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Transcriptomic dynamics and cell-to-cell communication during the transition of prospermatogonia to spermatogonia revealed at single-cell resolution

BMC Genomics volume 26, Article number: 58 (2025) Cite this article

Abstract

Background

Spermatogonia are essential for the continual production of sperm and regeneration of the entire spermatogenic lineage after injury. In mammals, spermatogonia are formed in the neonatal testis from prospermatogonia (also termed gonocytes), which are established from primordial germ cells during fetal development. Currently, the molecular regulation of the prospermatogonial to spermatogonia transition is not fully understood.

Results

In this study, we examined the gene expression patterns of prospermatogonia, spermatogonia and testicular somatic cells at 4 different stages, including embryonic day (E) 12.5, E17.5 and postnatal days (P) 1 and 6, using single-cell RNA sequencing (scRNA-seq). We identified 5 different molecular states in the prospermogonial population and revealed gene expression dynamics in corresponding testicular somatic cells. Specifically, we found that prospermatogonia mainly receive signals, while Leydig cells and peritubular myoid cells are the mediators for transmitting signals, indicating their potential roles in regulating the development and differentiation of prospermatogonia. Transcription regulon analyses revealed the involvement of basic helix-loop-helix (bHLH) transcription factors in directing prospermogonial fate decisions. We then disrupted this transcription network by ectopic expression of inhibitor of differentiation 2 (Id2), which is a negative regulator of bHLH transcription factors. The overexpression of Id2 in prospermatogonia caused severe defects in the progression of prospermatogonia to spermatogonia.

Conclusion

Together, these findings provide a crucial dataset for dissecting key genes that direct the establishment of the foundational spermatogonial pool and the fate transitions of different somatic cell lineages in the testis during fetal and neonatal periods of development.

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Background

In males, sperm are the only cells that can deliver genetic information to the next generation. The continual and robust production of sperm relies on spermatogonia that are established in the postnatal testis and contain spermatogonial stem cells (SSCs), progenitor spermatogonia and differentiating spermatogonia [1]. SSCs reside in a subpopulation of undifferentiated spermatogonia, and these cells self-renew to maintain a foundational stem cell pool while possessing the ability to differentiate into progenitor spermatogonia [2]. Differentiating spermatogonia undergo several rounds of expansion before entering the preleptotene stage of meiosis [3]. A heterogeneous spermatogonial population is formed in the neonatal testis under the guidance of intrinsic and somatic cell-derived factors [4]. Understanding the molecular mechanisms underlying the establishment and fate of spermatogonia is essential for treating fertility-related issues and developing fertility preservation technology for humans, endangered animals, and livestock species.

The origin of the spermatogonial lineage is traced back to primordial germ cells (PGCs) that arise in the proximal ectoderm at embryonic (E) 6.25 in mice. PGCs migrate to the gonad to form embryonic gonads together with somatic cells and then undergo sex differentiation at approximately E10.5 [5, 6]. After sex determination, germ cells begin to proliferate and then gradually enter mitotic arrest from E14.5 to quiescence at E16.5, when DNA methylation patterns are reestablished [7]. In XY gonads, germ cells transform into prospermatogonia (also termed gonocytes), which are the immediate precursors of the entire male spermatogenic lineage, including SSCs [8]. After birth, prospermatogonia gradually migrate from the center of the seminiferous tubule to the basement membrane and reenter the cell cycle during the first few days of life [9]. The transition from prespermatogonia to postnatal spermatogonia (including SSCs) is thought to occur within a broad period from P0 to P6 [10, 11]. The translocation of FOXO1 from the cytoplasm to the nucleus is a hallmark of the transition from prospermatogonia to undifferentiated spermatogonia, which is predominantly controlled by cell cycle regulators [12]. For example, Cyclin B2 (Ccnb2) deletion leads to an increase in CDK kinase activity, which prevents prospermatogonia from entering the cell cycle and differentiating into undifferentiated seprmatogonia [13, 14]. The overexpression of the RNA-binding protein Nanos2 inhibits the expression of Gfra1 and Zbtb16 in prospermatogonia, resulting in the failure of undifferentiated spermatogonial formation [15]. The RNA-binding protein Ddx20 plays a crucial role in promoting cell cycle reentry in prospermatogonia [16]. The absence of the Glis3 transcription factor also results in the retention of FOXO1 in the cytoplasm during prospermatogonia and impaired undifferentiated spermatogonial pool establishment [17]. Despite these findings, our understanding of the molecular control of the transition of prospermatogonia to spermatogonia remains limited.

Testicular somatic cells play essential roles in regulating the establishment of the foundational spermatogonial pool in the perinatal mouse testis. Sertoli cells are the only somatic cells in direct contact with germ cells and are essential for testis formation and spermatogenesis [18]. Sertoli cells dictate the fate of prospermatogonia in the fetal and neonatal testes [19, 20]. Fetal Leydig cells produce androgens (testosterone or androstenedione) to regulate fetal and neonatal testis development [21]. Peritubular myoid cells, the major cellular component of the basement membrane, also serve as crucial sources of the SSC niche factor glial cell line-derived neurotrophic factor (GDNF) [22]. Recent data revealed an essential role of prenatal testicular macrophages in testis development and spermatogenesis [23]. Notably, Sertoli cells play a significant role in directing testicular immune cell recruitment and macrophage differentiation, indicating the indispensable function of cell interactions in fetal and neonatal testis cord formation [24]. Testicular endothelial cells produce GDNF and other niche factors to influence SSC maintenance in the adult testis [25]. In the fetal testis, the inhibition of endothelial cell function impairs testis cord formation [26]. Despite these findings, the involvement of these testicular somatic cells in prospermatogonia fate decisions and their gene expression patterns during the prospermatogonia-to-spermatogonia transition have not been thoroughly determined.

Data generated from single-cell RNA sequencing (scRNA-seq) provide insights into prospermatogonial development and the corresponding somatic cells. Analysis of prospermatogonia from E16.5 to postnatal day 2 (P2) revealed heterogeneity in the prospermatogonia population and described the gene expression patterns of an SSC-enriched prospermatogonial subpopulation [27]. Systematic elucidation of germ cell fate transitions from E6.5 to adulthood at 28 time points revealed changes in gene expression and cell states during germline development [28]. Similarly, the assessment of whole testicular cells from E18.5, P2 and P7 using single-cell RNA sequencing (scRNA-seq) identified markers for distinct prospermatogonia fractions and defined the gene expression patterns of Sertoli cells, Leydig cells and peritubular myoid cells [29]. These studies generated key information on the kinetics of fate decisions and the dynamics of gene expression during the fetal and neonatal germ cell development.

Here, we conducted scRNA-seq analysis of testicular cells at 4 key time points to detect gene expression and cellular communication during the prospermatogonia-to-spermatogonia transition. We selected E12.5, E17.5, P1 and P6 to cover crucial periods of germ cell fate transitions. We also detected transcription factors that have potential roles in regulating germ cell fate and evaluated the functional involvement of basic helix-loop-helix (bHLH) transcription factors by overexpressing inhibitor of differentiation 2 (Id2), which is a negative regulator of bHLH transcription activity [30]. This study provides information on cell composition and interactions during fetal and neonatal testis development and highlights the potential role of bHLHs in regulating the molecular transition of prospermatogonia to spermatogonia.

Results

Identification of the testicular cell types present during germ cell development

We performed scRNA-seq analysis on dissociated cells from freshly isolated whole testes obtained from fetuses at embryonic day (E) 12.5 or E17.5 or from pups at postnatal days 1 and 6 (P1 and P6). A total of 13559, 8443, 11727 and 18689 cells were detected for the E12.5, E17.5, P1 and P6 testes, respectively. After poor-quality cells were filtered out, a total of 13225, 6949, 11175 and 18150 cells passed standard quality control and were retained for subsequent analysis (Fig. S1A & B). On average, 24745.32, 63152.46, 32661.2 and 11472.59 reads were detected per cell for the E12.5, E17.5, P1 and P6 testes, respectively (Supplementary Table 1). A total of 24554, 26164, 25200 and 25944 genes were detected for the E12.5, E17.5, P1 and P6 testes, respectively.

After quality control, data integration was performed at 4 time points, 49499 cells remained for subsequent analysis. We visualized transcriptome cluster maps after data integration using UMAP. Six main known testicular cell marker genes were identified (Dazl and Ddx4 for germ cells, Sox9 for Sertoli cells, Acta2 for peritubular myoid cells, Dlk1 and Cyp11a1 for Leydig cells, Tie1 for endothelial cells, and Adgre1 for macrophages) (Fig. 1A & B). The 6264 genes exhibiting enriched expression in each cell type were successfully recognized (Fig. 1C & Supplementary Table 2). Gene Ontology (GO) analysis identified functions enriched for each cell type (Fig. 1D). The genes in germ cells were enriched in the cell cycle and meiosis, whereas those in somatic cells were enriched mainly in the development of multiple organs and cell differentiation.

Fig. 1
figure 1

Identification of the major cell types in E12.5, E17.5, P1 and P6 mouse testes. A UMAP plot of cells from E12.5, E17.5, P1 and P6 mouse testes. Left: cell types; right: sample source. Number of cells in each cluster: germ cells (7017), Sertoli cells (22387), Leydig cells (14548), myoid cells (4089), endothelial cells (688), macrophages (552) and unknown cells (218). B Representative markers for each cell type. C Heatmap of differentially expressed genes (DEGs) from the six major cell types. Top: cell types and cell number; left: the number of DEGs; right: representative enriched genes for each cell type. D The most statistically significant Gene Ontology (GO) terms for each cell type

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Identification of germ cell clusters during testis development

We next performed a clustering analysis on germ cells from the E12.5, E17.5, P1 and P6 testicular datasets and generated eleven germ cell clusters (Fig. 2A & Supplementary Table 1). Following reclustering, we performed pseudotime analysis and found that germ cells formed a 5-state trajectory (Fig. 2B & Fig S1C). Previously studies have demonstrated that Prdm1, Dppa3 (also known as STELLA) and Rbm47 are markers of primordial germ cells (PGC) [31, 32]; Piwil4, Utf1 and Kdm1b are markers of prospermatogonia [29, 33]; Gfra1, Id4, Neurog3 and Zbtb16 are markers of undifferentiated spermatogonia containing enriched SSCs [34, 35]; and Kit, Rhox13, Dmrtb1 and Stra8 are markers of differentiated spermatogonia (Diff. SPG) [36, 37]. State 1 included PGC derived from E12.5. State 5 was a mixed population of cells, including prospermatogonia and undifferentiated spermatogonia, derived from E17.5, P1 and P6. States 2 and 4 were undifferentiated spermatogonia, while state 3 was mainly differentiating spermatogonia, all of which were from P6 (Fig. 2C and D). Amazingly, the cells in state 5 were Mki67-negative, indicating that these cells were in a nonproliferating state (Fig. 2C & Fig S1D & S1E). To define the molecular events involved in germ cell development, we identified genes enriched in cell subtypes along the trajectory pseudotime axis. This analysis revealed four distinct patterns of gene expression dynamics (Fig. 2E). Group-1 genes were enriched in primordial germ cells and are thus candidates involved in the regulation of PGC proliferation. The significantly enriched GO categories for the group-1 genes included ‘cell cycle’ and ‘cell division’ (Fig. 2E). The Group 2 genes were highly expressed in undifferentiated spermatogonia, including SSCs; the GO terms included ‘stem cell population maintenance’ and ‘cell proliferation’. The Group 3 genes were highly expressed in prospermatogonia and undifferentiated spermatogonia. The enriched GO terms for this group included ‘cell migration’ and ‘gamete generation’. The group 4 genes were specific for differentiating spermatogonia, and the enriched GO categories included ‘spermatogenesis’, ‘cell cycle process’ and ‘meiosis’ (Fig. 2E) (Supplementary Table 3).

Fig. 2
figure 2

Identification of germ cell clusters during testis development. A UMAP plot of germ cells from E12.5, E17.5, P1 and P6 mouse testes. Left: germ cell cluster; right: sample source. B Monocle pseudotime trajectory analysis of the germ cell states. Left: germ cell states; right: sample source. C Dot plot showing the expression patterns of marker gene sets in different states. The dot size indicates the fraction of cells with detectable expression of a given marker gene, and the color key indicates average gene expression levels in each cell type. D The expression patterns of marker genes in different states along the pseudotime axis. E Heatmap of DEGs from different germ cell subsets following the trajectory timeline shown in B. Top: pseudotime direction; left: the number of DEGs; right: representative genes, representative biological processes and P values

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Differential gene analysis of germ cells at different time points

We then performed a clustering analysis of E12.5, E17.5, P1 and P6 germ cells to detect changes in gene expression during cell fate transitions. For differentially expressed gene (DEG) screening, the absolute value of log2FC was greater than 1, and the false discovery rate (FDR) was less than 0.05. Compared with those of E12.5, 1377 DEGs were identified in germ cells of E17.5 testes, including 265 upregulated genes and 1112 downregulated genes. Compared with those at E17.5, 85 genes were upregulated, while 32 were downregulated. Compared with those in P1, 216 genes were upregulated and 114 were downregulated in P6 (Fig. 3A and B & Supplementary Table 4). GO analysis revealed that upregulated genes at E17.5 vs. E12.5 were associated with ‘spermatogenesis’, ‘cell differentiation’, ‘multicellular organism development’ and the ‘meiotic cell cycle’. The downregulated genes were associated with ‘translation’, ‘cell cycle’ and ‘mRNA processing’ (Fig S2A & 2B). The upregulated genes in P1 vs. E17.5 were associated with ‘G1/S transition of mitotic cell cycle’, ‘transcription’ and ‘cell differentiation’, while the downregulated genes were associated with ‘DNA methylation’, ‘development’ and ‘translation’ (Fig S2C & S2D). The upregulated genes in P6 vs. P1 were associated with ‘cell cycle’, ‘mitosis’ and ‘cell division’, while the downregulated genes were associated with ‘genetic imprinting’, ‘DNA methylation’ and ‘translation’ (Fig S2E&2F& Supplementary Table 5). Ninety-seven genes were highly expressed in the quiescent phase (E17.5 vs. E12.5 upregulation, P6 vs. E17.5 downregulation), and 378 genes were suppressed at this developmental point (E17.5 vs. E12.5 downregulation, P6 vs. E17.5 upregulation), indicating significant repression of gene expression during the process of prospermatogonia cell cycle arrest (Fig. 3C & Supplementary Table 6). By comparing the deferentially expressed genes at different developmental time point, we identified a group of genes that were highly or weakly expressed during the quiescent phase. To verify this result, we selected the protein BRDT, which was highly expressed in the quiescent phase, and the protein REST, which was lowly expressed in the quiescent phase. So next, we conducted immunostaining on BRDT and REST. Brdt was upregulated at E17.5 and downregulated at P6, while Rest was downregulated at E17.5 and upregulated at P6. The results revealed that BRDT was highly expressed in the E17.5 quiescent phase but not in P6 spermatogonia. REST was expressed in all the germ cells (Fig. 3D and E). Overall, this analysis described the dynamics of gene expression associated with cell fate transitions in the germline during the progression from prospermatogonia to spermatogonial development.

