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Myc and Dnmt1 impede the pluripotent to totipotent state transition in embryonic stem cells

Nature Cell Biology volume 21, pages 835–844 (2019)Cite this article

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Abstract

Totipotency refers to the ability of a cell to generate all of the cell types of an organism. Unlike pluripotency, the establishment of totipotency is poorly understood. In mouse embryonic stem cells, Dux drives a small percentage of cells into a totipotent state by expressing 2-cell-embryo-specific transcripts. To understand how this transition takes place, we performed single-cell RNA-seq, which revealed a two-step transcriptional reprogramming process characterized by downregulation of pluripotent genes in the first step and upregulation of the 2-cell-embryo-specific elements in the second step. To identify factors controlling the transition, we performed a CRISPR–Cas9-mediated screen, which revealed Myc and Dnmt1 as two factors preventing the transition. Mechanistic studies demonstrate that Myc prevents downregulation of pluripotent genes in the first step, while Dnmt1 impedes 2-cell-embryo-specific gene activation in the second step. Collectively, the findings of our study reveal insights into the establishment and regulation of the totipotent state in mouse embryonic stem cells.

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Fig. 1: RNA-seq indicates a step-wise pattern of transcriptome reprogramming from ESC to 2C-like cell transition.
Fig. 2: scRNA-seq reveals a transcriptional roadmap of the ESC to 2C-like cell transition.
Fig. 3: CRISPR–Cas9 screening identified regulators mediating the ESC to 2C-like cell transition.
Fig. 4: Myc impedes the repression of 2C+-downregulated genes at the early stage of the ESC to 2C-like cell transition.
Fig. 5: Dnmt1 impedes activation of 2C+-upregulated genes during the late stage of the ESC to 2C-like cell transition.
Fig. 6: Dnmt1 and Myc may impede the two-step transcriptional reprogramming of the ESC to 2C-like cell transition.

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Data availability

RNA-seq, Drop-seq, RRBS and CRISPR screen-related data, including the sgRNA read counts, that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) under the accession code GSE121459. Previously published sequencing data that were reanalysed here are available in the GEO under the accession codes GSE85766 (Promoter methylation in ESCs and 2C-like cells), GSE95517 (Dux ChIP-seq) and GSM1171648 (Myc ChIP-seq), and the samples GSM1966767, GSM1966768 and GSM1966769 (Zscan+ and MuERVL+ spontaneous 2C-like cell transcriptome)16,18,28. Source data for all figures have been provided as Supplementary Table 10. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

All of the codes used in this study are available from the corresponding author upon reasonable request.

References

  1. 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).

    Article  CAS  Google Scholar 

  2. Lu, F. & Zhang, Y. Cell totipotency: molecular features, induction, and maintenance. Natl Sci. Rev. 2, 217–225 (2015).

    Article  CAS  Google Scholar 

  3. Falco, G. et al. Zscan4: a novel gene expressed exclusively in late 2-cell embryos and embryonic stem cells. Dev. Biol. 307, 539–550 (2007).

    Article  CAS  Google Scholar 

  4. Macfarlan, T. S. et al. Embryonic stem cell potency fluctuates with endogenous retrovirus activity. Nature 487, 57–63 (2012).

    Article  CAS  Google Scholar 

  5. Yang, Y. et al. Derivation of pluripotent stem cells with in vivo embryonic and extraembryonic potency. Cell 169, 243–257.e25 (2017).

    Article  CAS  Google Scholar 

  6. Yang, J. et al. Establishment of mouse expanded potential stem cells. Nature 550, 393–397 (2017).

    Article  CAS  Google Scholar 

  7. Bao, S. et al. Derivation of hypermethylated pluripotent embryonic stem cells with high potency. Cell Res. 28, 22–34 (2018).

    Article  CAS  Google Scholar 

  8. Li, M. & Izpisua Belmonte, J. C. Deconstructing the pluripotency gene regulatory network. Nat. Cell Biol. 20, 382–392 (2018).

    Article  CAS  Google Scholar 

  9. Boskovic, A. et al. Higher chromatin mobility supports totipotency and precedes pluripotency in vivo. Genes Dev. 28, 1042–1047 (2014).

    Article  CAS  Google Scholar 

  10. Ishiuchi, T. et al. Early embryonic-like cells are induced by downregulating replication-dependent chromatin assembly. Nat. Struct. Mol. Biol. 22, 662–671 (2015).

