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DNMT3A-dependent DNA methylation is required for spermatogonial stem cells to commit to spermatogenesis

Nature Genetics volume 54, pages 469–480 (2022)Cite this article

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Abstract

DNA methylation plays a critical role in spermatogenesis, as evidenced by the male sterility of DNA methyltransferase (DNMT) mutant mice. Here, we report a division of labor in the establishment of the methylation landscape of male germ cells and its functions in spermatogenesis. Although DNMT3C is essential for preventing retrotransposons from interfering with meiosis, DNMT3A broadly methylates the genome (with the exception of DNMT3C-dependent retrotransposons) and controls spermatogonial stem cell (SSC) plasticity. By reconstructing developmental trajectories through single-cell RNA sequencing and profiling chromatin states, we found that Dnmt3A mutant SSCs can only self-renew and no longer differentiate in association with spurious enhancer activation that enforces an irreversible stem cell gene program. Our findings therefore highlight a key function of DNA methylation in male fertility: the epigenetic programming of SSC commitment to differentiation and lifelong spermatogenesis supply.

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Fig. 1: DNMT3A broadly methylates the male germ cell genome but is dispensable for retrotransposon silencing.
Fig. 2: Dnmt3A mutant males complete the first wave of spermatogenesis with reduced germ cell numbers.
Fig. 3: SSCs accumulate in absence of DNMT3A-dependent DNA methylation.
Fig. 4: DNMT3A is essential to SSC plasticity.
Fig. 5: DNA methylation controls stem cell gene expression by limiting enhancer activity in SSCs.
Fig. 6: Model of DNMT3A-dependent DNA methylation in spermatogenesis.

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Data availability

Data supporting the findings of this study are available in the main text and the supplementary materials. All sequencing data generated here have been deposited in the Gene Expression Omnibus under accession number GSE162724. Source data are provided with this paper.

Public datasets are available under the following accession numbers: H3K36me2 ChIP-seq in E16.5 prospermatogonia and WGBS on Nsd1 control and Nsd1KO mice in E16.5 prospermatogonia, GSE148150; H3K27ac and H3K4me1 ChIP-seq in ID4-GFPbright and ID4-GFPdim from mouse prepuberal testes, GSE131657; H3K27ac ChIP-seq in prospermatogonia at E13.5 and 1 dpp, DRA006633; assay for transposase-accessible chromatin with sequencing and H3K27me3 ChIP-seq in prospermatogonia at E13.5, E17.5, E19.5 and postnatal day 2, GSE121118. Source data are provided with this paper.

Code availability

We relied on existing published analysis tools, as indicated in Methods. No new analysis tools were generated; scripts used to analyze the data using the published tools are available upon request.

References

  1. Greenberg, M. V. C. & Bourc’his, D. The diverse roles of DNA methylation in mammalian development and disease. Nat. Rev. Mol. Cell Biol. 20, 590–607 (2019).

    Article  CAS  PubMed  Google Scholar 

  2. Weber, M. et al. Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nat. Genet. 39, 457–466 (2007).

    Article  CAS  PubMed  Google Scholar 

  3. Stadler, M. B. et al. DNA-binding factors shape the mouse methylome at distal regulatory regions. Nature 480, 490–495 (2011).

    Article  CAS  PubMed  Google Scholar 

  4. Xie, W. et al. Epigenomic analysis of multilineage differentiation of human embryonic stem cells. Cell 153, 1134–1148 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Gifford, C. A. et al. Transcriptional and epigenetic dynamics during specification of human embryonic stem cells. Cell 153, 1149–1163 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Yin, Y. et al. Impact of cytosine methylation on DNA binding specificities of human transcription factors. Science 356, eaaj2239 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Luo, C., Hajkova, P. & Ecker, J. R. Dynamic DNA methylation: In the right place at the right time. Science 361, 1336–1340 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Lord, T. & Oatley, J. M. Regulation of spermatogonial stem cell maintenance and self-renewal. In The Biology of Mammalian Spermatogonia 91–129 (Springer, 2017).

  9. Kubo, N. et al. DNA methylation and gene expression dynamics during spermatogonial stem cell differentiation in the early postnatal mouse testis. BMC Genomics 16, 624 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Bourc’his, D., Xu, G.-L., Lin, C.-S., Bollman, B. & Bestor, T. H. Dnmt3L and the establishment of maternal genomic imprints. Science 294, 2536–2539 (2001).

    Article  PubMed  Google Scholar 

  11. Kaneda, M. et al. Essential role for de novo DNA methyltransferase Dnmt3a in paternal and maternal imprinting. Nature 429, 900–903 (2004).

    Article  CAS  PubMed  Google Scholar 

  12. Barau, J. et al. The DNA methyltransferase DNMT3C protects male germ cells from transposon activity. Science 354, 909–912 (2016).

