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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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Updated: Jun 23, 2026

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

Automated high-dimensional flow cytometric data analysis.

Saumyadipta Pyne¹, Xinli Hu, Kui Wang

¹Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge MA 02142, USA.

Proceedings of the National Academy of Sciences of the United States of America

|May 16, 2009

Summary

This summary is machine-generated.

This study introduces a new statistical method for analyzing complex flow cytometry data. The approach automates cell population identification, improving rare cell detection and sample comparison for biological and clinical research.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 23, 2026

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

Area of Science:

Biotechnology
Computational Biology
Immunology

Background:

Flow cytometry enables rapid single-cell analysis using fluorescent markers.
Increasing cell surface marker detection necessitates advanced analytical methods beyond manual gating.
High-dimensional flow cytometry data presents analytical challenges.

Purpose of the Study:

To develop an automated, high-dimensional analytical method for flow cytometry data.
To address the complexities of analyzing large, multi-parameter flow cytometry datasets.
To improve the detection of rare cell populations and facilitate cross-sample analysis.

Main Methods:

A direct multivariate finite mixture modeling approach was employed.
Skew and heavy-tailed distributions were utilized to handle data complexities.
The method operates directly on high-dimensional data without projection or transformation.

Main Results:

The method successfully detects rare cell populations.
Robust modeling was achieved despite outliers and data skewness.
Accurate matching of cell populations across different samples was demonstrated.

Conclusions:

This multivariate finite mixture modeling approach offers a robust solution for high-dimensional flow cytometry data analysis.
The method enhances the ability to identify cell populations, detect rare events, and compare samples.
This advance supports the application of flow cytometry in complex biological and clinical research settings.