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. Researchers at the RIKEN Center for Biosystems Dynamics Research (BDR) have 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, the researchers 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 J, Ogawa T, Hojo N, Kryukov K, Shimizu K, Ikawa T, Imanishi T, Okazaki T, Shiroguchi K. (2022) Robotic data acquisition with deep learning enables cell image-based prediction of transcriptomic phenotypes. PNAS 120(1):e2210283120. [abstract]
