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

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

You might also read

Related Articles

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

Sort by

Same author

Evaluation of analysis modes for RNA coexpression in single-cell and bulk tissue.

bioRxiv : the preprint server for biology·2026

Same author

Persistent hindrances to data re-use in single-cell genomics.

Scientific data·2026

Same author

Molecular Characterization of the Progressive Landscape of Depression.

bioRxiv : the preprint server for biology·2026

Same author

Re-thinking neuropeptide therapeutics: What Neuropeptide Y and Galanin teach us about stress, resilience, and drug design.

Neuropeptides·2026

Same author

The α5-Containing GABA<sub>A</sub> Receptor as a Target for Disorders of Altered Dendritic Inhibition.

Biological psychiatry·2026

Same author

Peripheral GABAergic markers in late-life depression: Links to clinical outcomes.

Brain research·2026

Search research articles

Related Experiment Video

Updated: Nov 12, 2025

Author Spotlight: Enhancing Drug Discovery - Development of Automated, Standardized Protocols for Nuclei Extraction from Frozen Tissues

Author Spotlight: Enhancing Drug Discovery - Development of Automated, Standardized Protocols for Nuclei Extraction from Frozen Tissues

Published on: July 28, 2023

Estimating and Correcting for Off-Target Cellular Contamination in Brain Cell Type Specific RNA-Seq Data.

Jordan Sicherman^1,2, Dwight F Newton^3,4, Paul Pavlidis^2,5,6

¹Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada.

Frontiers in Molecular Neuroscience

|March 22, 2021

Summary

Analyzing minor cell populations is difficult. This study introduces a computational method to detect and control off-target contamination in single-cell type RNA sequencing (sctRNA-seq), improving gene expression analysis.

Keywords:

LCM-seq RNA-seq TRAP-seq brain contamination single cell

More Related Videos

Comprehensive Analysis of Transcription Dynamics from Brain Samples Following Behavioral Experience

Comprehensive Analysis of Transcription Dynamics from Brain Samples Following Behavioral Experience

Published on: August 26, 2014

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Related Experiment Videos

Last Updated: Nov 12, 2025

Author Spotlight: Enhancing Drug Discovery - Development of Automated, Standardized Protocols for Nuclei Extraction from Frozen Tissues

Author Spotlight: Enhancing Drug Discovery - Development of Automated, Standardized Protocols for Nuclei Extraction from Frozen Tissues

Published on: July 28, 2023

Comprehensive Analysis of Transcription Dynamics from Brain Samples Following Behavioral Experience

Comprehensive Analysis of Transcription Dynamics from Brain Samples Following Behavioral Experience

Published on: August 26, 2014

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Area of Science:

Molecular Genomics
Computational Biology
Transcriptomics

Background:

Profiling rare cellular populations presents significant challenges in molecular genomics.
Single-cell RNA sequencing (scRNA-seq) offers insights but faces practical and analytical hurdles.
Single-cell type RNA sequencing (sctRNA-seq) enriches targeted cells but risks contamination.

Purpose of the Study:

To develop and validate a computational approach for estimating and controlling off-target cell type contamination in sctRNA-seq datasets.
To assess the impact of contamination on downstream analyses.
To enhance the reliability of transcriptomic profiling from enriched cell populations.

Main Methods:

Leveraged existing single-cell sequencing datasets.
Applied a computational strategy to quantify off-target mRNA contamination.
Integrated contamination covariates into differential gene expression models.

Main Results:

Most sctRNA-seq datasets exhibited some level of off-target mRNA contamination.
Incorporating contamination covariates improved differential expression model quality.
The method led to the discovery of more differentially expressed genes in case/control comparisons.

Conclusions:

Off-target contamination is prevalent in sctRNA-seq data.
The developed computational method effectively detects and controls this contamination.
This approach enhances the accuracy and power of differential gene expression analyses in sctRNA-seq studies.