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Ultrasensitive Ribo-seq reveals translational landscapes during mammalian oocyte-to-embryo transition and pre-implantation development

Abstract

In mammals, translational control plays critical roles during oocyte-to-embryo transition (OET) when transcription ceases. However, the underlying regulatory mechanisms remain challenging to study. Here, using low-input Ribo-seq (Ribo-lite), we investigated translational landscapes during OET using 30–150 mouse oocytes or embryos per stage. Ribo-lite can also accommodate single oocytes. Combining PAIso-seq to interrogate poly(A) tail lengths, we found a global switch of translatome that closely parallels changes of poly(A) tails upon meiotic resumption. Translation activation correlates with polyadenylation and is supported by polyadenylation signal proximal cytoplasmic polyadenylation elements (papCPEs) in 3′ untranslated regions. By contrast, translation repression parallels global de-adenylation. The latter includes transcripts containing no CPEs or non-papCPEs, which encode many transcription regulators that are preferentially re-activated before zygotic genome activation. CCR4-NOT, the major de-adenylation complex, and its key adaptor protein BTG4 regulate translation downregulation often independent of RNA decay. BTG4 is not essential for global de-adenylation but is required for selective gene de-adenylation and production of very short-tailed transcripts. In sum, our data reveal intimate interplays among translation, RNA stability and poly(A) tail length regulation underlying mammalian OET.

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Fig. 1: Genome-wide mapping of translatome by Ribo-lite.
Fig. 2: Global translatome dynamics in mouse oocytes and pre-implantation embryos.
Fig. 3: Translatome dynamics and TE analyses.
Fig. 4: The role of CPE in translational repression and activation.
Fig. 5: CNOT6L and BTG4 mediate translation downregulation during oocyte maturation.
Fig. 6: Interplay among poly(A) tail, RNA stability and translation upon meiotic resumption.
Fig. 7: Pre-ZGA translation is essential for ZGA and maternal clearance.

Data availability

The sequencing datasets have been deposited in the Gene Expression Omnibus (GEO) under the accession code GSE165782. The proteomics data of FGOs have been deposited into ProteomeXchange under the code PXD032229. Publicly available datasets used in this work were from NCBI GEO accession number GSE37744 (HEK293-bulk-1 (ref. 32)), GSE94460 (HEK293-bulk-2 (ref. 37)), GSE78163 (low-input-brain34), GSE118564 (Poly-seq27), GSE121358 (SSP profiling28) and GSE135525 (Ribo-tag29). The published PAIso-seq is available in NCBI Sequence Read Archive under accession number PRJNA529588. We used mouse genome version mm9 and human genome version hg19. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

References

  1. Speed, R. M. Meiosis in the foetal mouse ovary. I. An analysis at the light microscope level using surface-spreading. Chromosoma 85, 427–437 (1982).

    CAS  PubMed  Article  Google Scholar 

  2. Hilscher, B. et al. Kinetics of gametogenesis. I. Comparative histological and autoradiographic studies of oocytes and transitional prospermatogonia during oogenesis and prespermatogenesis. Cell Tissue Res. 154, 443–470 (1974).

    CAS  PubMed  Article  Google Scholar 

  3. 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  Article  CAS  Google Scholar 

  4. Bachvarova, R. Gene expression during oogenesis and oocyte development in mammals. Dev. Biol. 1, 453–524 (1985).

    CAS  Google Scholar 

  5. Li, L., Zheng, P. & Dean, J. Maternal control of early mouse development. Development 137, 859–870 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. Pan, B. & Li, J. The art of oocyte meiotic arrest regulation. Reprod. Biol. Endocrinol. 17, 8 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  7. Clarke, H. J. Post-transcriptional control of gene expression during mouse oogenesis. Results Probl. Cell Differ. 55, 1–21 (2012).

    CAS  PubMed  Article  Google Scholar 

  8. Conti, M. & Franciosi, F. Acquisition of oocyte competence to develop as an embryo: integrated nuclear and cytoplasmic events. Hum. Reprod. Update 24, 245–266 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. Ozturk, S. The translational functions of embryonic poly(A)-binding protein during gametogenesis and early embryo development. Mol. Reprod. Dev. 86, 1548–1560 (2019).

    CAS  PubMed  Article  Google Scholar 

  10. Fox, C. A., Sheets, M. D. & Wickens, M. P. Poly(A) addition during maturation of frog oocytes: distinct nuclear and cytoplasmic activities and regulation by the sequence UUUUUAU. Genes Dev. 3, 2151–2162 (1989).

