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Reconstitution of the oocyte transcriptional network with transcription factors

Abstract

During female germline development, oocytes become a highly specialized cell type and form a maternal cytoplasmic store of crucial factors. Oocyte growth is triggered at the transition from primordial to primary follicle and is accompanied by dynamic changes in gene expression1, but the gene regulatory network that controls oocyte growth remains unknown. Here we identify a set of transcription factors that are sufficient to trigger oocyte growth. By investigation of the changes in gene expression and functional screening using an in vitro mouse oocyte development system, we identified eight transcription factors, each of which was essential for the transition from primordial to primary follicle. Notably, enforced expression of these transcription factors swiftly converted pluripotent stem cells into oocyte-like cells that were competent for fertilization and subsequent cleavage. These transcription-factor-induced oocyte-like cells were formed without specification of primordial germ cells, epigenetic reprogramming or meiosis, and demonstrate that oocyte growth and lineage-specific de novo DNA methylation are separable from the preceding epigenetic reprogramming in primordial germ cells. This study identifies a core set of transcription factors for orchestrating oocyte growth, and provides an alternative source of ooplasm, which is a unique material for reproductive biology and medicine.

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Fig. 1: Functional screening of genes involved in the PPT.
Fig. 2: Reconstitution of PPT in ES cells.
Fig. 3: Dispensability of epigenetic reprogramming for DIOL growth and de novo methylation.
Fig. 4: Maturation and developmental potential of DIOLs.

Data availability

The RNA-seq and methylome data have been deposited at the Gene Expression Omnibus (GEO) database under accession number GSE143218 and GSE143219, and DDBJ Sequence Read Archive (DRS001541 and DRS001547). There is no restriction on data availability. Source data are provided with this paper.

Code availability

Custom code used in this article can be accessed at https://github.com/nhamazaki/2020_DIOL.

References

  1. 1.

    Pan, H., O’Brien, M. J., Wigglesworth, K., Eppig, J. J. & Schultz, R. M. Transcript profiling during mouse oocyte development and the effect of gonadotropin priming and development in vitro. Dev. Biol. 286, 493–506 (2005).

    CAS  PubMed  Google Scholar 

  2. 2.

    Saitou, M. & Yamaji, M. Primordial germ cells in mice. Cold Spring Harb. Perspect. Biol. 4, a008375 (2012).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Nicholls, P. K. et al. Mammalian germ cells are determined after PGC colonization of the nascent gonad. Proc. Natl Acad. Sci. USA 116, 25677–25687 (2019).

    CAS  PubMed  Google Scholar 

  4. 4.

    McLaren, A. & Southee, D. Entry of mouse embryonic germ cells into meiosis. Dev. Biol. 187, 107–113 (1997).

    CAS  PubMed  Google Scholar 

  5. 5.

    Schultz, R. M., Letourneau, G. E. & Wassarman, P. M. Program of early development in the mammal: changes in the patterns and absolute rates of tubulin and total protein synthesis during oocyte growth in the mouse. Dev. Biol. 73, 120–133 (1979).

    CAS  PubMed  Google Scholar 

  6. 6.

    Sternlicht, A. L. & Schultz, R. M. Biochemical studies of mammalian oogenesis: kinetics of accumulation of total and poly(A)-containing RNA during growth of the mouse oocyte. J. Exp. Zool. 215, 191–200 (1981).

    CAS  PubMed  Google Scholar 

  7. 7.

    Soyal, S. M., Amleh, A. & Dean, J. FIGalpha, a germ cell-specific transcription factor required for ovarian follicle formation. Development 127, 4645–4654 (2000).

    CAS  PubMed  Google Scholar 

  8. 8.

    Pangas, S. A. et al. Oogenesis requires germ cell-specific transcriptional regulators Sohlh1 and Lhx8. Proc. Natl Acad. Sci. USA 103, 8090–8095 (2006).

    CAS  PubMed  ADS  Google Scholar 

  9. 9.

    Choi, Y., Yuan, D. & Rajkovic, A. Germ cell-specific transcriptional regulator sohlh2 is essential for early mouse folliculogenesis and oocyte-specific gene expression. Biol. Reprod. 79, 1176–1182 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Choi, Y., Ballow, D. J., Xin, Y. & Rajkovic, A. Lim homeobox gene, lhx8, is essential for mouse oocyte differentiation and survival. Biol. Reprod. 79, 442–449 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Rajkovic, A., Pangas, S. A., Ballow, D., Suzumori, N. & Matzuk, M. M. NOBOX deficiency disrupts early folliculogenesis and oocyte-specific gene expression. Science 305, 1157–1159 (2004).

