Improving drug discovery using image-based multiparametric analysis of epigenetic landscape

With the advent of automatic cell imaging and machine learning, high-content phenotypic screening has become the approach of choice for drug discovery due to its ability to extract drug specific multilayered data and compare it to known profiles. In the field of epigenetics such screening approaches has suffered from the lack of tools sensitive to selective epigenetic perturbations. Here we describe a novel approach Microscopic Imaging of Epigenetic Landscapes (MIEL) that captures patterns of nuclear staining of epigenetic marks (e.g. acetylated and methylated histones) and employs machine learning to accurately distinguish between such patterns (1). We demonstrated that MIEL has superior resolution compared to conventional intensity thresholding techniques and enables efficient detection of epigenetically active compounds, function-based classification, flagging possible off-target effects and even predict novel drug function. We validated MIEL platform across multiple cells lines and using dose-response curves to insure the robustness of this approach for the high content high throughput drug discovery.