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Related Experiment Video

Updated: Apr 1, 2026

An Optimized LIVE/DEAD Assay Coupled with Flow Cytometry for Quantifying Post-Stress Survival in Yeast Cells
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Single-cell viability assessment using YOLOv8n object detection.

Felix Peyre1, Allan Sauvat1, Marion Leduc1

  • 1Centre de Recherche des Cordeliers, Inserm UMRS 1138, Sorbonne Université, Université Paris Cité, Équipe labellisée par la Ligue contre le Cancer, Institut Universitaire de France, Paris, France; INSERM US23/CNRS UAR 3655, Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, Université Paris-Saclay, Villejuif, France.

Methods in Cell Biology
|March 30, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a new AI method using YOLOv8n for precise cellular viability assessment in drug discovery. It enables faster and more detailed toxicity predictions than traditional assays.

Area of Science:

  • Biotechnology
  • Drug Discovery
  • Artificial Intelligence in Medicine

Background:

  • Cellular viability assessment is crucial for high-throughput screening (HTS) in drug development.
  • Conventional methods (e.g., ATP assays) provide bulk measurements, limiting detailed analysis.
  • Adverse drug toxicity and therapeutic efficacy (oncology) require accurate viability data.

Purpose of the Study:

  • To present a novel methodology for assessing cellular viability using AI.
  • To enable precise and detailed viability assessments at single-cell resolution.
  • To offer rapid toxicity predictions for drug discovery applications.

Main Methods:

  • Utilized a trained YOLOv8n detection model for image analysis.
  • Applied the model to endpoint and kinetic cellular assays.
Keywords:
Drug discoveryDrug toxicity testingHigh-throughput screening

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  • Leveraged convolutional neural networks for biological image analysis.

    • Main Results:

    • Achieved single-cell resolution for cellular viability assessment.
    • Demonstrated rapid and precise toxicity predictions.
    • Provided more detailed viability data compared to bulk assays.

    Conclusions:

    • The YOLOv8n-based methodology offers a valuable advancement for drug discovery.
    • AI-driven image analysis enhances the precision of cellular viability assessment.
    • This approach supports more effective evaluation of drug toxicity and efficacy.