Single-cell dispensing and 'real-time' cell classification using convolutional neural networks for higher efficiency in single-cell cloning.

ABSTRACT: Single-cell dispensing for automated cell isolation of individual cells has gained increased attention in the biopharmaceutical industry, mainly for production of clonal cell lines. Here, machine learning for classification of cell images is applied for 'real-time' cell viability sorting on a single-cell printer. We show that an extremely shallow convolutional neural network (CNN) for classification of low-complexity cell images outperforms more complex architectures. Datasets with hundreds of cell images from four different samples were used for training and validation of the CNNs. The clone recovery, i.e. the fraction of single-cells that grow to clonal colonies, is predicted to increase for all the samples investigated. Finally, a trained CNN was deployed on a c.sight single-cell printe

SUBMITTER: Riba J 

PROVIDER: S-EPMC6985245 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature