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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: Nov 6, 2025

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

Data integration and visualization techniques for post-cytometric analysis of complex datasets.

Fanny Hedin¹, Maria Konstantinou¹, Antonio Cosma^1,2

¹Quantitative Biology Unit, National Cytometry Platform, Luxembourg Institute of Health, Esch-sur-Alzette, Luxembourg.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology

|May 6, 2021

Summary

This summary is machine-generated.

Innovative business intelligence tools can effectively analyze complex flow cytometry and mass cytometry data. These methods integrate diverse datasets, enhancing biological discovery across various research fields.

Keywords:

data analysis databases integration visualization

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Sample Preparation for Mass Cytometry Analysis

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Last Updated: Nov 6, 2025

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

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Published on: January 16, 2019

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

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Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

Area of Science:

Biotechnology
Bioinformatics
Immunology

Background:

High-throughput biological data generation, including flow cytometry and mass cytometry, is increasing.
Existing cytometry analysis software often lacks capabilities for metadata integration and complex visualization.
There is a need for accessible tools to analyze diverse, high-dimensional biological datasets.

Purpose of the Study:

To demonstrate the application of business intelligence data integration and visualization tools for analyzing cytometry datasets.
To showcase the adaptability of these tools for complex biological data analysis.
To highlight the potential for broader adoption of these tools in biological research.

Main Methods:

Utilized data integration and visualization tools commonly employed in the business sector.
Applied these tools to analyze mass cytometry data from a lung adenocarcinoma immune cell study.
Applied these tools to analyze flow cytometry data characterizing human immune cell CD marker expression.

Main Results:

The selected business intelligence tools proved effective for analyzing both mass and flow cytometry data.
Demonstrated successful integration of cytometry data with metadata and complex visualization.
The analysis facilitated a deeper characterization of immune cells in lung adenocarcinoma and human immune cell markers.

Conclusions:

Business intelligence tools offer a powerful and accessible approach for analyzing complex cytometry data.
These tools can overcome limitations of traditional cytometry analysis software.
The methodology is adaptable for diverse biological research applications, enhancing data interpretation and discovery.