Endometriosis is a common condition in women that causes chronic pain and infertility and is associated with an elevated risk of ovarian cancer. We profiled transcriptomes of >370,000 individual cells from endometriomas (n = 8), endometriosis (n = 28), eutopic endometrium (n = 10), unaffected ovary (n = 4) and endometriosis-free peritoneum (n = 4), generating a cellular atlas of endometrial-type epithelial cells, stromal cells and microenvironmental cell populations across tissue sites. Cellular and molecular signatures of endometrial-type epithelium and stroma differed across tissue types, suggesting a role for cellular restructuring and transcriptional reprogramming in the disease. Epithelium, stroma and proximal mesothelial cells of endometriomas showed dysregulation of pro-inflammatory pathways and upregulation of complement proteins. Somatic ARID1A mutation in epithelial cells was associated with upregulation of pro-angiogenic and pro-lymphangiogenic factors and remodeling of the endothelial cell compartment, with enrichment of lymphatic endothelial cells. Finally, signatures of ciliated epithelial cells were enriched in ovarian cancers, reinforcing epidemiologic associations between these two diseases.
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The data generated during this study are available at NCBI GEO under accession number GSE213216 and can also be accessed through https://github.com/lawrenson-lab/AtlasEndometriosis.
Scripts for the downstream analysis of the single-cell data are available on GitHub (https://github.com/lawrenson-lab/AtlasEndometriosis) and through Zenodo https://doi.org/10.5281/zenodo.6974609.
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Some of the specimens were collected as part of the Biologic and Epidemiologic Markers of Endometriosis (BEME) study. We thank all the patients who donated the specimens used in this study, and the team at the Biobank and Translational Research Laboratory who supported tissue procurement and histologic analyses, plus the Cedars-Sinai Applied Genomics, Computation and Translational core. We thank C. Chow and M. Ta at the Genetic Pathology Evaluation Centre (GPEC) for technical support for the mutation analyses. This study was supported in large part by a Leon Fine Translational Science Award from Cedars-Sinai Medical Center. K.L. is supported by a Liz Tilberis Early Career Award (grant no. 599175) and a Program Project Development grant (no. 373356) from the Ovarian Cancer Research Alliance, plus a Research Scholar’s Grant from the American Society (grant no. 134005). The research described was supported in part by the National Institutes of Health (NIH)/National Center for Advancing Translational Science (NCATS) UCLA CTSI Grant no. UL1TR001881 and in part by Cedars-Sinai Cancer, a Canadian Cancer Society Research Institute Impact grant (no. 705647, to D.G.H.) and a Canadian Institutes of Health Research Foundation grant (to D.G.H.). M.S.A. receives funds through the Canadian Institutes of Health Research (Early Career Investigator Grant in Maternal, Reproductive, Child & Youth Health), a Michael Smith Foundation for Health Research Scholar Award and the Janet D. Cottrelle Foundation Scholars program (managed by the BC Cancer Foundation). The GPEC receives core support from BC’s Gynecological Cancer Research team (OVCARE), and The VGH + UBC Hospital Foundation. Y.W. is a recipient of the North Family Health Research Award (administered by the VGH + UBC Hospital Foundation). M.D.P. is a recipient of grants from the NIH/National Institute of Biomedical Imaging and Bioengineering (NIBIB; grant no. U01NIBIB12482404) and NIH/National Institute of Allergy and Infectious Diseases (NIAID) (grant no. R01AI154535). The content of the manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
The authors declare no competing interests.
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(a) The number of genes detected per cell is not significantly different across the major classes of study, nor by fresh/frozen status. (b) Observed cell number is positively correlated with observed cell number (Pearson correlation and Analysis of Variance). (c) The number of cells passing QC filters and (d) the number of reads per cell is not significantly different across the major classes of study, nor by fresh/frozen status. Coral color denotes samples that were processed immediately - ‘Fresh’; teal denotes samples that were processed into single cells and viably cryopreserved and thawed before capture - ‘Frozen’. Number of samples in (a, b and d) – endometrioma, fresh = 6, frozen = 2; endometriosis, fresh = 12, frozen = 11; eutopic endometrium, fresh = 2, frozen = 8; no endometriosis detected, fresh = 2, frozen = 2; unaffected ovary, fresh = 1, frozen = 3. (e) Decision tree showing workflow for cell type assignment. Black boxes indicate action/processes, blue boxes indicate cell-type assignment endpoints. (f) Heatmap showing expression of cell-type specific markers, by cluster. Expression is scaled between 0-1. Possible cell type assignments are indicated by column labels with the number of cell-type specific genes for each cell type indicated in brackets. For a list of marker genes see Methods and panel (g). (g) Expression of cell-type specific markers across the 96 clusters. (h) UMAP plot with 114 clusters (using Seurat shared nearest neighbor (SNN) for cluster identification considering resolution parameter of 3). (i) Correlation values for pair-wise comparisons of the 6 unassigned clusters compared to a background distribution of correlation values for 100 pairs of clusters selected at random, red dots indicate the correlation value used for cell type assignment. (j) Pearson’s correlation between clusters, based on expression of a union set of 1,960 genes differentially expressed by one or more cluster (log2 FC > 0; p <0.05). Clusters with no cell markers were assigned the identity associated with the most correlated cell type with known identity (see Methods). (k) Correlation of gene expression across major cell types, comparing fresh and cryopreserved specimens. Correlation values shown above each plot, Pearson’s correlation. (l) Principal component analysis based on cell-type composition of endometriosis and control tissues. In the box and whisker plots shown in (a,c,d,i), boxes denote the interquartile range, bar denotes median. The limits of the whiskers represent 1.5 * IQR (interquartile range) and outlier values are indicated with individual dots. Red dots denote the correlation value for expression in the given cluster compared to the cluster used to assign identity.
Extended Data Fig. 2 Additional results, analysis of epithelial subgroups by ARID1A and KRAS mutation status.
(a) Cluster frequency for eutopic endometrial epithelial clusters with inclusion of patients taking exogenous hormones. (b) Visium analysis of an endometriosis lesion from patient BEME355. (c) ARID1A and KRAS expression across epithelial subtypes. (d) Clusters present in BEME355 and expression of key genes. (e) UMAP of endometrial-type epithelial cells by ARID1A protein expression status, (f) UMAP of cells by KRAS mutation status. UMAP structure from 3A. (g) Pathways enriched in lesions with endometrial-type epithelium exhibiting heterogeneous and homogenous positive staining for ARID1A. (h) Pathways enriched in KRAS mutant and wild-type endometrial-type epithelium. Pathway analyses were performed using the Reactome R package, with p-values calculated based on a hypergeometric model and a Bonferroni correction applied.
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Fonseca, M.A.S., Haro, M., Wright, K.N. et al. Single-cell transcriptomic analysis of endometriosis. Nat Genet 55, 255–267 (2023). https://doi.org/10.1038/s41588-022-01254-1
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