The human endometrium is a highly dynamic and complex tissue that undergoes cyclical shedding and regeneration and is the site of embryo implantation. Owing to the importance of the endometrium in human fertility and regenerative biology, extensive characterization of endometrial transformation has long been pursued[1–4]. Histologically, the menstrual cycle is divided into menstrual, proliferative, and secretory stages. Advancements in microarray and next-generation sequencing (NGS) have enabled researchers worldwide to understand the molecular dynamics of the endometrium[6,7]. Using whole transcriptomic profiling of the endometrium at different time points around the mid-secretory phase, Díaz-Gimeno et al. identified a gene signature of the window of implantation (WOI), which has been translated for use in clinics to guide embryo transfer in assisted reproduction treatment. Comparisons of the transcriptome between patients and controls revealed dysregulated genes and pathways contributing to the pathogenesis of recurrent implantation failure (RIF), recurrent pregnancy loss (RPL), endometriosis, and other uterine disorders. However, these characterizations at the bulk level only reflect average gene changes in the tissue. The human endometrium consists of multiple cell types, including epithelial, stromal, immune, perivascular, and endothelial cells, each having distinct cellular functions. Understanding single-cell activity is critical for understanding endometrial biology. Recently, single-cell approaches have been applied in endometrial research.
Single-cell RNA sequencing (scRNA-seq) is a popular technique in biomedical research worldwide. Unlike bulk RNA-seq, which measures the average expression in a mixture of cell populations, scRNA-seq facilitates transcript measurements in individual cells. Different methods have been developed for scRNA-seq using two major platforms, namely C1 and chromium. The C1 platform captures single cells at a low throughput and enables deep sequencing of full-length transcripts, whereas chromium allows a high number of single-cell detections and shallow sequencing of partial (3′ or 5′) transcripts[10,11]. Notably, both platforms have been widely used in extensive research.
In this review, we aimed to briefly discuss recent advances in scRNA-seq, with an emphasis on the biological findings and insights in endometrial research provided by single-cell analysis in endometrial research. We summarized the scRNA-seq techniques, discussed different digestion strategies for single-cell preparation of endometrial cells, and explored how single-cell analysis helps in the understanding of endometrial physiology and pathology.
Single-cell RNA sequencing
Early techniques, such as quantitative polymerase chain reaction (PCR), fluorescence-activated cell sorting (FACS), and immunofluorescence, can only be employed for a limited number of genes and rely on prior knowledge of cell identity markers. In the past decade, scRNA-seq has become the most revolutionized technique that can be used to examine individual cells in a sensitive and unbiased manner. Different scRNA-seq protocols have been summarized by Svensson et al. and Chen et al., including the well-known methods, SMART-seq2 and Chromium 10X (Table S1, https://links.lww.com/RDM/A7, Fig. 1)[10,12]. Each scRNA-seq protocol involves three important parts: single-cell isolation, library construction, and data analysis. The main differences among the protocols include single-cell isolation techniques and library construction methods.
Single-cell isolation determines the throughput of scRNA-seq protocols, which range from a limited number of cells by manual picking to tens of thousands of cells by random capture[13,14] and droplet emulsion[15–17] in a single experiment. Low-throughput isolation methods allow their application to rare and precious samples, whereas high-throughput methods facilitate the unbiased profiling and detection of rare cell types in the tissues of interest (Fig. 1). Single-cell library preparation is challenging due to the scarcity of starting material (10-30 pg of total RNA) in single cells. Thus, most scRNA-seq protocols amplify reverse-transcribed cDNA to obtain sufficient material for library construction. Although different amplification strategies (universal adaptor, template-switching oligonucleotide, in vitro transcription) have been utilized by different methods (Fig. 2), they typically utilize oligo-dT coupled with an adaptor sequence containing unique molecular identifiers (UMIs) and barcode sequences. UMIs are used to avoid amplification bias, while barcodes are used for pooling/multiplexing[16,17,19–21]. In general, to achieve high-throughput single cells with unique barcodes, the number of barcodes in the pool should be significantly larger than the input number of cells. Barcodes can be synthesized to a required length randomly (e.g., 10X Chromium) or combined from multiple designed barcodes using the split-pool method (e.g., InDrop) to achieve the desired number. scRNA-seq approaches are also available that can utilize combinatorial indexing without single-cell isolation or specialized equipment[22,23].
The resulting single-cell libraries from different strategies (Table S1 https://links.lww.com/RDM/A7, Fig. 2) can be sequenced to obtain either the full-length transcript or partial 5’/3’ of the transcript with different scales of cells and genes. The choice of protocol depends on various aspects, including the availability of study material, research questions, budget, and access to the required equipment. Detailed benchmark information can be found in the detection sensitivity and quantification accuracy of different protocols in related studies[24–26]. For endometrial research, as the tissue is relatively easy to access and has small cell populations (e.g., perivascular cells and endothelial cells), a high-throughput scRNA-seq protocol is recommended.
Sequencing data analysis
After library sequencing, scRNA-seq data have large uncertainty owing to the scarcity of the starting material, which easily leads to many dropouts (sparsity) and variations across cells. Considering single-cell data characteristics, different analysis methods have been developed in the past decade[27–29]. Luecken et al. proposed the best practices for single-cell analysis, which is a very useful tutorial for beginners.
In general, single-cell analysis involves pre-processing steps and downstream analysis (Fig. 3). During pre-processing, sequenced tags were first aligned to the corresponding genome/transcriptome, followed by quantification. Quality control is necessary to ensure that the analyzed data are from single and viable cells by filtering out doublets/empties and low-quality cell-based quality control metrics. Once high-quality data are obtained, different normalization methods will be performed to account for the count depth. Subsequently, the batch effect, which is introduced by sample/library preparation at different times/laboratories, should be removed to mitigate confounding effects that mask true biological findings. Numerous methods have been developed and evaluated for batch correction, the choice of which is dependent on data scenarios[27,31–36]. Dimensional reduction is another necessary step during preprocessing in terms of the computational burden and background noise. To retain only the most informative genes for dimensional reduction, highly variable genes are usually selected. Dimensional reduction algorithms, including principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP), are commonly used in single-cell analyses. After pre-processing, further downstream analyses (cell- and gene-level) were conducted to extract biological meanings. Cells can be primarily clustered according to their gene expression similarity using k-means clustering or graph-based community detection algorithms, followed by cluster annotation, which resolves cellular heterogeneity. Although some automatic annotation methods have been developed[37,38], manual annotation is the most effective annotation method and is based on gene signatures and biological knowledge of the studied cell types. Based on annotated clusters, trajectory analysis, cell-cell interaction analysis, and differential/dynamic gene expression analysis have been widely utilized to reveal biological significance. Trajectory analysis identifies developmental dynamics according to the hypothesis that scRNA-seq captures a snapshot of cells at different states, either based on gene expression distance (e.g., Monocle) or transcript splicing dynamics (e.g., Velocyto). Cell-cell communication analysis uncovers the interaction between cell types/subclusters using various methods[41–44]. Differential/dynamic gene expression analysis between different clusters or along the constructed trajectory helps in the discovery of key biological processes/regulators governing cellular structure/development. Different integrated toolkits, such as the Cellranger pipeline, Seurat, and Monocle, have been developed for simple usage. By using the methods available at each step, prior knowledge on their assumptions, algorithms, biases, and limitations will facilitate an appropriate selection.
