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Interpretable niche-based cell‒cell communication inference using multi-view graph neural networks.

Juntian Qi1,2,3, Zhengchao Luo4, Chuan-Yun Li5,6,7

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This study introduces STCase, a new tool for analyzing cell-cell communication at the single-cell level. It reveals niche-specific interactions crucial for understanding complex biological systems and diseases.

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Area of Science:

  • Computational Biology
  • Genomics
  • Systems Biology

Background:

  • Cell-cell communication (CCC) is vital for biological systems.
  • Existing CCC inference methods often overlook niche heterogeneity within cell types.
  • Understanding niche-specific interactions is crucial for biological and disease contexts.

Purpose of the Study:

  • To develop a novel tool, STCase, for single-cell/spot level CCC inference using spatial transcriptomics.
  • To identify distinct cellular niches and their specific CCC events.
  • To overcome limitations of current methods that aggregate cells by type.

Main Methods:

  • Spatial Transcriptomics-based cell-cell Communication And Subtype Exploration (STCase) tool.
  • Utilizes an interpretable multi-view graph neural network with CCC-aware attention.
  • Analyzes CCC events at the single-cell/spot resolution.

Main Results:

  • STCase accurately captures known immune-related CCC events in human bronchial glands.
  • Identified three distinct niches in oral squamous cell carcinoma, previously obscured by other methods.
  • Discovered niche-specific CCC events potentially impacting oral cancer prognosis.

Conclusions:

  • STCase provides a powerful method for high-resolution CCC analysis.
  • The tool effectively identifies niche heterogeneity and its role in biological processes.
  • Findings highlight the importance of niche-specific CCC in disease progression and suggest new avenues for therapeutic strategies.