Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages

Nature Cell Biology volume 16, pages 27–37 (2014)Cite this article

Subjects

Abstract

It is now recognized that extensive expression heterogeneities among cells precede the emergence of lineages in the early mammalian embryo. To establish a map of pluripotent epiblast (EPI) versus primitive endoderm (PrE) lineage segregation within the inner cell mass (ICM) of the mouse blastocyst, we characterized the gene expression profiles of individual ICM cells. Clustering analysis of the transcriptomes of 66 cells demonstrated that initially they are non-distinguishable. Early in the segregation, lineage-specific marker expression exhibited no apparent correlation, and a hierarchical relationship was established only in the late blastocyst. Fgf4 exhibited a bimodal expression at the earliest stage analysed, and in its absence, the differentiation of PrE and EPI was halted, indicating that Fgf4 drives, and is required for, ICM lineage segregation. These data lead us to propose a model where stochastic cell-to-cell expression heterogeneity followed by signal reinforcement underlies ICM lineage segregation by antagonistically separating equivalent cells.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Single-cell expression analysis of the lineage segregation within the ICM of the mouse blastocyst.
Figure 2: Correlation and hierarchy of gene expression is progressively established during lineage segregation within the ICM of the mouse blastocyst.
Figure 3: Heterogeneity in protein expression level of the EPI and PrE markers.
Figure 4: Cell position influences gene expression.
Figure 5: Comprehensive characterization of expression of Fgf signalling components in the early mouse embryo.
Figure 6: Fgf4 is required for driving lineage segregation between EPI and PrE in the early mouse embryo.
Figure 7: Schematic model for EPI versus PrE lineage segregation in the early mouse embryo, contrasting with mechanisms for embryo patterning in non-mammalian species.

Accession codes

Primary accessions

ArrayExpress

References

  1. Rossant, J. & Tam, P. P. Blastocyst lineage formation, early embryonic asymmetries and axis patterning in the mouse. Development 136, 701–713 (2009).

    Article  CAS  Google Scholar 

  2. Chazaud, C., Yamanaka, Y., Pawson, T. & Rossant, J. Early lineage segregation between epiblast and primitive endoderm in mouse blastocysts through the Grb2-MAPK pathway. Dev. Cell 10, 615–624 (2006).

    Article  CAS  Google Scholar 

  3. Plusa, B., Piliszek, A., Frankenberg, S., Artus, J. & Hadjantonakis, A. K. Distinct sequential cell behaviours direct primitive endoderm formation in the mouse blastocyst. Development 135, 3081–3091 (2008).

    Article  CAS  Google Scholar 

  4. Schrode, N. et al. Anatomy of a blastocyst: cell behaviors driving cell fate choice and morphogenesis in the early mouse embryo. Genesis 51, 219–233 (2013).

    Article  Google Scholar 

  5. Grabarek, J. B. et al. Differential plasticity of epiblast and primitive endoderm precursors within the ICM of the early mouse embryo. Development 139, 129–139 (2012).

    Article  CAS  Google Scholar 

  6. Lanner, F. & Rossant, J. The role of FGF/Erk signaling in pluripotent cells. Development 137, 3351–3360 (2010).

    Article  CAS  Google Scholar 

  7. Yamanaka, Y., Lanner, F. & Rossant, J. FGF signal-dependent segregation of primitive endoderm and epiblast in the mouse blastocyst. Development 137, 715–724 (2010).

    Article  CAS  Google Scholar 

  8. Arman, E., Haffner-Krausz, R., Chen, Y., Heath, J. K. & Lonai, P. Targeted disruption of fibroblast growth factor (FGF) receptor 2 suggests a role for FGF signaling in pregastrulation mammalian development. Proc. Natl Acad. Sci. USA 95, 5082–5087 (1998).

    Article  CAS  Google Scholar 

  9. Nichols, J., Silva, J., Roode, M. & Smith, A. Suppression of Erk signalling promotes ground state pluripotency in the mouse embryo. Development 136, 3215–3222 (2009).

    Article  CAS  Google Scholar 

  10. Cheng, A. M. et al. Mammalian Grb2 regulates multiple steps in embryonic development and malignant transformation. Cell 95, 793–803 (1998).

    Article  CAS  Google Scholar 

  11. Feldman, B., Poueymirou, W., Papaioannou, V. E., DeChiara, T. M. & Goldfarb, M. Requirement of FGF-4 for postimplantation mouse development. Science 267, 246–249 (1995).

    Article  CAS  Google Scholar 

  12. Wilder, P. J. et al. Inactivation of the FGF-4 gene in embryonic stem cells alters the growth and/or the survival of their early differentiated progeny. Dev. Biol. 192, 614–629 (1997).

