. Within the PPI network, 168 genes were given particular positions. The PPI network showed an enrichment P-value below 0.01, with 142 nodes and 450 edges. In addition, a network consisting of 30 DEGs and the genes close to them was constructed using FunRich. After that, the essential modules were located using the MCODE plugin. The MCODE scores were used to identify the top four functional clusters of modules. Out of the four modules, the first one has an MCODE score of 18, the second one is 4, the third one is 3, and the fourth one is 3.1. For every module, KEGG pathway analysis was performed
Fig. 1 In vitro cultivation of human spermatogonia achieved using CD49f- and matrix-based selection. Standard morphology of spermatogonia from the same patient. Observable were single and interconnected round cells with a high nucleus/cytoplasm ratio, which is characteristic of spermatogonia. All cell cultures had linked spermatogonia in the form of pairs, chains, and small groups/colonies. This is an electron microscope picture that shows the characteristics of cells and the different sizes of the nucleus of human spermatogonial stem cells grown in a laboratory setting. The scale bar for the ICC image is 25 µm, whereas for the electron microscopy image it is 2 µm
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Fig. 2 Microarray analysis of gene expression. (A-B) Correlation plot SSCs and Fibroblast, (C) Volcano plot for DEGs based on microarray, and (D) G-protein heat map
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Fig. 3 Performing GO enrichment analysis. The color corresponds to the corrected p-values (BH), while the size of the dots corresponds to the number of genes. (A1-3) refers to the biological processes,
molecular functions, and cellular components. (B1-3) refers to the network of these biological processes, molecular functions, and cellular components
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Fig. 4 Analysis of the hub genes in SSC using a PPI network and coexpression analysis. (A) PPI network of the key genes. (B) The coexpression analysis of the core genes was conducted to determine their
participation in signaling pathways. The red nodes represent genes with a high MCC score, whereas the yellow node represents genes with a low MCC value
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VWF and PECAM1 allowed clusters 12 and 17 to be recognized as endothelial cells. Additionally, we have isolated two distinct types of immune cells: cluster 11 had genes that are characteristic of macrophages (CD163 and CD14), while cluster 20 contained genes that are characteristic of T cells (CD38 and IL7R). The UMAP plots showed how the selected cell types'flag genes were expressed. Within the human testes, we have successfully identified and classified ten distinct kinds of testicular cells.
We used a Gene Ontology (GO) biological process enrichment analysis to find out which functional categories were linked to each cell type that was found. Based on our functional enrichment study, spermatogonia are highly enriched in processes related to DNA metabolism, DNA replication and repair, chromatin remodeling, and cell development control. There was a concentration of spermatocytes throughout the whole process of gamete creation, spermatogenesis, and the meiotic cell cycle. Spermatids have a role in the production of male gametes, oxidative phosphorylation, and the metabolism of peptides and ATP. The assembly of cell connections, ATP production, and extracellular matrix construction all include Sertoli cells. The functional categories of Leydig cells mostly pertain to the metabolic pathways of steroids and cholesterol. The endothelium and cell adhesion are two processes in which endothelial cells participate. Epithelial and tissue development were boosted by the concentration of myoid cells. There was an excess of cellular adhesion and mesenchymal growth in smooth muscle cells. When the immune system responded, macrophages and T cells played a role. Our functional experiments confirmed that human cell type annotation is accurate by revealing the roles played by different cell types (Fig. 6D-F).
Discovery of Marker Genes for Each Cell Type
To get a better grasp of how testicular cells evolve, we used quantitative analysis to find out what percentage of each cell type is present in human SSCs at each of the three stages of development. The germ cell population of the newborn D1 samples was completely composed of spermatogonia (2.91%). Adult D150 samples had a spermatocyte percentage of 15.29%, up from 31.67% in pubertal D75 samples. D75 samples had a lower percentage of spermatids (10.68%) compared to D150 samples (17.96%). As the testes matured, the percentage of Sertoli cells dropped from 84.22% in D1 samples to 20% in D75 samples and eventually to 1.46% in D150 samples. Only 3.28 percent of the total testicular cells were Leydig cells in the D1 samples. In the D75 samples, the percentage increased to 19.66%, while in the D150 samples, it reached 38.11% of the total count of testicular cells. Endothelial cells, peritubular myoid cells, and smooth muscle cells proliferated in the interstitial space surrounding
found to contain just spermatogonia and Sertoli cells. The light signal was observed in both D75 and D150 conditions; however, it was significantly more intense (or covered a larger area) in the D150 group.. The spermatogonia multiplied and differentiated into spermatocytes and spermatids at days 75 and 150. D150 displayed the haploid flagellated spermatozoa.
