Automated morphological classification and quantification of cerebrospinal fluid extracellular vesicles via AFM and machine learning

Abstract

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 (F₁ score of 85 ± 5%) and was successfully used to compare the 24 methods of preparation. Our analysis indicated that fixation had a very important role in both capturing and protection of EVs on a mica-based substrate, while critical point drying performed much better in retaining their morphology than hexamethyldisilazane. All tested functionalisations enabled good capture and visualisation of EVs, but (3-aminopropyl)triethoxysilane could cause flattening of EVs and NiCl₂ was more prone to formation of round artefacts during direct air-drying. Generally, ethanol gradient dehydration followed by critical point drying best preserved the EV morphology, while chemical dehydration with dimethoxypropane resulted in well-balanced shape distributions with lower aspect ratios. The highest aspect ratios were obtained by ethanol dehydration and critical point drying on NiCl₂-coated mica, for which all morphometric data agreed very well with the near-native EV morphology observed in liquid AFM images on the same type of substrate. These findings represent a promising first step towards utilising AFM images of EVs for diagnostic purposes.