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Single-cell multi-omics sequencing of human early embryos

Nature Cell Biology volume 20, pages 847–858 (2018)Cite this article

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A Publisher Correction to this article was published on 18 July 2018

Abstract

DNA methylation, chromatin states and their interrelationships represent critical epigenetic information, but these are largely unknown in human early embryos. Here, we apply single-cell chromatin overall omic-scale landscape sequencing (scCOOL-seq) to generate a genome-wide map of DNA methylation and chromatin accessibility at single-cell resolution during human preimplantation development. Unlike in mice, the chromatin of the paternal genome is already more open than that of the maternal genome at the mid-zygote stage in humans, and this state is maintained until the 4-cell stage. After fertilization, genes with high variations in DNA methylation, and those with high variations in chromatin accessibility, tend to be two different sets. Furthermore, 1,797 out of 5,155 (35%) widely open chromatin regions in promoters closed when transcription activity was inhibited, indicating a feedback mechanism between transcription and open chromatin maintenance. Our work paves the way for dissecting the complex, yet highly coordinated, epigenetic reprogramming during human preimplantation development.

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Fig. 1: Chromatin accessibility of human early embryos.
Fig. 2: Features of open chromatin and nucleosomes in mammalian development.
Fig. 3: Heterogeneity of TSS NDRs among individual cells in hESCs and from oocyte to the 4-cell stage.
Fig. 4: Differential DNA methylation and chromatin accessibility between parental genomes within each individual blastomere during human preimplantation development.
Fig. 5: Distinct features of chromatin accessibility between human and mouse embryos.
Fig. 6: Variance of DNA methylation and chromatin accessibility during human preimplantation development.
Fig. 7: Chromatin accessibility of repeat elements and the regulation network in human early embryos.
Fig. 8: Continual transcription is crucial for human preimplantation chromatin remodelling.

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Acknowledgements

This work was supported by grants from the Ministry of Science and Technology of China (2017YFA0102702 and 2017YFA0103402) and the National Natural Science Foundation of China (81521002 and 31625018). F.T. and J.Q. were also supported by a grant from the Beijing Municipal Science and Technology Commission (D151100002415000). This work was also supported by the Beijing Advanced Innovation Center for Genomics at Peking University. Some of the bioinformatics analyses were conducted on the Computing Platform at the Center for Life Science.

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  1. These authors contributed equally: Lin Li, Fan Guo, Yun Gao, Yixin Ren.

Authors and Affiliations

  1. Beijing Advanced Innovation Center for Genomics, Department of Obstetrics and Gynecology Third Hospital, College of Life Sciences, Peking University, Beijing, China

    Lin Li, Yun Gao, Yixin Ren, Peng Yuan, Liying Yan, Rong Li, Ying Lian, Jingyun Li, Boqiang Hu, Junpeng Gao, Lu Wen, Fuchou Tang & Jie Qiao

  2. Biomedical Institute for Pioneering Investigation via Convergence, Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Peking University, Beijing, China

    Lin Li, Yun Gao, Jingyun Li, Boqiang Hu, Junpeng Gao, Lu Wen & Fuchou Tang

  3. Center for Translational Medicine, Ministry of Education Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, China

    Fan Guo

  4. Key Laboratory of Assisted Reproduction, Ministry of Education, Beijing, China

    Yixin Ren, Peng Yuan, Liying Yan, Rong Li, Ying Lian & Jie Qiao

  5. Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, China

    Liying Yan & Jie Qiao

  6. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China

    Jingyun Li, Fuchou Tang & Jie Qiao

  7. Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

    Jingyun Li

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Contributions

F.T. and J.Q. conceived the project. F.G., Y.G., Y.R., P.Y., L.Y., R.L., Y.L., J.L. and J.G. performed the experiments. L.L. and B.H. conducted the bioinformatics analyses. F.T., L.L. and Y.G. wrote the manuscript with help from all authors.

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Correspondence to Fuchou Tang or Jie Qiao.

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Supplementary Figure 1 CNV and quality control of scCOOL-seq samples.

(a) Summary of CNV of each chromosome in single gametes, blastomeres and hESCs. The number of cells, including euploid and aneuploid cells, in each stage are listed on the left. Twenty-three α-Amanitin treated 8-cell blastomeres are not included. (b) Barplot showing the number of single cells with CNV on each chromosome. (c) Comparison analysis of average chromatin accessibility between euploid and aneuploid cells from zygote to TE. A two-tailed Student’s t-test was used to calculate the significance between two groups based on the normalized GCH methylation, that is, the average GCH methylation of each euploid or aneuploid cell is normalized by the mean value of all euploid cells within that stage. The mean value of all euploid cells in each stage was normalized to 1. P = 0.20. ns, not significant. The sample size in this panel corresponds to the euploid and aneuploid sample sizes provided in Fig. 1a (expect for euploid 8-cell blastomeres, n = 31 cells). The center represents mean, whereas error bars represent 1*SEM. (d) Boxplot showing the mapping rate of scCOOL-seq sample across each stage. (e-f) Boxplots showing the number of WCG (ACG/TCG) (e) and GCH (GCA/GCC/GCT) sites (f) covered in a single-cell sample across each stage at 1x depth. Each box in d-f represents median and 25% and 75% quantiles, while whiskers indicate 1.5 times of IQR. (g-h) Chromatin accessibility and DNA methylation coverage of RefSeq gene promoters (g) and CGIs (h) of scCOOL-seq libraries. Only regions covering 5 GCH or 3 WCG sites were calculated. The last row named ‘Mean’ is the mean coverage of all analyzed single cells. The sample size in d-h corresponds to the euploid sample sizes provided in Fig. 1a (expect for 8-cell, n = 31 cells).

Supplementary Figure 2 Chromatin accessibility around transcription start sites.

(a) Chromatin accessibility 2 kb upstream of the TSS, within the gene body, and 2 kb downstream of the TES of pools of samples across each stage. (b) Chromatin accessibility (upper panel) and DNA methylation level (lower panel) of H3K4me3 marked (in red) and unmarked (in blue) promoters. (c) Chromatin accessibility and DNA methylation levels of promoters with different expression levels. Genes were divided into 4 groups, including highly expressed (RPKM > 10), intermediately expressed (1 < RPKM ≤ 10), and lowly expressed genes (0.1 < RPKM ≤ 1) and genes that were not expressed (RPKM ≤ 0.1). The sample size in b-c corresponds to 3 euploid hESCs. (d) GO analysis of 8,385 genes that turned into an open state after fertilization (Related to Fig. 1d). The sample size in a, d corresponds to the euploid sample sizes provided in Fig. 1a (expect for 8-cell, n = 31 cells).

Supplementary Figure 3 Chromatin accessibility and DNA methylation level around the center of NDRs and NORs.

(a) Diagram of the number of TSS NDRs (NDRs containing TSS), proximal NDRs (NDRs within 2 kb upstream and downstream of the TSS) and distal NDRs (NDRs at least 2 kb away from the TSS) called at each stage from pools of samples. The last row named ‘Total’ is the total number of NDRs called from oocytes to hESCs. Overlapping NDRs longer than 10 bp were elongated and combined as one NDR. (b) Boxplot showing the dynamics of the length of TSS NDR distributions during human preimplantation development. The number of TSS NDR in each stage are shown in Supplementary Fig. 3a. Each box represents median and 25% and 75% quantiles, while whiskers indicate 1.5 times of IQR. (c) Chromatin accessibility and DNA methylation levels around the center of proximal NDRs and distal NDRs. (d) Chromatin accessibility and DNA methylation levels around the center of NORs. The sample size in a-d corresponds to the euploid sample sizes provided in Fig. 1a (expect for 8-cell, n = 31 cells).

Supplementary Figure 4 Heterogeneity of TSS NDRs among individual blastomeres from the 8-cell to blastocyst stage.