We used 82,027 testicular cells to conduct snRNA-Seq on the 10 ×Chromium platforms. A total of 24,122 cells from the newborn stage (D1), 26,628 cells from the pubertal stage (D75), and 33,569 cells from the mature stage (D150) were collected. Three biological replicates were used for each stage. From the sequencing libraries, we extracted 3145.9 GB of reads in total. Strict quality control approaches were used to select 74,578 cells with exceptional quality data for subsequent analysis. While there were an average of 39,584 readings per cell, 1,918 genes were identified in the median. Unique molecular identifiers (UMIs) had a Q30 value of 94.84%, barcodes 95.72%, and RNA readings 94%. With an average sequencing saturation of 58.09%, over 92.32 percent of the reads matched with the human reference genome (Fig. 6A-C).
Identification of Testicular Cell Types by Cell Cluster Analysis
After identifying several groups of cells using the UMAP technique, we described each group by analyzing their flag genes, which are unique to different kinds of universal mammalian cells. As a whole, 23 noteworthy cell clusters were discovered in the samples taken at D1, D75, and D150. Out of the total number of clusters, 8 consist of germ cells, 13 of somatic cells, and 2 of immunocyte cells. Out of all the clusters, only 9 and 13 showed spermatogonial marker gene expression (UTF1, UCHL1, and GFRA1). Cells displaying many genes associated with spermatocyte markers, including SYCP1, SYCP3, RAD51AP2, PIWIL1, SPATA16, and NME8, were seen in clusters 5, 8, 10, and 15. Genes specific to spermatogenesis, such as PRM1, TNP1, TNP2, and TPPP2, were found to be expressed in clusters 4 and 14. Only spermatogonia were found within the germ cells of the newborn D1 samples, as shown in Fig. 7. Specific markers for this cell type, including the AMH, SOX9, and WT1 genes, were expressed by the Sertoli cells, which were composed of clusters 1, 6, 7, 16, 18, and 23. Our analysis revealed that clusters 2, 3, and 22 are Leydig cells due to the presence of certain genes such as IGF1, LHCGR, HSD17B3, CYP11A1, HSD3B, and SRD5A1. Myoid cell marker genes, such as MYH11 and ACTA2, were shown to be expressed in Cluster 19. Cluster 21 showed that the NOTCH3 gene marker, which is exclusive to smooth muscle cells, was expressed. The presence of the flag genes
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and h. The microRNAs hsa-miR-3606-3p, hsa-miR-5692a, hsa-miR-6730-3p, hsa-miR-570-3p, hsa-miR-1275, hsamiR-30a-5p, hsa-miR-30e-5p, hsa-miR-3148, hsa-miR- 6721-5p, hsa-miR-1279, and hsa-miR-6867-5p shown superior clarity relative to other microRNAs. These microRNAs may regulate the expression levels of the SCRN1, CTSC, GCOM1, REP1N1, DNMT3A, STK4, and SPTS2L genes (Fig. 9B).
Discussion
Our study observed that human SSCs maintain a distinct morphology characterized by a high nucleus-to-cytoplasm ratio and the ability to form chains and clusters, even after extended culture periods. These morphological characteristics are in line with findings from Chan et al., who reported that SSCs consistently form clusters through intercellular bridges, reflecting their inherent properties for cell–cell communication and niche establishment [51]. The consistent observation across different studies and experimental conditions underscores the intrinsic properties of SSCs that support their role in spermatogenesis [52].
