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Single-cell transcriptomic atlas of the human endometrium during the menstrual cycle

Nature Medicine volume 26, pages 1644–1653 (2020)Cite this article

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Abstract

In a human menstrual cycle the endometrium undergoes remodeling, shedding and regeneration, all of which are driven by substantial gene expression changes in the underlying cellular hierarchy. Despite its importance in human fertility and regenerative biology, our understanding of this unique type of tissue homeostasis remains rudimentary. We characterized the transcriptomic transformation of human endometrium at single-cell resolution across the menstrual cycle, resolving cellular heterogeneity in multiple dimensions. We profiled the behavior of seven endometrial cell types, including a previously uncharacterized ciliated cell type, during four major phases of endometrial transformation, and found characteristic signatures for each cell type and phase. We discovered that the human window of implantation opens with an abrupt and discontinuous transcriptomic activation in the epithelia, accompanied with a widespread decidualization feature in the stromal fibroblasts. Our study provides a high-resolution molecular and cellular characterization of human endometrial transformation across the menstrual cycle, providing insights into this essential physiological process.

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Fig. 1: Human endometrium consists of seven cell types across the menstrual cycle.
Fig. 2: Marker validation and spatial visualization of endometrial ciliated cells.
Fig. 3: Construction of single-cell resolution trajectory of endometrial transformation across the human menstrual cycle.
Fig. 4: Temporal transcriptome dynamics of endometrial transformation across the human menstrual cycle.
Fig. 5: Deviating subpopulations of unciliated epithelia across the human menstrual cycle.
Fig. 6: Endometrial lymphocytes across the human menstrual cycle and their interactions with stromal fibroblasts during decidualization.

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Data availability

All raw and analyzed sequencing data associated with Figs. 1, 3–5 and 6a–c, all Extended Data figures and all supplementary figures in this study can be found at NCBI’s Gene Expression Omnibus (series accession code GSE111976) and Sequence Read Archive (accession code SRP135922). Data associated with Fig. 2, extracted from all representative fields of view, can be found in Supplementary Dataset 1. Source data are provided with this paper.

Code availability

Custom codes developed in this study for gene dispersion calculation, MI calculation and transcriptional regulator and secretory protein analyses are available at https://github.com/wanxinw/endometrium/. Specific codes are accessible, with no restrictions, upon request to W.W. (wanxinw@stanford.edu).

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Acknowledgements

We thank H. Ding for valuable discussions and advice; N. Neff and J. Okamoto for sequencing expertise; F. B. Yu, S. Darmanis and F. Zanini for technical expertise and discussions; S. Crasta and S. Kolluru for technical assistance; and the Stanford Cell Science Imaging Facility for assistance with imaging. This study was jointly supported by the March of Dimes, Chan Zuckerberg Biohub and MINECO/FEDER (no. SAF-2015-67164-R, to C.S.) (Spanish Government). W.W. was supported by the Stanford Bio-X Graduate Bowes Fellowship and Chan Zuckerberg Biohub. F.V. was supported by the Miguel Servet Program Type II of ISCIII (no. CPII18/00020) and the FIS project (no. PI18/00957). P.A. was supported by IVI-RMA Valencia. I.M. was supported by the Igenomix Foundation. M.M. and A.I. were supported by Chan Zuckerberg Biohub. W.P. was supported by the March of Dimes.

Author information

Author notes

  1. These authors contributed equally: Wanxin Wang, Felipe Vilella.

Authors and Affiliations

  1. Department of Bioengineering, Stanford University, Stanford, CA, USA

    Wanxin Wang, Alina Isakova, Wenying Pan & Stephen R. Quake

  2. Department of Obstetrics & Gynecology, Stanford University, Stanford, CA, USA

    Felipe Vilella, Inmaculada Moreno & Carlos Simon

  3. Igenomix Foundation, INCLIVA Health Research Institute, Valencia, Spain

    Felipe Vilella, Inmaculada Moreno & Carlos Simon

  4. IVI Valencia, Valencia, Spain

    Pilar Alama

  5. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Marco Mignardi & Stephen R. Quake

  6. Department of Obstetrics & Gynecology, University of Valencia, Valencia, Spain

    Carlos Simon

  7. Department of Applied Physics, Stanford University, Stanford, CA, USA

    Stephen R. Quake

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Contributions

W.W., W.P., F.V., C.S. and S.R.Q. conceived and designed the study. W.W., F.V., I.M. and A.I. performed experiments. W.W. performed single-cell experiments, RNAscope experiments and imaging. A.I. performed single-cell experiments. F.V. optimized the tissue dissociation protocol. I.M. performed tissue dissociation and immunofluorescence experiments. P.A. collected endometrial biopsies. W.W. and S.R.Q. analyzed single-cell RNA-seq data. M.M. and W.W. analyzed RNAscope data. W.W., F.V., C.S. and S.R.Q. interpreted the results. W.W., F.V., C.S. and S.R.Q. wrote the manuscript.

