Mapping single-cell transcriptomes of endometrium reveals potential biomarkers in cancer

Mapping single-cell transcriptomes of endometrium reveals potential biomarkers in cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mapping single-cell transcriptomes of endometrium reveals potential biomarkers in cancer Gang Xu, Tao Pan, Si Li, Jing Guo, Ya Zhang, Qi Xu, Renwei Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2645136/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Deconvolution of immune microenvironment that drive transcriptional programs throughout the menstrual cycle is key to understanding regulatory biology of endometrium. Methods We comprehensively analyzed single cell transcriptome of 59,397 cells across ten human endometrium samples. Cell specific expression of genes were revealed and transcription factors that potentially regulated these genes were identified by SCENIC. CellChat was used to analyze the cell-cell communications. The RNA-based molecular subtypes of human endometrial cancers were revealed by nonnegative matrix factorization analysis. Results Single cell transcriptome analyses revealed the dynamic cellular heterogeneity throughout the menstrual cycle. In particular, we identified two perivascular cell subtypes, four epithelial subtypes and four fibroblast cell types in endometrium. Moreover, we inferred the cell type-specific transcription factor (TF) activities and linked critical TFs to transcriptional output of diverse immune cell types, highlighting the importance of transcriptional regulation in endometrium. Dynamic interactions between various types of cells in endometrium contribute to a range of biological pathways regulating differentiation of secretory. Integration of the molecular biomarkers identified in endometrium and bulk transcriptome of 535 endometrial cancers (EC), we revealed five RNA-based molecular subtypes of EC with highly intratumoral heterogeneity and different clinical manifestations. Mechanism analysis uncovered clinically relevant pathways for pathogenesis of EC. Conclusions In summary, dynamic immune microenvironment analyses provide novel insights into future development of RNA-based treatments for endometriosis and endometrial carcinoma. single cell sequencing cell-cell interaction biological pathways molecular subtypes RNA bi-omarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background The endometrium is the layer forming the inner wall of the uterus of mammals. It reacts to both estrogen and progesterone, and therefore changes significantly with the estrus cycle and menstrual cycle. Dysfunctions of endometrium had been associated with various human diseases, including abnormal uterine bleeding, infertility, pre-eclampsia, endometriosis and endometrial carcinoma (EC) [ 1 , 2 ]. Thus, understanding the gene expression regulation during menstrual cycle in humans is crucial for understanding normal functions of endometrium and the mechanism of EC. With the development of high throughput sequencing technologies, single-cell RNA sequencing (scRNA-seq) as a revolutionary technology can uncover novel cell types, explore genetic and functional heterogeneity in various cellular contexts. Recently, scRNA-seq has been used to understanding the potential mechanisms of reproductive diseases. Wang et al. utilized scRNA-seq to analyze the cellular and molecular signatures of decidual and peripheral leukocytes in normal and unexplained recurrent miscarriage [ 3 ]. Liu et al. performed a scRNA-seq analysis of adenomyosis and supported the theory of adenomyosis derived from the invasion and migration of the endometrium [ 4 ]. Ma et al. performed single-cell analysis and identified nine cell types, and determined a potential developmental trajectory associated with endometriosis [ 5 ]. One recent study mapped the temporal and spatial dynamics of the human endometrium in vivo and in vitro [ 6 ]. Guo et al. presented a view of endometrial carcinoma at single-cell resolution and revealed the characteristics of endometrial epithelial cells in the endometrium [ 7 ]. However, the dynamic immune microenvironment in human endometrium is still unclear. Moreover, EC is the most commonly diagnosed gynecologic malignancy [ 8 ]. Traditional classification of EC is primary based either on clinical and endocrine features [ 8 , 9 ]. Histological subtyping is commonly used in clinical to guide prognosis and treatment decisions for EC patients, while ongoing researches are evaluating the potential molecular subtyping. An integrated genomic analysis had resulted in the molecular classification of endometrioid and serous carcinomas into four distinct subgroups [ 10 ]. The subtypes identified by the different classification systems correlate to some extent; however, the potential molecular pathways of different subtypes of EC are still unknown. Therefore, we interrogate the immune microenvironment of human endometrial cells during the proliferative and secretory phases of women menstrual cycle. We identified the diverse cell types in human endometrial and characterization of the RNA expression patterns in different cell types. In particular, we explored the transcriptional regulation and revealed the transcription factors (TFs) that exhibited high activities in cell types. Cell-cell communications were examined and further uncovered five RNA-based molecular subtypes of EC. Methods Transcriptome of human endometrium and endometrial cancers Single-cell RNA-sequencing was used to create a cell census of the human endometrium. We downloaded the raw sequencing data from ArrayExpress under the accession number E-MTAB-10287 [ 6 ]. In total, 11 samples obtained from five patients were sequenced, including four menstrual cycle stages (proliferative stage, early-secretory stage, mid-secretory stage and late-secretory stage). We selected 10 samples obtained from endometrium for further analysis. Gene expression profiles and clinical information of human endometrial cancers were obtained from The Cancer Genome Atlas (TCGA) project ( https://portal.gdc.cancer.gov/ ). The expressions of genes were measured by Fragments Per Kilobase of exon model per Million mapped fragments (FPKM). In total, 535 human endometrial cancers were included in our analysis. Processing of single cell sequencing data The 10x Genomics scRNA-seq data were first analyzed using Cellranger6.1.1 with the raw fastq files as input. The GRCh38 genome was used as the reference genome and default parameters were used. For each sample, the feature-barcode matrix was then converted into a Seurat object using the Seurat R package [ 11 ]. To enrich for high quality cells in each sample, we performed quality control (QC) for each sample dataset individually. First, we filtered cells that expressed less than 200 genes. For A13 and A30 patients, we excluded the cells with > 10% mitochondrial reads. For E1, E2 and E3, cells with > 20% mitochondrial reads were excluded. In addition, we filtered cells with hemoglobin protein-related reads > 5%. Next, we used the ‘isOutlier’ function in scater package to detect the outliers. The outlier cells were defined in each of the following metrics: log(UMI counts) (> 2 MADs, both), log(number of genes expressed) (> 2 MADs, both) and log(percent mitochondrial read count) (> 2 MADs, high end). We used the ‘CellCycleScoring’ in Seurat to calculate the cell cycle score for each cell and regression with the ‘vars.to.regress’. Cell cycle genes (G2/M and S) of were obtained from Seurat package. To reduce the false positive rate in doublet calling, only cells marked as doublets by both scDblFinder and doubletFinder [ 12 ] were removed from our analysis. Data normalization, feature selection and clustering The quality control was performed for single sample and the ‘CCA’ function in Seurat was used to integrate all samples. The read count matrices were normalized using ‘NormalizedData’ with ‘LogNormalize’ as the normalization method. Feature selection was performed by ‘FindVariableFeatures’ using the ‘vst’ method and the top 2,000 variable genes were identified. The top 2,000 most variable genes were summarized by principal component analysis (PCA). To identify groups of distinct cells, graph-based Lovain clustering was performed based on top 20 PCs. The ‘FindClusters’ function with a resolution of 0.2 was used to identify the cell clusters and UMAP plots were generated in R for visualization. Cell type annotation We performed cell types annotation based on two methods. One was gene signature enrichment and another one was reference-based annotation with the SingleR package [ 13 ]. First, the cell types were annotated based on signatures from ESTIMATE [ 14 ] and PangladoDB [ 15 ]. The ‘AddModuleScore’ was used to calculate the signature scores. The median scores of each cell clusters were calculated and if the median > 0.1, we considered the clusters as corresponding cell types. The primary annotations were performed by SingleR using the ‘HumanPrimaryCellAtlasData’ as reference dataset. The marker genes of cell types were obtained from literature or CellMarker database [ 16 ]. Identification of differentially expressed genes and functional annotations Differential gene expression analysis was performed by ‘FindAllMarkers’ in Seurat with the min.pct set to 0.25 and Wilcoxon’s rank sum test. Genes with p 0.25 were considered as up-regulated. For functional enrichment analysis, we selected top 50 highly expressed genes in each main cell type. For the cell subtypes, we used all differentially expressed genes. The functional enrichment analysis was performed by clusterProfiler [ 17 ] and heat maps were generated by ComplexHeatmap [ 18 ]. Gene sets functional scores To calculate the gene set functional scores, we used AUCell to perform this analysis [ 19 ]. The functional gene sets were obtained from the Molecular Signatures Database (MSigDB) hallmark gene set collection [ 20 ]. Wilcoxon’s rank sum test was used to compare the functional scores in different cell subtypes. P-values were adjusted by false discovery rate (FDR). The gene sets with log2FoldChange > 0 and p.adjust < 0.05 were considered as enriched in corresponding cell subtypes. Co-expression analysis of genes To investigate the expression correlation among differentially expressed genes in each cell type, we calculated the Spearman correlation coefficient (SCC) among top 50 differentially expressed genes based on the expression across cell types. The SCC matrix was visualized by heat map. Transcription factor activities analyses We used pySCENIC to identify the TF regulators in each cell types identified in human endometrium [ 19 , 21 ]. We first downloaded the motifs that allow using RcisTarget (mc9nr) from cisTarget database [ 22 ]. The input UMI count matrix was normalized (CPM) and log-transformed. Only genes in RcisTarget were included in further analysis. GENIE3 was performed to identify TF-modules in each cell type [ 23 ]. TF-modules having less than 10 genes were filtered out. The top 1 percentile of the number of detected genes per cell was used to calculate the AUCell enrichment of each TF regulon in each cell. ComplexHeatmap was used to generate the heat map of the activity matrix. Cell-cell interactions Cell-cell communication analysis was performed using CellChat (Version 1.1.0), based on the known ligand-receptor pairs in CellChatDB [ 24 ]. Briefly, the normalized genes expression matrix and cell type labels generated by Seurat were subjected as input for CellChat. For the main analyses the core functions ‘computeCommunProb’, ‘computeCommunProbPathway’ and ‘mergeCellChat’ were applied using default parameters. The ‘computeNetSimilarityPairwise’ function was used to calculate the similarity between pathways and pathways were clustered into different groups based on functional similarities. RNA-based molecular subtypes of human endometrial cancers To identify the molecular subtypes of human endometrial cancer, we performed clustering based on the gene expression. First, the ligand-receptor pairs prioritized in single cell data analysis were used in the clustering. Nonnegative matrix factorization (NMF) was performed based on R package [ 25 ]. The parameters ‘rank = 2:6, method = brunet’ were used in this analysis. Five molecular subtypes were identified based on the cophenetic curve. The survival analysis was performed by R packages (survival and survminer). Log-rank test was used to evaluate the difference of survival rates among subtypes. Results A single cell map of human endometrium To analyze the scRNA-seq cells from human endometrium through four menstrual cycle stages, we performed principal-component analysis (PCA) using the top 2,000 most variably expressed genes across 59,397 cells. Cells were clustered into transcriptionally distinct clusters with top 20 principal components (PCs). The cells were visualized using UMAP plot and revealed eight clusters that could be annotated to known cell types (Fig. 1 A). Moreover, we found that cells were clustered together based on cell types but not based on patient identify (Fig. 1 B). We then used well-known marker genes to define the identity of each cell cluster. For example, epithelial cells expressed KRT8 and PAEP, endothelial cells expressed VWF, fibroblast cells expressed APOD, DCN and COL3A1, perivascular (PV) cells expressed RGS5, smooth muscle cells expressed ACTG2 and MYH11, multi-potent stromal cells (MSC) expressed TOP2A and UBE2C, lymphoid and myeloid cells expressed CD74, NKG7 and GNLY (Fig. 1 A, E and Additional file 1: Fig. S1 ). Next, we calculated the proportion of cells during four menstrual cycle stages and different patients. We found that the proportions of cell types among stages and patients were significantly different (Fig. 1 C-D, p-values < 2.2E-16). PV cells were predominated in early-secretory stage and in A30 patient. Fibroblast cells decreased in early-secretory stage and immune cells were enriched in proliferative stage and late-secretory stage (Fig. 1 C). MSC decreased during the menstrual cycle stages. Moreover, we identified the highly expressed marker genes in each cell types. We found that C1QA, C1QB and C1QC were highly expressed in myeloid cells (Fig. 1 E), indicating their phagocytic ability [ 26 ]. Together, our comprehensive analysis provided a comprehensive catalog of the major cell types together with their cellular position in endometrium. Systematic discovery of cell type-specific RNAs in human endometrium We next explored the cell type-specific RNAs that could help explain distinct biological states of these cell types. Functional enrichment analysis revealed that genes highly expressed in fibroblast cells were significantly enriched in wound healing, regulation of vasculature development and regulation of angiogenesis (Fig. 2 A). Genes highly expressed in smooth muscle cells were enriched in wound healing, extracellular matrix organization and extracellular structure organization, whereas PV cell-specific genes were enriched in response to corticosteroid, steroid hormone and glucocorticoid (Fig. 2 A). Epithelial cell-specific genes were significantly enriched in epithelial cell proliferation and tissue migration, and endothelial cell-specific genes were enriched in regulation of vasculature development and angiogenesis (Fig. 2 A). MSCs are a population of self-renewing multipotent cells in the perivascular regions of the endometrium in both the basalis and functionalis [ 27 , 28 ]. We found that genes highly expressed in MSCs were enriched in sister chromatid segregation and nuclear division (Fig. 2 A). A large proportion of MSCs were in G2M stage of cell cycle (Fig. 2 B). In particular, we found that two proliferative marker genes, MKI67 and TOP2A, were highly expressed in MSC cells (Fig. 2 C-D). These results suggested that enriched functional analysis of each cluster supported their functions in human endometrium. Two newly discovered subtypes of perivascular cells We analyzed the PV population (n = 8,120) based on the known markers and re-clustered it into two distinct populations as indicated in the UMAP (Fig. 3 A and Additional file 1: Fig. S2A ). We analyzed the relative proportion of cells in each cluster and noted that two clusters exhibited similar proportion during menstrual cycle stages and patients (Fig. 3 B, p = 1.862E-8 and Additional file 1: Fig. S2B ). These observations were consistent with the results PV-MYH11 + are characteristic of myometrium while PV-STEAP4 + are only present in the endometrium [ 6 ]. We next analyzed the gene expression profiles and identified the top differentially expressed genes in two PV populations (Fig. 3 C). In particular, STEAP4 and MYH11 were separately expressed in two PV populations (Fig. 3 D-E). We found that several collagen-related genes (e.g., COL3A1, COL1A2, COL4A1 and COL1A1) were highly expressed in PV-STEAP4 populations (Fig. 3 C), which might be correlated with their roles in repair of endometrial damage and induced angiogenesis [ 29 ]. Moreover, IGFBP5 was highly expressed in PV-STEAP4 + subtypes, which is consistent with its roles in promoting angiogenic and neurogenic differentiation [ 30 , 31 ]. In contrast, the PV-MYH11 + populations were characteristic of myometrium and highly expressed MUSTN1 and MYH11. To further determine the specific roles of two PV populations that might contribute to human endometrium, we performed functional enrichment analysis based on the differentially expressed genes. We found that genes highly expressed in two PV populations were both significantly enriched in wound healing, response to oxidative stress and extracellular matrix organization (Additional file 1: Fig. S2C ). However, the proportions of genes in PV-STEAP4 + were much higher. In particular, genes highly expressed in PV-STEAP4 + were significantly enriched in extracellular matrix organization and structure organization, while genes in PV-MYH11 + were significantly enriched in muscle development related functions (Fig. 3 F). Cancer hallmark-related pathways also exhibited distinct activities in two PV populations. PV-STEAP4 + cells exhibited higher activities in immune and metabolism related functions and PV-MYH11 + cells exhibited higher activities in signaling and proliferative pathways, such as PI3K-AKT-mTOR (Fig. 3 G). It has been demonstrated that glycolysis is beneficial to angiogenesis and plays an important role in endometrial decidualization [ 32 – 34 ]. In addition, PI3K-AKT-mTOR pathway plays an important role in the decidualization of endometrium. Subpopulations of epithelial cells across human menstrual cycle The epithelial cell cluster (n = 5,758) was then assigned to four epithelial subtypes based on known markers obtained from the published literatures [ 35 , 36 ]. Three secretory glandular cells (secretory LGALS1+/PAEP+/MT+) and ciliated cells were identified (Fig. 4 A). We calculated the proportion of four subpopulations across menstrual cycle and found that ciliated epithelial cells were enriched in proliferative phase, secretory LGALS1 + cells were enriched in early and mid-secretory phases (Fig. 4 B, p < 2.2E-16). Examination of gene expression patterns of four epithelial subpopulations we revealed that ciliated cells expressed high levels of markers, such as the ciliated marker TPPP3, PIFO and FAM183A (Fig. 4 C- 4 D). The gene set enrichment analyses showed that genes up-regulated in ciliated cells were mainly enriched for cilium organization, cilium assembly, cilium movement functions (Fig. 4 E and Additional file 1: Fig. S3 ). This result is similar with one previous study [ 7 ]. The secretory MT + cell populations highly expressed genes in metallothionein (MT) family, such as MT2A, MT1G, MT1E, MT1X and MT1H (Fig. 4 E), which might contribute to impaired endometrial receptivity [ 37 ]. Moreover, higher proportion of secretory MT + cells was observed in late-secretory phase (Fig. 4 B). This is consistent with previous observations that MT genes were highly expressed in late-secretory phase [ 38 ]. The secretory PAEP + cell populations were enriched during the menstrual cycle and highly expressed CLDN3, CLDN4 and CXCL family genes (Fig. 4 E). Functional analysis revealed that genes up-regulated in this cell population were enriched in regulation of cell-cell adhesion, cell migration and reproductive development (Additional file 1: Fig. S3 ). We next performed functional analysis based on cancer hallmark-related pathways. Examination of the pathway activities we found that secretory populations exhibited higher pathway activities in numerous pathways (Fig. 4 F). The ciliate epithelial cells exhibited higher pathway activities in bile acid metabolism and mitotic spindle. The Wnt signaling pathway, DNA repair and G2M checkpoint pathways were enriched in secretory LGALS1 + cell populations (Fig. 4 F). In particular, epithelial mesenchymal transition was enriched in secretory LGALS1+, which was consistent with their roles in regeneration or differentiation of endometrium [ 27 ]. The inflammation-related pathways were significantly enriched in secretory PAEP + cell populations, such as IL6-JAK-STAT3 and IL2-STAT5 signaling pathways (Fig. 4 F). These results extended the transcriptional signature and potential pathways underlying the human endometrial epithelial cells. Fibroblast cells separate into four distinct cell types Fibroblasts (n = 21,865) were further separated into four clusters and annotated based on the highest expressing genes (Fig. 5 A). SPARACL1, ID4, MMP11 and EGR1 were highly expressed in corresponding clusters. We found that fibroblast ID4 + and SPARCL1 + cells were primarily observed in early- and late-secretory phases (Fig. 5 B). Fibroblast MMP11 + cells were primarily in the proliferative phase and EGR1 + cells were in mid-secretory phase (Fig. 5 B, p < 2.2E-16). The proportions were variable in different patients (Additional file 1: Fig. S4A ), suggesting the high heterogeneity among patients. Gene expression analysis revealed fibroblast SPARCL1 + highly expressed fibroblast-related genes, such as DCN, APOD, COL1A2 and COL15A1 (Fig. 5 C and Additional file 1: Fig. S4B ). In particular, four clusters highly expressed corresponding marker genes (Fig. 5 D). We next performed the functional enrichment analysis and found that genes up-regulated in fibroblast SPARCL1 + were significantly enriched in extracellular structure organization and negative regulation of locomotion (Fig. 5 E). Genes highly expressed in fibroblast ID4 + were enriched in epithelial cell proliferation, positive regulation of cytokine production and regulation of angiogenesis (Fig. 5 E). Numerous of transcription factors (TFs) were highly expressed in fibroblast EGR1 + sub-populations, such as JUNB, EGR1, FOS and JUN. We also observed high expression of GADD45B in this population, which plays important roles in DNA repair [ 39 ]. We also investigated the cancer hallmark pathway activities in different fibroblast populations and found that Wnt, MYC and peroxisome exhibited higher activities in ID4 + cell populations (Additional file 1: Fig. S4C ). Notch and PI3K-AKT signaling pathways were active in SPARCL1 + cell populations (Additional file 1: Fig. S4C ). Together, these results uncovered the potential pathways underlying different fibroblast populations in human endometrium. TF regulators of cell types in human endometrium It has been demonstrated that genes usually interact with each other to form a complex interaction network [ 40 ]. We thus analyzed the correlation between gene expressions and we found that genes highly expressed in cell types were co-expressed with each other (Fig. 6 A), indicating the modular programs associated with basic cellular functions of cells. TFs are important regulators and play important roles in regulating gene expression [ 41 ]. In addition, we calculated the module activities as the average expression of genes. We found that the expressions of several TFs (such as IRF8, SOX17, FOS and KLF2) were significantly correlated with the module activities (Additional file 1: Fig. S5 ). We thus identified the cell type-specific TFs based on the pySCENIC pipeline. In total, we identified 336 TFs exhibited high activities in different cell types and 62 TFs also exhibited differential expression (Fig. 6 B). We found that TFs exhibited high cell type- and phase-specificity. For example, SOX4 exhibited higher activity in proliferative phase (Fig. 6 B). SOX4 is closely associated with the development and progression of many malignant tumors and plays an important role in the cell growth and proliferation [ 42 ]. We also observed that the expressions of SOX4 in proliferative phase were significantly higher than secretory phases. In addition, MAF, KLF4, JUN, FOS and EGR1 exhibited higher activities in secretory phases (Fig. 6 C). Moreover, we found that several TFs exhibited cell type-specific activities in human endometrium. CD59 exhibited higher activities in endothelial cells, which is highly expressed in endothelial cells of human endometrium (Additional file 1: Fig. S6 ). It has been demonstrated that CD59 may be important in protection of endothelial cells against C-mediated damage at local sites of inflammation, thereby maintaining the vascular integrity [ 43 ]. E2F1 and EZH2 exhibited higher activities in MSC cell populations (Fig. 6 B). E2F1 plays a pivotal role in driving cells out of a quiescent state and into the S phase of the cell cycle [ 44 ] and the activity of EZH2 influences cell fate regulation [ 45 ]. Functional analysis of the targets of EZH2 revealed that they were significantly enriched in DNA replication and cell cycle, which is consistent with the proliferative state of MSC cells. EGR1 exhibited higher activities in fibroblast cells (Fig. 6 B), which had been demonstrated to contribute to inflammatory factors and fibrosis reduction [ 46 ]. The potential targets of EGR1 were significantly enriched in Wnt signaling pathway and cell growth (Fig. 6 D). In addition, FOS exhibited higher activities in fibroblast and smooth muscle cell populations (Fig. 6 B) and its targets were significantly enriched in muscle tissue development, Wnt signaling and cell differentiation (Fig. 6 D). It has been demonstrated that FOS plays an important role in control of fibroblast senescence and activation of programmed cell death [ 47 ]. Several TFs also exhibited higher activities in immune cells, such as STAT4, BATF, RUNX3 and EOMES (Fig. 6 B). These results suggest that the dynamic transcriptome during menstrual cycle were strictly regulated by TFs that were with dynamic activities. Cell-cell interactions in human endometrium The development of tissue and progression of complex diseases relies on a complex network of cell-cell interactions [ 48 ]. We thus inferred intercellular communications for human endometrium based on CellChat [ 24 ]. As a result, we found that the myeloid cells frequently interact with MSC and epithelial cells, while the fibroblast cells frequently interact with others (Fig. 7 A). Immune cells mainly function as signal input cells. As the signal output cells, epithelial and endothelial cells mainly interact with immune cells as targets (Fig. 7 A). Next, the 15 signaling pathways associated with inferred networks were mapped onto a two-dimensional manifold and clustered into four groups (Fig. 7 B-C). MIF, IGF, PTN and MK pathways were grouped into cluster 1, while group 4 was formed by pathways of VEGF, CXCL and VISFATIN (Fig. 7 C). We specifically examined how macrophage migration inhibitory factor (mif) communications among cell populations (Fig. 7 D). All cells function as signal output cells, although the intensity of interaction was different. When MSC and epithelial cells acted as signal input cells, the output intensity of cells was low. In this pathway, immune cells were mainly used as signal input cells, and the strength of intercellular interaction was high (Fig. 7 D). Macrophage migration inhibitory factor had been identified as a potential biomarker of endometriosis [ 49 , 50 ]. We also examined ligand receptor pairs that play a major role in this pathway and identified two ligand-receptor pairs MIF−(CD74 + CD44), and MIF−(CD74 + CXCR4) (Fig. 7 E). MIF exhibited high expression in all cell types while CXCR4, CD74 and CD44 exhibited higher expression in immune cells (Fig. 7 F). We also found that the SPP1 signaling pathway only took myeloid cells as signal output cells, and exported signals to fibroblasts, PV cells, SMC cells and lymphocytes in paracrine mode, and played a role in autocrine mode to a large extent (Fig. 7 G). The SPP1-(ITGA4 + ITGB1) and SPP1-CD44 plays important roles in cell-cell communications (Fig. 7 H). SPP1 and CD44 exhibited higher expression in immune cells while ITGB1 was highly expressed in all cell types (Fig. 7 I) Next, we compared the information flow for each signaling pathway. In signal input, several pathways only functions in one cell type, such as EGF and EDN in fibroblasts, VEGF and CXCL in endothelial cells, and ncWNT in PV cells (Fig. 7 J). MIF signaling pathway only participates in the signal input of immune cells and most of these pathways are involved in signal input patterns of myeloid cells, fibroblasts, and endothelial cells (Fig. 7 J). In the signal output part, we found that IL6, EGF, SPP1 and CCL only played a role in the signal output of immune cells. EDN only plays a role in signal output of endothelial cells. As with signal input patterns, most of these pathways were involved in signal output by fibroblasts and myeloid cells (Fig. 7 K). Together, cell-cell communications analysis enables multifaceted assessment of intercellular communication patterns in human endometrium. RNA-based molecular subtypes of human endometrial cancers Endometrial cancer (EC) is the most common gynecologic malignancy. We next explored the expression patterns of the ligand-receptor in EC. We found that the patients can be grouped into five clusters based on the ligand-receptor of the 15 pathways identified in cell-cell communication (Fig. 8 A). The survival rates for patients in five clusters were with significantly different and patients in cluster-5 were with poor survival (Fig. 8 B, log-rank test p = 0.00025). We found that patients in cluster-5 were with distinct expression of 17 genes, which were involved multiple cytokine-related pathways, including MIF, CXCL, SPP1 and VEGF. We next calculated the pathway activities for these pathways and found that the majority of pathway activities in cluster-5 were significantly higher than other clusters. For example, epidermal growth factor (EGF) was a conventional mitogenic factor that can stimulate the proliferation of various types of cells including epithelial cells and fibroblasts [ 51 ]. The activities of EGF in cluster-5 were significantly higher than that in cluster 1, 3, and 4 (Fig. 8 C). The EGFR family also plays an important role in maintaining epithelial homeostasis [ 52 ] and overexpressed in EC and ovarian cancers [ 53 , 54 ]. Compared with other clusters, we found that ERBB2 were highly expressed in patients of cluster-5 (Fig. 8 D). In addition, as a major ligand of IGF, IGF2 overexpression has been shown to play a role in many cancers [ 55 ]. However, there seem to be few reports about its role in endometrial cancer. We found that the activities of IGF pathway and expressions of IGF2 in patients of cluster-5 were significantly higher than cluster-2 and cluster-4 (Fig. 8 E-F). In contrast, there were several pathways (i.e., CXCL) had lower pathway activities in cluster-5 and the expressions of CXCR4 were also lower in patients of cluster-5. These results suggested that the genes identified here can be used to predict the prognostic survival of patients with endometrial cancer. Discussion In this study, our single transcriptome analysis yields a comprehensive catalog of the major subsets of uterine cells in endometrium. We comprehensively identified the diverse cell types in human endometrial and found that there were a higher proportion of fibroblast, PV and epithelial cells. We further characterized the gene expression patterns in different cell types and identified the cell type specific genes. In particular, we explored the transcriptional regulation and revealed the TFs that exhibited high activities in specific cell types. In addition, cell-cell communications were examined and revealed the critical ligand-receptor pairs. The expression patterns of identified ligand-receptor further uncovered five molecular subtypes of EC. All these results increased our understanding of the dynamic microenvironment of uterine cells in endometrium, as well as potential biomarkers for EC. In particular, we identified two perivascular cell subtypes, four epithelial subtypes and four fibroblast cell types in endometrium. We showed that the ciliated epithelium is a typical endometrial cell type, which is consistent with the results of one recent study [ 56 ]. Although the existence of ciliated cells in human endometrium was observed long times ago [ 57 ], the gene expression patterns and marker genes were not identified previously. We identified numerous of genes that showed higher expression in ciliated cells, such as TPPP3, CAPS, AGR3 and PIFO. AGR3 is a specialized member of the protein disulfide isomerase (PDI) family, and it has been found to play an unexpected role in the regulation of ciliary beat frequency and mucociliary clearance in the airway [ 58 ]. In total, we identified 4,038 immune cells, including monocyte, NK1, NK2 and T cells (Additional file 1: Fig. S7A ). Immune cells were mainly enriched in proliferation and late secretory stage, NK2 cells were mainly enriched in late secretory stage, and T cells were mainly distributed in proliferative stage (Additional file 1: Fig. S7B ). Monocyte cells highly expressed CD74 and LYZ, T cells highly expressed CD3D and IL7R, and NK cells highly expressed NKG7 and GNLY (Additional file 1: Fig. S7C ). In addition, the high expression of antigen presentation and phagocytic genes were observed in monocyte, such as HLA − DRA, HLA − DRB1, HLA − DPA1, C1QA, C1QB, and C1QC, indicating that they play an important role in the clearance of apoptotic cells after endometrial shedding (Additional file 1: Fig. S7D ). Functional enrichment analysis suggested that genes highly expressed in monocyte were significantly enriched in antigen processing and presentation, cell-cell adhesion and leukocyte migration (Additional file 1: Fig. S7E ). Moreover, genes highly expressed in T cells were enriched in T cell differentiation, T cell activation and leukocyte cell − cell adhesion (Additional file 1: Fig. S7E ). These results suggest that immune cells play an important role in the immune response, clearance of apoptotic cells and endometrial tissue remodeling after endometrium shedding. In addition, we found that genes highly expressed in cell types showed expression perturbations in cancer. For example, APOD, DCN, LUM and CFD were significantly highly expressed in fibroblast cells (Additional file 1: Fig. S8A ). DCN has been demonstrated to play tumor suppressive functions in cancer [ 59 , 60 ] and we found that it was lowly expressed in EC. CAV1 and VWF were highly expressed in endothelial cells but showed lower expression in EC (Additional file 1: Fig. S8B ). Epithelial-related genes, such as SCGB2A1 and CLDN4, were highly expressed in cancer (Additional file 1: Fig. S8C ). Moreover, we found that CCL3, C1QA, GZMB and IL32 were highly expressed in cancer (Additional file 1: Fig. S8D-E ). SCGB2A1 was found to be perturbed in various cancer types, such as breast and ovarian cancers [ 61 , 62 ]. We also found the expressions of several genes were associated with survival of cancer patients (Additional file 1: Fig. S8F ). These results highlight the critical roles of these genes in endometrial cancer. Conclusions In summary, deconvolution of immune microenvironment that drives transcriptional programs throughout the menstrual cycle is important to understanding RNA regulatory biology of endometrium. Our dynamic immune microenvironment analyses provide novel insights into future development of RNA-based treatments for endometriosis and endometrial carcinoma. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All datasets used in our study are from previously published studies. All the data generated in this study can be downloaded from https://figshare.com/projects/scRNAseq-Endometrium/147556. All the scripts can be downloaded from the following link: https://figshare.com/projects/scRNAseq-Endometrium/147556. Competing interests The authors declare that they have no competing interests. Funding This research was funded by Hainan Province Science and Technology Special Fund [ZDYF2021SHFZ051], Hainan Provincial Natural Science Foundation of China [820MS053, 822MS175, LCYX201202], the Major Science and Technology Program of Hainan Province [ZDKJ202003], Marshal Initiative Funding of Hainan Medical University [JBGS202103], National Natural Science Foundation of China [31871338, 31970646, 61873075, 32060152, 32070673, 32170676, 82072880], HMU Marshal Initiative Funding [HMUMIF-21024], project supported by Hainan Province Clinical Medical Center [QWYH202175], the National Key R&D Program of China [2018YFC2000100], Natural Science Foundation for Distinguished Young Scholars of Heilongjiang Province [JQ2019C004], Bioinformatics for Major Diseases Science Innovation Group of Hainan Medical University and Heilongjiang Touyan Innovation Team Program and Innovation Research Fund for Graduate Students [Qhys2021-348]. Author Contributions Conceptualization, Y.L. and Y.M.; methodology, G.X., T.P. and S.L.; formal analysis, G.X., T.P. and S.L.; investigation, Q.X. and Y.Z.; data curation, G.X., T.P. and S.L.; writing—original draft preparation, G.X.; writing—review and editing, Y.L., R.C. and Y.M.; visualization, G.X.; supervision, Y.L. and Y.M.; project administration, Y.L., R.C. and Y.M.; funding acquisition, Y.L., R.C. and Y.M. All authors have read and agreed to the published version of the manuscript. Acknowledgements Not applicable References Garrido-Gomez T, Dominguez F, Quinonero A, Diaz-Gimeno P, Kapidzic M, Gormley M, Ona K, Padilla-Iserte P, McMaster M, Genbacev O et al : Defective decidualization during and after severe preeclampsia reveals a possible maternal contribution to the etiology. Proc Natl Acad Sci U S A 2017, 114(40):E8468-E8477. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2645136","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":180417930,"identity":"09cbcb99-1639-49ef-88e4-213a75a8047b","order_by":0,"name":"Gang Xu","email":"","orcid":"","institution":"The First Affiliated Hospital of Hainan Medical University, Hainan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Xu","suffix":""},{"id":180417933,"identity":"39915abf-d556-4d04-b19d-60b36cd27106","order_by":1,"name":"Tao Pan","email":"","orcid":"","institution":"The First Affiliated 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01:29:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2645136/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2645136/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":33965544,"identity":"cd8dd352-da77-4d23-8f31-67e13a0c4621","added_by":"auto","created_at":"2023-03-08 15:02:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1343509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiversity of cell types in human endometrium delineated by single-cell transcriptomic analysis.\u003c/strong\u003e \u003cstrong\u003e(A-B)\u003c/strong\u003e UMAP projection of cells from 10 human endometrium samples. \u003cstrong\u003e(A)\u003c/strong\u003e colored by cell types and \u003cstrong\u003e(B)\u003c/strong\u003e colored by patients. \u003cstrong\u003e(C)\u003c/strong\u003e The cell fractions of different cell types originating from different phases of menstrual cycle. \u003cstrong\u003e(D)\u003c/strong\u003e The cell fractions of different cell types originating from different patients. \u003cstrong\u003e(E)\u003c/strong\u003e Bubble plots showing the expression levels of genes highly expressed in corresponding cell types.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2645136/v1/827c995cdd498b106efc7314.jpg"},{"id":33966521,"identity":"6dd35f44-7de6-455b-9a85-fa5942b7bcc6","added_by":"auto","created_at":"2023-03-08 15:10:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1762324,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression and functions of genes highly expressed in diverse cell types.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Heat map showing the expression levels of genes highly expressed in corresponding cell types. Enriched functions were indicated on the left. \u003cstrong\u003e(B) \u003c/strong\u003eUMAP plot, color-coded for cell cycle phases. \u003cstrong\u003e(C-D)\u003c/strong\u003e UMAP plot, color-coded for relative expression (lowest expression to highest expression, white to red) of marker genes.\u003cstrong\u003e (C)\u003c/strong\u003e for MKI67 and \u003cstrong\u003e(D)\u003c/strong\u003e for TOP2A.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2645136/v1/79abbccb6e7b57c5608826f4.jpg"},{"id":33965547,"identity":"044c6182-50d4-4a6e-a9d2-4859537514c1","added_by":"auto","created_at":"2023-03-08 15:02:31","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1856792,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePV cell clusters in human endometrium.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e UMAP plot of PV cells color-coded according to the assigned cell subtypes. \u003cstrong\u003e(B)\u003c/strong\u003e The cell fractions of different PV cell types originating from different phases of menstrual cycle. \u003cstrong\u003e(C)\u003c/strong\u003eBubble plots showing the expression levels of genes highly expressed in corresponding PV subtypes. \u003cstrong\u003e(D-E)\u003c/strong\u003e UMAP plot, color-coded for relative expression (lowest expression to highest expression, white to red) of marker genes.\u003cstrong\u003e (D)\u003c/strong\u003efor STEAP4 and \u003cstrong\u003e(E)\u003c/strong\u003e for MYH11. \u003cstrong\u003e(F)\u003c/strong\u003e Heat map showing the expression levels of genes highly expressed in corresponding PV cell subtypes. Enriched functions were indicated on the left. \u003cstrong\u003e(G)\u003c/strong\u003e Lollipop plot showing the differences of pathway activities between two PV subtypes.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2645136/v1/78928b15a67a594b28850ddf.jpg"},{"id":33966522,"identity":"79b13b92-0488-4349-9e13-df2f4e543b6c","added_by":"auto","created_at":"2023-03-08 15:10:31","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2474662,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEpithelial cell clusters in human endometrium. (A)\u003c/strong\u003eUMAP plot of epithelial cells color-coded according to the assigned cell subtypes.\u003cstrong\u003e (B)\u003c/strong\u003e The cell fractions of different epithelial cell types originating from different phases of menstrual cycle. \u003cstrong\u003e(C)\u003c/strong\u003e Bubble plots showing the expression levels of genes highly expressed in corresponding epithelial subtypes. \u003cstrong\u003e(D)\u003c/strong\u003e UMAP plot, color-coded for relative expression (lowest expression to highest expression, white to red) of marker genes. \u003cstrong\u003e(E)\u003c/strong\u003e Heat map showing the expression levels of genes highly expressed in corresponding epithelial cell subtypes. Enriched functions were indicated on the left. \u003cstrong\u003e(F)\u003c/strong\u003e Heat map showing the differences of pathway activities among epithelial subtypes.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2645136/v1/0ec9ead7455209475544d52b.jpg"},{"id":33966523,"identity":"c9c6c318-a527-49bb-994a-562e4820cac3","added_by":"auto","created_at":"2023-03-08 15:10:31","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1220913,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFibroblast cell clusters in human endometrium.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e UMAP plot of fibroblast cells color-coded according to the assigned cell subtypes. \u003cstrong\u003e(B)\u003c/strong\u003e The cell fractions of different fibroblast cell types originating from different phases of menstrual cycle. \u003cstrong\u003e(C)\u003c/strong\u003e Bubble plots showing the expression levels of genes highly expressed in corresponding fibroblast subtypes. \u003cstrong\u003e(D)\u003c/strong\u003e UMAP plot, color-coded for relative expression (lowest expression to highest expression, white to red) of marker genes. \u003cstrong\u003e(E)\u003c/strong\u003e Heat map showing the differences of pathway activities among fibroblast subtypes.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2645136/v1/eb86bbabdbc524a83abfd54d.jpg"},{"id":33965551,"identity":"00ae59a6-7fe8-41dc-973f-731f986fcbf6","added_by":"auto","created_at":"2023-03-08 15:02:31","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1655846,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTF regulators of cell types.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Co-expression of genes differentially expressed in cell type. \u003cstrong\u003e(B)\u003c/strong\u003e Heat maps showing the TF activities in each cell types. Each row represents a TF and column represents a cell. Numbers in the brackets were the potential target genes of TFs. \u003cstrong\u003e(C)\u003c/strong\u003e Violin plots showing the expression of TFs during menstrual cycle. \u003cstrong\u003e(D)\u003c/strong\u003e Bar plots showing the enriched functions of TF target genes.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2645136/v1/5699a5d3414cd8025d5c8991.jpg"},{"id":33966525,"identity":"a92a8aeb-c3dd-4ac5-ba03-ec2f04fd7ff9","added_by":"auto","created_at":"2023-03-08 15:10:31","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1259644,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell-cell communications in human endometrium. (A)\u003c/strong\u003e Cell-cell communications.\u003cstrong\u003e (B\u003c/strong\u003e) Jointly projecting and clustering signaling pathways into a shared two-dimension manifold according to their functional similarity. The size is proportional to the total communication probability. \u003cstrong\u003e(C)\u003c/strong\u003e Magnified view of each pathway group.\u003cstrong\u003e (D)\u003c/strong\u003e The inferred MIF signaling pathway network in human endometrium. Left and right portions show the autocrine and paracrine signaling, respectively. Circle sizes are proportional to the number of cells in each cell group and edge width represents the communication probability. \u003cstrong\u003e(E)\u003c/strong\u003e Relative contribution of each ligand-receptor pair to the overall MIF signaling pathway network. \u003cstrong\u003e(F)\u003c/strong\u003e Expression distribution of MIF signaling genes. \u003cstrong\u003e(G) \u003c/strong\u003eThe inferred SPP1 signaling pathway network in human endometrium. Left and right portions show the autocrine and paracrine signaling, respectively. Circle sizes are proportional to the number of cells in each cell group and edge width represents the communication probability. \u003cstrong\u003e(H)\u003c/strong\u003e Relative contribution of each ligand-receptor pair to the overall SPP1 signaling pathway network. \u003cstrong\u003e(I)\u003c/strong\u003e Expression distribution of SPP1 signaling genes.\u003cstrong\u003e (J-K) \u003c/strong\u003eThe dot plot showing the comparison of incoming and outgoing signaling patterns of secreting cells. The dot size is proportional to the contribution score computed from pattern recognition analysis. Higher contribution score implies the signaling pathway is more enriched in the corresponding cell group.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2645136/v1/608cc2c23f80b7f1e002c2ca.jpg"},{"id":33968479,"identity":"1122c00b-1336-4555-a8d3-3dde3adcb668","added_by":"auto","created_at":"2023-03-08 15:18:31","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1273029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular subtypes of EC.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Heat map showing the consensus matrix of EC patients. \u003cstrong\u003e(B)\u003c/strong\u003e The overall survival curves based on EC patients data, stratified by the expression patterns of ligand-receptor pairs. \u003cstrong\u003e(C)\u003c/strong\u003e Boxplots showing the EGF pathway activities in patients of different EC clusters. \u003cstrong\u003e(D)\u003c/strong\u003e Boxplots showing the expressions of ERBB2 in patients of different EC clusters.\u003cstrong\u003e (E)\u003c/strong\u003e Boxplots showing the IGF pathway activities in patients of different EC clusters. \u003cstrong\u003e(F)\u003c/strong\u003e Boxplots showing the expressions of IGF2 in patients of different EC clusters.