2023年1月3日
Robotic data acquisition with deep learning enables cell image-based prediction of transcriptomic phenotypes.
Proceedings of the National Academy of Sciences of the United States of America
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- 巻
- 120
- 号
- 1
- 開始ページ
- e2210283120
- 終了ページ
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1073/pnas.2210283120
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.
- リンク情報
- ID情報
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- DOI : 10.1073/pnas.2210283120
- PubMed ID : 36577074
- PubMed Central 記事ID : PMC9910600