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. 2021 Nov;54(11):e13128.
doi: 10.1111/cpr.13128. Epub 2021 Sep 23.

Deciphering mouse uterine receptivity for embryo implantation at single-cell resolution

Yi Yang  1 , Qiu-Yang Zhu  2 , Ji-Long Liu  2
Affiliations

Deciphering mouse uterine receptivity for embryo implantation at single-cell resolution

Yi Yang et al. Cell Prolif. 2021 Nov.

Abstract

Objectives: Mice are widely used as an animal model for studying human uterine receptivity for embryo implantation. Although transcriptional changes related to mouse uterine receptivity have been determined by using bulk RNA-seq, the data are of limited value because the uterus is a complex organ consisting of many cell types. Here, we aimed to decipher mouse uterine receptivity for embryo implantation at single-cell resolution.

Materials and methods: Single-cell RNA sequencing was performed for the pre-receptive and the receptive mouse uterus. Gene expression profiles in luminal epithelium and glandular epithelium were validated by comparing against a published laser capture microdissection (LCM)-coupled microarray dataset.

Results: We revealed 19 distinct cell clusters, including 3 stromal cell clusters, 2 epithelial cell clusters, 1 smooth muscle cell cluster, 4 endothelial cell clusters and 8 immune cell clusters. We identified global gene expression changes associated with uterine receptivity in each cell type. Additionally, we predicted signalling interactions for distinct cell types to understand the crosstalk between the blastocyst and the receptive uterus.

Conclusion: Our data provide a valuable resource for deciphering the molecular mechanism underlying uterine receptivity in mice.

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Conflict of interest statement