    CAS  PubMed  ADS  Google Scholar 

  12. 12.

    Falender, A. E., Shimada, M., Lo, Y. K. & Richards, J. S. TAF4b, a TBP associated factor, is required for oocyte development and function. Dev. Biol. 288, 405–419 (2005).

    CAS  PubMed  Google Scholar 

  13. 13.

    Grive, K. J., Seymour, K. A., Mehta, R. & Freiman, R. N. TAF4b promotes mouse primordial follicle assembly and oocyte survival. Dev. Biol. 392, 42–51 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Griffith, G. J. et al. Yin-yang1 is required in the mammalian oocyte for follicle expansion. Biol. Reprod. 84, 654–663 (2011).

    CAS  PubMed  Google Scholar 

  15. 15.

    Gazdag, E. et al. TBP2 is essential for germ cell development by regulating transcription and chromatin condensation in the oocyte. Genes Dev. 23, 2210–2223 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Choi, Y. et al. Microarray analyses of newborn mouse ovaries lacking Nobox. Biol. Reprod. 77, 312–319 (2007).

    CAS  PubMed  Google Scholar 

  17. 17.

    Joshi, S., Davies, H., Sims, L. P., Levy, S. E. & Dean, J. Ovarian gene expression in the absence of FIGLA, an oocyte-specific transcription factor. BMC Dev. Biol. 7, 67 (2007).

    PubMed  PubMed Central  Google Scholar 

  18. 18.

    Shin, Y. H. et al. Transcription factors SOHLH1 and SOHLH2 coordinate oocyte differentiation without affecting meiosis I. J. Clin. Invest. 127, 2106–2117 (2017).

    PubMed  PubMed Central  Google Scholar 

  19. 19.

    Wang, Z., Liu, C. Y., Zhao, Y. & Dean, J. FIGLA, LHX8 and SOHLH1 transcription factor networks regulate mouse oocyte growth and differentiation. Nucleic Acids Res. 48, 3525–3541 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Choi, Y. & Rajkovic, A. Characterization of NOBOX DNA binding specificity and its regulation of Gdf9 and Pou5f1 promoters. J. Biol. Chem. 281, 35747–35756 (2006).

    CAS  PubMed  Google Scholar 

  21. 21.

    Choi, M. et al. The oocyte-specific transcription factor, Nobox, regulates the expression of Pad6, a peptidylarginine deiminase in the oocyte. FEBS Lett. 584, 3629–3634 (2010).

    CAS  PubMed  Google Scholar 

  22. 22.

    Park, M. et al. Identification and characterization of LHX8 DNA binding elements. Dev. Reprod. 16, 379–384 (2012).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Hikabe, O. et al. Reconstitution in vitro of the entire cycle of the mouse female germ line. Nature 539, 299–303 (2016).

    PubMed  ADS  Google Scholar 

  24. 24.

    Da Silva-Buttkus, P. et al. Effect of cell shape and packing density on granulosa cell proliferation and formation of multiple layers during early follicle development in the ovary. J. Cell Sci. 121, 3890–3900 (2008).

    PubMed  Google Scholar 

  25. 25.

    Schultz, R. M., Stein, P. & Svoboda, P. The oocyte-to-embryo transition in mouse: past, present, and future. Biol. Reprod. 99, 160–174 (2018).

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Ram, P. T. & Schultz, R. M. Reporter gene expression in G2 of the 1-cell mouse embryo. Dev. Biol. 156, 552–556 (1993).

    CAS  PubMed  Google Scholar 

  27. 27.

    Davis, W., Jr & Schultz, R. M. Developmental change in TATA-box utilization during preimplantation mouse development. Dev. Biol. 218, 275–283 (2000).

    CAS  PubMed  Google Scholar 

  28. 28.

    Burns, K. H. et al. Roles of NPM2 in chromatin and nucleolar organization in oocytes and embryos. Science 300, 633–636 (2003).

    CAS  PubMed  ADS  Google Scholar 

  29. 29.

    Dong, J. et al. Growth differentiation factor-9 is required during early ovarian folliculogenesis. Nature 383, 531–535 (1996).

    CAS  PubMed  ADS  Google Scholar 

  30. 30.

    Leitch, H. G. & Smith, A. The mammalian germline as a pluripotency cycle. Development 140, 2495–2501 (2013).

    CAS  PubMed  Google Scholar 

  31. 31.

    Zhang, J. et al. OTX2 restricts entry to the mouse germline. Nature 562, 595–599 (2018).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  32. 32.

    Smallwood, S. A. et al. Dynamic CpG island methylation landscape in oocytes and preimplantation embryos. Nat. Genet. 43, 811–814 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Kobayashi, H. et al. High-resolution DNA methylome analysis of primordial germ cells identifies gender-specific reprogramming in mice. Genome Res. 23, 616–627 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Shirane, K. et al. Mouse oocyte methylomes at base resolution reveal genome-wide accumulation of non-CpG methylation and role of DNA methyltransferases. PLoS Genet. 9, e1003439 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Yagi, M. et al. Derivation of ground-state female ES cells maintaining gamete-derived DNA methylation. Nature 548, 224–227 (2017).

    CAS  PubMed  ADS  Google Scholar 

  36. 36.

    Veselovska, L. et al. Deep sequencing and de novo assembly of the mouse oocyte transcriptome define the contribution of transcription to the DNA methylation landscape. Genome Biol. 16, 209 (2015).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Dokshin, G. A., Baltus, A. E., Eppig, J. J. & Page, D. C. Oocyte differentiation is genetically dissociable from meiosis in mice. Nat. Genet. 45, 877–883 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Yamaguchi, S. et al. Tet1 controls meiosis by regulating meiotic gene expression. Nature 492, 443–447 (2012).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  39. 39.

    Hill, P. W. S. et al. Epigenetic reprogramming enables the transition from primordial germ cell to gonocyte. Nature 555, 392–396 (2018).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  40. 40.

    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).

    PubMed  PubMed Central  ADS  Google Scholar 

  41. 41.

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

    CAS  PubMed  ADS  Google Scholar 

  42. 42.

    Hayashi, K., Ohta, H., Kurimoto, K., Aramaki, S. & Saitou, M. Reconstitution of the mouse germ cell specification pathway in culture by pluripotent stem cells. Cell 146, 519–532 (2011).

    CAS  PubMed  Google Scholar 

  43. 43.

    Hamazaki, N., Uesaka, M., Nakashima, K., Agata, K. & Imamura, T. Gene activation-associated long noncoding RNAs function in mouse preimplantation development. Development 142, 910–920 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Shimamoto, S. et al. Hypoxia induces the dormant state in oocytes through expression of Foxo3. Proc. Natl Acad. Sci. USA 116, 12321–12326 (2019).

    CAS  PubMed  Google Scholar 

  45. 45.

    Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    CAS  Google Scholar 

  46. 46.

    Lê, S., Josse, J. & Husson, F. FactoMineR: an R package for multivariate analysis. J. Stat. Soft. 25, 1–18 (2008).

    Google Scholar 

  47. 47.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Huang, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protocols 4, 44–57 (2009).

    CAS  Google Scholar 

  49. 49.

    Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Bailey, T. L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Yang, Z. et al. Fast and sensitive detection of indels induced by precise gene targeting. Nucleic Acids Res. 43, e59 (2015).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Lonowski, L. A. et al. Genome editing using FACS enrichment of nuclease-expressing cells and indel detection by amplicon analysis. Nat. Protocols 12, 581–603 (2017).

    CAS  PubMed  Google Scholar 

  53. 53.

    Kobayashi, T. et al. Principles of early human development and germ cell program from conserved model systems. Nature 546, 416–420 (2017).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  54. 54.

    Yusa, K. et al. Targeted gene correction of α1-antitrypsin deficiency in induced pluripotent stem cells. Nature 478, 391–394 (2011).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  55. 55.

    Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, 17 (2005).

    MathSciNet  MATH  Google Scholar 

  56. 56.

    Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Hayashi, K. & Saitou, M. Generation of eggs from mouse embryonic stem cells and induced pluripotent stem cells. Nat. Protocols 8, 1513–1524 (2013).

    CAS  PubMed  Google Scholar 

  58. 58.

