A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases

Area of Science:

• Genomics
• Systems Biology
• Immunology

Background:

• Genomic medicine aims to identify biomarkers and therapeutic targets for complex diseases.
• Single-cell RNA sequencing (scRNA-seq) enables detailed analysis of gene expression across diverse cell types within organs.
• Complex diseases involve thousands of genes with variable expression across multiple cell types, posing challenges for traditional approaches.

Purpose of the Study:

• To develop and validate a systems-level strategy for prioritizing diagnostic biomarkers and therapeutic targets in complex diseases.
• To apply network tools to scRNA-seq data from arthritis models to identify cell-type-specific disease mechanisms.
• To investigate the potential of network centrality in prioritizing cell types and genes for therapeutic intervention.

Main Methods:

• Applied network tools to analyze scRNA-seq data from mouse arthritis models and human rheumatoid arthritis (RA).
• Constructed multicellular disease models (MCDMs) based on network analysis of disease-associated cell types and interactions.
• Validated the prioritization strategy using GWAS data, expression profiling from 13 diseases, and a mouse arthritis treatment study.

Main Results:

• Identified hundreds of cell-type-specific pathways, biomarkers, and drug targets in inflamed joints and lymph nodes.
• Demonstrated that network centrality of MCDM cell types correlates with genetic variant enrichment in RA, supporting its use for prioritization.
• Validated the strategy's potential across diverse diseases and in a preclinical arthritis model.

Conclusions:

• The developed network-based strategy effectively prioritizes diagnostic and therapeutic targets in complex diseases.
• Multicellular disease models offer a robust framework for understanding and targeting disease mechanisms at a systems level.
• This approach holds significant potential for advancing precision medicine by identifying optimal targets for patient treatment.