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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.
In...

Overview Of Cell Separation And Isolation

Overview Of Cell Separation And Isolation

Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.

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Related Experiment Video

Updated: Oct 7, 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 Statistical Method for Association Analysis of Cell Type Compositions.

Licai Huang¹, Paul Little¹, Jeroen R Huyghe¹

¹Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA.

Statistics in Biosciences

|January 10, 2022

Summary

We developed a new method to analyze cell type composition in tissue samples, revealing immune cell composition predicts colorectal cancer patient survival. This approach accounts for cell type dependencies, improving prognostic accuracy.

Keywords:

cell type composition genome-wide associations survival time

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Last Updated: Oct 7, 2025

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

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

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

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

Published on: March 1, 2017

Area of Science:

Computational Biology
Genomics
Cancer Research

Background:

Gene expression data from tissue samples reflect mixed cell types.
Estimating cell type composition is crucial for understanding tissue heterogeneity.
Cell type composition can correlate with clinical outcomes and individual characteristics.

Purpose of the Study:

To develop a novel method for cell type distance quantification accounting for dependencies.
To apply this method for association analysis between cell type composition and clinical data.
To assess the prognostic value of immune cell composition in colorectal cancer.

Main Methods:

Proposed a new method to quantify distances between cell types, considering their dependencies.
Utilized this distance metric for association analysis with clinical outcomes and genetic variants.
Applied the method to colorectal cancer tumor samples, analyzing immune cell composition against survival and SNP genotypes.

Main Results:

Immune cell type composition in colorectal cancer tumors demonstrates significant prognostic value.
The proposed distance metric improved survival time prediction accuracy compared to methods ignoring cell type dependencies.
Single nucleotide polymorphisms (SNPs) associated with survival time were enriched among those linked to immune cell composition.

Conclusions:

The developed method effectively quantifies cell type distances, accounting for interdependencies.
Immune cell composition is a valuable prognostic biomarker in colorectal cancer.
Integrating cell type composition analysis with genetic data offers deeper insights into cancer biology and patient outcomes.