    Yoshida, S., Sakakibara, Y. & Kitajima, T. S. Live imaging of intracellular dynamics during meiotic maturation in mouse oocytes. Methods Mol. Biol. 1457, 241–251 (2016).

    CAS  PubMed  Google Scholar 

  59. 59.

    Miura, F., Enomoto, Y., Dairiki, R. & Ito, T. Amplification-free whole-genome bisulfite sequencing by post-bisulfite adaptor tagging. Nucleic Acids Res. 40, e136 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Jühling, F. et al. metilene: fast and sensitive calling of differentially methylated regions from bisulfite sequencing data. Genome Res. 26, 256–262 (2016).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank all members of the Hayashi laboratory for their support and input. We are grateful to F. Arai, T. Matsuda and T. Ishiuchi for technical support and Y. Hayashi for comments. We thank the staff of the Research Support Center, Research Center for Human Disease Modeling, Kyushu University Graduate School of Medical Sciences for their technical assistance; we particularly thank M. Amago for support with the FACS sorting. We are grateful to Y. Hamazaki and KN international for proofreading. This study was supported in part by KAKENHI Grants-in-Aid from MEXT, Japan (numbers 17H01395, 18H05544 and 18H05545 to K.H., 15H06475, 16K18816, 16H06279 and 18K14605 to N. Hamazaki and 16H06527 to K.N.); by Management Expenses Grants of Kyushu University (K.H.); by the Advanced Computational Scientific Program of the Research Institute for Information Technology, Kyushu University; by the Uehara Memorial Foundation (K.H.); by the Takeda Science Foundation (K.H.); by a Hayashi Grant-in-Aid for Basic Medical Research (K.H.); by a JSPS Fellowship (N. Hamazaki); by the Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life Science Research) from AMED (JP19am0101103, 1804) (K.H.); by a Grant-in-Aid from The Open Philanthropy Project (K.H.); by MRC core funding (H.G.L.); and by a BBSRC grant (BB/R002703/1) (H.G.L.). H.G.L. is an Academic Clinical Lecturer and acknowledges support from the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre (BRC). The authors apologize to colleagues whose work could not be cited owing to length limitations.

Author information

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Authors

Contributions

N. Hamazaki and K.H. conceived and designed the project. N. Hamazaki and C.H. performed the molecular experiments, cellular experiments and analysis of RNA-seq data. O.H., S.S. and N. Hamazaki collected RNA-seq samples. N. Hamada and N. Hamazaki performed the immunostaining. F.M., H.A., T.I. and N. Hamazaki performed PBAT library construction and analysis of data. H.K. and T.S.K. performed live imaging of oocytes. N. Hamazaki, K.N., H.G.L. and K.H. prepared the figures and wrote the manuscript, incorporating feedback from all the authors.

Corresponding authors

Correspondence to Nobuhiko Hamazaki or Katsuhiko Hayashi.

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The authors declare no competing interests.

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Peer review information Nature thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Transcriptional signatures in PPT.

a, Schematic diagram of the transcriptome analysis. Each stage of the female germ line in vivo (circles) and in vitro (rectangles) was subjected to analysis. The top scale shows days of differentiation. The colour of the points corresponds to that in the PCA plot in Fig. 1a. DOB, day of birth. b, Dynamics of the X/A ratio. The ratio was determined by dividing the transcripts of X chromosomes by the average of the transcripts of autosomes. The putative half-active value (0.5) is shown by a red line. The expression profile is based on at least biologically duplicated samples. c, Expression dynamics of LTR families in oogenesis. The centre of the box plot is the median and the box correspond to the interquartile range (25th and 75th for bottom and top edge of the box, respectively), the distance between the first and third quartiles, the whiskers extend no more than 1.5 times the interquartile range. The expression profile shown is based on biologically triplicated samples. d, The ratio of expression levels of the AT-high-genes/AT-low-genes and GC-high/GC-low genes. Genes expressing any of the stages of the female germ line were classified into groups by the nucleotide compositions of their core promoters (−100 to −1 nt). High genes and low genes possessed higher AT or GC numbers compared to the (median +10) values and lower AT or GC numbers compared to the (median −10) values, respectively. e, Enrichment of the AT-rich sequences around the transcription start sites (TSSs). Shown are the AT ratios at −100 to +100 bp of the TSSs of genes whose expressions were up- or downregulated more than fourfold between IVD.D11 and IVD.D13. Local enrichment was observed at 20–30 bp upstream of the TSSs of these genes (orange box). Note that the TATA-boxes are known to be enriched at −20 to −30 bp of the TSS. f, Motif analysis of the sequence around the TSSs. Shown are de novo searched motifs within −40 bp to 0 bp of the upregulated genes (left) and similar motifs suggested by TOMTOM (right). P values were computed by a two-sided Fisher’s exact test for enrichment of the motif sequences. For the correction of multiple comparisons, P values are multiplied by the number of candidate motifs tested. g, Enrichment of the motifs. Shown are the distributions of each motif shown in f at −200 to +200 bp of the TSSs of the up- or downregulated genes (left) and the magnified view of the enrichment of TATA-box-like motifs at −100 bp to +100 bp of the TSSs (right).

