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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: Jul 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

EasyFlow: An open source, user friendly cytometry analyzer with graphic user interface (GUI).

Yitong Ma¹, Yaron Antebi²

¹Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.

Biorxiv : the Preprint Server for Biology

|August 23, 2023

Summary

This summary is machine-generated.

EasyFlow is a new, open-source graphical user interface (GUI) for flow cytometry analysis. It provides an accessible, locally installable tool for researchers, regardless of coding experience, to analyze flow cytometry data.

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Area of Science:

Biotechnology
Computational Biology
Immunology

Background:

Flow cytometry is a powerful technique for quantitative single-cell analysis, widely used in immunology and expanding into systems biology and microbiology.
Standardized data exchange via the Flow Cytometry Standard (FCS) format facilitates inter-machine compatibility.
Existing flow cytometry analysis software presents limitations, including high cost, complex coding prerequisites, or restrictions on large datasets.

Approach:

Development of EasyFlow, an open-source, cross-platform (Windows, MacOS) graphical user interface (GUI) for flow cytometry data analysis.
EasyFlow is available as a standalone application based on MATLAB or Python, requiring no prior coding knowledge for basic use.
The Python version (EasyFlowQ) leverages Matplotlib and Qt for advanced customization and extensibility by experienced users.

Key Points:

EasyFlow offers an intuitive, user-friendly interface for researchers with limited coding experience.
The software supports local installation and execution, overcoming limitations of online tools for large datasets.
EasyFlow provides a flexible platform, accommodating both novice users and advanced researchers seeking customization.

Conclusions:

EasyFlow democratizes flow cytometry data analysis by providing an accessible, open-source solution.
The tool bridges the gap between complex, costly software and the need for user-friendly, locally executable analysis platforms.
EasyFlow empowers a broader range of scientists to effectively utilize flow cytometry data for diverse research applications.