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
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
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Improving drug discovery using image-based multiparametric analysis of the epigenetic landscapeNCBI Gene Expression Omnibus, GSE134045.
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
Version 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.
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Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape
eLife 8:e49683.
https://doi.org/10.7554/eLife.49683
Further reading
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Esophageal cancer (EC) is a fatal digestive disease with a poor prognosis and frequent lymphatic metastases. Nevertheless, reliable biomarkers for EC diagnosis are currently unavailable. Accordingly, we have performed a comparative proteomics analysis on cancer and paracancer tissue-derived exosomes from eight pairs of EC patients using label-free quantification proteomics profiling and have analyzed the differentially expressed proteins through bioinformatics. Furthermore, nano-flow cytometry (NanoFCM) was used to validate the candidate proteins from plasma-derived exosomes in 122 EC patients. Of the 803 differentially expressed proteins discovered in cancer and paracancer tissue-derived exosomes, 686 were up-regulated and 117 were down-regulated. Intercellular adhesion molecule-1 (CD54) was identified as an up-regulated candidate for further investigation, and its high expression in cancer tissues of EC patients was validated using immunohistochemistry, real-time quantitative PCR (RT-qPCR), and western blot analyses. In addition, plasma-derived exosome NanoFCM data from 122 EC patients concurred with our proteomic analysis. The receiver operating characteristic (ROC) analysis demonstrated that the AUC, sensitivity, and specificity values for CD54 were 0.702, 66.13%, and 71.31%, respectively, for EC diagnosis. Small interference (si)RNA was employed to silence the CD54 gene in EC cells. A series of assays, including cell counting kit-8, adhesion, wound healing, and Matrigel invasion, were performed to investigate EC viability, adhesive, migratory, and invasive abilities, respectively. The results showed that CD54 promoted EC proliferation, migration, and invasion. Collectively, tissue-derived exosomal proteomics strongly demonstrates that CD54 is a promising biomarker for EC diagnosis and a key molecule for EC development.