    Article  CAS  PubMed  Google Scholar 

  13. Bourc’his, D. & Bestor, T. H. Meiotic catastrophe and retrotransposon reactivation in male germ cells lacking Dnmt3L. Nature 431, 96–99 (2004).

    Article  PubMed  CAS  Google Scholar 

  14. Zamudio, N. et al. DNA methylation restrains transposons from adopting a chromatin signature permissive for meiotic recombination. Genes Dev. 29, 1256–1270 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Yaman, R. & Grandjean, V. Timing of entry of meiosis depends on a mark generated by DNA methyltransferase 3a in testis. Mol. Reprod. Dev. 73, 390–397 (2006).

    Article  CAS  PubMed  Google Scholar 

  16. Kato, Y. et al. Role of the Dnmt3 family in de novo methylation of imprinted and repetitive sequences during male germ cell development in the mouse. Hum. Mol. Genet. 16, 2272–2280 (2007).

    Article  CAS  PubMed  Google Scholar 

  17. Lengner, C. J. et al. Oct4 expression is not required for mouse somatic stem cell self-renewal. Cell Stem Cell 1, 403–415 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Shovlin, T. C. et al. Sex-specific promoters regulate Dnmt3L expression in mouse germ cells. Hum. Reprod. 22, 457–467 (2007).

    Article  CAS  PubMed  Google Scholar 

  19. Shirane, K., Miura, F., Ito, T. & Lorincz, M. C. NSD1-deposited H3K36me2 directs de novo methylation in the mouse male germline and counteracts Polycomb-associated silencing. Nat. Genet. 52, 1088–1098 (2020).

    Article  PubMed  CAS  Google Scholar 

  20. Okano, M., Bell, D. W., Haber, D. A. & Li, E. DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell 99, 247–257 (1999).

    Article  CAS  PubMed  Google Scholar 

  21. Ohinata, Y. et al. Blimp1 is a critical determinant of the germ cell lineage in mice. Nature 436, 207–213 (2005).

    Article  CAS  PubMed  Google Scholar 

  22. Yoshida, S. et al. The first round of mouse spermatogenesis is a distinctive program that lacks the self-renewing spermatogonia stage. Development 133, 1495–1505 (2006).

    Article  CAS  PubMed  Google Scholar 

  23. Law, N. C., Oatley, M. J. & Oatley, J. M. Developmental kinetics and transcriptome dynamics of stem cell specification in the spermatogenic lineage. Nat. Commun. 10, 2787 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Chan, F. et al. Functional and molecular features of the Id4+ germline stem cell population in mouse testes. Genes Dev. 28, 1351–1362 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Helsel, A. R. et al. ID4 levels dictate the stem cell state in mouse spermatogonia. Development 144, 624–634 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Lord, T. & Oatley, J. M. A revised Asingle model to explain stem cell dynamics in the mouse male germline. Reproduction 154, R55–R64 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hermann, B. P. et al. The mammalian spermatogenesis single-cell transcriptome, from spermatogonial stem cells to spermatids. Cell Rep. 25, 1650–1667.e8 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Green, C. D. et al. A comprehensive roadmap of murine spermatogenesis defined by single-cell RNA-seq. Dev. Cell 46, 651–667.e10 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Vasiliauskaitė, L. et al. Defective germline reprogramming rewires the spermatogonial transcriptome. Nat. Struct. Mol. Biol. 25, 394–404 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Roy Choudhury, D. et al. Microarray-based analysis of cell-cycle gene expression during spermatogenesis in the mouse. Biol. Reprod. 83, 663–675 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Suzuki, S., McCarrey, J. R. & Hermann, B. P. An mTORC1-dependent switch orchestrates the transition between mouse spermatogonial stem cells and clones of progenitor spermatogonia. Cell Rep. 34, 108752 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. King, A. D. et al. Reversible regulation of promoter and enhancer histone landscape by DNA methylation in mouse embryonic stem cells. Cell Rep. 17, 289–302 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Jermann, P., Hoerner, L., Burger, L. & Schubeler, D. Short sequences can efficiently recruit histone H3 lysine 27 trimethylation in the absence of enhancer activity and DNA methylation. Proc. Natl Acad. Sci. U S A 111, E3415–E3421 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Brinkman, A. B. et al. Sequential ChIP-bisulfite sequencing enables direct genome-scale investigation of chromatin and DNA methylation cross-talk. Genome Res. 22, 1128–1138 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Cheng, K. et al. Unique epigenetic programming distinguishes regenerative spermatogonial stem cells in the developing mouse testis. iScience 23, 101596 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Yamanaka, S. et al. Broad heterochromatic domains open in gonocyte development prior to de novo DNA methylation.Dev. Cell 51, 21–34.e5 (2019).