Source data

Extended Data Fig. 2 Establishment and functional validation of knockout ES cell lines.

a, Heat map obtained by average linkage hierarchical clustering. The colour names at the right indicate the module assignment determined by WGCNA. Putative PPT-associated modules are written in red. Colour bars on the top of the heatmap indicate the groups in Extended Data Fig. 1a. b, Experimental scheme for the establishment of knockout ES lines. Shown are time course of cell culture for the establishment of knockout ES lines (top), the principal of indel detection by amplicon analysis (IDAA)51 (middle) and a representative IDAA profile of the Tbpl2 locus (bottom). For cell culture procedures, see Methods. For the amplicon analysis, amplicons derived from alleles with either deletion or insertion were analysed in a fragment analyser. The fragment analyser shows the size of the amplicons in x axis and their frequency in y axis. The sequences of the alleles in ES cells selected by the IDAA profiles were confirmed by Sanger sequencing. c, Summary of oocyte induction from knockout ES lines. The table shows results from the Cas9-mediated knockout lines. The number of ES cell lines tested for the IDAA profile (analysed lines) and knockout lines tested for oocyte induction (mutants) are shown. Among the 27 genes targeted, 19 genes were successfully disrupted. KO alleles of these lines were confirmed by Sanger sequencing. The bottom table shows eight genes (Kat8, Birc5, Sp110, Dynll1, Polr2j, Drap1, Stat3 and Dmap1) for which mutants could not obtained by the Cas9 system. These genes were rescued by the dox (Dox)-inducible expression vector. Shown are the numbers of ES cell lines tested for the IDAA profile (analysed lines), positive for the IDAA profile (candidate lines by PCR), and validated by Sanger sequence (seq-validated lines). Sp110 knockout ES cells could not be established in this study. In these ES cell lines, the gene expression was rescued by addition of Dox until after PGCLC differentiation, and then Dox was removed from the medium at IVD culture, by which 7 out of 8 genes were successfully disrupted in oocytes. Checkmarks indicate successful differentiation into the stage indicated at the top of the table. Pink boxes indicate the stage where differentiation was not observed. d, Oocyte derivation from knockout ES cell lines. The disrupted genes are shown at the left. Results of PGCLCs at 6 days of induction (PGCLCs. D6) and IVD at the days indicated are shown. BF, bright field. Scale bars, 200 μm. n = 3, biologically independent experiments. e, Quantification of oocyte formation in knockout lines. Box plots are as in Extended Data Fig. 1c. The oocyte numbers and sizes were measured by Stella-CFP signals. The values were compiled from biologically triplicated experiments.

Source data

Extended Data Fig. 3 Transcriptional properties of knockout oocytes.

a, X/A ratio in knockout oocytes. The ratio was determined as shown in Extended Data Fig. 1b. b, Expression dynamics of LTRs in knockout oocytes. c, Promoter usage of knockout oocytes. Shown are the ratios of expression levels of the AT-high-genes/AT-low-genes and GC-high/GC-low genes. Genes are classified as shown in Extended Data Fig. 1d. Expression profiles shown in ac are based on biologically duplicated samples. d, Reciprocal gene expression in each line of knockout oocytes. Shown are heat map of the expression of PPT-associated genes in the knockout oocytes. Differences of gene expression in the knockout oocytes at each stage compared to the wild-type are shown. Knockout oocytes arrested before PPT are highlighted in red. e, Imputed transcriptional network of PPT-associated genes from the RNA-seq data of KO-oocytes. Arrows indicate positive regulations. Line widths indicate the strength of the regulations. Arrow colours indicate the source genes of the arrows. Genes associated with the arrest of knockout oocytes before PPT are highlighted in red.

