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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: Jun 26, 2025

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

Multicolor Flow Cytometry-based Quantification of Mitochondria and Lysosomes in T Cells

Published on: January 9, 2019

TEAM: A MULTIPLE TESTING ALGORITHM ON THE AGGREGATION TREE FOR FLOW CYTOMETRY ANALYSIS.

John A Pura¹, Xuechan Li², Cliburn Chan²

¹Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans AfFaIRS MediCal Center, Durham, NC 27701.

The Annals of Applied Statistics

|May 13, 2024

Summary

This summary is machine-generated.

A new method called TEAM (Testing on the Aggregation tree Method) efficiently identifies immune cells responding to stimuli in flow cytometry data. This algorithm controls false discovery rates and offers powerful insights into cellular responses.

Keywords:

Flow cytometry aggregation tree distribution difference false discovery proportion (FDP)multiple testing

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Area of Science:

Immunology
Computational Biology
Biostatistics

Background:

Flow cytometry is a key single-cell assay in immunology for analyzing immune cell responses.
Identifying stimulus-responsive cells involves comparing protein expression probability density functions (pdfs) before and after stimulation.
Detecting differential pdfs is crucial for pinpointing responsive cell populations.

Purpose of the Study:

To develop a novel, computationally efficient method for identifying differential density regions in flow cytometry data.
To introduce TEAM (Testing on the Aggregation tree Method) for robust multiple testing and false discovery rate (FDR) control.
To pinpoint immune cell responses to stimuli with high statistical power and biological interpretability.

Main Methods:

Partitioning the sample space into bins to form hypotheses for differential pdfs.
Implementing TEAM, a multiple testing method using an aggregation tree for fine-to-coarse resolution testing.
Controlling the false discovery rate (FDR) to ensure reliable identification of differential regions.

Main Results:

TEAM successfully identified T cells responsive to cytomegalovirus (CMV)-pp65 antigen stimulation.
The method pinpointed enriched sets of monofunctional, bifunctional, and polyfunctional T cells.
TEAM demonstrated computational efficiency, analyzing large datasets faster than competing methods, with valid, powerful, and robust performance.

Conclusions:

TEAM is a statistically powerful and computationally efficient algorithm for flow cytometry data analysis.
The method provides meaningful biological insights by accurately identifying responsive immune cell populations.
TEAM offers a significant advancement in analyzing complex single-cell data for immunological studies.