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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: Jul 11, 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

MetaGate: Interactive Analysis of High-Dimensional Cytometry Data with Meta Data Integration.

Eivind Heggernes Ask^1,2, Astrid Tschan-Plessl^1,3, Hanna Julie Hoel¹

¹Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.

Biorxiv : the Preprint Server for Biology

|November 14, 2023

Summary

MetaGate offers interactive analysis and visualization for high-dimensional cytometry data, integrating clinical metadata for robust statistical insights. This platform enhances the study of immune cell populations in diseases like diffuse large B-cell lymphoma (DLBCL).

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

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Published on: December 12, 2019

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

Related Experiment Videos

Last Updated: Jul 11, 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

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

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

Flow cytometry and mass cytometry enable high-throughput protein quantification at the single-cell level, crucial for research and diagnostics.
Traditional manual gating for cytometry data analysis faces challenges with increasing data complexity and limitations in statistical testing and data sharing.
Existing analysis algorithms often lack integration with clinical data and cross-experiment comparability, hindering comprehensive biological interpretation.

Approach:

Developed MetaGate, a platform for interactive statistical analysis and visualization of high-dimensional cytometry data, incorporating clinical metadata.
MetaGate facilitates manual gating in standard software and employs a combinatorial gating system for defining cell populations.
A two-step process condenses raw cytometry data (FCS files), integrates manual gates, user-defined populations, and clinical metadata into a single project file.

Key Points:

MetaGate enables rapid statistical calculations and visualizations (box plots, heatmaps, volcano plots) from complex cytometry datasets.
Analysis of peripheral blood immune cells in diffuse large B-cell lymphoma (DLBCL) patients revealed expanded monocytic myeloid-derived suppressor cells.
A significant inverse correlation was observed between Natural Killer (NK) cell numbers and disease progression in DLBCL patients.

Conclusions:

MetaGate provides a powerful, integrated solution for analyzing complex cytometry data, addressing limitations of traditional methods.
The platform facilitates robust statistical analysis and visualization, crucial for understanding immune cell dynamics in diseases like DLBCL.
Findings in DLBCL patients highlight the utility of MetaGate in characterizing immune cell alterations and their correlation with disease progression.