The microarray research we conducted showed clear differences in gene expression patterns between SSCs and fibroblasts, emphasizing the distinctive molecular features of SSCs. Significantly, genes such as FLT1 (Fms-related receptor tyrosine kinase 1) and CSF1R (Colony Stimulating Factor 1 Receptor) were increased in expression in SSCs, indicating their role in the maintenance and renewal of stem cells. FLT1 and CSF1R have been associated with signaling pathways that provide support to stem cell niches, perhaps accounting for their increased expression in SSCs [53]. On the other hand, genes like TNFRSF11B (Tumor Necrosis Factor Receptor Superfamily Member 11b) and SIGMAR1 (Sigma Non-Opioid Intracellular Receptor 1) were found to be less active in SSCs compared to fibroblasts [54]. This suggests that there may be differences in the cellular signaling pathways that control cell growth and cell death.
The examination of gene expression differences allowed for a more in-depth understanding of the specific biological processes and pathways that are exclusive to SSCs. The upregulated pathways in SSCs were linked to cell communication, signal transduction, and receptor activation, which are crucial for maintaining stem cell characteristics and interacting with the milieu. This discovery aligns with other studies that highlight the significance of niche interactions in the functioning and survival of SSCs [55, 56]. Moreover, the enhancement of pathways associated with immune response in SSCs implies a possible function in immunological control within the testicular environment, which may be essential for safeguarding germ cells from immune assault.
the D150 sample. This shows that the mature testicular tissues'microenvironment was faithfully portrayed. During each of the three phases of testicular maturation, different proportions of immune cells, including macrophages and T cells, were detected. The percentage of immune cells was highest in the D1 sample at 8.11% and lowest in the D75 sample at 2.1%. Figures 7 and 8 show that 6.25 percent of the cells in the D150 sample were immune cells.
As a result, we produced the gene expression profiles for each unique cell type. The expression patterns of the three principal marker genes were shown using a heat map. We discovered 1402, 1167, and 424 genes that may serve as marker genes for spermatogonia, spermatocytes, and spermatids, respectively. A total of 1254 candidate marker genes for Sertoli cells, 783 for Leydig cells, 554 for myoid cells, 462 for smooth muscle cells, and 744 for endothelial cells in somatic cells were identified. Additionally, we have found 703 distinct genes that act as macrophage markers, as well as 337 genes that work as T cell markers. The cellular marker genes mentioned in the study demonstrated distinct or elevated expression levels in the corresponding cell types. The marker genes include existing ones such as PRM1, TNP1, PRM2, MYH11, and CD163, along with many potential novel marker genes. The novel marker genes, including NRXN3 (spermatogonia), SPAG16 (spermatocytes), ADGRB3 (Sertoli cells), and ACAA2 and GLDN (Leydig cells), have the ability to differentiate between distinct cell types in the testis.
miRNA-hub Gene Network
The ENCORI approach was used to discover the particular miRNAs that interact with the hub genes. The miRNAs designated as the target miRNAs of the hub genes were those predicted by a minimum of two databases, including miRanda, PITA, PicTar, and TargetScan. The miRNA-hub gene network was then displayed with Cytoscape software. During this phase, we identified 15 genes: SCRN1, CTSC, GCOM1, REP1N1, DNMT3A, STK4, SPTS2L, SCN2B, PLEKHG2, METTL6, NPY2R, NTN1, GRIK5, ANKRD11, ZNF519, and CD8A (Fig. 9A). Subsequently, we isolated and selected the most relevant microRNAs. The microRNAs listed are hsa-miR-513b-5p, hsa-miR- 548an, hsa-miR-205-3p, hsa-miR-513c-3p, hsa-miR-3714, hsa-miR-3613-3p, hsa-miR-3910, hsa-miR-8485, hsa-miR- 30c-5p, hsa-miR-8063, hsa-miR-548at-5p, hsa-miR-30d-5p,
Fig. 5 WGCNA analysis in SSC. (A) The co-expression network modules'cluster dendrogram was built by hierarchically grouping genes using the 1-TOM matrix. Each module was allocated distinct colors. (B) The links between modules and traits, and (C) each row represents a color module, while each column represents a clinical characteristic (cancer and normal). Each cell provides the corresponding correlation coefficient and P-value
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proliferation [62]. Additionally, cannabinoid receptors like CB1 and CB2, as well as the endocannabinoid receptor 2-AG, have been identified in murine ESCs, suggesting a broader role for GPCR-mediated signaling in stem cell survival and maintenance [63].
In addition to their role in stem cell survival, GPCRs are also implicated in the regulation of pluripotency and selfrenewal in both human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs). Gs- and Gi-coupled GPCRs, for instance, have been shown to influence pluripotency [64]. Inhibition of Gi with pertussis toxin (PTX) alters the typical flat colony morphology of hESCs/ hiPSCs to a multi-layered structure, which inhibits colony outgrowth without affecting cell proliferation, pluripotency, or survival. Conversely, activation of Gs has no significant impact on colony morphology [65]. While data on hPSCs is limited, evidence from murine ESCs suggests that the GαscAMP signaling pathway plays a critical role in maintaining the expression of key transcription factors necessary for pluripotency. Additionally, the cAMP/PKA signaling pathway has been implicated in self-renewal processes in murine ESCs [66]. Another important pathway in ESC self-renewal and pluripotency is the Wnt signaling pathway, which can proceed via the canonical Wnt/β-catenin route, the Wnt/planar cell polarity (PCP) route, or the Wnt/calcium route, each involving specific receptor interactions and co-receptors such as Frizzled (FZD) and LRP5/6 [67, 68].
The discovery of genes such as FLT1 and CSF1R as crucial factors in the maintenance of SSCs offers new avenues for studying the control of SSCs and possible treatments. The increased expression of these genes emphasizes their possible involvement in the exchange of information and interaction between stem cells and their surrounding environment, known as the niche. This connection is essential for the preservation and proper functioning of stem cells. These discoveries provide new opportunities for comprehending the molecular processes that govern SSC self-renewal and differentiation. This serves as a basis for devising precise methods to control SSC destiny for therapeutic applications.
The results of this research have important consequences for the advancement of treatments based on SSCs and techniques for preserving fertility. Gaining a comprehensive understanding of the molecular and cellular properties of SSCs is essential for the development of techniques to cultivate and sustain SSCs in a laboratory setting. These techniques might potentially be used in medical treatments, namely for addressing male infertility. Future research should prioritize the investigation of the precise functions of discovered genes and pathways in the maintenance and differentiation of SSCs. Furthermore, investigating the relationship between SSCs and their specific environment in a living organism will provide more profound understanding
The contrasting expression of FLT1 and CSF1R in SSCs as opposed to fibroblasts suggests that these genes likely have significant functions in SSC biology, presumably via regulating interactions within the stem cell niche and promoting self-renewal. The increased expression of these genes corresponds to the results reported by Venables et al., who showed that FLT1 has a role in angiogenesis and vascular development, which are essential for the stem cell niche [57, 58]. Conversely, the decrease in TNFRSF11B and SIGMAR1 expression may indicate that SSCs need less anti-apoptotic signaling compared to actively dividing fibroblasts, since SSCs remain largely dormant in vitro.
Our examination of individual cells'transcriptomes offered a detailed perspective on the diversity of SSCs, uncovering subsets with unique patterns of gene expression. Identifying individual subpopulations within SSC biology provides a higher degree of detail, allowing us to better comprehend the varied functions these subpopulations may have in spermatogenesis, such as self-renewal or differentiation [59]. The discovery of subpopulations exhibiting distinct gene expression patterns aligns with the results of Persio et al.[60], who used single-cell RNA sequencing to unveil infrequent subpopulations within the testicular environment that fulfill specific functions. These discoveries might provide valuable information for developing methods for treatments based on SSCs and preserving fertility.
Lysophospholipid signaling, involving sphingosine- 1-phosphate (S1P) and lysophosphatidic acid (LPA) interact with specific GPCRs, namely S1P1 -5 and LPA1 -5, to influence pathways such as phospholipase C (PLC), ERK1/2, adenylate cyclase, Ca2+ mobilization, and small GTPase activation. In human embryonic stem cells (hESCs), the expression of S1P1 - 3 and LPA1-5 receptors has been documented, highlighting their role in maintaining these cells in an undifferentiated state [61]. S1P, often in combination with platelet-derived growth factor (PDGF), sustains hESCs through Gi- and ERK-dependent mechanisms that promote cell survival, inhibit apoptosis, and enhance
Fig. 6 Identification of a NOA-specific hub gene in single cell. (A) A heatmap displaying the expression levels of cell cycle marker genes for each kind of cell. The various cell types are distinguished by the distinct hues shown in the uppermost section of the heat map. The gene IDs are shown on the left side of the heatmap. The right side of the heatmap displays a transition in gene expression levels from high to low, indicated by a color shift from yellow to purple. (B) The 20 samples were divided into fourteen clusters. Four different clusters were identified, demonstrating the variety of cell types involved in spermatogenesis. (C) The diagram displays 14 distinct kinds of cells found in the testicles, each represented by a different color. These include 3 types of germ cells (spermatogonia, spermatocytes, and spermatids), 5 types of somatic cells (Sertoli, Leydig, myoid, smooth muscle, and endothelial cells), and 2 types of immunocyte cells (macrophages and T cells) in the SSC. (D) Identification of marker genes unique to each cell type, (E–F) Identification of gene expression of G-protein
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Fig. 7 Single-cell transcriptomic analysis identified hub G-protein genes– ADGRG6, SIGMAR1, SSR1, SSR3 and TMEM87A as significantly associated with SSCs
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Fig. 8 Single-cell transcriptomic analysis identified hub G-protein genes– MPLKIP, GPR108, S1PR2, LEPROT and MMD as significantly associated with SSCs
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Fig. 9 Network of interactions between hub genes and targeted microRNAs. Hub genes are shown in green shapes, whereas targeted miRNAs are represented in red and blue shapes. The relationship between the hub genes and associated miRNAs is shown using arrows
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project: human spermatogonial stem cell ethics AUSMT1225, approved number: Ir.ausmt.rec.1402.05, 15 October 2022). All methods were carried out in accordance with relevant guidelines and regulations. Consent to Participate All patients have read and provided information and have had the opportunity to ask questions. They understand that their participation is voluntary and that they are free to withdraw at any time, without giving a reason and without cost. Consent to Publish All patients provided written informed consent for the use and publication of data derived from this study. Conflict of Interest It is declared by the remaining authors that there are no commercial or financial relationships that might conflict with the research.
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Conclusions
In this study, we combined microarray and single-cell transcriptomic analyses with immunofluorescence labeling to investigate the molecular characteristics of human spermatogonial stem cells (SSCs). Our data identified several differentially expressed G-protein-coupled receptor (GPCR)-related genes potentially involved in SSC regulation. Although preliminary, these findings provide a valuable resource for future investigations into SSC biology. However, further functional studies are required to validate the roles of these candidate genes and to fully understand their contribution to SSC self-renewal and interaction with the testicular niche.
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s12015-0 25-10942-4.
Acknowledgements We gratefully acknowledge the MazandBiomics Institute (www.mazandbiomics.ir), managed by Danial Hashemi Karoii and Zahra Hasani Mahforoozmahalleh, for their support and contributions to the bioinformatics analyses conducted in this study. We utilize Grammarly and Quilot for paraphrasing and revising the manuscript.
Authors'Contributions Danial Hashemi Karoii: writing—original draft preparation, statistical and bioinformatics analyses, formal analysis and investigation; Sobhan Bavandi: bioinformatics analyses (PPI analysis), Ali Shakeri Abroudi: bioinformatics analyses, Melika Djamali: bioinformatics analyses, Hossein Azizi: Conceptualization, project administration, validation and manuscript editing; and Thomas Skutella: funding acquisition, supervision, project administration, collection and processing of clinical samples and manuscript editing. All authors have read and agreed to the published version of the manuscript.
Funding This study was funded by the Institute for Anatomy and Cell Biology at the University of Heidelberg and the Amol University of Special Modern Technology, laying the groundwork for a synergistic partnership.
Data Availability The microarray data have been deposited in GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149512) under the accession number GSE149512.
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Ethics Approval All the gene expression data and clinical information used in this study have been approved by the local ethical committees Heidelberg (Ethics Committee of the Medical Faculty of Heidelberg University, title of the approved project: human spermatogonial stem cell ethics HSSC458, approved number: DFG18544, 29 October 2022) and Amol University of Special Modern Technologies (Iran National Committee for Ethics in Biomedical Research, title of the approved
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