    CAS  PubMed  Article  Google Scholar 

  11. Richter, J. D. CPEB: a life in translation. Trends Biochem. Sci. 32, 279–285 (2007).

    CAS  PubMed  Article  Google Scholar 

  12. Setoyama, D., Yamashita, M. & Sagata, N. Mechanism of degradation of CPEB during Xenopus oocyte maturation. Proc. Natl Acad. Sci. USA 104, 18001–18006 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. Mendez, R., Barnard, D. & Richter, J. D. Differential mRNA translation and meiotic progression require Cdc2-mediated CPEB destruction. EMBO J. 21, 1833–1844 (2002).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Stebbins-Boaz, B., Cao, Q., de Moor, C. H., Mendez, R. & Richter, J. D. Maskin is a CPEB-associated factor that transiently interacts with elF-4E. Mol. Cell 4, 1017–1027 (1999).

    CAS  PubMed  Article  Google Scholar 

  15. Cao, Q. & Richter, J. D. Dissolution of the maskin–eIF4E complex by cytoplasmic polyadenylation and poly(A)-binding protein controls cyclin B1 mRNA translation and oocyte maturation. EMBO J. 21, 3852–3862 (2002).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. Vassalli, J. D. et al. Regulated polyadenylation controls mRNA translation during meiotic maturation of mouse oocytes. Genes Dev. 3, 2163–2171 (1989).

    CAS  PubMed  Article  Google Scholar 

  17. Tay, J., Hodgman, R. & Richter, J. D. The control of cyclin B1 mRNA translation during mouse oocyte maturation. Dev. Biol. 221, 1–9 (2000).

    CAS  PubMed  Article  Google Scholar 

  18. Racki, W. J. & Richter, J. D. CPEB controls oocyte growth and follicle development in the mouse. Development 133, 4527–4537 (2006).

    CAS  PubMed  Article  Google Scholar 

  19. Gebauer, F., Xu, W., Cooper, G. M. & Richter, J. D. Translational control by cytoplasmic polyadenylation of c-mos mRNA is necessary for oocyte maturation in the mouse. EMBO J. 13, 5712–5720 (1994).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. Sha, Q. Q. et al. A MAPK cascade couples maternal mRNA translation and degradation to meiotic cell cycle progression in mouse oocytes. Development 144, 452–463 (2017).

    CAS  PubMed  Google Scholar 

  21. Virant-Klun, I., Leicht, S., Hughes, C. & Krijgsveld, J. Identification of maturation-specific proteins by single-cell proteomics of human oocytes. Mol. Cell Proteom. 15, 2616–2627 (2016).

    CAS  Article  Google Scholar 

  22. Wang, S. et al. Proteome of mouse oocytes at different developmental stages. Proc. Natl Acad. Sci. USA 107, 17639–17644 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. Gao, Y. et al. Protein expression landscape of mouse embryos during pre-implantation development. Cell Rep. 21, 3957–3969 (2017).

    CAS  PubMed  Article  Google Scholar 

  24. Israel, S. et al. An integrated genome-wide multi-omics analysis of gene expression dynamics in the preimplantation mouse embryo. Sci. Rep. 9, 13356 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  25. Potireddy, S., Vassena, R., Patel, B. G. & Latham, K. E. Analysis of polysomal mRNA populations of mouse oocytes and zygotes: dynamic changes in maternal mRNA utilization and function. Dev. Biol. 298, 155–166 (2006).

    CAS  PubMed  Article  Google Scholar 

  26. Chen, J. et al. Genome-wide analysis of translation reveals a critical role for deleted in azoospermia-like (Dazl) at the oocyte-to-zygote transition. Genes Dev. 25, 755–766 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. Sha, Q. Q. et al. CNOT6L couples the selective degradation of maternal transcripts to meiotic cell cycle progression in mouse oocyte. EMBO J. 37, e99333 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  28. Masek, T. et al. Identifying the translatome of mouse NEBD-stage oocytes via SSP-profiling; a novel polysome fractionation method. Int. J. Mol. Sci. 21, 1254 (2020).

    CAS  PubMed Central  Article  Google Scholar 

  29. Luong, X. G., Daldello, E. M., Rajkovic, G., Yang, C. R. & Conti, M. Genome-wide analysis reveals a switch in the translational program upon oocyte meiotic resumption. Nucleic Acids Res. 48, 3257–3276 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. Larsson, O., Tian, B. & Sonenberg, N. Toward a genome-wide landscape of translational control. Cold Spring Harb. Perspect. Biol. 5, a012302 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  31. Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. S. & Weissman, J. S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. Ingolia, N. T., Brar, G. A., Rouskin, S., McGeachy, A. M. & Weissman, J. S. The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments. Nat. Protoc. 7, 1534–1550 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. McGlincy, N.J. & Ingolia, N.T. Transcriptome-wide measurement of translation by ribosome profiling. Methods 126, 112–129 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  34. Hornstein, N. et al. Ligation-free ribosome profiling of cell type-specific translation in the brain. Genome Biol. 17, 149 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  35. Clamer, M., Lauria, F., Tebaldi, T. & Viero, G. Active ribosome profiling with RiboLace: from bench to data analysis. Methods Mol. Biol. 2252, 201–220 (2021).

