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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 24, 2026

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

Published on: March 24, 2023

Functional Virtual Flow Cytometry: A Visual Analytic Approach for Characterizing Single-Cell Gene Expression

Zhi Han^1,2, Travis Johnson², Jie Zhang^2,3

¹College of Software, Nankai University, Tianjin, China.

Biomed Research International

|August 12, 2017

Summary

This summary is machine-generated.

This study introduces a new workflow for analyzing single-cell RNA sequencing data, using gene coexpression modules to identify cell population patterns. This method enhances cell separation and interpretation, aiding in new biological discoveries.

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Published on: October 25, 2018

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Last Updated: Feb 24, 2026

Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells

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Published on: March 24, 2023

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

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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

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell transcriptome studies generate large datasets, posing challenges in extracting meaningful gene features for cell population analysis.
Traditional methods often struggle to effectively separate cell populations due to the high dimensionality of gene expression data.

Purpose of the Study:

To develop a novel workflow for detecting distribution patterns in cell populations using single-cell transcriptome data.
To leverage gene coexpression modules as features for improved cell population separation and interpretation.
To introduce interactive visualization tools aided by spatial statistical analysis for exploring cell distribution patterns.

Main Methods:

Utilized gene coexpression modules and summarized them into eigengenes as features for single-cell data analysis.
Applied a novel spatial statistical analysis with a clustering index parameter to scatter plot matrices (SPLOM) for interactive visualization.
Validated the workflow on large-scale datasets, including the Allen Brain scRNA-seq and a glioblastoma study.

Main Results:

The workflow effectively separates cell populations and facilitates prompt interpretation of gene features.
Interactive visualization aided by spatial statistics highlights significant 2D patterns in cell distribution.
Analysis of the Allen Brain dataset suggested a new hypothesis regarding glutamate metabolism in brain cell separation.
A unique cell migration signature was identified in a glioblastoma sample.

Conclusions:

The proposed workflow offers an effective approach for analyzing single-cell transcriptome data by utilizing gene coexpression modules.
This method enhances the ability to discover novel biological insights and hypotheses from complex single-cell studies.
The interactive visualization tools provide powerful means for exploring and interpreting cell population dynamics.