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Exploring the molecular mechanism of estrogen therapy effectiveness after TCRA in IUA patients at single-cell level

Biology Direct volume 19, Article number: 142 (2024) Cite this article

Abstract

Background

Intrauterine adhesion (IUA) is a common cause of clinically refractory infertility, and there exists significant heterogeneity in the treatment outcomes among IUA patients with the similar severity after transcervical resection of adhesion(TCRA). The underlying mechanism of different treatment outcomes occur remains elusive, and the precise contribution of various cell subtypes in this process remains uncertain.

Results

Here, we performed single-cell transcriptome sequencing on 10 human endometrial samples to establish a single-cell atlas differences between patients who responded to estrogen therapy and those who did not. The results showed increased infiltration of immune cells such as monocyte macrophages, T cells, and natural killer (NK) cells in patients who did not respond to estrogen therapy. Our findings indicate that distinct fibroblast subsets are implicated in the modulation of the Wnt, Hippo, and Hedgehog signaling pathways, as evidenced by functional enrichment analyses. This may have implications for the therapeutic efficacy in patients with IUA. Furthermore, we delineated the markers and transcriptional status of different macrophage subsets and identified two cell clusters, CXCL10high and CCL4L2high macrophage subsets, which are intimately associated with inflammation and fibrosis. The state of fibrosis and inflammatory response in human endometrial tissues with disparate treatment outcomes is revealed, and providing evidence to clarify the underlying determinants of sensitivity to estrogen therapy.

Conclusions

We described the transcriptional status of different cell subtypes in the two groups of patients, providing new ideas for exploring the molecular mechanism of the difference in the effectiveness of estrogen therapy in patients, and providing theoretical basis for providing precise and individualized treatment plans for IUA patients.

Introduction

Intrauterine adhesion is a common cause of refractory infertility, and the main pathological features of endometrium fibrosis caused by intra-uterine surgery, infection and other factors [1] lead to abnormal menstruation, amenorrhea, recurrent abortion, secondary infertility, etc [2], which seriously endangers the reproductive function of women of childbearing age. Even with a small number of successful pregnancies, the risk of pregnancy complications such as preterm birth, placental adhesion, and invasive placenta is significantly increased [3].

At present, the standard treatment for intrauterine adhesions is transcervical resection of adhesion (TCRA) and the application of intrauterine devices to prevent readhesions and estrogen-based drugs to promote endometrial regeneration [4]. Estrogen can promote endometrial regeneration by promoting endometrial proliferation and angiogenesis. However, for IUA patients, especially those with severe adhesions, the incidence of postoperative readhesions is still as high as 20-62.5% [5]. We observed that even in IUA patients with the same degree of adhesion, the same dose of estrogen after surgery resulted in significant differences in the degree of endometrial repair, and this difference determined whether reproductive outcomes were improved. This clinical phenomenon makes us think that further study of the interaction mechanism between endometrial regeneration process and microenvironment may be the key to uncover the heterogeneity of endometrial regeneration.

The advantage of single-cell sequencing technology is that it allows for in-depth study of diseases at the single-cell level, detecting differences between cells and their cooperative operational modes, and studying the issue of cellular heterogeneity [6]. In this study, we selected patients of childbearing age with the same degree of moderate intrauterine adhesions (IUA) and treated them with the same dose of estrogen therapy. Three to six months after TCRA, we monitored the thickness of the endometrium in the mid-luteal phase using color Doppler. Patients with endometrial thickness < 7 mm were considered to be estrogen treatment ineffective, also defined as endometrium no response (ENR), while those with endometrial thickness ≥ 7 mm were known as estrogen treatment effective which we defined as endometrium response (ER). We selected 5 patients from each group and collected endometrial samples from them under hysteroscopy for single-cell transcriptome sequencing analysis. We identified a total of 10 cell types, among which several cell types showed transcriptomic differences between the two groups. Furthermore, we found functional differences in subpopulations of cells such as macrophages and fibroblasts between the two groups. Cell-cell interaction analysis also revealed complex patterns of cellular interactions between the two groups.

Currently, there are few reports on the individual differences in estrogen treatment for IUA. We hope to use single-cell sequencing technology to reveal the intrinsic mechanisms underlying the differences in response to estrogen treatment among different individuals, providing a scientific basis for guiding clinical treatment.

Results

Cellular heterogeneity in the IUA endometrium characterized by scRNA-seq

To investigate the differences in treatment outcomes after estrogen therapy in patients with different degrees of intrauterine adhesions, we collected endometrial tissue samples from patients with effective estrogen treatment (ER) and ineffective estrogen treatment (ENR), each consisting of 5 cases, for scRNA-seq analysis (Fig. 1A). We constructed a single-cell map of endometrial tissue samples from 10 cases. Further analysis of approximately 85,837 sequencing cells that met the quality control criteria revealed an average of 8,583 cells per sample. Unsupervised clustering identified 20 cell clusters, including epithelial cells, immune cells, and endothelial or stromal cells (Supplementary Figure S1A). Further annotation of cell types using the SynEcoSys endometrial series database showed a total of 10 different cell types, including Epithelial cells (EpithelialCells), Endothelial cells (ECs), Fibroblasts, Mural cells (MuralCells), Proliferation cells, B cells (BCells), T and NK cells (TandNK), Neutrophils, Mast cells (MastCells), Mononuclear phagocytes (MPs), and Plasmacytoid dendritic cells (pDCs) (Fig. 1B C), with the proportions of epithelial cells and fibroblasts being 24.23% and 45.78%, respectively. The marker genes used and the number and proportion of each identified cell type in different samples are listed in Supplementary Figure S1B-S1C. The study showed that there is a variety of immune cell infiltration in the human endometrium, including NK cells, T cells, and macrophages. The single-cell transcriptome sequencing data of this study found that compared with the ER group samples, the ENR group had higher levels of T cell and NK cell infiltration in endometrial tissue. The proportion of mononuclear macrophages in the ENR group was slightly higher than that in the ER group(6.75%) at 8.86%. And the proportion of endothelial cells was not significantly different from that in the ER group. Interestingly, the proportion of epithelial cells in the ENR group was significantly higher than that in the ER group. We analyzed that this abnormal phenomenon may result from a combination of factors, including inadequate local blood supply, excessive fibroblast proliferation, or an abnormal local microenvironment (such as abnormal epithelial cell proliferation signals, an abnormal extracellular matrix, or overactivation of the immune response). Such conditions may inhibit the normal functional growth of epithelial cells and promote their aberrant proliferation, preventing their differentiation into a functional endometrium in ENR patients after estrogen therapy.Surprisingly, the proportion of fibroblasts in the ER group was higher than that in the ENR group, which may be due to overall differences in the number of various subgroups of fibroblasts with different functions or may be related to individual differences in sampling sites and patient samples (Fig. 1D). The subclustering and cell distribution of proliferating cells are shown in Figure S1D-F. Although this subpopulation contains a small number of cells, it may still play an important role in endometrial repair.

