4.7 Article

Mapping transcriptional heterogeneity and metabolic networks in fatty livers at single-cell resolution

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ISCIENCE
卷 26, 期 1, 页码 -

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CELL PRESS
DOI: 10.1016/j.isci.2022.105802

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Artificial intelligence

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Non-alcoholic fatty liver disease (NAFLD) is a complex disease with unclear molecular mechanisms. By using single-cell RNA sequencing, researchers identified distinct clusters of hepatocytes with different expression of the lipid synthesis driver Srebp1. Interestingly, Srebp1 was not a reliable predictor of hepatic lipid accumulation, suggesting the involvement of other factors in lipid metabolism. Computational network analyses revealed a strong association between NAFLD and high constitutive androstane receptor (CAR) expression, which interacted with multiple functional modules related to lipid metabolism. These findings provide insights into the cellular differences in lipid signatures and identify important functional networks involved in hepatic steatosis in both mice and humans.
Non-alcoholic fatty liver disease is a heterogeneous disease with unclear underlying molecular mechanisms. Here, we perform single-cell RNA sequencing of hepatocytes and hepatic non-parenchymal cells to map the lipid signatures in mice with non-alcoholic fatty liver disease (NAFLD). We uncover previously unidentified clusters of hepatocytes characterized by either high or low srebp1 expression. Surprisingly, the canonical lipid synthesis driver Srebp1 is not predictive of hepatic lipid accumulation, suggestive of other drivers of lipid metabolism. By combining transcriptional data at single-cell resolution with computational network analyses, we find that NAFLD is associated with high constitutive androstane receptor (CAR) expression. Mechanistically, CAR interacts with four functional modules: cholesterol homeostasis, bile acid metabolism, fatty acid metabolism, and estrogen response. Nuclear expression of CAR positively correlates with steatohepatitis in human livers. These findings demonstrate significant cellular differences in lipid signatures and identify functional networks linked to hepatic steatosis in mice and humans.

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