Placental function is essential for fetal development and establishing the foundations for lifelong health. The placental villous stroma is a connective tissue layer that supports the fetal capillaries and villous trophoblast. All the nutrients that cross the placenta must also cross the stroma, and yet little is known about this region. This study uses high‐resolution three‐dimensional imaging to explore the structural complexity of this region within the placental villi. Serial block‐face scanning electron microscopy and confocal microscopy were used to image the placental villous stroma in three‐dimensions. Transmission electron microscopy (TEM) was used to generate high resolution two‐dimensional images. Stereological approaches were used to quantify volumes of stromal constituents. Three‐dimensional imaging identified stromal extracellular vesicles, which constituted 3.9% of the villous stromal volume. These stromal extracellular vesicles were ovoid in shape, had a median length of 2750 nm (range 350–7730 nm) and TEM imaging confirmed that they were bounded by a lipid bilayer. Fifty‐nine per cent of extracellular vesicles were in contact with a fibroblast‐like stellate cell and these vesicles were significantly larger than those where no contact was observed. These stellate cells formed local networks with adherent junctions observed at contact points. This study demonstrates that the villous stroma contains extracellular macrovesicles which are considerably larger than any previously described in tissue or plasma. The size and abundance of these macrovesicles in the villous stroma highlight the diversity of extracellular vesicle biology and their roles within connective tissues.
Analysis of fibroblasts within placenta is necessary for research into placental growth-factors, which are linked to lifelong health and chronic disease risk. 2D analysis of fibroblasts can be challenging due to the variation and complexity of their structure. 3D imaging can provide important visualisation, but the images produced are extremely labour intensive to construct because of the extensive manual processing required. Machine learning can be used to automate the labelling process for faster 3D analysis. Here, a deep neural network is trained to label a fibroblast from serial block face scanning electron microscopy (SBFSEM) placental imaging.
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