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Flow Cytometry

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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Updated: Mar 27, 2026

Measurement of T Cell Alloreactivity Using Imaging Flow Cytometry

Measurement of T Cell Alloreactivity Using Imaging Flow Cytometry

Published on: April 19, 2017

Stochastic Measurement Models for Quantifying Lymphocyte Responses Using Flow Cytometry.

Andrey Kan^1,2, Damian Pavlyshyn^1,2, John F Markham^1,2

¹Division of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.

|January 8, 2016

Summary

This summary is machine-generated.

Researchers developed a new model to accurately analyze flow cytometry data, addressing variability in lymphocyte response measurements. This improves mathematical modeling for immune system studies.

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Last Updated: Mar 27, 2026

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Flow Cytometric Characterization of Murine B Cell Development

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Published on: January 22, 2021

Area of Science:

Immunology
Computational Biology
Biophysics

Background:

Adaptive immune responses involve B and T cell division and differentiation, crucial for host defense.
Flow cytometry is a key technique for tracking lymphocyte responses, providing data for mathematical modeling.
Variability in flow cytometry measurements, due to experimental noise and cell differences, challenges accurate mathematical modeling.

Purpose of the Study:

To investigate the nature of measurement errors in flow cytometry data from various experiments.
To evaluate the validity of assumptions made by current model-fitting methods.
To propose and validate a new measurement model for flow cytometry data.

Main Methods:

Analysis of flow cytometry measurement errors across diverse experimental datasets.
Characterization of the relationship between mean and variance of measurement noise.
Development and theoretical justification (maximum entropy) of a novel measurement model.
Empirical validation of the new model using collected flow cytometry data.

Main Results:

A power-law relationship (exponent 1.3-1.8) was found between the mean and variance of flow cytometry noise.
This relationship violates assumptions of common model-fitting methods like least squares and log-transformation.
The proposed new measurement model accurately describes the observed data and error characteristics.

Conclusions:

Current model-fitting methods for flow cytometry data may be unreliable due to violated assumptions.
The novel measurement model provides a more accurate and robust approach for analyzing flow cytometry data.
This work enhances quantitative studies of lymphocyte responses by improving data modeling.