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


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.

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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.

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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).

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Authors and Affiliations



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.

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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).

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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)

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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).

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