Functional module detection through integration of single-cell RNA sequencing data with protein-protein interaction...

Area of Science:

• Molecular Biology
• Bioinformatics
• Systems Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) enables cellular-level transcriptional analysis.
• scRNA-seq data reveals distinct cell clusters representing different transcriptional states.

Purpose of the Study:

• To present SCPPIN, a novel method for integrating scRNA-seq data with protein-protein interaction networks.
• To detect active biological modules within cells of varying transcriptional states.

Main Methods:

• Clustering scRNA-seq data and identifying differentially expressed genes.
• Constructing node-weighted protein-protein interaction networks.
• Applying an exact Steiner-tree approach to find maximum-weight connected subgraphs.

Main Results:

• SCPPIN expands differential gene expression analysis with protein interaction data.
• Different transcriptional states exhibit distinct, significantly enriched protein-protein interaction network subnetworks representing biological pathways.
• Identified proteins with crucial functions (e.g., receptors) that were not differentially expressed, offering insights beyond standard scRNA-seq analysis.

Conclusions:

• SCPPIN systematically analyzes scRNA-seq data by integrating protein interaction information.
• Detected modules aid in identifying and hypothesizing biological functions for specific cell clusters.
• The method reveals unexpected proteins involved in pathways, applicable across organisms and tissues.