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Robotic Data Acquisition with Deep Learning Enables Cell Image–based Prediction of Transcriptomic Phenotypes by Jianshi Jin; Taisaku Ogawa; Nozomi Hojo; Kirill Kryukov; Kenji Shimizu; Tomokatsu Ikawa; Tadashi Imanishi; Taku Okazaki; Katsuyuki Shiroguchi is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.
What is Robotic Data Acquisition with Deep Learning Enables Cell Image–based Prediction of Transcriptomic Phenotypes about?
## Significance Molecular cell phenotype identification is important for the investigation of the role of cells and for their medical applications. For this identification, single-cell whole-transcriptome analysis, which is an unbiased method, is a powerful tool. However, this method kills the target cell, which prevents further investigation of the cell. Here, we showed a successful noninvasive approach; label-free live-cell imaging with deep learning–based prediction can distinguish cell phenotypes defined by whole-transcriptome sequencing. This approach was enabled by linking cell images and the whole transcriptome for the same cell using our newly developed robot, the automated live imaging and cell picking system (ALPS). This noninvasive and unbiased determination of live-cell molecular phenotypes will be useful for cell dynamics studies.
Who reads Robotic Data Acquisition with Deep Learning Enables Cell Image–based Prediction of Transcriptomic Phenotypes?
It is typically read by researchers, students, and practitioners in Biochemistry, Genetics and Molecular Biology.
- Author
- Jianshi Jin; Taisaku Ogawa; Nozomi Hojo; Kirill Kryukov; Kenji Shimizu; Tomokatsu Ikawa; Tadashi Imanishi; Taku Okazaki; Katsuyuki Shiroguchi
- Publisher
- Proceedings of the National Academy of Sciences
- Published
- 2022
- Language
- EN
- Field
- Biochemistry, Genetics and Molecular Biology (Life Sciences)