Extended Data Fig. 4 Establishment of BVSCNCh-ES cells.

a, Knock-in of mCherry into the Npm2 locus. As shown in the schematic diagram (left), mCherry was inserted into the ATG of the endogenous Npm2 gene. Primers for genotyping (arrows) and the expected size of the amplicons are shown. The right images show the results of PCR using the primers and samples numbered. M, size marker. For gel source data, see Supplementary Fig. 1. b, mCherry expression in oocytes from BVSCNCh-ES cells. Oocytes were induced by culturing PGCLCs from BVSC H18 ES cells and BVSCNCh-ES cells aggregated with ovarian somatic cells. Note that NPM2-mCherry signals were detected at day 13 in nuclei of oocytes derived from BVSCNCh-ES cells. Scale bar, 100 μm. n = 9, biologically independent experiments. c, High magnification oocytes from BVSCNCh-ES cells. Shown are a merged view of oocytes derived from BVSCNCh-ES cells at 23 days of culture in the aggregate and magnified views of the secondary follicle. Scale bars, 20 μm (top), and 100 μm (bottom). n = 9, biologically independent experiments.

Extended Data Fig. 5 Requirement of PPT8 for oocyte-like cell induction from ES cells.

a, FACS analysis of BVSCNCh+PPT8 ES cells. Note that over 98% of BVSCNCh+PPT8 ES cells expressed Stella-ECFP at day 5 of Shield1-inducible overexpression of PPT8. n = 3, biologically independent experiments. For the gating strategy, see Supplementary Fig. 2. b, Reporter gene expression in BVSCNCh+PPT8 ES cells without Shield1. Scale bars, 100 μm. n = 8, biologically independent experiments. c, Overexpression of PPT8 in the ground state. BVSCNCh+PPT8 ES cells were cultured in 2i+LIF with Shield1. Note that Stella-ECFP, but not BLIMP1-mVenus or NPM2-mCherry, was clearly detectable at 1 day of the induction. Scale bars, 100 μm. n = 12, biologically independent experiments. d, Change in cell size and nuclear size upon overexpression of PPT8. Both the cell and nuclear sizes of BVSCNCh+PPT8 ES cells were increased at day 5 of the induction. P values were determined by two-sided Student’s t-test. e, DDX4 expression in BVSCNCh+PPT8 ES cells. At day 5 of the induction, DDX4 expression was detectable in some Stella-ECFP-positive cells. The dashed box in the merged image is enlarged at right. f, Dynamics of size of oocyte-like cells. FACS analysis shows the forward scatter of Stella-ECFP-positive cells (blue) and somatic cells (red) in the aggregates at the days indicated. For the gating strategy, see Supplementary Fig. 2. g, PPT8-dependent follicle formation. Shown are cultures of parental BVSCNCh-ES cells with ovarian somatic cells (top) and BVSCNCh+PPT8 ES cells with ovarian somatic cells without addition of Shield1 (bottom). Results in ag were reproducible in experiments repeated three times. Scale bars, 200 μm. h, Heat map of maternal gene expression in oocyte-like cells. i, Expression dynamics of LTRs in oocyte-like cells. j, X/A ratio in oocyte-like cells. The ratio was determined as shown in Extended Data Fig. 1b. k, Promoter usage in oocyte-like cells. Shown are the ratios of expression levels of the AT-high-genes/AT-low-genes and GC-high/GC-low genes. Genes are classified as shown in Extended Data Fig. 1d. The sample names in hk correspond to those in Fig. 2h. Expression profiles shown in hk are based on biologically duplicated samples.

