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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: Mar 18, 2026

Detection of Fluorescent Nanoparticle Interactions with Primary Immune Cell Subpopulations by Flow Cytometry

Detection of Fluorescent Nanoparticle Interactions with Primary Immune Cell Subpopulations by Flow Cytometry

Published on: March 28, 2014

HD Flow Cytometry: An Improved Way to Quantify Cellular Interactions with Nanoparticles.

Laura I Selby¹, Nachnicha Kongkatigumjorn², Georgina K Such²

¹Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia.

Advanced Healthcare Materials

|July 6, 2016

Summary

Histogram deconvolution flow cytometry improves nanomaterial-cell interaction quantification. This advanced algorithm accurately identifies positive cells in complex populations and visualizes results, outperforming current commercial methods.

Keywords:

cell interactions deconvolution flow cytometry nanoparticles

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Last Updated: Mar 18, 2026

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

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Published on: June 8, 2012

Area of Science:

Biotechnology
Nanotechnology
Analytical Chemistry

Background:

Accurate quantification of nanomaterial-cell interactions is crucial for understanding nanoparticle toxicity and drug delivery.
Existing flow cytometry methods struggle with highly overlapped cell populations, limiting precise analysis.
Visualizing nanomaterial uptake and distribution within cells remains a challenge.

Purpose of the Study:

To develop and validate a novel histogram deconvolution algorithm for enhanced flow cytometry analysis of nanomaterial-cell interactions.
To improve the accuracy of quantifying positive cells in complex, overlapped populations.
To provide a method for visualizing the output of nanomaterial-cell interaction analysis.

Main Methods:

Development of a histogram deconvolution algorithm tailored for flow cytometry data.
Application of the algorithm to analyze nanomaterial-cell interactions in highly overlapped populations.
Comparison of the algorithm's performance against commercially available methods.

Main Results:

The histogram deconvolution algorithm successfully identified positive cells in highly overlapped populations.
Accurate calculation of fluorescence intensity for the positive cell population was achieved.
The developed technique demonstrated superior performance compared to existing commercial methods.
The algorithm provided a valuable visualization of the analysis output.

Conclusions:

Histogram deconvolution flow cytometry offers a significant advancement in quantifying nanomaterial-cell interactions.
This method overcomes limitations of current techniques in analyzing complex cell populations.
The ability to visualize results enhances the interpretability and utility of flow cytometry data in nanomedicine and toxicology studies.