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Related Concept Videos

Reporter Genes02:11

Reporter Genes

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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.
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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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stGrads: decoding spatial gene expression gradients through proximity-driven analysis in complex tissues.

Yifan Fu1,2,3, Fan Zhang1, Feifan Zhang4

  • 1Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education; Key Laboratory of Innovation and Transformation of Advanced Medical Devices, Ministry of Industry and Information Technology; National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering); School of Engineering Medicine, Beihang University, No. 37 Xueyuan Road, Haidian District, 100191 Beijing, China.

Briefings in Bioinformatics
|April 30, 2026
PubMed
Summary
This summary is machine-generated.

Spatial transcriptomics reveals tissue heterogeneity. The new stGrads tool quantifies cell population influence on their niche, identifying spatial gradients and related genes in complex tissues.

Keywords:
gradient-related gene identificationspatial gradient modelingspatial transcriptomicsstGradstissue microenvironment

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Spatial transcriptomics enables exploration of gene expression heterogeneity within tissues.
  • Quantifying the impact of specific cell populations on their microenvironment is a significant challenge.

Purpose of the Study:

  • To introduce stGrads, a computational framework for characterizing spatial gradients induced by cell types.
  • To analyze gene expression and cell composition changes influenced by cellular proximity.

Main Methods:

  • stGrads integrates spatial proximity modeling with signal propagation for distance-dependent analysis.
  • The framework computes expression gradients, compositional shifts, and interaction strengths.
  • It is applicable to both spot-size and bin-size spatial transcriptomic data.

Main Results:

  • stGrads successfully identified disease-associated spatial gradients in multiple datasets.
  • The tool revealed local patterns of cellular responses and identified gradient-related genes.
  • Demonstrated the framework's utility in characterizing spatial interactions and functional responses.

Conclusions:

  • stGrads provides a generalizable and efficient method for analyzing spatial interactions in complex tissues.
  • The framework enhances understanding of cell-cell communication and tissue organization.
  • Facilitates downstream analysis of spatial transcriptomic data for biological discovery.