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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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Related Experiment Video

Updated: Sep 22, 2025

Analyzing Cellular Internalization of Nanoparticles and Bacteria by Multi-spectral Imaging Flow Cytometry

Analyzing Cellular Internalization of Nanoparticles and Bacteria by Multi-spectral Imaging Flow Cytometry

Published on: June 8, 2012

Identifying cell-to-cell variability in internalization using flow cytometry.

Alexander P Browning^1,2,3, Niloufar Ansari⁴, Christopher Drovandi^1,2,3

¹School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.

Journal of the Royal Society, Interface

|May 25, 2022

Summary

This summary is machine-generated.

Biological heterogeneity causes experimental variation. This study models cell internalization dynamics and variability, offering a new method to analyze single-cell data from internalization assays.

Keywords:

approximate Bayesian computation endocytosis flow cytometry heterogeneity internalization noise

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Last Updated: Sep 22, 2025

Analyzing Cellular Internalization of Nanoparticles and Bacteria by Multi-spectral Imaging Flow Cytometry

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Determination of the Relative Cell Surface and Total Expression of Recombinant Ion Channels Using Flow Cytometry

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Published on: September 28, 2016

Area of Science:

Cell biology
Biophysics
Mathematical biology

Background:

Biological heterogeneity significantly contributes to experimental variability in cellular dynamical processes.
Cellular internalization is crucial for therapeutics and viral entry, making cell-to-cell variability in this process biologically significant.
Many studies overlook cell-to-cell variability in internalization experiments.

Purpose of the Study:

To develop a mathematical model that captures the dynamics and cell-to-cell variability of cellular internalization.
To incorporate extrinsic noise from techniques like flow cytometry into the model.
To provide a broadly applicable framework for analyzing single-cell data from internalization assays.

Main Methods:

A simple mathematical model of internalization was developed.
A novel distribution-matching approximate Bayesian computation algorithm was used for model calibration.
The model was calibrated using flow cytometry data of anti-transferrin receptor antibody internalization in a human B-cell lymphoblastoid cell line.

Main Results:

The study identified parameter space regions consistent with experimental data, elucidating the nature of cell-to-cell variability.
The developed modeling framework is independent of sample size and signal-to-noise ratio.
The approach successfully quantifies cell-to-cell variability in internalization processes.

Conclusions:

The developed mathematical model and calibration algorithm effectively capture cellular internalization dynamics and variability.
This framework offers a robust method for analyzing single-cell data, particularly in internalization assays.
The approach is broadly applicable to studies of biological variability in cellular dynamical processes.