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Related Concept Videos

Flow Cytometry01:23

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.
In...
Methods to Assess Microbial Populations01:30

Methods to Assess Microbial Populations

Assessing microbial populations is crucial for understanding microbial roles in health, ecology, and industry. Various complementary techniques—both culture-based and molecular—enable detailed analysis of microbial abundance, diversity, and function.Viable Plate CountThe viable plate count is a traditional culture-based method used to estimate the number of living microbes in a sample. After serial dilution, the sample is spread onto nutrient agar plates. Each viable cell forms a visible...

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Related Experiment Video

Updated: Jun 6, 2026

Characterizing Microbiome Dynamics &#8211; Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
09:57

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

Published on: July 12, 2018

Multivariate data analysis methods for the interpretation of microbial flow cytometric data.

Hazel M Davey1, Christopher L Davey

  • 1Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Penglais, Aberystwyth, SY23 3DD, UK, hlr@aber.ac.uk.

Advances in Biochemical Engineering/Biotechnology
|November 12, 2010
PubMed
Summary
This summary is machine-generated.

Flow cytometry hardware advancements enable microbial analysis. New software and advanced methods like genetic programming improve data analysis, overcoming limitations of manual processing for microbial flow cytometry data.

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Area of Science:

  • Microbiology
  • Immunology
  • Cell Biology

Background:

  • Flow cytometry hardware is now sensitive enough for routine microbial analysis.
  • Software tools for analyzing, displaying, and disseminating flow cytometry data have lagged behind hardware advancements.
  • Manual analysis of individual samples remains a bottleneck in microbial flow cytometry.

Purpose of the Study:

  • To present new datasets illustrating common flow cytometry applications in microbiology.
  • To demonstrate advanced data analysis techniques for microbial flow cytometry.
  • To introduce novel software for automated data visualization in flow cytometry.

Main Methods:

  • Application of manual data analysis.
  • Utilizing automated visualization with newly developed software.
  • Employing genetic programming, principal components analysis, and artificial neural networks.

Main Results:

  • Demonstrated the effectiveness of advanced computational methods for microbial flow cytometry data.
  • Illustrated the application of these methods on two new microbiological datasets.
  • Showcased a new software tool for automated data visualization.

Conclusions:

  • Advanced computational methods can significantly enhance the analysis of microbial flow cytometry data.
  • The presented techniques overcome limitations associated with manual data analysis.
  • The described data analysis approaches are transferable to other cell types in flow cytometry applications.