Fig. 1
figure 1

Atlas of the endometrium of ER and ENR patients. A. Flowchart overview of the single-cell RNA sequencing. B. UMAP plots for all cell types, including epithelial cells, endothelial cells(ECs), fibroblasts, mural cells, B cells, T and NK cells, neutrophils, mast cells, mononuclear phagocytes(MPs), Plasmacytoid dendritic cells(pDCs). C. Heatmap showing the differentially expressed genes of different clusters, genes, and cells ordered by hierarchical clustering. D. Relative proportion of diffenent cell cluster in the endometrium of ER and ENR group samples

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Secretory cells are dominant in the endometrium epithelial cells

A total of 16,205 epithelial cells were identified among all analyzed cells. Based on the expression of specific marker genes, these epithelial cells were further divided into 2 subtypes, including ciliated cells (TPPP3, MS4A8, and C20orf85) and secretory cells (PAEP, CXCL14, and MT1G) (Fig. 2A). We found that the proportion of ciliated cells was higher in the ER group, while the ENR group had more secretory cells (Fig. 2B). And secretory cells dominated the epithelial cells in both groups. The secretory cells in the ER group significantly upregulated the expression of proteins belonging to the lipophilic protein subfamily, such as SCGB1D2, SCGB1D4, SCGB2A1, and SCGB2A2, as well as proteins from the matrix metalloproteinase family, such as MMP26 and MMP11 (Figure 2C). Additionally, the ER group showed higher expression of the main inhibitor of the Wnt signaling pathway, SFRP4. Analysis of the gene expression in the two groups revealed significant differences in the functional enrichment of differentially expressed genes, mainly manifested as the downregulation of genes related to the apoptotic signaling pathway in secretory cells of the ER group (Fig. 2D), while the upregulated genes were closely associated with extracellular matrix structure and collagen fiber formation (Fig. 2E). This suggests that there may be a correction of the apoptotic signaling pathway in the secretory cells of ER patients’ endometrium, leading to the restoration of normal biological cycles in secretory cells and ultimately inducing the formation of effective treatment outcomes. On the other hand, we conducted a preliminary analysis of the expression of several known embryo implantation window-related genes based on the intergroup differential gene list of secretory cells from ER and ENR patients (Supplementary Table S1). We found that ESR1, integrin, IGF1, and some other genes were highly expressed in the secretory cells of ER patients. The differences in the expression of these genes in secretory cells may, to some extent, contribute to the heterogeneity of therapeutic outcomes, thereby affecting patient fertility. Additionally, epithelial cells are the primary cell type affected by estrogen. We screened the GO and KEGG analysis results of differentially expressed genes between secretory cell groups, selected items related to estrogen action (P < 0.05), and identified three GO terms associated with the estrogen response (Table 1). This provides a basis for further investigation into the potential mechanisms of estrogen-related differential genes.

Fig. 2
figure 2

Single-cell transcriptional profiling of epithelial cells and the GO analysis of DEGs. A. UMAP plots of the epithelial cells. B. Relative proportion of epithelial cells subsets in ER and ENR group. C. The heatmap of differentially expressed genes in secretory cells between ER and ENR. D. The GO analysis of down-regulated DEGs shows top10 highlighted pathways in secretory cells of the ER group (n = 10, hypergeometric test, adjusted p-values obtained by Benjamini-Hochberg procedure). E. The GO analysis of up-regulated DEGs shows top10 highlighted pathways in secretory cells of the ER group

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Table 1 GO enrichment analysis results of secretory cells related to estrogen
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Fibroblast gene signatures uncover heterogeneity in estrogen treatment outcomes

