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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 18, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

MetaGate: Interactive analysis of high-dimensional cytometry data with metadata integration.

Eivind Heggernes Ask^1,2, Astrid Tschan-Plessl^1,3, Hanna Julie Hoel¹

¹Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.

Patterns (New York, N.Y.)

|July 31, 2024

Summary

This summary is machine-generated.

MetaGate is a new platform for analyzing complex flow cytometry data, improving statistical testing and data sharing. This tool identified key immune cell changes linked to diffuse large B cell lymphoma progression.

Keywords:

data analysis diffuse large B-cell lymphoma flow cytometry mass cytometry

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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

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

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Last Updated: Jun 18, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

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

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

Flow cytometry enables high-throughput single-cell protein quantification.
Increasing data complexity challenges existing bioinformatics tools for statistical analysis, data sharing, and clinical integration.

Purpose of the Study:

To develop MetaGate, an interactive platform for statistical analysis and visualization of high-dimensional cytometry data.
To address limitations in current bioinformatics tools for cytometry data analysis.

Main Methods:

Developed MetaGate, a platform integrating metadata for interactive analysis of manually gated high-dimensional cytometry data.
Implemented a data reduction algorithm using a combinatorial gating system for standardized data files.
Utilized a fast web-based user interface for generating figures and statistical analyses.

Main Results:

Demonstrated MetaGate's utility through mass cytometry analysis of peripheral blood immune cells.
Analyzed data from 28 diffuse large B cell lymphoma patients and 17 healthy controls.
Identified significant immune cell population alterations associated with disease progression.

Conclusions:

MetaGate offers a robust solution for analyzing complex cytometry data, enhancing statistical capabilities and data standardization.
The platform facilitates the identification of disease-specific immune signatures, as shown in diffuse large B cell lymphoma.