    CAS  PubMed  Article  Google Scholar 

  36. Gu, T. P. et al. The role of Tet3 DNA dioxygenase in epigenetic reprogramming by oocytes. Nature 477, 606–610 (2011).

    CAS  PubMed  Article  Google Scholar 

  37. Zhang, P. et al. Genome-wide identification and differential analysis of translational initiation. Nat. Commun. 8, 1749 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  38. Lee, M. T., Bonneau, A. R. & Giraldez, A. J. Zygotic genome activation during the maternal-to-zygotic transition. Annu. Rev. Cell Dev. Biol. 30, 581–613 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. Abe, K. et al. The first murine zygotic transcription is promiscuous and uncoupled from splicing and 3′ processing. EMBO J. 34, 1523–1537 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 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  Article  Google Scholar 

  41. Wang, R. et al. BMI1 deficiency results in female infertility by activating p16/p19 signaling and increasing oxidative stress. Int J. Biol. Sci. 15, 870–881 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. Subtelny, A. O., Eichhorn, S. W., Chen, G. R., Sive, H. & Bartel, D. P. Poly(A)-tail profiling reveals an embryonic switch in translational control. Nature 508, 66–71 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. Lim, J., Lee, M., Son, A., Chang, H. & Kim, V. N. mTAIL-seq reveals dynamic poly(A) tail regulation in oocyte-to-embryo development. Genes Dev. 30, 1671–1682 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. Eichhorn, S. W. et al. mRNA poly(A)-tail changes specified by deadenylation broadly reshape translation in Drosophila oocytes and early embryos. eLife 5, e16955 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  45. Liu, Y., Nie, H., Liu, H. & Lu, F. Poly(A) inclusive RNA isoform sequencing (PAIso-seq) reveals wide-spread non-adenosine residues within RNA poly(A) tails. Nat. Commun. 10, 5292 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  46. VanInsberghe, M., van den Berg, J., Andersson-Rolf, A., Clevers, H. & van Oudenaarden, A. Single-cell Ribo-seq reveals cell cycle-dependent translational pausing. Nature 597, 561–565 (2021).

    CAS  PubMed  Article  Google Scholar 

  47. Sendzikaite, G. & Kelsey, G. The role and mechanisms of DNA methylation in the oocyte. Essays Biochem. 63, 691–705 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  48. Sukari, A., Muqbil, I., Mohammad, R. M., Philip, P. A. & Azmi, A. S. F-BOX proteins in cancer cachexia and muscle wasting: emerging regulators and therapeutic opportunities. Semin. Cancer Biol. 36, 95–104 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. Welcker, M. & Clurman, B. E. FBW7 ubiquitin ligase: a tumour suppressor at the crossroads of cell division, growth and differentiation. Nat. Rev. Cancer 8, 83–93 (2008).

    CAS  PubMed  Article  Google Scholar 

  50. Abe, T. et al. Germ-cell-specific inflammasome component NLRP14 negatively regulates cytosolic nucleic acid sensing to promote fertilization. Immunity 46, 621–634 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. Hamatani, T. et al. Age-associated alteration of gene expression patterns in mouse oocytes. Hum. Mol. Genet 13, 2263–2278 (2004).

    CAS  PubMed  Article  Google Scholar 

  52. Spence, J. S. et al. IFITM3 directly engages and shuttles incoming virus particles to lysosomes. Nat. Chem. Biol. 15, 259–268 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. Grow, E. J. et al. Intrinsic retroviral reactivation in human preimplantation embryos and pluripotent cells. Nature 522, 221–225 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. Stein, P., Zeng, F., Pan, H. & Schultz, R. M. Absence of non-specific effects of RNA interference triggered by long double-stranded RNA in mouse oocytes. Dev. Biol. 286, 464–471 (2005).

    CAS  PubMed  Article  Google Scholar 

  55. Dai, X. X. et al. A combinatorial code for mRNA 3′-UTR-mediated translational control in the mouse oocyte. Nucleic Acids Res. 47, 328–340 (2019).