    Article  CAS  Google Scholar 

  11. Baker, C. L. & Pera, M. F. Capturing totipotent stem cells. Cell Stem Cell 22, 25–34 (2018).

    Article  CAS  Google Scholar 

  12. Lu, F., Liu, Y., Jiang, L., Yamaguchi, S. & Zhang, Y. Role of Tet proteins in enhancer activity and telomere elongation. Genes Dev. 28, 2103–2119 (2014).

    Article  CAS  Google Scholar 

  13. Rodriguez-Terrones, D. et al. A molecular roadmap for the emergence of early-embryonic-like cells in culture. Nat. Genet. 50, 106–119 (2018).

    Article  CAS  Google Scholar 

  14. Choi, Y. J. et al. Deficiency of microRNA miR-34a expands cell fate potential in pluripotent stem cells. Science 355, eaag1927 (2017).

    Article  Google Scholar 

  15. Whiddon, J. L., Langford, A. T., Wong, C. J., Zhong, J. W. & Tapscott, S. J. Conservation and innovation in the DUX4-family gene network. Nat. Genet. 49, 935–940 (2017).

    Article  CAS  Google Scholar 

  16. Hendrickson, P. G. et al. Conserved roles of mouse DUX and human DUX4 in activating cleavage-stage genes and MERVL/HERVL retrotransposons. Nat. Genet. 49, 925–934 (2017).

    Article  CAS  Google Scholar 

  17. De Iaco, A. et al. DUX-family transcription factors regulate zygotic genome activation in placental mammals. Nat. Genet. 49, 941–945 (2017).

    Article  Google Scholar 

  18. Eckersley-Maslin, M. A. et al. MERVL/Zscan4 network activation results in transient genome-wide DNA demethylation of mESCs. Cell Rep. 17, 179–192 (2016).

    Article  CAS  Google Scholar 

  19. Eidahl, J. O. et al. Mouse Dux is myotoxic and shares partial functional homology with its human paralog DUX4. Hum. Mol. Genet. 25, 4577–4589 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Kumar, R. M. et al. Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature 516, 56–61 (2014).

    Article  CAS  Google Scholar 

  21. Kolodziejczyk, A. A. et al. Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation. Cell Stem Cell 17, 471–485 (2015).

    Article  CAS  Google Scholar 

  22. Smith, A. Formative pluripotency: the executive phase in a developmental continuum. Development 144, 365–373 (2017).

    Article  CAS  Google Scholar 

  23. Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34, 184–191 (2016).

    Article  CAS  Google Scholar 

  24. Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).

    Article  Google Scholar 

  25. Chappell, J. & Dalton, S. Roles for MYC in the establishment and maintenance of pluripotency. Cold Spring Harb. Perspect. Med. 3, a014381 (2013).

    Article  Google Scholar 

  26. Kim, J. et al. A Myc rather than core pluripotency module accounts for the shared signatures of embryonic stem and cancer cells. Cell 143, 313–324 (2010).

    Article  CAS  Google Scholar 

  27. Nie, Z. et al. c-Myc is a universal amplifier of expressed genes in lymphocytes and embryonic stem cells. Cell 151, 68–79 (2012).

    Article  CAS  Google Scholar 

  28. Krepelova, A., Neri, F., Maldotti, M., Rapelli, S. & Oliviero, S. Myc and max genome-wide binding sites analysis links the Myc regulatory network with the polycomb and the core pluripotency networks in mouse embryonic stem cells. PLoS One 9, e88933 (2014).

    Article  Google Scholar 

  29. Percharde, M., Bulut-Karslioglu, A. & Ramalho-Santos, M. Hypertranscription in development, stem cells, and regeneration. Dev. Cell 40, 9–21 (2017).

    Article  CAS  Google Scholar 

  30. Lin, C. Y. et al. Transcriptional amplification in tumor cells with elevated c-Myc. Cell 151, 56–67 (2012).

    Article  CAS  Google Scholar 

  31. Jones, P. A. & Liang, G. Rethinking how DNA methylation patterns are maintained. Nat. Rev. Genet. 10, 805–811 (2009).

    Article  CAS  Google Scholar 

  32. Dan, J. et al. Zscan4 inhibits maintenance DNA methylation to facilitate telomere elongation in mouse embryonic stem cells. Cell Rep. 20, 1936–1949 (2017).

    Article  CAS  Google Scholar 

  33. De Iaco, A., Coudray, A., Duc, J. & Trono, D. DPPA2 and DPPA4 are necessary to establish a 2C‐like state in mouse embryonic stem cells. EMBO Rep. 20, e47382 (2019).

    Article  Google Scholar 

  34. Eckersley-Maslin, M. et al. Dppa2 and Dppa4 directly regulate the Dux-driven zygotic transcriptional program. Genes Dev. 33, 194–208 (2019).

