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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 16, 2026

Far-Red Fluorescent Senescence-Associated β-Galactosidase Probe for Identification and Enrichment of Senescent Tumor Cells by Flow Cytometry

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The curvHDR method for gating flow cytometry samples.

Ulrike Naumann¹, George Luta, Matthew P Wand

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

BMC Bioinformatics

|January 26, 2010

Summary

This summary is machine-generated.

A new statistical method, curvHDR, offers automated and semi-automated cell gating for high-throughput flow cytometry. This approach saves labor and matches human gating perception for efficient cellular analysis.

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Area of Science:

Biomedical Engineering
Computational Biology
Data Science

Background:

High-throughput flow cytometry generates large, multivariate cellular data.
Specialized processing is crucial for extracting clinically meaningful measurements.
Manual cell gating is a key but time-consuming and subjective step.

Purpose of the Study:

To develop an automated and semi-automated gating algorithm for flow cytometry data.
To address the limitations of manual gating in high-throughput experiments.

Main Methods:

Introduced curvHDR, a statistical procedure for cell gating.
Combines high negative curvature regions and highest density regions.
Designed for arbitrary dimensions, with a focus on up to three dimensions.

Main Results:

The curvHDR method demonstrates adaptability to human-perceived gates.
Developed accompanying software compatible with current flow cytometry informatics.
The procedure effectively handles complex data nuances.

Conclusions:

curvHDR accurately reflects human perception of useful gates.
Offers significant time savings in processing high-throughput flow cytometry data.
Maintains a high degree of efficacy in cellular analysis.