Automated morphological classification and quantification of cerebrospinal fluid extracellular vesicles

Morphology of extracellular vesicles (EVs) from cerebrospinal fluid is an important property that could uncover brain-related conditions. However, native morphology could get distorted during imaging, such as with atomic force microscopy (AFM) in air, which enables relatively simple visualisation and automated morphology assessment. Therefore, we compared 24 different preparation methods for the same sample of EVs according to the resulting size, height, aspect ratio and shape distributions obtained from the AFM images. We defined 5 different shape categories (round, flat, concave, single-lobed, and multilobed) and neglected other features that did not fit in either category and were considered artefacts. Artefacts affected the morphometric data (size, height, aspect ratio ranges and distributions), so their neglection was necessary for accurate morphometry. As this required a cumbersome and time-consuming manual search through all AFM images, we developed a computer program that facilitates the individual observation of each particle, enables manual shape identification and exports the resulting size and shape distribution from each AFM image. Since manual EV categorisation in the program still required significant time and proved to be quite subjective, we also employed machine learning for vesicle and shape recognition. A convolution neural network model was trained on a dataset of particles, for which 4 independent researchers provided consistent shape categorisations (