Multicolor super-resolution (SR) microscopy plays a critical role in cell biology research and can visualize the interactions between different organelles and the cytoskeleton within a single cell. However, more color channels bring about a heavier budget for imaging and sample preparation, and the use of fluorescent dyes of higher emission wavelengths leads to a worse spatial resolution. Recently, deep convolutional neural networks (CNNs) have shown a compelling capability in cell segmentation, super-resolution reconstruction, image restoration, and many other aspects. Taking advantage of CNN's strong representational ability, we devised a deep CNN-based instant multicolor super-resolution imaging method termed IMC-SR and demonstrated that it could be used to separate different biological components labeled with the same fluorophore, and generate multicolor images from a single super-resolution image in silico. By IMC-SR, we achieved fast three-color live-cell super-resolution imaging with ~100 nm resolution over a long temporal duration, revealing the complicated interactions between multiple organelles and the cytoskeleton in a single COS-7 cell.
Keywords: Convolutional neural network; Multicolor imaging; Super-resolution.
© The Author(s) 2021.