Leveraging Single-Cell RNA-seq Data to Uncover the Association Between Cell Type and Chronic Liver Diseases

Background: Components of liver microenvironment is complex, which leads to the difficulty in clarifying pathogenesis of chronic liver diseases (CLD). Genome-wide association studies (GWASs) have greatly revealed the genetic roles in CLD pathogenesis and prognosis, while single-cell RNA sequencing (scRNA-seq) enables interrogation of the cellular diversity and function of liver tissue at unprecedented resolution. Here, we integrated the GWAS and scRNA-seq data of CLD to uncover the significant cell types and provide clues for understanding on the pathogenesis. Methods: We downloaded the summary statistics, including one hepatocellular carcinoma (HCC) and two cirrhosis, and three scRNA-seq datasets of them. Specially, we defined the cell types for each scRNA-seq data. Then we used Rolypoly and LDSC-cts to integrate the GWAS and scRNA-seq. Finally, we analyzed one scRNA-seq data without association to CLD to valid the specificity of our findings. Results: After processing the scRNA-seq data, we obtain about 19,002~32,200 cells and defined 10~17 cell types. For the HCC analysis, we defined the association between B cell and HCC in two datasets. Rolypoly also identified the association (P=0.0040), when we integrated the two scRNA-seq datasets. In addition, we also defined natural killer (NK) cell as HCC-associated cell type in one dataset. In specificity analysis, we defined no significant cell type associated with HCC. Conclusion: In this integrative analysis, we identified B cell and NK cell as HCC-related cell type. More attention and verification should be paid to them in future research.