Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape
- Sanford Burnham Prebys Medical Discovery Institute, United States
- University of Toronto, Canada
- University of California, Santa Cruz, United States
- Cited 2
- Views 1,812
- Annotations
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.
Article and author information
Author details
Funding
California Institute for Regenerative Medicine (TG2-01162)
- Chen Farhy
Celgene (SCRA)
- Alexey V Terskikh
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Ross L Levine, Memorial Sloan Kettering Cancer Center, United States
Publication history
- Received: June 27, 2019
- Accepted: October 5, 2019
- Accepted Manuscript published: October 22, 2019 (version 1)
- Version of Record published: December 12, 2019 (version 2)
Copyright
© 2019, Farhy et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,812
- Page views
-
- 400
- Downloads
-
- 2
- Citations
Article citation count generated by polling the highest count across the following sources: PubMed Central, Crossref, Scopus.
Download links
Downloads (link to download the article as PDF)
Download citations (links to download the citations from this article in formats compatible with various reference manager tools)
Open citations (links to open the citations from this article in various online reference manager services)
Further reading
-
- Cancer Biology
- Chromosomes and Gene Expression
-
- Cancer Biology
- Cell Biology