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2645136/v1/e294386403c3e5ebe262033e.jpg"},{"id":34275438,"identity":"f33e2ffb-c62a-441c-b6c5-f52e061e282c","added_by":"auto","created_at":"2023-03-15 06:44:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1895738,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2645136/v1/dac3bf4e-e024-477e-86d8-231dc8502d6c.pdf"},{"id":33966526,"identity":"3ee141e3-a88a-4b6a-bfc5-d391c365bdcd","added_by":"auto","created_at":"2023-03-08 15:10:31","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":2466696,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-2645136/v1/a187d3fb1e0afd101fceed2d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mapping single-cell transcriptomes of endometrium reveals potential biomarkers in cancer","fulltext":[{"header":"Background","content":"\u003cp\u003eThe endometrium is the layer forming the inner wall of the uterus of mammals. It reacts to both estrogen and progesterone, and therefore changes significantly with the estrus cycle and menstrual cycle. Dysfunctions of endometrium had been associated with various human diseases, including abnormal uterine bleeding, infertility, pre-eclampsia, endometriosis and endometrial carcinoma (EC) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Thus, understanding the gene expression regulation during menstrual cycle in humans is crucial for understanding normal functions of endometrium and the mechanism of EC.\u003c/p\u003e \u003cp\u003eWith the development of high throughput sequencing technologies, single-cell RNA sequencing (scRNA-seq) as a revolutionary technology can uncover novel cell types, explore genetic and functional heterogeneity in various cellular contexts. Recently, scRNA-seq has been used to understanding the potential mechanisms of reproductive diseases. Wang et al. utilized scRNA-seq to analyze the cellular and molecular signatures of decidual and peripheral leukocytes in normal and unexplained recurrent miscarriage [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Liu et al. performed a scRNA-seq analysis of adenomyosis and supported the theory of adenomyosis derived from the invasion and migration of the endometrium [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Ma et al. performed single-cell analysis and identified nine cell types, and determined a potential developmental trajectory associated with endometriosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. One recent study mapped the temporal and spatial dynamics of the human endometrium in vivo and in vitro [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Guo et al. presented a view of endometrial carcinoma at single-cell resolution and revealed the characteristics of endometrial epithelial cells in the endometrium [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the dynamic immune microenvironment in human endometrium is still unclear.\u003c/p\u003e \u003cp\u003eMoreover, EC is the most commonly diagnosed gynecologic malignancy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Traditional classification of EC is primary based either on clinical and endocrine features [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Histological subtyping is commonly used in clinical to guide prognosis and treatment decisions for EC patients, while ongoing researches are evaluating the potential molecular subtyping. An integrated genomic analysis had resulted in the molecular classification of endometrioid and serous carcinomas into four distinct subgroups [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The subtypes identified by the different classification systems correlate to some extent; however, the potential molecular pathways of different subtypes of EC are still unknown.\u003c/p\u003e \u003cp\u003eTherefore, we interrogate the immune microenvironment of human endometrial cells during the proliferative and secretory phases of women menstrual cycle. We identified the diverse cell types in human endometrial and characterization of the RNA expression patterns in different cell types. In particular, we explored the transcriptional regulation and revealed the transcription factors (TFs) that exhibited high activities in cell types. Cell-cell communications were examined and further uncovered five RNA-based molecular subtypes of EC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eTranscriptome of human endometrium and endometrial cancers\u003c/p\u003e \u003cp\u003eSingle-cell RNA-sequencing was used to create a cell census of the human endometrium. We downloaded the raw sequencing data from ArrayExpress under the accession number E-MTAB-10287 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In total, 11 samples obtained from five patients were sequenced, including four menstrual cycle stages (proliferative stage, early-secretory stage, mid-secretory stage and late-secretory stage). We selected 10 samples obtained from endometrium for further analysis.\u003c/p\u003e \u003cp\u003eGene expression profiles and clinical information of human endometrial cancers were obtained from The Cancer Genome Atlas (TCGA) project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The expressions of genes were measured by Fragments Per Kilobase of exon model per Million mapped fragments (FPKM). In total, 535 human endometrial cancers were included in our analysis.\u003c/p\u003e \u003cp\u003eProcessing of single cell sequencing data\u003c/p\u003e \u003cp\u003eThe 10x Genomics scRNA-seq data were first analyzed using Cellranger6.1.1 with the raw fastq files as input. The GRCh38 genome was used as the reference genome and default parameters were used. For each sample, the feature-barcode matrix was then converted into a Seurat object using the Seurat R package [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. To enrich for high quality cells in each sample, we performed quality control (QC) for each sample dataset individually. First, we filtered cells that expressed less than 200 genes. For A13 and A30 patients, we excluded the cells with \u0026gt;\u0026thinsp;10% mitochondrial reads. For E1, E2 and E3, cells with \u0026gt;\u0026thinsp;20% mitochondrial reads were excluded. In addition, we filtered cells with hemoglobin protein-related reads\u0026thinsp;\u0026gt;\u0026thinsp;5%.\u003c/p\u003e \u003cp\u003eNext, we used the \u0026lsquo;isOutlier\u0026rsquo; function in scater package to detect the outliers. The outlier cells were defined in each of the following metrics: log(UMI counts) (\u0026gt;\u0026thinsp;2 MADs, both), log(number of genes expressed) (\u0026gt;\u0026thinsp;2 MADs, both) and log(percent mitochondrial read count) (\u0026gt;\u0026thinsp;2 MADs, high end). We used the \u0026lsquo;CellCycleScoring\u0026rsquo; in Seurat to calculate the cell cycle score for each cell and regression with the \u0026lsquo;vars.to.regress\u0026rsquo;. Cell cycle genes (G2/M and S) of were obtained from Seurat package. To reduce the false positive rate in doublet calling, only cells marked as doublets by both scDblFinder and doubletFinder [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] were removed from our analysis.\u003c/p\u003e \u003cp\u003eData normalization, feature selection and clustering\u003c/p\u003e \u003cp\u003eThe quality control was performed for single sample and the \u0026lsquo;CCA\u0026rsquo; function in Seurat was used to integrate all samples. The read count matrices were normalized using \u0026lsquo;NormalizedData\u0026rsquo; with \u0026lsquo;LogNormalize\u0026rsquo; as the normalization method. Feature selection was performed by \u0026lsquo;FindVariableFeatures\u0026rsquo; using the \u0026lsquo;vst\u0026rsquo; method and the top 2,000 variable genes were identified. The top 2,000 most variable genes were summarized by principal component analysis (PCA). To identify groups of distinct cells, graph-based Lovain clustering was performed based on top 20 PCs. The \u0026lsquo;FindClusters\u0026rsquo; function with a resolution of 0.2 was used to identify the cell clusters and UMAP plots were generated in R for visualization.\u003c/p\u003e \u003cp\u003eCell type annotation\u003c/p\u003e \u003cp\u003eWe performed cell types annotation based on two methods. One was gene signature enrichment and another one was reference-based annotation with the SingleR package [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. First, the cell types were annotated based on signatures from ESTIMATE [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and PangladoDB [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The \u0026lsquo;AddModuleScore\u0026rsquo; was used to calculate the signature scores. The median scores of each cell clusters were calculated and if the median\u0026thinsp;\u0026gt;\u0026thinsp;0.1, we considered the clusters as corresponding cell types. The primary annotations were performed by SingleR using the \u0026lsquo;HumanPrimaryCellAtlasData\u0026rsquo; as reference dataset. The marker genes of cell types were obtained from literature or CellMarker database [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIdentification of differentially expressed genes and functional annotations\u003c/p\u003e \u003cp\u003eDifferential gene expression analysis was performed by \u0026lsquo;FindAllMarkers\u0026rsquo; in Seurat with the min.pct set to 0.25 and Wilcoxon\u0026rsquo;s rank sum test. Genes with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and logfc\u0026thinsp;\u0026gt;\u0026thinsp;0.25 were considered as up-regulated. For functional enrichment analysis, we selected top 50 highly expressed genes in each main cell type. For the cell subtypes, we used all differentially expressed genes. The functional enrichment analysis was performed by clusterProfiler [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and heat maps were generated by ComplexHeatmap [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGene sets functional scores\u003c/p\u003e \u003cp\u003eTo calculate the gene set functional scores, we used AUCell to perform this analysis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The functional gene sets were obtained from the Molecular Signatures Database (MSigDB) hallmark gene set collection [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Wilcoxon\u0026rsquo;s rank sum test was used to compare the functional scores in different cell subtypes. P-values were adjusted by false discovery rate (FDR). The gene sets with log2FoldChange\u0026thinsp;\u0026gt;\u0026thinsp;0 and p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered as enriched in corresponding cell subtypes.\u003c/p\u003e \u003cp\u003eCo-expression analysis of genes\u003c/p\u003e \u003cp\u003eTo investigate the expression correlation among differentially expressed genes in each cell type, we calculated the Spearman correlation coefficient (SCC) among top 50 differentially expressed genes based on the expression across cell types. The SCC matrix was visualized by heat map.\u003c/p\u003e \u003cp\u003eTranscription factor activities analyses\u003c/p\u003e \u003cp\u003eWe used pySCENIC to identify the TF regulators in each cell types identified in human endometrium [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. We first downloaded the motifs that allow using RcisTarget (mc9nr) from cisTarget database [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The input UMI count matrix was normalized (CPM) and log-transformed. Only genes in RcisTarget were included in further analysis. GENIE3 was performed to identify TF-modules in each cell type [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. TF-modules having less than 10 genes were filtered out. The top 1 percentile of the number of detected genes per cell was used to calculate the AUCell enrichment of each TF regulon in each cell. ComplexHeatmap was used to generate the heat map of the activity matrix.\u003c/p\u003e \u003cp\u003eCell-cell interactions\u003c/p\u003e \u003cp\u003eCell-cell communication analysis was performed using CellChat (Version 1.1.0), based on the known ligand-receptor pairs in CellChatDB [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Briefly, the normalized genes expression matrix and cell type labels generated by Seurat were subjected as input for CellChat. For the main analyses the core functions \u0026lsquo;computeCommunProb\u0026rsquo;, \u0026lsquo;computeCommunProbPathway\u0026rsquo; and \u0026lsquo;mergeCellChat\u0026rsquo; were applied using default parameters. The \u0026lsquo;computeNetSimilarityPairwise\u0026rsquo; function was used to calculate the similarity between pathways and pathways were clustered into different groups based on functional similarities.\u003c/p\u003e \u003cp\u003eRNA-based molecular subtypes of human endometrial cancers\u003c/p\u003e \u003cp\u003eTo identify the molecular subtypes of human endometrial cancer, we performed clustering based on the gene expression. First, the ligand-receptor pairs prioritized in single cell data analysis were used in the clustering. Nonnegative matrix factorization (NMF) was performed based on R package [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The parameters \u0026lsquo;rank\u0026thinsp;=\u0026thinsp;2:6, method\u0026thinsp;=\u0026thinsp;brunet\u0026rsquo; were used in this analysis. Five molecular subtypes were identified based on the cophenetic curve. The survival analysis was performed by R packages (survival and survminer). Log-rank test was used to evaluate the difference of survival rates among subtypes.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA single cell map of human endometrium\u003c/p\u003e \u003cp\u003eTo analyze the scRNA-seq cells from human endometrium through four menstrual cycle stages, we performed principal-component analysis (PCA) using the top 2,000 most variably expressed genes across 59,397 cells. Cells were clustered into transcriptionally distinct clusters with top 20 principal components (PCs). The cells were visualized using UMAP plot and revealed eight clusters that could be annotated to known cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Moreover, we found that cells were clustered together based on cell types but not based on patient identify (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). We then used well-known marker genes to define the identity of each cell cluster. For example, epithelial cells expressed KRT8 and PAEP, endothelial cells expressed VWF, fibroblast cells expressed APOD, DCN and COL3A1, perivascular (PV) cells expressed RGS5, smooth muscle cells expressed ACTG2 and MYH11, multi-potent stromal cells (MSC) expressed TOP2A and UBE2C, lymphoid and myeloid cells expressed CD74, NKG7 and GNLY (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, E \u003cb\u003eand\u003c/b\u003e Additional file 1: \u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we calculated the proportion of cells during four menstrual cycle stages and different patients. We found that the proportions of cell types among stages and patients were significantly different (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-D, p-values\u0026thinsp;\u0026lt;\u0026thinsp;2.2E-16). PV cells were predominated in early-secretory stage and in A30 patient. Fibroblast cells decreased in early-secretory stage and immune cells were enriched in proliferative stage and late-secretory stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). MSC decreased during the menstrual cycle stages. Moreover, we identified the highly expressed marker genes in each cell types. We found that C1QA, C1QB and C1QC were highly expressed in myeloid cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE), indicating their phagocytic ability [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Together, our comprehensive analysis provided a comprehensive catalog of the major cell types together with their cellular position in endometrium.\u003c/p\u003e \u003cp\u003eSystematic discovery of cell type-specific RNAs in human endometrium\u003c/p\u003e \u003cp\u003eWe next explored the cell type-specific RNAs that could help explain distinct biological states of these cell types. Functional enrichment analysis revealed that genes highly expressed in fibroblast cells were significantly enriched in wound healing, regulation of vasculature development and regulation of angiogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Genes highly expressed in smooth muscle cells were enriched in wound healing, extracellular matrix organization and extracellular structure organization, whereas PV cell-specific genes were enriched in response to corticosteroid, steroid hormone and glucocorticoid (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Epithelial cell-specific genes were significantly enriched in epithelial cell proliferation and tissue migration, and endothelial cell-specific genes were enriched in regulation of vasculature development and angiogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). MSCs are a population of self-renewing multipotent cells in the perivascular regions of the endometrium in both the basalis and functionalis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. We found that genes highly expressed in MSCs were enriched in sister chromatid segregation and nuclear division (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). A large proportion of MSCs were in G2M stage of cell cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In particular, we found that two proliferative marker genes, MKI67 and TOP2A, were highly expressed in MSC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D). These results suggested that enriched functional analysis of each cluster supported their functions in human endometrium.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTwo newly discovered subtypes of perivascular cells\u003c/p\u003e \u003cp\u003eWe analyzed the PV population (n\u0026thinsp;=\u0026thinsp;8,120) based on the known markers and re-clustered it into two distinct populations as indicated in the UMAP (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA \u003cb\u003eand\u003c/b\u003e Additional file 1: \u003cb\u003eFig. S2A\u003c/b\u003e). We analyzed the relative proportion of cells in each cluster and noted that two clusters exhibited similar proportion during menstrual cycle stages and patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, p\u0026thinsp;=\u0026thinsp;1.862E-8 and Additional file 1: \u003cb\u003eFig. S2B\u003c/b\u003e). These observations were consistent with the results PV-MYH11\u0026thinsp;+\u0026thinsp;are characteristic of myometrium while PV-STEAP4\u0026thinsp;+\u0026thinsp;are only present in the endometrium [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. We next analyzed the gene expression profiles and identified the top differentially expressed genes in two PV populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In particular, STEAP4 and MYH11 were separately expressed in two PV populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-E). We found that several collagen-related genes (e.g., COL3A1, COL1A2, COL4A1 and COL1A1) were highly expressed in PV-STEAP4 populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), which might be correlated with their roles in repair of endometrial damage and induced angiogenesis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Moreover, IGFBP5 was highly expressed in PV-STEAP4\u0026thinsp;+\u0026thinsp;subtypes, which is consistent with its roles in promoting angiogenic and neurogenic differentiation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In contrast, the PV-MYH11\u0026thinsp;+\u0026thinsp;populations were characteristic of myometrium and highly expressed MUSTN1 and MYH11.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further determine the specific roles of two PV populations that might contribute to human endometrium, we performed functional enrichment analysis based on the differentially expressed genes. We found that genes highly expressed in two PV populations were both significantly enriched in wound healing, response to oxidative stress and extracellular matrix organization (Additional file 1: \u003cb\u003eFig. S2C\u003c/b\u003e). However, the proportions of genes in PV-STEAP4\u0026thinsp;+\u0026thinsp;were much higher. In particular, genes highly expressed in PV-STEAP4\u0026thinsp;+\u0026thinsp;were significantly enriched in extracellular matrix organization and structure organization, while genes in PV-MYH11\u0026thinsp;+\u0026thinsp;were significantly enriched in muscle development related functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Cancer hallmark-related pathways also exhibited distinct activities in two PV populations. PV-STEAP4\u0026thinsp;+\u0026thinsp;cells exhibited higher activities in immune and metabolism related functions and PV-MYH11\u0026thinsp;+\u0026thinsp;cells exhibited higher activities in signaling and proliferative pathways, such as PI3K-AKT-mTOR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). It has been demonstrated that glycolysis is beneficial to angiogenesis and plays an important role in endometrial decidualization [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In addition, PI3K-AKT-mTOR pathway plays an important role in the decidualization of endometrium.\u003c/p\u003e \u003cp\u003eSubpopulations of epithelial cells across human menstrual cycle\u003c/p\u003e \u003cp\u003eThe epithelial cell cluster (n\u0026thinsp;=\u0026thinsp;5,758) was then assigned to four epithelial subtypes based on known markers obtained from the published literatures [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Three secretory glandular cells (secretory LGALS1+/PAEP+/MT+) and ciliated cells were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). We calculated the proportion of four subpopulations across menstrual cycle and found that ciliated epithelial cells were enriched in proliferative phase, secretory LGALS1\u0026thinsp;+\u0026thinsp;cells were enriched in early and mid-secretory phases (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, p\u0026thinsp;\u0026lt;\u0026thinsp;2.2E-16). Examination of gene expression patterns of four epithelial subpopulations we revealed that ciliated cells expressed high levels of markers, such as the ciliated marker TPPP3, PIFO and FAM183A (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe gene set enrichment analyses showed that genes up-regulated in ciliated cells were mainly enriched for cilium organization, cilium assembly, cilium movement functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE \u003cb\u003eand\u003c/b\u003e Additional file 1: \u003cb\u003eFig. S3\u003c/b\u003e). This result is similar with one previous study [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The secretory MT\u0026thinsp;+\u0026thinsp;cell populations highly expressed genes in metallothionein (MT) family, such as MT2A, MT1G, MT1E, MT1X and MT1H (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), which might contribute to impaired endometrial receptivity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Moreover, higher proportion of secretory MT\u0026thinsp;+\u0026thinsp;cells was observed in late-secretory phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). This is consistent with previous observations that MT genes were highly expressed in late-secretory phase [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The secretory PAEP\u0026thinsp;+\u0026thinsp;cell populations were enriched during the menstrual cycle and highly expressed CLDN3, CLDN4 and CXCL family genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Functional analysis revealed that genes up-regulated in this cell population were enriched in regulation of cell-cell adhesion, cell migration and reproductive development (Additional file 1: \u003cb\u003eFig. S3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWe next performed functional analysis based on cancer hallmark-related pathways. Examination of the pathway activities we found that secretory populations exhibited higher pathway activities in numerous pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). The ciliate epithelial cells exhibited higher pathway activities in bile acid metabolism and mitotic spindle. The Wnt signaling pathway, DNA repair and G2M checkpoint pathways were enriched in secretory LGALS1\u0026thinsp;+\u0026thinsp;cell populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). In particular, epithelial mesenchymal transition was enriched in secretory LGALS1+, which was consistent with their roles in regeneration or differentiation of endometrium [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The inflammation-related pathways were significantly enriched in secretory PAEP\u0026thinsp;+\u0026thinsp;cell populations, such as IL6-JAK-STAT3 and IL2-STAT5 signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). These results extended the transcriptional signature and potential pathways underlying the human endometrial epithelial cells.\u003c/p\u003e \u003cp\u003eFibroblast cells separate into four distinct cell types\u003c/p\u003e \u003cp\u003eFibroblasts (n\u0026thinsp;=\u0026thinsp;21,865) were further separated into four clusters and annotated based on the highest expressing genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). SPARACL1, ID4, MMP11 and EGR1 were highly expressed in corresponding clusters. We found that fibroblast ID4\u0026thinsp;+\u0026thinsp;and SPARCL1\u0026thinsp;+\u0026thinsp;cells were primarily observed in early- and late-secretory phases (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Fibroblast MMP11\u0026thinsp;+\u0026thinsp;cells were primarily in the proliferative phase and EGR1\u0026thinsp;+\u0026thinsp;cells were in mid-secretory phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, p\u0026thinsp;\u0026lt;\u0026thinsp;2.2E-16). The proportions were variable in different patients (Additional file 1: \u003cb\u003eFig. S4A\u003c/b\u003e), suggesting the high heterogeneity among patients. Gene expression analysis revealed fibroblast SPARCL1\u0026thinsp;+\u0026thinsp;highly expressed fibroblast-related genes, such as DCN, APOD, COL1A2 and COL15A1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC \u003cb\u003eand\u003c/b\u003e Additional file 1: \u003cb\u003eFig. S4B\u003c/b\u003e). In particular, four clusters highly expressed corresponding marker genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). We next performed the functional enrichment analysis and found that genes up-regulated in fibroblast SPARCL1\u0026thinsp;+\u0026thinsp;were significantly enriched in extracellular structure organization and negative regulation of locomotion (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Genes highly expressed in fibroblast ID4\u0026thinsp;+\u0026thinsp;were enriched in epithelial cell proliferation, positive regulation of cytokine production and regulation of angiogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Numerous of transcription factors (TFs) were highly expressed in fibroblast EGR1\u0026thinsp;+\u0026thinsp;sub-populations, such as JUNB, EGR1, FOS and JUN. We also observed high expression of GADD45B in this population, which plays important roles in DNA repair [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. We also investigated the cancer hallmark pathway activities in different fibroblast populations and found that Wnt, MYC and peroxisome exhibited higher activities in ID4\u0026thinsp;+\u0026thinsp;cell populations (Additional file 1: \u003cb\u003eFig. S4C\u003c/b\u003e). Notch and PI3K-AKT signaling pathways were active in SPARCL1\u0026thinsp;+\u0026thinsp;cell populations (Additional file 1: \u003cb\u003eFig. S4C\u003c/b\u003e). Together, these results uncovered the potential pathways underlying different fibroblast populations in human endometrium.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTF regulators of cell types in human endometrium\u003c/p\u003e \u003cp\u003eIt has been demonstrated that genes usually interact with each other to form a complex interaction network [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. We thus analyzed the correlation between gene expressions and we found that genes highly expressed in cell types were co-expressed with each other (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), indicating the modular programs associated with basic cellular functions of cells. TFs are important regulators and play important roles in regulating gene expression [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In addition, we calculated the module activities as the average expression of genes. We found that the expressions of several TFs (such as IRF8, SOX17, FOS and KLF2) were significantly correlated with the module activities (Additional file 1: \u003cb\u003eFig. S5\u003c/b\u003e). We thus identified the cell type-specific TFs based on the pySCENIC pipeline. In total, we identified 336 TFs exhibited high activities in different cell types and 62 TFs also exhibited differential expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). We found that TFs exhibited high cell type- and phase-specificity. For example, SOX4 exhibited higher activity in proliferative phase (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). SOX4 is closely associated with the development and progression of many malignant tumors and plays an important role in the cell growth and proliferation [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. We also observed that the expressions of SOX4 in proliferative phase were significantly higher than secretory phases. In addition, MAF, KLF4, JUN, FOS and EGR1 exhibited higher activities in secretory phases (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoreover, we found that several TFs exhibited cell type-specific activities in human endometrium. CD59 exhibited higher activities in endothelial cells, which is highly expressed in endothelial cells of human endometrium (Additional file 1: \u003cb\u003eFig. S6\u003c/b\u003e). It has been demonstrated that CD59 may be important in protection of endothelial cells against C-mediated damage at local sites of inflammation, thereby maintaining the vascular integrity [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. E2F1 and EZH2 exhibited higher activities in MSC cell populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). E2F1 plays a pivotal role in driving cells out of a quiescent state and into the S phase of the cell cycle [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and the activity of EZH2 influences cell fate regulation [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Functional analysis of the targets of EZH2 revealed that they were significantly enriched in DNA replication and cell cycle, which is consistent with the proliferative state of MSC cells. EGR1 exhibited higher activities in fibroblast cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), which had been demonstrated to contribute to inflammatory factors and fibrosis reduction [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The potential targets of EGR1 were significantly enriched in Wnt signaling pathway and cell growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). In addition, FOS exhibited higher activities in fibroblast and smooth muscle cell populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB) and its targets were significantly enriched in muscle tissue development, Wnt signaling and cell differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). It has been demonstrated that FOS plays an important role in control of fibroblast senescence and activation of programmed cell death [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Several TFs also exhibited higher activities in immune cells, such as STAT4, BATF, RUNX3 and EOMES (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). These results suggest that the dynamic transcriptome during menstrual cycle were strictly regulated by TFs that were with dynamic activities.\u003c/p\u003e \u003cp\u003eCell-cell interactions in human endometrium\u003c/p\u003e \u003cp\u003eThe development of tissue and progression of complex diseases relies on a complex network of cell-cell interactions [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. We thus inferred intercellular communications for human endometrium based on CellChat [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. As a result, we found that the myeloid cells frequently interact with MSC and epithelial cells, while the fibroblast cells frequently interact with others (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Immune cells mainly function as signal input cells. As the signal output cells, epithelial and endothelial cells mainly interact with immune cells as targets (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Next, the 15 signaling pathways associated with inferred networks were mapped onto a two-dimensional manifold and clustered into four groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB-C). MIF, IGF, PTN and MK pathways were grouped into cluster 1, while group 4 was formed by pathways of VEGF, CXCL and VISFATIN (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe specifically examined how macrophage migration inhibitory factor (mif) communications among cell populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). All cells function as signal output cells, although the intensity of interaction was different. When MSC and epithelial cells acted as signal input cells, the output intensity of cells was low. In this pathway, immune cells were mainly used as signal input cells, and the strength of intercellular interaction was high (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Macrophage migration inhibitory factor had been identified as a potential biomarker of endometriosis [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. We also examined ligand receptor pairs that play a major role in this pathway and identified two ligand-receptor pairs MIF\u0026minus;(CD74\u0026thinsp;+\u0026thinsp;CD44), and MIF\u0026minus;(CD74\u0026thinsp;+\u0026thinsp;CXCR4) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). MIF exhibited high expression in all cell types while CXCR4, CD74 and CD44 exhibited higher expression in immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF). We also found that the SPP1 signaling pathway only took myeloid cells as signal output cells, and exported signals to fibroblasts, PV cells, SMC cells and lymphocytes in paracrine mode, and played a role in autocrine mode to a large extent (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). The SPP1-(ITGA4\u0026thinsp;+\u0026thinsp;ITGB1) and SPP1-CD44 plays important roles in cell-cell communications (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH). SPP1 and CD44 exhibited higher expression in immune cells while ITGB1 was highly expressed in all cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI)\u003c/p\u003e \u003cp\u003eNext, we compared the information flow for each signaling pathway. In signal input, several pathways only functions in one cell type, such as EGF and EDN in fibroblasts, VEGF and CXCL in endothelial cells, and ncWNT in PV cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eJ). MIF signaling pathway only participates in the signal input of immune cells and most of these pathways are involved in signal input patterns of myeloid cells, fibroblasts, and endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eJ). In the signal output part, we found that IL6, EGF, SPP1 and CCL only played a role in the signal output of immune cells. EDN only plays a role in signal output of endothelial cells. As with signal input patterns, most of these pathways were involved in signal output by fibroblasts and myeloid cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eK). Together, cell-cell communications analysis enables multifaceted assessment of intercellular communication patterns in human endometrium.