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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 20, 2025

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

Computational Analysis of Microbial Flow Cytometry Data.

Peter Rubbens¹, Ruben Props²

¹Flanders Marine Institute (VLIZ), Ostend, Belgium peter.rubbens@vliz.be ruben.props@ugent.be.

|January 20, 2021

Summary

This summary is machine-generated.

This review outlines computational methods for analyzing microbial flow cytometry data. It provides a comprehensive workflow from measurement to interpretation, addressing current challenges in microbial ecology.

Keywords:

bioinformatics cytometry data analysis fingerprinting microbial ecology multivariate statistics single cell

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Last Updated: Nov 20, 2025

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Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

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Published on: January 9, 2019

Area of Science:

Microbial Ecology
Cytometry
Bioinformatics

Background:

Flow cytometry is crucial for microbial community studies, offering high-resolution single-cell phenotypic data.
Existing commercial software lacks comprehensive computational analysis workflows for complex microbial flow cytometry data.
A standardized computational pipeline is needed for objective data processing and interpretation in microbial ecology.

Purpose of the Study:

To provide a comprehensive overview of the computational data analysis pipeline for microbial flow cytometry.
To highlight potentially useful computational methods with non-technical descriptions for each step.
To address open challenges and motivate standardized flow cytometry in microbial ecology.

Main Methods:

Review of existing literature and computational tools for flow cytometry data analysis.
Description of a complete data analysis pipeline from measurement to interpretation.
Identification and explanation of computational methods applicable to microbial ecology studies.

Main Results:

A structured overview of the entire computational workflow for microbial flow cytometry data.
Identification of key computational methods and their applications at each stage.
Discussion of current challenges and future directions for the field.

Conclusions:

Standardized computational analysis is essential for robust microbial flow cytometry studies.
This review offers a valuable resource for researchers in microbial ecology.
Adoption of outlined methods can advance the field and improve data interpretation.