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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: Jan 3, 2026

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

Automated Data Cleanup for Mass Cytometry.

Charles Bruce Bagwell¹, Margaret Inokuma¹, Benjamin Hunsberger¹

¹Verity Software House, Topsham, Maine.

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

|November 19, 2019

Summary

This summary is machine-generated.

Probability state modeling (PSM) automatically removes unwanted events like dead cells and debris from mass cytometry data. This enhances data quality for high-dimensional analysis, ensuring cleaner datasets for research.

Keywords:

Gaussian parameters probability state Modeling quality control unattended analysis

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Last Updated: Jan 3, 2026

Sample Preparation for Mass Cytometry Analysis

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Mass Cytometry: Protocol for Daily Tuning and Running Cell Samples on a CyTOF Mass Cytometer

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

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Area of Science:

Single-cell analysis
Biotechnology
Computational biology

Background:

Mass cytometry generates high-dimensional single-cell data.
Analyzing this complex data requires robust preprocessing steps.
Current data cleanup methods can be subjective and time-consuming.

Purpose of the Study:

To introduce an automated method for mass cytometry data cleanup.
To improve the accuracy and reproducibility of high-dimensional data analysis.
To remove unwanted events such as dead cells, debris, and aggregates.

Main Methods:

Developed a probability state modeling (PSM) approach.
Utilized QC measurements (DNA, live/dead, event length).
Incorporated four additional pulse-processing parameters for event detection.

Main Results:

PSM automatically identifies and removes unwanted events.
Generated FCS files with predominantly live and intact single cells.
Reduced subjectivity and bias in data cleanup compared to manual gating.

Conclusions:

PSM offers an automated, objective, and efficient solution for mass cytometry data cleanup.
The method enhances the quality of single-cell data for downstream analysis.
This approach is valuable for researchers working with high-dimensional cytometry data.