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Robotic data acquisition with deep learning enables cell image-based prediction of transcriptomic phenotypes.

Jianshi Jin1, Taisaku Ogawa1, Nozomi Hojo1

  • 1Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR), Suita, Osaka 565-0874, Japan.

Proceedings of the National Academy of Sciences of the United States of America
|December 28, 2022
PubMed
Summary

Researchers developed an automated live imaging and cell picking system (ALPS) for real-time, noninvasive single-cell whole-transcriptome analysis. This system links cell images to transcriptomes, enabling phenotype prediction from images using deep learning.

Keywords:
cell pickingdeep learningmicroscopyroboticssingle-cell RNA sequencing

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Area of Science:

  • Cell Biology
  • Genomics
  • Robotics

Background:

  • Single-cell whole-transcriptome analysis is crucial for defining cell phenotypes.
  • Current methods are incompatible with dynamic measurements like live-cell imaging.

Purpose of the Study:

  • To develop a system for real-time, noninvasive single-cell RNA sequencing compatible with live-cell imaging.
  • To enable prediction of cell phenotypes from microscopic images.

Main Methods:

  • Development of the automated live imaging and cell picking system (ALPS).
  • Integration of multiple imaging modes with single-cell RNA sequencing.
  • Application of cell image-based deep learning to predict transcriptome-defined phenotypes.

Main Results:

  • Successful linkage of cell images with whole-transcriptome data.
  • Noninvasive prediction of transcriptome-defined cell phenotypes using deep learning on cell images.
  • Demonstration of real-time whole-transcriptome analysis for live cells.

Conclusions:

  • The ALPS system enables noninvasive, real-time transcriptome analysis of live cells.
  • This data-driven approach provides a proof of concept for predicting cell phenotypes from images.
  • The methodology is adaptable for various cell types and transcriptome-defined phenotypes.