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

Xiangyu Ye; Julong Wei; Ming Yue; Yan Wang; Hongbo Chen; Yongfeng Zhang; Yifan Wang; Meiling Zhang; Peng Huang; Rongbin Yu

doi:10.3389/fgene.2021.637322

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

Front Genet. 2021 Mar 8:12:637322. doi: 10.3389/fgene.2021.637322. eCollection 2021.

Authors

Xiangyu Ye¹, Julong Wei², Ming Yue³, Yan Wang¹, Hongbo Chen⁴, Yongfeng Zhang⁴, Yifan Wang⁴, Meiling Zhang⁴, Peng Huang¹, Rongbin Yu¹

Affiliations

¹ Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
² Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, United States.
³ Department of Infectious Diseases, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
⁴ Department of Infectious Disease, Jurong Hospital Affiliated to Jiangsu University, Jurong, China.

Abstract

Background: Components of liver microenvironment is complex, which makes it difficult to clarify pathogenesis of chronic liver diseases (CLD). Genome-wide association studies (GWASs) have greatly revealed the role of host genetic background 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 made integrative analysis on the GWAS and scRNA-seq data of CLD to uncover CLD-related cell types and provide clues for understanding on the pathogenesis.

Methods: We downloaded three GWAS summary data and three scRNA-seq data on CLD. After defining the cell types for each scRNA-seq data, we used RolyPoly and LDSC-cts to integrate the GWAS and scRNA-seq. In addition, we analyzed one scRNA-seq data without association to CLD to validate the specificity of our findings.

Results: After processing the scRNA-seq data, we obtain about 19,002-32,200 cells and identified 10-17 cell types. For the HCC analysis, we identified the association between B cell and HCC in two datasets. RolyPoly also identified the association, when we integrated the two scRNA-seq datasets. In addition, we also identified natural killer (NK) cell as HCC-associated cell type in one dataset. In specificity analysis, we identified no significant cell type associated with HCC. As for the cirrhosis analysis, we obtained no significant related cell type.

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.

Keywords: GWAS; cell type; chronic liver diseases; integrated analysis; scRNA-seq.