    CAS  PubMed  Article  Google Scholar 

  56. Pique, M., Lopez, J. M., Foissac, S., Guigo, R. & Mendez, R. A combinatorial code for CPE-mediated translational control. Cell 132, 434–448 (2008).

    CAS  PubMed  Article  Google Scholar 

  57. Jariwala, N. et al. Role of the staphylococcal nuclease and tudor domain containing 1 in oncogenesis (review). Int. J. Oncol. 46, 465–473 (2015).

    CAS  PubMed  Article  Google Scholar 

  58. Su, Y.-Q. et al. Selective degradation of transcripts during meiotic maturation of mouse oocytes. Dev. Biol. 302, 104–117 (2007).

    CAS  PubMed  Article  Google Scholar 

  59. Bazzini, A. A., Lee, M. T. & Giraldez, A. J. Ribosome profiling shows that miR-430 reduces translation before causing mRNA decay in zebrafish. Science 336, 233–237 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. Collart, M. A. The Ccr4–Not complex is a key regulator of eukaryotic gene expression. Wiley Interdiscip. Rev. RNA 7, 438–454 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. Yu, C. et al. BTG4 is a meiotic cell cycle-coupled maternal-zygotic-transition licensing factor in oocytes. Nat. Struct. Mol. Biol. 23, 387–394 (2016).

    CAS  PubMed  Article  Google Scholar 

  62. Liu, Y. et al. BTG4 is a key regulator for maternal mRNA clearance during mouse early embryogenesis. J. Mol. Cell. Biol. 8, 366–368 (2016).

    CAS  PubMed  Article  Google Scholar 

  63. Bawankar, P., Loh, B., Wohlbold, L., Schmidt, S. & Izaurralde, E. NOT10 and C2orf29/NOT11 form a conserved module of the CCR4–NOT complex that docks onto the NOT1 N-terminal domain. RNA Biol. 10, 228–244 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. Cooke, A., Prigge, A. & Wickens, M. Translational repression by deadenylases. J. Biol. Chem. 285, 28506–28513 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. Zekri, L., Kuzuoglu-Ozturk, D. & Izaurralde, E. GW182 proteins cause PABP dissociation from silenced miRNA targets in the absence of deadenylation. EMBO J. 32, 1052–1065 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. Chekulaeva, M. et al. miRNA repression involves GW182-mediated recruitment of CCR4-NOT through conserved W-containing motifs. Nat. Struct. Mol. Biol. 18, 1218–1226 (2011).

    CAS  PubMed  Article  Google Scholar 

  67. Webster, M. W. et al. mRNA deadenylation is coupled to translation rates by the differential activities of Ccr4–Not nucleases. Mol. Cell 70, 1089–1100.e8 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. Eisen, T. J. et al. The dynamics of cytoplasmic mRNA metabolism. Mol. Cell 77, 786–799.e10 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. Liu, Y., Nie, H., Zhang, Y., Lu, F. & Wang, J. Comprehensive analysis of mRNA poly(A) tail reveals complex and conserved regulation. Preprint at bioRxiv https://doi.org/10.1101/2021.08.29.458068 (2021).

  70. Burton, A. et al. Heterochromatin establishment during early mammalian development is regulated by pericentromeric RNA and characterized by non-repressive H3K9me3. Nat. Cell Biol. 22, 767–778 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. Akiyama, T. et al. Transient bursts of Zscan4 expression are accompanied by the rapid derepression of heterochromatin in mouse embryonic stem cells. DNA Res. 22, 307–318 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  72. Tardat, M. et al. Cbx2 targets PRC1 to constitutive heterochromatin in mouse zygotes in a parent-of-origin-dependent manner. Mol. Cell 58, 157–171 (2015).

    CAS  PubMed  Article  Google Scholar 

  73. Aoki, F., Hara, K. T. & Schultz, R. M. Acquisition of transcriptional competence in the 1-cell mouse embryo: requirement for recruitment of maternal mRNAs. Mol. Reprod. Dev. 64, 270–274 (2003).

    CAS  PubMed  Article  Google Scholar 

  74. Wang, Q. & Latham, K. E. Requirement for protein synthesis during embryonic genome activation in mice. Mol. Reprod. Dev. 47, 265–270 (1997).

    CAS  PubMed  Article  Google Scholar 

  75. Wang, Q., Chung, Y. G., deVries, W. N., Struwe, M. & Latham, K. E. Role of protein synthesis in the development of a transcriptionally permissive state in one-cell stage mouse embryos. Biol. Reprod. 65, 748–754 (2001).