Corresponding authors

Correspondence to Carlos Simon or Stephen R. Quake.

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Competing interests

A patent disclosure has been filed for the study under the inventors S.R.Q., C.S., W.W. and F.V. C.S. is Founder and Head of the Scientific Advisory Board of Igenomix SL. P.A., I.M., M.M., A.I. and W.P. declare no competing interests.

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Peer review information Saheli Sadanand was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Data summary.

a, Number of single cells analyzed across the human menstrual cycle in the C1 dataset. b, C1: Relationship between the day of menstrual cycle (day) and endometrial phase/subphase assigned by single cell transcriptome data as in Fig. 4 for each woman (dot) in the C1 dataset. Arrows annotate discrepancy between day and phase/subphase where 1) orders were reversed for a pair of women between their orders in phase/subphase assignment and their cycle day (red) and 2) women on the same day of cycle were assigned into different phases or subphases (black). 10x: The day of menstrual cycle for each woman (dot) in the 10x dataset. Dots with * and # to the right are anchors shared between the two datasets (Methods). c, For each woman (dot: top, middle panel; bar: bottom panel) in the C1 dataset: (top) relationship between her pseudotime order (x-axis) and her cycle day, (middle) total number of single cells analyzed, (bottom) distribution of the six cell types identified by the C1 dataset. From left to right, women were ordered based on the median pseudotime of all her stromal fibroblasts and unciliated epithelia. Pseudotime and phase were as assigned in Fig. 3a. Related to Fig. 1, 3, 4.

Extended Data Fig. 2 Summary of cell types and states in the 10x validation dataset.

a, Dimension reduction (UMAP on top PCs) on 71032 single cells from 10 healthy human endometria. Integration was done across donors using Seurat IntegrateData() to decouple temporal signal. b, Distribution of cycling cells. c, Relative abundance of each cell type/subtype identified. Each black dot is a woman. Dots in pink are medians. d, Total number of cells analyzed per cell type/subtype. e, Expression of top discriminatory genes identified by the C1 dataset (Fig. 1b) and for smooth muscle cells. f, Expression of markers identifying cells with mesenchymal stem cell features in the human endometrium. g, Combinatorial expression of PDGFRB and MCAM in smooth muscle cells. In brackets: abundance of each of the four cell groups normalized by the total smooth muscle cell count. (Expression values were jittered (amount=0.1) for visualization.) h, Percent cells expressing SUSD2 in the four cell groups identified in (g). Related to Fig. 1, 5.

Extended Data Fig. 3 Constructing single cell resolution trajectories of the human menstrual cycle using mutual information (MI) based approach.

a, Dimension reduction (tSNE) using whole transcriptome data, that is all genes detected, for unciliated epithelia (epi) and stromal fibroblasts (str), respectively, led to identification of four major phases (insets). b, MI between expressions of each gene and time (red) or permutated time (black). Genes were ranked by their MI with time. c, tSNE using ‘time-associated genes’ for epi and str, respectively led to the identification of the same four major phases as in (a) and trajectories of endometrial transformation. In a, c main panels, cells were colored by the day of menstrual cycle (day). Related to Fig. 3.

Extended Data Fig. 4 Global temporal transcriptome dynamics across the human menstrual cycle.

a, MI between expressions of each pseudotime-associated gene (FDR<1E-05) and pseudotime (red) or permutated pseudotime (black) for unciliated epithelia (epi) and stromal fibroblasts (str). b, Dynamics of pseudotime- associated genes across the menstrual cycle. Related to Fig. 4.

Extended Data Fig. 5 Dynamics of WOI-associated genes in the 10x validation dataset.

a-b, Dimension reduction (UMAP on top PCs) on unciliated epithelia (a) and stromal fibroblasts (b) in the 10x dataset. Highlighted are anchors between the C1 and 10x datasets (Methods). Phases were assigned based on relative order between each cell group and the anchors. c, Relationship between the day of menstrual cycle and phase assignment. In blue are anchors. d-e, Dynamics of WOI-associated genes in Fig. 4 stratified by epithelial subpopulations (d) and stromal fibroblasts (e). MTIX, FGF7, and LMCD1 peaked at non-WOI phases in the C1 dataset and were included as references. White dots: medians; Boundaries of black bars: 25% and 75% quantiles. Highlighted in blue are cell groups where the anchors grouped with. Related to Fig. 4.

Extended Data Fig. 6 Dynamics of genes encoding transcriptional factors (TF) across the menstrual cycle.

a-b, All time-associated TFs for unciliated epithelia (epi, a) and stromal fibroblasts (str, b) (TFs bracketed by red bars were zoomed in c, d). c-d, TFs that are associated with the entrance/exit of WOI (bottom panel) or phase-defining (top) in epi (c) and str (d). e, Expression of TFs that are nuclear hormone receptors for steroid hormones estrogen (ESR1), progesterone (PGR), glucocorticoid (NR3C1), and androgen (AR). For heatmap, TFs were ordered first by the pseudotime of the major peak and then by the pseudotime of the peak’s inflection point. Full list of pseudotime-associated TFs can be found in Supplementary Table 2. Related to Fig. 4.

Extended Data Fig. 7 Dynamics of genes encoding secretory proteins (sec genes) across the menstrual cycle.

a-b, All time-associated sec genes for unciliated epithelia (epi, a) and stromal fibroblasts (str, b) (sec genes bracketed by blue bars were zoomed in c, d). c-d, Sec genes that are associated with the entrance/exit of WOI (bottom) in epi (c) and str (d). e-f, Expression of sec genes encoding canonical protein markers for decidualization during pregnancy in epi (e) and str (f). For heatmap, sec genes were ordered following the same strategy as in Extended Data Fig. 6. Full list of pseudotime-associated sec genes can be found in Supplementary Table 3. Related to Fig. 4.

Extended Data Fig. 8 Expression of genes that define human early-pregnant endometrial stromal fibroblast subtypes.

a, Abundance of stromal fibroblasts (str) and unciliated epithelia (epi) expressing IGFBP1, PRL, or both in non-pregnant human endometria across the menstrual cycle. Phases were assigned as in Extended Data Fig. 5. b, Distribution of stromal fibroblasts co-expressing IGFBP1 and PRL on the reduced dimension of all late-secretory stromal fibroblasts. c, Dispersion level of all genes detected in late-secretory stromal fibroblasts. Highlighted are genes reported to differentially express in the two early-pregnant subtypes that do not co-express IGFBP1 and PRL. d, Some of the subtype genes that are highly dispersed in (c), for example ACTA2, IGFBP1, showed a subtle expression gradient (arrow) on dimension-reduced late-secretory stromal fibroblasts. In c, d, red and blue indicate genes reported to define the two early-pregnant subtypes, respectively.

Extended Data Fig. 9 Dynamics of cell cycling activity across the menstrual cycle and transcriptomic signature of proliferative phases.

a-b, Endometrial G1/S and G2/M signatures for unciliated epithelia (epi, a) and stromal fibroblasts (str, b) identified in the C1 dataset. c-d, Distribution (left) and abundance (right) of cycling cells across major phases of the menstrual cycle. e-f, Top discriminatory genes for the two proliferative phases identified in epi (e) and str (f). Related to Fig. 4.

Extended Data Fig. 10 Deviating subpopulations in unciliated epithelia and their transcriptomic signatures across the menstrual cycle.

a. Dimension reduction (UMAP on top PCs) on all unciliated epithelia in the 10x validation dataset and distribution of cycling cells. Integration was done across donors using Seurat IntegrateData() to decouple temporal signal. b, Expression of markers differentially expressed between the two subpopulations that were identified in the C1 dataset. In black are genes in Fig. 5b. In orange are genes in Fig. 5c, d that were previously shown to be differentially expressed by glandular or luminal epithelia of human endometria in situ. c, Dynamics of differentially expressed genes between the two subpopulations in proliferative phases identified in the C1 dataset. Related to Fig. 5.

Supplementary information

Supplementary Information

Supplementary Figs. 1–4.

Reporting Summary

Supplementary Tables 1–5.

Supplementary Data 1

Supplementary Dataset 1. Data were extracted from all representative fields of view captured for Fig. 2a–d and were used to generate Fig. 2e.

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Wang, W., Vilella, F., Alama, P. et al. Single-cell transcriptomic atlas of the human endometrium during the menstrual cycle. Nat Med 26, 1644–1653 (2020). https://doi.org/10.1038/s41591-020-1040-z

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