Mapping Human Uterine Disorders Through Single-Cell Transcriptomics

Abstract

Disruptions in uterine tissue function contribute to disorders such as endometriosis, adenomyosis, endometrial cancer, and fibroids, which all significantly impact health and fertility. Advances in transcriptomics, particularly single-cell RNA sequencing, have revolutionized uterine biological research by revealing the cellular heterogeneity and molecular mechanisms underlying disease states. Single-cell RNA sequencing and spatial transcriptomics have mapped endometrial and myometrial cellular landscapes, which helped to identify critical cell types, signaling pathways, and phase-specific dynamics. Said transcriptomic technologies also identified stromal and immune cell dysfunctions, such as fibroblast-to-myofibroblast transitions and impaired macrophage activity, which drive fibrosis, chronic inflammation, and lesion persistence in endometriosis. For endometrial cancer, scRNA-seq uncovered tumor microenvironmental complexities, identifying cancer-associated fibroblast subtypes and immune cell profiles contributing to progression and therapeutic resistance. Similarly, studies on adenomyosis highlighted disrupted signaling pathways, including Wnt and VEGF, and novel progenitor cell populations linked to tissue invasion and neuroinflammation, while single-cell approaches characterized smooth muscle and fibroblast subpopulations in uterine fibroids, elucidating their roles in extracellular matrix remodeling and signaling pathways like ERK and mTOR. Despite challenges such as scalability and reproducibility, single-cell transcriptomic approaches may have potential applications in biomarker discovery, therapeutic target identification, and personalized medicine in gynecological disorders.

Keywords: adenomyosis; endometrial cancer; endometriosis; single-cell RNA sequencing; uterine disorders; uterine fibroids/leiomyoma.

Figure 1

Illustration of (A) the human uterus showing the different layers and the morphological and cellular changes based on the phase of the menstrual cycle (i.e., menstrual, proliferative and secretory phase). Red and blue lines represent estradiol (E2) and progesterone (P4) fluctuations, respectively; (B) single-cell sequencing workflow. Briefly, the single-cell process involves the collection and dissociation of cells; extraction of total RNA, cDNA synthesis and PCR amplification; library preparation and subsequent sequencing; computational data analysis (expression profiling single-cell wise, clustering, and cell type identification, with each colored dot in the cell clusters representing a specific cell type, with each colored dot in the cell clusters representing a specific cell type).

Figure 2

Representation of modified cell types, novel emerged cell types, key processes, and genes with altered expression under each condition compared to controls. Bold arrows indicate changes in cell abundance, while grey arrows denote changes in gene expression CAFs: cancer-associated fibroblasts; CCC: clear cell carcinoma; CNVs: copy number variations; Epi: epithelial cells; ECM: extracellular matrix; EEC: endometrioid endometrial cancer; EET: epithelial-to-endothelial transition; En: endothelial cells; FMT: fibroblast-to-myofibroblast transition; NK cells: natural killer cells; SMC: smooth muscle cell; USC: uterine serous carcinoma; VM: vasculogenic mimicry.