Fibroblasts were the most abundant cell type in the sequenced cells, and 7 cell subtypes were identified from the endometrial tissue samples of the two groups (Fig. 3A). The functional characteristics of FIB1, FIB2, and FIB4 subtypes, which highly express marker genes, include regulation of the extracellular matrix, pathway regulation, cell signaling transduction, cell adhesion, and cell cycle regulation among several biological processes (Fig. 3B). The FIB3 subtype shows high expression of genes mainly related to cell immune regulation and inflammatory responses (CXCL13, SERPING1, CEBPD, C1R, SCGB2A1), and its high expression of various genes related to lipid precursors (APOD, CFD) suggests that this cell subtype may originate from adipose tissue (Fig. 3B) [7], and it significantly increases in the ENR group patients. The FIB5 cell subtype is mainly involved in tissue repair and extracellular matrix formation processes (CCN2, COL4A1, COL4A2, BGN, COL6A3). Additionally, this cell subtype shows high expression of ACTA2, MYL9, HOPX, and other characteristic genes of myofibroblasts, which promote tissue fibrosis (Fig. 3B) [8, 9]. The high expression of genes in the FIB6 and FIB7 cell subtypes is related to cell proliferation (MKI67, BIRC5), chromosome stability (CENPF, HMGN2, H2AZ2, H2AZ1), DNA replication (TOP2A, MCM3, MCM4, MCM2, PCNA), indicating that these cells are in a proliferative state, belonging to proliferative fibroblasts (Fig. 3B) [10]. The proportion of the immune regulation-related FIB3 cell subtype is significantly higher in the ENR group than in the ER group, while the functionally complex FIB1 and FIB2 subtypes show the opposite trend (Fig. 3C). Further GO and KEGG enrichment analysis of different fibroblast subtypes showed that the upregulated differentially expressed genes (DEGs) in the FIB2 cells which significantly increased in ER patients were enriched in the Hedgehog signaling pathway (PTCH1, HHIP, PTCH2, GLI1) (Fig. 3D). Among them, the upregulated genes PTCH1, HHIP, and PTCH2 are major inhibitory factors of the Hedgehog signaling pathway, consistent with previous studies suggesting that abnormal activation of the Hedgehog signaling pathway may be related to the progression of fibrotic diseases [11,12,13,14]. In the ER group, the increase in the number of FIB2 cells (PTCH1, HHIP, PTCH2, GLI1) may lead to the inhibition of the Hedgehog signaling pathway, thereby alleviating endometrial fibrosis and resulting in different treatment outcomes. Furthermore, GO enrichment analysis of several cell subtypes showed that the functional enrichment was in the extracellular matrix, wound healing, epithelial cell proliferation, and collagen fiber tissue. GO enrichment analysis of the FIB1 cell subtype showed that DEGs were enriched in the Wnt signaling pathway (Fig. 3E) [15, 16], suggesting that differential activation of the Wnt signaling pathway in the FIB1 subtype may lead to the occurrence of different treatment outcomes. In addition, the proliferating fibroblast subgroup, constituting the highest proportion within the proliferating cell subgroup, exhibited high expression of the Wnt signaling pathway inhibitor SFRP4, as well as COL1A1 and COL3A1, which are involved in extracellular matrix synthesis (Figure S1F). This suggests that this subgroup may be mainly involved in endometrial repair and the pathological fibrosis process. Additionally, fibroblasts are the primary cell type affected by estrogen. We screened the GO and KEGG analysis results of differentially expressed genes between fibroblast subtypes, selected items related to estrogen action (P < 0.05), and identified three GO terms associated with the estrogen response (Table 1). This provides a basis for further investigation into the potential mechanisms of estrogen-related differential genes. Additionally, fibroblasts are predominant in endometrial tissue and represent one of the main cell types involved in the effects of estrogen. Therefore, we screened the results of the enrichment analysis of the inter-group differential genes for each fibroblast subgroup and found that FIB2 and FIB3 were the primary cell types associated with estrogen effects (Table 2), which may uncover the underlying molecular mechanisms of estrogen therapy response heterogeneity.

Monocle2 analysis simulated the dynamic transition of fibroblasts. We found that FIB3 cells were mainly distributed in the early stages of the trajectory path, while a small number of cells from the FIB2, FIB4, and FIB5 clusters were also distributed in the early stages. As the cells progressed along the pseudo-time developmental trajectory, two different differentiation pathways emerged. FIB1 cells differentiated later, while the proliferative FIB6 and FIB7 cells also appeared in the later stages of the trajectory due to cell proliferation activity (Fig. 3F).

Fig. 3
figure 3

The fibroblasts cell markers and trajectory branch. A. UMAP plots of the fibroblasts. B. The heatmap of clustered top DEGs across seven groups of fibroblasts. C. Proportion of fibroblasts subsets in ER and ENR group. D. The KEGG analysis of up-regulated DEGs shows mostly highlighted pathways in fibroblasts2 cluster. E. The GO analysis of up-regulated DEGs shows top10 highlighted terms in fibroblasts1 cluster. F. Pseudo-time trajectories developed through Monocle2 analysis for fibroblasts

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Table 2 GO enrichment analysis results of fibroblasts related to estrogen
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T and NK cells drive treatment outcome differences through a multidimensional immune network

In the ENR endometrial samples, the proportion of T and NK cells was significantly higher compared to the ER group. Subclustering of T and NK cell groups (7361 cells) and identification of cell types revealed 5 different types of T cells, in addition to Natural killer cells (NK) and Group 3 innate lymphoid cells (ILC3), including Natural killer T cells (NKT cells), T-helper 2 cells (Th2), CD4 + memory T cells (CD4Tmem), CD4 + regulatory T cells (CD4Treg), CD8 + effector T cells (CD8Teff) (Fig. 4A). After performing GO and KEGG enrichment analysis on different cell clusters, it was found that the proportion of NK cells and ILC3 cells was slightly higher in the ENR group compared to the ER group (Fig. 4B). DEGs downregulated in NK cells in the ER group were enriched in the IL-17 signaling pathway, antigen processing and presentation, TNF signaling pathway, cell apoptosis, MAPK signaling pathway, and Toll-like receptor signaling pathway (Fig. 4C), while upregulated DEGs were mainly enriched in pathways such as protein synthesis, protein digestion and absorption, and oxidative phosphorylation (Fig. 4D). These pathways and biological processes may be involved in the different treatment outcomes of the endometrium. ILC3 is a type of natural killer-like cell that is involved in regulating cell apoptosis, the TNF signaling pathway, and the IL-17 signaling pathway, which are crucial for regulating immune responses (Fig. 4E). GO enrichment analysis showed that upregulated DEGs in the 5 different subtypes of T cells were highly expressed in genes related to the ribosome pathway, indicating active protein synthesis processes in these T cells, which may be related to their functions. NKT cells have similar functions to NK cells, and the downregulated DEGs in the ER group were enriched in signaling pathways such as MAPK, TNF, and Toll-like receptors (Fig. 4F), which play critical roles in biological processes such as cell proliferation and immune responses. DEGs in CD4Treg were enriched in T cell activation, antigen processing and presentation, and MHC class II protein functions (Supplementary Figure S2A), which is consistent with the immunoregulatory function of CD4Treg.

Fig. 4
figure 4

Single-cell transcriptional profiling of Tand NK cells. A. UMAP plots of T and NK cells, and there are five subtypes of T cells, including Natural killer T cells(NKT cells), T-helper 2 cells(Th2), CD4 + memory T cells(CD4Tmem), CD4 + regulatory T cells(CD4Treg), CD8 + effector T cells(CD8Teff). B. Relative proportion of T and NK cell subsets in ER and ENR group. C. The KEGG analysis of down-regulated DEGs in NK cells of the ER group. D. The GO analysis of up-regulated DEGs shows top10 highlighted terms in NK cells of the ER group. E. The KEGG analysis of down-regulated DEGs shows top10 highlighted pathways in ILC3 of the ER group. F. The KEGG analysis of down-regulated DEGs shows top10 highlighted pathways in NKT cells

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Macrophage subsets drive endometrial treatment response and repair outcomes

Macrophages are important components of the immune system, and studies have shown that they play a crucial role in endometrial fibrosis and inflammatory reactions [17, 18]. In this study, we found that the proportion of mononuclear macrophages was slightly higher in the ENR group compared to the ER group. Subclustering of 3075 macrophages revealed 5 different cell clusters, each with its characteristic gene expression profile. For example, the Mac1 cell cluster was slightly less abundant in the ENR group compared to the ER group, while the Mac3 cell cluster was significantly less abundant in the ENR group compared to the ER group. In contrast, the Mac4(CCL4L2high) cell cluster was significantly enriched in the ENR group and highly expressed several characteristic genes related to inflammation, such as CCL4L2, CCL3L3, CXCL8, CCL3, CCL4, CXCL2, NFKBIA, IL1B, and CXCL3 (Fig. 5B and C) [19, 20]. This indicates that the Mac4 (CCL4L2high) cell cluster may play a key role in the sensitivity to oestrogen therapy and the degree of endometrial repair after damage. Furthermore, the Mac2 (CXCL10high) cell cluster highly expressed several characteristic genes of interferon response macrophages, such as CXCL10, CXCL9, GBP1, and ISG15, suggesting a potential function of interferon response. This cluster was more prevalent in the ENR group and exhibited a phenotype similar to M1 macrophages, which have pro-inflammatory properties [19, 21].

To further understand the biological functions of different cell clusters, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on these 5 cell clusters. The results showed that DEGs in the Mac1 cluster were significantly enriched in pathways related to cell adhesion, phagosomes, lysosomes, cell chemotaxis, and antigen processing and presentation (Supplementary Figure S3A-S3B), indicating the immune regulation and phagocytic functions of the Mac1 cell cluster. The Mac2(CXCL10high) cluster was enriched in pathways related to antigen processing and presentation, regulation of GTPase activity, and ribosomes (Supplementary Figure S3C-S3D), suggesting that the Mac2 (CXCL10high) cell subtype mainly regulates cell signal transduction, immune balance, protein synthesis, and other biological activities. DEGs downregulated in the Mac3 cluster and upregulated in the Mac5 cluster were mainly enriched in regulating aerobic glycolysis metabolism pathways (Supplementary Figure S3E-S3F). Both clusters are involved in regulating cell signal transduction and immune balance, indicating that the Mac3 and Mac5 cell clusters may have opposite regulatory effects on the aerobic glycolysis pathway, thus jointly affecting the pathway of endometrial repair in the ER group. The Mac4 (CCL4L2high) cluster showed differences in the regulation of signalling pathways such as the IL-17, NF-κB, TNF, and Apelin pathways (Fig. 5D). Additionally, GO enrichment analysis results showed that the upregulated DEGs were enriched in regulating leukocyte inflammatory responses (Fig. 5E). Therefore, the Mac4 (CCL4L2high) cluster may play an important role in regulating inflammatory responses. SCENIC analysis was applied to predict transcription factor, which identified a set of transcription factors associated with the biological signatures of macrophage cell subtypes (Fig. 5F).

Monocle2 analysis simulated the dynamic transition of macrophages and found that cell types distributed in the early trajectory path were mainly Mac2(CXCL10high) cells, Mac4 (CCL4L2high) cells, and a small number of Mac5 cells. As the pseudo-time trajectory extended, four different differentiation pathways of macrophages appeared, with Mac1 and Mac3 being two differentiated subtypes of macrophages (Fig. 5G).

Fig. 5
figure 5

Single-cell transcriptional profiling of the macrophages, and the pseudotime analysis. A. UMAP plots of macrophages. B. Proportion of different macrophages subsets in ER and ENR samples. C. The heatmap of clustered top DEGs across five groups of macrophages. D. The KEGG analysis of up-regulated(above) and down-regulated(below) DEGs both show top10 highlighted pathways in macrophages4. E. The GO analysis of up-regulated DEGs in macrophages4. F. Heatmap showing diferences in transcription factor (TF) activity across subclusters of basal cells scored by SCENIC. G. Pseudo-time trajectories developed through Monocle2 analysis for macrophages

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Neutrophils exacerbate inflammation and impair endometrial repair in ENR

Neutrophils are one of the main cells in the inflammatory process. In response to tissue damage, infection, or other inflammatory stimuli, neutrophils release inflammatory mediators in the damaged area and phagocytose and clear pathogens [22, 23]. In endometrial samples, the proportions of neutrophils in the ER group and ENR group were 1.54% and 1.15%, respectively. Compared with macrophages, T cells, and NK cells, the proportion of neutrophils is relatively small, but their role in the endometrial immune microenvironment cannot be ignored (Fig. 6A). The ENR group significantly expressed the pro-inflammatory factor IL-1β, while the ER group expressed TGFBI at a higher level (Fig. 6B). Enrichment analysis of DEGs between the two groups showed that the downregulated DEGs in the endometrial tissue of the ER group were enriched in antigen processing and presentation, immune cell activation, regulation of cell adhesion, and phagocytosis processes (Fig. 6C), while the upregulated DEGs were enriched in processes related to the extracellular matrix, collagen fibre tissue, and extracellular structural tissue (Fig. 6D). This suggests that neutrophils in the ENR group significantly upregulate the release of related inflammatory mediators, thereby increasing the activation of immune cells such as T cells and lymphocytes, enhancing the phagocytic and antigen-presenting abilities of macrophages and dendritic cells [24, 25], inducing exacerbated inflammatory reactions in endometrial tissue, thereby hindering endometrial repair to some extent.

Fig. 6
figure 6

Single-cell transcriptional profiling of neutrophils. A. Volcano plot of differentially expressed genes in ER and ENR. B. The heatmap of differentially expressed genes in ER and ENR. C. The GO analysis of up-regulated DEGs in neutrophils of the ER group. D. The GO analysis of down-regulated DEGs in macrophages2 of the ER group

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Cell-cell communication reveals distinct interaction patterns in ER and ENR

To understand the interactions between cells and analyse the communication network between them, we conducted cell communication analysis focusing on macrophages, fibroblasts, and epithelial cells. The cell communication analysis between fibroblast subtypes and epithelial cells, endothelial cells, macrophages, T cells, and NK cells showed (Fig. 7A and B) that ciliated cells had less cross-talk with other cells, while interactions among fibroblast subtypes were more active. Compared with the ER group, the interactions between FIB4 cluster cells and other cells were relatively reduced in the ENR group, and the cell cross-talk between macrophage clusters and fibroblasts and endothelial cells was also slightly reduced in the ENR group. Further analysis of the cell interactions between macrophage subtypes and epithelial cells, and endothelial cells showed that there were certain interactions between macrophage subtypes and epithelial and endothelial cells, and the interactions among macrophage subtypes were more active. The difference in interaction strength between cells in the ER and ENR groups was not significant (Fig. 7C and D). Further analysis of the receptor-ligand pairs between different cell types indicated that CD74 was one of the main receptors mediating interactions between macrophages and other cells (Supplementary Figure S4A-S4B). When analysing the ligand-receptor pairs between secretory cells and other cells, it was found that in the ENR group, MDK (Midkine protein) as a ligand interacted with multiple cells (Supplementary Figure S4C), suggesting that MDK may have potential significance in the formation of treatment outcomes in ENR patients.

Fig. 7
figure 7

Cell–Cell Interaction in ER and ENR samples. A and B. Abundance of connections between different cell types in ER (A) and ENR (B) samples shown by CellphoneDB. C and D. The number of interactions between different cell types in ER (C) and ENR (D) samples shown by Cellchat

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Discussion

The current status of oestrogen therapy for IUA and limitations of mechanistic studies

Oestrogen drugs are one of the main methods for treating intrauterine adhesions (IUA). However, there is significant variability in the extent of endometrial recovery after oestrogen therapy among different populations, which is not conducive to guiding clinical drug use for patients in whom the drug is ineffective. In recent years, there have been many studies on the mechanism of intrauterine adhesions, most of which have focused on the differences between normal and diseased groups, thereby overlooking the unpredictable outcomes in individuals after drug treatment, especially the different treatment outcomes caused by individual differences.

Single-cell RNA sequencing (scRNA-seq) can analyse tissue samples at the single-cell resolution and has been applied to many biological studies. It can objectively and unbiasedly annotate cell types based on the differential expression of characteristic marker genes, revealing the gene expression patterns of different cells. In this study, we performed single-cell transcriptome analysis of patients’ endometrial recovery after oestrogen therapy, divided into treatment-effective and treatment-ineffective groups, and obtained gene expression profiles of 85,837 single cells. Bioinformatic analysis was used to reveal cell and molecular changes associated with oestrogen therapy. Our results suggest that the endometrium remains in a thinned state after oestrogen therapy, possibly due to differences in the sensitivity of patients’ own cells to drug responses. There were significant differences in the activation status of epithelial cells and stromal cells (such as fibroblasts, macrophages, T cells, etc.) and immune responses among patients with different treatment outcomes.

Differences in endometrial fibroblasts and epithelial cells of IUA patients with different outcomes after oestrogen therapy

Fibroblasts and epithelial cells are the main components of the endometrium. Under normal circumstances, fibroblasts function to repair tissue damage. However, excessive deposition of extracellular matrix can lead to endometrial fibrosis, which is also the core mechanism of intrauterine adhesions (IUA). After endometrial injury, the local tissue initiates an inflammatory response, producing related inflammatory factors and cell mediators (TGF-β, etc.), stimulating fibroblast activation and proliferation, inducing collagen synthesis [26, 27], promoting excessive accumulation of extracellular matrix proteins such as collagen and fibronectin, disrupting tissue structure [28], forming scar tissue, and ultimately leading to endometrial dysfunction. In epithelial cells, after endometrial injury, excessive or sustained inflammatory responses can lead to epithelial cell damage and apoptosis, activate inflammatory factors, and increase the risk of adhesions. Epithelial cells can also regulate the activity of fibroblasts by secreting various growth factors, cytokines, and chemokines, causing them to become overly active, further promoting fibrosis.

Liu et al.‘s [29] study demonstrated that the transformation of adipocytes into myofibroblasts in dermal white adipose tissue (dWAT) can induce dermal fibrosis. It has been shown that the Hippo signalling pathway plays a crucial role in maintaining adipocyte characteristics. When it is inactive, energy-storing adipocytes are reshaped into myofibroblasts that produce extracellular matrix, thereby synergistically promoting adipocyte fibrosis with TGF-β signalling [30]. The Hippo signalling pathway is involved in endometrial fibrosis and interacts with other signalling pathways, such as TGF-β, Wnt/β-catenin, through various mechanisms [31]. Our study found that the FIB3 subset originates from adipose tissue and is significantly increased in ENR patient tissues. This suggests a potential mechanism in ENR patient endometria: due to dysregulation of the Hippo signalling pathway, some adipocytes are reshaped into myofibroblasts, producing a large amount of extracellular matrix, thereby promoting endometrial fibrosis and leading to poor efficacy of oestrogen therapy. In the ER group, the FIB1 cell subset showed high expression of various inhibitors of the Wnt signalling pathway, such as SFRP1, SFRP4, and APCDD1 [32,33,34,35], indicating that in ER patient endometria, the Fib1 subset inhibits the activation of the Wnt signalling pathway through molecules like SFRP1, SFRP4, and APCDD1, thereby inhibiting the process of endometrial fibrosis and achieving a good therapeutic outcome. Furthermore, the upregulated DEGs in the FIB2 cells were enriched in the Hedgehog signalling pathway (PTCH1, HHIP, PTCH2, GLI1)(11–14), where PTCH1, HHIP, and PTCH2 are major inhibitors of the Hedgehog signalling pathway. And FIB2 cell subpopulation has a numerical advantage in ER patients. Studies have shown that the Hedgehog signalling pathway is associated with various fibrotic diseases, including IUA, and excessive activation of the Hedgehog pathway can promote fibrosis progression. The upregulation of multiple Hedgehog signalling pathway inhibitors in the FIB2 cell subset inhibits Hedgehog signalling pathway activity, delaying fibrosis progression and promoting endometrial structural remodelling and tissue repair processes.

