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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: Jun 22, 2026

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Automation in high-content flow cytometry screening.

U Naumann¹, M P Wand

¹School of Mathematics and Statistics, The University of New South Wales, Sydney, Australia.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology

|June 24, 2009

Summary

This summary is machine-generated.

Automating data processing for high-content flow cytometric screening (FC-HCS) yields results comparable to manual methods. This advancement in cellular signature analysis can significantly enhance acute graft-versus-host-disease diagnosis and biomarker discovery.

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Area of Science:

Immunology
Bioinformatics
Biotechnology

Background:

High-content flow cytometric screening (FC-HCS) integrates automation, instrumentation, and bioinformatics for rapid sample analysis.
Acute graft-versus-host-disease (aGVHD) diagnosis and biomarker identification require efficient cellular signature definition.

Purpose of the Study:

To automate the data processing steps in FC-HCS for cellular signature definition in aGVHD.
To evaluate the efficacy of automated statistical methods compared to manual processing in FC-HCS.

Main Methods:

Application of advanced statistical methodologies for automated data processing of FC-HCS data.
Comparison of results from automated processing with those from manual analysis for aGVHD cellular signatures.

Main Results:

Automated data processing achieved results on par with manual processing for aGVHD cellular signature definition.
Demonstrated the effectiveness of statistical advancements in automating FC-HCS data analysis.

Conclusions:

Automation of FC-HCS data processing is feasible and effective for aGVHD analysis.
This automation holds significant potential for improving clinical diagnosis and biomarker identification in aGVHD.