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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.
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FIND: a new software tool and development platform for enhanced multicolor flow analysis.

Shareef M Dabdoub1, William C Ray, Sheryl S Justice

  • 1Biophysics Program, The Ohio State University, Columbus, Ohio, USA. dabdoub.2@osu.edu

BMC Bioinformatics
|May 17, 2011
PubMed
Summary
This summary is machine-generated.

New software automates multi-color flow cytometry analysis, enabling easier classification of high-dimensional data. This user-friendly platform supports automated gating and sharing of new analysis algorithms for flow cytometry (FC).

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Data Science

Background:

  • Flow cytometry (FC) identifies and counts cells using laser-activated fluorescence.
  • Current FC analysis software is limited to 2-3 dimensions, insufficient for high-dimensional data (20+ colors).
  • Manual analysis of high-dimensional FC data is error-prone and biased.

Purpose of the Study:

  • Develop user-friendly software for automated multi-color flow cytometry data analysis.
  • Create a platform for developing and disseminating new FC analysis tools.
  • Enable automated gating and graphical comparison of FC data.

Main Methods:

  • Developed a new software application for multi-color FC data analysis.
  • Implemented automated event classification and graphical comparison features.
  • Included a plugin system for researchers to share algorithms.

Main Results:

  • The software provides a user-friendly tool for automated gating of multi-color FC data.
  • Users can load, classify, and compare single or multiple datasets.
  • A plugin system allows for the implementation and sharing of new analysis algorithms.

Conclusions:

  • The FIND platform offers a powerful, user-friendly environment for FC data analysis.
  • It serves as a common platform for distributing new automated analysis techniques.
  • Facilitates global access to advanced automated flow cytometry analysis methods.