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

Position-effect Variegation02:32

Position-effect Variegation

6.3K
In 1928, a German botanist Emil Heitz observed the moss nuclei with a DNA binding dye. He observed that while some chromatin regions decondense and spread out in the interphase nucleus, others do not. He termed them euchromatin and heterochromatin, respectively. He proposed that the heterochromatin regions reflect a functionally inactive state of the genome. It was later confirmed that heterochromatin is transcriptionally repressed, and euchromatin is transcriptionally active chromatin.
6.3K
Gene Duplication and Divergence02:37

Gene Duplication and Divergence

6.0K
The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are...
6.0K
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

17.3K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
17.3K
Regulated mRNA Transport02:22

Regulated mRNA Transport

6.2K
In eukaryotes, transcription and translation are compartmentalized; an mRNA is first synthesized in the nucleus and then selectively transported to the cytoplasm for protein synthesis. Before transport, a pre-mRNA undergoes several steps of post-transcriptional modifications including splicing, 5' capping, and the addition of a poly-adenine tail. Various proteins bind to the pre-mRNA during these modifications. The mRNA transport takes place with the help of multiple proteins playing...
6.2K
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

872
The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
872
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

2.9K
2.9K

You might also read

Related Articles

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

Sort by
Same author

Meta-analysis of the clinical efficacy of microcatheter-assisted trabeculotomy in the treatment of glaucoma.

Journal of investigative medicine : the official publication of the American Federation for Clinical Research·2026
Same author

Interpretable modality-aware mapping of gene regulation in single-cell multiomics with scMAGCA.

Nature communications·2026
Same author

Bridging sequence-structure motifs and genetic variants for genome-wide dynamic RNA-protein interaction profiling.

Nature communications·2026
Same author

Olfactory Decline in Elderly at High Altitudes: A Narrative Review.

International journal of general medicine·2026
Same author

Gero-LLM: A Multimodal Large Language Model for Geroprotector Discovery via Cross-Modal Differentiated Mutual Learning.

IEEE journal of biomedical and health informatics·2026
Same author

Accurate and interpretable ADMET prediction: Integrating structural, geometric, and global molecular context representations.

European journal of medicinal chemistry·2026

Related Experiment Video

Updated: Jun 6, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

4.8K

Accurate Spatial Heterogeneity Dissection and Gene Regulation Interpretation for Spatial Transcriptomics using Dual

Zhuohan Yu1, Yuning Yang2, Xingjian Chen3

  • 1School of Artificial Intelligence, Jilin University, Jilin, 130012, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|November 28, 2024
PubMed
Summary

A new method called stDCL uses dual graph contrastive learning to identify spatial domains and interpret gene regulation in spatial transcriptomics data, improving tissue microenvironment analysis.

Keywords:
dual graph contrastive learninggene regulationgraph contrastive learningspatial heterogeneity

More Related Videos

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis
07:40

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

99
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

641

Related Experiment Videos

Last Updated: Jun 6, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

4.8K
Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis
07:40

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

99
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

641

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Spatial transcriptomics preserves gene expression and spatial context for high-resolution tissue analysis.
  • Understanding tissue microenvironments requires methods capturing spatial heterogeneity and gene regulation.
  • Current methods struggle to simultaneously characterize spatial structure and gene regulation.

Purpose of the Study:

  • To develop a novel method, stDCL, for identifying spatial domains and interpreting gene regulation from spatial transcriptomics data.
  • To enhance the analysis of spatial transcriptomics by integrating gene expression and spatial information.
  • To overcome limitations of existing methods in spatial dissection and gene interpretation.

Main Methods:

  • stDCL employs a dual graph contrastive learning approach.
  • A graph embedding autoencoder adaptively integrates gene expression and spatial data.
  • Contrastive learning ensures latent embeddings reflect spatial distribution and cluster similarity.

Main Results:

  • stDCL demonstrates superior accuracy in identifying spatial domains compared to state-of-the-art methods on cortex datasets.
  • The method reconstructs spatial hierarchical structures and refines differential expression analysis.
  • stDCL reveals gene regulation, high-resolution spatial heterogeneity, and embryonic developmental patterns.

Conclusions:

  • stDCL effectively identifies spatial domains and interprets gene regulation in spatial transcriptomics data.
  • The method accurately captures spatial heterogeneity and biological mechanisms.
  • stDCL shows potential in disease association studies, such as annotating astrocyte subtypes in Alzheimer's disease.