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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: Jul 11, 2026

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

Published on: August 15, 2013

Molecular identification using flow cytometry histograms and information theory.

Q Zeng¹, A J Young, A Boxwala

¹Decision System Group, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.

Proceedings. AMIA Symposium

|February 5, 2002

Summary

This study introduces an information theory approach for comparing flow cytometry histograms, improving the identification of unknown molecules. This method offers a more quantitative assessment of molecular similarity than traditional techniques.

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Last Updated: Jul 11, 2026

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Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

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Published on: March 24, 2023

Area of Science:

Biotechnology
Computational Biology
Immunology

Background:

Flow cytometry is a standard method for quantifying molecular expression on cells using fluorescence intensity histograms.
Current histogram comparison methods are insufficient for identifying unknown molecules and assessing molecular relationships.
Accurate histogram comparison is crucial for advancing flow cytometry as a knowledge discovery tool.

Purpose of the Study:

To develop and evaluate a novel information theory-based approach for comparing flow cytometry histograms.
To overcome the limitations of traditional methods in assessing histogram similarity and molecular relatedness.
To enhance the utility of flow cytometry for discovering unknown molecules.

Main Methods:

Applied information theory principles to histogram comparison.
Utilized histograms generated from 14 antibodies across 3 distinct cell types for testing.
Compared the performance of the information theory approach against traditional comparison methods.

Main Results:

The information theory approach demonstrated superior performance compared to traditional methods.
This novel method effectively identifies non-random correlations between histograms beyond simple similarity.
Provided a quantitative measure of similarity that surpasses traditional hypothesis testing for identity.

Conclusions:

Information theory offers a powerful framework for advancing flow cytometry data analysis.
The developed approach significantly improves the quantitative assessment of histogram similarity and molecular relatedness.
This method enhances the potential of flow cytometry for molecular discovery and knowledge generation.