\u003c/p\u003e \u003cp\u003eRNA-based molecular subtypes of human endometrial cancers\u003c/p\u003e \u003cp\u003eEndometrial cancer (EC) is the most common gynecologic malignancy. We next explored the expression patterns of the ligand-receptor in EC. We found that the patients can be grouped into five clusters based on the ligand-receptor of the 15 pathways identified in cell-cell communication (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). The survival rates for patients in five clusters were with significantly different and patients in cluster-5 were with poor survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB, log-rank test p\u0026thinsp;=\u0026thinsp;0.00025). We found that patients in cluster-5 were with distinct expression of 17 genes, which were involved multiple cytokine-related pathways, including MIF, CXCL, SPP1 and VEGF. We next calculated the pathway activities for these pathways and found that the majority of pathway activities in cluster-5 were significantly higher than other clusters. For example, epidermal growth factor (EGF) was a conventional mitogenic factor that can stimulate the proliferation of various types of cells including epithelial cells and fibroblasts [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The activities of EGF in cluster-5 were significantly higher than that in cluster 1, 3, and 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). The EGFR family also plays an important role in maintaining epithelial homeostasis [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and overexpressed in EC and ovarian cancers [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Compared with other clusters, we found that ERBB2 were highly expressed in patients of cluster-5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, as a major ligand of IGF, IGF2 overexpression has been shown to play a role in many cancers [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. However, there seem to be few reports about its role in endometrial cancer. We found that the activities of IGF pathway and expressions of IGF2 in patients of cluster-5 were significantly higher than cluster-2 and cluster-4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE-F). In contrast, there were several pathways (i.e., CXCL) had lower pathway activities in cluster-5 and the expressions of CXCR4 were also lower in patients of cluster-5. These results suggested that the genes identified here can be used to predict the prognostic survival of patients with endometrial cancer.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, our single transcriptome analysis yields a comprehensive catalog of the major subsets of uterine cells in endometrium. We comprehensively identified the diverse cell types in human endometrial and found that there were a higher proportion of fibroblast, PV and epithelial cells. We further characterized the gene expression patterns in different cell types and identified the cell type specific genes. In particular, we explored the transcriptional regulation and revealed the TFs that exhibited high activities in specific cell types. In addition, cell-cell communications were examined and revealed the critical ligand-receptor pairs. The expression patterns of identified ligand-receptor further uncovered five molecular subtypes of EC. All these results increased our understanding of the dynamic microenvironment of uterine cells in endometrium, as well as potential biomarkers for EC.\u003c/p\u003e \u003cp\u003eIn particular, we identified two perivascular cell subtypes, four epithelial subtypes and four fibroblast cell types in endometrium. We showed that the ciliated epithelium is a typical endometrial cell type, which is consistent with the results of one recent study [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Although the existence of ciliated cells in human endometrium was observed long times ago [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], the gene expression patterns and marker genes were not identified previously. We identified numerous of genes that showed higher expression in ciliated cells, such as TPPP3, CAPS, AGR3 and PIFO. AGR3 is a specialized member of the protein disulfide isomerase (PDI) family, and it has been found to play an unexpected role in the regulation of ciliary beat frequency and mucociliary clearance in the airway [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn total, we identified 4,038 immune cells, including monocyte, NK1, NK2 and T cells (Additional file 1: \u003cb\u003eFig. S7A\u003c/b\u003e). Immune cells were mainly enriched in proliferation and late secretory stage, NK2 cells were mainly enriched in late secretory stage, and T cells were mainly distributed in proliferative stage (Additional file 1: \u003cb\u003eFig. S7B\u003c/b\u003e). Monocyte cells highly expressed CD74 and LYZ, T cells highly expressed CD3D and IL7R, and NK cells highly expressed NKG7 and GNLY (Additional file 1: \u003cb\u003eFig. S7C\u003c/b\u003e). In addition, the high expression of antigen presentation and phagocytic genes were observed in monocyte, such as HLA\u0026thinsp;\u0026minus;\u0026thinsp;DRA, HLA\u0026thinsp;\u0026minus;\u0026thinsp;DRB1, HLA\u0026thinsp;\u0026minus;\u0026thinsp;DPA1, C1QA, C1QB, and C1QC, indicating that they play an important role in the clearance of apoptotic cells after endometrial shedding (Additional file 1: \u003cb\u003eFig. S7D\u003c/b\u003e). Functional enrichment analysis suggested that genes highly expressed in monocyte were significantly enriched in antigen processing and presentation, cell-cell adhesion and leukocyte migration (Additional file 1: \u003cb\u003eFig. S7E\u003c/b\u003e). Moreover, genes highly expressed in T cells were enriched in T cell differentiation, T cell activation and leukocyte cell\u0026thinsp;\u0026minus;\u0026thinsp;cell adhesion (Additional file 1: \u003cb\u003eFig. S7E\u003c/b\u003e). These results suggest that immune cells play an important role in the immune response, clearance of apoptotic cells and endometrial tissue remodeling after endometrium shedding.\u003c/p\u003e \u003cp\u003eIn addition, we found that genes highly expressed in cell types showed expression perturbations in cancer. For example, APOD, DCN, LUM and CFD were significantly highly expressed in fibroblast cells (Additional file 1: \u003cb\u003eFig. S8A\u003c/b\u003e). DCN has been demonstrated to play tumor suppressive functions in cancer [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] and we found that it was lowly expressed in EC. CAV1 and VWF were highly expressed in endothelial cells but showed lower expression in EC (Additional file 1: \u003cb\u003eFig. S8B\u003c/b\u003e). Epithelial-related genes, such as SCGB2A1 and CLDN4, were highly expressed in cancer (Additional file 1: \u003cb\u003eFig. S8C\u003c/b\u003e). Moreover, we found that CCL3, C1QA, GZMB and IL32 were highly expressed in cancer (Additional file 1: \u003cb\u003eFig. S8D-E\u003c/b\u003e). SCGB2A1 was found to be perturbed in various cancer types, such as breast and ovarian cancers [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. We also found the expressions of several genes were associated with survival of cancer patients (Additional file 1: \u003cb\u003eFig. S8F\u003c/b\u003e). These results highlight the critical roles of these genes in endometrial cancer.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, deconvolution of immune microenvironment that drives transcriptional programs throughout the menstrual cycle is important to understanding RNA regulatory biology of endometrium. Our dynamic immune microenvironment analyses provide novel insights into future development of RNA-based treatments for endometriosis and endometrial carcinoma.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eAll datasets used in our study are from previously published studies. All the data generated in this study can be downloaded from https://figshare.com/projects/scRNAseq-Endometrium/147556. All the scripts can be downloaded from the following link: https://figshare.com/projects/scRNAseq-Endometrium/147556.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was funded by Hainan Province Science and Technology Special Fund [ZDYF2021SHFZ051], Hainan Provincial Natural Science Foundation of China [820MS053,\u0026nbsp;822MS175, LCYX201202], the Major Science and Technology Program of Hainan Province [ZDKJ202003], Marshal Initiative Funding of Hainan Medical University [JBGS202103], National Natural Science Foundation of China [31871338, 31970646, 61873075, 32060152, 32070673, 32170676, 82072880], HMU Marshal Initiative Funding [HMUMIF-21024], project supported by Hainan Province Clinical Medical Center [QWYH202175], the National Key R\u0026amp;D Program of China [2018YFC2000100], Natural Science Foundation for Distinguished Young Scholars of Heilongjiang Province [JQ2019C004], Bioinformatics for Major Diseases Science Innovation Group of Hainan Medical University and Heilongjiang Touyan Innovation Team Program and Innovation Research Fund for Graduate Students [Qhys2021-348].\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eConceptualization, Y.L.\u0026nbsp;and Y.M.; methodology, G.X., T.P. and S.L.; formal analysis, G.X., T.P. and S.L.; investigation, Q.X. and Y.Z.; data curation, G.X., T.P. and S.L.; writing\u0026mdash;original draft preparation, G.X.; writing\u0026mdash;review and editing, Y.L.,\u0026nbsp;R.C. and Y.M.; visualization, G.X.; supervision, Y.L. and Y.M.; project administration, Y.L.,\u0026nbsp;R.C. and Y.M.; funding acquisition, Y.L.,\u0026nbsp;R.C. and Y.M. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGarrido-Gomez T, Dominguez F, Quinonero A, Diaz-Gimeno P, Kapidzic M, Gormley M, Ona K, Padilla-Iserte P, McMaster M, Genbacev O \u003cem\u003eet al\u003c/em\u003e: Defective decidualization during and after severe preeclampsia reveals a possible maternal contribution to the etiology. Proc Natl Acad Sci U S A 2017, 114(40):E8468-E8477.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalker MS, Christian M, Steel JH, Nautiyal J, Lavery S, Trew G, Webster Z, Al-Sabbagh M, Puchchakayala G, Foller M \u003cem\u003eet al\u003c/em\u003e: Deregulation of the serum- and glucocorticoid-inducible kinase SGK1 in the endometrium causes reproductive failure. 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Oncology letters 2020, 20(4):24.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"single cell sequencing, cell-cell interaction, biological pathways, molecular subtypes, RNA bi-omarkers","lastPublishedDoi":"10.21203/rs.3.rs-2645136/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2645136/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDeconvolution of immune microenvironment that drive transcriptional programs throughout the menstrual cycle is key to understanding regulatory biology of endometrium.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe comprehensively analyzed single cell transcriptome of 59,397 cells across ten human endometrium samples. Cell specific expression of genes were revealed and transcription factors that potentially regulated these genes were identified by SCENIC. CellChat was used to analyze the cell-cell communications. The RNA-based molecular subtypes of human endometrial cancers were revealed by nonnegative matrix factorization analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSingle cell transcriptome analyses revealed the dynamic cellular heterogeneity throughout the menstrual cycle. In particular, we identified two perivascular cell subtypes, four epithelial subtypes and four fibroblast cell types in endometrium. Moreover, we inferred the cell type-specific transcription factor (TF) activities and linked critical TFs to transcriptional output of diverse immune cell types, highlighting the importance of transcriptional regulation in endometrium. Dynamic interactions between various types of cells in endometrium contribute to a range of biological pathways regulating differentiation of secretory. Integration of the molecular biomarkers identified in endometrium and bulk transcriptome of 535 endometrial cancers (EC), we revealed five RNA-based molecular subtypes of EC with highly intratumoral heterogeneity and different clinical manifestations. Mechanism analysis uncovered clinically relevant pathways for pathogenesis of EC.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn summary, dynamic immune microenvironment analyses provide novel insights into future development of RNA-based treatments for endometriosis and endometrial carcinoma.\u003c/p\u003e","manuscriptTitle":"Mapping single-cell transcriptomes of endometrium reveals potential biomarkers in cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-08 15:02:25","doi":"10.21203/rs.3.rs-2645136/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b096f2c-d38c-4bc2-ae33-4b60621a3c1d","owner":[],"postedDate":"March 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-18T10:59:45+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-08 15:02:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2645136","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2645136","identity":"rs-2645136","version":["v1"]},"buildId":"0SHbDDIpRTBOrFPTvp6pu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}