    CAS  PubMed  Article  Google Scholar 

  76. Xiang, K. & Bartel, D. P. The molecular basis of coupling between poly(A)-tail length and translational efficiency. eLife 10, e66493 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  77. Liu, H. Y. et al. The NOT proteins are part of the CCR4 transcriptional complex and affect gene expression both positively and negatively. EMBO J. 17, 1096–1106 (1998).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  78. Chen, J. et al. Purification and characterization of the 1.0 MDa CCR4–NOT complex identifies two novel components of the complex. J. Mol. Biol. 314, 683–694 (2001).

    CAS  PubMed  Article  Google Scholar 

  79. Collart, M. A. Global control of gene expression in yeast by the Ccr4–Not complex. Gene 313, 1–16 (2003).

    CAS  PubMed  Article  Google Scholar 

  80. Mostafa, D. et al. Essential functions of the CNOT7/8 catalytic subunits of the CCR4–NOT complex in mRNA regulation and cell viability. RNA Biol. 17, 403–416 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  81. Morgan, M. et al. mRNA 3′ uridylation and poly(A) tail length sculpt the mammalian maternal transcriptome. Nature 548, 347–351 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  82. Chang, H. et al. Terminal uridylyltransferases execute programmed clearance of maternal transcriptome in vertebrate embryos. Mol. Cell 70, 72–82.e7 (2018).

    CAS  PubMed  Article  Google Scholar 

  83. Xiao, Z. et al. De novo annotation and characterization of the translatome with ribosome profiling data. Nucleic Acids Res. 46, e61 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  84. Solter, D. & Knowles, B. B. Immunosurgery of mouse blastocyst. Proc. Natl Acad. Sci. USA 72, 5099–5102 (1975).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  85. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    CAS  PubMed  Article  Google Scholar 

  86. Zhang, B. et al. Allelic reprogramming of the histone modification H3K4me3 in early mammalian development. Nature 537, 553–557 (2016).

    CAS  PubMed  Article  Google Scholar 

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

    CAS  PubMed  Article  Google Scholar 

  88. Trapnell, C. et al. Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  89. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  90. Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).

    CAS  PubMed  Article  Google Scholar 

  91. Dennis, G. Jr. et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 4, P3 (2003).

    PubMed  Article  Google Scholar 

  92. Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  93. Wu, J. et al. The landscape of accessible chromatin in mammalian preimplantation embryos. Nature 534, 652–657 (2016).

    CAS  PubMed  Article  Google Scholar 

  94. Li, F., Xing, X., Xiao, Z., Xu, G. & Yang, X. RiboMiner: a toolset for mining multi-dimensional features of the translatome with ribosome profiling data. BMC Bioinformatics 21, 340 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgements

We are grateful to members of the Xie laboratory for discussion and comments during the preparation of the manuscript, and the Animal Center and Biocomputing Facility at Tsinghua University for their support. We thank R. Méndez for kindly providing scripts for the CPE analysis. We thank P. Wang and J. Na for help in establishing the oocyte injection systems. We appreciate N. Ingolia for discussion on the Ribo-seq method. We thank Z. Xiao and X. Yang for help in establishing Ribo-seq data analysis pipeline. We thank F. Lu, Y. Liu and H. Nie for help and discussion in establishing PAIso-seq experiment and data analysis. We thank W. Hu and K. Kee for providing reagents. We thank Ying Yang and Yungui Yang for providing the YTHDF3 antibody. This work was funded by the National Natural Science Foundation of China (31988101 to W.X.), the National Key R&D Program of China (2021YFA1100102 and 2019YFA0508901 to W.X.), the National Natural Science Foundation of China (31725018 to W.X.; 31930033 and 32170812 to L. Li), the Tsinghua-Peking Center for Life Sciences (W.X.) and the Beijing Municipal Science and Technology Commission (grant Z181100001318006 to W.X.). W.X. is a recipient of an HHMI International Research Scholar award.

Author information

Authors and Affiliations

Authors

Contributions

Z.X. and W.X. conceived and designed the project. Z.X. developed Ribo-lite and performed Ribo-lite experiments with help from Z.Z., Q.W. and J.F. K.X., Z.L. and F.K. collected oocytes and embryos with help from L. Liu, L.W. and Z.X. Z.X., F.K. and Q.W. performed RNA-seq. Z.L. and K.X. optimized IVM culture advised by H.W. K.X., F.K. and Z.L. performed oocyte injections and embryo drug treatment. Y.Q. and L. Li provided Btg4/ mice for Ribo-lite and RNA-seq analyses. Q.S. and H.F. provided Cnot6l/ mice for Ribo-lite and RNA-seq analyses. Q.W. prepared reporters and mRNA for injection. S.J., Y.C. and H.Z. generated and analysed the FGO MS data supervised by H.D. F.L. and X.Y. advised and helped data analysis. Z.X. and W.X. analysed the data with help from G.Y. and B.L. Z.X. and W.X. prepared most figures and wrote the manuscript with help from all authors.