Single-cell preparation of endometrial cells
A determining step in the application of scRNA-seq is the preparation of single cells for scRNA-seq library construction. In the endometrium, connective stromal cells can be easily digested, whereas digestion of the epithelial glands is refractory. Table S2, https://links.lww.com/RDM/A7, summarizes the protocols used to obtain the desired cell type(s) from various endometrial samples, including full-thickness endometrium, endometrial biopsy, decidua, and ex vitro cultures. In general, these protocols differ in the enzymes used, digestive conditions, duration, and desired cell type collection.
For full-thickness endometria, different protocols were used based on the desired cell type for collection. Wu et al. employed a two-stage dissociation protocol with collagenase V and trypLE to mainly obtain endometrial stromal and epithelial cells, respectively. Our group employed collagenase III and negative selection using magnetic beads to obtain stromal cells. In these studies, the tissues were digested via incubation at 37 °C, but for different durations, with or without intermediate collection of the dispersed cells (Table S2, https://links.lww.com/RDM/A7). For endometrial biopsy, a single-stage digestion protocol using collagenase IA/IV with incubation at 37°C was adopted by Brosens’ lab (collagenase IA) and Lin’s lab (collagenase IV) to collect endometrial cells. In contrast, Wang et al. and Queckbörner et al. followed a two-stage protocol and opted to performed overnight incubation at 4°C with collagenase A1 and dispase II for the first-stage digestion of stromal cells. In the second stage, Wang et al. digested the remaining tissue using TrypLE Select for 20 min at 37°C with intermittent pipetting homogenization for epithelial cells, whereas Queckbörner et al. further digested the tissue using collagenase III and DNASE for 45 min at 37°C with agitation. Recently, a specialized equipment (Miltenyi GentleMACS Dissociator) and cold-active proteases or dispases were employed for endometrial biopsy dissociation. Decidua is another type of endometrial biopsy specimen. Simple one-stage digestion protocols using collagenase V/IV or a commercial kit with incubation at 37°C for different durations were adapted in several studies[52–55]. Notably, some studies used the GentleMACS dissociator[52,54]. For ex vivo cultures, including monolayer cells and organoids, simple trypsinization at 37°C was utilized to obtain single cells[36,56]. DNaseI is not consistently used in the aforementioned protocols.
After single cells are collected from digestion, red blood cells and dead cells are usually removed. Two methods are currently available to remove red blood cells: Ficoll-Paque density gradient and red blood cell lysis. Based on our experience, the Ficoll gradient centrifugation method causes less damage to cells but achieves low elimination efficiency compared to the lysis method. Therefore, most studies have used commercial kits to lyse red blood cells in single-cell suspensions. Removal of dead cells using a commercial kit (Miltenyi Biotec) is sometimes applied when the cell viability is not qualified for single-cell isolation and library construction.
In summary, various digestion protocols have been utilized in single-cell endometrial research and their use may affect the final cell composition, viability, and gene profile of the cell preparations. Indeed, high temperatures impose severe stress on cells, inducing changes in the gene expression profile. Thus, standardization of endometrial dissociation is critical for the replication and generalization of biological findings. In addition to tissue digestion, preprocessing and preservation of tissues before digestion are also important in scRNA-seq studies. Some studies preserved the tissue until further processing, while others processed the fresh tissue immediately. Although one study reported small variations in bulk-RNA expression in uterine myometrium tissue when stored in different ways, the impact of storage on endometrium and single-cell RNA expression is unknown. Altogether, we suggest standard pre-processing and digestion of endometrial tissues in scRNA-seq experiments, which will benefit biological generalization and reproducibility.
Application of single-cell analysis in endometrial research
The endometrium and lining of the uterine cavity undergo cyclical shedding and regeneration in women of reproductive age and become receptive to embryos in the WOI. The endometrium differs in morphology during the menstrual, proliferative, and early, mid, and late secretory phases. The human endometrium consists of two layers: the functionalis and basalis. The upper functionalis layer of the endometrium sheds and regenerates every menstrual cycle, while the basal layer of the endometrium is a constant layer that facilitates endometrial regeneration. Since the first single-cell study of the endometrium, in which a single-cell sequencing protocol was successfully applied to endometrial stromal cells, numerous groups have utilized scRNA-seq to understand endometrial physiology and pathology under different conditions (Fig. 4). As described in the previous section, single-cell analysis can resolve cellular heterogeneity by clustering similar cells into the same group, while separating dissimilar cells; construct a pseudotime trajectory computationally for a biological process based on the transcriptomic capture of unsynchronized cells from the tissue; resolve functional processes according to differential gene expression across clusters or trajectories; infer cell-cell communication by ligand-receptor analysis. In this section, we discussed the biological findings of different single-cell analyses of the endometrium.