    Article  CAS  Google Scholar 

  13. Kang, M., Piliszek, A., Artus, J. & Hadjantonakis, A. K. FGF4 is required for lineage restriction and salt-and-pepper distribution of primitive endoderm factors but not their initial expression in the mouse. Development 140, 267–279 (2013).

    Article  CAS  Google Scholar 

  14. Chisholm, J. C. & Houliston, E. Cytokeratin filament assembly in the preimplantation mouse embryo. Development 101, 565–582 (1987).

    CAS  PubMed  Google Scholar 

  15. Morris, S. A. et al. Origin and formation of the first two distinct cell types of the inner cell mass in the mouse embryo. Proc. Natl Acad. Sci. USA 107, 6364–6369 (2010).

    Article  CAS  Google Scholar 

  16. Guo, G. et al. Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Dev. Cell 18, 675–685 (2010).

    Article  CAS  Google Scholar 

  17. Kurimoto, K. et al. An improved single-cell cDNA amplification method forefficient high-density oligonucleotide microarray analysis. Nucleic Acids Res. 34, e42 (2006).

    Article  Google Scholar 

  18. Kurimoto, K., Yabuta, Y., Ohinata, Y. & Saitou, M. Global single-cell cDNA amplification to provide a template for representative high-density oligonucleotide microarray analysis. Nat. Protocols 2, 739–752 (2007).

    Article  CAS  Google Scholar 

  19. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    Article  CAS  Google Scholar 

  20. Tang, F. et al. RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nat. Protocols 5, 516–535 (2010).

    Article  CAS  Google Scholar 

  21. Pelkmans, L. Cell Biology. Using cell-to-cell variability–a new era in molecular biology. Science 336, 425–426 (2012).

    Article  CAS  Google Scholar 

  22. Eldar, A. & Elowitz, M. B. Functional roles for noise in genetic circuits. Nature 467, 167–173 (2010).

    Article  CAS  Google Scholar 

  23. Hu, M. et al. Multilineage gene expression precedes commitment in the hemopoietic system. Genes Dev. 11, 774–785 (1997).

    Article  CAS  Google Scholar 

  24. Pina, C. et al. Inferring rules of lineage commitment in haematopoiesis. Nat. Cell Biol. 14, 287–294 (2012).

    Article  CAS  Google Scholar 

  25. Moignard, V. et al. Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysis. Nat. Cell Biol. 15, 544 (2013).

    Article  Google Scholar 

  26. Buganim, Y. et al. Single-cell expression analyses during cellular reprogramming reveal an early stochastic and a late hierarchic phase. Cell 150, 1209–1222 (2012).

    Article  CAS  Google Scholar 

  27. Frankenberg, S. et al. Primitive endoderm differentiates via a three-step mechanism involving Nanog and RTK signaling. Dev. Cell 21, 1005–1013 (2011).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  29. Gerbe, F., Cox, B., Rossant, J. & Chazaud, C. Dynamic expression of Lrp2 pathway members reveals progressive epithelial differentiation of primitive endoderm in mouse blastocyst. Dev. Biol. 313, 594–602 (2008).

    Article  CAS  Google Scholar 

  30. Artus, J., Piliszek, A. & Hadjantonakis, A. K. The primitive endoderm lineage of the mouse blastocyst: sequential transcription factor activation and regulation of differentiation by Sox17. Dev. Biol. 350, 393–404 (2011).

    Article  CAS  Google Scholar 

  31. Widmer, C. et al. Molecular basis for the action of the collagen-specific chaperone Hsp47/SERPINH1 and its structure-specific client recognition. Proc. Natl Acad. Sci. USA 109, 13243–13247 (2012).

    Article  CAS  Google Scholar 

  32. Tarkowski, A. K. & Wroblewska, J. Development of blastomeres of mouseeggs isolated at the 4- and 8-cell stage. J. Embryol. Exp. Morphol. 18, 155–180 (1967).

    CAS  PubMed  Google Scholar 

  33. Silva, J. & Smith, A. Capturing pluripotency. Cell 132, 532–536 (2008).

    Article  CAS  Google Scholar 

  34. Wennekamp, S., Mesecke, S., Nedelec, F. & Hiiragi, T. A self-organization framework for symmetry breaking in the mammalian embryo. Nat. Rev. Mol. Cell Biol. 14, 454–461 (2013).

    Article  CAS  Google Scholar 

  35. Dietrich, J. E. & Hiiragi, T. Stochastic patterning in the mouse pre-implantation embryo. Development 134, 4219–4231 (2007).

    Article  CAS  Google Scholar 

  36. Xiong, F. et al. Specified neural progenitors sort to form sharp domains after noisy shh signaling. Cell 153, 550–561 (2013).

