Methods for High-Content, High-Throughput Image-Based Cell Screening
نویسندگان
چکیده
Visual inspection of cells is a fundamental tool for discovery in biological science. Modern robotic microscopes are able to capture thousands of images from massively parallel experiments such as RNA interference (RNAi) or small-molecule screens. Such screens also benefit from lab automation, making large screens, e.g., genome-scale knockdown experiments, more feasible and common. As such, the bottleneck in large, imagebased screens has shifted to visual inspection and scoring by experts. In this paper, we describe the methods we have developed for automatic image cytometry. The paper demonstrates illumination normalization, foreground/background separation, cell segmentation, and shows the benefits of using a large number of individual cell measurements when exploring data from highthroughput screens.
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