Metastasis is the main cause of cancer-related death and therapies specifically targeting metastasis are highly needed. Cortical cell polarity (CCP) is a prometastatic property of circulating tumor cells affecting their ability to exit blood vessels and form new metastases that constitute a promising point of attack to prevent metastasis. However, conventional fluorescence microscopy on single cells and manual quantification of CCP are time-consuming and unsuitable for screening regulators. In this study, we developed an imaging flow cytometry-based method for high-throughput screening of factors affecting CCP in melanoma cells. The artificial intelligence-supported analysis method we developed is highly reproducible, accurate, and orders of magnitude faster than manual quantification. Additionally, this method is flexible and can be adapted to include additional cellular parameters. In a small-scale pilot experiment using polarity-, cytoskeleton-, or membrane-affecting drugs, we demonstrate that our workflow provides a straightforward and efficient approach for screening factors affecting CCP in cells in suspension and provide insights into the specific function of these drugs in this cellular system. The method and workflow presented here will facilitate large-scale studies to reveal novel cell-intrinsic as well as systemic factors controlling CCP during metastasis.
Keywords: artificial intelligence; cortical cell polarity; high-throughput screening; image analysis; imaging flow cytometry; melanoma; metastasis.
© 2023 The Authors. Journal of Cellular Biochemistry published by Wiley Periodicals LLC.