    Article  CAS  Google Scholar 

  37. Kay, R. R. & Thompson, C. R. Forming patterns in development without morphogen gradients: scattered differentiation and sorting out. Cold Spring Harb. Perspect. Biol. 1, a001503 (2009).

    Article  Google Scholar 

  38. Ohnishi, Y. et al. Small RNA class transition from siRNA/piRNA to miRNA during pre-implantation mouse development. Nucleic Acids Res. 38, 5141–5151 (2010).

    Article  CAS  Google Scholar 

  39. Irizarry, R. A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003).

    Article  Google Scholar 

  40. Kauffmann, A., Gentleman, R. & Huber, W. arrayQualityMetrics–a bioconductor package for quality assessment of microarray data. Bioinformatics 25, 415–416 (2009).

    Article  CAS  Google Scholar 

  41. Hornik, K. A CLUE for CLUster Ensembles. J. Statist. Software 14 (2005).

Download references

Acknowledgements

We thank A. Courtois for help with image analysis, R. Niwayama for quantitative protein expression analysis, S. Salvenmoser and R. Bloehs for technical assistance, and EMBL Genomics Core Facility for technical support. We also thank the members of the Hiiragi, Hadjantonakis, Huber and Saitou laboratories for helpful and stimulating discussions. Work in the laboratory of T.H. is supported by the Max Planck Society, European Research Council under the European Commission FP7, Stem Cell Network North Rhine Westphalia, German Research Foundation (Deutsche Forschungsgemeinschaft), and World Premier International Research Center Initiative, Ministry of Education, Culture, Sports, Science and Technology, Japan. Work in the laboratory of A.-K.H. is supported by the National Institutes of Health (NIH) NIH RO1-HD052115 and RO1-DK084391 (AKH) and NYSTEM. W.H. acknowledges financial support from the European Commission FP7-Health through the RADIANT project. Y.O. is supported by Naito and Uehara Memorial Foundation fellowships, and by Marie Curie FP7 IIF fellowship (no. 273193).

Author information

Authors and Affiliations

  1. Developmental Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany

    Yusuke Ohnishi & Takashi Hiiragi

  2. Genome Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany

    Wolfgang Huber & Andrzej K. Oleś

  3. Institute for Integrated Cell-Material Sciences, Kyoto University, Yoshida-Ushinomiya-cho, Sakyo-ku, Kyoto 606-8501, Japan

    Akiko Tsumura & Mitinori Saitou

  4. Developmental Biology Program, Sloan-Kettering Institute, 1275 York Avenue, Box 371, New York, New York 10065, USA

    Minjung Kang, Panagiotis Xenopoulos & Anna-Katerina Hadjantonakis

  5. Department of Anatomy and Cell Biology, Graduate School of Medicine, Kyoto University, Yoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8501, Japan

    Kazuki Kurimoto & Mitinori Saitou

  6. JST, ERATO, Yoshida-Konoe-cho, Sakyo-ku, Kyoto 606-8501, Japan

    Kazuki Kurimoto & Mitinori Saitou

  7. Computational Biology and Bioinformatics Group, Max Planck Institute for Molecular Biomedicine, Röntgenstrasse 20, 48149 Münster, Germany

    Marcos J. Araúzo-Bravo

  8. Center for iPS Cell Research and Application, Kyoto University, 53 Kawahara-cho, Shogoin Yoshida, Sakyo-ku, Kyoto 606-8507, Japan

    Mitinori Saitou

Authors
  1. Yusuke Ohnishi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wolfgang Huber
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Akiko Tsumura
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Minjung Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Panagiotis Xenopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kazuki Kurimoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Andrzej K. Oleś
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Marcos J. Araúzo-Bravo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Mitinori Saitou
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Anna-Katerina Hadjantonakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Takashi Hiiragi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.O., A.T. and T.H. designed the study, Y.O. performed most of the experiments, A.T., K.K. and M.S. contributed to establishing the method of single-cell gene expression analysis in the mouse preimplantation embryo, Y.O., A.T., M.K. and P.X. collected the single-cell samples, W.H. and A.K.O. performed statistical analysis, and M.J.A.-B. contributed to initial analyses of the data. Y.O., A.-K.H. and T.H. wrote the manuscript.

Corresponding author

Correspondence to Takashi Hiiragi.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Performance of spike RNA amplification.

Each blue line represents the outcome of spike RNA amplification for each experimental sample that is used for microarray (66 cells in total including 36 cells from 6 embryos for E3.25, 22 cells from 3 embryos for E3.5, and 8 cells from one embryo for E4.5). Box plot shows the performance of spike RNA amplification for all samples including those used only for additional qPCR (grey, 154 cells in total including 50 cells from 6 embryos for E3.25, 43 cells from 3 embryos for E3.5 and 61 cells from 3 embryos for E4.5). Those single-cell cDNAs of highest quality with minimal deviation from the ideal value (red line) are processed for microarray analysis. Based on this performance, we defined 20 copies as the minimum amount of mRNAs that we can detect quantitatively.

