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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: Dec 18, 2025

Analyzing Platelet Subpopulations by Multi-color Flow Cytometry

Analyzing Platelet Subpopulations by Multi-color Flow Cytometry

Published on: June 10, 2025

Multi-set Pre-processing of Multicolor Flow Cytometry Data.

Rita Folcarelli¹, Gerjen H Tinnevelt^1,2, Bart Hilvering³

¹Radboud University, Institute for Molecules and Materials, Analytical Chemistry, P.O. Box 9010, 6500 GL, Nijmegen, The Netherlands. chemometrics@science.ru.nl.

Scientific Reports

|June 18, 2020

Summary

This summary is machine-generated.

This study introduces a novel multi-set preprocessing method for Flow Cytometry data. It balances sample importance, improving immunological insights and classification performance by accounting for varying cell counts.

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Area of Science:

Biotechnology
Computational Biology
Immunology

Background:

Flow Cytometry measures multiple single-cell markers simultaneously.
Existing methods struggle with the 'multi-set' data structure arising from varying cell counts per sample.
Standard preprocessing overemphasizes samples with higher cell numbers.

Purpose of the Study:

To develop a 'multi-set' preprocessing technique for Flow Cytometry data.
To correct for cell number differences and balance sample influence.
To enhance data analysis for improved immunological insight and classification.

Main Methods:

Proposed a novel 'multi-set' preprocessing approach.
Implemented data balancing to account for varying cell counts across samples.
Demonstrated benefits through case examples including variability reduction and enhanced response detection.

Main Results:

The multi-set preprocessing method effectively balances the relative importance of each sample.
It reduces measurement-to-measurement variability.
Class-based multi-set preprocessing enhances the studied response compared to controls.

Conclusions:

Adjusting data analysis to accommodate the 'multi-set' structure significantly benefits Flow Cytometry data.
This approach improves immunological insight and classification performance.
The method utilizes all collected data from expensive analyses effectively.