Single-cell network biology enabling cell-type-resolved disease genetics.

Area of Science:

• Computational biology
• Genomics
• Systems biology

Background:

• Gene network models are crucial for understanding biological functions and identifying disease genes.
• Interpreting disease genetics requires a cellular context, which traditional organism-level datasets lack.
• Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to distinguish cell states and understand disease-driving cellular biology.

Purpose of the Study:

• To present methods for inferring gene functional interaction networks using scRNA-seq data.
• To introduce a compendium of cell-type-specific gene networks (CGNs) as a resource for disease genetics.
• To highlight the potential of single-cell network biology in mapping the gene-cell-disease axis.

Main Methods:

• Development of reference-based and de novo inference methods for gene networks from scRNA-seq datasets.
• Leveraging transcriptome-wide profiles from abundant cell samples to model systemic CGNs.
• Utilizing computational distinction of cell states enabled by scRNA-seq technology.

Main Results:

• Successful inference of gene functional interaction networks from scRNA-seq data.
• Creation of a compendium of CGNs providing cell-type-resolved genetic insights.
• Demonstration of CGNs' utility in understanding gene-cell-disease relationships.

Conclusions:

• Single-cell network biology is a key approach for dissecting complex gene-cell-disease relationships.
• scRNA-seq-derived CGNs offer novel insights into disease mechanisms and potential drug targets.
• The presented compendium serves as a valuable resource for advancing cell-type-resolved disease genetics research.