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

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Multi-parametric cytometry from a complex cellular sample: Improvements and limits of manual versus

F Gondois-Rey^1,2,3,4, S Granjeaud^2,3,4,5, P Rouillier^2,3,4,5

¹Team Immunity and Cancer, Inserm, U1068, CRCM, Marseille, F-13009, France.

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

|April 10, 2016

Summary

New computational tools enhance multi-parametric flow cytometry analysis by overcoming limitations of manual gating. The Multi-Experiment Viewer software (MeV) integrates unsupervised clustering for improved immunomonitoring of complex samples.

Keywords:

cluster computation flow cytometry data analysis heatmaps manual gating multi-experiment viewer population annotation

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Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

Area of Science:

Immunology
Computational Biology
Biotechnology

Background:

Multi-parametric flow cytometry generates complex, high-dimensional data.
Classical analysis methods struggle with multi-dimensional data, limiting potential.
Adoption of advanced computational tools by flow cytometrists remains low.

Purpose of the Study:

To integrate unsupervised computational tools for managing multi-stained samples in flow cytometry.
To compare the advantages and limitations of unsupervised tools against manual gating.
To optimize manual gating strategies and introduce validated automated analysis software.

Main Methods:

Analysis of a single peripheral blood mononuclear cell (PBMC) tube with 11 populations and 9 fluorescent markers.
Investigation of manual gating variability and optimization strategies.
Introduction and validation of the Multi-Experiment Viewer (MeV) software for automated analysis.

Main Results:

Unsupervised tools, particularly MeV, facilitate merging clusters and interactive population annotation.
Automated gating identified targeted and unexpected populations, though minor heterogeneity required adjustments.
MeV simultaneously displays Mean Fluorescence Intensity (MFI) and marker signatures for enhanced population identification.

Conclusions:

The described procedure effectively manages high numbers of multi-stained samples.
Unsupervised computational tools, integrated via MeV, improve multi-parametric flow cytometry analysis.
This approach offers an improved alternative to classic manual gating for immunomonitoring.