High-grade serous carcinoma of the ovary, fallopian tube and peritoneum (HGSC), the most common type of ovarian cancer, ranks among the deadliest malignancies. Many HGSC patients have excess fluid in the peritoneum called ascites. Ascites is a tumour microenvironment (TME) containing various cells, proteins and extracellular vesicles (EVs). We isolated EVs from patients' ascites by orthogonal methods and analyzed them by mass spectrometry. We identified not only a set of 'core ascitic EV-associated proteins' but also defined their subset unique to HGSC ascites. Using single-cell RNA sequencing data, we mapped the origin of HGSC-specific EVs to different types of cells present in ascites. Surprisingly, EVs did not come predominantly from tumour cells but from non-malignant cell types such as macrophages and fibroblasts. Flow cytometry of ascitic cells in combination with analysis of EV protein composition in matched samples showed that analysis of cell type-specific EV markers in HGSC has more substantial prognostic potential than analysis of ascitic cells. To conclude, we provide evidence that proteomic analysis of EVs can define the cellular composition of HGSC TME. This finding opens numerous avenues both for a better understanding of EV's role in tumour promotion/prevention and for improved HGSC diagnostics.

• Keywords
• ascites, extracellular vesicles (EV), fallopian tube and peritoneum (HGSC), high-grade serous carcinoma of the ovary, macrophage, ovarian cancer (OC), tandem mass spectrometry (MS/MS), tumour microenvironment (TME),
• MeSH
• Ascites metabolism   pathology   MeSH
• Extracellular Vesicles * metabolism   MeSH
• Humans MeSH
• Tumor Microenvironment MeSH
• Ovarian Neoplasms * diagnosis   MeSH
• Proteomics MeSH
• Cystadenocarcinoma, Serous * diagnosis   genetics   metabolism   MeSH
• Check Tag
• Humans MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Small extracellular vesicles (sEVs) are cell-derived vesicles of nanoscale size (~30-200 nm) that function as conveyors of information between cells, reflecting the cell of their origin and its physiological condition in their content. Valuable information on the shape and even on the composition of individual sEVs can be recorded using transmission electron microscopy (TEM). Unfortunately, sample preparation for TEM image acquisition is a complex procedure, which often leads to noisy images and renders automatic quantification of sEVs an extremely difficult task. We present a completely deep-learning-based pipeline for the segmentation of sEVs in TEM images. Our method applies a residual convolutional neural network to obtain fine masks and use the Radon transform for splitting clustered sEVs. Using three manually annotated datasets that cover a natural variability typical for sEV studies, we show that the proposed method outperforms two different state-of-the-art approaches in terms of detection and segmentation performance. Furthermore, the diameter and roundness of the segmented vesicles are estimated with an error of less than 10%, which supports the high potential of our method in biological applications.

• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH