Visual observation is a powerful approach for screening bioactive compounds that can facilitate the discovery of attractive druggable targets following their chemicobiological validation. So far, many high-content approaches, using sophisticated imaging technology and bioinformatics, have been developed. In our study, we aimed to develop a simpler method that focuses on intact cell images because we found that dynamic changes in morphology are informative, often reflecting the mechanism of action of a drug. Here, we constructed a chemical-genetic phenotype profiling system, based on the high-content cell morphology database Morphobase. This database compiles the phenotypes of cancer cell lines that are induced by hundreds of reference compounds, wherein those of well-characterized anticancer drugs are classified by mode of action. Furthermore, we demonstrate the applicability of this system in identifying NPD6689, NPD8617, and NPD8969 as tubulin inhibitors.
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