Deconvolution of bulk endometrial tissue identifies cell type proportions and expression signatures associated with endometrial function and disease

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Abstract

Background

Human endometrium is a complex tissue lining the inside of the uterus and is essential for female fertility. It comprises several cell types, including endometrial stromal and epithelial cells, whose structure and function change across the menstrual cycle in response to steroid hormones. Although numerous bulk tissue studies have been carried out to investigate factors regulating gene expression in eutopic endometrium, these are complicated by variations in cell composition, cell specific expression profiles and the presence of endometrial pathologies. This study aims to investigate changes in the cellular and molecular environment of the endometrium across the menstrual cycle in women with and without endometriosis.

Methods

Using RNA sequencing data generated from the eutopic endometrium of 206 European women with and without endometriosis at different menstrual cycle phases, we estimated cell type proportions and cell type specific gene expression profiles using computational deconvolution methods.

Results

Cell type proportions varied across menstrual cycle phases and disease states. Women with endometriosis had lower proportions of luminal and ciliated epithelia at mid-secretory phase. We further analysed the estimated cell type specific gene expression profiles and identified differentially expressed (DE) genes within cycle phases between women with and without endometriosis in different cell types at the mid-secretory phase. Known genes associated with endometrial receptivity were DE including downregulation of PTGS1 and upregulation of POSTN in stromal fibroblasts and glandular epithelia in women with endometriosis. DE genes were enriched in RNA metabolism and biogenesis pathways that can mediate fundamental mechanisms of cell proliferation and migration.

Conclusions

Our findings suggests that cell type deconvolution of RNA-sequencing data can improve our understanding of the role of cell type specific gene expression in endometrium, offering insights into potential disease relevant cell types and target genes.

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