    Article  CAS  PubMed  Google Scholar 

  38. Kawabata, Y. et al. Sex-specific histone modifications in mouse fetal and neonatal germ cells. Epigenomics 11, 543–561 (2019).

    Article  CAS  PubMed  Google Scholar 

  39. Smith, A. M. et al. A novel mode of enhancer evolution: the Tal1 stem cell enhancer recruited a MIR element to specifically boost its activity. Genome Res. 18, 1422–1432 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Domcke, S. et al. Competition between DNA methylation and transcription factors determines binding of NRF1. Nature 528, 575–579 (2015).

    Article  CAS  PubMed  Google Scholar 

  41. Lea, A. J. et al. Genome-wide quantification of the effects of DNA methylation on human gene regulation. Elife 7, 1–27 (2018).

    Article  Google Scholar 

  42. Stephens, D. C. & Poon, G. M. K. Differential sensitivity to methylated DNA by ETS-family transcription factors is intrinsically encoded in their DNA-binding domains. Nucleic Acids Res. 44, 8671–8681 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Song, H.-W. & Wilkinson, M. F. Transcriptional control of spermatogonial maintenance and differentiation. Semin. Cell Dev. Biol. 30, 14–26 (2014).

    Article  PubMed  CAS  Google Scholar 

  44. Zhang, T., Oatley, J., Bardwell, V. J. & Zarkower, D. DMRT1 is required for mouse spermatogonial stem cell maintenance and replenishment. PLoS Genet. 12, e1006293 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Wu, H. et al. Dnmt3a-dependent nonpromoter DNA methylation facilitates transcription of neurogenic genes. Sci. (80-.). 329, 444–448 (2010).

    Article  CAS  Google Scholar 

  46. Izzo, F. et al. DNA methylation disruption reshapes the hematopoietic differentiation landscape. Nat. Genet. 52, 378–387 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ketkar, S. et al. Remethylation of Dnmt3a−/− hematopoietic cells is associated with partial correction of gene dysregulation and reduced myeloid skewing. Proc. Natl Acad. Sci. U S A 117, 3123–3134 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Challen, G. A. et al. Dnmt3a is essential for hematopoietic stem cell differentiation. Nat. Genet. 44, 23–31 (2012).

    Article  CAS  Google Scholar 

  49. Jeong, M. et al. Loss of Dnmt3a immortalizes hematopoietic stem cells in vivo. Cell Rep. 23, 1–10 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Lewandoski, M., Wassarman, K. M. & Martin, G. R. Zp3–cre, a transgenic mouse line for the activation or inactivation of loxP-flanked target genes specifically in the female germ line. Curr. Biol. 7, 148–151 (1997).

    Article  CAS  PubMed  Google Scholar 

  51. Miura, F. et al. Highly efficient single-stranded DNA ligation technique improves low-input whole-genome bisulfite sequencing by post-bisulfite adaptor tagging. Nucleic Acids Res. 47, e85–e85 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Skene, P. J. & Henikoff, S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. Elife 6, 1–35 (2017).

    Article  Google Scholar 

  53. Didion, J. P., Martin, M. & Collins, F. S. Atropos: specific, sensitive, and speedy trimming of sequencing reads. PeerJ 5, e3720 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Illingworth, R. S. et al. Orphan CpG islands identify numerous conserved promoters in the mammalian genome. PLoS Genet. 6, e1001134 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Feng, H., Conneely, K. N. & Wu, H. A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data. Nucleic Acids Res. 42, e69–e69 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  57. Velte, E. K. et al. Differential RA responsiveness directs formation of functionally distinct spermatogonial populations at the initiation of spermatogenesis in the mouse. Development 146, dev173088 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Lane, N. et al. Resistance of IAPs to methylation reprogramming may provide a mechanism for epigenetic inheritance in the mouse. Genesis 35, 88–93 (2003).

    Article  CAS  PubMed  Google Scholar 

  60. Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

We are grateful for support and feedback from members of the Bourc’his laboratory. We thank N. Fayaubost and the Institut Curie in vivo experiment platform for animal care, M. Greenberg for critical reading of the manuscript and N. Servant for bioinformatic assistance. We thank A. Clark for the Prdm1-Cre and Oct4-eGFP mice, J. Oatley for the Id4-GFP mouse and K. Laband and J. Dumont for the anti-SYCP3 antibody. We acknowledge the ICGex NGS platform of Institut Curie (supported by grants ANR-10-EQPX-03, Equipex and ANR-10-INBS-09-08, France Génomique) and the Cell and Tissue Imaging Platform-PICT-IBiSA (member of France-Bioimaging, ANR-10-INBS-04) of the Genetics and Developmental Biology Department (UMR3215/U934) of the Institut Curie. The laboratory of D.B. is part of the LABEX DEEP (ANR-11-LABX-0044 and ANR-10-IDEX-0001-02). This work was supported by a grant from the Agence Nationale pour la Recherche (ANR-17-CE12-00013-01), the Fondation Bettencourt Schueller and the Fondation pour la Recherche Médicale (FRM Team Label). M.D. was supported by PhD fellowships from Région Île-de-France and Fondation pour la Recherche Médicale. L.B. is the recipient of a PhD Boehringer Ingelheim Fonds fellowship.

