SSBD:database

Detail of PBMC-timelapse-plate3_91

Jianshi Jin, Taisaku Ogawa, Nozomi Hojo, Kirill Kryukov, Kenji Shimizu, Tomokatsu Ikawa, Tadashi Imanishi, Taku Okazaki, Katsuyuki Shiroguchi (2023) 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, Volume 120, Number 1, pp. e2210283120

Published in 2023 Jan 3 (Electronic publication in Dec. 28, 2022, midnight )

• doi: 10.1073/pnas.2210283120
• pmid: 36577074
  

(Abstract) 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.

• Transcriptome
• Deep Learning[major]
• Robotics[major]
• Image Processing, Computer-Assisted/methods
• Robotic Surgical Procedures[major]
• Gene Expression Profiling
• Phenotype