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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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Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells
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Comparative exploration of multidimensional flow cytometry software: a model approach evaluating T cell

Timothy T Spear1, Michael I Nishimura1, Patricia E Simms2

  • 1Department of Surgery, Cardinal Bernardin Cancer Center, Loyola University Chicago, Maywood, Illinois, USA; and.

Journal of Leukocyte Biology
|May 28, 2017
PubMed
Summary
This summary is machine-generated.

Selecting the right flow cytometry analysis software is crucial for complex immune cell studies. Automated Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE) and Simplified Presentation of Incredibly Complex Evaluations (SPICE) offer user-friendly options for analyzing T cell polyfunctionality.

Keywords:
ACCENSEPestleSPICEcomputational biology/bioinformaticspolychromatic flow cytometry

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Area of Science:

  • Immunology
  • Bioinformatics
  • Computational Biology

Background:

  • Polychromatic flow cytometry enables simultaneous analysis of numerous immune cell markers.
  • Complex datasets from flow cytometry necessitate advanced analytical software.
  • Limited access to bioinformatics support hinders researchers in choosing appropriate analysis tools.

Purpose of the Study:

  • To comparatively assess multidimensional flow cytometry software packages for ease of use, cost, and suitability for publication-quality data visualization.
  • To identify optimal software for analyzing complex immune cell datasets, specifically T cell polyfunctionality.

Main Methods:

  • Multidimensional flow cytometry was used to analyze 7 parameters, including 6 intracellular cytokines and degranulation, on TCR-transduced T cells.
  • Various software packages were evaluated for their ability to handle complex data (128 possible combinations of positivity/negativity).
  • Automated Classification of Cellular Expression by Nonlinear Stochastic Embedding (ACCENSE) and coupled analysis in Pestle/Simplified Presentation of Incredibly Complex Evaluations (SPICE) were specifically assessed.

Main Results:

  • ACCENSE and SPICE demonstrated the most user-friendly interfaces and generated the most readable graphical outputs for analyzing T cell polyfunctionality.
  • These tools effectively evaluated the effects of altered antigen-specific stimulation on T cell polyfunctionality.
  • The study identified specific software capable of managing the complexity of high-parameter flow cytometry data.

Conclusions:

  • ACCENSE and SPICE are recommended for researchers and Shared Resource Laboratories (SRLs) needing to analyze complex flow cytometry data, particularly for immune cell functional assays.
  • This comparative assessment provides a model for selecting appropriate software to address challenging biologic questions from high-dimensional datasets.
  • Further development and awareness of flow cytometry analysis tools are essential for advancing biological data interpretation.