An Open-Source Framework for Automated High-Throughput Cell Biology Experiments - Katunin, Pavel; Zhou, Jianbo; Shehata, Ola M.; Peden, Andrew A.; Cadby, Ashley; Nikolaev, Anton (Frontiers in Cell and Developmental Biology, 2021)
Modern data analysis methods, such as optimization algorithms or deep learning have been successfully applied to a number of biotechnological and medical questions. For these methods to be efficient, a large number of high-quality and reproducible experiments needs to be conducted, requiring a high degree of automation. Here, we present an open-source hardware and low-cost framework that allows for automatic high-throughput generation of large amounts of cell biology data. Our design consists of an epifluorescent microscope with automated XY stage for moving a multiwell plate containing cells and a perfusion manifold allowing programmed application of up to eight different solutions. Our system is very flexible and can be adapted easily for individual experimental needs. To demonstrate the utility of the system, we have used it to perform high-throughput Ca2+ imaging and large-scale fluorescent labeling experiments.