Establishment of DNA methylation during primate germ cell development

Domain: neuroscience
Published: 2026-04-08
Editorial outcome: major_revision

Abstract
DNA methylation is almost completely erased throughout the genome in primordial germ cells, and then reestablished during mammalian germ cell development. In this study, we demonstrate that in three primate species—marmosets, macaques, and humans–de novo methylation occurs postnatally in prospermatogonia in males and growing oocytes in females. In monkey prospermatogonia, de novo methylation is a prolonged process spanning 6 months to 1 year, primarily occurring within the first year after birth. In human testes, this process may occur more slowly over an extended period. Single-cell bisulfite sequencing analyses in spermatogonia of three species revealed that all genomic regions acquire DNA methylation gradually. However, DNA methylation levels increase faster in genic regions compared to intergenic regions. Unlike in mice, mitotic divisions occur during the establishment of methylation in prospermatogonia. The established methylation is likely maintained because maintenance methyltransferase DNMT1 is specifically expressed during the mitotic stage. Our findings show notable differences in the de novo DNA methylation processes in male germ cells between mice and primates.

Introduction
During germ cell development, DNA methylation is erased and reestablished. The reestablished DNA methylation is crucial for retrotransposon suppression and genomic imprinting1,2. The process has been extensively studied in mice. In male mouse germ cells, DNA methylation is established within a brief period from embryonic day 15 (E15.5) to 1 day after birth in the prospermatogonium stage. In female germ cells, it occurs during oocyte growth within developing follicles.

Molecular mechanisms and chromatin dynamics of DNA methylation establishment are well-characterized in mouse germ cells. de novo DNA methyltransferases DNMT3A, 3B, and 3C catalyze methylation at CpG cytosines, with distinct genomic region preferences3-5. DNMT3A methylates most of the genome, while DNMT3B and DNMT3C target active retrotransposon promoters and centromere repeats. Primates lack DNMT3C, which evolved from DNMT3B in muroid rodents. Maintenance DNA methyltransferase DNMT1 adds methyl groups to newly synthesized DNA strands to maintain the already established methylation pattern. During de novo methylation in germ cells, mouse germ cells do not divide, suggesting DNMT1 does not contribute to the establishment6,7.

PIWI-interacting RNAs (piRNAs) are 25–30 nt small RNSAs. piRNAs are incorporated into PIWI proteins and mediate epigenetic silencing during DNA methylation establishment in male germ cells. The primary targets of piRNAs are active retrotransposons that are normally repressed by methylation8-10. DNA methylation of retrotransposon sequences is similarly erased and reestablished during germ cell development. This reestablishment of retrotransposon DNA methylation occurs in a manner dependent on PIWI-piRNAs. PIWIL4 (also known as MIWI2 in mice) drives these epigenetic functions, while other PIWI proteins (PIWIL1 and PIWIL2) regulate the posttranscriptional processes. PIWIL4 uses piRNAs as guides to target nascent RNAs11 and interact with SPOCD1, linking them to epigenetic functions through DNMT3A/3B/3C and chromatin remodeling complexes like NURD and BAF12. This pathway mediates DNA methylation at the later stage during the establishment process13.

Additionally, differentially accessible domains (DADs) in mouse prospermatogonia acquire DNA methylation later than other regions. In DADs, increased chromatin accessibility coincides with the initiation of DNA methylation establishment14, suggesting accessibility is necessary for methylation. However, the extent to which these mechanisms are conserved in primates remains unclear.

In humans, DNA methylation in the female germline is established during oocyte growth15. However, the timing of this process in the human male germline remains unclear. In marmoset monkeys, DNA methylation establishment in male germ cells occurs postnatally during the prepubertal stage16. It begins by 4 months of age and continues until puberty at approximately 8 months16. As puberty timing differs between marmosets and humans, the human establishment stage cannot be directly inferred from marmosets. As such, insights from other primates with extended prepubertal stages could help extrapolate these findings to humans.

Here, we investigated the timing of de novo methylation in the germlines of three primate species (marmosets, cynomolgus monkeys, and humans). Additionally, we analyzed the complete de novo methylation processes in testicular germ cells using single-cell bisulfite sequencing (scBS-seq) in the three species. Our study highlights both similarities and differences in de novo DNA methylation between mice and primates.

Results
Establishment of DNA methylation in primate germ cells
To pinpoint the timing of DNA methylation establishment in primate germ cells, immunohistological analysis was performed using an antibody specific to 5-methylcytosine (5mC) in the ovaries of two primate species: cynomolgus monkeys and marmosets. In both species, DNA methylation signals were absent in oocytes of primordial and primary follicles (Figs. 1A, B). Most oocytes in secondary follicles displayed signals in both species (Figs. 1A–D). A similar pattern was observed in ovaries from both young and aged monkeys (Figs. 1C, D). These findings indicate that DNA methylation is established in oocytes of secondary follicles. The stage of DNA methylation in oocytes hence appears to be largely conserved between mice and monkeys17.

In mice, DNA methylation in male germ cells occurs between E15.5 and 1 day after birth. Germ cells in this period, termed prospermatogonia, later differentiate into spermatogonial stem cells within several days after birth. In primates, primordial germ cells differentiate into prospermatogonia during midgestation and remain in this stage until puberty. As puberty in primates occurs much later after birth, primates have an extended postnatal prospermatogonium phase. To approximate the timing of de novo methylation in primate testes, immunohistochemical analyses were conducted. In accordance with Langenstroth-Rower et al.16, 5mC signals in marmoset testicular germ cells were absent or faint at 21 days of age, varied at 3 months, and strong at 9 months (Fig. 1E). Similarly, in cynomolgus monkeys, testicular germ cells showed no or weak 5mC signals at 29 days but displayed strong signals at 6 months (Fig. 1F). These observations indicate that de novo DNA methylation in these primates occurs postnatally, predominantly within several months after birth.

Single-cell analyses of de novo methylation in marmosets
To investigate de novo methylation dynamics with greater precision during marmoset postnatal development, scBS-seq analysis was performed alongside simultaneous scRNA-seq analyses (see later) from the same cells. A total of 103 FACS-purified marmoset germ cells, spanning 10 developmental stages (1 day to 12 months of age), were sequenced (Supplementary Data 1, 2, and 3). Conversion rates were estimated to be 99.2%–99.4% using externally added unmethylated lambda DNA (Supplementary Data 3). Among the 103 cells analyzed, two exhibited low mapping rates (1% and 0.5%), and one was identified as a somatic cell based on DNA methylation patterns at imprinted loci (Supplementary Data 3, 4). After excluding these three, a total of 100 cells (4−16 per stage) were retained for analysis (Supplementary Data 2). The average genome coverage rate was 24.2%, ranging from 10.4% to 34.4% (see Supplementary Data 3 for detailed mapping statistics). The average DNA methylation level in 1-day-old germ cells (Fig. 2, Supplementary Data 3) was at its minimum (4.9%), closely resembling the level reported for iPS cell-derived early prospermatogonia (4.3%) in a previous study18. This observation suggests that de novo methylation primarily occurs postnatally. A modest rise in DNA methylation was noted from 1 day (4.9%) to 1.1 months (10%) (Fig. 2). This gradual increase continued from 1.1 to 2.5 months (20%), although one 2.5-month prospermatogonia exhibited a markedly higher methylation level (61%). Following 2.5 months, the sharpest increase was observed up to 6.4 months (57%), during which varying methylation levels were detected even among prospermatogonia from the same testis (Fig. 2). Immunostaining analyses also revealed varied signal intensities among germ cells in 3-month-old testes (Fig. 1E). Beyond this period, DNA methylation levels continued to rise, with an additional 11.1% increase noted from pubertal 8.5-month prospermatogonia/spermatogonial stem cells (60%) to 12.5-month spermatogonial stem cells (71%) with potential for spermatogenesis. This level approached that of sperm (76%), as determined by bisulfite sequencing of bulk DNA.

de novo DNA methylation patterns in the marmoset male germ cells
Substantial variation in DNA methylation levels was observed among germ cells within the same testes (Fig. 2), reflecting unsynchronized developmental progression. To examine methylation dynamics in detail, cells were grouped into seven categories (G1–G7: G1, 0%−10%, 11 cells; G2, 10%−20%, 16 cells; G3, 20%−30%, 8 cells; G4, 30%−40%, 10 cells; G5, 40%−50%, 21 cells; G6, 50%−60%, 19 cells; G7, 60%−80%, 15 cells) according to total DNA methylation levels (Fig. 2). Most G1 and G2 cells originated from samples aged 1 day to 2.5 months. G3−G5 were predominantly from 3.4−5 months, while G6 and G7 were mainly derived from 6.4−8.5 and 12.5 months, respectively.

