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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: Aug 12, 2025

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

A Guide on Analyzing Flow Cytometry Data Using Clustering Methods and Nonlinear Dimensionality Reduction (tSNE or

Thomas A Ujas¹, Veronica Obregon-Perko², Ann M Stowe³

¹Department of Neurology, Department of Neuroscience, The University of Kentucky, Lexington, KY, USA.

Methods in Molecular Biology (Clifton, N.J.)

|January 30, 2023

Summary

This guide details computational methods for analyzing complex flow cytometry data, enhancing rigor in immune cell research after stroke and other injuries.

Keywords:

Flow cytometry Flow cytometry analysis FlowJo™Nonlinear dimensionality reduction UMAP tSNE

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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

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Published on: December 12, 2019

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Area of Science:

Neuroscience
Immunology
Computational Biology

Background:

Flow cytometry is crucial for identifying immune cells in the brain after injury.
Traditional manual gating limits the analysis of high-parameter flow cytometry data.
Unbiased computational approaches offer improved scientific rigor.

Purpose of the Study:

To provide a step-by-step guide for analyzing complex flow cytometry data using computational methods.
To demonstrate unsupervised clustering and nonlinear dimensionality reduction techniques.
To apply these methods to neuroinflammation studies post-stroke.

Main Methods:

Utilizing FlowJo™ Software v10 for data analysis.
Implementing pre-processing steps for complex datasets.
Performing unsupervised clustering and nonlinear dimensionality reduction.

Main Results:

The described methods enable robust analysis of high-parameter flow cytometry data.
These techniques facilitate the identification of neuroinflammatory responses.
The approach is applicable to various flow cytometry datasets.

Conclusions:

Modern computational approaches enhance the analysis of flow cytometry data.
Unbiased methods improve scientific rigor in immune cell subset identification.
These techniques are valuable for studying neuroinflammation and other biological processes.