    Article  CAS  Google Scholar 

  35. Campbell, A. E. et al. NuRD and CAF-1-mediated silencing of the D4Z4 array is modulated by DUX4-induced MBD3L proteins. eLife 7, e31023 (2018).

    Article  Google Scholar 

  36. Percharde, M. et al. A LINE1-nucleolin partnership regulates early development and ESC identity. Cell 174, 391–405.e19 (2018).

    Article  CAS  Google Scholar 

  37. 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).

    Article  CAS  Google Scholar 

  38. The ENCODE Project Consortium An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  39. Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).

    Article  CAS  Google Scholar 

  40. Chen, R., Wu, X., Jiang, L. & Zhang, Y. Single-cell RNA-seq reveals hypothalamic cell diversity. Cell Rep. 18, 3227–3241 (2017).

    Article  CAS  Google Scholar 

  41. Criscione, S. W., Zhang, Y., Thompson, W., Sedivy, J. M. & Neretti, N. Transcriptional landscape of repetitive elements in normal and cancer human cells. BMC Genomics 15, 583 (2014).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  43. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    Article  CAS  Google Scholar 

  44. Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

    Article  Google Scholar 

  45. McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).

    Article  CAS  Google Scholar 

  46. Kramer, A., Green, J., Pollard, J. Jr. & Tugendreich, S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics 30, 523–530 (2014).

    Article  Google Scholar 

  47. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411 (2018).

    Article  CAS  Google Scholar 

  48. Lun, A. T., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).

    Article  Google Scholar 

  49. McDavid, A. et al. Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments. Bioinformatics 29, 461–467 (2013).

    Article  CAS  Google Scholar 

  50. McInnes, L., Healy, J., Saul, N. & Großberger, L. UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3, 861 (2018).

    Article  Google Scholar 

  51. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  Google Scholar 

  52. Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).

    Article  CAS  Google Scholar 

  53. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  Google Scholar 

  54. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

  55. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

Download references

Acknowledgements

We thank S. L. Pfaff for providing the MERVL-tdTomato reporter, F. Lu for assistance with the establishment of the reporter cell line and Z. Chen for critical reading of the manuscript. This project was supported by the NIH (R01HD092465) and HHMI. Y.Z. is an Investigator of the Howard Hughes Medical Institute.

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Author notes

  1. These authors contributed equally: Xudong Fu, Xiaoji Wu, Mohamed Nadhir Djekidel.

Authors and Affiliations

  1. Howard Hughes Medical Institute, Boston, MA, USA

    Xudong Fu, Xiaoji Wu, Mohamed Nadhir Djekidel & Yi Zhang

  2. Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA, USA

    Xudong Fu, Xiaoji Wu, Mohamed Nadhir Djekidel & Yi Zhang

  3. Harvard Stem Cell Institute, Boston, MA, USA

    Xudong Fu, Xiaoji Wu, Mohamed Nadhir Djekidel & Yi Zhang

  4. Department of Genetics, Harvard Medical School, Boston, MA, USA

    Xudong Fu, Xiaoji Wu, Mohamed Nadhir Djekidel & Yi Zhang

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  1. Xudong Fu
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Contributions

Y.Z. conceived the project; X.F., X.W. and Y.Z. designed the experiments; X.F. and X.W. performed the experiments. M.N.D. performed bioinformatics analyses. All authors were involved in the interpretation of data. X.F., X.W. and Y.Z. wrote the manuscript.

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Correspondence to Yi Zhang.

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Integrated supplementary information

Supplementary Figure 1 Transcriptomic anlysis indicated the existance of an intermediate cell state during ESC to 2C-like cell transition.

