Myometrium

Parturition is a well-orchestrated process characterized by increased uterine contractility, cervical ripening, and activation of the chorioamniotic membranes; yet, the transition from a quiescent to a contractile myometrium heralds the onset of labor. However, the cellular underpinnings of human parturition in the uterine tissues are still poorly understood. Herein, we performed a comprehensive study of the human myometrium during spontaneous term labor using single-cell RNA sequencing (scRNA-Seq). First, we established a single-cell atlas of the human myometrium and unraveled the cell type-specific transcriptomic activity modulated during labor. Major cell types included distinct subsets of smooth muscle cells, monocytes/macrophages, stromal cells, and endothelial cells, all of which communicated and participated in immune (e.g., inflammation) and nonimmune (e.g., contraction) processes associated with labor. Furthermore, integrating scRNA-Seq and microarray data with deconvolution of bulk gene expression highlighted the contribution of smooth muscle cells to labor-associated contractility and inflammatory processes. Last, myometrium-derived single-cell signatures can be quantified in the maternal whole-blood transcriptome throughout pregnancy and are enriched in women in labor, providing a potential means of noninvasively monitoring pregnancy and its complications. Together, our findings provide insights into the contributions of specific myometrial cell types to the biological processes that take place during term parturition.

ObjectiveTo estimate the phenotypic characterization of fibrotic process in adenomyosis occurring at the inner or the outer myometrium.MethodsEight cases of adenomyosis occurring at the inner myometrium (Subtype I) and 10 cases of adenomyosis occurring at the outer myometrium (Subtype II), and 10 normal counterparts were used in this study. A immunohistochemical study for smooth muscle cells (SMCs) was performed using cytoskeletal proteins, Type I and III collagen, TGF-β and its signaling molecules.ResultsAn increased expression of Type I collagen was observed in the extracellular matrix of adenomyotic foci. In normal uteri, immunostaining of SMC differentiation marker proteins (Desmin, Smoothelin, Myosin heavy chain (MHC)) were absent or only found in low numbers at the inner myometrium, while all of these marker proteins were clearly stained at the outer myometrium. In both types of adenomyotic foci, Desmin, Smoothelin, and MHC commonly showed a negative staining at the adjacent area to the glands. A significant staining of Non-muscle myosin IIB, TGF-β, and phosphorylated TGF-β type I receptors were found only at the SMCs of Subtype II adenomyosis. The Smad3/2 ratio of Subtype II adenomyosis was significantly higher than that of Subtype I.ConclusionsThe inner myometrium of normal uteri was composed of undifferentiated phenotypes of SMCs, while the outer myometrium was composed of terminally differentiated SMCs. Various fibrotic processes have been suggested in the development of uterine adenomyosis. Distinct expression patterns of fibrosis related proteins have been shown to be implicated with differences in the subtypes of adenomyosis.

Myometrial invasion affects the prognosis of endometrial cancer. However, discrepancies exist between pre-operative magnetic resonance imaging staging and post-operative pathological staging. This study aims to validate the accuracy of artificial intelligence (AI) for detecting the depth of myometrial invasion using a deep learning technique on magnetic resonance images. We obtained 4896 contrast-enhanced T1-weighted images (T1w) and T2-weighted images (T2w) from 72 patients who were diagnosed with surgico-pathological stage I endometrial carcinoma. We used the images from 24 patients (33.3%) to train the AI. The images from the remaining 48 patients (66.7%) were used to evaluate the accuracy of the model. The AI then interpreted each of the cases and sorted them into stage IA or IB. Compared with the accuracy rate of radiologists' diagnoses (77.8%), the accuracy rate of AI interpretation in contrast-enhanced T1w was higher (79.2%), whereas that in T2w was lower (70.8%). The diagnostic accuracy was not significantly different between radiologists and AI for both T1w and T2w. However, AI was more likely to provide incorrect interpretations in patients with coexisting benign leiomyomas or polypoid tumors. Currently, the ability of this AI technology to make an accurate diagnosis has limitations. However, in hospitals with limited resources, AI may be able to assist in reading magnetic resonance images. We believe that AI has the potential to assist radiologists or serve as a reasonable alternative for pre-operative evaluation of the myometrial invasion depth of stage I endometrial cancers.

Uterine fibroids (UFs) are the most common benign gynecologic tumours affecting women of reproductive age. This study aims to deepen the understanding of UFs complex aetiology through harnessing the power of 3D organoid models derived from human myometrial stem cells to emulate the in vivo behaviour of these tumours. Isolated SCs were cultured over 7 days under a defined culture system. Immunohistochemistry, Immunofluorescence, organoid stiffness, RNA Sequencing was conducted, and differential gene expression was assessed using RT-PCR. The derived organoids exhibited diverse populations of cells, including stem cells, smooth muscle, and fibroblasts. Excessive ECM deposition was shown via Collagen and Fibronectin expression. We confirmed that our organoids expressed oestrogen receptor in a pattern similar to that in their corresponding tissue, as well as responded to steroid hormone. Interestingly, we revealed significant racial disparities in ECM accumulation within organoids derived from different racial groups. This augmented ECM deposition is theorised to enhance tissue stiffness, as assessed using Young's modulus. Additionally, our research demonstrated significant decreases in fibrotic markers upon treatment with Vitamin D3 and Doxercalciferol. Furthermore, the pro-fibroid effects of environmental phthalates further elucidate the potential factors contributing to UF pathology. The 3D organoid model can serve as a robust platform to study the underlying molecular mechanisms of UFs, besides offering invaluable insights for potential therapeutic interventions.