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Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

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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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Discrimination of Seven Immune Cell Subsets by Two-fluorochrome Flow Cytometry
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Data File Standard for Flow Cytometry, Version FCS 3.2.

Josef Spidlen1, Wayne Moore2, David Parks3

  • 1Informatics, BD Life Sciences - FlowJo, Ashland, Oregon, USA.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology
|September 4, 2020
PubMed
Summary
This summary is machine-generated.

Flow Cytometry Standard (FCS) 3.2 enhances data compatibility with mixed data types and explicit keywords for precise analysis. This revised standard ensures broader interoperability for flow cytometry data.

Keywords:
FCSFCS 3.2bioinformaticsdata standardfile formatflow cytometry

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Area of Science:

  • * Flow Cytometry Data Standards
  • * Bioinformatics
  • * Computational Biology

Background:

  • * The Flow Cytometry Standard (FCS) facilitates data exchange across different hardware and software.
  • * Previous FCS versions required improvements for precise instrument condition and measurement feature capture.
  • * Community and industry feedback highlighted needs for enhanced data representation.

Purpose of the Study:

  • * To introduce FCS 3.2, a revised flow cytometry data standard.
  • * To improve the precision of capturing instrument conditions and measurement features.
  • * To ensure continued uniform file format for cross-platform analysis.

Main Methods:

  • * Revision of the existing FCS file structure and features.
  • * Implementation of support for mixed data types (integers and floating-point).
  • * Addition of explicit keywords for dyes, detectors, and analytes; formalization of measurement types.

Main Results:

  • * FCS 3.2 supports mixed data types, allowing for more accurate representation of integer and floating-point measurements within a single dataset.
  • * New keywords improve the reliability of identifying dyes, detectors, and analytes.
  • * Clarifications and formalizations enhance the overall specification, though changes impact compatibility with older readers.

Conclusions:

  • * FCS 3.2 offers significant improvements for flow cytometry data standardization.
  • * The new version enhances data precision and analytical reliability.
  • * While introducing advancements, users should be aware of compatibility issues with legacy FCS file readers.