Gradual establishment of global DNA methylation raises two possibilities regarding the pace of local methylation acquisition: (1) DNA methylation is acquired quickly (e.g., within one week, as observed in mouse prospermatogonia) at the regional level but the establishment occurs at different developmental stages depending on genomic region (fast pattern, Fig. 3A top), or (2) methylation occurs gradually at the regional level as well (gradual pattern, Fig. 3A bottom). To discern these patterns, the genome was divided into 500-kbp tiles, and the average methylation level for each group was calculated within each tile, with results displayed along each chromosome (Fig. 3B, Supplementary Fig. 1). DNA methylation levels were gradually increased across genomic regions (Fig. 3B), supporting the gradual pattern. However, close inspection revealed that the final (G7) levels varied significantly among regions. Furthermore, slight differences in establishment kinetics between regions were revealed by comparing methylation acquisition between early (G3–G1, red line in Fig. 3B top) and late (G7–G5, blue line in Fig. 3B top) stages (Fig. 3B). Thus, at the local level, de novo methylation proceeds gradually, but establishment kinetics slightly differs among genomic regions.

Some genomic regions exhibited preferential DNA methylation acquisition during the early stage, reaching a plateau more rapidly (Fig. 3B, red arrows), while others demonstrated a slower and continuous increase until the later stages (Fig. 3B, black arrows). To elucidate the regional differences in DNA methylation establishment patterns, genomic tiles were categorized into three groups (fast, intermediate, and slow) based on the speed of methylation acquisition. This categorization was achieved by comparing methylation differences between the early two stages (G3–G1) and the final two stages (G7 –G5) (Fig. 3C; see methods). Slow regions, characterized by lower methylation in (G3–G1) than in (G7–G5), were predominantly associated with intergenic regions and retrotransposons enriched in intergenic regions (LINE and LTR elements) (Figs. 3B, D, Supplementary Figs. 1, 2A, B). Conversely, fast regions were predominantly associated with genic regions, including promoters, exons, and introns, along with annotations enriched in these regions, such as ATAC peaks and CpG islands (Supplementary Figs. 2A, B). These findings reveal that, at the macroscopic scale, genic regions seem to more easily acquire DNA methylation than intergenic regions (Fig. 3E).

The final DNA methylation levels varied significantly between genomic regions. To elucidate the basis for these differences, average methylation levels were assessed across structural annotations (exons, introns, intergenic regions, flanking regions, and promoters) and functional annotations (CpG islands and ATAC peaks) (Fig. 3F, Supplementary Figs. S2-4). Exons (77%) and introns (79%) showed higher methylation levels than intergenic (72%) and gene flanking (65%) regions in G7 (Fig. 3F). However, although active regulatory elements, such as promoters (excluding low CpG promoters), CpG islands, and ATAC peaks from testicular germ cells at 1 and 3 months of age, were mainly located in genic regions (Figs. S2A, B), they were essentially devoid of methylation (Figs. 3F, G). Notably, when active regions (promoters, CpG islands, and ATAC peaks) and their surrounding sequences (±2 kb from the center) were excluded (Fig. 3G, bottom), the genome-wide average methylation level increased by approximately 10%. The enrichment of these active regions within genic regions (Figs. S2A, B) suggests that genic regions have a stronger innate tendency to acquire DNA methylation than indicated by this analysis.

Mechanisms of de novo DNA methylation in marmoset male germ cells
Association of active transcription with efficient DNA methylation
Previous studies in mouse oocytes have shown that DNA methylation establishment at genic regions depends on transcriptional activity19,20. To test whether transcription similarly influences the rapid kinetics of methylation at genic versus intergenic regions in primates, we performed correlative analyses of scBS-seq, scRNA-seq, and ASTAC-seq data from marmoset prospermatogonia. Genes were divided into three groups—high, mid, and low—based on expression levels, and DNA methylation at gene bodies was compared (Fig. 3H). Across all groups (G1–G7), methylation levels were positively correlated with mRNA expression levels. We further classified genes into fast, intermediate, and slow categories according to the speed of methylation acquisition in gene bodies, then examined promoter ATAC peaks (Fig. 3I). This revealed clear differences in promoter accessibility: fast genes displayed the highest accessibility, followed by intermediate and slow genes. Together, these findings indicate that efficient methylation acquisition at genic regions in primate prospermatogonia depends on transcription of genes with accessible promoters.

Expression of the components involved in de novo DNA methylation
DNMT3A and DNMT3B catalyze the transfer of methyl groups onto unmethylated cytosines in CpGs with the assistance of catalytically inactive DNMT3L4,5,21. The expression of these de novo DNA methyltransferases was investigated through simultaneous scRNA-seq data analysis. Average expression levels were assessed across groups (G1–G7) categorized by DNA methylation levels (Fig. 4A). Non-smooth expression patterns across development were likely due to the limited number of cells per group. During de novo DNA methylation in marmoset male germ cells, DNMT3A expression was detected across all groups, whereas DNMT3B and DNMT3L expression was primarily restricted to the later groups (Figs. 4A, B). We next performed immunofluorescence analyses of the three DNMT3 proteins. In neonatal testes, DNMT3A was detected in the nuclei of a small subset of germ cells, while many showed weak cytoplasmic staining (Fig. 4C). Similar weak cytoplasmic staining was also observed for DNMT3B, whereas DNMT3L expression was absent. By 3 months of age, all three DNMT3 proteins exhibited clear nuclear staining in a fraction of germ cells, though signal intensities varied and some cells lacked expression entirely. At 9 months, DNMT3A and DNMT3L were expressed in ~90% and ~20% of spermatogonial stem cell-like cells, respectively. DNMT3B showed weak cytoplasmic expression in a small subset of these cells. Additionally, DNMT3A and DNMT3B expression was observed in Sertoli cells of 9-month-old testes. In mouse prospermatogonia, the PIWI-piRNA pathway regulates de novo methylation of retrotransposons8,9. Similarly, expression of key PIWI-piRNA-mediated DNA methylation components, PIWIL4, SPOCD1, and TEX15, was observed during de novo methylation stage in marmosets (Figs. 4A, B).

Cell division during de novo DNA methylation
Unlike the quiescent G0 state of mouse prospermatogonia during de novo DNA methylation, our simultaneous scRNA-seq analyses revealed that marmoset prospermatogonia undergo cell division (Fig. 4B). Of the 88 cells that were in the de novo methylation process (methylation level: 10%−70%), 11 cells (DNA methylation levels: 12%, 12%, 40%, 40%, 43%, 46%, 56%, 60%, 62%, 68%, 70%) were likely in the mitotic phases (S and G2/M) (Fig. 4B, Supplementary Data 3). This prompts us to question whether the amount of the established DNA methylation is decreased due to the division. Interestingly, DNMT1 expression is observed exclusively in the dividing cells, whereas DNMT3A/B expression is mainly observed in quiescent cells (Figs. 4B, D). This differential DNMT1 expression pattern suggests that the established methylation is likely maintained after cell division, during the establishment stage.

