scMLnet

scMLnet infers multilayer signaling networks from single-cell RNA sequencing (scRNA-seq) data to determine how cellular microenvironments regulate gene expression.

Key Features:

• Multilayer Network Construction: Constructs multilayer signaling networks by integrating intercellular ligand-receptor interactions with intracellular receptor–transcription factor (TF) and TF–target gene relationships.
• Cell-type-specific Integration: Integrates cell-type specific gene expression data with prior knowledge of biological networks and statistical inference methods to identify active pathways.
• Visualization Capabilities: Provides visualization of constructed signaling pathways between a central cell and its neighboring cells.
• Application to COVID-19 Datasets: Applied to scRNA-seq datasets from COVID-19 patients to investigate microenvironmental regulation of ACE2 and its correlations with cytokines EGF, IFN-γ, and TNF-α in the context of SARS-CoV-2 infection.
• Experimental Validation: Predictions of cytokine-driven elevation of ACE2 were experimentally validated in human lung cells and related signaling pathways were shown to be activated during SARS-CoV-2 infection.
• Methodological Comparison: Summarizes and compares methods for inferring inter-/intra-cellular signaling networks from scRNA-seq data.
• Implementation: Implemented using R (version 3.6.0) and Python (version 3.7).

Scientific Applications:

• Gene Expression Regulation: Models intercellular communications and intracellular gene regulatory networks to elucidate how microenvironments influence gene expression at single-cell resolution.
• Disease Mechanism Elucidation: Identifies key regulators and pathways, informing studies of disease progression and potential interventions such as anti-cytokine strategies in COVID-19.

Methodology:

Constructs and analyzes multilayer signaling networks from scRNA-seq by integrating ligand–receptor interactions with receptor–TF and TF–target relationships using cell-type specific expression, prior biological network knowledge, and statistical inference methods; implemented in R 3.6.0 and Python 3.7.