In Biochemical and biophysical research communications
To enable large-scale screening of signaling molecules and drugs that regulate cellular contractility-associated mechanotransduction, we previously modified, particularly in terms of the capability of efficiently collecting big data, conventional methodologies using wrinkled substrates to determine the cellular contractility. Here, we present a new system to perform the wrinkle-based cell force assay in a multi-well plate format conformed to standardized geometric configurations and compatible with available technologies such as automated plate readers. With this highly improved throughput in terms of hardware as well as software using a deep learning-based technology, we evaluated the effect of treating cells with various types of pharmacological inhibitors on the cellular contractility. We found opposite responses of cells to the inhibitors between the contractility and collective migration activities. While similar inverse relationships between the contractility and migration have been reported in separate studies, our results here with the high-throughput screening system more broadly generalized these observations.
Nehwa Foncham Jermia, Matsui Tsubasa S, Honghan Li, Matsunaga Daiki, Sakaguchi Yoshiyuki, Deguchi Shinji
Cell assay, Cell contractility, Deep learning, High-throughput screening, Mechanobiology