Fig. 3
figure 3

Analysis of DEGs of germ cells from E12.5, E17.5, P1 and P6 mouse testes. A Volcano plot of pairwise comparisons of DEGs from E12.5, E17.5, P1 and P6 mouse germ cells. B Comparison of the number of DEGs in pairs from E12.5, E17.5, P1 and P6 mouse germ cells. The black circles represent DEGs at the corresponding time points. C Venn diagram showing the number of genes active and inactive in the quiescent phase of the germ cell. D Coimmunofluorescence staining for BRDT and LIN28A in cross-sections of testes from E12.5‒P6 male mice. Scale bar = 25 μm. n = 3. E Coimmunofluorescence staining for REST and LIN28A in cross-sections of testes from E12.5‒P6 male mice. Scale bar = 25 μm. n = 3

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Changes in gene expression and the molecular states of sertoli cells at different time points

Because Sertoli cells are master regulators of germ cell fates [20], we then performed a re-clustering analysis on Sertoli cells from the E12.5, E17.5, P1 and P6 datasets (Supplementary Table 1). As a result, 12 different Sertoli cell clusters were identified (Fig. 4A). The cells from different developmental points were assigned to all clusters, and pseudotime analysis led to the identification of 5 different patterns of gene expression (Fig. 4B and C). The types of genes with significantly enriched GO terms in groups 1 and 2 included ‘cell cycle’, ‘cell division’ and ‘DNA replication’. The group 3 genes were enriched in ‘translation’ and ‘response to estradiol’, indicating a role for estrogen in Sertoli cell development. The Group 4 genes were enriched in ‘gene expression’ and ‘gonadal development’, while Group 5 genes were enriched in ‘apoptosis’ and ‘cell adhesion’ (Fig. 4C & Supplementary Table 3). Next, we performed gene expression analysis and revealed that, compared with those in E12.5 Sertoli cells, 104 genes were differentially expressed at E17.5, including 78 upregulated genes and 26 downregulated genes. Compared with those at E17.5, four genes were upregulated, and 48 genes were downregulated in P1 Sertoli cells. Compared with those in P1, 10 genes were upregulated and 60 were downregulated in P6 Sertoli cells (Fig S3A & Supplementary Table 4). GO analysis revealed that upregulated genes at E17.5 vs. E12.5 were associated with ‘transcription’, ‘retinoic acid’ and ‘cell adhesion’. The downregulation of this gene was associated with ‘transcription’, ‘cell proliferation’ and ‘cell adhesion’ (Fig S3B & 3C). The downregulated genes in P1 vs. E17.5 were associated with ‘transcription’, ‘apoptosis’, and ‘cell differentiation’ (Fig S3D). The downregulated genes in P6 vs. P1 were associated with ‘translation’, ‘cell proliferation’ and ‘gene expression’ (Fig S3E & Supplementary Table 5).

Fig. 4
figure 4

Identification of Sertoli cell clusters during testis development. A UMAP plot of Sertoli cells from E12.5, E17.5, P1 and P6 mouse testes. Left: Sertoli cell clusters; right: sample source. B Monocle pseudotime trajectory analysis of Sertoli cell states. Upper left: pseudotime anlaysis; upper right: Sertoli cell clusters; lower left: sample source; lower right: Sertoli cell states. C Heatmap of DEGs from different SC subsets following the trajectory timeline shown in B. Top: Pseudotime direction; right: the number of DEGs and the representative biological processes and P values

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Changes in gene expression and the molecular states of Leydig cells and peritubular myoid cells at different time points

Similarly, we performed re-clustering analysis on the Leydig cells at the 4 time points and generated 10 clusters (Fig. 5A & Supplementary Table 1). In sharp contrast to Sertoli cells, these cells at different developmental points presented biased distribution patterns in each cluster. For example, fetal Leydig cells from E12.5 gonads were assigned to clusters 4, 8, and 3, while the cells from P1 were predominantly in clusters 0 and 1. The results of pseudotime analysis revealed the different molecular states involved in Leydig cell development, and specifically, genes enriched along the pseudotime axis trajectory in cells exhibited 5 different patterns (Fig. 5B). Interestingly, genes in groups 1 and 2 were involved in mitotic cell cycle regulation, while those in group 5 were associated with steroid biosynthesis. Interestingly, the cells in this state were mainly from E12.5 and E17.5 (Fig. 5C & Supplementary Table 3). Differential gene expression analysis revealed that, compared with those at E12.5, 115 genes were upregulated, and 18 genes were downregulated at E17.5. Fewer than 32 genes were differentially expressed among E17.5, P1 and P6 Leydig cells (Fig S4A & Supplementary Table 4). GO analysis revealed that the genes whose expression was upregulated at E17.5 compared with that at E12.5 were associated with ‘cell adhesion’, ‘cell migration’ and ‘gene expression’ (Fig. S4B & Supplementary Table 5).

Fig. 5
figure 5

Identification of Leydig cell clusters during testis development. A UMAP plot of Leydig cells from E12.5, E17.5, P1 and P6 mouse testes. Left: Sertoli cell clusters; right: sample source. B Monocle pseudotime trajectory analysis of the Leydig cell states. Upper left: pseudotimes; upper right: Leydig cell clusters; lower left: sample source; lower right: Leydig cell states. C Heatmap of DEGs from different Leydig cell subsets following the trajectory timeline shown in B. Top: Pseudotime direction; right: the number of DEGs and the representative biological processes and P values

