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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: Jun 9, 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

Parameter optimization for stable clustering using FlowSOM: a case study from CyTOF.

Weiyang Tao¹, Anirban Sinha¹, Khadir Raddassi¹

¹Immunology Discovery Research, AbbVie Cambridge Research Center, Cambridge, MA, United States.

Frontiers in Immunology

|October 24, 2024

Summary

This summary is machine-generated.

Critical modifications to the FlowSOM package improve high-dimensional cell phenotyping with CyTOF. Optimized parameters enable reliable analysis of complex immune cell data for disease research.

Keywords:

FlowSOM clustering cyTOF high-dimensional dataset parameter optimization

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Last Updated: Jun 9, 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

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

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Published on: January 16, 2019

Area of Science:

Immunology
Computational Biology
Bioinformatics

Background:

High-dimensional cell phenotyping using CyTOF is crucial for understanding health and disease.
Analyzing complex CyTOF data requires advanced computational methods like machine learning for automated gating.

Purpose of the Study:

To critically modify and optimize the machine learning-based FlowSOM package for reliable CyTOF data analysis.
To address bugs and enhance the scalability of the FlowSOM pipeline for large datasets.

Main Methods:

Modified the FlowSOM package to fix existing bugs and improve scalability.
Performed precise parameter optimization to assess its impact on clustering outcomes.
Validated the enhanced FlowSOM pipeline on a large external immunological dataset.

Main Results:

Critical modifications and parameter optimization significantly improved the reliability of CyTOF data analysis.
Identified key parameters influencing clustering outcomes in FlowSOM.
Demonstrated the necessity of data-specific parameter tuning for accurate immune cell population definition.

Conclusions:

The modified FlowSOM pipeline provides a reliable method for high-dimensional cell phenotyping using CyTOF.
Tailored parameter optimization is essential for interrogating immune cell populations in immune disorders.
This work enhances the utility of CyTOF for immunological research and disease studies.