High-throughput cell spheroid production and assembly analysis by microfluidics and deep learning

Abstract

3D cell culture models are important tools in translational research but have been out of reach for high-throughput screening due to complexity, requirement of large cell numbers and inadequate standardization. Microfluidics and culture model miniaturization technologies could overcome these challenges. Here, we present a high-throughput workflow to produce and characterize the formation of miniaturized spheroids using deep learning. We train a convolutional neural network (CNN) for cell ensemble morphology classification for droplet microfluidic minispheroid production, benchmark it against more conventional image analysis, and characterize minispheroid assembly determining optimal surfactant concentrations and incubation times for minispheroid production for three cell lines with different spheroid formation properties. Notably, this format is compatible with large-scale spheroid production and screening. The presented workflow and CNN offer a template for large scale minispheroid production and analysis and can be extended and re-trained to characterize morphological responses in spheroids to additives, culture conditions and large drug libraries.