Optimization of trajectory length in light scattering imaging for nanoparticle diffusion analysis

Small extracellular vesicles (sEVs) are nanoscale bioparticles released from various cells and have important applications in clinical and basic science. In nanoscale particle tracking, the tracking trajectory length is important for the accurate sizing of nanoparticles (NP). Here, a light scattering imaging system uses a 786.4 nm laser source to collect the side scatter of individual nanoscale particles with a 10X objective lens and a CMOS camera is introduced. Supervised sliding window analysis is tested for optimized NP trajectory segmentation, followed by a machine learning algorithm that classifies Brownian motion and non-Brownian diffusions based on tracked trajectory features. Supervised sliding window analysis allows the differentiation of non-Browian diffusions with a high accuracy of 93.8% and precise sizing of standard polystyrene NPs. Imaging and size measurements of 120 nm NPs, 65 nm NPs, and plasma-derived sEVs show that optimizing the trajectory length combined with purifying the non-Brownian diffusion improves the sizing accuracy. Nanoscale EVs are expected to be reliable biomarkers for many diseases, especially those associated with cancer, where reliable and accurate size estimation methods based on light scattering imaging have potential applications.