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Figures

FIGURE 1
FIGURE 1
Single‐cell transcriptome analysis of uterine receptivity in mice. (A) A flow chart overview of this study. (B) The pre‐receptive and the receptive uterus from mice were collected on gestational days (GD) 3 and 4 respectively. (C) The morphology of embryos recovered from the oviduct on GD3 and from the uterus on GD4. (D) Haematoxylin/eosin staining of mouse uterus from GD3 and GD3 showing all the three layers, endometrium, myometrium and perimetrium. (E) Single‐cell RNA‐seq data pre‐processing and quality control. Cells with detected genes fewer than 200 or more than 6000 were removed. Only cells with total mitochondrial gene expression below 25% were kept. (F). Scatter plots showing the correlation between single‐cell RNA‐seq and bulk RNA‐seq. For single‐cell RNA‐seq data, gene expression levels were averaged and normalized as transcripts per million (TPM). For bulk RNA‐seq data, gene expression levels were measured as transcripts per kilobase million (TPM)
FIGURE 2
FIGURE 2
Identification of different cell types in mouse uterus by using canonical gene markers. (A) TSNE visualization of cell clusters in mouse uterus by integrating GD3 and GD4 data. Single cells were grouped by cellular origin (left) and cell clusters (right). E, epithelial cells; Ep, proliferating epithelial cells; S1, superficial stromal cells; S2, deep stromal cells; S1p, proliferating superficial stromal cells; SMC, smooth muscle cells; PC, pericytes; VEC, vascular endothelial cells; VECp, proliferating vascular endothelial cells; LEC, lymphatic endothelial cells; VECp, proliferating lymphatic endothelial cells; M, macrophages; DC, dendritic cells; pDC, plasmacytoid dendritic cells; M/DCp, proliferating mixed macrophages and dendritic cells; NK, natural killer cells; NKT, natural killer T cells; T, T cells; NK/NKT/Tp, proliferating mixed natural killer cells, natural killer T cells and T cells; B, B cells. (B‐H) TSNE plots showing the expression pattern of canonical marker genes for stromal cells (B), epithelial cells (C), smooth muscle cells (D), endothelial cells (E), antigen‐presenting cells (F), lymphocytes (G) and proliferating cells (H). Dashed lines give the boundaries of the specific cell clusters
FIGURE 3
FIGURE 3
Cell population shifts and gene expression changes in receptive uterus compared to pre‐receptive uterus. (A) Bar plot showing the cell population change of 14 major cell types in mouse uterus on GD4 compared to GD3. Cell types with p < 0.05 by chi‐square test were labelled in red. (B) The distribution of differentially expressed genes in each cell type (logFC >0.25 and p < 0.05). (C) Gene ontology enrichment analysis of differentially expressed genes. Significant hits (p < 0.05) were shown as colour circles, while non‐significant ones were displayed in grey
FIGURE 4
FIGURE 4
Dividing epithelial cells into sub‐clusters. (A) Selection of epithelial cells from uterine cells. (B) Visualizing sub‐clusters of epithelial cells by TSNE plot. LE, luminal epithelial cells; LEp, proliferating luminal epithelial cells; GE, glandular epithelial cells; GEp, proliferating glandular epithelial cells. (C) The expression pattern of marker genes for sub‐clusters of epithelial cell. (D) Immunohistochemical analysis of Foxa2 expression. Bar = 50 μm
FIGURE 5
FIGURE 5
Identification of differentially expressed genes in LE cells. (A) Scatter plot for the comparison of gene expression levels in LE cells between GD3 and GD4. The threshold values for differentially expressed genes were logFC >0.25 and p < 0.05. Down‐regulated genes, up‐regulated gene and non‐changed genes were shown in green, red and blue respectively. (B) Gene ontology (GO) enrichment analysis of differentially expressed genes. Differentially expressed genes were grouped based on MGI GOslim terms under the biological process categories. Significantly enriched GO terms (p < 0.01) were coloured in red. (C) Gene network underlying differentially expressed genes. Up‐regulated genes were coloured in red, and down‐regulated genes were coloured in green. Hub genes, which are defined as genes with degree values exceeding the mean plus two standard deviations, were displayed in the centre of the network
FIGURE 6
FIGURE 6
Identification of differentially expressed genes in GE cells. (A) Scatter plot for the comparison of gene expression levels in GE cells between GD3 and GD4. Non‐changed genes were marked in blue, while differently expressed genes (logFC >0.25 and p < 0.05) were denoted in red or green. (B) GO enrichment analysis of differentially expressed genes. Significantly enriched GO terms (p < 0.01) were coloured in red. (C) Gene network for differentially expressed genes. Hub genes were displayed in the centre of the network
FIGURE 7
FIGURE 7
Validating single‐cell RNA‐seq data by laser capture microdissection (LCM)‐coupled microarray data. (A) A comparison of differential expressed genes in LE. Left: MA plot showing differential expressed genes in LE in the LCM‐coupled microarray data. Fold change >2 was used to select differently expressed genes. Down‐regulated genes, up‐regulated gene and non‐changed genes were shown in green, red and blue respectively. Right: Venn diagram showing the overlap of differentially expressed genes between our single‐cell RNA‐seq data and LCM‐coupled microarray data. (B) A comparison of differential expressed genes in GE. Left: MA plot showing differential expressed genes in LE in LCM‐coupled microarray data. Right: Venn diagram showing the overlap of differentially expressed genes between our single‐cell RNA‐seq data and LCM data. DG, down‐regulated genes; UG, up‐regulated genes. P values were calculated using the hypergeometric test, and the background parameter N was set to 15963
FIGURE 8
FIGURE 8
Ligand‐receptor interactions between the blastocyst and the uterus on GD4. (A) TSNE clustering of single cells from mouse E3.5 blastocysts which were recovered from GD4 uterus. TE, trophectoderm; ICM/EPI, inner cell mass/epiblast; PE, primitive endoderm. (B) TSNE map showing the expression pattern of well‐known marker genes. (C) Network plot showing the ligand‐receptor interaction events between blastocysts and the uterus on GD4. Cell‐cell communication is indicated by the connected line. The thickness of the lines is correlated with the total number of ligand‐receptor interaction events. The interactions that are likely associated with the establishment of uterine receptivity were coloured in red. The red node indicates cells from blastocysts. The green nodes and the blue nodes are non‐immune and immune cells from the uterus respectively. Abbreviations for cell types are listed in Figure 2. (D) Dot plot showing selected ligand‐receptor interactions underlying TE‐LE and TE‐GE crosstalk. P values are indicated by circle size, and means of the average expression level of interacting molecule are indicated by colour
FIGURE 9
FIGURE 9
Comparison of uterine receptivity between mice and humans at single‐cell resolution. (A) TSNE visualization of single‐cell RNA‐seq data collected from the pre‐receptive phase (day 17 of the menstrual cycle, D17) and the receptive phase (day 22 of the menstrual cycle, D17) of human endometrium. Single cells were coloured by tissue source (left) and cell clusters (right). LE, epithelial cells; GE, glandular epithelial cells; cE, ciliated epithelial cells (cE); LE/GEp, proliferating mixed luminal and glandular epithelial cells; S1, superficial stromal cells, S1p, proliferating stromal cells; SMC, smooth muscle cells; SMCp, proliferating smooth muscle cells; M/DC, mixed macrophages and dendritic cells; NK/NKT/T, mixed NK, NKT and T cells; VEC/LEC, mixed vascular and lymphatic endothelial cell; VEC/LECp, proliferating mixed vascular and lymphatic endothelial cells. (B‐G) TSNE plots showing the expression pattern of canonical marker genes for epithelial cells (B), stromal cells (C), smooth muscle cells (D), proliferating cells (E), endothelial cells (F) and immune cells (G). (H‐I) Venn diagram showing the overlap of differentially expressed genes between mice and humans in 4 major cell types. Down‐regulated genes (H) and up‐regulated genes (I) were compared seperately. P values were calculated using the hypergeometric test. The background N was set to 20366, and this parameter was estimated by averaging single‐cell RNA‐seq data to produce pseudo‐bulk RNA‐seq data
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