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Cells respond to many types of information, often through receptor proteins positioned on the membrane. They respond to chemical signals, such as hormones, neurotransmitters, and other signaling molecules, initiating a series of molecular reactions to produce an appropriate response. This is called signal transduction. Cells also coordinate different responses elicited by the same signaling molecule via mediators, allowing molecular cross-talk.
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Once a ligand binds to a receptor, the signal is transmitted through the membrane and into the cytoplasm. The continuation of a signal in this manner is called signal transduction. Signal transduction only occurs with cell-surface receptors, which cannot interact with most components of the cell, such as DNA. Only internal receptors can interact directly with DNA in the nucleus to initiate protein synthesis. When a ligand binds to its receptor, conformational changes occur that affect the...
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Imaging Spatial Reorganization of a MAPK Signaling Pathway Using the Tobacco Transient Expression System
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Protocol for reconstructing spatially aware receptor-TF-target signaling cascades using spatial transcriptomics.

Yao Li1, Xiaobin Liu2, Xinkai Yan3

  • 1BGI Research, Qingdao 266555, China; Department of Food Science and Nutrition, The Hong Kong Polytechnic University, Hong Kong 999077, China.

STAR Protocols
|December 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces SpaGRN, a new method for gene regulatory network analysis that considers spatial information. It reconstructs spatially aware signaling pathways in tissues, improving understanding of complex biological systems.

Keywords:
BioinformaticsComputer sciencesDevelopmental biologySingle cellSystems biology

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Area of Science:

  • Spatial transcriptomics
  • Systems biology
  • Bioinformatics

Background:

  • Gene regulatory networks (GRNs) are crucial for cellular function.
  • Existing GRN inference methods often overlook spatial context in transcriptomics data.
  • Understanding spatial relationships is vital for complex tissues like embryos and tumors.

Purpose of the Study:

  • To present a novel protocol for reconstructing spatially aware gene regulatory networks.
  • To integrate extracellular signaling and spatial dependencies for improved GRN inference.
  • To enable systematic mapping of signaling pathways in complex biological samples.

Main Methods:

  • Utilizing the SpaGRN statistical framework.
  • Integrating spatial autocorrelation and co-expression analysis.
  • Performing cis-regulatory motif enrichment and receptor-associated regulon inference.
  • Employing 3D visualization techniques for spatial data representation.

Main Results:

  • Successful reconstruction of receptor-transcription factor-target regulatory cascades with spatial awareness.
  • Demonstration of SpaGRN's capability in analyzing complex tissues.
  • Development of a comprehensive protocol for spatial GRN inference.

Conclusions:

  • SpaGRN provides a robust framework for spatially resolved GRN analysis.
  • The protocol facilitates a deeper understanding of signaling pathways in developmental and disease contexts.
  • This approach enhances the analysis of spatial transcriptomics data by incorporating spatial constraints.