Single-cell transcriptome analysis reveals defective decidua stromal niche attributes to recurrent spontaneous abortion

Abstract

Objectives: Successful pregnancy involves the homeostasis between maternal decidua and fetoplacental units, whose disruption contributes to compromised pregnancy outcomes, including recurrent spontaneous abortion (RSA). The role of cell heterogeneity of maternal decidua in RSA is yet to be illustrated.

Materials and methods: A total of 66,078 single cells from decidua samples isolated from patients with RSA and healthy controls were analysed by unbiased single-cell RNA sequencing (scRNA-seq).

Results: Our scRNA-seq results revealed that stromal cells are the most abundant cell type in decidua during early pregnancy. RSA samples are accompanied by aberrant decidualization and obviously obstructed communication between stromal cells and other cell types, such as abnormal activation of macrophages and NK cells. In addition, the over-activated TNF superfamily member 12 (TNFSF12, TWEAK) and FASLG in RSA are closely related to stromal cell demise and pregnancy failure.

Conclusions: Our research reveals that the cell composition and communications in normal and RSA decidua at early pregnancy and provides insightful information for the pathology of RSA and will pave the way for pregnancy loss prevention.

FIGURE 1

Overview of the 66,078 single cells from normal or RSA decidua. (A) Summary of the sample origins and analysis workflow. The decidua tissue was collected and processed into a single‐cell suspension and single‐cell RNA sequencing was performed using the 10× Genomics platform followed by bioinformatics analysis. (B) UMAP of the 66,078 cells with its sample type of origin (RSA or normal), the associated cell type and the proportion of each cell type in decidua samples. (C) Dotplot map showing the expression of classical cell type‐specific marker genes in each cluster

FIGURE 2

Characterization of different stromal cells in normal decidua samples. (A) Feature plot showing the expression of PR, HAND2, WT1 and FOXO1 in decidua stroma cells; (B) Feature plot showing the expression of PRL, IGFBP1, PLA2G2A and MME among different DS subsets; (C) Feature plot and violin plot showing the expression of SFRP4, DIO2 and WNT5A among distinct stromal subsets; (D–F) The genes with higher levels in DS1, DS2 and DS3 than FB1 and FB2, based on the results of TCseq (D); the functional enrichment of these genes (E); and oxidative phosphorylation associated genes in these DSs specific genes (F); (G–I) The genes with higher levels in FBs than DSs, based on the results of TCseq (G); The functional enrichment of these FBs specific genes (H) and autophagy‐associated genes in FBs specific genes (I). (J, K) Feature plot showing the higher expression of PKM and COX6A1 in DS subsets with lower expression in FB subsets. (L, M) Violin plot showing the higher expression of ATG7 and PRKAG2 in FB subsets with lower expression in DS subsets. (N) Heat map showing DS subsets were enriched for oxidative phosphorylation regulating genes and FB subsets were enriched for autophagy and histone modification associated genes

FIGURE 3

Defective stromal decidualization contributes to RSA. (A) The proportion of each cell type in normal and RSA decidua; (B) The cell number of each DS subset in different groups; (C, D) Feature plot showing the expression of LEFTY2 and MMP10 in DS5 subsets in normal and RSA decidua; (E, F) Dot plot showing the function enrichment analysis of DEGs among each DS subset between normal and RSA decidua and oxidative phosphorylation, ferroptosis, autophagy, huntington disease and endocytosis associated genes; (G, H) Feature plot and boxplot plot showing the expression of BAD and GADD45G in normal and RSA decidua by scRNA‐seq and the confirmation by real‐time PCR

FIGURE 4

Stromal cell development progress based on molecular trajectory analysis. (A, B) Pseudotime ordering of DS1, DS2 and DS3 in normal and RSA samples by monocle analysis. Each dot represents one cell and each branch represents one cell state. A was labelled with cell states and B was labelled with developmental pseudotime. (C) Heatmap showing the significant enriched gene dynamics along pseudotime; (D–G) Violin plot showing the expression profile of ATF3, MME, SEMA3B and WNT4 among normal DS subset; (H) Heatmap showing the decidualization related gene dynamics among different cell fates by branch analysis; (I, J) The expression of WNT4 and PLA2G2A along pseudotime among DS subsets in the normal and RSA group

FIGURE 5

Cell connection among cell types in the decidua niche. (A) Heatmap showing the total number of interactions between cell types in normal and RSA samples utilizing CellPhoneDB; (B, C) The sending signalling and receiving signalling among different cell types in normal and RSA samples; (D) The detailed ligand‐receptor connections among DS, FB, PV and Endo subsets in normal samples. The top labelled subsets were as senders and the bottom subsets were as receivers. The colour darkness of dots represents the p‐values, and the size of the dots corresponds to the value of Ligand‐receptor interactions, scale on the right

FIGURE 6

Cell‐cell cross‐talk between different cell types in normal decidua. (A) Heatmap showing the sending signal patterns of all cell types in normal decidua by CellChat analysis, and the first pattern included DS1, DS2, DS3, FB1, FB2, PV, Epi1, Epi2 and Endo1, with the secretion of WNT, ncWNT, SEMA3, PDGF, ANGPT (Angiopoietin), ANGPTL (Angiopoietin like) and PRL signalling; (B) Circle plot showing the inferred connection between canonical WNT signalling networks among DS subsets and endothelial cells in normal decidua; (C) Heatmap showing the relative importance of each cell type as sender and receiver for WNT signalling in normal decidua; (D) Violin plot showing the expression of major ligand‐receptors of WNT and ncWNT signalling pathways in normal decidua; (E) Heatmap showing the relative importance of each cell type as sender and receiver for SEMA3 signalling in normal decidua; (F) Circle plot showing the inferred SEMA3 signalling networks among different cell types in normal decidua; (G) Heatmap showing the receiving signal patterns of all cell types in normal decidua, and the first pattern included DS1, DS2, DS3, FB1, FB2 and PV, shared a very similar signalling pattern including EDN (Endothelin), EGF (Epidermal growth factor), PDGF and others; (H, I) Circle plot showing the inferred EDN and EGF signalling networks among different cell types in normal decidua

FIGURE 7

Derailed communications contributed to RSA. (A) Heatmap showing the receiving signal patterns of all cell types in RSA decidua by CellChat analysis; and the signalling received by DS1, DS2, DS3, DS4, DS5, FB1, FB2 and PV was clustered to the same one; (B) Circle plot showing the abnormal TWEAK signalling networks among different cell types in RSA decidua; (C, D) Violin plot showing the expression of ligand‐receptor pair of TWEAK (TNFSF12 and TNFRSF12A) signalling among each cell type in normal and RSA decidua; (E, F) Circle plot showing FASLG signalling networks among different cell types in normal and RSA decidua; (G) Violin plot showing the expression of ligand‐receptor pair of FASLG (FAS and FASLG) signalling among each cell type in normal and RSA decidua; (H) Heatmap with binarized regulon activity showing the enriched regulatory landscape of DS, Macro, NK and Endo cell types in normal decidua by SCENIC. The column shows a single cell while the row shows regulons. For the regulons of particular interest, their representative binding motif was visualized in the right panel. ‘Black’ depicts active, while ‘white’ represents inactive