A review of the biomechanical properties of single extracellular vesicles. Issue 1 (27th October 2020) - LeClaire, Michael; Gimzewski, James; Sharma, Shivani
Abstract: Extracellular vesicles (EVs) are a unique, heterogeneous class of biological nanoparticles secreted by most cells. As potential a class of novel diagnostics and therapeutics, the physio‐chemical characterization as well as the biomolecular composition of EVs are widely investigated. However, there is emerging evidence suggesting that biomechanical analysis of lipid‐bilayer membrane‐bound single EVs may provide key insights into their biological structure, biomarker functions, and potential therapeutic functions. In this review, we focus on the unique biomechanical properties of single EVs such as elasticity, stiffness, and deformability. We compare common indentation models used in atomic force microscopy (AFM)‐based biomechanical analysis of EVs, as well as the benefits and drawbacks of each model encompassing the heterogeneous EV sub‐populations—mainly the small EVs (or exosomes). Next, we discuss high‐throughput approaches to determine the biomechanical landscape of EVs that may overcome some of the challenges associated with the accurate determination of particle sizes and particle‐by‐particle indentations. Finally, we highlight exciting new opportunities for EV biomechanical fingerprinting emanating from machine learning capabilities. In particular, we propose multi‐parametric AFM structure‐mechanical analysis to further advance label‐free, orthogonal biophysical understanding of EVs beyond biomolecular or particle size analysis, with significant implications for research and clinical use. Abstract : This review discusses the biomechanical properties of extracellular vesicles (EVs), unique and heterogenous biological nanoparticles secreted by cells. We examine the indentation models used to obtain mechanical properties for single vesicles from atomic force microscopy (AFM) experiments. We also review the current state of high‐throughput nanomechanical screening of EVs, and the potential applicability of machine learning techniques to this field.