a, Images showing Dux induction can gradually induce 2C-like cells generation in cultured ESCs. Scale bar: 0.1 mm. Experiment was repeated independently twice with similar results. b, The 2C-like cell percentage of two independent ESC clones after one day of Dux induction. c, Dux transgene mRNA level relative to Gapdh in two independent ESC clones. b-c, Shown are mean ± SD, n=3 biologically independent cell cultures. P values (indicated as numbers in the graphs) are calculated by unpaired t-test, two-tailed, two-sample unequal variance. Experiments were repeated independently twice with similar results. d, Bar plots showing the -log10(q value) (right-tailed Fisher-exact test) of the GO terms of upregulated and downregulated genes in 2C-like cells. e, Box plot showing the distance of the nearest gene TSS (n=Stable: 24,739, Down: 2,466, Up: 2,146 genes) to MERVL repeats (median: Stable: ~213kb, Down: ~242.5kb, Up: ~93kb). f, Scatter plot comparing 2-cell-embryo-specific genes expression profile between spontaneous 2C-like cells and Dux-induced 2C-like cells (Pearson correlation, r=0.9, n=1,400 genes). g, The percentage of 2C-like cells after 2-hour and 24-hour culture of D1 2C+ cells. h, Scatter plot comparing gene expression profile between cells which exit from Dux-induced 2C-like state (D2 2C− cells) and D1 2C− cells, (Pearson correlation, r=0.95, n=27,221 genes/repeats), supporting that Dux-induced 2C-like cells can exit from 2C-like state spontanesouly. i, Relative percentage of genes and repeats in each category. j, Box plot showing the log2 expression level of 2C+-upregulated genes/repeats (n=2,976 genes/repeats) and 2C+-downregulated genes/repeats (n=2,726 genes/repeats) in each of the D0 2C−, D1 2C−, and D1 2C+ cell populations. e, j, P values were calculated by two-tailed mann-whitney U-test. The black central line is the median, boxes limits indicate the upper and lower quartiles, whiskers indicate the 1.5 interquartile range, dots represent outliers. k, Pie chart reporting the percentage of Group1 and Group2 genes in 2C+-upregulated genes and Dux bound 2C+-upregulated genes. d-k were based on two independent biological replicates of RNA-seq experiments. The effect-size of each statistical analysis and statistical source data can be found in Supplementary Table 10.

Supplementary Figure 2 Single-cell transcriptomic analyses revealed an intermediate state from ESC to 2C-like cell transition.

a, Violin plot showing the log2 expression of representative genes or repeats in each time point (n= 0h: 738, 12h: 456 cells, 24h: 568, 36h: 871). Violin plots show the kernel density estimation for the distribution of the gene expression in each cluster. The width of the plot represents the proportion of data with the corresponding expression value. b, UMAP plot showing the expression of representative genes in each sequenced cells (n=2,633 cells). c, UMAP plot showing the subpopulations of of cluster 1 cells (n=1,780 cells). Around 7% pluripotent cells (n=115 cells) belong to the minor subcluster b. d, Box plots comparing the gene expression between subcluster a (n=1,665 cells) and subcluster b cells (n=115 cells). The black central line is the median, boxes limits indicate the upper and lower quartiles, whiskers indicate the 1.5 interquartile range, dots represent outliers. P values (shown as p) are calculated by two-tailed Mann-Whitney U-test and effect-sizes (shown as r) are calculated as \(Z/\sqrt N\) where Z is the Z value of the Mann–Whitney U-test and N is the number of samples. e, FACS histogram plot showing the expression of Rex1 in mESC culture. About 5% of cells are Rex1-negative. Experiment was repeated independently three times with similar results. f, Bar plot showing the proportion of the different cell clusters at different time point of Dux induction. g, The partial 2-cell embyro-specfic genes and elements activated in D1 2C− cells leads to less dramatic transcriptional variability between D1 2C− cells and cluster 2 cells compared to that of cluster 1 cells (n=470 genes/repeats). The red dots show the top 30 genes/repeats that define the variance between D1 2C− and cluster 1 cells. Correlation was calculated using spearman correlation. Statistical source data can be found in Supplementary Table 10.

Supplementary Figure 3 Single-cell transcriptomic analyses revealed the transcriptional dynamics from ESC to 2C-like cell transition.

a, Scatter plots showing cells (n=2,633 cells) along the projected pseudo-time following the dynamics of the ESC to 2C-like cell transition. b, Cell distribution along with the pseudotime in each time point. Cells collected at the early hours of Dux-induction (0 h and 12 h) are located at the beginning of the timeline while cells collected at the late hours (24 h and 36 h) are gradually located towards the end, supporting that the projected timeline captures the transcriptional changes during Dux-induced 2C-like transition. c, Mean expression of representative genes in each cell cluster. d, Violin plot showing the expression of representative transcripts in each cell cluster (n=1,780 cells, 512 cells and 341 cells respectively for cluster 1, 2, and 3). Violin plots show the kernel density estimation of the distribution for the expression of the genes in each cluster. The width of the plot represents the proportion of data with the corresponding expression value. e, Box plot showing the expression of the detected 2C+-downregulated pluripotent genes (n=135 genes), 2C+-upregulated 2-cell-embyro-specific genes (n=75 genes) and activated repeats (n=501 repeats) in each cell cluster when analyzed after subcluster b cells are excluded. The black central line is the median, boxes limits indicate the upper and lower quartiles, whiskers indicate the 1.5 interquartile range, dots represent outliers. P values (shown as p) are calculated by two-tailed Mann-Whitney U-test and effect-sizes (shown as r) are calculated as \(Z/{\sqrt{N}}\) where Z is the Z value of the Mann–Whitney U-test and N is the number of samples. Statistical source data can be found in Supplementary Table 10.