Unders tanding endometrial physiology
Cellular heterogeneities in the endometrium
Essential progress has been made in deciphering the cellular heterogeneity of the endometrium using scRNA-seq. Consistently, five major cell types, namely epithelial, stromal, endothelial, supporting, and immune cells, have been recovered in the endometrium from different layers and phases[46,49,50,60,61]. However, within each cell type, different levels of cellular heterogeneity have been discovered across different studies. Glandular (secretory), luminal, and ciliated epithelial cells have been identified by scRNA-seq in the endometrium[49,60,61]. Although the number of ciliated epithelial cells is scarce and far less than that of glandular and luminal epithelial cells, its presence was validated using RNA and protein co-staining of specific marker genes, including CDHR3, C11orf88, C20orf85, and FAM183A, revealed by scRNA-seq. Interestingly, an SOX9+ epithelial population was identified by Garcia-Alonso et al. by integrating data from another study[49,60]. Within this subpopulation, the non-cycling SOX9+LGR5+ sub-cluster located in the surface epithelium, non-cycling SOX9+LGR5- subcluster in the basal glands, and cycling SOX9+ subcluster in the regenerating superficial layer were further defined. As SOX9 and LGR5 have been widely studied as stem/progenitor cell markers[62,63], the SOX9+ epithelial population was suggested to label similar cell identities in the endometrium. Compared with epithelial cell identities, stromal cells are more complex. Stromal cells were classified as non-decidualized (mostly in the proliferative phase), decidualized (in the secretory phase) stromal, or a unique fibroblast cell population located in the basalis. A distinct proliferative stromal cell subpopulation with a distinct expression pattern of proliferative genes was defined by Lv et al. using samples from the proliferative phase. The non-decidualized stromal and proliferative stromal cell subpopulations were validated in an independent study that applied scRNA-seq to principal stromal cells from the proliferative phase. The supporting cell in the endometrium was mainly annotated as pericytes or perivascular cells[49,50,60,61]. Although subclusters of supporting cells were consistently identified by Queckbörner et al. and Garcia-Alonso et al., a remarkable difference was identified. Queckbörner et al. classified a mural subpopulation (CSPG4+CNN1lowMYH11low) and a contractile subpopulation (CSPG−CNN1highMYH11high) from the superficial layer, whereas Garcia-Alonso et al. defined two subpopulations as PV-STEAP4 (endometrial origin) and PV-MYH11 (myometrial origin) from the entire thickness. Whether high MYH11-expressing clusters are similar should be determined in further study. If MYH11 is expressed in the superficial endometrial layer and myometrium, MYH11 might not be a tissue location marker. Endothelial cells comprise a small subpopulation in the endometrium, and the lymphatic endothelial, vein, and artery endothelial subpopulations have been identified by scRNA-seq[60,61]. Immune cells are one of the most studied cell types in the endometrium and have a variety of subtypes. Macrophages, natural killer cells, T cells, and their subpopulations have been differentially detected in various studies[49,60,61]. Other minor cell types, including natural killer T cells, mast cells, dendritic cells, and B cells, were found by scRNA-seq. Similar to the non-pregnant endometrium, subpopulations of epithelial cells, decidual stromal cells, supporting cells, and immune cells are present in the pregnant decidua. Although the relationship between subpopulations in non-pregnant and pregnant endometria remains largely unknown, one study reported the development of a peri-implantation stromal subpopulation into one decidual stromal subpopulation. Cellular heterogeneity was also investigated in the mouse endometrium at the single-cell level[65–67]. The mouse endometrium contains the same main cell type as that of the human endometrium. Subpopulations of each main cell type can be found in the mouse endometrium. Moreover, the markers labeling each subpopulation are similar to those of humans. Thus, the mouse endometrium demonstrated similar cellular composition at the single-cell and molecular levels.
In bioinformatics, cellular heterogeneity is determined by clustering resolution and manual annotation, which are largely subjective. Therefore, different studies have reported different sub-cluster numbers for each main cell type. For example, the study by Wu et al. reported greater sub-clusters than recent studies. Accordingly, an integrated analysis of existing datasets is beneficial for constructing a consensus on the cellular heterogeneity of the human endometrium. In contrast, the endometrium is a very complex tissue whose molecular profile is menstrual cycle- and pregnancy status-dependent; thus, the spatiotemporal dynamics of different subpopulations must be revealed by combining different sources of datasets. Nevertheless, the application of scRNA-seq in endometrial research has allowed us to understand the widespread heterogeneity that requires extensive functional analysis and validation.
Endometrial stem cells (eSCs) have been actively studied since their discovery decades ago. Cyclical endometrial epithelial and stromal regeneration have been attributed to endometrial epithelial progenitors and endometrial mesenchymal stromal cells (eMSCs), respectively. Despite extensive research on eSCs in recent years, knowledge of their identity, location, and regulatory mechanisms remains largely unknown. However, the recent application of scRNA-seq in eSC research has advanced our understanding of eSCs.
ScRNA-seq identified potential epithelial stem cell populations and markers in primary endometrial tissues/cells. A SOX9+ epithelial sub-population is suggested to be responsible for epithelial regeneration. This sub-population was found to contain non-cycling SOX9+LGR5+ cells in the surface epithelium, SOX9+LGR5− cells in the basal glands, and proliferative SOX9+ cells in the regenerating superficial layer, suggesting a continuous landscape of stem cell activity from quiescent non-cycling SOX9+ cells to activated cycling SOX9+ cells. This SOX9+ cell population was induced by estrogen treatment in ex vivo endometrial epithelial organoids (EEO), indicating the activation role of estrogen in stem cell differentiation. EEO has been developed and characterized as an effective ex vivo model for studying endometrial biology[69–71]; its long-term culture suggests the presence of epithelial stem cells, which ensures their proliferation. A stem cell population responsive to hormones in EEO was also identified in another study. SOX9 and LGR5 were remarkably expressed in this population based on a manual check of the supplementary data; however, this result was not included in the main text. Nonetheless, the relationship between the stem cell population identified by Fitzgerald et al. and the SOX9+ population revealed by Garcia-Alonso et al. requires further comparison[56,60]. ScRNA-seq of developing mouse uterine epithelial cells revealed an Aldh1a1-marking stem cell population in which Lgr5 was also highly expressed. LGR5 has been identified as an epithelial stem cell in the uterus of mice. Of note, human SOX9+LGR5+ cells are located in the surface epithelium, whereas mouse Lgr5-expressing cells are located at the tip of the gland. Thus, the ability of SOX9 and LGR5 to co-mark epithelial stem/progenitor cells in the human endometrium requires further validation.
ScRNA-seq answers clear questions in endometrial stromal stem cell research. Endometrial MSCs can be enriched by two sets of surface markers: the co-expression of CD140b (PDGFRβ) and CD146 (MCAM) or expression of the single marker, SUSD2. These cells display the properties of MSC in vitro. Two sets of markers were discovered with limited known perivascular markers based on the hypothesis that the perivascular niche residues mesenchymal stem cells. At the global and single-cell levels, PDGFRβ, MCAM, and SUSD2 were not exclusively expressed in a single endometrial cell cluster either individually or in combination, indicating their inability to identify a pure stem cell population[49,50]. A more comprehensive perivascular niche for stromal regeneration was constructed by Queckbörner et al., among which new progenitor sub-populations (CSPG4+CNN1lowMYH11low and CSPG4−CNN1highMYH11high) were proposed through expression and trajectory analysis. However, the perivascular niche for in vivo regeneration remains controversial. The stem cell identity and regenerative ability of eMSCs were validated using in vitro assays and expression profile analysis at the single-cell level. However, the in vivo eMSC identity was questioned through a comparison analysis between in vivo stromal cells co-expressing PDFGRB and MCAM and cultured eMSC. A change in the phenotype of endometrial stromal cells upon culture was reported in another single-cell study. Endometrial stromal cells without specific marker enrichment exhibit mesenchymal stem cell properties in vitro, suggesting the bias/ineffectiveness of current isolation marker sets. In mice, CD34+KLF4+ and Misr2+ stromal cells have been suggested as stem cells in the endometrium[77,78]; however, neither was reported in the human endometrium by scRNA-seq. Collectively, these results indicate that the identity of stem cells in endometrial stromal cells remains elusive. Such identification might be achieved by integrating the analysis, given the availability of datasets covering the functionalis and basalis from different phases.
Although the identification of endometrial stem cells responsible for endometrial regeneration requires further validation and exploration, proliferative activity, which is also essential for regeneration, has been consistently observed in epithelial and stromal cell sub-populations during the proliferative phase[50,60,61].
Differentiation and decidualization
The epithelial and stromal cell differentiation of the estradiol-primed endometrium is important events during decidualization, which include the accumulation of specialized uterine immune cells and vascular remodeling. Decidualization is critical for normal endometrial function, such as receptivity for embryo implantation and pregnancy maintenance. The hormone signaling pathway is essential for decidualization.
Epithelial cell differentiation has been extensively examined at the single-cell level. In vivo, the identified ciliated epithelial cells persist from proliferative to secretory phases, suggesting that ciliary differentiation is induced by estrogen alone. Ciliary differentiation is further enhanced by progesterone addition[56,60]. Unlike ciliary differentiation, secretory epithelial cell differentiation depends on progesterone administration[56,60]. A secretory phenotype is mainly observed in the secretory phase or upon progesterone treatment[56,60]. The balance between ciliary and secretory lineage differentiation is governed by the WNT and NOTCH signaling pathways, and WNT activation facilitates ciliary differentiation, while NOTCH activation facilitates secretory differentiation. Compared with epithelial cell differentiation, stromal cell differentiation in the endometrium remains elusive. ScRNA-seq identified proliferative stromal cells in the proliferative phase and decidualised stromal cells in the secretory phase. Activation of the nuclear progesterone receptor and cyclic AMP/protein kinase A (PKA) pathways induces decidual stromal cell differentiation in vitro. Recently, this axis was questioned by a new axis, PGE2/EP2/PKA, which induces endometrial stromal cell decidualization . The two mechanisms of decidualization differ in senescent decidual cell generation highlighting the need to dissect the in vivo events[47,80]. EGF and IHH signaling pathways have also been proposed by scRNA-seq to modulate stromal cell proliferation and decidualization; however, only proliferative activities have been testified in stromal cells.
Successful decidualization prepares the endometrium for embryo implantation. By performing scRNA-seq of the human endometrium across the menstrual cycle and using a mutual information-based approach, Wang et al. for the first time established a molecular menstrual cycle with WOI definition, which is highly correlated with canonical menstrual dating. Based on the molecular menstrual cycle, a discontinuous expression change in the unciliated epithelial cells was observed upon WOI opening, suggesting a sudden activation of secretory differentiation. In contrast, decidualization in stromal cells is a gradual process, with the observation of clearer modules of genes with continuous upregulation. Moreover, stromal cell decidualization occurred before WOI. Unlike the abrupt opening of the WOI in unciliated epithelial cells, the closure of the WOI is continuous in both cell types. By comparing the WOI definitive genes to the translated WOI signature, we found few overlaps (data not shown), suggesting a substantial difference between bulk- and single-cell studies. The mechanisms of decidualization and establishment of the WOI are essential for reproductive disorders and might be revealed by detailed analysis of specific gene sets, including global transcriptional regulators, nuclear receptors for steroid hormones, and genes encoding secretory proteins. Decidualization is deemed incomplete without the stimuli of embryos. Accordingly, Diniz-da-Costa et al. discovered a highly proliferative decidual precursor sub-population around stromal spiral arterioles as progenitors of a stromal decidual sub-population in early pregnant decidua, supporting the above notion of incomplete decidualization during WOI and further decidualization upon embryo attachment[55,64]. Further decidualization has been supported by the detection of widespread endometrial expression changes upon embryo implantation[66,67].
Crosstalk between cell types in the endometrial microenvironment
Interactions between different endometrial cell-type shape signaling compartments and endometrial function. Using scRNA-seq data, several methods have been developed to investigate cell-cell communication[41–43], with remarkable crosstalk found between different cell types in the endometrium. Among them, epithelial and endothelial cells demonstrated the highest number of connections with the remaining cell types. Epithelial secretory lineage differentiation is regulated by the WNT and NOTCH signaling pathways. The expression of WNT agonists in the proliferative phase and the expression of WNT inhibitors during decidualization in stromal cells strongly indicate its role in modulating the WNT pathway in epithelial cells, which finally affects epithelial lineage differentiation. The expression of WNT5A by stromal and perivascular cells suggests its role in regulating epithelial polarity through receptors expressed by epithelial cells. As signal receivers, stromal and perivascular cells can be regulated by epithelial and natural killer cells that produce EGFs. The infiltration of lymphocytes, which increased in abundance in the secretory phase, had a unique expression profile, particularly in CD3− and CD3+ subsets during endometrial decidualization. Their direct interplay with stromal cells was revealed by the ligand-receptor expression profile (e.g., IL15 in stromal cells and IL2RB in lymphocytes) and the spatial proximity between the sub-populations of the two cell types in situ. Using ligand-receptor analysis at the single-cell level, bioinformatics methods have proposed numerous interactions between different cell types. How these interactions shape endometrial function requires further investigation.
Exploring endometrial pathology
Endometrium remodels cyclically with menstruation, regeneration, differentiation, and decidualization. Dysfunctions at either stage can lead to endometrial disorders. Using scRNA-seq, some endometrial disorders were investigated at the cellular and molecular levels, which revealed new mechanisms. In this section, we aimed to mainly discuss endometriosis, adenomyosis, thin endometrium, and recurrent pregnancy loss (RPL), which have been studied using scRNA-seq technology.
Endometriosis and adenomyosis
Endometriosis is a benign gynecological disease that affects approximately 10% of the female population and is characterized by the presence of ectopic (outside the uterus) endometrial-like tissues and infertility. The etiology and pathology of endometriosis remain unclear, although the retrograde menstruation theory has long been proposed. Endometrial stem/progenitor cells have long been reported in endometriotic lesions. Consistently, signatures of the suggested epithelial stem/progenitor sub-populations (SOX9+ and SOX9+LGR5+) by scRNA-seq were upregulated in endometriotic peritoneal lesions compared to normal peritoneum, suggesting that epithelial stem/progenitor cells are a driver of endometriosis. Endometrial stromal cells were also found to contribute to endometriotic pathogenesis by identifying specific stromal sub-populations in ectopic, eutopic, and normal endometria. Dysregulation of StAR in endometriotic stromal cells may lead to abnormal steroid hormone synthesis, causing endometriosis, suggesting the involvement of hormone-induced endometrial regeneration and differentiation. Moreover, fewer activated T cells, a decreased percentage of NK cells, and an increased proportion of macrophages were observed in endometriosis. The unique immune microenvironment of endometriotic lesions might be caused by the unique perivascular mural cells in endometriotic lesions, which affect immune cell trafficking.
Adenomyosis, which is also characteristic of ectopic endometrial growth, is an endometrial disorder characterized by the growth of endometrial tissue into the myometrium. By employing scRNA-seq, Liu et al. not only identified important roles of conventional functional programs, including cancer, cell motility, inflammation, cell proliferation, and angiogenesis in the development of adenomyosis but also revealed a novel mechanism for adenomyosis progression by identifying the epithelial-endothelial transition cluster with the formation of vasculogenic mimicry in the ectopic lesions of adenomyosis. As adenomyosis is characterized by thickening and disruption of the endo-myometrial junctional zone, myometrial dysregulation might also be crucial for adenomyosis pathology. Comparing smooth muscle cells from adenomyosis to those from normal controls may help in the identification of new mechanisms for adenomyosis.
Thin endometrium, diagnosed with endometrial thickness less than 7 mm by ultrasound, has been recognized as a crucial factor for infertility and adverse pregnancy outcomes. Thin endometrium might be related to endometrial proliferation owing to its insufficiency. A recent scRNA-seq study associated thin endometrium with proliferative stromal cell sub-population; cellular senescence of stromal, epithelial, and perivascular cells; collagen overdeposition; and decreased number of T cells, NK cells, and macrophages, which collectively reduced endometrial proliferation. Mechanistic studies suggested that dysregulated SEMA3, EGF, PTN, and TWEAK signaling pathways are responsible for compromised endometrial proliferation, providing therapeutic insights for thin endometrium.
Recurrent pregnancy loss
RPL, also referred to as recurrent miscarriage (RM), develops with multiple contributing factors, such as fetal chromosomal abnormalities and abnormal endometrial decidualization. To decipher the underlying mechanism of RPL, several research groups have utilized scRNA-seq technology.
Dysregulation of the immune microenvironment has been identified in both peripheral and decidual leukocytes, contributing to RPL[53,54]. In particular, enhanced pro-inflammatory status in peripheral blood and preferential immune activation in decidual leukocytes may lead to embryo rejection. NK cells are the most abundant immune cell type in the decidua. Different sub-populations of decidual NK (dNK) cells have been identified in normal early pregnancy. In RPL, an abnormal abundance of dNK subsets has been suggested as the cause of RPL[52–54]. In particular, a decrease in the dNK subset supporting embryonic growth, decrease in the dNK subset (CSF1+ CD59+ KIRs-expressing), decrease in the dNK subset with angiogenic function, increase in the dNK subset characteristic of cytotoxic and immune activation, and increase in the dNK subset (CD56+CD16+) in RPL highlighted the role of dNKs in the pathogenesis of RPL; however, the function of these subsets requires further investigation. Besides dNKs, different macrophage polarization, accumulation of activated dendritic cell sub-population, and abnormal distribution of T cell subtypes were indicated in RPL[52–54,86,87].
Dysregulation of decidual stromal cells has been implicated in the development of RPL. Abnormal decidualization of stromal cells in early pregnancy decidua can lead to a destructive stromal niche that aberrantly activates immune cells, such as dNK cells, leading to stromal cell demise contributing to RPL. Aberrant decidualization of stromal cells before embryo implantation has been suggested in RPL. Senescent decidual stromal cells during peri-implantation are required for normal decidualization, and the abnormal senescent decidual response is associated with RPL. The development of a decidual progenitor population during WOI into the decidual stromal cell sub-population in early pregnancy highlights the possibility of pre-pregnancy screening and intervention in preventing RPL[47,64].
Conclusion and perspective
Herein, we summarized the scRNA-seq technology and discussed its application in endometrial research. Plate-based, bead-based, and combinatorial index-based scRNA-seq protocols have been developed and compared in terms of sensitivity, accuracy, and capacity to recover known information[24–26]. Although 10X chromium has the highest reproducibility and is now the most popular platform, the decision on which platform to use depends on the balance between the research aim and funding. However, for the endometrium, droplet-based methods are recommended owing to the relatively easy tissue accessibility and the existence of a rare cell population. Although this study mainly discussed scRNA-seq, other single-cell technologies involving other single/combined genomic level(s) (genome, methylation, chromatin accessibility, protein) and spatial information are rapidly being developed to reconstruct the molecular regulatory architecture for a given tissue. Within this context, a combination of different methods can help identify complementary information. For example, shallow, but high-throughput scRNA-seq coupled with deep sequencing of a newly identified cell type can help in biological discovery and detailed characterization. Furthermore, scRNA-seq captures cellular heterogeneity without cell positional information, whereas single-spot spatial transcriptomic sequencing can capture the local microenvironment using tissue sections and spatial barcodes. By combining scRNA-seq and spatial transcriptomic data, researchers can map the identified cell population to tissue space. Single-cell research has entered a multi-omics era with the development of multi-omics detection within one cell[11,89]. Multi-omics is useful for determining molecular mechanisms; however, challenges and difficulties in data analysis remain to be overcome..
In the past decades, scRNA-seq has been utilized in many research areas, such as cancer, immunology, and embryogenesis. ScRNA-seq and spatial transcriptomic sequencing have also benefited endometrial research, facilitating further understanding of endometrial physiology and pathology[49,60,61]. Herein, we summarized recent findings obtained using scRNA-seq in endometrial physiology (heterogeneity, regeneration, differentiation, and decidualization) and pathology (endometriosis, adenomyosis, thin endometrium, and RPL). However, most findings were obtained from pure data analysis without experimental validation. Moreover, different studies processed their samples with different protocols and analyzed their data using different methods, leading to the question: Could the conclusions be generalized? Therefore, in the future, a study that integrates current scRNA-seq datasets from endometrial research to establish a comprehensive framework would be meaningful. Regarding integration, researchers should standardize the protocols to avoid batch effects and choose appropriate integration methods during data analysis. Batch effect is the biggest analysis problem in scRNA-seq data owing to tissue processing at different times[27,29]. To resolve this problem, single-cell nuclear RNA sequencing (snRNA-seq) can be utilized. Using this method, the collected tissues can be archived in liquid nitrogen and processed in the same batch for nuclear extraction. SnRNA-seq produces transcriptomic data that are highly correlated with that of scRNA-seq, suggesting the ability of snRNA-seq to reveal biological insights; however, snRNA-seq should also be used for its lack of some cell types and generation of some cells with unknown identity. Although scRNA-seq helps advance our knowledge of the endometrium, its application remains limited (Fig. 4). More assessments should be performed to identify the regulatory network and mechanism of endometrial regeneration, differentiation, and decidualization, and how endometrial dysregulation contributes to the development of reproductive disorders. Owing to the accumulation of endometrial data at the transcriptomic level, other genomic levels, such as epigenetic regulation and chromatin remodeling, are unknown at the single-cell level. Therefore, in addition to scRNA-seq, the application of these techniques should help establish a comprehensive regulatory network of endometrial functions. In the future, we foresee that the challenges will be multi-omics data integration/analysis and biological interpretation.
Supplementary information is linked to the online version of the paper on the Reproductive and Developmental Medicine website.
D.C. reviewed the literature and wrote the paper with contributions from J.W. W.S.B.Y. supervised the study. W.S.B.Y. and Y.Y. critically reviewed the manuscript. All authors have read and approved the final manuscript.
This study was supported by funding from the Shenzhen Knowledge Innovation Programme of the Shenzhen Science and Technology Innovation Commission (JCYJ20180508153031952), and The University of Hong Kong - Shenzhen Hospital Scientific Research Training Plan (HKUSZH20192003).
Conflicts of interest
All authors declare that they have no conflict of interest.
. Mumusoglu S, Polat M, Ozbek IY, et al. Preparation of the endometrium for frozen embryo transfer: a systematic review. Front Endocrinol. 2021;12:688237. doi:10.3389/fendo.2021.688237.
. Critchley HOD, Maybin JA, Armstrong GM, et al. Physiology of the endometrium and regulation of menstruation. Physiol Rev. 2020;100(3):1149–1179. doi:10.1152/physrev.00031.2019.
. Maybin JA, Critchley HO. Menstrual physiology: implications for endometrial pathology
and beyond. Hum Reprod Update. 2015;21(6):748–761. doi:10.1093/humupd/dmv038.
. Gargett CE, Schwab KE, Deane JA. Endometrial stem/progenitor cells: the first 10 years. Hum Reprod Update. 2016;22(2):137–163. doi:10.1093/humupd/dmv051.
. Noyes RW, Hertig AT, Rock J. Dating the endometrial biopsy. Am J Obstet Gynecol. 1975;122(2):262–263. doi:10.1016/s0002-9378(16)33500-1.
. Sebastian-Leon P, Devesa-Peiro A, Aleman A, et al. Transcriptional changes through menstrual cycle reveal a global transcriptional derepression underlying the molecular mechanism involved in the window of implantation. Mol Hum Reprod. 2021;27(5):gaab027. doi:10.1093/molehr/gaab027.
. Ruiz-Alonso M, Blesa D, Simón C. The genomics of the human endometrium. Biochim Biophys Acta. 2012;1822(12):1931–1942. doi:10.1016/j.bbadis.2012.05.004.
. Díaz-Gimeno P, Horcajadas JA, Martínez-Conejero JA, et al. A genomic diagnostic tool for human endometrial receptivity based on the transcriptomic signature. Fertil Steril. 2011;95(1):50–60, 60.e1. doi:10.1016/j.fertnstert.2010.04.063.
. Devesa-Peiro A, Sebastian-Leon P, Garcia-Garcia F, et al. Uterine disorders affecting female fertility: what are the molecular functions altered in endometrium? Fertil Steril. 2020;113(6):1261–1274. doi:10.1016/j.fertnstert.2020.01.025.
. Svensson V, Vento-Tormo R, Teichmann SA. Exponential scaling of single-cell RNA-seq in the past decade. Nat Protoc. 2018;13(4):599–604. doi:10.1038/nprot.2017.149.
. Kashima Y, Sakamoto Y, Kaneko K, et al. Single-cell sequencing techniques from individual to multiomics analyses. Exp Mol Med. 2020;52(9):1419–1427. doi:10.1038/s12276-020-00499-2.
. Chen X, Teichmann SA, Meyer KB. From tissues to cell types and back: single-cell gene expression analysis of tissue architecture. Annu Rev Biomed Data Sci. 2018;1(1):29–51. doi:10.1146/annurev-biodatasci-080917-013452.
. Gierahn TM, Wadsworth MH 2nd, Hughes TK, et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat Methods. 2017;14(4):395–398. doi:10.1038/nmeth.4179.
. Fan HC, Fu GK, Fodor SP. Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science. 2015;347(6222):1258367. doi:10.1126/science.1258367.
. Macosko EZ, Basu A, Satija R, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161(5):1202–1214. doi:10.1016/j.cell.2015.05.002.
. Klein AM, Mazutis L, Akartuna I, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell. 2015;161(5):1187–1201. doi:10.1016/j.cell.2015.04.044.
. Zheng GX, Terry JM, Belgrader P, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun. 2017;8:14049. doi:10.1038/ncomms14049.
. Zhu YY, Machleder EM, Chenchik A, et al. Reverse transcriptase template switching: a SMART approach for full-length cDNA library construction. Biotechniques. 2001;30(4):892–897. doi:10.2144/01304pf02.
. Sasagawa Y, Nikaido I, Hayashi T, et al. Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biol. 2013;14(4):R31. doi:10.1186/gb-2013-14-4-r31.
. Picelli S, Björklund AK, Faridani OR, et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods. 2013;10(11):1096–1098. doi:10.1038/nmeth.2639.
. Jaitin DA, Kenigsberg E, Keren-Shaul H, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science. 2014;343(6172):776–779. doi:10.1126/science.1247651.
. Cao J, Packer JS, Ramani V, et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science. 2017;357(6352):661–667. doi:10.1126/science.aam8940.
. Rosenberg AB, Roco CM, Muscat RA, et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science. 2018;360(6385):176–182. doi:10.1126/science.aam8999.
. Svensson V, Natarajan KN, Ly LH, et al. Power analysis of single-cell RNA-sequencing experiments. Nat Methods. 2017;14(4):381–387. doi:10.1038/nmeth.4220.
. Ziegenhain C, Vieth B, Parekh S, et al. Comparative analysis of single-cell RNA sequencing methods. Mol Cell. 2017;65(4):631–643.e4. doi:10.1016/j.molcel.2017.01.023.
. Ding J, Adiconis X, Simmons SK, et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol. 2020;38(6):737–746. doi:10.1038/s41587-020-0465-8.
. Chen W, Zhao Y, Chen X, et al. A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples. Nat Biotechnol. 2021;39(9):1103–1114. doi:10.1038/s41587-020-00748-9.
. Saelens W, Cannoodt R, Todorov H, et al. A comparison of single-cell trajectory inference methods. Nat Biotechnol. 2019;37(5):547–554. doi:10.1038/s41587-019-0071-9.
. Kharchenko PV. The triumphs and limitations of computational methods for scRNA-seq. Nat Methods. 2021;18(7):723–732. doi:10.1038/s41592-021-01171-x.
. Luecken MD, Theis FJ. Theis, Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol. 2019;15(6):e8746. doi:10.15252/msb.20188746.
. Stuart T, Butler A, Hoffman P, et al. Comprehensive integration of single-cell data. Cell. 2019;177(7):1888–1902.e21. doi:10.1016/j.cell.2019.05.031.
. Tran HTN, Ang KS, Chevrier M, et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 2020;21(1):12. doi:10.1186/s13059-019-1850-9.
. Butler A, Hoffman P, Smibert P, et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411–420. doi:10.1038/nbt.4096.
. Haghverdi L, Lun ATL, Morgan MD, et al. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol. 2018;36(5):421–427. doi:10.1038/nbt.4091.
. Korsunsky I, Millard N, Fan J, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019;16(12):1289–1296. doi:10.1038/s41592-019-0619-0.
. Cao D, Chan RWS, Ng EHY, et al. Single-cell RNA sequencing of cultured human endometrial CD140b(+)CD146(+) perivascular cells highlights the importance of in vivo microenvironment. Stem Cell Res Ther. 2021;12(1):306. doi:10.1186/s13287-021-02354-1.
. Abdelaal T, Michielsen L, Cats D, et al. A comparison of automatic cell identification methods for single-cell RNA sequencing data. Genome Biol. 2019;20(1):194. doi:10.1186/s13059-019-1795-z.
. Clarke ZA, Andrews TS, Atif J, et al. Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods. Nat Protoc. 2021;16(6):2749–2764. doi:10.1038/s41596-021-00534-0.
. Trapnell C, Cacchiarelli D, Grimsby J, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32(4):381–386. doi:10.1038/nbt.2859.
. La Manno G, Soldatov R, Zeisel A, et al. RNA velocity of single cells. Nature. 2018;560(7719):494–498. doi:10.1038/s41586-018-0414-6.
. Efremova M, Vento-Tormo M, Teichmann SA, et al. CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat Protoc. 2020;15(4):1484–1506. doi:10.1038/s41596-020-0292-x.
. Jin S, Guerrero-Juarez CF, Zhang L, et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun. 2021;12(1):1088. doi:10.1038/s41467-021-21246-9.
. Cabello-Aguilar S, Alame M, Kon-Sun-Tack F, et al. SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics. Nucleic Acids Res. 2020;48(10):e55. doi:10.1093/nar/gkaa183.
. Dimitrov D, Türei D, Boys C, et al. Comparison of resources and methods to infer cell-cell communication from single-cell RNA data. bioRxiv. 2021. doi:10.1101/2021.05.21.445160.
. Satija R, Farrell JA, Gennert D, et al. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015;33(5):495–502. doi:10.1038/nbt.3192.
. Wu B, Li Y, Liu Y, et al. Cell atlas of human uterus. bioRxiv. 2018;267849. doi:10.1101/267849.
. Lucas ES, Vrljicak P, Muter J, et al. Recurrent pregnancy loss is associated with a pro-senescent decidual response during the peri-implantation window. Commun Biol. 2020;3(1):37. doi:10.1038/s42003-020-0763-1.
. Ma J, Zhang L, Zhan H, et al. Single-cell transcriptomic analysis of endometriosis provides insights into fibroblast fates and immune cell heterogeneity. Cell Biosci. 2021;11(1):125. doi:10.1186/s13578-021-00637-x.
. Wang W, Vilella F, Alama P, et al. Single-cell transcriptomic atlas of the human endometrium during the menstrual cycle. Nat Med. 2020;26(10):1644–1653. doi:10.1038/s41591-020-1040-z.
. Queckbörner S, von Grothusen C, Boggavarapu NR, et al. Stromal heterogeneity in the human proliferative endometrium-a single-cell RNA sequencing study. J Pers Med. 2021;11(6):448. doi:10.3390/JPM11060448.
. Courtois E, Tan Y, Flynn W, et al. Single cell analysis of endometriosis reveals a coordinated transcriptional program driving immunotolerance and angiogenesis across eutopic and ectopic tissues. bioRxiv. 2021. doi:10.21203/RS.3.RS-745435/V1.
. Chen P, Zhou L, Chen J, et al. The immune atlas of human deciduas with unexplained recurrent pregnancy loss. Front Immunol. 2021;12:689019. doi:10.3389/fimmu.2021.689019.
. Guo C, Cai P, Jin L, et al. Single-cell profiling of the human decidual immune microenvironment in patients with recurrent pregnancy loss. Cell Discov. 2021;7(1):1. doi:10.1038/s41421-020-00236-z.
. Wang F, Jia W, Fan M, et al. Single-cell immune landscape of human recurrent miscarriage. Genomics Proteomics Bioinformatics. 2021;19(2):208–222. doi:10.1016/j.gpb.2020.11.002.
. Vento-Tormo R, Efremova M, Botting RA, et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature. 2018;563(7731):347–353. doi:10.1038/s41586-018-0698-6.
. Fitzgerald HC, Dhakal P, Behura SK, et al. Self-renewing endometrial epithelial organoids of the human uterus. Proc Natl Acad Sci USA. 2019;116(46):23132–23142. doi:10.1073/pnas.1915389116.
. O’Flanagan CH, Campbell KR, Zhang AW, et al. Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses. Genome Biol. 2019;20(1):210. doi:10.1186/s13059-019-1830-0.
. Mutter GL, Zahrieh D, Liu C, et al. Comparison of frozen and RNALater solid tissue storage methods for use in RNA expression microarrays. BMC Genomics. 2004;5:88. doi:10.1186/1471-2164-5-88.
. Krjutškov K, Katayama S, Saare M, et al. Single-cell transcriptome analysis of endometrial tissue. Hum Reprod. 2016;31(4):844–853. doi:10.1093/humrep/dew008.
. Garcia-Alonso L, Handfield LF, Roberts K, et al. Mapping the temporal and spatial dynamics of the human endometrium in vivo and in vitro. Nat Genet. 2021;53(12):1698–1711. doi:10.1038/s41588-021-00972-2.
. Lv H, Zhao G, Jiang P, et al. Deciphering the endometrial niche of human thin endometrium at single-cell resolution. Proc Natl Acad Sci USA. 2022;119(8):e2115912119. doi:10.1073/pnas.2115912119.
. Jo A, Denduluri S, Zhang B, et al. The versatile functions of Sox9 in development, stem cells, and human diseases. Genes Dis. 2014;1(2):149–161. doi:10.1016/j.gendis.2014.09.004.
. Leung C, Tan SH, Barker N. Recent advances in Lgr5(+) stem cell research. Trends Cell Biol. 2018;28(5):380–391. doi:10.1016/j.tcb.2018.01.010.
. Diniz-da-Costa M, Kong CS, Fishwick KJ, et al. Characterization of highly proliferative decidual precursor cells during the window of implantation in human endometrium. Stem Cells. 2021;39(8):1067–1080. doi:10.1002/stem.3367.
. Kirkwood PM, Gibson DA, Smith JR, et al. Single-cell RNA sequencing redefines the mesenchymal cell landscape of mouse endometrium. FASEB J. 2021;35(4):e21285. doi:10.1096/fj.202002123R.
. He JP, Tian Q, Zhu QY, et al. Single-cell analysis of mouse uterus at the invasion phase of embryo implantation. Cell Biosci. 2022;12(1):13. doi:10.1186/s13578-022-00749-y.
. Yang Y, Zhu QY, Liu JL. Deciphering mouse uterine receptivity for embryo implantation at single-cell resolution. Cell Prolif. 2021;54(11):e13128. doi:10.1111/cpr.13128.
. Chan RW, Schwab KE, Gargett CE. Clonogenicity of human endometrial epithelial and stromal cells. Biol Reprod. 2004;70(6):1738–1750. doi:10.1095/biolreprod.103.024109.
. Turco MY, Gardner L, Hughes J, et al. Long-term, hormone-responsive organoid cultures of human endometrium in a chemically defined medium. Nat Cell Biol. 2017;19(5):568–577. doi:10.1038/ncb3516.
. Boretto M, Cox B, Noben M, et al. Development of organoids from mouse and human endometrium showing endometrial epithelium physiology and long-term expandability. Development. 2017;144(10):1775–1786. doi:10.1242/dev.148478.
. Boretto M, Maenhoudt N, Luo X, et al. Patient-derived organoids from endometrial disease capture clinical heterogeneity and are amenable to drug screening. Nat Cell Biol. 2019;21(8):1041–1051. doi:10.1038/s41556-019-0360-z.
. Wu B, An C, Li Y, et al. Reconstructing lineage hierarchies of mouse uterus epithelial development using single-cell analysis. Stem Cell Rep. 2017;9(1):381–396. doi:10.1016/j.stemcr.2017.05.022.
. Seishima R, Leung C, Yada S, et al. Neonatal Wnt-dependent Lgr5 positive stem cells are essential for uterine gland development. Nat Commun. 2019;10(1):5378. doi:10.1038/s41467-019-13363-3.
. Schwab KE, Gargett CE. Co-expression of two perivascular cell markers isolates mesenchymal stem-like cells from human endometrium. Hum Reprod. 2007;22(11):2903–2911. doi:10.1093/humrep/dem265.
. Masuda H, Anwar SS, Bühring HJ, et al. A novel marker of human endometrial mesenchymal stem-like cells. Cell Transplant. 2012;21(10):2201–2214. doi:10.3727/096368911X637362.
. Queckbörner S, Syk Lundberg E, Gemzell-Danielsson K, et al. Endometrial stromal cells exhibit a distinct phenotypic and immunomodulatory profile. Stem Cell Res Ther. 2020;11(1):15. doi:10.1186/s13287-019-1496-2.
. Yin M, Zhou HJ, Lin C, et al. CD34(+)KLF4(+) stromal stem cells contribute to endometrial regeneration and repair. Cell Rep. 2019;27(9):2709–2724.e3. doi:10.1016/j.celrep.2019.04.088.
. Saatcioglu HD, Kano M, Horn H, et al. Single-cell sequencing of neonatal uterus reveals an Misr2+ endometrial progenitor indispensable for fertility. Elife. 2019;8:e46349. doi:10.7554/eLife.46349.
. Maruyama T, Yoshimura Y. Molecular and cellular mechanisms for differentiation and regeneration of the uterine endometrium. Endocr J. 2008;55(5):795–810. doi:10.1507/endocrj.k08e-067.
. Stadtmauer DJ, Wagner GP. Single-cell analysis of prostaglandin E2-induced human decidual cell in vitro differentiation: a minimal ancestral deciduogenic signaldagger. Biol Reprod. 2022;106(1):155–172. doi:10.1093/biolre/ioab183.
. Zondervan KT, Becker CM, Missmer SA. Endometriosis. N Engl J Med. 2020;382(13):1244–1256. doi:10.1056/NEJMra1810764.
. Sourial S, Tempest N, Hapangama DK. Theories on the pathogenesis of endometriosis. Int J Reprod Med. 2014;2014:179515. doi:10.1155/2014/179515.
. Liu Z, Sun Z, Liu H, et al. Single-cell transcriptomic analysis of eutopic endometrium and ectopic lesions of adenomyosis. Cell Biosci. 2021;11(1):51. doi:10.1186/s13578-021-00562-z.
. Benagiano G, Brosens I, Habiba M. Structural and molecular features of the endomyometrium in endometriosis and adenomyosis. Hum Reprod Update. 2014;20(3):386–402. doi:10.1093/humupd/dmt052.
. Ford HB, Schust DJ. Recurrent pregnancy loss: etiology, diagnosis, and therapy. Rev Obstet Gynecol. 2009;2(2):76–83.
. Zheng Q, Xu X, Yang F, et al. Single cell transcriptome analysis of decidua macrophages in normal and recurrent spontaneous abortion patients. bioRxiv. 2021. doi:10.1101/2021.03.23.436615.
. Liu H, Lin XX, Huang XB, et al. Systemic characterization of novel immune cell phenotypes in recurrent pregnancy loss. Front Immunol. 2021;12:657552. doi:10.3389/fimmu.2021.657552.
. Du L, Deng W, Zeng S, et al. Single-cell transcriptome analysis reveals defective decidua stromal niche attributes to recurrent spontaneous abortion. Cell Prolif. 2021;54(11):e13125. doi:10.1111/cpr.13125.
. Perkel JM. Single-cell analysis enters the multiomics age. Nature. 2021;595(7868):614–616. doi:10.1038/d41586-021-01994-w.
. Tang X, Huang Y, Lei J, et al. The single-cell sequencing: new developments and medical applications. Cell Biosci. 2019;9:53. doi:10.1186/s13578-019-0314-y.
. Basile G, Kahraman S, Dirice E, et al. Using single-nucleus RNA-sequencing to interrogate transcriptomic profiles of archived human pancreatic islets. Genome Med. 2021;13(1):128. doi:10.1186/s13073-021-00941-8.
. Ramsköld D, Luo S, Wang YC, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol. 2012;30(8):777–782. doi:10.1038/nbt.2282.
. Hagemann-Jensen M, Ziegenhain C, Chen P, et al. Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nat Biotechnol. 2020;38(6):708–714. doi:10.1038/s41587-020-0497-0.
. Islam S, Kjällquist U, Moliner A, et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 2011;21(7):1160–1167. doi:10.1101/gr.110882.110.
. Islam S, Zeisel A, Joost S, et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat Methods. 2014;11(2):163–166. doi:10.1038/nmeth.2772.
. Hochgerner H, Lönnerberg P, Hodge R, et al. STRT-seq-2i: dual-index 5’ single cell and nucleus RNA-seq on an addressable microwell array. Sci Rep. 2017;7(1):16327. doi:10.1038/s41598-017-16546-4.
. Natarajan KN. Single-cell tagged reverse transcription (STRT-Seq). Methods Mol Biol. 2019;1979:133–153. doi:10.1007/978-1-4939-9240-9_9.
. Hashimshony T, Wagner F, Sher N, et al. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2012;2(3):666–673. doi:10.1016/j.celrep.2012.08.003.
. Hashimshony T, Senderovich N, Avital G, et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 2016;17:77. doi:10.1186/s13059-016-0938-8.
. Sasagawa Y, Danno H, Takada H, et al. Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads. Genome Biol. 2018;19(1):29. doi:10.1186/s13059-018-1407-3.
. Habib N, Avraham-Davidi I, Basu A, et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat Methods. 2017;14(10):955–958. doi:10.1038/nmeth.4407.
. Han X, Wang R, Zhou Y, et al. Mapping the mouse cell atlas by microwell-seq. Cell. 2018;172(5):1091–1107.e17. doi:10.1016/j.cell.2018.02.001.
. Hendriks GJ, Jung LA, Larsson AJM, et al. NASC-seq monitors RNA synthesis in single cells. Nat Commun. 2019;10(1):3138. doi:10.1038/s41467-019-11028-9.
. Sheng K, Zong C. Single-cell RNA-seq by multiple annealing and tailing-based quantitative single-cell RNA-seq (MATQ-Seq). Methods Mol Biol. 2019;1979:57–71. doi:10.1007/978-1-4939-9240-9_5.
. Lebrigand K, Magnone V, Barbry P, et al. High throughput error corrected Nanopore single cell transcriptome sequencing. Nat Commun. 2020;11(1):4025. doi:10.1038/s41467-020-17800-6.