Corresponding authors

Correspondence to Lei Li or Wei Xie.

Ethics declarations

Competing interests

A patent for Ribo-lite has been issued to W.X. and Z.X (patent number: ZL 2019 1 0609393.9, issue number: CN 112195226 B). The remaining authors declare no competing interests.

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Nature Cell Biology thanks Shintaro Iwasaki and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Comparison of RPF-based methods with different library construction strategies.

(a) Schematics showing workflows for different Ribo-seq based methods including the conventional Ribo-seq32, Ligation-free Ribo-seq34 and Ribo-lite. (b) Barcharts showing the marker contamination for 500-cell samples by PAGE purification with RNA (left) or ssDNA (right) markers. Results are from two (for RNA marker) and three (for ssDNA marker) independent experiments. (c) A table showing the RPF-based methods and datasets that are used for comparison, including two conventional Ribo-seq32,37, Ligation-free Ribo-seq34 and Ribo-lite. (d) Bar plots showing the percentages of reads that are adapter, rRNA or clean reads (non-rRNA mapped reads) in different datasets. Results are from two independent experiments. (e) Bar plots showing the metagene read distribution of the 5′-end of reads from Ribo-seq and mRNA-seq data around the translation start codon and stop codon. Read position relative to ORFs (0, 1, 2) are shown in different colors. The schematic shows the ribosome binding around start/stop codon. Results are from two independent experiments.

Extended Data Fig. 2 Validation of Ribo-lite data in mouse oocytes and early embryos.

(a) DNA (Hoechst 33342) and α-tublin staining of LPI and MII oocytes. Representative images from two independent experiments are shown. Scale bar: 10 μm. (b) Bar plots showing the percentages of sequencing reads that are adapters, rRNA reads, or clean reads (non-rRNA mapped reads). Results are from two independent experiments. (c) Bar plots shows the numbers of genes detected by Ribo-lite (FPKM > 1). Results are from two independent experiments. (d) Bar plots showing the RPF 5′-end metagene distribution around the start codon. Read positions relative to ORFs (0, 1, 2) are shown in different colors. Representative results are from two independent experiments. (e) Bar plots showing the percentages of footprints that match these reading frames. Results are from two independent experiments. (f) Bar plots showing the percentages of mapped reads with periodicity, as analyzed by RiboCode83. Results are from two independent experiments. (g) Bar plots showing the read distribution in 5′UTR, CDS and 3′UTR regions. Results are from two independent experiments. (h) Heat map showing the read length distribution for Ribo-lite data. Representative results are from two independent experiments.

Extended Data Fig. 3 Comparison of Ribo-lite and mRNA-seq data for mouse oocytes and early embryos.

(a) Heat maps showing the expression levels of stage-specific genes sorted by Ribo-lite data and the mapped mRNA-seq data (left). Heat maps showing the expression levels of stage-specific genes sorted by mRNA-seq data and the mapped Ribo-lite data (right). Results are from two independent experiments. (b) Heat maps showing the Spearman correlation values for Ribo-lite and mRNA-seq data. Results are from two independent experiments. (c) The UCSC browser views of Mos, Btg4, Cnot7, Cnot6l and Cpeb1 in mRNA-seq and Ribo-lite. Representative results are from two independent experiments. (d) Heat maps showing the mRNA and translatome levels of 15 known dormant RNAs and 3 protein known to be down-regulated upon meiotic resumption in oocytes from different datasets27,28,29.

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Extended Data Fig. 4 The comparison of oocyte translatomes generated by Ribo-lite, Ribo-tag, Poly-seq and SSP-profiling.

