I. Rosenhek-Goldian, P.A. Karam, I. Azuri, E. Dekel, M.I. Morandi, S.R. Cohen, N. Regev-Rudzki
Weizmann Institute of Science,
Israel
Keywords: chemical research support, life sciences core facilities, biomolecular sciences, Weizmann Institute of Science, Rehovot, Israel
Summary:
Atomic force microscopy (AFM) stands out as a powerful tool for rendering high quality images of extracellular vesicles (EVs), attached to a surface under wet conditions. In this strategy, a probe attached to a cantilever reconstructs the topographic surface of EVs and can measure some of their nanomechanical properties. Nevertheless, comparison of mechanical properties between different EVs populations may be complicated, in particular when comparing EVs of different sizes (due to tip-EV contact area and underlying substrate influence on the measured values). To properly measure the difference in biophysical properties between the two distinct malaria-derived EV subpopulations - small (10-70 nm) and large (30-500 nm), we avoid these two complications by using puncture tests on supported lipid bilayers created from these EV subpopulations. AFM puncture analysis combined with machine learning methods emphasizes the difference in biophysical properties between the two subpopulations.1 We have also applied atomic force microscopy (AFM) to study the mechanical changes occurring in red blood cells (RBCs) exposed to malaria derived EVs, finding that this exposure reduces the stiffness of the membrane, and therefore primes naïve RBCs for parasite invasion, eventually increasing the parasite’s spread in the body. Furthermore, high-resolution images of dried cells with exposed cytoskeleton show distinct morphological differences associated with the breakdown and softening of the cell structure. Even when the extent of damage was not always clear from human SPM expert inspection of the images, convolutional neural network (CNN) analysis was able to differentiate between healthy and damaged cytoskeleton images.2,3 1. Abou Karam P. et al. (2022) EMBO Reports 23:e54755. 2. Dekel E. et al. (2020) Nature Communications, 12, 1172. 3. Azuri, I. et al. (2021) Beilstein Journal of Nanotechnology, 12, 878–901.