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Related Experiment Video

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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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Single-Cell Radiation Response Scoring with the Deep Learning Algorithm CeCILE 2.0.

Sarah Rudigkeit1, Judith Reindl1

  • 1Section Biomedical Radiation Physics, Institute for Applied Physics and Measurement Technology, Universität der Bundeswehr München, 85577 Neubiberg, Germany.

Cells
|December 22, 2023
PubMed
Summary

A new deep-learning algorithm, CeCILE 2.0, analyzes individual cell responses to radiation. This tool quantifies cellular viability, cell cycle, and survival, aiding in understanding radiation effects on mammalian cells.

Keywords:
cell survivalcell viabilitydeep learningphase-contrastsingle-cell response

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Area of Science:

  • Cell biology
  • Radiation biology
  • Bioinformatics

Background:

  • External stressors like ionizing radiation significantly impact mammalian cell life, division, and survival.
  • Understanding the precise mechanisms of radiation effects requires detailed analysis of individual cell responses.
  • Manual analysis of large datasets from long-term cell tracking is infeasible.

Purpose of the Study:

  • To introduce CeCILE 2.0, a deep-learning algorithm for automated cell localization, classification, and tracking in live-cell videos.
  • To enable comprehensive analysis of single-cell and cell composite responses to stressors, particularly X-ray radiation.
  • To characterize radiation-induced abnormalities and derive insights into cellular radiation response.

Main Methods:

  • Development of a deep-learning-based algorithm named CeCILE 2.0 (Cell classification and in vitro lifecycle evaluation).
  • Utilizing live-cell phase-contrast videos for cell tracking and analysis.
  • Integration of a fully automated workflow for single-cell and cell composite analysis.

Main Results:

  • CeCILE 2.0 successfully localizes, classifies, and tracks cells in videos.
  • The algorithm facilitates conclusions on cell viability, cell cycle, and survival under radiation.
  • Radiation-specific abnormalities during cell division were characterized.

Conclusions:

  • CeCILE 2.0 is a powerful tool for quantifying cellular responses to external stressors like radiation.
  • It allows for the characterization of individual cell responses within a broader context.
  • This represents the first integrated workflow for comprehensive single-cell analysis of cellular radiation response.