MiceVAPORDot: A novel automated approach for high-throughput behavioral characterization during E-cigarette exposure in mice

Abstract

AbstractThe prosperity of electronic nicotine delivery systems (ENDS) or e-cigarette use has been regarded to lead an increasing risk of nicotine addiction, especially among youth. Understanding and evaluating the behaviors induced by ENDS are fundamental to the study of neuropsychiatric effects of e-cigarettes. However, little is known regarding the behavioral features during e-cigarette exposure in mice. Current behavioral assessments for nicotine addiction are based on nicotine withdrawal-induced anxiety which can be only performed after ENDS exposures. Here we developed MiceVAPORDot, a novel high-throughput tool for automatedin situbehavioral characterization during e-cigarette exposure. The integration of a deep learning-based animal pose tracking method by MiceVAPORDot allows precise characterization on behavioral phenotypes of e-vapor exposed mice, which were unable revealed by traditional evaluation methodology such as conditioned place preference and elevated plus maze tests. The behavioral fingerprints recognized by MiceVAPORDot can be used for high-throughput screening on incentive nature of e-cigarette flavors as well as medications for smoking cessation.