Single-cell transcriptomics uncovers zonation of function in the mesenchyme during liver fibrosis
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE137720 | GEO | 2019/11/01
REPOSITORIES: GEO
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Project description:Liver zonation characterizes the separation of metabolic pathways along the lobules and is required for optimal hepatic function. Wnt signaling is a master regulator of spatial liver zonation. A perivenous-periportal Wnt activity gradient orchestrates metabolic zonation by activating genes in perivenous hepatocytes, while suppressing genes in their periportal counterparts. The understanding of liver gene zonation and zonation regulators in disease was limited. Non-alcoholic steatohepatitis (NASH) is a chronic liver disease characterized by fat accumulation, inflammation, and fibrosis. Here, we investigated the perturbation of liver gene zonation in a mouse NASH model by combination of spatial transcriptomics, bulk RNAseq and in situ hybridization. Wnt target genes represented a major subset of genes showing altered spatial expression in the NASH liver. The altered Wnt target gene expression level and zonation spatial pattern were in line with the upregulation of Wnt regulators and the augmentation of Wnt signaling. Particularly, we found that the Wnt activator Rspo3 expression was restricted to the perivenous zone in control livers, whereas expanded to the periportal zone in NASH livers. AAV8-mediated RSPO3 overexpression in controls resulted in a zonation change, and further amplified the disturbed zonation of Wnt target genes in NASH, but had no impacts on steatosis, inflammation and fibrosis. In summary, our study demonstrated the alteration of Wnt signaling in a mouse NASH model, leading to perturbed liver zonation.
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Project description:Cell type identity is encoded by gene regulatory networks (GRN), in which transcription factors (TFs) bind to enhancers to regulate target gene expression. In the mammalian liver, lineage TFs have been characterized for the main cell types, including hepatocytes. Hepatocytes cover a relatively broad cellular state space, as they differ significantly in their metabolic state, and function, depending on their position with respect to the central or portal vein in a liver lobule. It is unclear whether this spatially defined cellular state space, called zonation, is also governed by a well-defined gene regulatory code. To address this challenge, we have mapped enhancer-GRNs (eGRNs) across liver cell types at high resolution, using a combination of single-cell multi-omics, spatial omics, GRN inference, and deep learning. We found that zonated variation in gene expression in hepatocytes, liver sinusoidal endothelial cells and hepatocellular stellate cells corroborate cell state changes in transcription and chromatin accessibility with spatial transcriptomics. eGRN mapping suggests that zonation states in hepatocytes are driven by the repressors Tcf7l1 and Tbx3, that modulate the core hepatocyte GRN, controlled by Hnf4a, Cebpa, Hnf1a, Onecut1 and Foxa1, among others. To investigate how these TFs cooperate with cell type TFs, we performed an in vivo Massively Parallel Reporter Assay (MPRA) on 12,000 hepatocyte enhancers and used these data to train a hierarchical deep learning model (called DeepLiver) that exploits both enhancer accessibility and activity. DeepLiver confirms Cebpa, Onecut, Foxa1, Hnf1a and Hnf4a as drivers of enhancer specificity in hepatocytes; Tcf7l1/2 and Tbx3 as regulators of the zonation state; and Hnf4a, Hnf1a, AP-1 and Ets as activators. Finally, taking advantage of in silico mutagenesis predictions from DeepLiver and MPRA, we confirmed that the destruction of Tcf7l1/2 or Tbx3 motifs in zonated enhancers abrogates their zonation bias. Our study provides a multi-modal explanation of the regulatory code underlying hepatocyte identity and their zonation state, that can be exploited to engineer enhancers with desired activity levels and zonation patterns.
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Project description:Cell type identity is encoded by gene regulatory networks (GRN), in which transcription factors (TFs) bind to enhancers to regulate target gene expression. In the mammalian liver, lineage TFs have been characterized for the main cell types, including hepatocytes. Hepatocytes cover a relatively broad cellular state space, as they differ significantly in their metabolic state, and function, depending on their position with respect to the central or portal vein in a liver lobule. It is unclear whether this spatially defined cellular state space, called zonation, is also governed by a well-defined gene regulatory code. To address this challenge, we have mapped enhancer-GRNs (eGRNs) across liver cell types at high resolution, using a combination of single-cell multi-omics, spatial omics, GRN inference, and deep learning. We found that zonated variation in gene expression in hepatocytes, liver sinusoidal endothelial cells and hepatocellular stellate cells corroborate cell state changes in transcription and chromatin accessibility with spatial transcriptomics. eGRN mapping suggests that zonation states in hepatocytes are driven by the repressors Tcf7l1 and Tbx3, that modulate the core hepatocyte GRN, controlled by Hnf4a, Cebpa, Hnf1a, Onecut1 and Foxa1, among others. To investigate how these TFs cooperate with cell type TFs, we performed an in vivo Massively Parallel Reporter Assay (MPRA) on 12,000 hepatocyte enhancers and used these data to train a hierarchical deep learning model (called DeepLiver) that exploits both enhancer accessibility and activity. DeepLiver confirms Cebpa, Onecut, Foxa1, Hnf1a and Hnf4a as drivers of enhancer specificity in hepatocytes; Tcf7l1/2 and Tbx3 as regulators of the zonation state; and Hnf4a, Hnf1a, AP-1 and Ets as activators. Finally, taking advantage of in silico mutagenesis predictions from DeepLiver and MPRA, we confirmed that the destruction of Tcf7l1/2 or Tbx3 motifs in zonated enhancers abrogates their zonation bias. Our study provides a multi-modal explanation of the regulatory code underlying hepatocyte identity and their zonation state, that can be exploited to engineer enhancers with desired activity levels and zonation patterns.
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