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Authors and Affiliations

  1. Institut Curie, Paris Sciences et Lettres Research University, INSERM U934, CNRS UMR3215, Paris, France

    Mathilde Dura, Aurélie Teissandier, Mélanie Armand, Lorraine Bonneville, Mathieu Schulz & Deborah Bourc’his

  2. Institute of Molecular Biology, Mainz, Germany

    Joan Barau

  3. Paris University and Paris-Saclay University, CEA, UMR SGCSR/E008, Fontenay-aux-Roses, France

    Clémentine Lapoujade & Pierre Fouchet

  4. Biotechnology and Cell Signaling, University of Strasbourg, CNRS UMR7242, Illkirch Cedex, France

    Michael Weber

  5. ICGex Next-Generation Sequencing Platform, Institut Curie, Paris Sciences et Lettres Research University, Paris, France

    Laura G. Baudrin & Sonia Lameiras

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Contributions

M.D. and D.B. designed and conceived the study. Most experiments were performed and analyzed by M.D. M.A. assisted in the histology, immunofluorescence and microscopy. J.B. performed the WGBS experiment. L.B., M.S. and M.A. assisted in CUT&RUN experiments. C.L. and P.F. performed in vitro SSC derivation. L.G.B. and S.L. performed NGS experiments and supervised generation of single-cell and NGS data. A.T. performed the bioinformatic analyses. M.W. provided the Dnmt3A mutant mouse strain. D.B. and M.D. interpreted data and wrote the paper. All authors reviewed and approved the final manuscript.

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Correspondence to Deborah Bourc’his.

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Extended data

Extended Data Fig. 1 DNMT3A is not required for retrotransposon silencing.

a, Representative FACS plots of live Oct4-GFP-gated cells (prospermatogonia) from E18.5 male embryos. b, Genome browser representation showing DNA methylation levels (WGBS) in E18.5 WT, Dnmt3AKO, Dnmt3LKO and Dnmt3CKO prospermatogonia at paternally imprinted regions (highlighted in pink). H19-Igf2 (top) and Dlk1-Gtl2 (middle) loci show decreased DNA methylation in Dnmt3AKO and Dnmt3LKO, while the Rasgrf1 locus (bottom)—previously described as a DNMT3C target12—shows decreased DNA methylation in Dnmt3CKO and Dnmt3LKO. c, Same as b, focusing on a region whereby an ancient L1MdF element shows hypomethylation throughout its length in Dnmt3AKO (pink), while a recent L1MdA element shows hypomethylation over its promoter in Dnmt3CKO (yellow) and over its body in Dnmt3AKO (pink). d and e, Metaplots of DNA methylation levels over full-length MERVL (c) and IAPEz (d) comparing WT, Dnmt3AKO, Dnmt3LKO, and Dnmt3CKO. IAP methylation levels were less decreased compared to other retrotransposon families, in agreement with a subset of IAPEz copies resisting DNA methylation reprogramming59. f, Correlation plot showing that H3K36me2 enrichment19 correlates with the extent of DNA methylation loss in Dnmt3AKO (R = 0.6, left) but not in Dnmt3CKO (R = 0.2, right). Dots represent the count of 10 kb windows. R = linear correlation coefficient. g, Venn diagram representing the overlap between DMRs in Dnmt3AKO and Dnmt3CKO prospermatogonia (E18.5) and in Nsd1KO prospermatogonia (E16.5)19. h, Model of division of function for de novo DNA methylation in fetal male germ cells. DNMT3C selectively methylates the promoters of evolutionarily young retrotransposons. DNMT3A methylates the rest of the genome, including the body of young retrotransposons and the entire length of old retrotransposons. DNMT3L is a universal co-factor that stimulates both activities. i, Fold-change expression (KO/WT) of different retrotransposon families in 19 dpp testes, assessed by RT-qPCR normalized by beta-actin expression. Data are mean ± normalized SD (black bar) from at least three biological replicates (one-sided student t-test over WT, *p < 0.05, **p < 0.005, ***p < 0.0005). j, RNA-seq expression in log(TPM + 1) (TPM, transcript counts per million) of meiotic genes in prospermatogonia in 19 dpp and 25 dpp Dnmt3AKO and WT testes and in public datasets from 20 dpp Dnmt3CKO and WT12.

Extended Data Fig. 2 Dnmt3A mutants complete the first wave of spermatogenesis until spermatozoa with a reduced germ cell numbers.

a, Representative photograph of a smaller constitutive Dnmt3AKO animal with WT littermate at 10ddp (left) and 25 dpp (middle), and a germ cell-conditional Prdm1-Dnmt3AcKO animal with its control littermate at 25 dpp (right). All animals are males. b, Genomic qPCR of Dnmt3A copy number. Dnmt3AWT, Dnmt3A2lox, Dnmt3A2lox/KO, Dnmt3AKO correspond to liver DNA. Genomic DNA from Prdm1-Dnmt3AcKO (# indicates individual replicates) was extracted from EpCAM-pos, β2M-neg FACS-sorted germ cells (10 dpp). Germ cell purity assessed by TRA98 staining was #2: 79%, #3: 96.8%, #4: 93.3%. c, Representative image of TRA98 and DNMT3A staining on testis sections from E18.5 Prdm1-Dnmt3AcKO and control (scale, 20 μm). d, Testicular weight normalized by body weight at 10 dpp, 19 dpp, 8 weeks and 6 months. Data are mean ± normalized SD (black bar) from biological replicates, and individual points represent biological replicates (one-sided student t-test over WT, *p < 0.05, **p < 0.005, ***p < 0.0005). WT: 10 dpp n = 13, 19 dpp n = 12, 8 weeks n = 7, 6 months n = 4. Dnmt3AKO: 10 dpp n = 5, 19 dpp n = 5. Prdm1-Dnmt3AcKO: 8 weeks n = 4, 6 months n = 2. Dnmt3LKO: 10 dpp n = 4, 19 dpp n = 3, 8 weeks n = 5, 6 months n = 3. Dnmt3CKO: 10 dpp n = 4, 19 dpp n = 4, 8 weeks n = 3, 6 months n = 3. e, Mean of tubule surface (mm2 in Dnmt3AKO, Prdm1-Dnmt3AcKO and controls at 10 dpp, 25 dpp, 6 weeks, 8-9 weeks and 6 months. Data are mean ± normalized SD (black bar) from biological replicates, and individual points represent biological replicates, (one-sided student t-test, p values as in d). WT: 10 dpp n = 3, 25 dpp n = 3, 6 weeks n = 2, 8–9 weeks n = 3, 6 months n = 2. Dnmt3AKO: 10 dpp n = 3, 25 dpp n = 3. Prdm1-Dnmt3AcKO: 6 weeks n = 3, 8–9 weeks n = 4, 6 months n = 2. f, (Left) Representative images of testis sections from 6-week-old germ cell-conditional Prdm1-Dnmt3AcKO mutants and controls (scale, 50 μm). (Right) Percentage of tubule classes per genotype. Data are mean ± normalized SD (black bar) from biological replicates, n = number of animals. Control genotypes: Dnmt3A2lox/KO; Prdm1-Cre0/0 and Dnmt3AKO/WT; Prdm1-CreTg/0.

Extended Data Fig. 3 Dnmt3A mutants produce meiotic and post-meiotic cells.

a, (Right) Representative image of SYCP3 staining on testis sections from Dnmt3AKO, Prdm1-Dnmt3AcKO and controls at 25 dpp, 6 weeks, 8–9 weeks and 6 months (scale, 20 μm). (Left) Quantification of SYCP3-positive cells normalized per tubule. Control genotypes: 25 dpp n = 3 (Dnmt3AWT), 6 weeks n = 2 (Dnmt3AKO/WT; Prdm1-CreTg/0 and Dnmt3A2lox/KO; Prmd1-Cre0/0), 8–9 weeks n = 3 (Dnmt3A2lox/KO; Prmd1-Cre0/0 (n = 2) and Dnmt3A2lox/WT; Prdm1-Cre0/0 (n = 2)), 6 months n = 2 (Dnmt3AKO/WT; Prmd1-CreTg/0 and Dnmt3A2lox/WT; Prmd1-Cre0/0). Dnmt3AKO: 25 dpp n = 3. Prdm1-Dnmt3AcKO: 6 weeks n = 3, 8–9 weeks n = 4, 6 months n = 2. Data are mean ± SD (black bar). One-sided student t-test over WT *p < 0.05, **p < 0.005, ***p < 0.0005). b, Percentage of first meiotic prophase stages assessed by double-immunofluorescence against SYCP3 and γH2AX on WT and Dnmt3AKO (25 dpp) meiotic spreads (representative image on Fig. 2e). Data are mean for biological replicates, n = number of animals. At least 100 cells/replicate were counted. c, RT-PCR of spermatid markers (Sp10 and Tp2) in Dnmt3AKO/WT and Dnmt3AKO/KO testes (25 dpp). Two biological replicates per genotype. d, Representative photograph of epididymis spermatozoa in 6 week-old conditional Dnmt3AcKO mutant and control littermates (Dnmt3AKO/WT; Prdm1-CreTg/0). Scale, 20 μm.