Supplementary Figure 2 Immunofluorescence single-section images of the E4.5 (>150 cell stage) blastocyst stained for Serpinh1 (a) and P4ha2 (b), PrE markers newly identified in the microarray analysis, indicating the lineage-specific expression in PrE. Scale bars; 10 μm.

Supplementary Figure 3 qPCR data for the expression of seven PrE differentiation stage markers used in Fig. 2b,c.

Each dot represents the gene expression pattern of single cells derived from E3.25 ICM (purple), E3.5 PrE (light green), and E4.5 PrE (dark green) cells with Y-axis indicating the estimated copy number (86 cells in total including 33 cells from 4 embryos for E3.25, 22 cells from 3 embryos for E3.5 PrE, and 31 cells from 3 embryos for E4.5 PrE). The within-group means and the binning thresholds are shown as horizontal dotted lines (light grey) and horizontal solid lines (dark grey), respectively.

Supplementary Figure 4 All possible and equally optimal orders of the genes (Y-axis) used in Fig. 2c to examine the potential hierarchy in gene activation during the E3.25 to E3.5 transition (see Methods).

A total of seven equally optimal solutions are available for aligning the genes upregulated during the E3.25 to E3.5 transition, including one shown in Fig. 2c. Note that there was only one solution for the E3.5 to E4.5 transition, as shown in Fig. 2c.

Supplementary Figure 5 Comprehensive characterization of expression of Fgf signalling components within the early mouse embryo.

Box plots showing the mRNA expression level of Fgf ligands and downstream cytoplasmic signal effectors, collected for each stage from single-cell microarray analysis (66 WT cells including 36 cells from 6 embryos for E3.25, 11 and 11 cells from 3 embryos for E3.5 EPI and PrE, and 4 and 4 cells from one embryo for E4.5 EPI and PrE cells, respectively; and 35 Fgf4−/− cells including 17 cells from 3 embryos for E3.25, 8 cells from one embryo for E3.5 and 10 cells from one embryo for E4.5).

Supplementary Figure 6 Scatter plots showing the early lineage marker expressions in individual WT and Fgf4−/− ICM cells.

Each dot represents the expression of lineage markers in single blastomere, analysed by qPCR (33 cells from 4 embryos for E3.25 WT and 9 cells from one embryo for E3.25 Fgf4−/−, and 43 cells (21 and 22 cells for EPI and PrE, respectively) from 3 embryos for E3.5 WT and 8 cells from one embryo for E3.5 Fgf4−/−). The gene expression levels are normalised to that of Gapdh (x or y = 0). The colour code is the same as shown in Fig. 6a. In WT cells, each combination of two marker genes exhibits statistically significant correlation (E3.25: r = 0.35, p = 4×10−2 (Gata6 vs. Fgfr2); r = −0.46, p = 7×10−3 (Nanog vs. Fgfr2) and E3.5: r = −0.42, p = 5×10−3 (Nanog vs. Gata6); r = 0.54, p = 2×10−4 (Gata6 vs. Fgfr2); r = −0.66, p = 2×10−6 (Nanog vs. Fgfr2); Pearson’s correlation coefficient), except for Nanog vs. Gata6 at E3.25 (r = −0.07, p = 0.7). However, the correlation is lost in Fgf4−/− cells (E3.25: r = 0.34, p = 0.4 (Gata6 vs. Nanog); r = 0.01, p = 1 (Gata6 vs. Fgfr2); r = 0.30, p = 0.4 (Nanog vs. Fgfr2) and E3.5: r = 0.25, p = 0.5 (Nanog vs. Gata6); r = 0.05, p = 0.9 (Gata6 vs. Fgfr2); r = −0.04, p = 0.9 (Nanog vs. Fgfr2); Pearson’s correlation coefficient).

Supplementary information

Supplementary Information

Supplementary Information (PDF 1499 kb)

Supplementary Table 1

Supplementary Information (XLSX 70 kb)

Supplementary Table 2

Supplementary Information (XLSX 47 kb)

Supplementary Table 3

Supplementary Information (XLSX 43 kb)

Immunofluorescence staining of the E3.5 blastocyst.

Z-scanning sections of one of the four embryos used for the quantitative protein expression analysis in Fig. 3b. Serpinh1, Gata6 and Nanog are labelled in blue, red and green, respectively. (MOV 2159 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Ohnishi, Y., Huber, W., Tsumura, A. et al. Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages. Nat Cell Biol 16, 27–37 (2014). https://doi.org/10.1038/ncb2881

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ncb2881

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing