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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: Jul 26, 2025

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

A Bayesian feature allocation model for identifying cell subpopulations using CyTOF data.

Arthur Lui¹, Juhee Lee², Peter F Thall³

¹Department of Statistics, Baskin School of Engineering, University of California Santa Cruz, 1156 High Street, Santa Cruz, CA, 95064, USA.

Journal of the Royal Statistical Society. Series C, Applied Statistics

|June 16, 2023

Summary

This summary is machine-generated.

A new Bayesian model identifies cell subpopulations from cytometry data across multiple samples. This approach improves the discovery of novel cell subsets, particularly for natural killer (NK) cells, aiding cancer immunotherapy research.

Keywords:

clustering latent features natural killer cells non-ignorable missing data subpopulations

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Last Updated: Jul 26, 2025

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Published on: October 25, 2018

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A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

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Published on: March 1, 2017

Area of Science:

Computational biology
Immunology
Statistical modeling

Background:

Cytometry by time of flight (CyTOF) generates high-dimensional data for cell analysis.
Identifying distinct cell subpopulations is crucial for understanding cellular functions and disease states.
Existing methods often analyze samples independently, potentially missing cross-sample patterns.

Purpose of the Study:

To develop a Bayesian feature allocation model (FAM) for robust cell subpopulation identification.
To enable simultaneous analysis of multiple CyTOF datasets.
To account for technical artifacts like missing data in mass cytometry.

Main Methods:

Bayesian feature allocation model (FAM) using a finite Indian buffet process.
Joint analysis of multiple CyTOF samples.
Incorporation of a static missingship mechanism for non-ignorable missing data.

Main Results:

The FAM effectively clusters cells into subpopulations based on marker expression patterns.
Simultaneous multi-sample analysis identified cell subpopulations missed by conventional methods.
Application to natural killer (NK) cell data revealed potential novel NK cell subsets.

Conclusions:

The FAM provides a powerful framework for dissecting cellular heterogeneity in multi-sample CyTOF data.
Identified novel NK cell subsets may offer insights into NK cell biology and cancer immunotherapy.
This statistical approach can advance the development of improved NK cell-based therapies.