Gene Deconvolution Reveals Aberrant Liver Regeneration and Immune Cell Infiltration in Alcohol‐Associated Hepatitis. Issue 2 (15th June 2021) - Kim, Adam; Wu, Xiaoqin; Allende, Daniela S.; Nagy, Laura E.
Abstract : Background and Aims: Acute liver damage causes hepatocyte stress and death, but in chronic liver disease impaired hepatocyte regeneration and immune cell infiltration prevents recovery. While the roles of both impaired liver regeneration and immune infiltration have been studied extensively in chronic liver diseases, the differential contribution of these factors is difficult to assess. Approach and Results: We combined single‐cell RNA‐sequencing (RNA‐seq) data from healthy livers and peripheral immune cells to measure cell proportions in chronic liver diseases. Using bulk RNA‐seq data from patients with early alcohol‐associated hepatitis, severe AH (sAH), HCV, HCV with cirrhosis, and NAFLD, we performed gene deconvolution to predict the contribution of different cell types in each disease. Patients with sAH had the greatest change in cell composition, with increases in both periportal hepatocytes and cholangiocyte populations. Interestingly, while central vein hepatocytes were decreased, central vein endothelial cells were expanded. Endothelial cells are thought to regulate liver regeneration through WNT signaling. WNT2, important in central vein hepatocyte development, was down in sAH, while multiple other WNTs and WNT receptors were up‐regulated. Immunohistochemistry revealed up‐regulation of FZD6, a noncanonical WNT receptor, in hepatocytes in sAH. Immune cell populations also differed in disease. In sAH, a specific group of inflammatory macrophages was increased and distinct from the macrophage population in patients with HCV. Network and correlation analyses revealed that changes in the cell types in the liver were highly correlated with clinical liver function tests. Conclusions: These results identify distinct changes in the liver cell populations in chronic liver disease and illustrate the power of using single‐cell RNA‐seq data from a limited number of samples in understanding multiple different diseases.