Automated single-molecule imaging in living cells

• Published in: Nature communications Vol. 9; no. 1; pp. 3061 - 11
• Main Authors: Yasui, Masato, Hiroshima, Michio, Kozuka, Jun, Sako, Yasushi, Ueda, Masahiro
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 03.08.2018; Nature Publishing Group; Nature Portfolio
• Subjects: 14/56; 14/63; 631/1647/245/2225; 631/57/2265; 631/80/86/2368; 96/95; Animals; Artificial Intelligence; Automation; Cell Line; Cell Membrane; Cell Tracking - methods; Cells (biology); Cricetulus; Diffusion coefficient; Dose-Response Relationship, Drug; Epidermal growth factor; Epidermal growth factor receptors; Feasibility studies; Fluorescent Dyes; Growth factor receptors; Growth factors; Humanities and Social Sciences; Image acquisition; Kinetics; Models, Biological; multidisciplinary; Pharmacology; Receptors; Science; Science (multidisciplinary); Single Molecule Imaging - methods
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-018-05524-7

An automated single-molecule imaging system developed for live-cell analyses based on artificial intelligence-assisted microscopy is presented. All significant procedures, i.e., searching for cells suitable for observation, detecting in-focus positions, and performing image acquisition and single-molecule tracking, are fully automated, and numerous highly accurate, efficient, and reproducible single-molecule imaging experiments in living cells can be performed. Here, the apparatus is applied for single-molecule imaging and analysis of epidermal growth factor receptors (EGFRs) in 1600 cells in a 96-well plate within 1 day. Changes in the lateral mobility of EGFRs on the plasma membrane in response to various ligands and drug concentrations are clearly detected in individual cells, and several dynamic and pharmacological parameters are determined, including the diffusion coefficient, oligomer size, and half-maximal effective concentration (EC 50 ). Automated single-molecule imaging for systematic cell signaling analyses is feasible and can be applied to single-molecule screening, thus extensively contributing to biological and pharmacological research. Large scale live cell screens often lack single-molecule resolution. Here the authors present an artificial intelligence-assisted TIRF microscope with automated cell searching and focusing, and use it for high-throughput single-molecule imaging of EGFR dynamics in response to various stimuli.