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It has been reported that Leydig cells and peritubular myoid cells originate from common progenitors in the fetal gonad [38]. We then evaluated the transcriptomic changes in the peritubular myoid cells at different stages. These cells were assigned to 10 clusters, and similar to Leydig cells, 5 patterns of gene expression were detected in peritubular myoid cells integrated from the E12.5, E17.5, P1 and P6 testes (Fig. 6A-C). Germ expression was unique to the cells from E12.5 according to the pseudotime trajectory analysis. The genes associated with E12.5 were in states 3 and 4, and these genes were associated with the cell cycle, cell division and DNA replication (Fig. 6D). As expected, dramatic changes in gene expression were observed between the cells from E12.5 and those from E17.5 because 135 genes were upregulated, whereas only 5 genes were downregulated in E17.5 compared with those in E12.5 (Fig S5A & Supplementary Table 4). GO analysis revealed that upregulated genes in E17.5 vs. E12.5 were associated with ‘transcription’, ‘cell proliferation’ and ‘cell adhesion’ (Fig S5B). Compared with those at E17.5, 15 genes were upregulated, and 53 were downregulated in P1. Compared with those in P1, 5 genes were upregulated, and 83 were downregulated in P6 (Fig S5A & Supplementary Table 4). These results indicated the repression of gene expression during this stage of development. The downregulated genes in P1 were associated with ‘cell cycle’ and ‘cell differentiation’, and the downregulated genes in P6 were associated with ‘transcription’, ‘cell differentiation’, ‘cell migration’, and ‘cytoplasmic translation’ (Fig. S5C & Supplementary Table 5).

Fig. 6
figure 6

Identification of myoid cell clusters during testis development. A UMAP plot of myoid cells from E12.5, E17.5, P1 and P6 mouse testes. Left: Myoid cell clusters; right: sample source. B Monocle pseudotime trajectory analysis of myeloid cell states. Upper left: pseudotimes; upper right: Myoid cell clusters; lower left: sample source; lower right: Myoid cell states. C Heatmap of DEGs from different myeloid cell subsets following the trajectory timeline shown in B. Top: Pseudotime direction; right: the number of DEGs and the representative biological processes and P values

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Differential gene analysis of macrophages and endothelial cells at different time points

We performed a clustering analysis of macrophages from E12.5, E17.5, P1 and P6 testes (Supplementary Table 1). The results revealed 6 clusters of macrophages, and compared with those at E12.5, there were 61 DEGs at E17.5, including 51 upregulated genes and 10 downregulated genes. Compared with those in E17.5, there were 293 downregulated genes in P1. There were 56 DEGs in D6 compared with P1, including 38 upregulated genes and 18 downregulated genes (Fig S6A & Supplementary Table 4). GO analysis revealed that upregulated genes in E17.5 vs. E12.5 were associated with ‘transcription’, ‘immune system process’ and ‘apoptotic cell clearance’ (Fig S6B). The downregulated genes in P1 vs. E17.5 were associated with ‘ERK1 and ERK2 cascade’, ‘immune system process’ and ‘endocytosis’ (Fig S6C). The upregulated genes in P6 vs. P1 were associated with ‘immune response’ and ‘immune system process’, while the downregulated genes were associated with ‘angiogenesis’, ‘peptide secretion’ and ‘apoptotic process’ (Fig S6D & Supplementary Table 5).

We performed a clustering analysis of E12.5, E17.5, P1 and P6 endothelial cells. Compared with those at E12.5, 95 DEGs were identified at E17.5, including 91 upregulated genes and 4 downregulated genes. Compared with those in E17.5, there were 119 DEGs in P1, including 5 upregulated genes and 114 downregulated genes. There were 11 DEGs in P6 compared with P1, including 5 upregulated genes and 6 downregulated genes (Fig S7A & Supplementary Table 4). GO analysis revealed that the genes whose expression was upregulated at E17.5 vs. E12.5 were associated with ‘endothelial cell proliferation’, ‘transcription’ and ‘glycolytic process’ (Fig S7B). The downregulated genes in P1 vs. E17.5 were associated with ‘cell cycle’, ‘cell proliferation’, and ‘cell migration’ (Fig S7C & Supplementary Table 5).

CellChat identifies signaling pathways in different groups of cells

CellChat compares the total number of interactions and interaction strength of the inferred cell‒cell communication networks from different biological conditions. With the development process, the total number of interactions first increased and then decreased (E12.5:681, E17.5:737, P1:561, P6:519), and the intensity of interactions decreased (E12.5:27.514; E17.5:19.819; P1:18.147; P6:11.91). Further research revealed that Leydig cells and myoid cells interact strongly with other types of cells and that both send signals and receive signals (Fig. 7A and B). Germ cells receive signals, and Sertoli cells receive and send signals with an intensity between them (Fig. 7B). Overall, the intensity of information exchange between cells tended to decrease as development progressed. Next, outgoing or incoming signals between different datasets are compared to identify signaling pathways that show different signal patterns. Among the incoming signals, the WNT signaling pathway was strongest in the germ cells at E12.5, and conversely, the GRN signaling pathway was weakest in the germ cells at E12.5. IGF and MIF have stronger signals in germ cells after birth. In the efferent signaling pathway, the VEGF signaling pathway was more strongly activated in Sertoli cells before birth, and the GDNF and MIF signaling pathways were more strongly activated in Sertoli cells after birth (Fig. 7C).

Fig. 7
figure 7

Cell‒cell communication between different cell populations at different time points. A Circular plot showing the difference in the number of interactions in the cell‒cell communication network between different cell populations. The nodes in the figure represent different cell types; a larger node circle indicates a greater number of cells of that cell type, a thicker line indicates a greater number of interactions detected between different cell types, and the color of the line is consistent with the color of the ligand cell. B Changes in signals sent and received between different cell types at different time points. C Comparison of incoming (above) or outgoing (below) signals associated with each cell population at different time points

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Identification of transcription factors (TFs) involved in germ cell development

Based on the matrix of germ cells obtained by Seurat analysis, 1186 potential transcription factors were identified by running GENIE3 according to SCIENC, and 11,725 binding sites of transcription factors were inferred in germ cells. A regulon open heatmap was drawn on the basis of the AUC binary matrix to identify regulons open in cells, which was beneficial for determining the function of cell subsets. The results revealed that state 2 and state 4 shared similar open regulons (Fig. 8A), while the rest had their own transcriptional networks. By clustering cells with the AUC matrix, we revealed the network state of transcription factors and motif binding that occurs repeatedly in each state. Specifically, the transcription regulators Nanog, Sox2, Hesx1, Tbx2, and Ezh2 were specific for PGCs, while previously identified transcription factors associated with SSC maintenance, including Foxc2, Sox3, and Foxo1, were active in undifferentiated spermatogonia, as were Barhl2, Foxa1, Mef2c, Elk3, and Tcf7l2, which have not been studied in the germline. Brca1, E2f8, E2f2, Dhhac6, Kdm5a, Bclaf1 and other factors were enriched in differentiating spermatogonia (Fig. 8A).

Fig. 8
figure 8

Analysis of transcription factors of germ cells in different states. A Heatmaps of the regulatory activity of germ cells in different states were drawn on the basis of the area under the curve (AUC) values generated by SCENIC. Each row in the map represents a regulon, each column represents a cell, and the color represents the size of the AUC value. B Expression patterns of transcription factors regulating bHLH proteins in different states along the pseudotime axis. C SCENIC software was used to construct a heatmap of the regulon activity of bHLH proteins in germ cells according to the binary AUC of the regulon. Each row in the map represents a regulon, each column represents a cell, and the color represents the size of the AUC value. D Expression patterns of BHLH-related genes in different states on the pseudotime axis

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Among these transcription factors, we observed dynamic changes in the expression of several bHLH transcription factors, which recognize E-box motifs and function as potent regulators of stem and progenitor cell fate decisions in various somatic tissues [30, 39]. For example, Usf1 and Tcf4 were enriched in PGCs and quiescent prospermatogonia but absent in differentiating spermatogonia (Fig. 8B & C). Interestingly, inhibitors of DNA binding/differentiation (Id1–4) were present in germ cells throughout development (Fig. 8D). These data indicate that bHLH transcription factors may play functional roles in regulating the fate transitions of prospermatogonia.

Id2 overexpression affects spermatogenesis in the testis

A few basic helix-loop-helix (bHLH) transcription factors are known to play critical roles in germ cell development [40]. Id proteins repress differentiation and promote cell division by dimerizing with and inhibiting the action of bHLH transcription factors, including those that bind to E-box motifs [41]. TF enrichment analysis revealed that bHLH-related genes have multiple motif binding sites in germ cells (Fig. 8B and D). As a member of the Id protein family, Id2 is a primary partner of bHLH [42]; therefore, we chose to overexpress Id2 to disrupt the balance of transcription programs regulated by bHLH transcription factors. Immunofluorescence staining revealed that the Id2 protein was enriched in the nucleus of prospermatogonia in E17.5 testes (Fig. 9A). To explore whether Id2 plays a functional role in germ cell development and differentiation, we conditionally overexpressed Id2 in fetal gonocytes at embryonic day (E) 14.5 by crossing Vasa-Cre transgenic mice and Id2 OE mice. Compared with those of the control mice, the testes of the 35-day Id2 OE(Vasa−Cre) mice were smaller, and the testis/body weight ratio was significantly lower (1.35 ± 0.37 vs. 3.91 ± 0.27 Control, P < 0.01) (Fig. 9B & C); moreover, no significant difference in body mass was detected. The sperm density of the Id2 OE (Vasa−Cre) group was also significantly lower than that of the control group (0.27 ± 0.09 vs. 14.51 ± 5.26 Control, P < 0.05) (Fig. 9D). Further histological analysis of the seminiferous tubules revealed abnormal spermatogenesis (Fig. 9E). Moreover, an abundance of exfoliated germ cells was observed in the lumen of the epididymides of Id2 OE (Vasa−Cre) mice (Fig. 9F). Notably, the cauda epididymis of the Id2 OE (Vasa−Cre) group contained extensively round and multinucleated giant cells with a low sperm mass (Fig. 9F). The number of germ cells per 500 Sertoli cells in the Id2 OE (Vasa−Cre) group was significantly lower than that in the control group (599.70 ± 326.90 vs. 4141 ± 543.70 Control, P < 0.01) (Fig. 9G & H). These results indicate that the overexpression of Id2 leads to germ cell loss in some spermatogenic tubules in the testis and that Id2 plays a negative regulatory role in spermatogenesis.

Fig. 9
figure 9

Id2 overexpression results in the abnormal development of some germ cells. A Coimmunofluorescence staining of ID2 in testes at different developmental stages in male mice. n = 3. B Representative images of testes from 35-day-old control and Id2-OE (Vasa−Cre) male mice. n = 3. C Ratios of testis weight to body weight in adult control and Id2-OE (Vasa−Cre) male mice. n = 4. D Comparisons of sperm density between control and Id2-OE (Vasa−Cre) male mice. n = 5. E Representative images of hematoxylin and eosin (H&E)-stained testes from 3-month-old control and Id2-OE (Vasa−Cre) male mice. Scale bar = 200 μm. n = 3. F Representative images of hematoxylin and eosin (H&E)-stained cauda epididymides from 3-month-old control and Id2-OE (Vasa-Cre) male mice. The black arrows indicate exfoliated germ cells. Scale bar = 50 μm. n = 3. G Coimmunofluorescence staining for TRA98 (a germ cell marker) and SOX9 (a Sertoli cell marker) in cross-sections of testes from three-month-old male control and Id2-OE (Vasa−Cre) mice. Scale bar = 50 μm. n = 3. H The number of germ cells per 500 Sertoli cells from adult control and Id2-OE (Vasa−Cre) male mice. n = 3. The data are presented as the means ± SDs of 3–5 mice. ** indicates a significant difference at P < 0.01

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Id2 overexpression inhibited the recovery of the prospermatogonia cell cycle after birth

To verify the function of Id2 in spermatogenesis, we performed immunofluorescence staining for TRA98 (a germ cell marker), SOX9 (a Sertoli cell marker), TRA98 and EDU (proliferating cells) in P0 and P3 control and Id2 OE (Vasa−Cre) mouse testicular sections. The number of prospermatogonia cells corresponding to 500 Sertoli cells and the proportion of proliferating prospermatogonia cells were counted (Fig S8). In P0 mice, we found no difference in the number of prospermatogonia cells between Id2 OE (Vasa−Cre) and control mice (106.70 ± 4.41 vs. 103.3 ± 1.67 Control, P > 0.05) (Fig S8A & C). Similar to the controls, the germ cells also did not proliferate (data not shown). These results indicated that the overexpression of Id2 at E14.5 did not affect the development of later prospermatogonia cells. At P3, compared with that in the control group, the number of germ cells per 500 Sertoli cells in the Id2 OE (Vasa−Cre) group decreased by 40.84% (77.50 ± 5.95 vs. 131 ± 9.93 in the control group, P < 0.01) (Fig S8B & C), and the proportion of proliferating germ cells decreased by 24.91% (30.15 ± 2.28 vs. 40.15 ± 2.91 in the control group, P < 0.05) (Fig S8D & E). These results showed that Id2 overexpression inhibited the recovery of the prospermatogonia cell cycle after birth, resulting in a decrease in the number of germ cells.

Overexpression of Id2 leads to failure of the prospermatogonia-to-SSC transition

After birth, mouse prospermatogonia cells gradually differentiate into spermatogonia, and a proportion of these cells form SSCs [43]. To determine whether the Id2 protein is also involved in the regulation of the spermatogonial cell cycle and SSC pool, we detected the number of germ cells and proliferating cells in the P6 control and Id2 OE (Vasa−Cre) groups. The results revealed that the number of undifferentiated spermatogonial cells per 500 Sertoli cells in the Id2 OE (Vasa−Cre) group was significantly lower than that in the control group (57.33 ± 2.19 vs. 200 ± 6.81 Control, P < 0.0001) (Fig. 10A & B). Next, EdU was used to determine the percentage of proliferating germ cells, and the percentage of proliferating cells in the Id2 OE (Vasa−Cre) decreased but was not significantly different from that in the control (16.46 ± 4.03 vs. 25.33 ± 2.96 Control, P > 0.05) (Fig. 10C & D). Caspase 3 was used to determine the percentage of apoptotic germ cells, and the percentage of apoptotic Id2 OE (Vasa−Cre) cells decreased significantly compared with that of control cells (3.02 ± 0.91 vs. 0.26 ± 0.12 Control, P < 0.05) (Fig. 10E & G). Foxo1 is a novel marker of prospermatogonia, and its cytoplasmic-to-nuclear translocation marks the prospermatogonia-to-SSC transition [44]. We detected Foxo1 expression and found that the proportion of Foxo1-expressing cells in the nucleus of the Id2 OE (Vasa−Cre) group was significantly lower than that in the control group (32.03 ± 2.18 vs. 55.87 ± 5.92 Control, P < 0.01) (Fig. 10F & H). These results suggest that the overexpression of Id2 leads to a decrease in the number of spermatogonia but does not affect the regulation of the spermatogonial cell cycle. In addition, overexpression of Id2 leads to failure of the prospermatogonia-to-SSC transition, which may be the cause of abnormal spermatogenesis.

Fig. 10
figure 10

Id2 overexpression affects the differentiation of germ cells. A Coimmunofluorescence staining for PLZF (an undifferentiated spermatogonial cell marker) and SOX9 (a Sertoli cell marker) in cross-sections of testes from D6 male control and Id2-OE (Vasa−Cre) mice. Scale bar = 50 μm. n = 3. B The number of undifferentiated spermatogonial cells per 500 Sertoli cells from P6 control and Id2-OE (Vasa−Cre) male mice. n = 3. C Coimmunofluorescence staining for TRA98 and EDU (a cellular proliferative marker) in cross-sections of testes from P6 male control and Id2-OE (Vasa−Cre) mice. Scale bar = 50 μm. n = 3. (D) The percentage of germ cells remaining proliferating in all cross-sections of testes from P6 controls and Id2-OE(Vasa−Cre) male mice. n = 3. E Coimmunofluorescence staining for TRA98 and CASPASE3 (cellular apoptotic markers) in cross-sections of testes from P6 male control and Id2-OE (Vasa−Cre) mice. Scale bar = 50 μm. n = 4. F Immunofluorescence staining for Foxo1 in cross-sections of testes from P6 male control and Id2-OE(Vasa−Cre) mice. Scale bar = 50 μm. n = 4. G The percentage of germ cells in the apoptotic state in all cross-sections of testes from P6 control and Id2-OE (Vasa−Cre) male mice. n = 4. H The percentage of gonocytes to SSCs in all cross-sections of testes from P6 control and Id2-OE(Vasa−Cre) male mice. n = 4. The data are presented as the means ± SDs of 3–4 mice. * indicates a significant difference at P < 0.05. ** indicates a significant difference at P < 0.01. **** indicates a significant difference at P < 0.0001

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Discussion

In this study, we used scRNA-seq to molecularly characterize testicular germ and somatic cells during the perinatal period. We assigned the germ cells at the 4 time points into 5 different states and found that MKi67-negative and nonproliferating State 5 was a hybrid population of ProSPGs and SSCs. According to the results of the pseudotime analysis, with the development of germ cells, PGCs entered the stationary phase, some were still in the stationary phase after birth, and others further proliferated and differentiated. These cells, which are still quiescent after birth, are in the same state as quiescent cells. We further explored this question by extracting germ cells after integrating data from E17.5, P1 and P6 (Fig S1F & S1G). Through pseudotime analysis, we found that some cells in the stationary phase after birth further proliferate and differentiate, while others are still in a stationary state.

From the PGC to the quiescent phase of germ cells, dramatic epigenetic changes occur in both germ cells and somatic cells, especially in germ cells, Leydig cells and myoid cells. In addition, their ability to communicate with other cells increased. From stasis to birth, the genes in different cells changed little, except for those in macrophages, endothelial cells and germ cells. Germ cells and myoid cells presented the greatest genetic changes from birth to the establishment of the SSC library. Overall, the results of the cell communication analysis revealed that germ cells received signals mainly and that Leydig cells and myoid cells were the mediators that receive signals and transmit signals. We also found that Leydig cells communicate more strongly with other cells than do Sertoli cells. This may be because, on the one hand, testicular Leydig cells are the main source of androgens [45]. Androgens diffuse into the seminiferous tubules and blood vessels in the interstitial space and control the signaling pathways of male germ cells by binding to androgen receptors (ARs) on peritubular myoid cells and Sertoli cells, thereby participating in spermatogenesis [46, 47]. On the other hand, testicular Leydig cells interact with surrounding cells by secreting local signaling molecules such as prostaglandins, cytokines and growth factors [48, 49].In addition, the extracellular matrix (ECM) also plays an important role in the functional regulation of testicular Leydig cells. The extracellular matrix not only provides the structural support necessary for cell growth and function but also influences cell behavior and signaling through interactions with cell surface receptors. Components of the ECM, such as collagen, glycosaminoglycans, and elastin, play pivotal roles in maintaining the normal function of testicular Leydig cells and facilitating their communication with other cells [50]. Finally, intercellular communication between macrophages and Leydig cells contributes to a favorable state of germ cell development [51].

By comparing the DEGs among the four time points, 378 genes were downregulated in the quiescent phase (i.e., downregulated in E17.5 vs. downregulated in E12.5, upregulated in D6 vs. upregulated in E17.5). These genes included genes that play a role in spermatogenesis, such as Ccnb1 [52], Atf1 [53], and Glis3 [54]. Genes related to embryonic development and the cell cycle, such as Ccna2 [55], Ccnb2 [56], and the Mcm complex [57]. Other genes related to DNA replication and cell proliferation, such as Top2a [58], Mki67 [59], and Ube2c [60], and 97 genes, including genes that play a role in spermatogenesis, such as Ddx25 [61], Piwil4 [62], and Dnmt3l [63], are upregulated in the stationary phase. The genes related to embryonic development include Dnmt3a [64], Tdrd9 [65], and Mael [66].

In addition, we investigated the bHLH-related gene Id2 and found that it is widely expressed in the testis. When Id2 was overexpressed in fetal prospermatogonias at E14.5, the testis density decreased, the sperm density decreased, and some spermatogenic tubules exhibited severe disruption; these findings were significantly different from those in the control group. Moreover, many exfoliated germ cells appear in the cauda epididymis. Further research revealed that Id2 overexpression resulted in the inhibition of the prospermatogonia cell cycle after birth recovery and the failure of the prospermatogonia-to-SSC transition, which may be responsible for abnormal spermatogenesis.

Materials and methods

Animals

This study was carried out in strict accordance with the guidelines of the Institutional Animal Care and Use of Laboratory Animals and was approved by the Animal Welfare and Ethics Committee at the Northwest Institute, Chinese Academy of Sciences. Samples were collected from wild-type C57BL/6 mice at E12.5, E17.5, P1 and P6. The Id2-OE line was generated as described previously [67]. In the presence of the tissue-specific Cre enzyme, Neo is deleted, and Id2 is specifically expressed in specific tissues under the regulation of the CAG promoter. Vasa-Cre mice were obtained from the Jackson Laboratory (018980, B6; FVB-Tg (Vasa-Cre)71Dcas/Knwj). The Id2-OE females were mated with Vasa-Cre+ males to obtain Vasa-Cre; Id2-OE males (Id2 OE(Vasa−Cre)). The animals were identified by using genotyping PCR. Briefly, two pairs of PCR primers were designed, in which Screen1-cag-F was in the CAG region, Screen1-neo-R was in the loxp-Neo-loxp (LNL) region, screen2-gfp-F was in the GFP region, and screen2-pa-R was in the globin PA region. When the transgenic vector is randomly inserted and integrated into the genome, the above two pairs of PCR primers produce corresponding PCR bands. The primers used to detect the flox allele are listed in supplementary Table 7. All the mice were housed in an environment controlled for light (12 h on/off) and temperature (21 to 23 °C) with ad libitum access to water and food.

The mice used in the paper were anesthetized with the anesthetic agent avertin (tribromoethanol, TBE), followed by neck breakage and sacrifice. The anesthetic dose was injected intraperitoneally at 0.125–0.24 mg/g.

Testis sample preparation

Each testicular sample analyzed by scRNA-seq was pooled from eight mice. Single-cell suspensions of testes were prepared as described previously [68]. Briefly, testes were collected and digested with 5 ml of 0.25% trypsin-EDTA for 5 min at 37 °C, after which 1 ml of 1 mg/ml DNase I was added, followed by pipetting, and this operation was repeated until the testes were digested into single cells. The digestion was stopped by adding 10% fetal bovine serum (FBS), and the cells were filtered through 40 μm cell strainers. The single-cell suspension was centrifuged at 400 × g for 5 min and resuspended in 1 ml of DPBS-S. Then, 3 ml of red blood cell lysis buffer (Solarbio, China) was added to remove the red blood cells. The DPBS was washed twice and centrifuged at 1500 rpm for 5 min, after which the supernatant was removed. The cells were then resuspended in cold sample buffer provided by a single-cell capture and cDNA synthesis kit (Cat. No. 633731), and 27,200 cells with a viability rate of > 85% per sample were counted for single-cell library construction and sequencing.

scRNA-seq library construction and sequencing

We performed single-cell capture and cDNA synthesis with the BD RhapsodyTM Single-Cell Analysis System (Doc ID: 210966 Rev.1.0 protocol) to capture single cells and synthesize cDNA. Library preparation was carried out according to the BD RhapsodyTM System mRNA Whole Transcriptome Analysis (WTA) and AbSeq Library Preparation Protocol. Briefly, the cell suspension was loaded into a special U-shaped slot by using the BD Rhapsody Cartridge Kit (Cat. No. 633733), and the cells naturally settled into the micropores. Excess beads with cell tags and unique molecular identification (UMI) sequences were then added to ensure that most of the beads fell into the micropores, where the beads captured the mRNA released after cell lysis. After the cell capture beads were retrieved and subjected to several washes, standard sequencing libraries were constructed through reverse transcription with the help of a BD RhapsodyTM cDNA Kit (Cat. No. 633773) and cDNA amplification by using a BD RhapsodyTM WTA Amplification Kit (Cat. No. 633801). Finally, 150 bp paired-end sequencing was performed on the Illumina HiSeq 2000 platform (sequenced by Novogene).

scRNA-seq data analysis

Quality control and analysis of the raw data were conducted according to the BD Phapsody pipeline. The BD pipeline processes the fastq sequences generated by sequencing through quality filtering, reference gene alignment, and expression quantification to generate a cell to gene expression matrix file. The specific analysis steps were as follows: (1) low-quality reads were removed based on the read length (R1 < 66, R2 < 64), average quality score (< 20), single nucleotide value (SNF, R1 SNF ≥ 0.55, R2 SNF ≥ 0.80), and (2) filtered Reads1 identified cell label (CLS), common sequence (L), unique molecular identifier (UMI) and poly T tail. The cell label consists of three parts: CLS1, CLS2, and CLS3. Reads were first filtered according to a preset method: CLS1: position 1–9, CLS2: position 22–30, and CLS3: position 44–52, and the reads that exactly matched this were selected. UMI consists of 8 nucleotides immediately downstream of CLS3, with at least 6 T in the 8 bases following UMI (3). Bowtie2 (v2.2.9) was used to align the filtered reads2 to the genome. An effective R2 alignment must meet the following conditions: the reads are uniquely aligned to the transcript, the reads are aligned starting from the first 5 nucleotides, the length of the alignment is > 60, and the reads cannot be aligned on phiX174. (4) The data of valid reads1 and reads2 were further analyzed, and valid reads1 contained CLSs, UMI sequences without N, and Ploy T tails; valid reads2 were the only reads that aligned the transcript sequence, the start contained the correct PCR2 primer sequence, and the alignment length was > 60. (5) Errors caused by base substitution were eliminated using recursive substitution error correction (RSEC), followed by distribution-based error correction (DBEC) to eliminate errors caused by library preparation and sequence deletion. The BD pipeline identifies the number of cells based on the second-order import algorithm.

Seurat analysis

The cell to gene expression matrix file produced by the BD Phapsody pipeline was imported into R, and we used the Seurat (v4.0.6, https://github.com/satijalab/seurat) package to conduct quality control, cell selection, data normalization, variable gene analysis, dimensional reduction, clustering of the cells, identification of differentially expressed genes, and functional analysis of the differentially expressed genes [69, 70]. The CreateSeuratObject function was used to create Seurat objects and remove low-quality cells or genes. The parameters were set to retain genes expressed in at least 3 cells, with at least 200 genes expressed in each cell, a maximum of 7500 genes expressed and a mitochondrial proportion of less than 30%. The generated data were normalized by “LogNormalize”, and hypervariable genes were identified using “FindVariableFeatures” with a threshold of 2000. In the subsequent principal component analysis (PCA) and cluster analysis, 10 to 20 principal components were selected and checked in duplicate in FALSE for nonlinear dimensional reduction analysis (uniform manifold approximation and projection for dimension reduction, UMAP), which reduced the cell distribution to two-dimensional space for display and downstream analysis [71]. Cluster analysis was performed on single samples using “FindClusters” with the parameter “resolution = 0.4”, and the data structure was visualized by UMAP. The genes in the clusters were identified by “FindAllMarkers”, and then, the marker genes were screened with the parameters “min.pct = 0.25”, “logfc.threshold = 0.25” and “p_val_adj < 0.05”. Integration of different samples was performed by using the CCA + MNN method; that is, the FindIntegrationAnchors function was used to identify anchors, and these anchors were subsequently passed to the IntegrateData function, which allows for data integration and correction of batch effects.

Cell Type Identification and Functional Enrichment Analysis

On the basis of published cluster-enriched genes and known marker genes for testicular germ cells and somatic cells, the relationship of each cluster to a cell type was established. DEGs in the same cell type at four time points were identified via the FindMarker function in the Seurat package (v4.0.6, https://github.com/satijalab/seurat), with parameters set to “min.pct = 0.25”, “logfc.threshold = 0.25” and “p_val < 0.05”. Using the DAVID web server (DAVID: Functional Annotation Tools (ncifcrf.gov)), the functional enrichment of each cell type marker gene in each sample and the DEGs between samples were analyzed by GO and KEGG, with P < 0.05 as a significant filter criterion. The results were visualized by using the ggplot2 package (v3.3.5, https://www.r-graph-gallery.com/ggplot2-package.html).

