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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 29, 2025

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

Determining clinically relevant features in cytometry data using persistent homology.

Soham Mukherjee¹, Darren Wethington^2,3, Tamal K Dey¹

¹Computer Science Department, Purdue University, West Lafayette, Indiana, United States of America.

Plos Computational Biology

|March 21, 2022

Summary

This summary is machine-generated.

Persistent homology analysis reveals distinct topological features in cytometry data, identifying significant differences in T-cell protein expression between COVID-19 patients and healthy controls.

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Last Updated: Sep 29, 2025

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

Area of Science:

Immunology
Computational Biology
Topology

Background:

Cytometry generates high-dimensional data challenging manual interpretation.
Standard Boolean gating misses subtle topological features and is affected by batch effects.
Persistent homology offers a novel approach to analyze complex cytometry data.

Purpose of the Study:

To apply persistent homology to cytometry data for distinguishing between COVID-19 patients and healthy controls.
To identify differences in non-naïve CD8+ T cell protein expression.
To explore topological data analysis for novel insights in cytometry.

Main Methods:

Utilized persistent homology to analyze cytometry data from COVID-19 patients and healthy controls.
Employed a decision-tree classifier to identify key proteins (T-bet, Eomes, Ki-67).
Computed Wasserstein distances between persistence diagrams to quantify data structure differences.

Main Results:

Systematic structural differences were found in protein expression between COVID-19 patients and healthy controls.
T-bet and Eomes expression were significantly downregulated in COVID-19 patient non-naïve CD8+ T cells.
Persistent homology effectively identified differences masked by standard analysis.

Conclusions:

Persistent homology is a powerful tool for uncovering hidden patterns in cytometry data.
Downregulation of T-bet and Eomes suggests reduced canonical effector CD8+ T cells in COVID-19 patients.
This topological approach enhances the discovery of biological insights from cytometry experiments.