Extended Data Fig. 4 Accumulation of ID4-eGFP positive germ cells in Dnmt3AKO testes.

a, Representative image of SOX9 (Sertoli cell marker) staining on testis sections from Dnmt3AKO, Prdm1-Dnmt3AcKO and controls at 10 dpp, 25 dpp, 6 weeks, 8–9 weeks and 6 months. Scale, 20 μm. b, (Left) Representative microscopy images of double-immunofluorescence detection of SOX9 (Sertoli cell marker) and ID4-eGFP (SSC) at 10 dpp. Scale, 20 μm. (Right) Quantification of the number of ID4-eGFP positive cells per mm2 of tubule in Dnmt3AKO and WT. Data are mean ± SD (black bar). WT, n = 3 and Dnmt3AKO, n = 3 (one-sided student t-test over WT). c, (Left) Representative FACS analysis plots of live ID4-eGFP-positive gated testicular cells from 10 dpp mice. ID4-eGFP cells were divided into three classes: GFP dim, GFP med and GFP bright. (Right) Representative count of cell numbers in each GFP subclass in Dnmt3AKO and WT. (Far right) Cell count in percentage for each GFP subclass in WT and Dnmt3AKO for two biological replicates. #1 et #2 represent individual replicates.

Extended Data Fig. 5 scRNA-seq quality controls and cluster annotation of germ and somatic cells.

a, (Left) UMAP dimensionality reduction representation of scRNA-seq data from 10 dpp testicular cells before batch effect correction of eight samples, each sample is one animal of the genotype of interest (41,582 cells). Colors represent different conditions, see key. (Right) UMAP dimensionality reduction representation of the same sample after batch effect correction (see Methods, integrated data). b, UMAP representation of scRNA-seq data from 10 dpp testicular cells (n = 8 animals from different genotypes) with a color gradient representing the expression of known markers of germ cells (Dazl and Ddx4), Sertoli cells (Sox9 and Amh), peritubular myeloid cells (PTM) (Myh11 and Acta2) and Leydig cells (Dlk1 and Cyp11a1). c, UMAP representation of scRNA-seq data from 10 dpp testicular cells, colors represent cellular types based on marker expression of b. d, Unbiased cell clustering onto an UMAP representation of scRNA-seq data from 10 dpp testicular cells demonstrated 24 clusters, each color represents a cluster annotated by a number (from 0 to 23). e, Violin plots show expression of key cell-type specific markers among cell clusters to distinguish those containing germ cells (Ddx4-positive) from testicular somatic cells (Gata4-positive). Cluster 8 presented expression of the two markers: this germ cell cluster was considered as somatically contaminated—probably due to cellular doublets—and was removed from further analysis. Cluster in black was removed from analysis because of poor quality control (see Extended Data Fig. 4f). f, Quality control of scRNA-seq integrated data split by cellular cluster, presenting the number of UMIs (left panel), the number of detected genes (middle panel) and the percentage of mitochondrial reads (right panel). n = 8 animals from different genotypes. Box plots show the median as the center line, box limits correspond to upper and lower quartiles, and whiskers cover 1.5x the interquartile range.

Extended Data Fig. 6 Supporting evidence for naming final germ cell clusters.

a, Unbiased cell clustering onto an UMAP representation of scRNA-seq data from 10 dpp germ cells. Identification of 18 clusters (0 to 17). b, Violin plots show expression of key cell-type specific markers among germ cell clusters to distinguish those containing germ cells (Ddx4-positive) from testicular somatic cells (Gata4-positive). Clusters 8 and 16 were removed from further analysis because of somatic contamination, probably due to cellular doublets. c, Quality control of scRNA-seq integrated data on germ cell clusters. Box plots show the median as the center line, box limits correspond to upper and lower quartiles, and whiskers cover 1.5x the interquartile range. Cluster 14, 15 and 17 were removed for poor sequencing quality. d, UMAP dimensionality reduction representation of scRNA-seq integrated data from 10 dpp germ cells (6,638). Colors represent different samples and conditions, red: Dnmt3AWT FACS-enriched germ cells, orange: Dnmt3AKO FACS-enriched germ cells, dark green: Dnmt3ACTL (Dnmt3A2lox/KO; Prdm1-Cre0/0) FACS-enriched germ cells, light green: Prdm1-Dnmt3AcKO (Dnmt3A2lox/KO; Prdm1-CreTg/0) FACS-enriched germ cells, dark blue: Dnmt3AWT all testicular cells, light blue: Dnmt3AKO all testicular cells, brown: Dnmt3LWT FACS-enriched germ cells, yellow: Dnmt3LKO FACS-enriched germ cells. e, Heatmap of pairwise Spearman correlation matrix among the 8 germ cell clusters. f, Heatmap of single-cell expression profile of markers defined in only one cluster for each germ cell cluster. Numbers on the upper row represent the number of cells in each germ cell cluster. g, Table of top 10 markers for each germ cell cluster. Marker numbers correspond to the total number of identified markers that give a signature for each cluster. h, Bar plot showing the percentage of cells normalized to WT per cell type. i, Violin plots show expression of Dnmt3 genes across germ cell populations. j, (Left) Representative image of GFRA1 and Phospho-Histone 3 (PH3) staining on testis sections from Dnmt3AKO and WT littermates at 10 dpp and 25 dpp (scale, 20 μm). (Right) Percentage of double GFRA1 and PH3-positive cells. Data are mean ± SD (black bar), individual points represent biological replicates (n = 3). One-sided student t-test over WT.

Extended Data Fig. 7 DNMT3A and DNMT3L are essential to SSC plasticity.

a, Pseudotime trajectory of Dnmt3A mutants (mixed Dnmt3AKO and Prdm1-Dnmt3AcKO) germ cells from 10 dpp testes. The percentage of each cell type is indicated for every pseudotime segments. The two highest values are colored in purple. b, c, Pseudotime trajectory of germ cells from 10 dpp testes. (Left) Dnmt3LWT germ cells (FACS-sorted). (Right) Dnmt3LKO germ cells (FACS-sorted). In (b), cells were ordered from beginning (dark blue) to the end (light blue). In (c), cells were colored following germ cell cluster allocation, see key above. Patterns may differ from Dnmt3A mutant pseudotime (Fig. 4a, b), due to the use of different versions of the 10X Genomics scRNA-seq kit (V2 for Dnmt3L- versus V3 for Dnmt3A-related samples). RNA velocity could not be performed on Dnmt3LKO, due to insufficient germ cells (1,031 WT and 1,182 Dnmt3LKO) and the use of anterior version of the scRNA-seq kit. d, Representative FACS plots of live EpCAM-pos, β2M-neg gated testicular cells from 8 dpp Dnmt3AKO and WT males. EpCAM-pos cells were divided into EpCAMlow and EpCAMhigh populations. e, Morphological observation of germ cell clusters derived from EpCAMlow, β2M-neg cells of 8 dpp WT mice after 11 days of culture. f, Table showing the number of in vitro germ cell clusters forming from EpCAMlow and EpCAMhigh spermatogonia at 8 dpp and EpCAMtotal (= EpCAM low + high) spermatogonia at 21 dpp. At 8 dpp, the results come from one biological replicate (n = 1 Dnmt3AKO and n = 1 WT). At 21 dpp, the results come from two biological replicates (n = 2 Dnmt3AKO and n = 2 WT).

Extended Data Fig. 8 Relationship between gene expression, H3K27me3 or H3K9me3 distribution, and DNA methylation in Dnmt3AKO SSCs (ID4-eGFPbright+med).

a, Gene Ontology analysis (Metascape60) of misregulated genes (bulk RNA-seq on ID4-GFPbright+med cells). The top 20 GO terms are represented. Black bars: GO term enrichment with p value < 1e-10; grey bars: enrichment with p-value < 1e-6. P value is calculated with hypergeometric statistical test. b, Heatmap showing levels of H3K27ac, H3K27me3 and H3K9me3 enrichment in RPKM-normalized CUT&RUN signal for ID4-eGFPbright+med cells (SSCs) from 10 dpp Dnmt3AKO and WT males. Enrichment is assessed + /−5 kb from the center of the peak. Number of biological replicates for H3K27ac and H3K27me3: Dnmt3AKO = 3, WT = 4; for H3K9me3: Dnmt3AKO = 2 (one biological replicate was further divided into 2 technical replicates), WT = 3. c, Pie chart showing genomic annotation of each H3K9me3 peak category. d, Heatmap focusing on i) Dnmt3AKO-lost H3K9me3 peaks (log2 FC) compared to WT (Left), ii) differential expression (log2 FC) of genes associated with H3K9me3 peaks (TSS+/− 5 kb from the peak) between Dnmt3AKO and WT—from ID4-eGFPbright+med RNA-seq—(Center), and iii) percentage of DNA methylation loss in Dnmt3AKO versus WT on H3K9me3 peak location—from E18.5 WGBS—(Right). Rows are ordered according to gene expression changes: top, not differentially expressed; middle, upregulated; bottom, downregulated. e, Heatmap focusing on Dnmt3AKO-gained H3K9me3 peaks (log2 FC) compared to WT, legend as in d. Rows are ordered according to gene expression changes: top, not differentially expressed; bottom, downregulated (no upregulated genes). f, Heatmap showing levels of H3K27me3 enrichment in RPKM-normalized CUT&RUN signal for ID4-eGFPbright+med germ cells (SSCs) in 10 dpp Dnmt3AKO and WT males. Peaks are divided into three categories: not differentially enriched (Dnmt3AKO = WT), down-enriched (Dnmt3AKO < WT), up-enriched (Dnmt3AKO > WT) (FDR < 5% and FC > 1). g, Genomic annotation of each H3K27me3 peak category. h, Heatmap focusing on Dnmt3AKO-gained H3K27me3 peaks (log2 FC) compared to WT (left), legend as in d. Rows are ordered according to gene expression changes: top, not differentially expressed; middle, upregulated; bottom, downregulated. i, Genomic annotation of Dnmt3AKO-gained H3K27me3 peaks, associated with downregulated genes and >30% DNA methylation loss on H3K27me3 peak location.