(a) Heat map showing the Spearman correlation between Ribo-lite, Ribo-tag29, Poly-seq27 and SSP-profiling28. NEBD (nuclear envelope breakdown) oocytes28 were released from FGO for 80 min. Results are from two independent experiments. (b) Scatter plots showing the comparison of RNA with translatome in FGOs (or NEBD oocytes for SSP-profiling) in different studies. Red dots represent known dormant mRNAs. The black line represents the linear regression line. Mos and Plat are marked. Results are from two independent experiments. (c) Scatter plots showing the translation efficiency (TE, translatome/mRNA) detected in different studies in FGOs. As mRNA-seq data without selection are unavailable for SSP-profiling, non-polysome mRNA data were used to calculate TE. Right, boxplots showing the TE of dormant RNAs detected in different studies in FGOs. Results are from two independent experiments. N = 17 genes are analyzed for FGO TE. Centre line, median; box, 25th and 75th percentiles; whiskers, 1.5×IQR. (d) Scatter plots showing the comparison of mRNA levels and translatome levels detected by different methods in LPI and MII oocytes. (e) Boxplots showing the TE of dormant RNAs during meiotic resumption. N = 17 genes are analyzed for TE. Centre line, median; box, 25th and 75th percentiles; whiskers, 1.5×IQR. (f) Scatter plots showing the relationship between TE detected by Ribo-lite, Ribo-tag, Poly-seq and SSP-profiling and poly(A) tail length detected by PAIso-seq in FGO45. (g) Scatter plots showing the relationship between translatome detected by Ribo-lite, Ribo-tag, Poly-seq and SSP-profiling and protein levels detected by MS in FGOs.

Extended Data Fig. 5 Ribo-lite using 50 HEK293 cells or single oocytes.

(a) Bar plots showing the percentages of sequence reads that are adapters, rRNA reads or clean reads. Representative results are from 19 (for FGOs) and 18 (for MII oocytes) independent experiments. (b) Bar plots showing the number of genes detected by Ribo-lite (FPKM > 1). Representative results are from 19 (for FGOs) and 18 (for MII oocytes) independent experiments. (c) Spearman correlation of RPF signals between low-input (1,000) and 50 HEK293 cells, as well as between low-input FGO and MII oocytes and single oocytes. Representative results are from 19 (for FGOs) and 18 (for MII oocytes) independent experiments. (d) Scatter plots showing the Ribo-lite signal between low-input data and 50 HEK293 cells or single oocytes. The Spearman correlation values are also shown. Representative results are from 19 (for FGOs) and 18 (for MII oocytes) independent experiments. (e) Bar charts showing the RPF metagene distribution around start codon for low-input cells, 50 HEK293 cells, or single oocytes. Position relative to ORFs (0, 1, 2) are shown in different colors. Representative results are from 19 (for FGOs) and 18 (for MII oocytes) independent experiments. (f) Bar plots showing the percentages of footprints that match the reading frames. Representative results are from 19 (for FGOs) and 18 (for MII oocytes) independent experiments. (g) Bar plots showing the percentages of mapped reads with periodicity, as analyzed by RiboCode83. Representative results are from 19 (for FGOs) and 18 (for MII oocytes) independent experiments. (h) Bar plots showing the read coverages at 5′UTR, CDS and 3′UTR regions. Representative results are from 19 (for FGOs) and 18 (for MII oocytes) independent experiments. (i) Box plots showing the RPF levels for known dormant genes using low-input or single-cell oocytes. Centre line, median; box, 25th and 75th percentiles; whiskers, 1.5×IQR. N = 17 genes are analyzed.

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Extended Data Fig. 6 Representative genes with distinct translatome dynamics during OET and analysis for bottom 500 TE genes.

(a-d) Graphs showing the RPF levels for representative genes or gene families for OET up-regulated (a), maternal-specific (b), OET down-regulated (c), and embryo-specific (d) gene groups. Results are from two independent experiments. (e) Western blot results for CNOT8, YTHDF3, BTG4, CPEB1, and TUBLIN (representative results shown are from two biologically independent experiments). (f) Heat maps showing the percentages of bottom TE genes overlapped with bottom TE genes at other stages. Results are from two independent experiments. (g) Heat map showing the enriched GO terms from bottom TE genes for each stage. The color shows the enrichment from Fisher’s Exact P value. Results are from two independent experiments.

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Extended Data Fig. 7 papCPE-mediated translational repression in FGOs.

(a) Bar plots showing the numbers of genes with different numbers of PAS. (b) Jitter plots comparing the number of papCPE (with different definition) to the FGO TE (log2) (genes with one PAS). Unpaired two-sided t-test was used. Results are from two independent experiments. (c) Scatter plots comparing the poly(A) tail length45 with FGO TE (log2) for all the genes, or expressed genes in FGOs with one PAS and different types of CPEs. Results are from two independent experiments. (d) Left, jitter plots comparing the distance between papCPE and PAS with FGO TE (log2) for genes with one PAS and one papCPE. Right, jitter plots comparing the FGO TE (log2) of papCPE overlapping with PAS or not overlapping with PAS. P values, Mann-Whitney-U test. Results are from two independent experiments. (e) Similar analysis as (d) but for genes with one PAS and two papCPEs. P values, Mann-Whitney-U test. Results are from two independent experiments. (f) A jitter plot comparing the CPE-CPE distance with FGO TE (log2) for genes with one PAS and two papCPEs. Results are from two independent experiments. (g) A jitter plot comparing the FGO TE of different location scenarios of two papCPEs with PAS, for genes with one PAS and two papCPEs. P values, Mann-Whitney-U test. Results are from two independent experiments. (h) A jitter plot comparing FGO TE for genes with 0, 1, or both PASs with at least two papCPEs. P values, Mann-Whitney-U test. Results are from two independent experiments.

