RT Journal Article
SR Electronic
T1 High-throughput cell spheroid production and assembly analysis by microfluidics and deep learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.10.02.510497
DO 10.1101/2022.10.02.510497
A1 Martin Trossbach
A1 Emma Åkerlund
A1 Krzysztof Langer
A1 Brinton Seashore-Ludlow
A1 Haakan N. Joensson
YR 2022
UL http://biorxiv.org/content/early/2022/10/05/2022.10.02.510497.abstract
AB 3D cell culture models are an important tool in translational research but have been out of reach for high-throughput screening due to complexity, requirement of large cell numbers and inadequate standardization. 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, benchmark it against more conventional image analysis, and characterize spheroid assembly determining optimal surfactant concentrations and incubation times for spheroid 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.Competing Interest StatementThe authors have declared no competing interest.