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

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...
6.7K
Combinatorial Gene Control02:33

Combinatorial Gene Control

9.5K
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 Position Affects Gene Expression02:35

Chromatin Position Affects Gene Expression

24.6K
Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
Topologically Associated Domains (TADs)
The 3-dimensional positioning of chromatin in the nucleus influences the...
24.6K
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

16.2K
Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
16.2K
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

5.4K
5.4K
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...
82.2K

You might also read

Related Articles

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

Sort by
Same author

Multifunctional spatiotemporally programmed microneedle patches for the reconstruction of antibacterial and immunoregenerative homeostasis in periodontitis therapy.

Acta biomaterialia·2026
Same author

Genotype epigenome phenotype integration reveals peripheral immune contributions to type I bipolar disorder.

Nature communications·2026
Same author

Clotrimazole-Mediated Autophagy to Protect Against Cisplatin-Induced Ototoxicity via the AMPK/mTOR/TFEB Pathway in Mice.

Antioxidants & redox signaling·2026
Same author

Generalized SIMEX Method: Polynomial Approximation for Extrapolation.

Statistics in medicine·2026
Same author

Corrigendum to "Inhibition of CISD1 attenuates cisplatin-induced hearing loss in mice via the PI3K and MAPK pathways" [Biochem. Pharmacol. 223 (2024) 116132].

Biochemical pharmacology·2026
Same author

Machine Learning-Assisted Detection of Phosgene and Acetyl Chloride via a Dual-Probe Fluorescent Platform with Differential Reactivity.

Analytical chemistry·2026
Same journal

STED: flexible cross-modal topic modeling infers cell-type-specific regulatory landscapes from bulk epigenomics.

Briefings in bioinformatics·2026
Same journal

A knowledge-guided deep learning framework for quantitative nucleic acid testing.

Briefings in bioinformatics·2026
Same journal

Optimal transport for label transfer in single-cell multi-omics integration.

Briefings in bioinformatics·2026
Same journal

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same journal

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026
Same journal

Transformers for single-cell RNA sequencing: a survey.

Briefings in bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jan 9, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

624

SpaTM: topic models for inferring spatially informed transcriptional programs.

Adrien Osakwe1, Wenqi Dong2, Qihuang Zhang1,3

  • 1Quantitative Life Sciences Program, McGill University, 550 Sherbrooke W., Montreal, QC, H3A 1E3, Canada.

Briefings in Bioinformatics
|December 8, 2025
PubMed
Summary
This summary is machine-generated.

The Spatial Topic Model (SpaTM) offers a unified framework for analyzing spatial transcriptomics data. It enables both annotation-guided and annotation-free approaches, improving interpretability and performance in tasks like spatial domain identification and gene program discovery.

Keywords:
Bayesian inferenceneighbour predictionspatial segmentationspatial transcriptomicstopic modelling

More Related Videos

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

2.9K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.2K

Related Experiment Videos

Last Updated: Jan 9, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

624
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

2.9K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.2K

Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Spatial transcriptomics integrates gene expression with tissue architecture, crucial for understanding biological systems.
  • Current analysis methods often require multiple tools and lack interpretability for both guided and unguided spatial data exploration.

Purpose of the Study:

  • To introduce the Spatial Topic Model (SpaTM), a novel framework for analyzing spatial transcriptomics data.
  • To provide a unified and interpretable approach for both annotation-guided and annotation-free analysis of spatial transcriptomes.
  • To enhance gene program discovery and spatial domain inference.

Main Methods:

  • Developed SpaTM, a topic-modelling framework for spatial transcriptomics.
  • Benchmarked SpaTM against state-of-the-art methods for spatial label prediction and clustering.
  • Applied SpaTM to analyze transcriptional programs in human brain and ductal carcinoma samples.

Main Results:

  • SpaTM demonstrated competitive performance in spatial label prediction and clustering.
  • The framework successfully learned gene programs representing histology and inferred spatial domains.
  • SpaTM facilitated the integration of spatial transcriptomics tasks and analysis of large-scale atlases.

Conclusions:

  • SpaTM offers a unified, interpretable framework for spatial transcriptomics analysis.
  • The model enables competitive performance across multiple tasks while inferring biologically relevant gene programs.
  • SpaTM enhances the understanding of tissue architecture and disease mechanisms through integrated spatial analysis.