Source data

Extended Data Fig. 6 Identification of a minimum set of genes for DIOL induction.

a, Subtraction assay for eight transgenes. A total of 49 BVSCNCh-ES cell lines, which randomly lacked transgenes, were subjected to DIOL induction. The number of oocytes and distribution of oocyte size in each cell lines are shown. On the basis of the number of DIOLs induced from the ES cell lines, we found that Dynll1, Sub1 and Sohlh1 were dispensable for DIOL induction (see Mix8-Sub1_3, Mix8_8, Mix8-Dynll1_1, Mix8_3 and Mix8-Sohlh1_3). DIOLs were induced from ES cell lines containing ‘NFTLS’ (see Mix5_7 and Mix5_2) or ‘NFTL’ (see Mix5_3), whereas no DIOLs were induced from ES cell lines containing fewer than four transgenes among the lines tested. The values of the DIOL number and size were compiled from biologically triplicated experiments except Mix8-Sohlh1_3 (biologically duplicated experiments). Box plots as in Extended Data Fig. 1c. b, Representative images of NFTLS- and NFTL-induced DIOLs at 21 days of culture. Scale bars, 200 μm. n = 3, biologically independent experiments. c, Knock-in of tdTomato into the Padi6 locus. For further subtraction assay, another reporter ES cells, BVSC‘Ptd’-ES cells, were made by knocking-in tdTomato into the Padi6 gene locus in BVSC ES cells. Primers for genotyping (arrows) and the expected size of the amplicons are shown. The images show the results of PCR using the primers and samples numbered. M, size marker. Cre-mediated loxP excision was made in the clone 3 shown in the left image. For gel source data, see Supplementary Fig. 1. d, The subtraction assay using BVSCPtd-ES cells. Seventeen ES cell lines, which randomly lacked transgenes among NFTLS, were subjected to DIOL induction. The images show expression of Stella-ECFP and PADI6-tdTomato (Ptd) in DIOL.D2 and PADI6-tdTomato in DIOL.D21 plus somatic cells. The set of transgenes is shown below each clone. Stella-ECFP expression was observed all clones containing Figla. Clones, which did not show PADI6-tdTomato expression in DIOLs, were not subjected to reaggregation with somatic cells. Note that no DIOLs were induced from BVSCPtd ES cells that lacked any of the NFTL genes. Scale bars, 100 μm (DIOL.D2) and 200 μm (DIOL+S.D21). n = 3, biologically independent experiments. e, Variation of DIOL induction among the transgenic BVSCNCh clones. All three of the BVSCNCh-ES cells harbouring the 8 factors showed a robust induction of DIOLs, whereas 2 of 6 NFTLS clones and 1 of 2 NFTL clones showed the DIOL induction.

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Extended Data Fig. 7 DIOL induction from somatic cells.

a, A schematic protocol of DIOL induction from MEFs. BVSCNCh PPT8-ES cells (Mix8_6, see Extended Data Fig. 6a) were injected into blastocysts, followed by transplantation into pseudopregnant females. At 11 days after transplantation, MEFs were collected from female chimaera embryos, which had Stella-ECFP-positive PGCs or oocytes as shown in the images. BVSCNCh MEFs were selected by puromycin and cultured with Shield1 and gonadal somatic cells. b, No DIOL formation from MEFs. Images show MEFs and reaggregates at the indicated days of culture. Experiments were performed five times. Scale bars, 100 μm (MEFs) and 200 μm (reaggregates). c, A schematic protocol of DIOL induction from iPS cells. BVSC iPS cells were derived from the tail of a 10-week-old female BVSC mouse by overexpression of Pou5f1, Sox2, Klf4 and Myc. Expression vectors for DD-tagged NOBOX, FIGLA, TBPL2 and STAT3 were transfected into BVSC iPS cells. BVSC iPS cells containing all the expression vectors were cultured with Shield1 and gonadal somatic cells. d, DIOL induction from BVSC iPS cells. Images show reaggregates on day 21 of culture. The results were reproducible in experiments repeated three times. Scale bars, 200 μm.

Extended Data Fig. 8 Dispensability of PGC specification for DIOL formation.

a, Heat map of genes essential for PGC specification. The expression profile is based on at least biologically duplicated samples. b, Deletion of the Prdm1 gene by Cas9. gRNAs for the deletion of exons of the Prdm1 gene and primers for detection of the deletions are shown. The numbers above the primer indicate locations in the genome. The right image shows PCR results using the primers. The numbers and M indicate the ES cell lines analysed and the size marker, respectively. Red dots indicate Prdm1-knockout ES lines. For gel source data, see Supplementary Fig. 1. c, DIOL induction with somatic cells from Prdm1-knockout ES cells. Note that Stella-ECFP- and NCh-positive oocytes were induced in the absence of Prdm1. Scale bars, 200 μm. n = 6, biologically independent experiments.

