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

By Chen Farhy, Santosh Hariharan, Jarkko Ylanko, Luis Orozco, Fu-Yue Zeng, Ian Pass, Fernando Ugarte, Camilla Forsberg, Chun-Teng Huang, David Andrews, Alexey V. Terskikh

Posted 05 Feb 2019
bioRxiv DOI: 10.1101/541151 (published DOI: 10.7554/eLife.49683)

With the advent of automatic cell imaging and machine learning, high-content phenotypic screening has become the approach of choice for drug discovery because it can extract drug-specific multi-layered data, which could be compared to known profiles. In the field of epigenetics, such screening approaches have suffered from a lack of tools sensitive to selective epigenetic perturbations. Here we describe a novel approach, Microscopic Imaging of Epigenetic Landscapes (MIEL), which captures the nuclear staining patterns of epigenetic marks (e.g., acetylated and methylated histones) and employs machine learning to accurately distinguish between such patterns. We validated the fidelity and robustness of the MIEL platform across multiple cells lines using dose-response curves. We employed MIEL to uncover the mechanism by which bromodomain inhibitors synergize with temozolomide-mediated killing of human glioblastoma lines. To explore alternative, non-cytotoxic, glioblastoma treatment, we screen the Prestwick chemical library and documented the power of MIEL platform to identify epigenetically active drugs and accurately rank them according to their ability to produce epigenetic and transcriptional alterations consistent with the induction of glioblastoma differentiation.

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