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

  1. Chen Farhy
  2. Santosh Hariharan
  3. Jarkko Ylanko
  4. Luis Orozco
  5. Fu-Yue Zeng
  6. Ian Pass
  7. Fernando Ugarte
  8. E Camilla Forsberg
  9. Chun-Teng Huang
  10. David W Andrews
  11. Alexey V Terskikh  Is a corresponding author
  1. Sanford Burnham Prebys Medical Discovery Institute, United States
  2. University of Toronto, Canada
  3. University of California, 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.

Data availability

Sequencing data have been deposited in GEO under accession code GSE134045

The following data sets were generated

Article and author information

Author details

  1. Chen Farhy

    Sanford Burnham Prebys Medical Discovery Institute, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Santosh Hariharan

    Biological Sciences Platform, Sunnybrook Research Institute, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
  3. Jarkko Ylanko

    Biological Sciences Platform, Sunnybrook Research Institute, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
  4. Luis Orozco

    Sanford Burnham Prebys Medical Discovery Institute, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Fu-Yue Zeng

    Sanford Burnham Prebys Medical Discovery Institute, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Ian Pass

    Sanford Burnham Prebys Medical Discovery Institute, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Fernando Ugarte

    Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. E Camilla Forsberg

    Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Chun-Teng Huang

    Sanford Burnham Prebys Medical Discovery Institute, La Jolla, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. David W Andrews

    Biological Sciences Platform, Sunnybrook Research Institute, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9266-7157
  11. Alexey V Terskikh

    Sanford Burnham Prebys Medical Discovery Institute, La Jolla, United States
    For correspondence
    terskikh@sbpdiscovery.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4641-3997

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

  1. Ross L Levine, Memorial Sloan Kettering Cancer Center, United States

Publication history

  1. Received: June 27, 2019
  2. Accepted: October 5, 2019
  3. Accepted Manuscript published: October 22, 2019 (version 1)
  4. 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.

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  1. Chen Farhy
  2. Santosh Hariharan
  3. Jarkko Ylanko
  4. Luis Orozco
  5. Fu-Yue Zeng
  6. Ian Pass
  7. Fernando Ugarte
  8. E Camilla Forsberg
  9. Chun-Teng Huang
  10. David W Andrews
  11. Alexey V Terskikh
(2019)
Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape
eLife 8:e49683.
https://doi.org/10.7554/eLife.49683

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