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Cell signaling characterization for spatial transcriptomics (ST) data using network analysis.

Azka Javaid1, H Robert Frost1

  • 1Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA.

Complex Networks & Their Applications. International Conference on Complex Networks and Their Applications
|June 13, 2025
PubMed
Summary
This summary is machine-generated.

We developed a network analysis method to map cell-cell communication in spatial transcriptomics (ST) data. This approach quantifies signaling activity by modeling ligand-receptor interactions, revealing biologically plausible communication patterns.

Keywords:
CentralityNetwork analysisSpatial Transcriptomics

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Area of Science:

  • Computational Biology
  • Spatial Transcriptomics
  • Systems Biology

Background:

  • Spatial Transcriptomics (ST) enables gene expression analysis within tissue context.
  • Understanding cell-cell communication is crucial for interpreting tissue architecture and function.
  • Existing methods may not fully capture the spatial dynamics of ligand-receptor interactions.

Purpose of the Study:

  • To introduce a novel network analysis-based method for quantifying cell-cell communication in ST data.
  • To model ligand-receptor interactions using a weighted, directed network approach.
  • To validate the method's ability to capture spatial signaling heterogeneity.

Main Methods:

  • Constructed a network model where nodes are ST locations and edge weights reflect ligand-receptor expression and spatial distance.
  • Utilized weighted in-degree centrality to quantify signaling activity for specific interactions.
  • Validated the method on a real ST dataset comparing it with five existing strategies.

Main Results:

  • The method successfully captures simultaneous expression heterogeneity of ligands and receptors.
  • Generated biologically plausible cell communication profiles for Wnt3-Fzd1, Ephb1-Efnb3, and Ptprc-Cd22 interactions.
  • Demonstrated the importance of using low-dimensional embeddings for network modeling in ST data.

Conclusions:

  • The network analysis approach provides a robust framework for inferring cell-cell communication from ST data.
  • The method effectively models spatial signaling, considering both expression levels and physical proximity.
  • Low-dimensional gene embeddings are critical for building accurate network models in spatial transcriptomics.