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Flow Cytometry01:23

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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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An Automated Method to Perform The In Vitro Micronucleus Assay using Multispectral Imaging Flow Cytometry
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MuPETFlow: multiple ploidy estimation tool from flow cytometry data.

C Gómez-Muñoz1, G Fischer2

  • 1Laboratory of Computational, Quantitative and Synthetic Biology (CQSB), Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Paris-Seine (IBPS), Sorbonne Université, Paris, F-75005, France. cintia.gomez_munoz@sorbonne-universite.fr.

BMC Genomics
|March 27, 2025
PubMed
Summary
This summary is machine-generated.

We developed MuPETFlow, a user-friendly tool to automate ploidy estimation from flow cytometry data. This software simplifies complex analyses, enabling faster and more accurate ploidy and genome size predictions for large datasets.

Keywords:
CytometryGenomeGraphical user interfacePloidySoftware

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Area of Science:

  • Genomics
  • Computational Biology
  • Cytometry

Background:

  • Ploidy estimation from flow cytometry data typically requires complex scripting and manual sample curation.
  • Existing methods for ploidy analysis are often time-consuming and may lack automation for large-scale projects.

Purpose of the Study:

  • To develop an automated, user-friendly tool for ploidy estimation using flow cytometry data.
  • To streamline the analysis of cell cycle phases and genome size determination.

Main Methods:

  • Development of MuPETFlow, a Shiny graphical user interface (GUI) tool.
  • Implementation of automated peak detection for cell cycle phases.
  • Linear regression using standards for ploidy and genome size predictions.
  • Benchmarking with yeast and plant datasets for validation.

Main Results:

  • MuPETFlow successfully automates ploidy estimation for multiple flow cytometry files.
  • The tool accurately visualizes histograms, detects cell cycle phase peaks, and predicts ploidy and genome size.
  • Benchmarking confirmed consistent ploidy results compared to known datasets.
  • Performance and peak detection were comparable to existing tools.

Conclusions:

  • MuPETFlow offers unique in-app ploidy detection, multi-sample visualization, and automation.
  • The tool significantly accelerates flow cytometry data analysis, particularly for large-scale genomic projects.
  • MuPETFlow enhances accessibility and efficiency in ploidy and genome size determination.