Supplementary Figure 4 CRISPR-Cas9 screen identified novel regulators mediating ESC to 2C-like transition.

a,b, RRA score as calculated by MAGeCK tool of negative effect (a) and positive effect (b) of genes on the ESC to 2C-like cell transition from the screen. The results were based on two biologically independent screens.

Supplementary Figure 5 Myc perturbation facilitats ESC to 2C-like cell transition.

a, Western-blot showing the sgRNA efficiency. Unprocessed raw blots can be found in Supplementary Fig. 7. b, Relative percentage of 2C-like cells after 24-hour culture of D1 2C+ cells isolated from ESC infected with sgGFP or sgMyc. Shown are mean ± SD, n=3 biologically independent cell cultures. c, Relative percentage of spontaneous 2C-like cells and representative FACS plot of ESC culture. Shown are mean ± SD, n=3 biologically independent cell cultures. d, Myc knockdown decreases total RNA amount per cell in ESCs (left panel) and D1 2C+ cells (right panel), confirming the transcrition amplification effect of Myc in mESCs. Shown are mean ± SD, n=3 biologically independent cell cultures. b-d, P values (indicated as numbers in the graphs) are calculated by unpaired t-test, two-tailed, two-sample unequal variance. Statistical source data can be found in Supplementary Table 10. a-d, experiments were repeated independently twice with similar results.

Supplementary Figure 6 Dnmt1 and Myc perturbation facilitates ESC to 2C-like cell transition.

a, Western-blot showing the sgRNA efficiency. Unprocessed raw blots can be found in Supplementary Fig. 7. b, Relative percentage of 2C-like cells after 24-hour culture of D1 2C+ cells isolated from ESC infected with sgGFP or sgDnmt1. Shown are mean ± SD, n=3 biologically independent cell cultures. c, Relative percentage of spontaneous 2C-like cells and representative FACS plot of ESC culture. Shown are mean ± SD, n=3 biologically independent cell cultures. b-c, P values (indicated as numbers in the graphs) are calculated by unpaired t-test, two-tailed, two-sample unequal variance. d, Mean promoter methylation change from ESC to 2C-like cell of 2C+-upregulated (n=1,611 genes) and 2C+-downregulated genes (n=1,398 genes). The black central line is the median, boxes limits indicate the upper and lower quartiles, whiskers indicate the 1.5 interquartile range, dots represent outliers. P values (shown as p) are calculated by two-tailed Mann-Whitney U-test and effect-sizes (shown as r) are calculated as \(Z/{\sqrt{N}}\) where Z is the Z value of the Mann–Whitney U-test and N is the number of samples. e, The percentage of 2C-like cells with prolonged Dux induction. Prolonged Dux induction did not induce complete 2C-like transition as Dux induction initiates an unsynchronized 2C-like transition (Fig. 1b) and cannot maintain 2C-like state (Supplementary Fig. 1g). f, Bootstrapped correlation analysis showing that the Zscan4+ intermediate cells reported by Rodriguez-Terrones et al. are assigned to clusters 1 and 3 of our single-cell analysis but cannot be firmly assigned to cluster 2, the intermediate state. g, Density plot showing the distribution of cells from the different clusters generated in our study and study from Rodriguez-Terrones et al. along the pseudotime. Cells were ordered using the 93 genes selected by Rodriguez-Terrones et al. after technical batch removal using the MNN method. We note that the date from Rodriguez-Terrones et al. did to capture the intermediate state (Cluster 2) identified in our study. h-i, FACS histogram plot showing the expression of Myc (h) or Dnmt1 (i) in mESCs. a-c, e, h-i, experiments were repeated independently twice with similar results. Statistical source data can be found in Supplementary Table 10.

Supplementary Figure 7 Unprocessed original scans of blots.

Unprocessed images of immunoblots shown in Supplementary Fig. 5a and Supplementary Fig. 6a are provided.

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Fu, X., Wu, X., Djekidel, M.N. et al. Myc and Dnmt1 impede the pluripotent to totipotent state transition in embryonic stem cells. Nat Cell Biol 21, 835–844 (2019). https://doi.org/10.1038/s41556-019-0343-0

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