Non-CG methylation
A previous study in mice reported markedly high levels of non-CG methylation (~8% of all non-CG sites), peaking in neonatal prospermatogonia22. To assess whether this is conserved across species, we analyzed CH methylation (H = A, C, or T) in marmoset prospermatogonia. Quantification showed that the fraction of methylated cytosines among all CH dinucleotides ranged from 0.7% to 1.1% during the G1–G7 transition, in contrast to the sharp increase in CG methylation (Fig. 4E). In G7, ~4.5% of all cytosines were methylated (vs. 10.8% in mouse neonatal testis germ cells22), with 3.5% (3.3% in mice) at CG sites and 1.0% (7.5% in mice) at CH sites. CH methylation occurred most frequently at CA dinucleotides, particularly at CAC and CAG trinucleotides, which showed an increasing trend across G1–G7 (Fig. 4F, left). Since CAC and CAG are preferred targets of DNMT3A and DNMT3B, respectively23-25, these results indicate that CH methylation depends on the expression of these enzymes. For CG methylation, CGC and CGG are reported targets of DNMT3A and DNMT3B25. These trinucleotides were enriched among methylated CpG sites but showed a decreasing trend from G1 to G7 (Fig. 4F, right), likely reflecting preferential methylation at early stages and reduced availability later. Collectively, these findings demonstrate that CH methylation levels are lower in marmosets than in mice, likely due to the active proliferation of marmoset prospermatogonia, as non-CG methylation is not maintained during DNA replication.

Accessibility
In mouse testes, certain genomic regions, termed DADs, resist methylation during the early phase of de novo methylation14. DADs acquire DNA methylation only after increased accessibility during the late de novo methylation stage14. To explore the presence of DAD-like regions in marmosets, scATAC-seq analyses were conducted on newborn and 3-month-old marmoset testes. Newborn and 3-month-old germ cells showed similar patterns of chromatin accessibility peaks (Fig. 3B, Supplementary Figs. 1). Despite no clear differences in accessibility, the entire genomic regions of 3-month-old germ cells displayed increased DNA methylation to the level almost proportional to the final levels (Figs. 3B, Supplementary Fig 1). Consequently, DAD-like regions were not evident during the establishment phase in marmosets.

Conservation of the timing of de novo DNA methylation in cynomolgus monkeys
scBS-seq analysis was performed on postnatal cynomolgus monkeys to accurately assess DNA methylation levels during male germ cell development. Thirty germ cells spanning seven developmental stages were analyzed (Fig. 5A; Supplementary Data 1, 2, and 5). In 9-day prospermatogonia, the average DNA methylation level was very low (3.6%) (Fig. 5A, Supplementary Data 5). Levels increased in 60-day prospermatogonia (28%) and further increased in 6-month prospermatogonia (58%), with substantial variation among cells at these stages. In 1.1-year prospermatogonia, the level nearly plateaued (70%), with only slight increases observed thereafter (72% in 1.6-year prospermatogonia, 70% in 1.7-year prospermatogonia). Adult spermatogonial stem cells (10 years) exhibited a methylation level of 75%. Thus, although puberty onset varies between marmosets (9 months) and cynomolgus monkeys (4 years), de novo methylation in male germ cells occurs at similar ages.

To examine the pattern of methylation establishment, 30 cells were grouped into five categories (G1–G5) according to total DNA methylation levels (Fig. 5B). Average methylation levels in each group were then plotted across 500-kb genomic tiles. As in marmosets, DNA methylation appeared to accumulate gradually and in parallel across the genome (Fig. 5B). Analysis of non-CG methylation revealed that only 1.0%–1.8% of all CH dinucleotides were methylated (Fig. 5C), far lower than the ~8% observed in mouse neonatal prospermatogonia22. Preferential targeting of CAG/CAC and CGG/CGC trinucleotides was evident for CH and CG methylation, respectively (Fig. 5D). These findings indicate a conserved mode of non-CG methylation among primates but underscore a striking divergence from rodents.

To investigate genomic context-dependent methylation kinetics in cynomolgus monkeys, genomic tiles were categorized into three groups: fast, intermediate, and slow (Figs. 5B, E, Supplementary Fig. 5). As observed in marmosets, annotations associated with genic and intergenic regions were enriched in fast and slow groups, respectively (Figs. 5F, Supplementary Figs. 2B, 5). Additionally, for the final methylation level (G5), genic regions (exon 80% and intron 82%) acquired higher methylation levels than intergenic (77%) and gene flanking regions (68%) (Figs. 5G, Supplementary Fig. 5). Hence, the timing and pattern appeared consistent between cynomolgus monkeys and marmosets.

Establishment of DNA methylation in human germ cells
By extrapolating findings from the two monkeys, establishment stages in humans were predicted to occur in secondary follicle oocytes and testicular germ cells between 0 and 1 years of age. Adult ovaries excised from uterine cancer patients (Supplementary Data 6) were analyzed via immunohistochemistry with 5mC antibodies. Although we cannot exclude the possibility that cancer tissues influence the developmental establishment of oocyte methylation, normal tissues resected from cancer patients provide valuable insights into human in vivo biology. Consistent with the findings in monkeys: no 5mC signals were detected in primary follicle oocytes (Fig. 6A), and weak signals appeared in secondary follicle oocytes, with stronger signals in antral follicle oocytes. Additionally, these results align with bisulfite analyses by Yan et al., demonstrating variable levels of DNA methylation in human growing oocytes15.

To examine the timing of de novo methylation during human male germ cell development, immunofluorescence analyses using a 5mC antibody were performed on aborted normal gonad (E21-week), testicular biopsies from patients with severe combined immunodeficiency (for fertility preservation, 3 months), medulloblastoma (for fertility preservation, 7 months), and unilateral undescended testis (biopsy of normal testis, 8 years) (Fig. 6B, Supplementary Data 6). Because these diseases were observed outside the examined testis, they most likely had little effect on the results. In addition, we examined surgically removed testes from testicular teratoma patients (5 months and 1-year 5-months (Fig. 6C, Supplementary Data 6). Because the testicular tissues analyzed were directly in contact with the teratomas, their developmental progression may have been partially influenced by the tumor environment. No or very weak methylation signal was detected in germ cells from the aborted fetus (E21). Weak signals were observed in 3-month germ cells of biopsy samples. However, 5-month germ cells (surgical sample from a teratoma patient) showed no or very weak signals. Clear, although not strong, signals were present in some germ cells in 7-month testes (biopsy). Stronger 5mC signals were observed in most germ cells at 1 year and 5 months (surgical samples from teratoma patients), with levels almost comparable to those in 8-year testis germ cells (biopsy). However, some germ cells in 8-year sample showed nearly completely negative staining (Fig. 6B right, yellow arrows). This is possibly due to unfavorable cellular conditions for 5mC immunostaining (e.g., degradation of DNA, demethylation, difficulty in accessing epitopes), as many Sertoli cells also showed negative staining. Similar negative staining was observed in testes after 9 months of storage, although this was not detected when the same sample (a 1-year-5-month testis from a teratoma patient) was examined immediately after collection (Supplementary Fig. 6). Considering considerable variations in sample state (e.g., fixative, storage time, storage conditions, and disease) (Supplementary Data 6), accurately comparing methylation levels across samples is difficult. However, the data provide some preliminary information about the timing. The timing of de novo methylation in human germ cells hence seems slightly later than that in marmosets and cynomolgus monkeys.

To quantitatively assess methylation levels, single-cell bisulfite sequencing was carried out on human germ cells. Prospermatogonia from three teratoma patients (1-year-0-month, 1-year-5-months (also examined in Fig. 6C), and 1-year-8-months) and spermatogonial stem cells from a 29-year-old obstructive azoospermia patient with normal spermatogenesis were analyzed (Supplementary Data 6). The three 1-year-old prospermatogonia exhibited average methylation levels of 41.4% (23.1–54.4%, 1-year-0-month), 53.1% (32.0–62.4%, 1-year-5-months), and 32.6% (10.2–54.7%, 1-year-8-months) (Fig. 7A, Supplementary Data 7). No correlation between methylation levels and ages in these 1-year-old samples is likely due to individual variation and/or effects of teratoma. In contrast, adult spermatogonia showed an average level of 75.7% (73.3–76.8%). Although teratoma may have influenced the developmental timing of prospermatogonia within the same testes, prospermatogonia across all 1-year-old samples remained in an intermediate phase of DNA methylation establishment.

