Utilizing sc-linker to integrate single-cell RNA sequencing and human genetics to identify cell types and driver...

Genome-wide association studies (GWAS) provide a powerful method for identifying the loci and genes that contribute to disease. However, in many cases, the specific cell types and states that confer disease risk through these genes remain unknown. Determining this relationship is crucial for identifying pathogenic processes and developing therapeutic strategies.

In this study, we utilized the sc-linker framework developed by Jagadeesh, which is an integrated framework that combines single-cell RNA sequencing (scRNA-seq), epigenomic single nucleotide polymorphism (SNP)-to-gene mapping, and GWAS summary statistics to infer potential cell types and diseases affected by genetic variations.

Using normal cell type programs in the sc-linker, we identified type 2 alveolar cells in normal lung tissues that are closely associated with non-small cell lung cancer (NSCLC). Additionally, we identified cancer-associated fibroblasts (CAFs) associated with lung cancer using disease-dependent programs. By integrating extensive single-cell data from NSCLC, we discerned heterogeneity among CAFs subgroups. Finally, using MAGMA, we identified RAB31 as a driver gene in disease-related fibroblasts. Proteins from the RAB family are involved in the dynamic regulation of cell membrane compartments and are dysregulated in various tumor types, potentially altering biological properties such as the proliferation, migration, and invasion of cancer cells. We found that the Ras-related protein Rab-31 (RAB31) was significantly overexpressed in tumor-associated fibroblasts compared to that in normal fibroblasts and was closely associated with poor prognosis in patients with NSCLC.

By integrating scRNA-seq, epigenomic, and GWAS datasets, we found that ACT2 and CAFs have specific disease heritability in lung cancer and identified the driver gene RAB31 as a potential therapeutic target.