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Related Concept Videos

Flow Cytometry01:23

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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence.

Matthew A Rodrigues1, María Gracia García Mendoza2, Raymond Kong2

  • 1Amnis Flow Cytometry, Luminex Corporation; mrodrigues@luminexcorp.com.

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|February 13, 2023
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Summary

Artificial intelligence (AI) combined with imaging flow cytometry (IFC) offers a new method for scoring the micronucleus (MN) assay. This approach enables fully automated genetic toxicity testing, improving upon traditional microscopy.

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Area of Science:

  • Toxicology and Pharmacology
  • Genetics and Genomics
  • Computational Biology

Background:

  • The micronucleus (MN) assay is a regulatory standard for assessing chemical genotoxicity.
  • Traditional light microscopy for MN scoring is time-consuming and subjective.
  • Flow cytometry offers high throughput but lacks visual confirmation capabilities.

Purpose of the Study:

  • To describe the development and application of an AI-driven deep learning model for automated MN assay scoring.
  • To integrate AI with imaging flow cytometry (IFC) for enhanced MN assay analysis.
  • To validate AI-based scoring against manual microscopy for genetic toxicity assessment.

Main Methods:

  • Utilized artificial intelligence (AI) and convolutional neural networks to create a deep learning model.
  • Employed imaging flow cytometry (IFC) for high-throughput image acquisition and automated analysis of MN assay data.
  • Developed a workflow for training AI models and applying them to score MN assay events.

Main Results:

  • The AI deep learning model successfully scored all key events in the MN assay using IFC data.
  • Results generated by the AI model demonstrated strong agreement with manual microscopy scoring.
  • The combined IFC and AI approach enables fully automated scoring of the MN assay.

Conclusions:

  • AI, particularly deep learning models, can automate the scoring of MN assay data acquired by IFC.
  • This integrated approach provides a robust, efficient, and objective method for genotoxicity testing.
  • Combining IFC and AI represents a significant advancement in the automated evaluation of genetic toxicity.