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

Combinatorial Gene Control

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...

Co-activators and Co-repressors

Co-activators and Co-repressors

Gene transcription is regulated by the synergistic action of several proteins that form a complex at a gene regulatory site. This is observed in eukaryotes, where the regulation of gene expression is a complex process. Regulatory proteins in eukaryotes can broadly be classified into two types – regulators that bind directly to specific DNA sequences and co-regulators that associate with regulatory proteins but cannot directly bind to the DNA. These co-regulators are further divided into...

Cooperative Binding of Transcription Regulators

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome. Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...

Ribosome Profiling

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...

General Transcription Factors

General Transcription Factors

Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...

Master Transcription Regulators

Master Transcription Regulators

Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...

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

Application of large language models to the annotation of cell lines and mouse strains in genomics data.

bioRxiv : the preprint server for biology·2026

Same author

Thiorphan reprograms neurons to promote functional recovery after spinal cord injury.

Nature·2025

Same author

Global partnerships in rare disease research.

Disease models & mechanisms·2025

Same author

Revealing function-altering MECP2 mutations in individuals with autism spectrum disorder using yeast and Drosophila.

Genetics·2025

Same author

Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics.

PLoS computational biology·2025

Same journal

A human-specific genetic modifier reconfigures large-scale cortical network dynamics underlying behavioral performance.

bioRxiv : the preprint server for biology·2026

Same journal

<i>Staphylococcus aureus</i> uses a eukaryotic-like uridyltransferase to make UDP-GlcNAc for cell wall synthesis.

bioRxiv : the preprint server for biology·2026

Same journal

Dynamic redistribution of eIF4F controls cap-dependent translation initiation.

bioRxiv : the preprint server for biology·2026

Same journal

When does additional information improve accuracy of RNA secondary structure prediction?

bioRxiv : the preprint server for biology·2026

Same journal

Normative brain-state trajectories reveal deviation from healthy aging in Alzheimer's disease.

bioRxiv : the preprint server for biology·2026

Same journal

Noradrenergic infraslow rhythm during sleep is the critical link between heart-rate dynamics and memory consolidation.

bioRxiv : the preprint server for biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 29, 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

Identifying Reproducible Transcription Regulator Coexpression Patterns with Single Cell Transcriptomics.

Alexander Morin^1,2,3, C Pan Chu^1,2,3, Paul Pavlidis^1,2

¹Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada.

Biorxiv : the Preprint Server for Biology

|April 1, 2024

Summary

This summary is machine-generated.

This study maps gene expression coordination with human and mouse transcription regulators (TRs) using single-cell RNA sequencing data. Findings reveal reproducible coexpression patterns, aiding in identifying gene regulatory interactions across species.

More Related Videos

Reusable Single Cell for Iterative Epigenomic Analyses

Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Related Experiment Videos

Last Updated: Jun 29, 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

Reusable Single Cell for Iterative Epigenomic Analyses

Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Single-cell transcriptomics enables detailed study of gene transcription regulation.
Understanding transcription regulator (TR) gene partners is crucial for deciphering cellular processes.

Purpose of the Study:

To identify gene partners coexpressed with human and mouse transcription regulators (TRs) across diverse biological contexts.
To assess the consistency and reproducibility of TR coexpression profiles within and across species.

Main Methods:

Assembled 120 human and 103 mouse single-cell RNA sequencing datasets (>28 million cells).
Constructed single-cell coexpression networks for human and mouse TRs.
Prioritized reproducible coexpression patterns across cell types and species.
Integrated coexpression data with ChIP-seq information to identify candidate regulatory interactions.

Main Results:

Developed single-cell coexpression rankings for each TR, recovering known targets.
Demonstrated that aggregated coexpression data rivals ChIP-seq in identifying TR targets.
Identified candidate regulatory interactions supported by both coexpression and ChIP-seq data across species.
Highlighted interactions for the neural TR ASCL1.

Conclusions:

Aggregated single-cell coexpression networks provide a robust method for identifying TR gene partners.
Cross-species analysis of coexpression patterns enhances the reliability of identified regulatory interactions.
The compiled information serves as a valuable resource for the research community, exemplified by ASCL1 interactions.