Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan.
Department of Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213, USA.
Sci Rep. 2017 Sep 29;7(1):12454. doi: 10.1038/s41598-017-12378-4.
In the last decade, high-content screening based on multivariate single-cell imaging has been proven effective in drug discovery to evaluate drug-induced phenotypic variations. Unfortunately, this method inherently requires fluorescent labeling which has several drawbacks. Here we present a label-free method for evaluating cellular drug responses only by high-throughput bright-field imaging with the aid of machine learning algorithms. Specifically, we performed high-throughput bright-field imaging of numerous drug-treated and -untreated cells (N = ~240,000) by optofluidic time-stretch microscopy with high throughput up to 10,000 cells/s and applied machine learning to the cell images to identify their morphological variations which are too subtle for human eyes to detect. Consequently, we achieved a high accuracy of 92% in distinguishing drug-treated and -untreated cells without the need for labeling. Furthermore, we also demonstrated that dose-dependent, drug-induced morphological change from different experiments can be inferred from the classification accuracy of a single classification model. Our work lays the groundwork for label-free drug screening in pharmaceutical science and industry.
在过去的十年中，基于多变量单细胞成像的高通量筛选已被证明在药物发现中是有效的，可以评估药物诱导的表型变化。不幸的是，这种方法本质上需要荧光标记，而荧光标记有几个缺点。在这里，我们提出了一种无需标记即可评估细胞药物反应的方法，仅通过高通量明场成像并借助机器学习算法即可实现。具体来说，我们通过光流控时间拉伸显微镜对大量药物处理和未处理的细胞（N=~240,000）进行高通量明场成像，其高通量高达 10,000 个细胞/秒，并将机器学习应用于细胞图像，以识别其形态变化，这些变化对于人眼来说过于微妙而无法察觉。因此，我们实现了 92%的高精度，无需标记即可区分药物处理和未处理的细胞。此外，我们还证明，来自不同实验的、剂量依赖性的、药物诱导的形态变化可以从单个分类模型的分类准确性中推断出来。我们的工作为药物筛选在制药科学和工业中的无标记应用奠定了基础。
