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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: Feb 16, 2026

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

A Beginner's Guide to Analyzing and Visualizing Mass Cytometry Data.

Abigail K Kimball¹, Lauren M Oko², Bonnie L Bullock³

¹Department of Anesthesiology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045.

Journal of Immunology (Baltimore, Md. : 1950)

|December 20, 2017

Summary

This summary is machine-generated.

Mass cytometry enables deep single-cell analysis, but choosing the right data analysis algorithm can be challenging. Integrating multiple algorithms provides complementary insights for high-dimensional datasets.

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Last Updated: Feb 16, 2026

Sample Preparation for Mass Cytometry Analysis

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Published on: April 29, 2017

Isolation and Staining of Mouse Skin Keratinocytes for Cell Cycle Specific Analysis of Cellular Protein Expression by Mass Cytometry

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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
Biotechnology

Background:

Mass cytometry offers high-dimensional single-cell analysis, expanding phenotypic and functional characterization.
This platform necessitates novel data analysis approaches beyond traditional flow cytometry.
Beginners face challenges due to numerous algorithms and lack of consensus on best practices.

Purpose of the Study:

To compare five cytometry by time-of-flight (CyTOF) analysis platforms: viSNE, SPADE, X-shift, PhenoGraph, and Citrus.
To identify considerations and challenges for users of these high-dimensional data analysis methods.
To provide a practical guide for analyzing complex CyTOF datasets.

Main Methods:

Analysis of a single mass cytometry dataset using five distinct computational algorithms.
Comparative evaluation of algorithm performance and biological insights generated.
Development of annotated workflows and figures for practical guidance.

Main Results:

Each algorithm revealed common and unique biological insights from the high-dimensional data.
Identified key considerations and challenges associated with using different CyTOF analysis platforms.
Demonstrated the value of integrating multiple algorithms for comprehensive data interpretation.

Conclusions:

Integrating multiple CyTOF analysis algorithms is beneficial for gaining complementary insights.
A practical guide is provided to aid investigators in analyzing high-dimensional datasets.
Emphasizes the need for careful algorithm selection and integration in mass cytometry studies.