Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 120, Issue 1, Pages -Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.2210283120
Keywords
robotics; microscopy; deep learning; -cell RNA sequencing; cell picking
Categories
Funding
- Japan Society for the Promotion of Science (JSPS) KAKENHI [18H05411]
- Mochida Memorial Foundation for Medical and Pharmaceutical Research
- Sumitomo Foundation
- Special Postdoctoral Researchers Program of RIKEN
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Developed a multifunctional robot system ALPS for single-cell RNA sequencing of cells observed under multiple imaging modes. Successfully predicted transcriptome-defined cell phenotypes using cell image-based deep learning. This work provides a new approach to determine live-cell whole transcriptome in real time.
Single-cell whole-transcriptome analysis is the gold standard approach to identifying molecularly defined cell phenotypes. However, this approach cannot be used for dynam-ics 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 transcrip-tome, we successfully predicted transcriptome-defined cell phenotypes in a noninvasive manner using cell image-based deep learning. This noninvasive approach opens a win-dow 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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