Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape
Alexey V. Terskikh
Posted 05 Feb 2019
bioRxiv DOI: 10.1101/541151 (published DOI: 10.7554/eLife.49683)
Posted 05 Feb 2019
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.
- Downloaded 1,065 times
- Download rankings, all-time:
- Site-wide: 24,278
- In cell biology: 839
- Year to date:
- Site-wide: 75,977
- Since beginning of last month:
- Site-wide: 100,452
Downloads over time
Distribution of downloads per paper, site-wide
- 27 Nov 2020: The website and API now include results pulled from medRxiv as well as bioRxiv.
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!