Extended Data Fig. 9 Relationship between H3K27ac distribution, gene expression and DNA methylation in Dnmt3AKO SSCs (ID4-eGFPbright+med).

a, Genomic annotation of each category of H3K27ac peaks. b, Quantification of intersection between H3K27ac peaks from Dnmt3AKO, Dnmt3AWT and WT SSCs (ID4-eGFPbright), and progenitors (ID4-eGFPdim) (from published data36), down-enriched peaks and up-enriched peaks. Intersections lower than 200 were removed from the figure. c, Heatmap focusing on i) Dnmt3AKO-lost H3K27ac peaks (log2 FC) compared to WT (left), ii) differential expression (log2 FC) of genes associated with these peaks (TSS + /−5 kb from the peak) between Dnmt3AKO and WT—from ID4-eGFPbright+med RNA-seq—(right). Rows are ordered according to gene expression changes: Cat. 1: 1,499 peaks associated with 1,561 non differentially expressed genes; Cat. 2: 10 peaks associated with 10 upregulated genes; Cat. 3: 85 peaks associated with 64 downregulated genes. d, (Left) Heatmap showing levels of H3K27ac peak enrichment in RPKM-normalized CUT&RUN signal from Dnmt3AKO and WT ID4-GFPbright+med germ cells as in Fig. 5d. (Right) Enrichment in H3K27ac, ATAC-seq and H3K27me3 from public prospermatogonia ChIP-seq data37,38 (RPKM-normalized signal) at H3K27ac peaks described here in Dnmt3AKO and WT. e, Heatmap focusing on i) Dnmt3AKO-gained H3K27ac peaks (log2 FC) compared to WT that in addition lost above 30% DNA methylation in Dnmt3AKO condition, from E18.5 WGBS (the two first columns), and ii) differential expression (log2 FC) of genes associated with H3K27ac peaks (TSS + /−5 kb from the peak) between Dnmt3AKO and WT—from ID4-eGFPbright+med RNA-seq—(center) Rows are ordered according to gene expression changes: top, differentially expressed, n = 1,083; middle, upregulated, n = 142; bottom, downregulated, n = 8. No Dnmt3AKO-gained H3K27ac peaks were associated with unexpressed genes. f, Genome browser representation of the Gata2 locus, showing H3K27ac enrichment (grey), DNA methylation (yellow) and RNA expression (red) for Dnmt3AKO and WT. Regions showing Dnmt3AKO de novo H3K27ac peaks associated with >30% DNA methylation loss are shaded in light blue; regions showing de novo H3K27ac peaks at regions not controlled by DNA methylation are shaded in grey-blue.

Extended Data Fig. 10 TFs and putative gene targets.

a, Violin plots showing expression levels of TF factors that have the best match with the motifs present in Dnmt3AKO-specific H3K27ac peaks. Expression is represented across SSCs and spermatogonia subtypes discriminated by scRNA-seq for Dnmt3AKO and WT. Upper panel: 17 TFs with SSC/spermatogonia expression, lower panel: 18 TFs not expressed in SSC/spermatogonia. b, Venn diagram representation of the overlap between 142 upregulated genes associated with Dnmt3AKO-gained H3K27ac peaks that lose DNA methylation (see Fig. 5e), and 116 upregulated genes defined as putative targets of TFs with binding motif sites within Dnmt3AKO-gained H3K27ac peaks (see Fig. 5i).

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Dura, M., Teissandier, A., Armand, M. et al. DNMT3A-dependent DNA methylation is required for spermatogonial stem cells to commit to spermatogenesis. Nat Genet 54, 469–480 (2022). https://doi.org/10.1038/s41588-022-01040-z

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