Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human...

Area of Science:

• Genomics
• Computational Biology
• Immunology

Background:

• Genome-wide association studies (GWAS) identify genetic loci linked to disease but often fail to pinpoint the specific cell types mediating these effects.
• Understanding cell-type specific gene functions is crucial for elucidating disease mechanisms and developing targeted therapies.

Purpose of the Study:

• To develop and validate a computational framework, sc-linker, for integrating single-cell RNA sequencing (scRNA-seq), epigenomic data, and GWAS summary statistics.
• To infer cell types and biological processes through which genetic variants influence complex diseases.
• To uncover novel cell-disease associations and validate known ones.

Main Methods:

• Integration of scRNA-seq data with epigenomic SNP-to-gene maps and GWAS summary statistics.
• Development of a framework (sc-linker) to infer cell-type specific disease enrichments.
• Application of the framework to analyze major depressive disorder, ulcerative colitis, multiple sclerosis, and autoimmune diseases.

Main Results:

• sc-linker successfully recapitulated known cell-disease relationships and identified novel associations.
• Specific cell types were linked to diseases: γ-aminobutyric acid-ergic neurons for major depressive disorder, M-cells for ulcerative colitis, and complement cascade in multiple sclerosis.
• Autoimmune diseases showed distinct patterns, with immune cell programs in both healthy and disease states, and disease-dependent epithelial cell programs, suggesting roles in disease response.

Conclusions:

• The sc-linker framework offers a powerful approach to identify cell types and processes underlying genetic contributions to disease.
• This method enhances the interpretation of GWAS findings by linking genetic risk to specific cellular contexts.
• The findings provide insights into disease pathogenesis and potential therapeutic targets across various complex conditions.