转录组
表型
人工智能
计算生物学
计算机科学
细胞
计算机视觉
生物
基因
基因表达
遗传学
作者
Jiashun Jin,Taisaku Ogawa,Nozomi Hojo,Kirill Kryukov,Kenji Shimizu,Tomokatsu Ikawa,Tadashi Imanishi,Taku Okazaki,Katsuyuki Shiroguchi
出处
期刊:Proceedings of the National Academy of Sciences
日期:2022-12-28
卷期号:120 (1)
标识
DOI:10.1073/pnas.2210283120
摘要
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.
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