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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: Nov 2, 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

CytoPy: An autonomous cytometry analysis framework.

Ross J Burton¹, Raya Ahmed¹, Simone M Cuff¹

¹Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, United Kingdom.

Plos Computational Biology

|June 8, 2021

Summary

This summary is machine-generated.

CytoPy is a new Python framework for automated cytometry data analysis. It offers an iterative environment and an algorithm-agnostic design, facilitating open-source cytometry bioinformatics and T cell subset phenotyping.

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Area of Science:

Computational Biology
Bioinformatics
Immunology

Background:

Cytometry technologies are advancing, enabling higher dimensional single-cell data acquisition.
New computational methods are needed to analyze this high-dimensional data effectively.
Widespread adoption of computational tools in immunology remains a challenge.

Purpose of the Study:

To present CytoPy, a Python framework for automated cytometry data analysis.
To provide a data-centric and iterative analytical environment.
To foster open-source cytometry bioinformatics within the Python ecosystem.

Main Methods:

Developed CytoPy, a Python framework integrating a document-based database.
Designed an algorithm-agnostic platform for flexible analysis.
Applied the CytoPy pipeline to phenotype T cell subsets and analyze inflammatory infiltrates.

Main Results:

CytoPy successfully phenotyped T cell subsets in whole blood, robust to batch effects.
The framework demonstrated efficacy in analyzing inflammatory infiltrates in infection models.
CytoPy provides an open-source, iterative, and data-centric analytical environment.

Conclusions:

CytoPy offers a powerful and adaptable solution for high-dimensional cytometry data analysis.
The framework promotes open-source collaboration in cytometry bioinformatics.
CytoPy facilitates robust immunophenotyping, even with complex datasets and batch variations.