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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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Updated: Jan 4, 2026

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

Minimizing Batch Effects in Mass Cytometry Data.

Ronald P Schuyler¹, Conner Jackson¹, Josselyn E Garcia-Perez¹

¹Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO, United States.

Frontiers in Immunology

|November 5, 2019

Summary

This summary is machine-generated.

Batch effects in Cytometry by Time-Of-Flight (CyTOF) experiments are reduced using a novel method with technical replicates. This approach minimizes variability across multiple runs, enabling more accurate single-cell analysis and comparisons over time.

Keywords:

anchor barcode clinical studies human immunology mass cytometry normalization

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Last Updated: Jan 4, 2026

Sample Preparation for Mass Cytometry Analysis

Sample Preparation for Mass Cytometry Analysis

Published on: April 29, 2017

Single-cell Analysis of Immunophenotype and Cytokine Production in Peripheral Whole Blood via Mass Cytometry

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Published on: June 26, 2018

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

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

Published on: November 2, 2012

Area of Science:

Immunology
Biotechnology
Computational Biology

Background:

Cytometry by Time-Of-Flight (CyTOF) enables high-dimensional single-cell analysis.
Sample barcoding reduces technical variability within a batch but necessitates multiple batches for larger studies.
Inter-batch variability requires robust adjustment methods for accurate data comparison.

Purpose of the Study:

To develop and validate an automated batch adjustment method for CyTOF data.
To mitigate technical variability across multiple experimental batches.
To improve the reliability of single-cell data comparisons over extended periods.

Main Methods:

Incorporation of technical replicate samples (anchors) within each CyTOF run.
Automated determination and application of batch-specific adjustment parameters using anchors.
Validation of the method using manual gating and unsupervised clustering on anchor and independent replicate sets.

Main Results:

The developed method effectively reduces batch effects in anchor and validation replicate samples.
Quantification of cell subpopulations and mean signal intensity showed significant mitigation of variability.
The procedure operates without manual intervention, streamlining data processing.

Conclusions:

Technical replicates provide a robust internal standard for batch effect correction in CyTOF.
This automated method enhances the accuracy and comparability of CyTOF data across multiple batches and time points.
The approach supports more reliable large-scale studies and longitudinal analyses using CyTOF.