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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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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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Making the most of high-dimensional cytometry data.

Felix Md Marsh-Wakefield1,2,3, Andrew J Mitchell4, Samuel E Norton5,6

  • 1Vascular Immunology Unit, Discipline of Pathology, The University of Sydney, Sydney, NSW, Australia.

Immunology and Cell Biology
|April 2, 2021
PubMed
Summary
This summary is machine-generated.

High-dimensional cytometry unlocks new immunology insights. This guide helps researchers design experiments for better data collection and analysis, maximizing the potential of this powerful technology.

Keywords:
Analysisexperimental designflow cytometryhigh-dimensional datamass cytometry

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Area of Science:

  • Immunology
  • Biotechnology
  • Data Science

Background:

  • High-dimensional cytometry is revolutionizing immunology research.
  • Numerous analysis tools exist, but experimental design remains a challenge.
  • Data underutilization stems from data size and complexity.

Purpose of the Study:

  • To explain concepts for designing high-dimensional cytometry experiments.
  • To provide considerations for maximizing quality data collection.
  • To aid both new and experienced users in experimental design.

Main Methods:

  • Conceptual explanation of experimental design principles.
  • Considerations for data acquisition and marker selection.
  • Guidance on data analysis workflows for high-dimensional data.

Main Results:

  • Underutilization of high-dimensional cytometry data is common.
  • Effective experimental design is crucial for meaningful data.
  • Clear guidelines can improve data quality and interpretation.

Conclusions:

  • Transitioning to high-dimensional cytometry requires a shift in experimental design thinking.
  • Proper design maximizes the utility of complex immunological datasets.
  • This article provides a framework for successful high-dimensional cytometry experiments.