Decrypting spatiotemporal code of human endometrial receptivity

Domain: biology
Published: 2025-12-21
Editorial outcome: major_revision

Abstract
The mechanism underlying the establishment of human endometrial receptivity remains elusive, constituting a significant obstacle to advancing our knowledge of female infertility. Via integrating high-resolution spatiotemporal and single-cell transcriptomic profiling and in situ sequencing, we construct a spatiotemporal atlas of human endometrial receptivity at single-cell resolution. Our study depicts detailed spatial molecular topography governing the opening and closing of human endometrial receptivity. Notably, the results indicate that stromal-specific NR2F1, CEBPD and epithelial-specific SGK1, KLF5 and ELF3 are closely associated with the opening of implantation window. Furthermore, cholesterol metabolism is found to promote ciliogenesis. Remarkably, we identify stroma- and epithelium-specific factors and unravel a previously unappreciated role of FGFs-FGFR2 signaling pathway mediating stroma-epithelium crosstalk in epithelial differentiation during the onset of receptivity. This spatiotemporally resolved receptivity code provides insights into the female fertility, with potential implications for the intervention of female infertility.

Introduction
Appropriate preparation of the human endometrium is a critical prerequisite for successful embryo implantation, whose deficiency contributes to female infertility and adverse pregnancy outcomes, affecting women worldwide at childbearing age. Despite significant advancements in assisted reproductive technology (ART), the incidence of embryo implantation failure remains high, ascribed to both seed (embryo) and soil (endometrium)1. While significant advances have been made to improve the development of the embryo, the development of a receptive endometrium has thus emerged as another important determinant of ART outcomes2.

As the site of embryo implantation and subsequent embryonic development in humans, the endometrium undergoes complex and dynamic alterations throughout the menstrual cycle, orchestrated by ovarian hormones3. The endometrial epithelium and stroma undergo profound proliferation after menstruation, contributing to their re-thickening under the influence of preovulatory estrogen. Following ovulation triggered by luteinizing hormone (LH), the endometrium transitions into the secretory phase, characterized by the expansion of glands and stromal edema, driven by elevated progesterone level4. The mid-secretory phase, occurring approximately 7–10 days post-LH surge, is considered a critical window for embryo implantation, known as the implantation window or endometrial receptivity5. In the absence of an implanted embryo, the endometrium transits into the refractory phase or late-secretory phase, in preparation for the subsequent menstruation. Therefore, elucidating the molecular mechanisms underlying the onset of receptivity is crucial for optimizing the timing of embryo transfer in in vitro fertilization and embryo transfer (IVF-ET).

Accompanied by the advancement of genetic models, several key regulators for endometrial receptivity have been identified, including leukemia inhibitory factor (Lif), signal transducer and activator of transcription 3 (Stat3), heart and neural crest derivatives expressed 2 (Hand2), and msh homeobox 1 (Msx1)6–10. However, the prediction of endometrial receptivity in humans remains contentious due to the lack of reliable markers specific to human endometrial receptivity11–13. Although single-cell RNA sequencing (scRNA-seq) has been applied to understand the gene signature of human endometrial receptivity in normal or Asherman’s Syndrome endometrium14,15 and spatial transcriptomics has been utilized to resolve the gene localization in proliferative and secretory endometrium using 10X Visium with 55μm resolution16, the spatiotemporal code around the timing of implantation at high resolution remains to be untangled.

To comprehensively decrypt the spatiotemporal molecular and cellular landscapes of endometrial receptivity, we integrated spatiotemporal enhanced resolution transcriptomic sequencing (Stereo-seq, 500nm resolution), high-throughput in situ sequencing (ISS), and scRNA-seq to decipher the black box of spatial transcriptomics of implantation. In this study, we constructed a spatial atlas of the human endometrium, spanning the transition from the opening to the closing of the implantation window. Our findings unravel that both stroma and epithelium undergo proliferation, differentiation, and degradation along specific pathways. Especially, we dissect out spatial and cell-type-specific markers for the opening of the implantation window, including stromal NR2F1, CEBPD, and epithelial SGK1. Notably, we find that cholesterol metabolism plays a critical role in ciliogenesis. Furthermore, fibroblast growth factors (FGFs) and interleukin 15 (IL-15), derived from the stroma, were found to be important for epithelial differentiation and the dynamic regulation of natural killer (NK) cells during the peri-implantation period, respectively. This study provides insights into the molecular mechanisms of endometrial receptivity establishment through the lens of spatial transcriptomics, providing valuable insights for refining therapeutic strategies in IVF-ET to combat human infertility.

Results
Single-cell spatial atlas of human endometrial receptivity
The human endometrium undergoes dynamic remodeling driven by fluctuating ovarian steroid hormones and is classically divided into proliferative (follicular) and secretory (luteal) phases within an average 28-day menstrual cycle5. A transient window of receptivity during the secretory phase is critical for embryo implantation and the establishment of pregnancy. However, precisely identifying this transient window remains challenging due to the difficulty in accurately determining the timing of ovulation, inter-individual differences in follicular dynamics, stochastic variation in hormonal responses, asynchronous follicle development rates, and so on. To address this, human chorionic gonadotropin (hCG) was administered to the preovulatory follicle to simulate natural cycles17. The endometrial biopsies were collected at 2 (LH2, pre-receptive phase), 7 and 9 (LH7 and LH9, receptive phase), and 12 days (LH12, refractory phase) after hCG injection. These samples were subjected to Stereo-seq and scRNA-seq to decode the spatial-temporal transcriptomic landscapes of receptivity at single-cell resolution (Fig. 1A, Supplementary Fig. 1A). Since progesterone receptor (PR) expression is reported to peak in the epithelium during the early secretory phase and diminish thereafter18,19, PR staining was employed to confirm the sample stages (Supplementary Fig. 1B).

scRNA-seq was applied to interpret the single-cell transcriptomic atlas of human endometrium during the peri-receptive period. After quality control, 82,980 cells were retained for further analyses. The results of scRNA-seq revealed that the superficial endometrium consisted of a variety of cell types, including proliferating stroma (DNA topoisomerase II alpha, TOP2A), stroma (estrogen receptor 1, ESR1), endothelium (platelet and endothelial cell adhesion molecule 1, PECAM1), epithelium (paired box 8, PAX8), cilium (forkhead box J1, FOXJ1), pericyte (regulator of G protein signaling 5, RGS5), NK (granzyme B, GZMB), macrophage (CD14 molecule, CD14), and other lymphocyte (CD96 molecule, CD96) (Fig. 1B, C), which is aligned with previous works3,20 and listed in Supplementary Data 1. Among the major cell populations, the proportion of proliferating stromal cells decreased substantially, while NK cells significantly increased from LH2 to LH12 (Fig. 1D). After integrating our data with recently published scRNA-seq data covering LH3-LH1115, high concordance in major cell-type composition was noticed (Supplementary Fig. 1C–E).

To further depict the spatial distribution and dynamic organization of these distinct cell types around receptivity, Stereo-seq was applied to capture the spatial transcriptomic landscapes of the endometrium during the establishment of receptivity. To enhance the accuracy of spatial cell annotation, particularly for cell types with smaller populations and diffuse distributions, such as endothelial cells and immune cells, Stereo-seq was integrated with scRNA-seq to annotate cells in the endometrium. This combined approach mapped these cell types across 179,520 spatial spots in the endometrium with high precision (Fig. 1E, Supplementary Fig. 2A–C). Our spatial-temporal transcriptomic analysis revealed that stromal cells were the most abundant cell type. Consistent with scRNA-seq results, a notable increase in NK cells was observed at LH9 and LH12 (Fig. 1F). Meanwhile, multiplexed immunofluorescence (mIF) staining was performed to validate the major cell types in the endometrium (Fig. 1G). Together, these results provided a comprehensive depiction of the spatial distribution and dynamic changes in cell populations within the human endometrium during the peri-implantation window.

Molecular and spatial characterizations of stroma
Stromal cells represent the most abundant cell type in the endometrium, which are critical for the establishment of uterine receptivity and the maintenance of pregnancy. The localization of stromal cells was confirmed by the expression of HAND2 and platelet-derived growth factor receptor alpha (PDGFRα) (Supplementary Fig. 2D). Pericytes have been identified as a significant source of endometrial mesenchymal stem/stromal cells (MSCs), marked by high expression of sushi domain containing 2 (SUSD2), cluster of differentiation 146 (CD146), and platelet-derived growth factor receptor beta (PDGFRβ)21. In our study, the transcription factor myocyte enhancer factor 2C (MEF2C) was specifically expressed in pericytes. Immunofluorescence co-localization analysis confirmed that MEF2C expression precisely overlapped with established pericyte markers NG2 and RGS5 (Supplementary Fig. 2E).

To better characterize the molecular heterogeneity within stromal cells, we re-clustered stroma population into four subclusters: proliferating stromal cells (proStr), marked by classical DNA replication genes, such as TOP2A and marker of proliferation Ki-67 (MKI67); undifferentiated stromal cells (undiffStr), enriched for Wnt pathway-related genes associated with maintaining undifferentiated state, including secreted frizzled related protein 4 (SFRP4) and patched 1 (PTCH1); differentiated stromal cells (diffStr), which highly expressed aldehyde dehydrogenase 1 family member A3 (ALDH1A3), nuclear receptor subfamily 2 group F member 1 (NR2F1); and degrading stromal cells (deStr), characterized by the expression of genes such as insulin-like growth factor binding protein 3 (IGFBP3), centrin-like protein (CTN4), and unc-51 like kinase 4 (ULK4) (Fig. 2A–C, Supplementary Fig. 3A). Cell cycle analysis of these subclusters revealed that proStr were predominantly enriched in the S and G2M phases (Fig. 2D). Immunofluorescence staining of WT1 transcription factor (WT1) and MKI67 confirmed significant proliferation in both the stroma and epithelium at LH2. Accompanied by elevated progesterone secreted from the corpus luteum after ovulation, the proliferation of both stroma and epithelium gradually ceased. However, cell proliferation continued in some WT1-negative cells, peaking at LH12, indicating the propagation of other cell types such as immune cells or endothelial cells (Fig. 2E). The subpopulations of stromal cells were further confirmed by integrating our data with recently published work (Supplementary Fig. 3B, C).

To delineate the dynamic changes of stromal cells spatially, these subsets were mapped onto Stereo-seq data. Stromal cells at LH2 were primarily undiffStr and proStr, while the LH7 and LH9 endometrium were mainly characterized by diffStr, and deStr predominantly emerged at LH12 (Fig. 2F). Notably, SFRP4 was highly expressed at LH2, which was critical to maintain the undifferentiated status of stroma.

Our Stereo-seq revealed that NR2F1 exhibited dynamic alterations during the natural cycle in human endometrium with prominent expression in stromal cells at LH7 and LH9, coinciding with the timing of embryo implantation. Interestingly, while NR2F2 exhibited high stromal expression levels comparable to NR2F1, its expression remained remarkably stable from LH2 to LH12 in both scRNA-seq and Stereo-seq analyses (Supplementary Fig. 3D, E). Serpin family G member 1 (SERPING1), a type of serine protease inhibitor involved in the regulation of the complement cascade22, was highly expressed from LH7 to LH12, while thrombospondin 2 (THBS2), a member of the thrombospondin family participating in negative regulation of angiogenesis, was predominantly expressed at LH12 (Fig. 2G). These results were further corroborated by high-throughput probe-based ISS (Fig. 2H, Supplementary Fig. 3F). Together, these studies revealed a detailed spatiotemporally dynamic code of distinct stromal cells across the phases of the endometrial cycle at high spatial resolution (Supplementary Fig. 4A).

The stromal determinant of receptivity establishment
To comprehensively elucidate the functional characteristics of the four stromal cell populations, genes specifically enriched in each population were subjected to the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. This analysis unraveled distinct functional hallmarks of each stromal subset: genes related to cell cycle regulation were predominantly enriched in proStr; undiffStr exhibited significant enrichment in the mTOR and Hedgehog signaling pathways; and diffStr were prominently characterized by enrichment in the extracellular matrix (ECM)-receptor interaction pathways (Fig. 3A, B, Supplementary Fig. 4B). Notably, deStr displayed marked enrichment of genes linked to autophagy and apoptosis pathways, such as B-cell lymphoma 2 (BCL2), growth arrest and DNA damage-inducible alpha and gamma (GADD45A and GADD45G), Sequestosome 1 (SQSTM1), along with canonical autophagy markers microtubule-associated protein 1 light chain 3 alpha and beta (MAP1LC3A and MAP1LC3B) and the apoptosis regulator myeloid cell leukemia 1 (MCL1) (Fig. 3A–C, Supplementary Fig. 4B). Differentially expressed gene (DEG) analysis between stromal cells elucidated that forkhead box O1 (FOXO1), a key gene critical for stromal cell differentiation and an early marker for stromal cell decidualization23, was obviously upregulated in diffStr compared to undiffStr (Fig. 3D, Supplementary Fig. 4C). Additionally, the augmented phospholipase A2 group IIA (PLA2G2A) and prostaglandin D2 synthase (PTGDS) in diffStr signified a potential role of stromal prostaglandin in receptivity opening (Fig. 3D). Meanwhile, there were substantial DEGs between diffStr and deStr, indicating that both the transitions from pre-receptivity to receptivity and from receptivity to the refractory state involved profound transformation of stromal cells (Fig. 3D, E). Furthermore, the comparison of stromal cells between LH2, LH7/9, and LH12 also manifested similar results. Cellular proliferation was the predominant feature of stromal cells at LH2. Upon transitioning to LH7/9, stromal cells exhibited marked upregulation of genes linked to the FOXO1 signaling pathway. Notably, as the process advanced to LH12, stromal cells displayed a distinct shift toward apoptosis, characterized by the elevated expression of cell death-associated genes (Supplementary Fig. 4D).

Given that diffStr encompassed stromal cells from both LH7 and LH9 stages, we also compared gene expression between these two stages. The number of DEGs between LH7 and LH9 diffStr was substantially smaller than in comparisons with DEGs between diffStr and other subtypes. Notable differences included lower expression of ArfGAP with GTPase domain, ankyrin repeat and PH domain 1 (AGAP1), Rho GTPase activating protein 28 (ARHGAP28) and thrombospondin type 1 domain containing 4 (THSD4) at LH7 and reduced expression of lipopolysaccharide binding protein (LBP), arachidonate 15-lipoxygenase type B (ALOX15B) and phospholipase A2 group IIA (PLA2G2A) at LH9 (Supplementary Fig. 5A–C).

To excavate the critical determinant of stroma transformation, regulon analysis was applied, leveraging Single-cell regulatory network inference and clustering (SCENIC). Both regulon activity and expression of the E2F family and forkhead box M1 (FOXM1) were highly enriched in stromal proliferation (Supplementary Fig. 5D), consistent with previous works24. Meanwhile, homeobox A10 (HOXA10) and transcription factor 4 (TCF4) were overtly decreased from proStr and undiffStr to diffStr (Fig. 3F), implying their roles in maintaining the undifferentiated state of stroma. HOXA10 was reported to be closely associated with endometrial receptivity25, while our results showed that although HOXA10 expression was high at LH2, it decreased markedly from LH7 onward in our Stereo-seq and scRNA-seq data (Supplementary Fig. 5E–I), consistent with previous reports showing peak expression in the proliferative and early secretory phases followed by a decline3. Interestingly, we unraveled the previously unappreciated role of CCAAT enhancer binding protein delta (CEBPD) in stroma differentiation (Fig. 3F, G). While CEBPB was consistently expressed and showed no dynamic change in both scRNA-seq and Stereo-seq compared with CEBPD (Supplementary Fig. 5J, K). The results of IHC confirmed the expression and dynamic changes of CEBPD in human endometrium (Supplementary Fig. 5L). The highly expressed and increased activation of AP1 family (JunD proto-oncogene, JUND; Jun proto-oncogene, JUN; Fos proto-oncogene, FOS) and X-box binding protein 1 (XBP1) at LH12 suggested their involvement in stromal degradation and the initiation of menstruation (Fig. 3G, Supplementary Fig. 5D). The dynamic spatial localization of TCF4, CEBPD, and FOS further supported the above speculations (Fig. 3H).

The signal pathway projection further confirmed that IHH and mitophagy were specifically enriched in undiffStr and diffStr, respectively (Fig. 3I, J). Trajectory analysis revealed that IHH-dominant undiffStr exhibited lower pseudotime scores, while deStr displayed the highest pseudotime scores, indicating the differential potential of stromal cells during transformation (Fig. 3K, L). This result is consistent with our previous work that the derailed IHH-PTCH pathway is important for the pathological causes of thin endometrium26. Intriguingly, insulin-like growth factor binding protein 1 (IGFBP1) and prolactin (PRL), two critical markers for stromal cell pre-decidualization and decidualization, only manifested minimal expression in terms of both cell ratio and gene level in stroma in later LH stages based on Stereo-seq and scRNA-seq (Supplementary Fig. 5M, N). Collectively, these results in the stroma provided a comprehensive spatial framework for understanding the molecular dynamics of stromal cells during receptivity onset.

Spatial identification and visualization of epithelium
Epithelium mediates the first direct interplay between the implanting embryo and the receptive endometrium. To extrapolate the diversity and dynamic roles of epithelial cells in endometrial receptivity establishment, we subclustered epithelial cells into seven distinct populations: (1) proliferating epithelial cells (proEpi) with high levels of proliferative genes such as MKI67 and TOP2A; (2) luminal epithelial cells (LE) expressing leucine rich repeat containing G protein-coupled receptor 5 (LGR5) and claudin 22 (CLDN22); (3) glandular epithelial cells (GLE) expressing hydroxysteroid 17-beta dehydrogenase 2 (HSD17B2) and laminin subunit beta 1 (LAMB1); (4) differentiated epithelial cells (diffEpi) expressing metallothionein 2A (MT2A) and metallothionein 1M (MT1M); (5) degraded epithelial cells (deEpi) expressing DNA polymerase gamma 2 (POLG2); (6) senescent epithelial cells (seneEpi) expressing gremlin 2 (GREM2) and disks large MAGUK scaffold protein 2 (DLG2); and (7) Cilium (ciliated epithelial cells) characterized by expression of dynein axonemal heavy chain 12 (DNAH12), cadherin related family member 3 (CDHR3), and HYDIN axonemal central pair apparatus protein (HYDIN) (Fig. 4A, B, Supplementary Fig. 6A). The classification of epithelial subpopulations was further confirmed by projection of recently published data to our data (Supplementary Fig. 6B).

