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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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AI and flow cytometry.

Dawei Lin1, Anupama Gururaj1, Sheng Lin-Gibson2

  • 1Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD 20892, United States.

Journal of Immunology (Baltimore, Md. : 1950)
|November 10, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) are advancing biotechnology, but inconsistent flow cytometry (FCM) data hinders progress. This workshop addresses data quality and AI-readiness to unlock FCM

Keywords:
Artificial intelligence (AI) and machine learning (ML) models, Experimental design incorporating reference materialsData repositories and data standardsFlow cytometry (FCM)translational and clinical applications

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Data Science

Background:

  • Artificial intelligence (AI) and machine learning (ML) are increasingly vital in biotechnology and the bioeconomy.
  • Flow cytometry (FCM) is a critical high-throughput single-cell analysis technology for biotechnology innovation.
  • Significant variations in FCM data quality and consistency across studies create data silos, limiting AI applications.

Purpose of the Study:

  • To address challenges in flow cytometry (FCM) data quality and consistency for AI applications.
  • To identify solutions for creating AI-ready reference data in FCM.
  • To foster advancements in AI/ML applications for FCM data analysis.

Main Methods:

  • Focus on essential measurements for standardized FCM data.
  • Development of reference controls to ensure data consistency.
  • Exploration of current AI/ML models applicable to FCM data.
  • Establishing AI-ready reference datasets for FCM.

Main Results:

  • Identified key challenges in FCM data quality and consistency.
  • Proposed solutions including essential measurements and reference controls.
  • Highlighted the need for AI-ready reference data.
  • Reviewed current AI/ML models for FCM data analysis.

Conclusions:

  • Standardized, high-quality FCM data is essential for effective AI/ML implementation.
  • Developing AI-ready reference datasets will accelerate AI applications in FCM.
  • Collaboration and standardized approaches are crucial for advancing AI in biotechnology through FCM.