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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 17, 2026

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Merging mixture components for cell population identification in flow cytometry.

Greg Finak¹, Ali Bashashati, Ryan Brinkman

¹Computational Biology Unit, Clinical Research Institute of Montreal, 110 Pine Avenue West, Montreal, QC, Canada H2W1R7.

Advances in Bioinformatics

|January 6, 2010

Summary

This summary is machine-generated.

This study introduces a novel framework for identifying cell subpopulations in flow cytometry data, improving accuracy and automating cell count estimation. The flowMerge software enhances high-throughput analysis pipelines.

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Published on: November 19, 2019

Area of Science:

Computational Biology
Bioinformatics
Data Analysis

Background:

Flow cytometry is crucial for cell analysis but identifying distinct cell subpopulations can be challenging.
Existing methods like Gaussian mixture models and flowClust may struggle with complex data distributions.

Purpose of the Study:

To present a framework for robust cell subpopulation identification in flow cytometry data.
To improve the accuracy of cell subpopulation estimation and automate the process.

Main Methods:

Developed a framework integrating a cluster merging algorithm with the flowClust methodology.
Implemented automated selection for the number of cell subpopulations.
Demonstrated a method for summarizing complex merged subpopulations.

Main Results:

The cluster merging algorithm significantly improves model fit compared to standard methods.
The framework provides a more accurate estimate of distinct cell subpopulations, especially for complex data.
Successfully identified limitations and failure cases of the algorithm.

Conclusions:

The proposed framework enhances cell subpopulation identification in flow cytometry.
It offers automated, accurate, and robust analysis suitable for high-throughput pipelines.
The flowMerge package provides accessible software for downstream data analysis.