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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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Related Experiment Video

Updated: Jul 8, 2026

Analysis of T-cell Receptor-Induced Calcium Influx in Primary Murine T-cells by Full Spectrum Flow Cytometry
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Published on: December 16, 2022

Cytometry-acquired calcium-flux data analysis in activated lymphocytes.

A S Kaposi1, G Veress, B Vásárhelyi

  • 1Research Group of Pediatrics and Nephrology, Hungarian Academy of Sciences, Semmelweis University, Budapest 1083, Hungary.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology
|January 1, 2008
PubMed
Summary
This summary is machine-generated.

A new algorithm analyzes flow cytometry calcium flux data, identifying the hormesis function to characterize lymphocyte activation kinetics. This method allows for robust statistical comparison of cellular responses.

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Area of Science:

  • Immunology
  • Biophysics
  • Computational Biology

Background:

  • Flow cytometry generates large datasets for calcium signaling analysis.
  • Existing algorithms are inadequate for statistical analysis of high-throughput flow cytometry data.
  • Accurate analysis of calcium flux is crucial for understanding lymphocyte activation.

Purpose of the Study:

  • Develop a robust algorithm for statistical analysis of flow cytometry calcium flux data.
  • Enable statistical comparison between different calcium flux measurements.
  • Characterize lymphocyte activation kinetics and data distribution.

Main Methods:

  • Applied flow cytometry to measure calcium levels in stimulated lymphocytes.
  • Developed a novel algorithm fitting functions to median values of time-course data.
  • Utilized the hormesis function as the best fit for calcium flux data.
  • Reanalyzed published calcium flux data from CD3+ and Jurkat cells.

Main Results:

  • Identified the hormesis function as the optimal fit for calcium flux data.
  • Calculated key biological descriptors including peak time and signal amplitude.
  • Demonstrated statistically significant differences in cell activation kinetics.
  • Successfully characterized kinetics and distribution of calcium flux data.

Conclusions:

  • The developed algorithm provides a reliable tool for analyzing flow cytometry calcium flux data.
  • This approach enhances the characterization of lymphocyte activation.
  • Facilitates robust statistical comparisons of cellular signaling dynamics.