In this study, significantly upregulated differentially expressed genes (DEGs) in the ER group secretory cells, including SCGB1D2, SCGB1D4, SCGB2A1, SCGB2A2 (Secretoglobin family), are members of the lipophilic protein subfamily and part of the Uteroglobin (UG) superfamily. Although the biological activities of most individual SCGBs have not been fully characterized, the known functions of this family of proteins suggest they possess anti-inflammatory, tissue repair, and immune regulatory properties. They also exhibit anti-chemotactic, anti-allergic, anti-tumour, and embryonic growth stimulation activities [36, 37]. The tissue-specific expression of their genes is regulated by steroids. Studies have shown that SCGB1D2 mRNA is highly expressed in primary breast cancer and positively correlates with estrogen receptors [38,39,40]. Therefore, we speculate that in ER patients, there may be a regulatory mechanism in the endometrium whereby exogenous estrogen stimulation upregulates the expression of SCGB family proteins, inducing an increase in estrogen receptor expression. This increased sensitivity to estrogen therapy could lead to a favourable therapeutic outcome. Additionally, MMP26 and MMP11, significantly upregulated DEGs in the ER group secretory cells, belong to the matrix metalloproteinase (MMP) family, mainly responsible for degrading the extracellular matrix (ECM) [41,42,43,44]. Zhao et al.‘s [42] study indicates that besides the excessive accumulation of ECM, impaired ECM degradation is also an important cause of fibrosis. Macrophages interact closely with the main molecular system, MMP, that promotes ECM degradation, and are involved in the occurrence and repair of tissue fibrosis. In our study, MMP11 was also highly expressed in macrophages in the ER group, suggesting a possible interaction between macrophages, MMPs, and ECM in the endometrium of patients, affecting ECM degradation and participating in fibrosis and endometrial tissue remodelling. This may be one of the reasons for the different therapeutic outcomes of estrogen therapy in different patients.

The role of macrophages in the different phases of endometrial repair following estrogen therapy for intrauterine adhesions

Endometrial repair at the site of injury requires the involvement of the inflammatory process; excessive inflammation can lead to dysregulation of endometrial repair mechanisms, fibrosis, and scar formation. Timely resolution of the inflammatory response is crucial for functional endometrial repair. Among various endometrial cells, macrophages are the key players in the generation and resolution of inflammation, and their changes in number and function will have significant impacts on the repair of the endometrium. Therefore, macrophages are attractive targets for therapeutic intervention in inflammatory diseases.

A study using mesenchymal stem cell exosome-coated collagen scaffold CS/Exos for the treatment of intrauterine adhesions suggested that CS/Exos can induce endometrial regeneration and restoration of fertility by promoting macrophage polarization towards the M2 phenotype, reducing inflammation, and increasing anti-inflammatory responses [45]. In 2023, Hu Yali’s team at Nanjing Drum Tower Hospital validated through single-cell transcriptome sequencing combined with molecular biology experiments that the CD301 + macrophage subset plays a key role in promoting endometrial fibrosis. Targeted deletion of CD301 + macrophages or drug intervention to inhibit their downstream pathways can effectively alleviate endometrial fibrosis and improve pregnancy outcomes [17]. These results suggest that macrophages play a crucial role in inflammation and fibrosis, exerting complex bidirectional regulatory effects.

Enrichment of the IL-17 signaling pathway, NF-κB signaling pathway, TNF signaling pathway, and Apelin signaling pathway in the DEGs of Mac4(CCL4L2high) cluster macrophages after estrogen therapy indicates potential differential mechanisms in endometrial repair among different IUA patients. It has been reported that aerobic glycolysis can induce fibrosis in organs such as the lungs, kidneys, and heart by promoting collagen deposition and inducing myofibroblast differentiation [46,47,48]. The Mac2 (CXCL10high) subset, which is highly expressed in the ENR group, overexpresses characteristic genes of interferon-responsive macrophages (CXCL10, CXCL9, GBP1, ISG15), indicating that this cell subset has interferon response function and a phenotype similar to M1 macrophages, with pro-inflammatory characteristics [49, 50]. This suggests that after endometrial injury, the body produces interferons due to exogenous stimuli, activates Mac2 (CXCL10high) interferon-responsive macrophages, and then regulates the expression of inflammation-related genes or regulates the activity of various immune cells such as T cells, B cells, etc., intervening in the endometrial repair process.

Neutrophil expression patterns and their predictive value in the outcome of estrogen therapy for intrauterine adhesions

IUA is essentially a uterine endometrial fibrosis disease, characterized by a progressive chronic process driven by the interaction of inflammatory cytokines and fibrotic factors. When injuries such as surgery or infections occur in the endometrium, the repair process exhibits a dynamic nature. Processes such as inflammation, immune regulation, endometrial epithelial regeneration, and vascular repair in the microenvironment are interactive, highly coordinated, continuous events. Any abnormalities in these processes may lead to repair disorders, endometrial fibrosis, subsequent scar replacement, and loss of the necessary conditions for fertilized egg implantation. It has been reported that after liver injury, leukocytes rapidly infiltrate the liver parenchyma, promoting inflammation and fibrosis by producing soluble mediators that can activate other immune cells and non-parenchymal cell populations [51]. Neutrophils are also involved in tissue fibrosis development in pulmonary fibrosis [52]. Therefore, the changes in inflammatory cells in the uterine cavity microenvironment after estrogen treatment are an important indicator of the degree of endometrial repair.

Neutrophils are divided into two subtypes, N1 and N2, based on their different inflammatory responses and immune regulatory characteristics [53, 54]. N1 neutrophils mainly exhibit a pro-inflammatory phenotype, while N2 neutrophils are associated with anti-inflammatory effects and tissue repair promotion [55, 56]. The balance between these two subtypes is crucial for maintaining normal immune and inflammatory responses. In this study, neutrophils in the ENR group showed significantly higher expression of the pro-inflammatory marker IL1β [55], reflecting an N1 macrophage phenotype, which exacerbates tissue inflammation, promotes fibrosis progression, and leads to adverse treatment outcomes. In contrast, neutrophils in the ER group showed significantly higher expression of the gene TGFBI induced by the anti-inflammatory factor TGF-β [55]. Previous studies have confirmed that TGFBI has characteristics of inhibiting tissue inflammation [57], indicating that there may be a potential mechanism in ER patients involving TGF-β-induced high expression of TGFBI, which could alleviate endometrial tissue inflammation and inhibit fibrosis. These findings suggest that the expression of pro-inflammatory N1 neutrophils and anti-inflammatory N2 neutrophils may have potential predictive value for the degree of endometrial repair in patients after estrogen therapy.

Conclusions

Our single-cell sequencing study reveals the transcriptomic profiles of IUA patients with different estrogen therapy outcomes and provides a fundamental framework for understanding cellular and molecular mechanisms and transcriptomic patterns of estrogen therapy effectiveness. To our knowledge, our study is the first attempt to use single-cell sequencing technology to study the differences in endometrial recovery after estrogen drug treatment in different IUA patients objectively identifying key cell types and molecules that cause different treatment outcomes due to tissue heterogeneity after oestrogen therapy. We believe that these detailed information can provide a new entry point for studying the mechanism of different treatment outcomes of estrogen therapy in IUA patients, and provide a new theoretical basis for better treatment of the disease. In future studies, more experimental verification is still needed to prove the idea.

Materials and methods

Patients and tissue sample collection

In this study, we selected patients of childbearing age with moderate IUA who were admitted to the hysteroscopic diagnosis and Treatment Center of Ningxia Medical University General Hospital between January 2022 and June 2023. And treated them with the same dose of estrogen therapy after TCRA. Three to six months after TCRA, we monitored the thickness of the endometrium in the mid-luteal phase using color Doppler, monitoring is performed during each menstrual cycle, with measurements taken at least three times. Patients with endometrial thickness of less than 7 mm for three or more consecutive times were defined as estrogen treatment ineffective (ENR), while those with endometrial thickness ≥ 7 mm were considered estrogen treatment effective (ER). We strictly selected 5 patients from each group.And during the luteal phase, we collected endometrial samples from them under hysteroscopy for single-cell transcriptome sequencing analysis.

Tissue dissociation and single-cell suspension preparation

The fresh tissues were stored in the sCelLiveTM Tissue Preservation Solution (Singleron) on ice post-surgery within 30 min. The tissue specimens underwent a triple wash with Hanks Balanced Salt Solution (HBSS) to eliminate any remaining blood components. Subsequently, the tissue was fragmented into small pieces on ice, and then digested with 3 mL sCelLiveTM Tissue Dissociation Solution (Singleron) by Singleron PythoN™ Tissue Dissociation System at 37 °C for 15 min. The cell suspension was collected and filtered through a 40-micron sterile cell strainer. The GEXSCOPE® red blood cell lysis buffer (RCLB, Singleron) was added into the cell suspension, and the mixture[Cell: RCLB = 1:2 (volume ratio)] was incubated at room temperature for 5–8 min to remove residual red blood cells. The mixture was then centrifuged at 300 × g 4 ℃ for 5 min to remove supernatant, and the cells were re-suspended with PBS.

Single cell RNA sequencing

Single-cell suspensions (2 × 105 cells/mL) with PBS were loaded onto microwell chip using the Singleron Matrix® Single Cell Processing System. Barcoding Beads with barcode labels are subsequently collected from the microwell chip, followed by reverse transcription of the mRNA captured by the Barcoding Beads and to obtain cDNA, and PCR amplification. The amplified cDNA is then fragmented and ligated with sequencing adapters. The scRNA-seq libraries were constructed according to the protocol of the GEXSCOPE® Single Cell RNA Library Kits (Singleron) [58]. Individual libraries were diluted to 4 nM, pooled, and sequenced on Illumina novaseq 6000 with 150 bp paired end reads.

Primary analysis of raw read data (scRNA-seq)

Raw reads were processed to generate gene expression profiles using CeleScope v1.5.2(Singleron Biotechnologies) with default parameters. Briefly, Barcodes and UMIs were extracted from R1 reads and corrected. Adapter sequences and poly A tails were trimmed from R2 reads and the trimmed R2 reads were aligned against the GRCh38 (hg38) transcriptome using STAR(v2.6.1a). Uniquely mapped reads were then assigned to exons with FeatureCounts(v2.0.1). Successfully Assigned Reads with the same cell barcode, UMI and gene were grouped together to generate the gene expression matrix for further analysis.

Date quality control, dimension-reduction and clustering

Scanpy v1.8.1 [59] was used for quality control, dimensionality reduction and clustering under Python 3.7. For each sample dataset, we filtered expression matrix by the following criteria: (1) cells with gene count less than 200 or with top 2% gene count were excluded; (2) cells with top 2% UMI count were excluded; (3) cells with mitochondrial content > 20% were excluded; (4) genes expressed in less than 5 cells were excluded. After filtering, 85,837 cells were retained for the downstream analyses, with on average 2052.24 genes and 5491.07 UMIs per cell. The raw count matrix was normalized by total counts per cell and logarithmically transformed into normalized data matrix. Top 2000 variable genes were selected by setting flavor = ‘seurat’. Principle Component Analysis (PCA) was performed on the scaled variable gene matrix, and top 20 principle components were used for clustering and dimensional reduction. Batch effect between samples was removed by Harmonypy v0.0.5 [60] using the top 20 principal components from PCA.Cells were separated into 20 clusters by using Louvain algorithm and setting resolution parameter at 1.2. Cell clusters were visualized by using Uniform Manifold Approximation and Projection (UMAP).

Cell type recognition with Cell-ID

Cell-ID [61] is multivariate approach that extracts gene signatures for each individual cell and perform cell identity recognition using hypergeometric tests(HGT). Dimensionality reduction was performed on normalized gene expression matrix through multiple correspondence analysis, where both cells and genes were projected in the same low dimensional space. Then a gene ranking was calculated for each cell to obtain most featured gene sets of that cell. HGT were performed on these gene sets against endometrium reference from SynEcoSysTM database [62], which contains all cell-type’s featured genes in the specific tissue. Identity of each cell was determined as the cell-type has the minimal HGT p value. For cluster annotation, Frequency of each cell-type was calculated in each cluster, and cell-type with highest frequency was chosen as cluster’s identity.

The cell type identification of each cluster was determined according to the expression of canonical markers from the reference database SynEcoSysTM (Singleron Biotechnology). SynEcoSysTM contains collections of canonical cell type markers for single-cell seq data, from CellMakerDB, PanglaoDB and recently published literatures. The canonical markers and their corresponding cell types were listed in Supplementary Figure S1B. Doublet cells were estimated based on the expression pattern of canonical cell markers. Any clusters enriched with multiple cell type-specific markers were excluded for downstream analysis.

Differentially expressed genes (DEGs) analysis (Seurat)

To identify differentially expressed genes (DEGs), we used the Seurat FindMarkers() function based on Wilcoxon rank sum test with default parameters, and selected the genes expressed in more than 10% of the cells in both of the compared groups of cells and with an average log(Fold Change) value greater than 0.25 as DEGs. Adjusted p value was calculated by Bonferroni Correction and the value 0.05 was used as the criterion to evaluate the statistical significance.

Pathway enrichment analysis

To investigate the potential functions of differential expression genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used with the “clusterProfiler” R package v 4.0.0 [63]. Pathways with p_adj value less than 0.05 were considered as significantly enriched. Selected significant pathways were plotted as bar plots.

Cell-cell interaction analysis

CellChat (version 1.6.1) [64] was used to analyze the intercellular communication networks from scRNA-seq data. A CellChat object was created using the R package process. Cell information was added into the meta slot of the object. The ligand-receptor interaction database was set, and the matching receptor inference calculation was performed.

Cell-cell interaction between different cell types were predicted based on known ligand–receptor pairs by Cellphone DB (v4.0.0) [65] version. Permutation number for calculating the null distribution of average ligand-receptor pair expression in randomized cell identities was set to 1000. Individual ligand or receptor expression was thresholded by a cutoff based on the average log gene expression distribution for all genes across each cell type. Predicted interaction pairs with p value < 0.05 and of average log expression > 0.1 were considered as significant and visualized by heatmap_plot and dot_plot in CellphoneDB.

Pseudotime trajectory analysis: monocle2

Cell differentiation trajectory of monocyte subtypes was reconstructed with the Monocle2 v 2.22.0 [66]. For constructing the trajectory, top 2000 highly variable genes were selected by Seurat(v4.1.0) FindVairableFeatures(), and dimension-reduction was performed by DDRTree(). The trajectory was visualized by plot_cell_trajectory() function in Monocle2.

Transcription factor regulatory network analysis (pySCENIC)

The transcription factor network of macrophage was constructed by pySCENIC (v0.11.0) using scRNA expression matrix and transcription factors in AnimalTFDB. First, GRNBoost2 predicted a regulatory network based on the co-expression of regulators and targets. CisTarget was then applied to exclude indirect targets and to search transcription factor binding motifs. After that, AUCell was used for regulon activity quantification for every cell. Cluster-specific TF regulons were identified according to Regulon Specificity Scores (RSS) and the activity of these TF regulons were visualized in heatmaps.

Data availability

The sequencing data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

Not applicable.

Funding

This work was supported by the National Natural Science Foundation of China (No. 82260300); Central Guiding Local Science and Technology Development Project of Ningxia (No. 2022YDDF0155); Ningxia Science and Technology Innovation Leading Talent Project (No. 2022GKLRLX010).

Author information

Authors and Affiliations

  1. Department of Obstetrics, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, 750004, China

    Yue Du

  2. Department of Obstetrics and Gynecology, Gansu Provincial Hospital, Lanzhou, Gansu, 730000, China

    Ruzhen Shuai

  3. Department of Beijing National Biochip Research Center Sub-Center in Ningxia, Institute of Medical Sciences, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, 750004, China

    Sang Luo, Yiran Jin & Dan Liu

  4. Key Laboratory of Ministry of Education for Fertility Preservation and Maintenance, Ningxia Medical University, Yinchuan, Ningxia, 750004, China

    Sang Luo & Dan Liu

  5. The First School of Clinical Medicine, Ningxia Medical University, Yinchuan, Ningxia, 750004, China

    Fengjuan Xu & Jingyi Zhang

  6. Department of Gynecology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, 750004, China

    Dan Liu

  7. Department of Obstetrics and Gynecology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China

    Limin Feng

Authors
  1. Yue Du
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  2. Ruzhen Shuai
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  3. Sang Luo
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  4. Yiran Jin
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  5. Fengjuan Xu
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  6. Jingyi Zhang
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  7. Dan Liu
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  8. Limin Feng
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Contributions

Conceptualization, Yue Du, Dan Liu, and Limin Feng; formal analysis, Yue Du, Ruzhen Shuai, Fengjuan Xu, and Jingyi Zhang; investigation, Yue Du, Ruzhen Shuai, Fengjuan Xu, and Jingyi Zhang; methodology, Yue Du, Dan Liu, Limin Feng, Ruzhen Shuai, Sang Luo, and Yiran Jin; supervision, Yue Du, Dan Liu, Sang Luo, and Yiran Jin; visualization, Yue Du, Ruzhen Shuai, Sang Luo, and Yiran Jin; writing-original draft, Yue Du, Dan Liu; writing-review & editing, Yue Du, Dan Liu, Ruzhen Shuai, Sang Luo, Yiran Jin.

Corresponding authors

Correspondence to Dan Liu or Limin Feng.

Ethics declarations

Ethics approval and consent to participate

All individuals provided informed consent to participate in this study. And this work has been approved by Medical Research Ethics Committee of General Hospital of Ningxia Medical University(No. KYLL-2023-0368).

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All authors have approved the manuscript for publication.

Competing interests

The authors declare no competing interests.

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Du, Y., Shuai, R., Luo, S. et al. Exploring the molecular mechanism of estrogen therapy effectiveness after TCRA in IUA patients at single-cell level. Biol Direct 19, 142 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13062-024-00583-x

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13062-024-00583-x

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Biology Direct

ISSN: 1745-6150

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