Integration analysis of different cell subtypes at four time points

To explore the process of perinatal cell development in mice, an integrated analysis of samples from four time points was performed, and then different cell types were extracted for subsequent analysis. Anchors were identified using the FindIntegrationAnchors functions in the Seurat toolkit (v4.0.6, https://github.com/satijalab/seurat), which were then passed to the IntegrateData function in the Seurat toolkit and returned to the Seurat object. Therefore, a new multisample analysis method was obtained by integrating the corresponding expression matrices (parameters “dims = 1:10–20”). Cell clustering analysis was performed again by using the UMAP method, and the parameter resolution in FindClusters was 0.4.

Cell trajectory analysis

Using pseudotemporal analysis methods, the differentiation trajectories of cells and the developmental process of sequential cells were detected according to differences in cell clusters, states and pseudotimes. This study used the Monocle 2 [72] package (v2.18.0) to analyze the developmental trajectories of different cells at 4 time points. The top 1000 DEGs were selected as ranking genes and further subjected to pseudotime analysis. Store the deg in a Seurat object and then reduce it to two dimensions using the DDRTree (discriminational dimension reduction with trees) method. Heatmaps of the genes used in the pseudotime analysis were generated by the plot_pseudotime_heatmap function in the Seurat toolkit.

Germ cell transcription factor analysis

For germ cell transcription factor analysis, we used the SCENIC (single-cell regulatory network inference and clustering) [73] (v1.2.4) function in the Seurat toolkit. The input data were the germ cell expression matrix after integration of the 4 time points in the Seurat dataset. SCENIC performed transcription factor analysis through the following three steps: the first step involved constructing a co-expression network, which was completed by using GENIE3 (Gene Network Inference with Ensemble of Trees) (v1.12.0) software; the second step was conducted to construct a TF-target network, which was completed by RcisTarget (v1.10.0) software, which used the databases mm9–500 bp-upstream-7 species.mc9nr.feather and mm9–tss-centered-10 kb-7 species.mc9nr.feather; and the third step was done to calculate the activity of regulons, which was performed by AUCell (v1.12.0) software. The visualization of the results was also completed by SCENIC software. There are two main types of result graphs that are output by default. One was the regulon activity heatmap based on the AUC value. Each row in the figure represents a region, each column is a cell, and the color represents the AUC value. This type of heatmap was mainly used to compare the activity of the same regulons in different cells; the other type was the openness heatmap of regulons based on the AUC binary matrix, which was used to find open regulons in cells.

Cell‒cell communication analysis

For cell‒cell communication analysis, we used the CellChat (v1.5.0) [74] function in the Seurat toolkit. We first ran CellChat separately on the dataset at each point in time and then merged the different CellChat objects together. Because the cell type is slightly different at each time point, we used the function liftCellChat to boost the cell group to the same cell marker for all datasets and then performed a comparative analysis as a joint analysis of datasets with the same cell type components. For the combined dataset, we compare the number of interactions between different cell populations, compare the major sources and targets in 2D space, and finally compare the outgoing and incoming signals associated with each cell population.

Histological and immunohistochemical staining

Histological and immunohistochemical staining of testicular sections was performed as previously described [19]. Briefly, mouse testes were fixed in Bouin’s solution or 4% paraformaldehyde (PFA), and after dehydration, the tissues were embedded in paraffin (Leica, Germany). The paraffin-embedded tissues were then cut into 4 μm sections with a microtome (Leica RM2235). The sections were deparaffinized, rehydrated, and stained with hematoxylin and eosin (H&E) for histological analysis or processed for immunohistochemical staining. The sections were boiled in 10 mM sodium citrate (pH 6.0) for antigen retrieval. The sections were incubated with 10% normal blocking serum for 1 h at RT. Primary antibodies (Supplementary Table 7) were diluted in antibody dilution buffer and incubated overnight at 4 °C. The sections were then washed in PBS and incubated with secondary antibodies (Supplementary Table 7) for 2 h at RT. For immunohistochemical staining, after being washed 3 times in PBS for 10 min each, a DAB kit (Zsbio, China) was used for positive signal detection. For immunofluorescence staining, after being washed 3 times in PBS for 10 min each time, the sections were stained with Hoechst 33,342 (H33342) (Sigma, St. Louis, Missouri, USA) for 1 min and mounted in 50% glycerol before being examined under a microscope (Leica, Mannheim, Germany). Images were examined by using a microscope (Nikon ECLIPSE E200, Japan) and captured by CCD (MshOt MS60, China).

Ethynyl-2’-deoxyuridine (EdU) proliferation assay

The mice were treated with 50 mg/kg body weight EdU (RIBOBIO, China) via intraperitoneal injection. Two hours after the EdU injections, the testes were collected and fixed in 4% PFA. EdU incorporation was detected in cross-sections of testes using the Cell LightTM EdU Apollo 567 In Vivo Kit (RIBOBIO, China). After EdU labeling, cross-sections were costained with TRA98 to identify proliferative germ cells.

Statistical analysis

All the quantitative data are presented as the means ± SDs of at least three biological replicates. Differences between means were examined via the general linear model one-way ANOVA or t test function of GraphPad Prism 9.5.1 (La Jolla, CA, USA). Differences between means were considered significant at P < 0.05.

Data availability

Single-cell RNA-seq data have been uploaded to NCBI ( BioProject: PRJNA996315).

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Funding

This study was supported by grants from the National Natural Science Foundation of China (31571539 and 31771656). Q.E. Yang was supported by the “100 Talents” project from the Chinese Academy of Sciences and the “1000 Talents” project from the Qinghai Provincial Government.

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Author notes

  1. Zhen He and Rong-Ge Yan contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, Qinghai, 810008, China

    Zhen He, Rong-Ge Yan, Qin-Bang Shang & Qi-En Yang

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Zhen He, Rong-Ge Yan & Qi-En Yang

  3. Qinghai Key Laboratory of Animal Ecological Genomics, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, Qinghai, 810008, China

    Zhen He, Rong-Ge Yan, Qin-Bang Shang & Qi-En Yang

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  1. Zhen He
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Contributions

Q.E.Y. conceived the project and designed the experiments. Z.H. and R.G.Y. performed the experiments and analyzed the data. Q.B.S. assisted with the experiments. Q.E.Y. and Z.H. wrote the manuscript, which was edited by all the authors.

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Correspondence to Qi-En Yang.

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All animal experiments involving animals were conducted according to the Guide for the Care and Use of Laboratory Animals and were approved by the Animal Welfare and Ethics Committee at the Northwest Institute, Chinese Academy of Science (Approval number: SYXK 2022-0001).

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He, Z., Yan, RG., Shang, QB. et al. Transcriptomic dynamics and cell-to-cell communication during the transition of prospermatogonia to spermatogonia revealed at single-cell resolution. BMC Genomics 26, 58 (2025). https://doi.org/10.1186/s12864-025-11244-2

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  • DOI: https://doi.org/10.1186/s12864-025-11244-2

Keywords

BMC Genomics

ISSN: 1471-2164

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