Comparison of nanoimaging and nanoflow based detection of extracellular vesicles at a single particle resolution - Shihan Xu; Zhengrong Zhang; Bridgette C. Melvin; Nibedita Basu Ray; Seiko Ikezu; Tsuneya Ikezu (Journal of Extracellular Biology)
Abstract The characterization of single extracellular vesicle (EV) has been an emerging tool for the early detection of various diseases despite there being challenges regarding how to interpret data with different protocols or instruments. In this work, standard EV particles were characterized for single CD9+, single CD81+ or double CD9+/CD81+ tetraspanin molecule positivity with two single EV analytic technologies in order to optimize their EV sample preparation after antibody labelling and analysis methods: NanoImager for direct stochastic optical reconstruction microscopy (dSTORM)‐based EV imaging and characterization, and Flow NanoAnalyzer for flow‐based EV quantification and characterization. False positives from antibody aggregates were found during dSTORM‐based NanoImager imaging. Analysis of particle radius with lognormal fittings of probability density histogram enabled the removal of antibody aggregates and corrected EV quantification. Furthermore, different machine learning models were trained to differentiate antibody aggregates from EV particles and correct EV quantification with increased double CD9+/CD81+ population. With Flow NanoAnalyzer, EV samples were prepared with different dilution or fractionation methods, which increased the detection rate of CD9+/CD81+ EV population. Comparing the EV phenotype percentages measured by two instruments, differences in double positive and single positive particles existed after percentage correction, which might be due to the different detection limit of each instrument. Our study reveals that the characterization of individual EVs for tetraspanin positivity varies between two platforms—the NanoImager and the Flow NanoAnalyzer—depending on the EV sample preparation methods used after antibody labelling. Additionally, we applied machine learning models to correct for false positive particles identified in imaging‐based results by fitting size distribution data.