Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Transcription Factors02:16

Transcription Factors

75.8K
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...
75.8K
General Transcription Factors01:30

General Transcription Factors

5.2K
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...
5.2K
Combinatorial Gene Control02:33

Combinatorial Gene Control

8.3K
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...
8.3K
Chromatin Immunoprecipitation- ChIP02:36

Chromatin Immunoprecipitation- ChIP

11.1K
Chromatin immunoprecipitation, or ChIP, is an antibody-based technique used to identify sites on DNA that bind to transcription factors of interest or histone proteins. It also helps determine the type of histone modifications such as acetylation, phosphorylation, or methylation.
Types of ChIP
ChIP can be divided into two types - X-ChIP and N-ChIP. X-ChIP involves in vivo cross-linking of histones and regulatory proteins to DNA, fragmenting the DNA by sonication, and isolating the protein-DNA...
11.1K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

2.0K
2.0K
RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

9.2K
Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...
9.2K

You might also read

Related Articles

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

Sort by
Same author

Alteration of individual morphological brain networks in preschool children with autism spectrum disorder.

Brain imaging and behavior·2026
Same author

Layered Copper-Anthraquinone Coordination Polymer Cathode Leveraging Dual-Redox Sites and Facilitated Ion Diffusion for High-Performance Lithium-Ion Batteries.

Angewandte Chemie (International ed. in English)·2026
Same author

Altered static and dynamic functional network connectivity in Parkinson's disease: A multisite functional magnetic resonance imaging study.

IBRO neuroscience reports·2026
Same author

The effectiveness of a plant-based milk with fermented brown rice on constipation symptoms via gut microbiota modulation: a double-blind randomized controlled trial.

European journal of nutrition·2026
Same author

Self-Adaptive AdamW-Guided Optimization: A Learning-Driven Metaheuristic for Solving Complex Real-World Engineering Problems.

Entropy (Basel, Switzerland)·2026
Same author

Liver transplantation promotes early neural reorganization in minimal hepatic encephalopathy: a longitudinal resting state fMRI study.

Metabolic brain disease·2026

Related Experiment Video

Updated: Jun 21, 2025

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations
11:36

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

Published on: April 21, 2023

2.0K

STAN, a computational framework for inferring spatially informed transcription factor activity.

Linan Zhang, April Sagan, Bin Qin

    Biorxiv : the Preprint Server for Biology
    |July 9, 2024
    PubMed
    Summary

    We developed STAN, a computational method to map transcription factor (TF) activity within tissues using spatial transcriptomics. STAN reveals how TF networks influence cell identity and function in their local microenvironment.

    More Related Videos

    Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
    12:54

    Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

    Published on: March 7, 2018

    13.5K
    Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences
    11:25

    Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences

    Published on: February 11, 2019

    7.9K

    Related Experiment Videos

    Last Updated: Jun 21, 2025

    Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations
    11:36

    Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

    Published on: April 21, 2023

    2.0K
    Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
    12:54

    Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

    Published on: March 7, 2018

    13.5K
    Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences
    11:25

    Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences

    Published on: February 11, 2019

    7.9K

    Area of Science:

    • Genomics
    • Computational Biology
    • Cellular Biology

    Background:

    • Transcription factors (TFs) regulate cellular responses to environmental and signaling cues.
    • Cellular fate and function are influenced by neighboring cells and TF activity.
    • Spatial transcriptomics (ST) provides tissue-level mRNA expression but hasn't been fully utilized to estimate TF activity.

    Purpose of the Study:

    • To develop a computational method for predicting spatially informed TF activity from ST data.
    • To integrate TF-target gene information, gene expression, and spatial data.
    • To reveal the role of TF networks in cellular identity and spatial organization.

    Main Methods:

    • Introduced STAN (Spatially informed Transcription factor Activity Network), a linear mixed-effects model.
    • Integrated TF-target gene priors, mRNA expression, spatial coordinates, and imaging data.
    • Applied STAN to lymph node, breast cancer, and glioblastoma ST datasets.

    Main Results:

    • STAN successfully predicted spot-specific TF activities.
    • Identified TFs associated with distinct cell types, spatial domains, and pathological regions.
    • Revealed TF associations with ligand-receptor interactions in the tissue microenvironment.

    Conclusions:

    • STAN enhances the utility of ST data for understanding TF roles in cellular contexts.
    • The method elucidates the interplay between TF activity and spatial organization.
    • STAN provides a framework for dissecting TF-driven cellular heterogeneity in tissues.