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Elife. 2019 Oct 22;8. pii: e49683. doi: 10.7554/eLife.49683. [Epub ahead of print]

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

Author information

1
Sanford Burnham Prebys Medical Discovery Institute, La Jolla, United States.
2
Biological Sciences Platform, Sunnybrook Research Institute, University of Toronto, Toronto, Canada.
3
Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, United States.

Abstract

High-content phenotypic screening has become the approach of choice for drug discovery due to its ability to extract drug-specific multi-layered data. In the field of epigenetics, such screening methods 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 and employs machine learning to accurately distinguish between such patterns. We validated the MIEL platform across multiple cells lines and using dose-response curves, to insure the fidelity and robustness of this approach for high content high throughput drug discovery. Focusing on noncytotoxic glioblastoma treatments, we demonstrated that MIEL can identify and classify epigenetically active drugs. Furthermore, we show MIEL was able to accurately rank candidate drugs by their ability to produce desired epigenetic alterations consistent with increased sensitivity to chemotherapeutic agents or with induction of glioblastoma differentiation.

KEYWORDS:

cancer biology; human; human biology; medicine

PMID:
31637999
DOI:
10.7554/eLife.49683
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Conflict of interest statement

CF, SH, JY, LO, FZ, IP, FU, EF, CH, DA, AT The authors declare that no competing interests exist.

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