Meanwhile, to thoroughly elucidate the signature of epithelium, we analyzed subcluster-specific genes for functional enrichment. GLE cell-specific genes were highly enriched in “allograft rejection” and “viral infection”, implicating a role in maintaining the endometrial microenvironment via cytokine secretion. Under the influence of accumulated serum progesterone level, proEpi transitioned into diffEpi characterized by enriched FOXO signaling and MAPK resistance signaling (Fig. 4C, D), aligned with the previously reported genetic evidence that epithelial FOXO1 expression and its nuclear translocation are critical for embryo implantation in mice19. SeneEpi exhibited autophagy-related signatures, whereas deEpi were predominantly associated with apoptosis-related pathways. Interestingly, Cilium was associated with NOTCH signaling and motor proteins, which were implicated in ciliogenesis and movement. Immunostaining of E-Cadherin and acetylated Tubulin documented a significantly higher enrichment of Cilium at LH7 (Fig. 4E, Supplementary Fig. 6C).

After projecting these cells into Stereo-seq, proEpi and GLE were abundant during the early LH2 stage, with most LE transitioning into diffEpi by LH7. It was interesting to notice that most of the diffEpi turned into seneEpi with residual diffEpi localized in the same gland at LH9. By the end of LH cycle when the implantation window closed, almost all epithelium transitioned into seneEpi (Fig. 4F, G, Supplementary Fig. 6A). Luminal epithelium, which forms glandular structures through invagination, showed specific expression of genes such as prostaglandin-endoperoxide synthase 1 (PTGS1) and LGR5 in our study (Supplementary Fig. 6D). Immunostaining revealed that COX1, coded by PTGS1, was specifically expressed in LH2 luminal epithelium with also lower expression in glandular epithelium in later stages. While COX2, another member of the cyclooxygenase family, was more specifically localized in luminal epithelium (Supplementary Fig. 6E). Since double deletion of these two genes contributes to complete embryo implantation failure27, our results underscore the potential role of PTGS1 and PTGS2-derived prostaglandins in human embryo implantation.

Surprisingly, co-immunostaining of forkhead box A2 (FOXA2) and keratin 8 (CK8) suggested that FOXA2 was expressed unevenly in glandular epithelium, indicating the unexpected heterogeneity of glandular epithelium (Supplementary Fig. 6F). Indeed, we noticed that Cbp/p300 interacting transactivator with Glu/Asp rich carboxy-terminal domain 4 (CITED4) was gradually decreased in glandular epithelium accompanied with cumulatively increased dipeptidyl peptidase 4 (DPP4) from LH2 to LH12 with the co-expression of these two genes in the same cell at LH7 and LH9 (Fig. 4H, Supplementary Fig. 6G). A similar scenario was also observed for achaete-scute family bHLH transcription factor 5 (ASCL5) and GADD45A (Supplementary Fig. 6H, I). The increased expression of apoptosis-related GADD45A was further reinforced by cleaved Caspase 3 in LH12 epithelium (Supplementary Fig. 6J).

Apart from these genes, we also noticed that MSX1 was highly expressed in LH2 epithelium and gradually decreased to LH12 (Fig. 4I, J, Supplementary Fig. 7A, B). Dual specificity phosphatase 2 (DUSP2), a nuclear phosphatase that inactivates mitogen-activated protein kinase28, exhibited transiently high expression at LH7 (Fig. 4I, J, Supplementary Fig. 7A, B). Serum/glucocorticoid regulated kinase 1 (SGK1), a critical gene mediating rapid progesterone responses29,30, was found highly expressed at LH7 and LH9, heralding its potential role in receptivity opening and closing (Fig. 4I, J, Supplementary Fig. 7A, B). Besides, deregulated SGK1 was documented to be crucial for embryo implantation failure and human recurrent pregnancy loss (RPL)30. In line with previous works, the expressions of glutathione peroxidase 3 (GPX3) and progestagen-associated endometrial protein (PAEP), hallmarks associated with uterine milk in the secretory phase3,31, were persistently increased after the endometrium entered the secretory phase and thereafter (Fig. 4I, J, Supplementary Fig. 7A, B). Additionally, stage-specific gene expression was further revealed, including distal-less homeobox 5 (DLX5), PKHD1-like 1 (PKHD1L1) at LH2, disks large MAGUK scaffold protein 5 (DLG5), and metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) at LH7, C-X-C motif chemokine ligand 14 (CXCL14), DEP domain containing MTOR interacting protein (DEPTOR), and FOXO1 at LH9 (Supplementary Fig. 8A, B). Together, these findings highlighted the dynamic progression of epithelial cell states and gene expression patterns after progesterone exposure, shedding light on the molecular mechanisms underpinning receptivity establishment.

Epithelial cholesterol metabolism is critical for ciliogenesis
To further delineate the molecular characteristics of the epithelial subclusters, we conducted a comparative analysis of DEGs across these populations. The gene expression profiles revealed striking differences among the subclusters (Fig. 5A, Supplementary Fig. 8C). Leveraging SCENIC analysis, we further investigated transcriptional regulon activities, uncovering distinct patterns of transcription factor (TF) enrichment specific to each epithelial subtype. Notably, the expression and regulon activity of FOXO1, a critical epithelial regulator of endometrial receptivity19, were enriched in proEpi and diffEpi, highlighting its role in human embryo implantation. The seneEpi displayed enrichment of senescence-associated transcription factors, such as TP53 and JUN, while the proliferation-related TFs E2F1 and E2F8 were predominantly expressed and activated in proEpi cells (Fig. 5B). While integrins are widely reported as implantation markers, our data revealed that only ITGA3 and ITGB4 were markedly upregulated in the epithelium during receptivity, suggesting that the α3β4 heterodimer may be the predominant integrin involved in embryo attachment (Supplementary Fig. 8D–F).

Interestingly, newly identified cyclic adenosine monophosphate responsive element-binding protein 3 like 1 (CREB3L1), a critical regulator for stromal cell decidualization32, was significantly enriched in proEpi, suggesting its involvement in epithelial differentiation during the proliferative phase (Fig. 5C). Beyond previously reported epithelial-specific SOX1733, we identified other unappreciated epithelial-specific transcription factors, including homer scaffold protein 2 (HOMER2), LH7 specific KLF transcription factor 5 (KLF5) and E74 like ETS transcription factor 3 (ELF3) (Fig. 5D, Supplementary Fig. 9A). LIF-STAT3 signaling pathway was reported to be critical for embryo implantation7,34–37, we therefore detected the expression of genes associated with LIF-STAT3 signaling pathway. It was interesting that the expression of LIF and its receptor IL6ST was increased at LH9, while interleukin 6 (IL6) remained low and LIFR showed no significant changes (Supplementary Fig. 9B). Furthermore, phosphorylated STAT3 (p-STAT3) was primarily localized in stroma at LH2 and LH7, significantly upregulated in the epithelium at LH9, and markedly decreased in both the epithelium and stroma by LH12 (Supplementary Fig. 9C). These results indicated that LIF-STAT3 played an important role in the establishment of endometrial receptivity in humans. Although both LH7 and LH9 belong to the mid-secretory phase, the transient activation of p-STAT3 in the LH9 epithelium implied that LH9 might represent the critical time point for the opening of the implantation window.

Additionally, FOXJ1, a central regulator of ciliogenesis, was exclusively expressed in Cilium, consistent with previous studies3. Cilia were reported to secrete extracellular vesicles, termed ectosomes, at their tip to transport bioactive enzymes, peptide products, or remove activated receptors from the cell surface38. Cholesterol had been reported as essential for ciliogenesis39. Intriguingly, we also found that cholesterol biosynthesis rate-limiting enzymes 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) and squalene epoxidase (SQLE) were specifically expressed in epithelium. More importantly, the expression of HMGCR peaked at LH7 (Fig. 5E, Supplementary Fig. 10A). To validate this, primary epithelium-derived organoids were treated with cholesterol or the HMGCR inhibitor atorvastatin. Results showed that the expression of FOXJ1 and Ac-Tubulin was significantly increased in the presence of cholesterol (Fig. 5F, Supplementary Fig. 10B) and inhibited by atorvastatin (Fig. 5F). In a word, the epithelium underwent dynamic spatial-temporal changes of cell types across the LH cycle and was critical for embryo implantation.

