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Related Concept Videos

Reporter Genes02:11

Reporter Genes

Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
Commonly used reporter...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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...
Combinatorial Gene Control02:33

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...
Gene Duplication and Divergence02:37

Gene Duplication and Divergence

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 characterized.
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

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Related Experiment Video

Updated: Jun 16, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

geneSCOPE: gene spatial co-occurrence of pairwise expression.

Shicheng Zhang1, Koichi Saeki2, Hiroshi Haeno2

  • 1Graduate School of Biological Sciences, Tokyo University of Science, Yamazaki 2669, Noda City, Chiba 278-0022, Japan.

Briefings in Bioinformatics
|June 14, 2026
PubMed
Summary

geneSCOPE is a new framework for spatial transcriptomics that uses spatial statistics and network analysis to reveal gene expression patterns. It improves cross-study comparability and identifies cell-cell interactions in tissues like colorectal cancer.

Keywords:
Lee’s LMorisita’s Iδcell–cell interactionsgeneSCOPEspatial transcriptomics

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Related Experiment Videos

Last Updated: Jun 16, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Spatial transcriptomics enables context-dependent gene expression analysis.
  • Existing methods lack consistent scale accounting and rely on user-defined graphs, limiting comparability.
  • There is a need for scalable and interpretable spatial analysis frameworks.

Purpose of the Study:

  • To introduce geneSCOPE (gene Spatial Co-Occurrence of Pairwise Expression), a novel framework for gene-centric spatial analysis.
  • To integrate spatial statistics and network analysis for capturing measurement scale and spatial information.
  • To enhance cross-study comparability and interpretability in spatial transcriptomics.

Main Methods:

  • Molecules binned on a grid with scale determined by Morisita's Iδ-width curves.
  • Pairwise spatial associations quantified using Lee's L statistic.
  • Spatial gene network assembly and module identification via consensus clustering.
  • Cell-cell interaction identification based on spatial association and co-expression.

Main Results:

  • geneSCOPE applied to human colorectal cancer and lymph node data.
  • Identified spatial gene modules corresponding to microanatomical compartments.
  • Highlighted intercellular interactions at tumor-stroma interfaces, including LGR5 and C3 patterns.
  • Demonstrated superior concordance with known gene interactions compared to other methods.

Conclusions:

  • geneSCOPE offers a scalable, interpretable, and cross-study comparable framework for spatial transcriptomics.
  • The method effectively captures spatial gene expression patterns and cell-cell interactions.
  • geneSCOPE advances the analysis of tissue microenvironments and disease contexts.