To investigate regional establishment patterns, single cells were sorted into seven groups (G1–G7) based on methylation levels. Genomic tile (500-kbp) analyses revealed that DNA methylation levels were gradually increased across genomic regions (Fig. 7B, Supplementary Fig. 7). The methylation levels of non-CpG sites in human prospermatogonia (1.5–2.1%, G1–G6 in Fig. 7C) were slightly higher than those in monkey prosermatogonia (marmoset: 0.7%–1.0%; cynomolgus: 1.0%–1.8%), yet remained lower than in mouse prospermatogonia (~8%)22. Moreover, human spermatogonial stem cells in humans (G7 in Fig. 7C) displayed elevated methylation not only at CpG sites (75.7%) but also at non-CpG sites (5.4%) compared with marmoset (CpG: 63.4% and non-CpG: 1.1%) and cynomolgus monkey (CpG: 64.0% and non-CpG: 1.5%). This pattern indicates robust de novo methylation activity in human spermatogonial stem cells. Genomic tiles were sorted into three classes (slow, intermediate, fast) according to the acquisition pattern of DNA methylation, and genomic-context-dependent methylation kinetics were evaluated. As in monkeys, genic (intergenic) regions tended to gain methylation faster (slower) in development (Fig. 7B, D, E, F). Among the analyzed sequences, introns exhibited the highest levels in prospermatogonia (G1–G6 in Fig. 7F), whereas LINE sequences were most highly methylated (85.3%) in spermatogonial stem cells (G7 in Fig. 7F).

Methods
Testis samples
Testis sampling was approved by the animal ethics committees of the National Center for Child Health and Development (NCCHD), CLEA Japan, Central Institute of Experimental Animals, and Tsukuba Primate Research Center (NIBIOHN). Details of the marmosets and cynomolgus monkeys used in sequencing analyses are provided in Supplementary Data 1. Samples were obtained via hemicastration under anesthesia or from monkeys euthanized for other purposes. Details of human samples used in this study are summarized in Supplementary Data 6. The study’s experiments were approved by the ethics committees of the NCCHD (2022-138 and 2024-044), Kanagawa Cancer Center (2022EKI97), and University of California San Francisco (#23-39078). This study was conducted in accordance with all relevant ethical regulations concerning research involving human samples.

Histological examination
Testes were fixed in 4% PFA, 10% formalin, or Bouin’s solution, embedded in paraffin, and sectioned. Antigen retrieval was performed using a citrate buffer pH 6.0 (5mC, MAGEA3/4, DDX4, and DNMT3B) or a Tris buffer pH 9.0 (DNMT3A and 3L) at 100°C for 15 min. Blocking was conducted with BOND Primary Antibody Diluent (DNMT3L and MAGEA3/4) or 5% skim milk/0.1% Tween 20/PBS (5mC, DDX4, DNMT3A, and 3B). Antibody reactions utilized BOND Primary Antibody Diluent (MAGEA3/4, DNMT3A, and 3B) or Can Get Signal Solution A (5mC, DDX4, and DNMT3L) (TOYOBO). Primary antibodies used are 5mC (ab10805, Abcam, 1:50), MAGEA3/4 (MABC1150, Merck, 1:500), DDX4 (AF2030, R&D, 1:500), DNMT3A (ab188470, Abcam, 1:500), DNMT3B (NB300-516-0.025 mg, Novus 1:200), DNMT3L (ab194094, Abcam, 1:150).

Germ cell sorting
Monkey (human) testes were dissociated using 1 mg/mL collagenase type I (type IV) in DMEM at 37°C with shaking for 30 min. After centrifugation (200 g, 5 min), the samples were digested with 0.25% trypsin-EDTA at 37C for 10 min. Reactions were halted by adding an equal volume of 10% FCS/DMEM. DNase I was added to a final concentration of 25 μg/mL, and the cells were incubated at room temperature for 1 min. After filtering through a 60-μm strainer, cells were centrifuged, and the supernatant was discarded. The washing process was repeated twice using 10% FCS/DMEM. Collagenase treatment was omitted for testes from younger animals (<6 months old), with only trypsin treatment applied.

For fluorescence-activated cell sorting (FACS), monkey cells were stained with CD9-FITC (MCA469FT, BioRad, 1:50) and CD90-PE (555596, BD, 1:100) in 10% FCS/DMEM containing 25 μg/mL DNase1 for at least 30 min. Human cells were stained with SSEA4-Alexa488 (560308, BD, 1:100) in the same medium. DAPI was added to the FACS buffer (0.5% FCS/DMEM containing 25 μg/mL DNase1) to exclude dead cells. Sorting was performed using a SH800 (SONY). The proportion of germ cells in the FACS-purified population was confirmed via immunofluorescence with the MAGEA3/4 antibody (Fig. S8A, B). The germ cell proportions after sorting are shown in Supplementary Data 2.

RNA and DNA purification from the same single cell
FACS-purified testicular cells were washed twice with 0.04% BSA/PBS and resuspended in 0.04% BSA/PBS for single-cell pickup. Single cells were isolated using a mouth pipette and placed into PCR tubes (MicroAmp 8-Tube Strip, Applied Biosystems). To each tube, 3.5 μL lysis buffer (1.14 U/μL RNAse Inhibitor, 0.54% Triton X-100) containing DynaBeads MyOne Carboxylic Acid (Thermo Fisher) was added. The beads bound to the nuclei, allowing separation of the cytoplasmic supernatant. For DNA extraction, 5 μL DNA isolation buffer (20 mM Tris-Cl, 2 mM EDTA, 20 mM KCl, 0.3% Triton X-100, 1 mg/mL proteinase K, 0.1 pg/μL λDNA, 2 ng/μL carrier RNA) was added to the beads, followed by incubation at 50℃ for 10 min. Proteinase K was inactivated at 75℃ for 30 min. scRNA-seq and scBS-seq libraries were then prepared from the cytoplasmic RNA and nuclear DNA fractions, respectively18,30.

scBS-seq and bulk BS-seq library preparation
scBS-seq libraries were prepared as per Zhou et al.30 with minor modifications. Unmethylated lambda DNA (Promega) was added prior to the bisulfite reaction. The MethylCode Bisulfite Conversion kit (Invitrogen) was used for bisulfite conversion of single-cell DNA. Four rounds of random priming were performed using a random primer with an A:T:G:C ratio of 4:4:1:1, minimizing GC-rich sequence amplification bias. After ExonucleaseⅠ treatment to remove residual primers, second-strand synthesis and PCR amplification were conducted. Quality was assessed via a Bioanalyzer (Agilent), followed by paired-end 150-bp sequencing using Illumina NovaSeq.

Bulk DNA was used for generating the sperm bisulfite-seq library (see Supplementary Methods for sperm isolation). The library was made with two modifications: (1) a single round of first-strand random priming with a biotinylated random primer and (2) purification of the first DNA strand using streptavidin beads, as per Miura et al.31.

scBS-seq data processing
Adapter and low-quality reads were trimmed using trim_galore (v.0.6.10) with parameters: --quality 20 --stringency 3 --length 50 --clip_R1 9 --clip_R2 9 --paired --trim1 --phred33. Clean reads were mapped to the marmoset genome (calJac4) using Bismark (v.0.24.2) in paired-end mode with parameters: --bowtie2 --fastq --non_directional. PCR duplicates were removed using the samtools (v.1.21) markup command. Bismark methylation extractor, Bismark2bedGraph, and Coverage2cytosine analyses were performed sequentially for downstream processing. Among 103 marmoset cells, 30 cynomolgus monkey cells, and 46 human cells sequenced, we excluded 3, 0, and 4 cells, respectively. This exclusion was done based on two criteria: (1) coverage rate (<1%) and (2) somatic cell contamination assessed via methylation levels at maternally methylated imprinted loci (MEST/PEG1, RB1, PLAGL1/ZAC1) (Supplementary Data 3, 5, and 7). The information of the imprinted loci is described in Supplementary Data 4.

Data from both DNA strands were merged to calculate CpG site methylation rates. The average methylation rate per cell was derived by averaging genome-wide CpG site methylation rates. Based on these averages, cells were classified into seven groups for marmosets (Group 1: 0%–10%, 11 cells; Group 2: 10%–20%, 16 cells; Group 3: 20%–30%, 8 cells; Group 4: 30%–40%, 9 cells; Group 5: 40%–50%, 22 cells; Group 6: 50%–60%, 19 cells; Group 7: 60%–80.1%, 15 cells), five groups for cynomolgus monkeys (Group 1: 2.5%–4.8%, 5 cells; Group 2: 28.4%–41.1%, 4 cells; Group 3: 52%–59.5%, 6 cells; Group 4: 66%–67.7%, 6 cells; Group 5: 68.2%–72.8%, 9 cells), and seven groups for humans (Group 1: 0%–20%, 1 cell; Group 2: 20%–30%, 6 cells; Group 3: 30%–40%, 7 cells; Group 4: 40%–50%, 10 cells; Group 5: 50%–60%, 7 cells; Group 6: 60%–70%, 3 cells; Group 7: 70%–80%, 8 cells).

scRNA-seq library preparation
The scRNA-seq library preparation was conducted as previously described18. Before cDNA synthesis, ERCC RNA (Invitrogen, diluted 1:500,000) was added to the cytoplasmic RNA. cDNA was synthesized using cytoplasmic RNA, an oligo dT primer containing cell barcode sequences, template switch (TS) oligo DNA, and SuperScript II reverse transcriptase (Thermo Fisher). To prevent concatemerization of the TS oligo, nonnatural nucleotides were added to its 5′ end32. After purifying the cDNA with AMPure beads, qPCR was performed using primers detecting ERCC and the germ cell-specific gene DDX4 to evaluate the cDNA synthesis efficiency and confirm germ cell identity. cDNAs from 11 to 12 cells with different unique barcodes were pooled, and PCR was performed using a biotinylated primer targeting the 5′ end of the oligo dT sequence and an unmodified primer annealing to the 5′ end of the TS oligo. Fragmentation was carried out with the NEBNext Ultra II FS DNA Library Prep Kit for Illumina, and biotinylated DNA was purified using streptavidin beads. Adapter ligation and PCR amplification were performed according to the NEBNext Ultra II FS DNA Library Prep Kit protocol. Each library pool was labeled with a unique dual index (NEBNext Multiplex Oligos for Illumina, 96 Unique Dual Index). After quality assessment using Bioanalyzer (Agilent), paired-end 150-bp sequencing was performed on an Illumina NovaSeq platform.

Data availability
scRNA/scBS/scATAC-seq data have been deposited in the DDBJ database under accession numbers: DRA016143 (https://ddbj.nig.ac.jp/search/entry/sra-submission/DRA016143) and DRA016233 (https://ddbj.nig.ac.jp/search/entry/sra-submission/DRA016233) and in the NBDC Human database under accession numbers: JGAS000887 (https://humandbs.dbcls.jp/en/hum0542-v1) and JGAS000888 (https://humandbs.dbcls.jp/en/hum0544-v1). Source data are provided with this paper.

Code availability
Publicly available software and packages were mainly used to analyze scBS-seq data: for processing BAM files, Samtools version 1.21 (https://doi.org/10.1093/gigascience/giab008); for trimming adapter sequences, Trim Galore version 0.6.10 (https://zenodo.org/records/7598955), Cutadapt version 4.9 (https://doi.org/10.14806/ej.17.1.200), FastQSC version 0.12.1; for mapping to the genome, Bismark version 0.24.2 (https://doi.org/10.1093/bioinformatics/btr167), Bowtie2 version 2.5.4 (https://doi.org/10.1109/SFCS.2000.892127); for annotation and analyzing data, bedtools version 2.31.0 (https://doi.org/10.1093/bioinformatics/btq033), Homer version 5.1 (https://doi.org/10.1016/j.molcel.2010.05.004); for displaying data on chromosomes, karyoploteR version 1.32.0 (https://doi.org/10.1093/bioinformatics/btx346), bismap version 1.2.1 (https://doi.org/10.1093/nar/gky677). Seurat version 5.1.0 (https://doi.org/10.1038/s41587-023-01767-y) and Cell Ranger were used to analyze scRNA-seq data. Cell Ranger and ArchR were used to analyze scATAC-seq data. The R and shell scripts used in the ATAC analyses can be obtained from GitHub (https://github.com/Hattyoriiiiiii/scatac-caljac-watanabe)33. Detailed information on analysis methods is provided in the respective sections of the Methods and Supplementary Methods.

