Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape
Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape
复制标题
DOI：
10.7554/elife.49683
复制
发表时间：
2019-10-22
期刊：
影响因子：
7.7
通讯作者：
Terskikh, Alexey V.
中科院分区：
文献类型：
--
作者：
Farhy, Chen;Hariharan, Santosh;Terskikh, Alexey V.
高含量表型筛选因其能够提取药物特异性的多层数据而成为药物发现的首选方法。在表观遗传学领域，由于缺乏对选择性表观遗传扰动敏感的工具，这种筛选方法受到了影响。在这里，我们描述了一种新的方法，表观遗传景观的显微成像(MIEL)，它捕捉到表观遗传标记的核染色模式，并使用机器学习来准确区分这些模式。我们使用剂量-反应曲线跨多个细胞系验证了MIEL平台，以确保该方法在高含量高通量药物发现中的保真度和稳健性。将重点放在非细胞毒性胶质母细胞瘤的治疗上，我们证明了MIEL可以识别和分类表观遗传活性药物。此外，我们还表明，MIEL能够根据候选药物产生所需的表观遗传学改变的能力对候选药物进行准确的排序，这些改变与化疗药物的敏感性增加或胶质母细胞瘤分化的诱导一致。
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.