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

Elife. 2019 Oct 22:8:e49683. doi: 10.7554/eLife.49683.


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; epigenetics; glioblastoma; high content screening; human; human biology; medicine.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antineoplastic Agents / therapeutic use*
  • Biomarkers, Tumor / genetics*
  • Biomarkers, Tumor / metabolism
  • Brain Neoplasms / drug therapy
  • Brain Neoplasms / genetics
  • Brain Neoplasms / metabolism
  • Brain Neoplasms / pathology
  • Cell Line, Tumor
  • Cell Nucleus / drug effects
  • Cell Nucleus / genetics
  • Cell Nucleus / metabolism
  • Dose-Response Relationship, Drug
  • Drug Discovery / methods*
  • Epigenesis, Genetic / drug effects*
  • Glioblastoma / drug therapy
  • Glioblastoma / genetics
  • Glioblastoma / metabolism
  • Glioblastoma / pathology
  • High-Throughput Screening Assays*
  • Histones / genetics*
  • Histones / metabolism
  • Humans
  • Image Processing, Computer-Assisted / statistics & numerical data
  • Machine Learning
  • Microscopy, Fluorescence
  • Neoplasm Proteins / genetics*
  • Neoplasm Proteins / metabolism


  • Antineoplastic Agents
  • Biomarkers, Tumor
  • Histones
  • Neoplasm Proteins

Associated data

  • GEO/GSE134045