Extended Data Fig. 9 Dispensability of epigenetic reprogramming for DIOL formation and de novo methylation in full-grown DIOLs.

a, Level of DNA methylation in the genome of DIOLs. Shown are the mean percentages with s.d. of methylated CpG, CHG and CHH (in which H correspond to A, T or C) in the genomes of ES cells and DIOL.D5 in the biologically duplicated samples. b, A violin plot showing the CpG methylation levels for each 10-kb window. c, Violin plots showing the distribution of CpG methylation in repetitive elements classified by repeat masker (http://www.repeatmasker.org). DNA, DNA repeat elements; LINE, long interspersed nuclear elements; RC, rolling circle; RNA, RNA repeats including RNA, tRNA, rRNA, small nuclear RNA (snRNA), small conditional RNA (scRNA) and signal recognition particle (srpRNA); SINE, short interspersed nuclear elements; simple repeats, microsatellites. d, Formation of transzonal projections between the DIOL and surrounding granulosa cells. Images show a representative DIOL at 9 days of IVG culture stained with anti-GFP antibody for Stella-ECFP (green), FOXL2 (red), phalloidin (white) and DAPI (blue). The box in the merged image is shown at the right image. Note that transzonal projections stained with phalloidin bridge between the DIOL and surrounding granulosa cells (arrowheads). Formation of transzonal projections was observed in all 12 of the DIOL-granulosa cell complexes tested. Scale bar, 10 μm. n = 10, biologically independent DIOL–granulosa cell complexes. e, DNA methylation patterns across chromosome 15 estimated by a locally estimated scatterplot smoothing regression fitting. Red and blue lines indicate the mean DNA methylation levels of ES cells or DIOLs and in vivo oocytes, respectively. The shaded areas indicate the 95% confidence intervals. f, The frequency of reads with a different level of CpG methylation in imprinting loci. The y axis shows the frequency of reads at the maternal imprinting loci indicated. The x axis shows the percentage of CpG methylation in each read. Note that the frequency of completely methylated reads was more than 50 in all loci, suggesting that a portion of the MII-DIOLs completed the maternal imprinting. The methylome profile is based on biologically duplicated samples, except in the case of ngOocytes and fgOocytes, for which single samples were used.

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Extended Data Fig. 10 Lack of meiosis in DIOLs.

a, Chromosome structure in DIOLs. Shown are representative images of the immunofluorescence analysis of centrosomes and H3K9me3 in a DIOL and an oocyte in vivo soon after germinal vesicle breakdown. Bivalent structures of the chromosomes were observed in the oocyte in vivo (n = 6) but not in the DIOL (n = 20). Scale bar, 10 μm. b, Heat map of genes essential for meiosis. The expression profile is based on biologically duplicated samples. c, SYCP3 expression in DIOLs. A synaptonemal structure was seen in IVD.D7 but not at DIOL+S.D5 irrespective of the presence of retinoic acid (RA) (1 μM). Scale bar, 10 μm. A similar result was observed in three independent reconstituted ovaries. Scale bar, 10 μm. d, Percentage of DIOLs with a polar body. Shown are the number of fgDIOLs and MII-DIOLs in each experiment. e, Heat map of gene expression representing oocyte maturation.

Supplementary information

Supplementary Information

This file contains Supplementary Figures 1 and, Supplementary Tables 1 and 2 and a Supplementary Discussion. Supplementary Figure 1 shows source data for gel electrophoresis, Supplementary Figure 2 shows FACS gating strategy, Supplementary Tables 1 shows the list of oligonucleotides used in this study, Supplementary Tables 2 shows the list of antibodies used in this study and the Supplementary Discussion provides additional discussion of possible role of the transcription factors and meiosis induction in DIOLs.

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Video 1

: Live imaging analysis of chromosomal segregation in fgDIOL maturation. Microtubules and chromosomes were visualized by expression of EGFP-Map4 (green) and H2B-mCherry (magenta), respectively, in WT fg oocytes and fgDIOLs. SC and Npm2-mCherry expression are detected in the cytoplasm of DIOLs. Shown are combined time-laps movies that were acquired at 5-min intervals for 14 h after induction of meiotic resumption.

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Hamazaki, N., Kyogoku, H., Araki, H. et al. Reconstitution of the oocyte transcriptional network with transcription factors. Nature 589, 264–269 (2021). https://doi.org/10.1038/s41586-020-3027-9

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