Label-free detection of cellular drug responses by high-throughput bright-field imaging and machine learning.

ABSTRACT: 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

SUBMITTER: Kobayashi H 

PROVIDER: S-EPMC5622112 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature