Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

In vitro NIH3T3 mouse embryonic fibroblast cell model does not predict AAV2 or AAVdj-mediated cell transformation.

Toxicology and applied pharmacology·2025

Same author

Thymic lymphoma detection in RORγ knockout mice using 5-hydroxymethylcytosine profiling of circulating cell-free DNA.

Toxicology and applied pharmacology·2023

Same author

A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.

Inflammatory bowel diseases·2022

Same author

The MicroRNA-based Liquid Biopsy Improves Early Assessment of Lethal Acetaminophen Poisoning: A Case Report.

The American journal of case reports·2020

Same author

Dynamic changes in epithelial cell morphology control thymic organ size during atrophy and regeneration.

Nature communications·2019

Same author

Network assessment of demethylation treatment in melanoma: Differential transcriptome-methylome and antigen profile signatures.

PloS one·2018

Search research articles

Related Experiment Video

Updated: Jul 10, 2026

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

Electronically subtracting expression patterns from a mixed cell population.

Mark M Gosink¹, Howard T Petrie, Nicholas F Tsinoremas

¹Scientific Computing, Scripps Florida, 5353 Parkside Dr Jupiter, FL 33458, USA. gosink@scripps.edu

Bioinformatics (Oxford, England)

|October 25, 2007

Summary

This summary is machine-generated.

This study introduces a novel computational method to electronically subtract gene expression from mixed cell samples. This technique accurately identifies gene expression patterns in minor cell populations, aiding biomarker discovery.

More Related Videos

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Published on: January 19, 2017

Simple, Affordable, and Modular Patterning of Cells using DNA

Simple, Affordable, and Modular Patterning of Cells using DNA

Published on: February 24, 2021

Related Experiment Videos

Last Updated: Jul 10, 2026

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

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Published on: January 19, 2017

Simple, Affordable, and Modular Patterning of Cells using DNA

Simple, Affordable, and Modular Patterning of Cells using DNA

Published on: February 24, 2021

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Biological samples often contain multiple cell types.
Identifying specific cell-type biomarkers is challenging and labor-intensive.
Current methods require physical separation of cell populations.

Purpose of the Study:

To develop a computational method for isolating gene expression profiles from mixed cell populations.
To enable the identification of biomarkers in minor or difficult-to-isolate cell types.
To improve the analysis of complex biological samples.

Main Methods:

Developed a gene expression subtraction methodology.
Applied the method to simulated and real-world microarray data.
Utilized computational subtraction to deconvolve mixed cell expression profiles.

Main Results:

The method accurately identifies gene expression in cell types comprising as little as 5% of a mixture.
Successfully re-analyzed microarray data from viral infections and T-cell populations.
Significantly enhanced the identification of genes within specific subcomponent cell populations.

Conclusions:

The developed methodology offers a powerful tool for analyzing gene expression in complex biological samples.
This approach reduces the need for extensive laboratory separation of cells.
Facilitates biomarker discovery and a deeper understanding of cellular contributions in mixed tissues.