Extended Data Fig. 8 Translational regulation of Snd1 3′UTR reporters with non-papCPEs or papCPEs during oocyte maturation.

(a) Representative images are shown (from two biologically independent experiments). mCherry mRNA serves as the injection control. Scale bar: 200 μm.

Extended Data Fig. 9 Translation down-regulation during oocyte maturation are mediated by CNOT6L and BTG4.

(a) Line graphs showing the transcription and translation levels of mRNA-destabilized and mRNA-stable genes. GO terms are also shown. Results are from two independent experiments. (b) Bar plots showing the mRNA and translation levels for Cnot6, Cnot6l, Cnot7, Cnot8 and Btg4. Results are from two independent experiments. (c) The UCSC browser views of mRNA-seq signals and Ribo-lite signals of Het oocytes and KO oocytes for Cnot6l and Btg4. Results are from two independent experiments. The knocked out regions are shaded. (d) Venn diagram showing the overlap between RNA targets (up-regulated mRNA DEGs) identified in Cnot6l KO MII oocytes or Btg4 KO MII oocytes and down-regulated mRNA DEGs from FGO to MII stage. Results are from three (for Btg4 samples) and one (for Cnot6l samples) biologically independent experiment. (e) A similar comparative analysis as (d) but for RPF targets or down-regulated RPF DEGs. Results are from three (for Btg4 samples) and one (for Cnot6l samples) biologically independent experiment. (f) Codon adaptation index (cAI) is shown for genes that are regulated by CNOT6L or BTG4, or both, based on Ribo-lite data (results are from three (for Btg4 samples) and one (for Cnot6l samples) biologically independent experiment). P values (two-sided Mann-Whitney-U test) are also shown. Number of genes analyzed here are global (n = 19,952), overlap (n = 55), CNOT6L specific (n = 57) and BTG4 specific (n = 378). Centre line, median; box, 25th and 75th percentiles; whiskers, 1.5×IQR.

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Extended Data Fig. 10 Poly(A) tail length in FGOs and MII oocytes and translation in early embryos.

(a) A scatter plot showing the correlation of poly(A) tail length data generated in this study and the published data45. (b) Histograms showing the poly(A) tail length distributions in FGOs and MII oocytes at the gene (left) and transcript (right) levels. Results shown are from one (for FGO sample) and two (for MII oocytes samples) biologically independent experiments. (c) Histograms showing the poly(A) tail length distribution for short-tailed (<50 nt) and long-tailed (>70 nt) genes (defined in MII oocytes) in FGOs and MII oocytes. Results shown are from one (for FGO sample) and two (for MII oocytes samples) biologically independent experiment. (d) Heat maps showing translation and mRNA levels for heterochromatin regulators. Results are from two independent experiments. (e) Schematic of the CHX and DRB treatment experiments in early embryos. (f) Boxplots showing RPF levels of DRB-sensitive genes in early 2-cell (left) and MII-E2C translationally up-regulated genes (right) in MII oocytes in early 2-cell embryos. Results are from one biologically independent experiment. N = 276 for DRB-sensitive genes and n = 839 for MII-E2C translationally up-regulated genes. Centre line, median; box, 25th and 75th percentiles; whiskers, 1.5×IQR.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–6.

Reporting Summary.

Supplementary Table 1

Supplementary Table 1. Ribo-lite and RNA-seq gene FPKM table for oocytes and embryos with replicate data. Supplementary Table 2. Ribo-lite and RNA-seq gene average FPKM and calculated TE table for oocytes and embryos. Supplementary Table 3. Identified RPF DEGs and mRNA DEGs for consecutive stages from the Ribo-lite and mRNA-seq data.

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Source Data Fig. 4

Statistical source data.

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Source Data Extended Data Fig. 3

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Unprocessed western blots and gels.

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Xiong, Z., Xu, K., Lin, Z. et al. Ultrasensitive Ribo-seq reveals translational landscapes during mammalian oocyte-to-embryo transition and pre-implantation development. Nat Cell Biol 24, 968–980 (2022). https://doi.org/10.1038/s41556-022-00928-6

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