The molecular characterizations of the endothelium in receptivity
Endothelial cells were classified into three subtypes according to the endothelium atlas40, including venous endothelial cells (C-C motif chemokine ligand 14, CCL14; ryanodine receptor 2, RYR2), arterial endothelial cells (gap junction protein alpha 4, GJA4; regulator of calcineurin 2, RCAN2) and proliferating endothelial cells (proEndo) (aurora kinase B, AURKB; kinesin family member 2C, KIF2C) (Fig. 6A–C). Notably, the proportion of proEndo peaked at LH7 and LH9 and was validated by the expression of MKI67 (Fig. 6D). According to KEGG analysis, LH2 endothelial-specific genes were primarily enriched in “FOXO signaling pathway”, which was closely relevant to endothelial quiescence41. By LH12, pathways related to apoptosis and the AGE-RAGE signaling axis were prominently enriched (Fig. 6E). Interestingly, atypical chemokine receptor 1 (ACKR1), a critical mediator in the transport of chemokines42, was predominantly expressed at later LH stages, implicating the role of endothelial cells in facilitating intercellular communication during this phase (Fig. 6F). Furthermore, spatial transcriptomic analysis revealed distinct localization of endothelial cells expressing ACKR1, roundabout guidance receptor 4 (ROBO4) and kinase insert domain receptor (KDR) within the endometrium (Fig. 6G). In brief, our results unraveled the spatiotemporal characteristics of the endothelium in the receptivity window.

Spatial resolution of the immune microenvironment
For immune cells annotated with Azimuth package43, we identified 12 immune cell subclusters within the endometrium, including mast cells (Mast), CD4 positive T cells (CD4_T), CD8 positive T cells (CD8_T), double-negative T cells (dnT), regulatory T cells (T_reg), proliferating natural killer T cells (pro_NKT), CD56 positive natural killer cells (CD56_NK), CD16 positive natural killer cells (CD16_NK), B cells (B), macrophage (Mac), and two types of dendritic cells: plasmacytoid dendritic cells (pDC) and conventional dendritic cells (cDC) (Fig. 6H, I). Co-immunostaining of MKI67 and CD56 revealed substantial accumulation of CD56+ NK cells, known for their robust cytokine production and rapid mediation of antibody-dependent cell-mediated cytotoxicity (ADCC), in the endometrium at LH9 (Fig. 6J, Supplementary Fig. 10C). Stereo-seq and ISS were also performed to verify the above changes of CD56+ NK subclusters (Fig. 6K, L). This accumulation of NK cells at LH12 likely correlated with processes involved in endometrial shedding. Functional enrichment analysis of DEGs across LH stages indicated that “DNA replication” was markedly upregulated in NK cells during LH7/LH9, as evidenced by high expression of cyclin A2 (CCNA2), cyclin-dependent kinase 6 (CDK6), and proliferating cell nuclear antigen (PCNA). Conversely, genes in the terms of “p53 signaling pathway” and “apoptosis” were increased at LH12, indicating a preparatory phase for endometrial shedding (Fig. 6M, N, Supplementary Fig. 10D). Additionally, macrophages, marked by CD14, were significantly enriched in the endometrium at LH12. This macrophage accumulation likely reflected their role in phagocytic clearance of cellular debris, contributing to tissue homeostasis and repair (Fig. 6O). Collectively, by integrating Stereo-seq, scRNA-seq, and ISS technologies, we obtained a comprehensive spatiotemporal map of endothelium and immune cells across the LH stages, highlighting the intricate coordination of the immune microenvironment in the maintenance of endometrial homeostasis and remodeling.

The dominant role of stroma in spatial communications
Stromal cells were the most abundant cell type, indicating their critical role in maintaining endometrial homeostasis. To elucidate the interactions between stromal cells and other cellular types, CellPhoneDB was applied to identify potential ligand-receptor pair-mediated communications. Ranking cellular interactions by the number of connections revealed stromal cells, endothelial cells, and epithelial cells as the most active participants in endometrial signaling (Fig. 7A). Specifically, intercellular crosstalk between the epithelium and stroma was predominantly mediated by signal molecules such as fibroblast growth factor 2 (FGF2), FGF7 and bone morphogenetic protein 4 (BMP4) (Fig. 7B). Among them, the FGF7-FGFR2 signaling axis emerged as particularly significant, playing a critical role in epithelial cell differentiation and maturation44. FGF7 was predominantly expressed in diffStr, whose receptor FGFR2 was specifically localized to epithelial cells, suggesting a key regulatory role of FGF7-FGFR2 signaling in driving epithelial differentiation and functional maturation (Fig. 7B, C). Consistently, spatial transcriptomic visualization demonstrated robust FGF7 expression in stromal compartments from LH7 to LH9, with FGFR2 being distinctly enriched in epithelial regions (Fig. 7D). Ligand-receptor pairs such as IHH_HHIP, WNT4_SFRP4, WNT9A_SFRP4, WNT7A_SFRP, and WNT5B_SFRP4 were primarily observed in LH2, indicating their roles in maintaining the undifferentiated state of the endometrium. Additionally, the E2-ESR1 axis was predominantly enriched in LH2, which is associated with the thickening of the endometrium by promoting cell proliferation (Fig. 7C).

IL-15 plays a critical role in endorsing the survival, proliferation, and cytotoxic activity of NK cells. In the endometrium, IL-15 was mostly expressed in diffStr and deStr. Ligand-receptor interaction analyses demonstrated the presence of IL-15-IL2RB-IL2RG signaling between stromal and NK cells, implying the role of stromal cells in supporting the proliferation and activation of CD56+ NK cells (Fig. 7E). Stereo-seq analysis confirmed that IL-15 was specifically localized to the stromal compartment, with its expression markedly increased from LH7 onward. Concurrently, its receptors, IL2RB and IL2RG, were distinctly upregulated in NK cells, underscoring a spatially coordinated regulatory network (Fig. 7F). Signals originating from the epithelium and NK cells that targeted stromal compartments were also examined (Supplementary Fig. 11A, B). In summary, stromal cells dynamically responded to changes in the endometrium by secreting cytokines such as FGF2 and FGF7, which promoted epithelial differentiation via FGFR2. Moreover, stromal cells produced IL-15 to modulate NK cell activity (Fig. 7G). Our spatial-temporal map of receptivity not only uncovers the cell-specific expression code of genes associated with receptivity opening, closing, and menstruation preparation, but also depicts the potential spatial crosstalk underlying this process (Fig. 8).

Methods
Human endometrium sample collection
The study was approved by the Ethics Committee of Nanjing Drum Tower Hospital, Nanjing University Medical School (IRB reference number 2013-081-01) and was conducted according to all relevant ethical regulations. Written informed consent was obtained from all participants, and no financial compensation was provided. Detailed clinical information on these 21 subjects is provided in Supplementary Data 2.

To investigate the molecular signatures associated with endometrial receptivity, we recruited 40 females undergoing in vitro fertilization and embryo transfer (IVF-ET) cycles due to male factors. Inclusion criteria were as follows: (1) 23–35 years old, (2) regular menstrual cycle ranging from 21 to 35 days, (3) 18.5kg/m² ≤body mass index (BMI)<24kg/m², (4) absence of other endocrine or immune-related disorders. Follicle development was monitored starting on day 10 of the natural menstrual cycle. Subcutaneous administration of 250μg recombinant hCG (OVIDREL, Merck Serono) was performed on the day when the dominant follicle reached a diameter of ≥16mm, which was designated as LH0. Ovulation occurred around 38h later56. Endometrial biopsies were subsequently collected on LH2, LH7, LH9, and LH12 (n=10 for each group). Of these, 21 subjects subsequently achieved a confirmed clinical pregnancy, and their endometrial samples were included in further high-throughput profiling and experimental validation. Eight samples, two per time point, were subjected to single-cell RNA sequencing (scRNA-seq) analysis. From these, two representative samples per time point were also analyzed using spatiotemporal enhanced resolution omics-sequencing (Stereo-seq, n=4) and high-throughput in situ sequencing (ISS, n=4). The remaining samples were used for experimental validation, including immunostaining and organoid experiments.

Culture of human endometrial epithelial organoids
The procedure of the development of human endometrial epithelial organoids was modified from a previous study57. Briefly, endometrial biopsy from three independent individuals was minced and digested with collagenase type I. After 30min, the digestion was stopped by adding FBS, and the mixture was passed through a 100-μm cell strainer. Then, the strainer was inverted, and the gland structures were backwashed and pipetted up and down to partially dissociate cells. Four times the volume of ice-cold Matrigel was added and mixed thoroughly, and 20μL of the cell-Matrigel mixture was plated on the preheated 48-well plate. After the cell-Matrigel mixture set, each drop was overlaid with 250μL Expansion Medium (ExM).

To mimic the secretory phase epithelium, estrogen, progesterone (P), and 8-bromoadenosine 3′,5′-cyclic monophosphate (8Br-cAMP) were sequentially supplemented in the ExM for 4 days. Specifically, when the diameter of most organoids reached 150 μm, the medium was replaced with ExM supplemented with 10nM E2, which was counted as day 1 (D1) of hormone stimulation. The medium was changed daily for 2 days. On D3, the medium was replaced with ExM supplemented with 10nM E2, 1μM P and 0.5mM 8Br-cAMP, and was changed daily for a 2-day culture. To evaluate the impact of cholesterol metabolism on epithelial ciliogenesis, atorvastatin (8μM) was added starting on D2, or cholesterol (50/250μM) was added starting on D3. The expression changes of ciliogenesis-related markers FOXJ1 and acetylated α-tubulin were then assessed. To determine the effects of FGF2 and FGF7 on epithelial differentiation, FGF10 was removed from the original ExM to avoid interference, and FGF2/FGF7 (100ng/mL) were added from D3. Chemicals, peptides, recombinant proteins, and commercial assays used in this paper are listed in Supplementary Table 1.

Immunofluorescence
Frozen sections from the human endometrium were fixed at room temperature with freshly prepared 4% paraformaldehyde (PFA) for 60min, followed by 4 times washing in PBST buffer (PBS+0.1% Triton X-100) for 5min each time. Blocking with 5% bovine serum albumin (BSA) at room temperature for 1h reduced the non-specific binding of antibodies. Primary antibodies were diluted with blocking solution in appropriate proportions according to the ratio recommended in the antibody instructions and incubated at 4°C overnight. Secondary antibodies or DAPI at a 1:200 dilution in PBS buffer were incubated for 30min at room temperature. The antibodies used in this paper are listed in the Supplementary Table 2.

Hematoxylin and eosin (H&E) staining
Frozen tissue sections of the human endometrium were used for H&E staining. Firstly, tissue sections were fixed with 4% PFA for 30min and washed 3 times with PBS for 5min each time. Then, sections were stained with hematoxylin for 3min, immersed in PBS for 5min, washed again with water for 40min, and stained with eosin staining solution for 2min. Next, tissue sections were dehydrated with 50% ethanol, 70% ethanol, 80% ethanol, 95% ethanol, and anhydrous ethanol for 10s each sequentially, and then replaced with xylene twice for 5min each time. Finally, the tissue sections were sealed with neutral resin.

10X Genomics single-cell RNA-seq of human endometrium
The human endometrium was rinsed with ice-cold DPBS and chopped using scalpels into small pieces and enzymatically digested in 5mL DPBS containing 1mg/mL collagenase type IV. The supernatant was diluted with DPBS and passed through a 40μm cell sieve. The flow-through was centrifuged and resuspended in 1mL of red blood cell lysis buffer for 2min. The cells were collected by spinning down at 500rpm for 5min and resuspended in DPBS for subsequent 10× scRNA sequencing (Chromium GEM-X Single-Cell 3’ Reagent Kits v4).

Stereo-seq sample preparation and sequencing
For Stereo-seq, the human endometrium was embedded in Tissue-Tek O.C.T. Compound and snap-frozen on top of dry ice. The frozen samples were stored at −80°C until further use. Frozen sections with a thickness of 10μm were mounted onto a Stereo-seq chip (1cm×1cm), which is composed of numerous 200 nm-diameter DNA nanoballs (DNBs) with 500nm for the distance from the center to center of DNBs (500nm resolution). Tissue sections adjacent to Stereo-seq were retained for H&E staining. The tissue sections were fixed with pre-cooled methanol at −20°C for 30min and then stained with ssDNA Assay Kit. Tissue permeabilization was performed using PR Enzym for 18min. Reverse transcription was conducted at 42°C using the Gene Expression kit. For library construction, cDNA was fragmented and amplified by a Stereo-seq DNA library kit, after which sequencing libraries were constructed for the subsequent sequencing process.

Processing of Stereo-seq data
Paired-end FASTQ reads were generated by the MGI DNBSEQ-Tx sequencer, where the coordinate identity (CID) and molecular identifier (MID) were obtained from Read 1, and the cDNA sequence was included in Read 2. The raw FASTQ files were subjected to the Stereo-seq Analysis Workflow (SAW). Briefly, the preprocessed reads were aligned to the human GRCh38 reference genome using STAR. Low-quality reads were filtered out using HandleBam. The final expression matrix was constructed from exonic reads containing CID information, with genes as rows and DNBs as columns. The resulting GEF file from SAW was subjected to downstream analysis or visualization using different bin sizes. The size of these bins determines the resolution of the spatial transcriptome. A smaller bin size (e.g., bin10 representing 10×10 DNB bins) allows for more precise localization of gene expression patterns.

In situ sequencing (ISS)
In situ sequencing exploits a novel probing and barcoding approach for improving signal intensity and specificity. Frozen tissue sections of human endometrium were subjected to in situ sequencing performed by Genedenovo Biotechnology Co., Ltd (Guangzhou, China). In brief, tissue sections of human endometrium in 10μm thickness were fixed in 4% PFA for 5min, followed by washing twice with diethylpyrocarbonate-treated PBS. Tissue sections were dehydrated in an ethanol series and then were permeabilized with 0.1 M HCl at 37°C for 5min after 3 times washes. Tissue sections were incubated at 37°C for 4h in the probe hybridization mixtures containing RNA molecular markers. Next, the sections were incubated with the ligature mixture, circularization mixture including splint primers, and amplification mixture for rolling circle amplification (RCA). For sequencing, the ligation sequence, imaging, and stripping off were applied for each cycle until all cycles were achieved (four cycles at most).

Multiplexed immunofluorescence staining
The frozen tissues were sectioned from the human endometrium and attached to the slide provided in the FluoXpert mIF kit. These sections were fixed with 4% PFA for 15min and washed 3 times with PBS, and then submerged in PBS and stored at 4°C. Before the staining experiment, the tissue sections were re-fixed with 4% PFA for 15min, and then washed 3 times with PBS and permeabilized with 0.5% Triton X-100 for 15min at room temperature prior to flow cell assembly. Different antibodies were added to the FluoXpert mIF reagent cartridge. Multiplexed immunofluorescence was then performed with DNBSEQ-G400RS FluoXpert.

Data processing of scRNA-seq data
Single-cell libraries were sequenced on Illumina HiSeq X Ten instruments using 150bp paired-end sequencing. The raw data were processed using Cell Ranger (v5.0.1) with default parameters and aligned to the GRCh38 genome reference and annotation. The downstream analyses, including preprocessing, normalization, identification of variable features, data scaling, dimensional reduction, clustering, and top marker identification, were mostly fulfilled by Seurat (v5.0.3). We filtered cells with >20% mitochondrial content (percent. mt), unique feature counts (nFeature_RNA) over 10,000 or under 500, or RNA counts (nCount_RNA) exceeding 30,000 or below 1000, resulting in 82,980 cells remaining. Batch effects across datasets from different libraries were addressed using the RunHarmony function. FindAllMarkers identified the marker genes of each cluster mostly. For subset analysis, we followed a similar approach: selecting the top 2000 variable features, scaling the data, recomputing PCA, clustering, and visualizing with UMAP. If necessary, RunHarmony was used to adjust for batch effects. The differential expression genes were calculated utilizing the FindMarkers function in Seurat with the Wilcoxon test (fold change <0.5 or >2 and p-value <0.05) and visualized using the R package scRNAtoolVis. The DEGs of stromal cells, epithelial cells, and NK cells were provided in Supplementary Data 3.

Mapping cell types from scRNA-seq onto Stereo-seq
To visualize spatial distribution patterns of the identified cell types, we mapped them onto Stereo-seq data based on our scRNA-seq datasets (LH2, LH7, LH9, and LH12 endometrium). The FindTransferAnchors method in Seurat was applied to identify anchors between the scRNA-seq and Stereo-seq datasets. The TransferData function was then used to transfer scRNA-seq clusters to selected spots in the Stereo-seq data. Each spot was assigned a potential cell type based on the scRNA-seq cluster with the highest transfer score. Subcluster annotation for Stereo-seq was performed in the same manner as above.

Cell-type annotation using TACCO
To accurately annotate cell types in Stereo-seq spatial transcriptomic data, we applied TACCO (Transfer of Annotations to Cells and their COmbinations)58. scRNA-seq data from human endometrium were used as the reference for cell-type signatures. Both scRNA-seq and Stereo-seq data were normalized and log-transformed prior to analysis. TACCO assigns probabilistic cell-type labels to each spatial spot, accounting for spot composition and gene capture variability. The cell type with the highest probability was assigned as the primary annotation. This approach enabled reliable annotation of small or sparsely distributed cell populations, including epithelial subtypes and immune cells. Analyses were performed using TACCO v0.0.2 (https://github.com/simonwm/tacco) with default parameters.

Cell-cell interaction analysis by CellPhoneDB and NicheNet
To explore potential interactions between different cell types, cell-cell interaction analysis was conducted using CellPhoneDB, a public database of receptor-ligand interactions, including heteromeric complexes with subunit structures. Briefly, a log2-normalized count matrix was subsampled to 500 cells per cluster. Significant ligand-receptor pairs were identified after filtering. For each identified pair, the total number across clusters within the same cellular module was calculated, allowing cellular-module-specific ligand-receptor pairs to be determined based on enrichment scores. To identify potential ligands influencing latent phenotypes, NicheNet (v2.2.0) was used to investigate potential interactions and target genes between specified cell types.

Single-cell regulatory network inference and clustering (SCENIC) analysis
Following cluster annotation, we performed pySCENIC analysis using a three-step workflow based on human cisTarget resources. First, we converted the scRNA-seq gene expression matrix into a loom file using Python. Next, we used the pyscenic grn, pyscenic ctx, and pyscenic aucell functions to infer the gene regulatory network, predict regulon activity, and identify the key transcription factors and their target genes in each cell type. AUCell then scored the activity of each regulon in individual cells, producing a binarized activity matrix.

RNA velocity analysis
Unspliced and spliced RNA were extracted from the BAM file using velocyto, resulting in a data matrix that was preprocessed with scVelo. Preprocessing steps included normalization, feature gene selection, and PCA for dimensionality reduction. Default parameters were then used to estimate dynamic parameters and compute gene-wise RNA velocity vectors. These velocity vectors were subsequently projected onto the UMAP space for visualization and analysis. To explore dynamic gene expression along the velocity and latent time, a differential kinetic test was performed using the scv.tl.differential_kinetic_test function.

Statistics & reproducibility
No statistical method was used to predetermine sample size. Statistical analyses were performed using GraphPad Prism (v.9.0) (for experimental data), R (v.4.1.0), and RStudio (2021.09.1). Comparisons between groups were conducted using Student’s t-tests and Wilcoxon rank-sum tests, and ANOVA was used for continuous variables. P<0.05 was considered to be statistically significant. For organoid experiments, three independent samples were utilized, and one-way ANOVA was applied for multiple group comparisons. Software and algorithms used in this paper are listed in the Supplementary Table 3.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript by Liu et al. explores the molecular and cellular dynamics of the human endometrium that govern the acquisition of receptivity for embryo implantation. To achieve this, the authors adopt a comprehensive multi-omics strategy integrating spatiotemporal enhanced-resolution transcriptomics, single-cell RNA sequencing, and high-throughput in situ hybridization. This approach offers a novel and valuable perspective on the endometrial landscape during the implantation window, addressing a critical gap in current reproductive biology. Nevertheless, several aspects related to data interpretation, methodological transparency, and the clarity of presentation require substantive refinement to align with the scientific and editorial standards of journal.\nMajor Points:\n1. The authors fail to include several key publications in the field that are critical for contextualizing their work. Notably, Garcia-Alonso et al. (Nat. Genet. 53, 1698–1711, 2021) mapped the temporal and spatial dynamics of the human endometrium both in vivo and in vitro, offering significant insights that should be acknowledged. Similarly, Santamaria et al. (Nat. Commun. 14, 5890, 2023) provided a detailed single-cell resolution analysis of the endometrial receptivity in controls and patients with Asherman’s syndrome. Additional pertinent studies should also be considered. Given these established findings, the authors shuld clarify what specific novelty their study provides beyond the current body of evidence. After reviewing these key references and other relevant literature, it remains unclear how this work advances the field or offers unique insights. A more detailed discussion situating the current findings within this context is necessary in the abstract, introduction, results and discussion.\n2. The abstract reads more like a narrative and lacks essential scientific details that should be incorporated. It would benefit from a clearer presentation of the study's objectives, methods, key results, and conclusions. Including specific data points or main findings will provide the reader with a concise and informative overview of the work, improving its scientific rigor and impact.\n3. The authors intermingle information regarding human and murine endometrial receptivity, which generates more confusion than clarity. The physiological differences between species, particularly in endometrial function and receptivity, can lead to misinterpretation when not clearly delineated. To enhance the focus and coherence of the manuscript, I recommend removing the data related to murine endometrial receptivity and concentrating exclusively on human data, which is directly relevant to the study’s objectives\n4. Provide functional validation (or at least temper interpretations) for key signaling pathways like FGFs-FGFR2.\n5. The regulatory function of IL-15 in uterine NK cell activation and maintenance is well-established in the literature. However, the manuscript presents this interaction as a novel discovery, when in fact it represents a spatially resolved confirmation of previously described biology. To clarify the true scope of the study’s innovation, the authors should include a concise discussion of canonical implantation markers, such as LIF, HOXA10, and integrins, and explicitly address how their spatial atlas corroborates, refines, or contradicts existing models of human endometrial receptivity.\n6. In the manuscript the authors mentioned analysis at “bin 10” level, I understand that the meaning that transcript counts were aggregated in 10×10 micron bins or similar to balance resolution and sensitivity. This is acceptable, but details should be provided in the Methods (e.g. the exact resolution achieved, and how many transcripts or genes per bin were typically detected).\n7. The manuscript does not clearly specify the number of individual donors sampled at each time point. The current phrasing appears to imply that only one biopsy was analyzed per stage, which, if accurate, represents a substantial limitation. If the study indeed includes only one sample per time point (i.e., a total of four patients), the authors must explicitly acknowledge the resulting constraints on generalizability and the inability to perform robust statistical comparisons across menstrual phases. The apparent lack of multiple patients per time point is a significant limitation. Individual variation in endometrial gene expression is known to be high.\n8. The authors should provide a detailed assessment of the spatial transcriptomic data coverage and quality. Specifically, it is important to clarify the extent of endometrial tissue profiled per sample. Were full cross-sections of the tissue analyzed, or only selected regions? Including representative images, preferably in the Supplementary Information, showing the distribution of Stereo-seq spots overlaid on histological sections would help readers better assess the spatial resolution and anatomical representation. In addition, reporting key technical metrics.\n9. The Results section refers to multiple cellular subpopulations, but it is essential that these classifications are clearly supported by marker genes previously validated in the literature. To ensure biological accuracy and reproducibility, the manuscript should include a detailed description or a dedicated figure panel that presents the specific marker genes used to define each cluster, along with appropriate references to prior studies where these markers have been established.\n10. The strategy used to integrate scRNA-seq data with Stereo-seq should be explained.\n11. The manuscript’s use of ISS and immunofluorescence is a strong point in terms of validation. To further enhance confidence, the authors might consider quantifying these validations.\nMinor Comments\n1. The first paragraph of Mat & Meth (618-625) is more appropriate for a textbook that for a specialized scientific paper, please remove.\n2. Terminology Consistency. The manuscript sometimes interchanges terms like \"epithelial differentiation,\" \"epithelial remodeling,\" and \"epithelial transformation.\" Consistency would aid clarity.\n3. Statistical Analysis Details. The statistical thresholds for DEGs (e.g., adjusted p-values, log fold changes) should be explicitly stated in the methods.\n4. Potential Clinical Applications. The study hints at clinical applications (e.g., endometrial dating, IVF optimization). A dedicated paragraph discussing how these findings could inform current diagnostic tools (like ERA) or therapeutic strategies would enhance translational relevance.\n5. Supplementary Materials. Ensure that all supplementary tables and figures (especially regarding antibodies, sequencing data, and detailed DEGs) are accessible and well-organized.", "reviewer_2": "This manuscript describes results of scRNAseq and spatial transcriptomics of 40 human endometrial biopsies obtained from women with male factor infertility. The goal was to map gene expression changes at single cell resolution in the pre-receptive phase (LH+2), receptive phase (LH+7, LH+9) and post-receptive/pre-menstrual phase (LH= 12). The “day of LH surge” was pre-defined from days after human choriogonadotropin alfa administration following dominant follicle development assessed by ultrasound. The investigators used standard techniques and data analyses. Key findings included endometrial stromal fibroblasts undergoing marked molecular changes from de-differentiated, to differentiated, to degrading stromal stages. Cell-cell communication analyses using inferred ligand-receptor pairs revealed stromal-epithelial and stromal-immune crosstalk that could be important for receptivity establishment. The authors also included endometrial organoids although the utilization of these in the analyses is unclear.\nTo assure rigor of the data, there are numerous items that need to be addressed. These include:\n1. Introduction, Lines 50-51: while the endometrium plays a critical role in implantation failure, based on many clinical observational studies, some may argue embryo-related factors could play a relatively larger role (and is equally incompletely understood as well). Softening the tone of this sentence is recommended.\n2. More information about the human subjects and ultrasound findings and circulating steroid hormone levels after hCG injection to estimate day of ovulation and any correlations between observed signatures and hormonal environment would add more rigor to the data.\n3. Ovidrel trigger was performed when dominant follicle reached 16mm. The authors should clarify findings regarding endometrial thickness at time of Ovidrel trigger. Endometrial thickness at time of Ovidrel trigger has been postulated to reflect subsequent endometrial receptivity and associated with live birth rates following embryo transfer.\n4. Did the subjects have luteal support with exogenous progesterone after Ovidrel trigger prior to the biopsy?\n5. More clarity on patient characteristics is warranted. The study relies on the assumption that the sampled subjects are “fertile” and the findings they describe are what would be considered “normal/fertile” physiology in establishing endometrial receptivity. However, these were patients getting fertility treatment. Just because the diagnosis is “male factor”, it does not automatically imply there is not a concomitant female factor. Information on whether the female partners had ever had a prior pregnancy (thus proven fertility), if they underwent a comprehensive evaluation – normal uterine cavity, absence of endometriosis, uterine fibroids, an imaging workup, and past treatment history of the female participants should be described.\n6. Regarding the method of endometrial tissue sampling (e.g., was it uniform?), and prior infertility treatment history (e.g., history of failed embryo transfers, uterine pathology/ treatment, and female age) to ensure no confounding factors when comparing across groups. A better sample selection for the study objective would be females with no uterine pathology and demonstrated prior fertility. Baseline clinical characteristics and prior treatment history of the patient subjects are necessary to contextualize the results/conclusions.\n7. 20% mitochondrial content is a rather high threshold for filtering. What was the distribution of mitochondrial content % from the scRNA output?\n8. Was ambient RNA addressed and filtered?\n9. The scRNA UMAPs consistently demonstrated a low proportion of epithelial cells relative to the entire endometrium. Of note, only about 83k cells were retained and analyzed– which is a very low number for 40 total patient samples This is quite unusual based on existing literature and raises questions regarding sample quality, experimental processing, and/or cell type annotation. Do the authors have an explanation for the low recovery? Were many cells filtered out during quality control – and if this is the case, any concerns from the authors regarding quality of sample processing?\n10. Figure 1C – the authors should provide additional canonical cell type markers for each major cell type in their violin plot to demonstrate confidence for their cell type annotation. For example, ESR1 is widely expressed across multiple cell types (as demonstrated by the authors’ Figure as well), and should not be the primary cell type marker reported for stromal cells (Line 125). For example – why not use PGFRA and/or COL6A1?\n11. Figure 1F – visually, uterine NK cells seem most abundant at LH+9 (and dramatically more abundant than at LH+7), which does not correspond with the result shown in Figure 1D, which suggests uNK is most abundant at LH+12, with relative similar proportion between LH+7 and LH+9. Please explain.\n12. Lines 391-393 – Typically, Ovidrel+6/7 or LH+6/7 is when an embryo is placed during an IVF cycle. Are the authors implying that LH+9 could reflect the critical opening of implantation window (via LIF-STAT3), and do the authors then think that transferring at LH+7 is “too early”?\n13. There is a lot of interest in the functional roles of uterine NK cells in establishing endometrial receptivity and facilitating implantation/invasion. Aside from the relative abundance across the secretory stages, it is imperative for the authors to perform DEGs and pathway analyses specific to uNK by LH+2 vs LH+7/9 vs LH+12.\n14. Figure 3 – Additional in-depth analyses on functional enrichment using stromal molecular changes/DEGs throughout the secretory transformation is warranted, given their proposed significance in this study and in the literature for establishing endometrial receptivity and could add biological relevance to the authors’ findings.\n15. DEG figures in Figure 3 and Figure 5 – what are the distributions of the adjusted p-values? Would be helpful to include volcano plots of logFC vs adj p values in supplemental data sections. In Methods, there was no description of how these DEG analyses were conducted – were these done with MAST, Pseudobulk, or other methods?\n16. Figure 5F, ciliogenesis is hard to appreciate at the resolution given in the figure. The frames for H&E staining and FOXJ1 staining are also not matched – making it hard to interpret.\n17. Ovidrel was used in this study, which is a form of luteal support and could also produce additional endogenous progesterone beyond a “natural” level (hence the value of serum P4 levels). Could this influence the study’s findings/conclusions? Does this affect the generalizability of this study’s results to endometrial receptivity in a natural menstrual cycle setting?\n18. Many figures show only 1 sample for analysis, which diminishes the rigor of the approach and confidence in the generalizability of observations to other samples in the cohort and the conclusions rendered.\n19. More details on specific thresholds/parameters used in different analytical functions/packages used in this study are warranted.\n20. It is unclear how this study’s results compare with those of Cao et al Nat Commun 2025, wherein 28 endometrial biopsies spanning 5 time points in the secretory phase (LH+3, LH+5, LH+7, LH+9, LH+11) were analyzed by scRNAseq.\n21. Regarding #20 above, a comprehensive Discussion would be a great contribution if the authors can synthesize their results, present a model, and compare their data (unique or confirmatory) relative to the vast literature on genes associated with endometrial receptivity and the window of implantation - a critical time in the process of pregnancy initiation.\n22. Also, how might the authors’ results compare/contrast with current receptivity tests used clinically?\n23. Minor: Page 108-109 – challenges in defining the WOI is attributed to difficulty in defining ovulation by US. There could be other causes – e.g., stochastic events in follicle dynamics, different rates of follicle development, etc.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "THe manuscript by Liu and coworkers describes the cell specific expression of genes in the human endometrium during pregnancy. This manuscript uses spatial and single cell transcriptomics to define cell identity , changes in cell populations and cell to cell communication. They have identified stromal expression of FGF7 and IL 15 as critical regulators of epithelial and Natural Killer cell biology. All in all this is a descriptive manuscript that joins numerous publications describing the cell specific expression of genes in the human endometrium during the peri implantation period. The uniqueness of this manuscript is the use of spatial transcriptomics integrated with the single cell analysis. However this is still a descriptive report that gives a limited conclusion. There are several important issues that should be integrated.\n1. Since uterine biology is governed by estrogen and progesterone signaling, an identification of the steroid regulated networks deemed from this manuscript should be highlighted. This is especially true since the cistrome for ESR1 and PGR have been described.\n2. It is suprizing so little is given to describe glandular epithelium since much has been described regarding organoid gene expression and single cell analysis of uterine glands.\n3. Since this manuscript attempts to identify the regulation of receptivity , a working model of the spatial temporal gene expression regulating receptivity should be delineated.", "reviewer_5": "Manuscript: ‘Decrypting the spatiotemporal code of human endometrial receptivity’\nLiu et al present integrated single cell (10X) and spatial (Stereo-seq, ISS, immunofluorescence) analysis of human endometrial biopsies throughout the implantation window. A novel aspect of the manuscript is the use of IVF participant samples to permit accurate timing of this process, therefore providing a rare and valuable insight into the healthy endometrial composition over time with single-cell-level spatial resolution (the majority of other published spatial atlassing work is not of equivalent cellular resolution). The manuscript characterizes the detailed cellular composition of this tissue over time, including stromal, epithelial and immune compartments. Samples are by necessity surface-level biopsies ie deeper tissues are not investigated. This manuscript provides valuable reference data useful for future comparisons with diseased samples to understand female fertility and the implantation window.\nOverall, this manuscript is worthy of publication, subject to the comments below:\n(1) In line 159/160/fig S3A the authors state that they identify MEF2C as a pericyte marker (pericytes being interesting as a potential source of endometrial mesenchymal stromal / stem cells). Is this marker known in the literature? Can you provide protein immunostaining / RNA staining of MEF2C together with a commonly known pericyte marker to confirm that these are pericytes?\n(2) Line 190: Interestingly, the authors find dynamic gene expression alterations in NR2F1, but not NR2F2 (NR2F2 having been identified as critical for implantation in mice). Can the data for NR2F2 be shown for comparison to NR2F1, to confirm it is clearly expressed in human and does not change with time?\n(3) In several of the figures the authors indicate that Stereoseq data is ‘bin10 / bin20’ etc. Could you also equate this to size for readers more familiar with other methods to allow easier interpretation of the level of resolution (e.g. 10µm2)?\n(4) In line 248 / fig 3G/F the authors demonstrate that CEBPD, but not CEBPB shows dynamic expression over time (primarily expressed in proStr and undiffStr). Is this reflected in protein expression as well as RNA? Can you show the CEBPB data for comparison?\n(5) Figure 4E E-Cad/Ac-Tub staining is shown to demonstrate higher enrichment of cilium at LH7. Is it feasible to computationally quantify this staining across the images, as it can be difficult to judge whether single snapshots are representative, especially as they contain different amounts of epithelium. Cilium does not appear increased in panels 4F/G at LH7 vs LH2?\n(6) Figure 5F: The ability to modify FOXJ1 (ciliated cell) abundance in endometrial epithelial organoids is interesting. Do you know if these organoids represent a particular stage of the implantation cycle and contain multiple epithelial populations corresponding to the in vivo data? (While this would be interesting to know, it is appreciated that this is not the focus of this manuscript).\n(7) Fig 6L: Can the authors mention RRM2 in the text to explain the reason for this staining?\n(8) Line 472: The FGF7-FGFR2 signalling axis is interesting; do the authors know if FGF7 / FGFR2 are expressed in their organoid model or the effect of FGF7 on organoid maturation? Again, it is appreciated that the organoids are not the primary focus of this manuscript.\n(9) Materials and methods: please provide information on the 10X single cell kit / version used?\n(10) Please indicate number of samples used for each technique / timepoint in the manuscript (ie single cell, Stereo-seq, ISS, Multiplexed IF)."}, "author_response": "Dear Reviewers,\nMy co-authors join me in expressing our sincere appreciation to the reviewers\nfor their thoughtful and very positive comments. We have critically reviewed\neach comment and our responses are elaborated below. The reviewers’\ncomments are followed by our responses highlighted in blue. Figure citations\nare primarily used in this response letter unless otherwise noted.\nReviewer #1 (Remarks to the Author)\n[...]\n(See full rebuttal text in the provided document)"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I appreciate the thorough and constructive responses provided by the authors. The revised manuscript reflects substantial improvements in clarity, rigor, and transparency. The authors improved the literature context and clearly defined the novel contribution of their study, avoiding any overstatement of novelty. They rewrote the abstract to make it clearer and more data-focused, and removed potentially confusing ancillary information (such as mouse experiments) to sharpen the paper’s focus.\nIn addition, they strengthened their key claims with new experimental validation, which significantly reinforced the conclusions. They expanded the discussion to include classical implantation markers and placed their findings in the proper context, ensuring that established biology was not implied to be “new.” They also added essential methodological details regarding spatial resolution and data yield, along with visual aids to demonstrate tissue coverage. The cohort size was clarified, and limitations due to sample numbers were acknowledged, while external comparisons were used to increase confidence in the results. Finally, they validated their cell type definitions using known markers and cross-study comparisons, thereby improving the credibility of all downstream analyses.\nHowever, a few critical points remain that should be addressed prior to publication:\n1. Sample size: While the authors now clarify donor numbers, the effective use of two biological replicates per LH stage for scRNA-seq and spatial profiling remains a significant limitation. I recommend adding a concise but explicit statement in the Discussion acknowledging the restricted statistical power and generalizability of stage-specific conclusions due to inter-individual variability.\n2. Functional assay transparency: The newly added organoid experiments are valuable. However, the Methods section should clearly state the number of biological replicates, statistical analyses performed, and provide a more detailed description of the experimental design to ensure reproducibility.\n3. Data availability and reusability: I recommend confirming that all raw and processed data (Stereo-seq, scRNA-seq, ISS) will be made available in a public repository (e.g., GEO), in line with community standards.\nOverall, the revised manuscript represents a good contribution to the field.", "reviewer_2": "The authors have extensively revised the manuscript, which is now easier to follow. In response to the reviewers’ comments, the authors have included key reference to studies by others re scRNAseq and spatial transcriptomics in the human endometrial window of implantation (WOI) and provide data in the rebuttal demonstrating comparable findings. They have also added clinical metadata for enrolled subjects, functional validation of FGF7 upregulating some key epithelial receptivity markers in an organoid model of human epithelium in the appropriate hormonal context of the WOI, demonstrate cholesterol increasing ciliated epithelial Ac-tubulin expression in the organoid model, DEGs of uNKs showing a transition from LH+2 of Wnt signaling to LH+7/9 proliferative signatures and functional enrichment of stromal cells from TGF  and FOXO1 signaling to apoptosis at LH+12. Other significant additions include updated Materials & Methods and new figures, e.g., volcano plots for DEGs and full cross-sections and staining w/ ssDNA providing insights into how representative the spatial data are in the revised manuscript, and clarification on lineage markers for cell types/subtypes with appropriate attribution to original authors. Moreover, the strategy to integrate stereo-seq, scRNAseq, and spatial/in situ sequencing (ISS) is provided as are validation studies using ISS and IF. Some syntax and terminology consistency have been added, as well as details about statistical analyses, among other revisions/additions.\nOverall, the manuscript is greatly improved, and the data are exciting with new insights into cell types and key features in the opening and closing of the WOI in human endometrium. This said, there are a few items that warrant further clarification:\n1. Reviewer 1 had inquired about the number of subject samples used at each time point for scRNAseq, and in the rebuttal, the authors state that n=2 per time point were used (small # for making comparisons across the WOI) and that 4 subject samples were eliminated because of “high interindividual variation”. What were the criteria to discard these samples specifically?\n2. In the response to Reviewer 2, the authors state that only samples from subjects who achieved pregnancy in a subsequent IVF cycle were used for the sc and spatial transcriptomic analyses. A new table, Supplemental Data 1, shows n=6 subjects had subsequent IVF pregnancies, and of these, 3 were sampled at LH+7, 2 sampled at LH+9, 1 at LH+12, and 0 at LH+2. This does not add up to n=2 subjects sampled at each of the time pointe LH+2, LH+7, LH+9, and LH+12 as stated in the text or in comment 2 above. Please clarify.\n3. The authors comment on ESR and PGR expression as key lineage markers for the proStroma, stroma, pericytes, epithelium, and ciliated epithelial cells and not in endothelial cells or immune cells (Figure 10 in response to Reviewer 2). A key characteristic of the transition shortly after the LH surge from the proliferative to secretory phase is downregulation of ESR1 and PGR (driven by progesterone from the corpus luteum) in epithelium and maintenance of some stromal cell PGR expression throughout the secretory phase. Please explain if there are differences in ESR1 and PGR in the cell types with progression from LH+2 and LH+7, LH+9, and LH+12.\n4. Please clarify if the comparative analysis (https://doi.org/10.5281/zenodo.16851856 published on Zenodo August 13, 2025) and noted in the response to the reviewers includes data from this manuscript as well as the data of Cao 2025. Another resource is the control group from Ma et al 2023.\n5. While  3 5 is upregulated in the WOI (high at LH+7), what about other integrins – e.g.,  v 3, shown to be regulated in epithelium in the WOI and dysregulated in endometrial inflammatory disorders? Could the lack of detection be due to the limited # of luminal epithelial cells in the current dataset?\n6. As HoxA10 is most highly expressed in stroma at LH+7 and decreases thereafter, what to the authors think its role is in the WOI?\n7. Discussion would benefit from:\no Comment on optimal timing of ET in IVF cycles LH+7 or LH+9 based on the data herein?\no A final section of strengths and weaknesses of the study\no Suggest deleting the final comment on the ERA as the authors appropriately discussed this earlier.\n8. Other items – language and syntax still need further editing to communicate intent effectively throughout the manuscript.\nSome examples include:\no Line 34: as no knockdown studies were conducted, so the authors are advised to temper the statement on line 34 to reflect cholesterol metabolism promotes ciliogenesis.\no Line 47: poor endometrial maturation is not a leading cause of female infertility – rather ovarian age is. Suggest this statement reflects that endometrial deficiencies can contribute to female infertility and poor pregnancy outcomes.\no Line 52: Suggest “tuning of endometrium” be re-worded to “development of a receptive endometrium”", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "The authors have addressed many of the issues raised in the review and have revised the manuscript to produce a valuable asset for the investigation of uterine biology. They", "reviewer_5": "Thank you to the authors for responding to my comments. I still have some concern about the n number for the spatial analysis not being robust (n=1 each for Stereo-seq and ISS); however I do appreciate these experiments are extremely expensive and it is not, at this stage, viable to generate more of this data for the manuscript (for future grants I hope increased n number will be considered). The data is nonetheless valuable and should be shared with the community, especially as some of the differences in expression between time points appear quite large and are validated by multiple methods.\nThe authors have clearly taken time to address the comments raised and the new data & responses are convincing. I would recommend that the manuscript is published."}, "author_response": "Dear Reviewers,\nMy co-authors join me in expressing our sincere appreciation to the reviewers for their\nthoughtful and very positive comments. We have reviewed each comment and our\nresponses are elaborated below. The reviewers’ comments are followed by our responses\nhighlighted in blue.\nReviewer #1 (Remarks to the Author):\n[...]\n(See full rebuttal text in the provided document)"}, {"round": 3, "reviewer_comments": {"reviewer_1": "Thank you for the thoughtful and comprehensive revision. The manuscript is now much clearer, methodologically transparent, and well contextualized, and I appreciate the added functional experiments, data availability, and improvements in figures and text. Before recommending acceptance, I have two minor requests that will further enhance clarity and reusability for the community. First, please include a concise supplementary table mapping each annotated cluster to a small set of canonical marker genes with supporting references, so readers can quickly verify the basis for cell-type labels and reuse the atlas with confidence. Second, although the Discussion acknowledges sample size constraints, I recommend adding an explicit sentence that highlights the n=1 per time point for the spatial datasets and briefly notes its implications, and reflecting this detail in the relevant figure legends to ensure it is visible to readers. With these small adjustments, I would be satisfied to recommend the manuscript for publication.", "reviewer_2": "The authors have responded to most of the reviewers' comments. However, why some individuals were excluded due to high inter-individual variability remains unclear and is important — particularly given (1) the small sample size and (2) how markedly different some samples appear, which could reflect experimental variability (sampling, tissue processing) or noise from clinical/metadata-related factors. Taken together, these issues raise concerns about the rigor of the conclusions.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Dear Reviewers,\nMy co-authors join me in expressing our sincere appreciation to the reviewers for their\nthoughtful and very positive comments. We have reviewed each comment and our\nresponses are elaborated below. The reviewers’ comments are followed by our responses\nhighlighted in blue.\nReviewer #1 (Remarks to the Author):\n[...]\n(See full rebuttal text in the provided document)"}]

Decision letter