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This study introduces a user-friendly algorithm and protocol to analyze cell communication networks (interactomes) using single-cell RNA sequencing (scRNA-seq). The method identifies how these interactions change during viral infection in mouse lungs.

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Area of Science:

  • Cellular biology
  • Immunology
  • Bioinformatics

Background:

  • Mammalian organs rely on complex cell-to-cell signaling via ligand-receptor interactions for development and repair.
  • Single-cell RNA sequencing (scRNA-seq) generates vast data for studying these interactions, but analysis tools are often inaccessible to bench scientists.
  • Existing computational methods require advanced programming skills, limiting broader adoption.

Purpose of the Study:

  • To develop an intuitive, quantitative algorithm and optimized protocol for constructing and comparing cellular interactomes.
  • To address the gene dropout issue in scRNA-seq data and enable cost-efficient cell type representation.
  • To identify known and novel ligand-receptor interactions and their alterations during viral infection.

Main Methods:

  • Developed a quantitative, intuitive algorithm for interactome construction and comparison.
  • Optimized an experimental protocol for scRNA-seq data acquisition.
  • Employed cell lineage normalization after cell sorting for cost-efficient cell type representation.
  • Utilized a numeric representation of ligand-receptor interactions to identify significant changes.

Main Results:

  • A minimum of 90 cells per cell type in scRNA-seq data effectively compensates for gene dropout, achieving sensitivity comparable to bulk RNA sequencing.
  • The algorithm successfully constructed and compared interactomes in control and Sendai virus-infected mouse lungs.
  • Identified known and potential novel ligand-receptor interactions, revealing changes induced by viral infection.

Conclusions:

  • The developed experimental and computational approaches provide an accessible method for dissecting intercellular communication.
  • This approach can identify critical ligand-receptor interactions and their dynamics in response to stimuli like viral infection.
  • The methodology is generalizable to various organs and human samples, facilitating broader research in cell signaling.