SLAS 2019 AI in Process Automation Symposium

Application of Machine Learning Methods To Improve Hit Calling Accuracy For High Content Image Based Screens
Formation of α-synuclein inclusions is linked to pathogenesis of Parkinson’s disease. A high-content imaging screen was conducted to probe Biogen’s chemogenomic set for inhibitors of α-synuclein inclusion formation in M17 neuroblastoma cell line stably expressing α-syn 3K-GFP. Confidence in hits was hindered by high hit rate and apparent unwanted mechanism of inclusion reduction by many compounds (e.g. cytotoxicity). Improved hit-calling accuracy is much needed in this high-information content space.
 We generated a work flow to process complex, large number of image-based descriptors obtained from a high-content image-based screening campaign to identify inhibitors of α-synuclein inclusion formation. In this study, along with the user defined image analysis sequence in Columbus software, we applied a combination of unsupervised and supervised machine learning models to high-content image-based screening data of Biogen’s chemogenomic set of 3070 compounds. We demonstrated the value of this approach in confirming the hits in relation to current hit-calling and evaluate additional hits.