SLAS 2019 Advanced 3D Human Models and High-Content Analysis Symposium

Machine learning has seen some revolutionary and remarkable developments over the last few years. Exciting breakthroughs have been achieved in artificial neural networks, in particular around deep network architectures (= deep learning), which work based on raw-pixel image information and provide a classification accuracy exceeding human expert judgement. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, including image processing and analysis and are widely implemented in academia and industry.

Genedata has developed the first commercially available solution for deep learning-based High Content Image analysis. Genedata Imagence® allows for the application of deep networks to the analysis of High Content Imaging, creating a workflow that cuts image analysis time, increases data quality, reproducibility of results and seamlessly integrates with Genedata Screener® for image data analysis. This deep learning approach outperforms conventional approaches for feature extraction and phenotype classification.

Here we provide the evaluation of a recent pilot of Genedata Imagence® for High Content Analysis. We compare the Genedata Imagence® workflow with our conventional in-house High Content Imaging and Analysis workflow through a series of assays that have been developed within the target validation biology group at LifeArc. These include, immunocytochemistry of shRNA knock-down clones, antibody internalisation assay, High Content cell health assay, growth cone collapse assay and osteoclast differentiation assay.The generation of training data and subsequent training of the network was carried out for each assay and results of the data analysis were compared to in-house conventional analysis. We discuss a few limitations but show overall an excellent data quality produced by this novel deep learning module for High Content Image analysis.