(a) RNA expression levels of 3 classes of genes during human preimplantation development. A two-tailed Student’s t-test was used to calculate the significance between comparisons. ns, not significant. The numbers of gene promoters in 3 classes defined in each stage were shown on the left of heat map in Fig. 3e and Supplementary Fig. 4c. (b) Coefficient of variation (CV) of expression levels of homogeneously open, divergent and homogeneously closed genes among individual cells during human preimplantation development. A two-tailed Student’s t-test was used to calculate the significance between comparisons. ns, not significant. The numbers of gene promoters in 3 classes defined in each stage were shown on the left of heat map in Fig. 3e and Supplementary Fig. 4c. Each box in a-b represents median and 25% and 75% quantiles, while whiskers indicate 1.5 times of IQR. (c) Heat map showing chromatin accessibility, DNA methylation levels and enriched GO terms of homogeneously open, divergent and homogeneously closed genes from the 8-cell to blastocyst stage. “n” on the left represents the number of promoters in each class. The sample size in a-c corresponds to the euploid sample sizes provided in Fig. 1a (expect for 8-cell, n = 31 cells).

Supplementary Figure 5 Differential DNA methylation and chromatin accessibility between parental genomes within each aneuploid blastomere during human preimplantation development.

(a) The global pattern of differential DNA methylation levels (left panel) and chromatin accessibility (right panel) between parental genomes within individual aneuploid blastomeres. The green bar represents the difference between euploid oocytes and sperm. (b) The difference in DNA methylation levels and chromatin accessibility between parental genomes within individual aneuploid blastomeres in different genomic regions. The green bar represents the difference between euploid oocytes and sperm. The sample size in a-b corresponds to the aneuploid sample sizes provided in Fig. 1a.

Supplementary Figure 6 Chromatin accessibility evolutionary comparison and the linkage between DNA methylation variance and DNA methylation level of gene promoter regions during human preimplantation development.

(a) Chromatin accessibility evolutionary comparison between human and mouse preimplantation embryos was performed using t-SNE (t-Distributed Stochastic Neighbor Embedding) analysis based on 15,057 human-mouse homologous proximal NDRs longer than 100 bp (NDRs within 2 kb upstream and downstream of the TSS) called from oocytes to ESCs. (b) Density plot of the relationship between the DNA methylation variance of the promoter regions (1 kb upstream and 0.5 kb downstream of the TSS) and the DNA methylation level of the corresponding regions among individual cells in each stage. The sample size in a-b corresponds to the euploid sample sizes provided in Fig. 1a (expect for 8-cell, n = 31 cells).

Supplementary Figure 7 The linkage between DNA methylation reprogramming and chromatin remodeling of gene promoter regions during human preimplantation development.

(a) Density plot of the relationship between the DNA methylation variance of the promoter regions (1 kb upstream and 0.5 kb downstream of the TSS) and the chromatin accessibility of the corresponding regions (200 bp upstream and 100 bp downstream of the TSS) among individual cells in each stage. (b) Density plot of the relationship between the chromatin accessibility variance of the promoter regions and the DNA methylation level of the corresponding regions among individual cells in each stage. The sample size in a-b corresponds to the euploid sample sizes provided in Fig. 1a (expect for 8-cell, n = 31 cells).

Supplementary Figure 8 Chromatin accessibility of repeat elements, subfamilies and around putative TF binding sites.

(a) The chromatin accessibility (left panel) and DNA methylation level (right panel) of SVAs. (b) The chromatin accessibility (left panel) and DNA methylation level (right panel) of the subfamilies of LINEs. L1, red line; L2, blue line. (c) The chromatin accessibility (left panel) and DNA methylation level (right panel) of the subfamilies of SINEs. Alu, red line; MIR, blue line. In a-c, the center represents mean, whereas error bars represent 1*SEM. (d) Boxplot showing the chromatin accessibility of repeat elements during human preimplantation development. Each box represents median and 25% and 75% quantiles, while whiskers indicate 1.5 times of IQR. (e) Heat map showing the openness of SOX2 and NANOG putative binding sites identified in hESCs during human preimplantation development (see the legend of Fig. 7d for details.). The sample size in a-e corresponds to the euploid sample sizes provided in Fig. 1a (expect for 8-cell, n = 31 cells). (f) Venn diagram shows the overlap between human-mouse homologous de-novo predicted enhancers based on open chromatin and low-methylated regions (LMRs). De-novo predicted enhancers in human preimplantation development and hESCs are listed in the Supplementary Table 4.

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Li, L., Guo, F., Gao, Y. et al. Single-cell multi-omics sequencing of human early embryos. Nat Cell Biol 20, 847–858 (2018). https://doi.org/10.1038/s41556-018-0123-2

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