Statics & Reproducibility
The specific statistical test used, sample size (n), and P values are reported in the corresponding figure. Unless otherwise indicated, tests were two‑sided, and a significance level of α = 0.05 was used. Reproducibility of the immunostaining results was described in the legends of the corresponding figures. The sample size for the scBS-seq data was first determined based on the predicted variation in methylation levels from immunofluorescence analyses. The linear increase pattern across developmental stages confirmed that our sample size is sufficient to understand overall developmental methylation dynamics.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript by Kojima et al. presents a detailed analysis of the timing and pattern of DNA methylation establishment during germ cell development across primate species. The authors utilize single-cell bisulfite sequencing (scBS-seq) alongside complementary scRNA-seq analyses to provide important insights into the dynamics of epigenetic reprogramming in primate germ cells, emphasizing species-specific differences compared to mouse models. The manuscript is concise and well-written; however, several points require attention before it can be recommended for publication. These points are outlined below.\nWhile the authors mention in the abstract that the manuscript deals with three primate species: \"we demonstrate that in three primate species - marmosets, macaques, and humans - de novo methylation occurs postnatally in prospermatogonia in males and growing oocytes in females\", it is worth noting that the human data is very limited, devoid of sequencing controls, and in terms of sample size (of course completely understandable given the logistical and ethical difficulties in obtaining such human samples), not comparable to the datasets from marmosets and cynomolgus monkeys. While the human data might offer some preliminary insights and suggest conservation of the described mechanisms, further work is needed to unequivocally determine the dynamics of 5mC deposition in the postnatal germline.\nWhile the study identifies differential 5mC kinetics between genic and intergenic regions, further mechanistic insight into why genic regions preferentially and more rapidly acquire methylation would enhance the impact. For example, could the authors utilize their RNA-seq data and classify genes according to expression levels to identify whether de novo 5mC in gene bodies correlates with increased transcriptional activity?\nThe work by Kubo et al. (BMC Genomics, 2015) reported very high levels of CH methylation (mCH) in rodent neonatal PSGs: \"Approximately 10.8% of all cytosines were methylated in this cell type, compared with 3–4% in somatic tissues, and more than two-thirds of 5mCs (7.5% of all cytosines) were at CH sites (Fig. 2a).\" Could the authors leverage their scBS-seq data (perhaps by pooling data to achieve greater depth) and analyze mCH? This would represent another important point the authors could easily address. The abundance and origin of the mCH signal, especially in the germline, has been much debated. The authors could use their data to demonstrate or dispute whether this is a conserved mechanism in mammals by quantifying mCH at various developmental stages and determining motif occurrence (e.g., CAC or CAG). The discovery that primate prospermatogonia undergo mitotic divisions during DNA methylation establishment, with DNMT1 expression implying methylation maintenance during mitosis, is particularly compelling. One could therefore hypothesize that mCH is diluted during these stages, as mCH mostly accumulates in non-dividing tissues. In any case, this would be an important analysis to perform.\nSome quality control details for single-cell analyses (e.g., dropout rates, technical replicates, controls for bisulfite conversion efficiency) should be explained to strengthen the technical rigor of the reported findings.\nCould the authors please clarify why certain germ cells showed negative staining for 5mC in older samples and discuss whether this might indicate technical issues or biological variability?", "reviewer_2": "In this manuscript, Kojima et al. investigate the timing of de novo DNA methylation during primate germ cell development, employing a combination of immunostaining, single-cell bisulfite sequencing (scBS-seq), and transcriptomic analyses. The study addresses an important question in the field of epigenetic reprogramming and offers valuable insights into methylation dynamics in non-human primates. The methodologies are largely appropriate, and the data presented are of high quality. However, the manuscript does not sufficiently address the mechanisms regulating the timing of methylation acquisition during germ cell development. Several critical issues need to be addressed to strengthen the manuscript and fully support the authors' conclusions.\nMajor Comments:\n1. In Figures 1E and 1F, the authors demonstrate germ cell-specific acquisition of 5-methylcytosine (5mC). However, somatic cells appear to exhibit a progressive reduction in 5mC staining intensity with increasing age. This observation is not discussed or quantified. A systematic quantification of 5mC levels in somatic cells over developmental time points would help clarify whether global methylation loss in somatic cells accompanies germline reprogramming, which could have broader implications for understanding tissue-specific methylation dynamics.\n2. In Figure 3B, cells are categorized into G1-G7 groups based on methylation levels. However, the manuscript does not adequately explain how these groupings correspond to developmental age or biological progression. As the classification is based on methylation states rather than chronological age, the authors should explicitly state this distinction. Including a schematic or trajectory plot mapping how cells from different developmental time points distribute across G1-G7 groups would enhance clarity and help contextualize the methylation progression relative to development.\n3. The observation that genic regions preferentially gain methylation compared to intergenic regions is interesting. However, the role of chromatin accessibility (ATAC-seq peaks) in this process requires further explanation. Specifically, it is unclear whether ATAC signal intensity at earlier stages correlates with subsequent methylation acquisition. A more detailed description of how chromatin state influences methylation dynamics, and whether open chromatin regions are preferentially methylated during later stages (G5-G7), would substantially strengthen the mechanistic interpretation.\n4. In Figure 3B, the authors report that DNMT3A is expressed across all groups, while DNMT3B and DNMT3L expression is more restricted to later stages. Interestingly, both DNMT3A and DNMT3B show biphasic expression patterns, with peaks at early (G2) and later (G5-G6) stages. This biphasic regulation is intriguing but is not discussed in the manuscript. A brief discussion exploring potential regulatory mechanisms underlying these expression patterns - such as the involvement of transcriptional regulators or cell cycle dynamics - would add depth to the interpretation.\n5. While the transcriptomic data for DNMT3A, DNMT3B, and DNMT3L are informative, it is important to validate these findings at the protein level. Immunostaining or other protein-level analyses across developmental stages would provide critical validation of the temporal regulation of de novo methyltransferases.\n6. In Figures 6 and 7, the authors assess methylation levels in oocytes and testicular cells obtained from uterine cancer patients and testicular biopsies, respectively. The use of cancer-derived samples may confound the interpretation of developmental methylation patterns due to potential alterations in the epigenetic landscape associated with malignancy. The authors should more thoroughly discuss the limitations of using cancer samples for developmental studies and, if possible, consider validating key findings in non-cancerous control samples.\nMinor Comments:\nSeveral figures would benefit from more detailed legends, explicitly stating the number of biological replicates and statistical analyses performed.\nThe manuscript would benefit from careful proofreading to correct minor grammatical errors and improve overall clarity.\nSummary:\nOverall, this manuscript addresses an important question and is based on impressive technical execution. However, significant revisions are necessary to strengthen the mechanistic insights and address potential confounding factors.", "reviewer_3": "Establishment of DNA methylation during germ cell development is well-characterized in mice, but our knowledge on this process in primates including humans is very limited. In this manuscript, Kojima et al. report their findings on the mode of DNA methylation acquisition in germ cells, mostly male germ cells, of marmosets, macaques, and humans although their molecular data are limited to marmosets and macaques. They find that prospermatogonia of primates undergo de novo methylation primarily during the first year after birth, unlike those of mice, where methylation is acquired during the fetal stage. Furthermore, they find that primate prospermatogonia undergo mitotic divisions, which indicates that establishment of methylation involves maintenance through DNA replication. These findings are novel and important for understanding the process of germ cell development in primates and have implications to fertility preservation for cancer patients, especially young male patients. Most of the mechanistic aspects remain to be elucidated, but I understand that such studies need time, especially for primates. Overall, the study provides important information. I should like to raise several points for improvement.\n1. Lines 139-153: The authors discuss the pattern of DNA methylation acquisition across the genome by providing two hypothetical models (Fig. 3A). While I see the value of such models, they are not properly described in the text and, furthermore, not used in an effective way in deriving the conclusion. I request a more careful discussion with clear and correct wording.\n2. Lines 283-289: The authors discuss possible mechanisms for the faster methylation acquisition in genic regions, but I was surprised to find that they do not touch upon H3K36me. It is established that both DNMT3A and DNMT3B recognize H3K36me, which often marks genic and intergenic regions differently. Indeed, a study in mice showed that acquisition of methylation in male germ cells depends on H3K36me (Shirane et al. Nat. Genet. 2020). The authors should discuss this point.\n3. Lines 320-339: The authors discuss possible mechanisms underlying the prolonged methylation acquisition in primates. While a difference in SAM concentration is an interesting possibility, this is totally speculative. I therefore suggest a more concise discussion on this point. On the other hand, I do not understand why they do not discuss their own finding, the lack of DAD-like regions in primates. The authors should discuss this point.\n4. A problem of this manuscript is that the writing occasionally lacks smoothness, likely due to the authors’ English writing ability. I strongly recommend English editing. Below are some points to be improved but there are many more.\n- Line 43: To make this sentence meaningful, please add a brief phrase describing what is DNMT1.\n- Line 80: Do the authors mean to describe methylation in the ovaries including somatic cells? Please correct it.\n- Line 97: 5-methylcytosine and its abbreviation 5mC should be introduced here.\n- Line 121: As scRNA-seq data do not appear in this section, it would be good to announce their appearance in later sections, by adding (see later).\n- Line 148: A definition should be provided for the term “final level”. Is it a methylation level at 12.5M or of G7?\n- Lines 154-180: The words “genomic structures”, “features”, and “annotations” are intermingled in these paragraphs but are not properly used. Some sentences even have grammatical errors. Please tidy up.\n- Line 195: “senescence G0 state” should be “quiescent G0 state”?"}, "author_response": "Point-by-point response to the reviewer’s comments.\nThe reviewer’s comments are presented in blue font, and our replies are described in red.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe manuscript by Kojima et al. presents a detailed analysis of the timing and pattern of DNA methylation establishment during germ cell development across primate species. The authors utilize single-cell bisulfite sequencing (scBS-seq) alongside complementary scRNA-seq analyses to provide important insights into the dynamics of epigenetic reprogramming in primate germ cells, emphasizing species-specific differences compared to mouse models. The manuscript is concise and well-written; however, several points require attention before it can be recommended for publication. These points are outlined below.\n①While the authors mention in the abstract that the manuscript deals with three primate species: \"we demonstrate that in three primate species - marmosets, macaques, and humans - de novo methylation occurs postnatally in prospermatogonia in males and growing oocytes in females\", it is worth noting that the human data is very limited, devoid of sequencing controls, and in terms of sample size (of course completely understandable given the logistical and ethical difficulties in obtaining such human samples), not comparable to the datasets from marmosets and cynomolgus monkeys. While the human data might offer some preliminary insights and suggest conservation of the described mechanisms, further work is needed to unequivocally determine the dynamics of 5mC deposition in the postnatal germline.\nResponse: In the revised version, we incorporated scBS-seq data for human prospermatogonia and spermatogonial stem cells (lines 366-389, Fig 7). This analysis confirms that human germ cells exhibit a similar annotation-dependent DNA methylation establishment kinetics to those of monkey germ cells. However, the establishment in humans appears to begin later and take longer. Some of the samples examined here were derived from testes with teratoma; their developmental progression may be affected. Future studies using normal tissues are required to confirm the results. We clearly described this in the revised version. In addition, we added one testicular sample for immunostaining analysis of 5-mC (3-month-old testicular sample from a testicular tumor (hemangioma) patient, Figure 6D). This analysis confirmed that de novo methylation is initiated at 3-month-old testes. Adding human sequencing data and new immunostaining data increased the significance of our paper. We thank the reviewer for the comment.\n②While the study identifies differential 5mC kinetics between genic and intergenic regions, further mechanistic insight into why genic regions preferentially and more rapidly acquire methylation would enhance the impact. For example, could the authors utilize their RNA-seq data and classify genes according to expression levels to identify whether de novo 5mC in gene bodies correlates with increased transcriptional activity?\nResponse: We thank the reviewer for this valuable suggestion. We analyzed our RNA-seq data from marmoset prospermatogonia and classified genes into three categories based on expression levels—high, mid, and low—to examine their methylation status. This analysis revealed that DNA methylation levels in genic regions are positively correlated with transcription levels: highly expressed genes showed higher DNA methylation levels within gene bodies than weakly expressed genes. This finding is consistent with the previous studies showing that de novo DNA methylation in oocytes depends on transcriptional activity. We have incorporated these results into the revised manuscript (lines 214–228, Fig. 3H and I).\n③The work by Kubo et al. (BMC Genomics, 2015) reported very high levels of CH methylation (mCH) in rodent neonatal PSGs: \"Approximately 10.8% of all cytosines were methylated in this cell type, compared with 3–4% in somatic tissues, and more than two-thirds of 5mCs (7.5% of all cytosines) were at CH sites (Fig. 2a).\" Could the authors leverage their scBS-seq data (perhaps by pooling data to achieve greater depth) and analyze mCH? This would represent another important point the authors could easily address. The abundance and origin of the mCH signal, especially in the germline, has been much debated. The authors could use their data to demonstrate or dispute whether this is a conserved mechanism in mammals by quantifying mCH at various developmental stages and determining motif occurrence (e.g., CAC or CAG). The discovery that primate prospermatogonia undergo mitotic divisions during DNA methylation establishment, with DNMT1 expression implying methylation maintenance during mitosis, is particularly compelling. One could therefore hypothesize that mCH is diluted during these stages, as mCH mostly accumulates in non-dividing tissues. In any case, this would be an important analysis to perform.\nResponse: We thank the reviewer for raising this important point. As suggested, we analyzed CH methylation levels in marmoset, cynomolgus monkey, and human prospermatogonia. Our analysis demonstrated that CH methylation remained consistently low in primate prospermatogonia (marmoset: 0.7%–1.1%; cynomolgus: 1.0%–1.8%; humans: 1.5-2.1%). This contrasts sharply with the situation in mouse prospermatogonia (7.5%) and highlights species-specific features of CH methylation during this stage of germ cell development.\nOur detailed analysis further revealed that methylated CH motifs are enriched for CA dinucleotides, showing an increasing trend during development. Moreover, the CAC and CAG trinucleotides were the most prevalent motifs among methylated CA sequences. Since the preferred target sequences for DNMT3A and DNMT3B are CAC and CAG, respectively, these findings suggest that CH methylation in primate prospermatogonia depends on DNMT3A/DNMT3B activity. Consistently, we confirmed an increasing trend in DNMT3A and DNMT3B mRNA levels, correlating with the rise in mCH levels.\nAs proposed by the reviewer, the substantial differences between mouse and primates may be explained by the fact that primate prospermatogonia remain mitotically active at this stage. We have incorporated these results into the revised manuscript (lines 267–285, 316-321, and 387-394, Fig. 4E, F, 5C, D, 7C).\n④Some quality control details for single-cell analyses (e.g., dropout rates, technical replicates, controls for bisulfite conversion efficiency) should be explained to strengthen the technical rigor of the reported findings.\nResponse: As suggested, we have added the information on dropout rates, number of replicates at each stage, conversion rates, and genome coverage rates necessary for evaluating scBS-seq data quality (lines 136–142). We believe that including these details further demonstrates the accuracy and reliability of our dataset.\n⑤Could the authors please clarify why certain germ cells showed negative staining for 5mC in older samples and discuss whether this might indicate technical issues or biological variability?\nResponse: In the revised version, we added new data showing that long-term storage results in similar negative staining. We compared the staining pattern before and after storage for several months. Negative staining was observed only after staining. These data and the discussions of the data have been incorporated into the manuscript and the supplementary figure (lines 359–369, Figure S6).\nReviewer #2 (Remarks to the Author):\nIn this manuscript, Kojima et al. investigate the timing of de novo DNA methylation during primate germ cell development, employing a combination of immunostaining, single-cell bisulfite sequencing (scBS-seq), and transcriptomic analyses. The study addresses an important question in the field of epigenetic reprogramming and offers valuable insights into methylation dynamics in non-human primates. The methodologies are largely appropriate, and the data presented are of high quality. However, the manuscript does not sufficiently address the mechanisms regulating the timing of methylation acquisition during germ cell development. Several critical issues need to be addressed to strengthen the manuscript and fully support the authors' conclusions.\nMajor Comments:\n1. In Figures 1E and 1F, the authors demonstrate germ cell-specific acquisition of 5-methylcytosine (5mC). However, somatic cells appear to exhibit a progressive reduction in 5mC staining intensity with increasing age. This observation is not discussed or quantified. A systematic quantification of 5mC levels in somatic cells over developmental time points would help clarify whether global methylation loss in somatic cells accompanies germline reprogramming, which could have broader implications for understanding tissue-specific methylation dynamics.\nResponse: We thank the reviewer for highlighting this issue. The images shown in Figures 1E and 1F were indeed prone to misinterpretation. To improve visibility of weak 5mC signals in germ cells, we had increased the exposure time of early-age samples. We believe that these differences in exposure time, combined with variations in tissue quality, may have led to a misunderstanding of the methylation status of somatic cells. To resolve this, we repeated the immunostaining and captured images using identical exposure times.\nFurthermore, we replaced the lower-quality samples with higher-quality ones (marmoset 33-day-old testis → 21-day-old testis; cynomolgus monkey 1-year- and 11-month-old testis → 1-year- and 9-month-old testis; Figure 1E, 1F, and Table S1). We believe the new images more accurately represent actual DNA methylation levels.\n2. In Figure 3B, cells are categorized into G1-G7 groups based on methylation levels. However, the manuscript does not adequately explain how these groupings correspond to developmental age or biological progression. As the classification is based on methylation states rather than chronological age, the authors should explicitly state this distinction. Including a schematic or trajectory plot mapping how cells from different developmental time points distribute across G1-G7 groups would enhance clarity and help contextualize the methylation progression relative to development.\nResponse: We thank the reviewer for this comment. As suggested, we have revised the manuscript (lines 160–167) and the corresponding figure (Figure 2, 5A, and 7A) to clarify the relationships among grouping, methylation levels, and developmental stages.\n3. The observation that genic regions preferentially gain methylation compared to intergenic regions is interesting. However, the role of chromatin accessibility (ATAC-seq peaks) in this process requires further explanation. Specifically, it is unclear whether ATAC signal intensity at earlier stages correlates with subsequent methylation acquisition. A more detailed description of how chromatin state influences methylation dynamics, and whether open chromatin regions are preferentially methylated during later stages (G5-G7), would substantially strengthen the mechanistic interpretation.\nResponse: We appreciate the reviewer’s suggestion. We analyzed ATAC-seq data from marmoset prospermatogonia to assess the relationship between chromatin accessibility and DNA methylation acquisition at genic regions. This analysis revealed that “fast” genes display greater promoter accessibility than “intermediate” and “slow” genes (Figure 3I, lines 222–226). This finding is consistent with our additional results showing that methylation acquisition at gene bodies positively correlates with transcription levels (Figure 3H, lines 219–222). These new results have been incorporated into the revised manuscript.\n4. In Figure 3B, the authors report that DNMT3A is expressed across all groups, while DNMT3B and DNMT3L expression is more restricted to later stages. Interestingly, both DNMT3A and DNMT3B show biphasic expression patterns, with peaks at early (G2) and later (G5-G6) stages. This biphasic regulation is intriguing but is not discussed in the manuscript. A brief discussion exploring potential regulatory mechanisms underlying these expression patterns - such as the involvement of transcriptional regulators or cell cycle dynamics - would add depth to the interpretation.\nResponse: We consider the biphasic pattern observed to be largely attributable to technical variation resulting from the limited number of cells and the small number of UMIs per cell. We have added this explanation to the revised version (lines 235–237).\n5. While the transcriptomic data for DNMT3A, DNMT3B, and DNMT3L are informative, it is important to validate these findings at the protein level. Immunostaining or other protein-level analyses across developmental stages would provide critical validation of the temporal regulation of de novo methyltransferases.\nResponse: We appreciate this comment. We performed immunofluorescence analyses as suggested. These analyses support the overall expression patterns revealed by scRNA-seq and also provide two new insights. First, they showed substantial variation in expression levels among individual cells. Second, they suggested that DNMT3A and DNMT3B may be regulated post-transcriptionally through nuclear import and export. These data have been added to the revised manuscript (lines 239–249, Figure 4C).\n6. In Figures 6 and 7, the authors assess methylation levels in oocytes and testicular cells obtained from uterine cancer patients and testicular biopsies, respectively. The use of cancer-derived samples may confound the interpretation of developmental methylation patterns due to potential alterations in the epigenetic landscape associated with malignancy. The authors should more thoroughly discuss the limitations of using cancer samples for developmental studies and, if possible, consider validating key findings in non-cancerous control samples.\nResponse: We agree with this comment. We used several cancer patient-derived tissues for methylation analyses. The potential impact of cancer on testis/ovary development depends on the cancer type. We consider that testicular teratoma may affect the developmental progression of testicular tissues because of their close proximity. By contrast, other cancers originated outside the testes/ovaries, and therefore the associated risk should be minimal. We have added sentences in the revised version explaining these limitations for each cancer type (lines 335–338, 348-353, 380-383, and 407-409).\nMinor Comments:\nSeveral figures would benefit from more detailed legends, explicitly stating the number of biological replicates and statistical analyses performed.\nThe manuscript would benefit from careful proofreading to correct minor grammatical errors and improve overall clarity.\nResponse: In accordance with the reviewer’s comments, we carefully reviewed the manuscript, figures, and legends. Detailed information—including the number of replicates and the statistical analyses performed—was added to the figures and their legends. In addition, the manuscript was edited to correct grammatical errors and enhance overall readability.\nSummary:\nOverall, this manuscript addresses an important question and is based on impressive technical execution. However, significant revisions are necessary to strengthen the mechanistic insights and address potential confounding factors.\nResponse: We thank the reviewer for recognizing the importance of our study and for the positive evaluation of our methodology.\nReviewer #3 (Remarks to the Author):\nEstablishment of DNA methylation during germ cell development is well-characterized in mice, but our knowledge on this process in primates including humans is very limited. In this manuscript, Kojima et al. report their findings on the mode of DNA methylation acquisition in germ cells, mostly male germ cells, of marmosets, macaques, and humans although their molecular data are limited to marmosets and macaques. They find that prospermatogonia of primates undergo de novo methylation primarily during the first year after birth, unlike those of mice, where methylation is acquired during the fetal stage. Furthermore, they find that primate prospermatogonia undergo mitotic divisions, which indicates that establishment of methylation involves maintenance through DNA replication. These findings are novel and important for understanding the process of germ cell development in primates and have implications to fertility preservation for cancer patients, especially young male patients. Most of the mechanistic aspects remain to be elucidated, but I understand that such studies need time, especially for primates. Overall, the study provides important information. I should like to raise several points for improvement.\n1. Lines 139-153: The authors discuss the pattern of DNA methylation acquisition across the genome by providing two hypothetical models (Fig. 3A). While I see the value of such models, they are not properly described in the text and, furthermore, not used in an effective way in deriving the conclusion. I request a more careful discussion with clear and correct wording.\nResponse: We appreciate the reviewer for highlighting this issue. As suggested, we extensively revised this section. We believe that, in the revised version, the hypotheses facilitate a clearer understanding of the results and contribute to a stronger conclusion (lines 168–182).\n2. Lines 283-289: The authors discuss possible mechanisms for the faster methylation acquisition in genic regions, but I was surprised to find that they do not touch upon H3K36me. It is established that both DNMT3A and DNMT3B recognize H3K36me, which often marks genic and intergenic regions differently. Indeed, a study in mice showed that acquisition of methylation in male germ cells depends on H3K36me (Shirane et al. Nat. Genet. 2020). The authors should discuss this point.\nResponse: As suggested by the reviewer, we now discuss the potential involvement of H3K36me2/3 marks in differential methylation between genic and intergenic regions (lines 423–430, Figure 4A). We thank the reviewer for this valuable input.\n3. Lines 320-339: The authors discuss possible mechanisms underlying the prolonged methylation acquisition in primates. While a difference in SAM concentration is an interesting possibility, this is totally speculative. I therefore suggest a more concise discussion on this point. On the other hand, I do not understand why they do not discuss their own finding, the lack of DAD-like regions in primates. The authors should discuss this point.\nResponse: We appreciate the reviewer’s thoughtful suggestion. Accordingly, we have shortened the discussion on SAM concentration and, instead, added discussion on DAD-like regions in the revised version (lines 463–476).\n4. A problem of this manuscript is that the writing occasionally lacks smoothness, likely due to the authors’ English writing ability. I strongly recommend English editing. Below are some points to be improved but there are many more.\n- Line 43: To make this sentence meaningful, please add a brief phrase describing what is DNMT1.\n- Line 80: Do the authors mean to describe methylation in the ovaries including somatic cells? Please correct it.\n- Line 97: 5-methylcytosine and its abbreviation 5mC should be introduced here.\n- Line 121: As scRNA-seq data do not appear in this section, it would be good to announce their appearance in later sections, by adding (see later).\n- Line 148: A definition should be provided for the term “final level”. Is it a methylation level at 12.5M or of G7?\n- Lines 154-180: The words “genomic structures”, “features”, and “annotations” are intermingled in these paragraphs but are not properly used. Some sentences even have grammatical errors. Please tidy up.\n- Line 195: “senescence G0 state” should be “quiescent G0 state”?\nResponse: We sincerely thank the reviewer for these comments. The manuscript has been carefully edited to improve readability and accuracy."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have satisfactorily addressed all of my questions. The newly added analyses, including those on non-canonical DNA methylation, add substantial depth to the dataset and significantly enhance the value of this manuscript as a community resource.", "reviewer_2": "Based on the revisions described and the authors’ point-by-point responses, the concerns raised have been satisfactorily addressed. The authors have:\nClarified and corrected the interpretation of 5mC immunostaining by repeating experiments with standardized exposure and improved sample quality.\nClearly explained the basis of the G1–G7 grouping and its relationship to methylation progression and developmental stages.\nStrengthened the mechanistic interpretation of methylation acquisition by integrating chromatin accessibility and transcriptional data.\nAppropriately addressed the apparent biphasic DNMT expression patterns and supported transcriptomic findings with new protein-level validation.\nTransparently discussed the limitations associated with the use of cancer-derived samples.\nImproved figure clarity, legends, and overall manuscript quality through careful editing.\nTaken together, these revisions substantially strengthen the manuscript and fully support the authors’ conclusions. I accept the manuscript in the present format.", "reviewer_3": "The authors have appropriately addressed almost all of the points I raised. Furthermore, the addition of scBS-seq data for human prospermatogonia and spermatogonial stem cells is impressive and further strengthens the manuscript.\nOne minor point: the clause added in the Abstract to explain the maintenance methyltransferase DNMT1 (“…, which adds a methyl group to the newly synthesized DNA strand using the information regarding the methylation status of the template strand, …”) seems overly detailed and could be deleted."}, "author_response": ""}]

Decision letter