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

Nature Cell Biology volume 24, pages 968–980 (2022)Cite this article

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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.

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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.

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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

Author notes

  1. These authors contributed equally: Zhuqing Xiong, Kai Xu, Zili Lin, Feng Kong, Qiujun Wang, Yujun Quan.

Authors and Affiliations

  1. Center for Stem Cell Biology and Regenerative Medicine, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China

    Zhuqing Xiong, Kai Xu, Feng Kong, Qiujun Wang, Zhuoning Zou, Ling Liu, Shuyan Ji, Hongmei Zhang, Guang Yu, Bofeng Liu, Lijuan Wang & Wei Xie

  2. Peking University–Tsinghua University–National Institute of Biological Sciences Joint Graduate Program, School of Life Sciences, Tsinghua University, Beijing, China

    Zhuqing Xiong & Fajin Li

  3. Tsinghua-Peking Center for Life Sciences, Beijing, China

    Kai Xu, Feng Kong, Qiujun Wang, Zhuoning Zou, Ling Liu, Shuyan Ji, Hongmei Zhang, Jianhuo Fang, Guang Yu, Bofeng Liu, Lijuan Wang & Wei Xie

  4. College of Animal Science and Technology College, Beijing University of Agriculture, Beijing, China

    Zili Lin

  5. State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Yujun Quan & Lei Li

  6. University of Chinese Academy of Sciences, Beijing, China

    Yujun Quan & Lei Li

  7. Fertility Preservation Laboratory, Reproductive Medicine Center, Guangdong Second Provincial General Hospital, Guangzhou, China

    Qian-qian Sha

  8. MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China

    Fajin Li, Jianhuo Fang & Xuerui Yang

  9. Center for Synthetic & Systems Biology, Tsinghua University, Beijing, China

    Fajin Li, Jianhuo Fang & Xuerui Yang

  10. School of Life Sciences, Tsinghua University, Beijing, China

    Yuling Chen & Haiteng Deng

  11. Institute of Animal Science, Jiangsu Academy of Agricultural Sciences, Nanjing, China

    Huili Wang

  12. MOE Key Laboratory for Biosystems Homeostasis & Protection and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou, China

    Heng-yu Fan

  13. Key Laboratory of Reproductive Dysfunction Management of Zhejiang Province, Assisted Reproduction Unit, Department of Obstetrics and Gynecology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China

    Heng-yu Fan

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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.

Peer review

Peer review information

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

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Source data

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.

Source data

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.

Source data

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.

Source data

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.

Source data

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.

Source data

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Unprocessed western blots and gels.

Source Data Extended Data Fig. 9

Statistical source data.

Source Data Extended Data Fig. 10

Statistical source data.

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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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