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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 3, 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

Analysis of flow cytometry data using an automatic processing tool.

David Jeffries¹, Irfan Zaidi, Bouke de Jong

¹Medical Research Council (MRC) Gambia, Banjul, The Gambia. djeffries@mrc.gm

Cytometry. Part a : the Journal of the International Society for Analytical Cytology

|July 10, 2008

Summary

This summary is machine-generated.

Manual flow cytometry analysis is labor-intensive and inconsistent. An automatic processing tool (APT) was developed to rapidly and consistently analyze large cytometry datasets, improving objectivity and efficiency in cell population identification.

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Area of Science:

Immunology
Computational Biology
Biotechnology

Background:

Manual analysis of flow cytometry data is time-consuming, subjective, and challenging for large datasets.
Advances in flow cytometry generate vast amounts of data, necessitating efficient and objective analysis methods.

Purpose of the Study:

To develop an automatic processing tool (APT) for rapid, consistent, and objective analysis of flow cytometry data.
To automate the definition and description of cell populations, overcoming limitations of manual analysis.

Main Methods:

Developed an expert system using image processing, smoothing, and clustering algorithms.
Integrated algorithms into a graphical user interface (GUI) to create the APT.
Validated the APT using datasets from CMV-infected infants and applied it to identify regulatory T-cells in HIV-infected adults.

Main Results:

The APT demonstrated close agreement with manual analyses by five immunologists (concordance correlation coefficient = 0.96).
The tool processed approximately 100 data files per hour, significantly improving efficiency.
APT provided consistent criteria for data analysis, enhancing objectivity.

Conclusions:

The automatic processing tool (APT) offers a reliable and objective alternative to manual flow cytometry data analysis.
APT enhances efficiency and consistency, particularly for large-scale immunological studies.
The tool shows promise for reproducible cell population identification in clinical and research settings.