In Proceedings of the National Academy of Sciences of the United States of America
Single-cell whole-transcriptome analysis is the gold standard approach to identifying molecularly defined cell phenotypes. However, this approach cannot be used for dynamics measurements such as live-cell imaging. Here, we developed a multifunctional robot, the automated live imaging and cell picking system (ALPS) and used it to perform single-cell RNA sequencing for microscopically observed cells with multiple imaging modes. Using robotically obtained data that linked cell images and the whole transcriptome, we successfully predicted transcriptome-defined cell phenotypes in a noninvasive manner using cell image-based deep learning. This noninvasive approach opens a window to determine the live-cell whole transcriptome in real time. Moreover, this work, which is based on a data-driven approach, is a proof of concept for determining the transcriptome-defined phenotypes (i.e., not relying on specific genes) of any cell from cell images using a model trained on linked datasets.
Jin Jianshi, Ogawa Taisaku, Hojo Nozomi, Kryukov Kirill, Shimizu Kenji, Ikawa Tomokatsu, Imanishi Tadashi, Okazaki Taku, Shiroguchi Katsuyuki
2023-Jan-03
cell picking, deep learning, microscopy, robotics, single-cell RNA sequencing
