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Related Concept Videos

Transcription Factors02:16

Transcription Factors

82.2K
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...
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General Transcription Factors01:30

General Transcription Factors

6.7K
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...
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Combinatorial Gene Control02:33

Combinatorial Gene Control

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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...
9.5K
Chromatin Immunoprecipitation- ChIP02:36

Chromatin Immunoprecipitation- ChIP

12.1K
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...
12.1K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

2.4K
2.4K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

7.1K
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...
7.1K

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Related Experiment Video

Updated: Jan 13, 2026

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

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

Linan Zhang1, April Sagan2, Bin Qin3

  • 1Department of Applied Mathematics, School of Mathematics and Statistics, Ningbo University, Ningbo, Zhejiang 315211, China.

Nucleic Acids Research
|January 12, 2026
PubMed
Summary

We developed STAN, a computational method to map transcription factor (TF) activity within tissues using spatial transcriptomics. This reveals how TF networks influence cell identity and spatial organization in various diseases and biological contexts.

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Area of Science:

  • Computational Biology
  • Genomics
  • Systems Biology

Background:

  • Transcription factors (TFs) regulate cellular responses and are influenced by microenvironments.
  • Spatial transcriptomics (ST) offers insights into tissue microenvironments but has not been fully leveraged to infer TF activity.
  • Understanding TF roles in cell identity and spatial organization is crucial.

Purpose of the Study:

  • To develop a computational approach for inferring spatially resolved TF activity from ST data.
  • To investigate the relationship between TF activity, cell identity, and tissue architecture.
  • To enhance the analytical capabilities of ST data for biological discovery.

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 histological features.
  • Applied STAN to diverse ST datasets (lymph node, brain, breast cancer, glioblastoma).

Main Results:

  • STAN successfully predicted spot-specific TF activities.
  • Identified TFs associated with distinct cell types, spatial regions, and pathological zones.
  • Revealed TF involvement in ligand-receptor interactions within tissue microenvironments.

Conclusions:

  • STAN effectively infers TF activity from ST data, providing spatial context.
  • The approach enhances the utility of ST for understanding TF roles in cellular function and tissue organization.
  • Highlights the intricate interplay between TF networks and spatial biology across various biological systems.