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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: Apr 29, 2026

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

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Published on: January 16, 2019

AutoGate: automating analysis of flow cytometry data.

Stephen Meehan¹, Guenther Walther, Wayne Moore

¹Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA.

Immunologic Research

|May 15, 2014

Summary

This summary is machine-generated.

New software, AutoComp and AutoGate, automates flow cytometry data analysis. This technology enhances the processing and analysis of complex biological data, improving efficiency in research and clinical applications.

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Last Updated: Apr 29, 2026

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Published on: January 16, 2019

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Area of Science:

Biotechnology
Immunology
Medical Diagnostics

Background:

Flow cytometry is crucial in biology and medicine for applications like HIV monitoring, bone marrow transplant tracking, and leukemia characterization.
Despite advancements in flow cytometry instrumentation, automated data processing and analysis tools have not kept pace.
This gap hinders efficient utilization of flow cytometry data in critical areas such as stem cell research and HIV drug development.

Purpose of the Study:

To develop automated software solutions for flow cytometry data compensation and subset analysis.
To address the limitations of manual data processing in flow cytometry.
To create user-friendly tools that enhance the accuracy and efficiency of flow data analysis.

Main Methods:

Developed AutoComp software to automate the compensation task by acquiring sample and reagent labels.
Developed AutoGate software utilizing statistical algorithms for automatic detection and delineation of cell subsets.
Implemented a user-guided analysis process creating "analysis trees" for reproducible results.

Main Results:

AutoComp successfully automates flow data compensation.
AutoGate accurately detects, displays, and delineates cell subsets using statistical algorithms.
The developed software generates analysis "trees" for automated guidance of similar sample analyses.
Early versions are undergoing testing at leading research institutions (Stanford, Emory, NIH).

Conclusions:

Automated flow analysis software (AutoComp/AutoGate) significantly improves data processing and analysis efficiency.
These tools address a critical need for advanced analytical capabilities in flow cytometry.
The software is expected to be distributed free of charge to academic and government users, promoting wider accessibility.