A predictive machine learning model for cannabinoid effect based on image detection of reactive oxygen species in microglia

Abstract

Neuroinflammation is a key feature of human neurodisease including neuropathy and neurodegenerative disease and is driven by the activation microglia, immune cells of the nervous system. During activation microglia release pro-inflammatory cytokines as well as reactive oxygen species (ROS) that can drive local neuronal and glial damage. Phytocannabinoids are an important class of naturally occurring compounds found in the cannabis plant (Cannabis sativa) that interact with the body’s endocannabinoid receptor system. Cannabidiol (CBD) is a prototype phytocannabinoid with anti-inflammatory properties observed in cells and animal models. We measured ROS in human microglia (HMC3) cells using CellROX, a fluorescent dynamic ROS indicator. We tested the effect of CBD on ROS level in the presence of three known immune activators: lipopolysaccharide (LPS), amyloid beta (Aβ42), and human immunodeficiency virus (HIV) glycoprotein (GP120). Confocal microscopy images within microglia were coupled to a deep learning model using a convolutional neural network (CNN) to predict ROS responses. Our study demonstrates a deep learning platform that can be used in the assessment of CBD effect in immune cells using ROS image measure.