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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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Updated: Mar 10, 2026

Single-cell Analysis of Bacillus subtilis Biofilms Using Fluorescence Microscopy and Flow Cytometry

Single-cell Analysis of Bacillus subtilis Biofilms Using Fluorescence Microscopy and Flow Cytometry

Published on: February 15, 2012

Probing bacterial cell biology using image cytometry.

Julie A Cass¹, Stella Stylianidou¹, Nathan J Kuwada²

¹Department of Physics, University of Washington, Seattle, WA, 98195, USA.

Molecular Microbiology

|December 10, 2016

Summary

This summary is machine-generated.

Researchers developed new image cytometry tools for rapid analysis of bacterial cell imaging data. This automated framework enables detailed population-level insights from high-throughput fluorescence microscopy experiments.

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Last Updated: Mar 10, 2026

Single-cell Analysis of Bacillus subtilis Biofilms Using Fluorescence Microscopy and Flow Cytometry

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Published on: February 15, 2012

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Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

Published on: August 15, 2013

Area of Science:

Microbiology
Cell Biology
Biotechnology

Background:

Automated fluorescence microscopy generates large datasets for bacterial cell analysis.
Current analysis methods struggle with speed and reliability for high-throughput experiments.

Purpose of the Study:

To develop an automated image cytometry framework for analyzing bacterial cell imaging data.
To enable fast and reliable quantification of numerous cell descriptors.

Main Methods:

Developed a novel image cytometry framework and associated tools.
The framework visualizes and gates over 70 cell descriptors, including fluorescence, morphology, and proliferation.
Inspired by flow cytometry principles for population-level analysis.

Main Results:

The tools facilitate analysis of high-density bacterial cultures and complete cell cycles.
Demonstrated broad applicability across four distinct experimental applications.
Enabled efficient analysis of multi-well imaging data.

Conclusions:

The developed image cytometry framework provides a powerful solution for analyzing large-scale bacterial fluorescence microscopy data.
This approach offers a fast, reliable, and versatile method for extracting detailed cell population insights.