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Flow Cytometry

Flow Cytometry

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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Related Experiment Video

Updated: Jan 17, 2026

Quality-Controlled Sputum Analysis by Flow Cytometry

Quality-Controlled Sputum Analysis by Flow Cytometry

Published on: August 9, 2021

AI in flow cytometry: Current applications and future directions.

Alice Yue¹, Ryan R Brinkman², Veronica Nash³

¹Zhejiang University, Zhejiang, China.

Cytometry. Part B, Clinical Cytometry

|September 23, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) integration in flow cytometry enhances cell analysis for research and diagnostics. This review covers current AI applications and future potential in flow cytometry, improving assay design and data interpretation.

Keywords:

AI assay design automation controls design data analysis flow cytometry procedures reagent design standardization

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Last Updated: Jan 17, 2026

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Area of Science:

Immunology and cellular biology
Biotechnology and bioinformatics

Background:

Flow cytometry is a crucial technique for analyzing cellular properties in research and diagnostics.
Current limitations in flow cytometry include challenges in assay design and data analysis.

Purpose of the Study:

To review current applications of artificial intelligence (AI) in flow cytometry.
To explore future directions for AI integration in flow cytometry.

Main Methods:

Literature review of AI applications in flow cytometry.
Analysis of AI's impact on various aspects of flow cytometry workflows.

Main Results:

AI is being applied to reagent selection, instrument standardization, panel design, data analysis, and quality control.
AI demonstrates potential to significantly improve the efficiency and accuracy of flow cytometry.

Conclusions:

AI integration offers transformative potential for flow cytometry across research, clinical trials, and diagnostics.
Future AI development in flow cytometry will likely focus on automated assay design and advanced data interpretation.