Single-cell spatial reconstruction reveals global division of labor in the mammalian liver
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE84498 | GEO | 2016/11/29
SECONDARY ACCESSION(S): PRJNA329488
REPOSITORIES: GEO
Similar Datasets
Project description:The mammalian liver is composed of repeating hexagonal units termed lobules. Spatially-resolved single-cell transcriptomics revealed that about half of hepatocyte genes are differentially expressed across the lobule. Technical limitations impede reconstructing similar global spatial maps of other hepatocyte features. Here, we used zonated surface markers to sort hepatocytes from defined lobule zones with high spatial resolution. We applied transcriptomics, miRNA array measurements and Mass spectrometry proteomics to reconstruct spatial atlases of multiple zonated hepatocyte features. We found that protein zonation largely overlapped mRNA zonation, with the periportal HNF4α as an exception. We identified zonation of miRNAs such as miR-122, and inverse zonation of miRNAs and their hepatocyte target genes, implying potential regulation through zonated mRNA degradation. These targets included the pericentral Wnt receptors Fzd7 and Fzd8 and the periportal Wnt inhibitors Tcf7l1 and Ctnnbip1. Our approach facilitates reconstructing spatial atlases of multiple cellular features in the liver and other structured tissues.
2019-07-26 | GSE134827 | GEO
Project description:Hepatocytes of the mammalian liver are organized in liver lobules and operate in a spatially-dependent manner. Cells in different positions along the lobule’s porto-cenrtal axis, defined by the directionality of blood flow, express different genes and perform different liver tasks. Gradients of the transcriptome along liver lobule axis has been recently established, yet not for the hepatocyte proteome. We used two surface markers whose levels are inversely zonated – CD73 with a decreasing gradient from pericentral to periportal hepatocytes and E-cadherin with increasing gradient from portal to central hepatocytes. By staining for both surface markers, we efficiently isolated bulk populations of hepatocytes from distinct lobule layers by Fluorescence Activated Cell Sorting (FACS). Over all, we sorted 100,000 hepatocytes from each of eight spatially distinct populations, from five different mice. Cells were washed, digested by trypsin and subjected to LC-MS/MS. More cells from same populations from the same mice were also collected for mRNA sequencing and microRNA microarray profiling, to achieve a multi-omic view on spatially sorted hepatocytes, for better understanding of the transcriptomic and post-transcriptomic levels of regulation of liver zonation.
2019-08-06 | PXD014512 | Pride
Project description:Single cell RNA sequencing, spatial reconstruction of the liver lobule after acetaminophen (300 mg/kg)
2022-04-13 | GSE200771 | GEO
Project description:Human hepatocytes differ in gene expression and function across the hexagonal lobules of the tissue microarchitecture, a phenomenon referred to as liver zonation. Hepatocytes also display intra-lobular differences in cell size, but to what extent zonal expression and function correlate with cell size in isolated human hepatocytes is not entirely clear. Here, we first used our accumulated experience of nearly 100 hepatocyte isolations to assess the impact of donor background and process parameters on hepatocyte quality. We observed substantial inter-batch variability in cell size distributions and a tendency for overall cell size to affect the outcome of hepatocyte isolation and cryopreservation. We further separated cells into different size fractions and analyzed them with label-free quantitative proteomics. This showed that protein abundances in different hepatocyte size fractions recapitulated the in vivo expression patterns of well-known zonal markers. We also found that proteins with sequential enrichment across fractions largely represented biological processes with known zonal specificity. This was confirmed by differences in the metabolic activity of zonated CYP enzymes. Altogether, our results show that hepatocyte size corresponds to zonal origin, and that our size fractionation approach can be used to study zone-specific liver functions in vitro.
2021-01-26 | PXD013142 | Pride
Project description:To investigate the underlying molecular principles of metabolic and morphogenic zonation of the human liver lobule, we generated an integrated epigenetic map across three zones (pericentral, intermediate and periportal) by methylation and transcriptomic analysis of hepatocytes captured by laser micro-dissection. We observe a deep link between epigenetic zonation of human liver and a zonated expression of metabolic and morphogenic pathways: Key transcriptionally zonated enzymes in xenobiotic and glutamine metabolism show a strong anticorrelated methylation gradient indicating that zonal expression of these genes is partly controlled by epigenetic programs. Zonated DNA-methylation at binding sites of uniformly expressed transcription factors such as HNF4A and RXRα points to an epigenetic layer in regulatory networks and a zonated activity of their nuclear ligands and drugs such as fibrates and bile acids. The same holds for genes of the wnt morphogen pathway whose expression show a zonated methylation at binding sites of TCF4L2. A strong pericentral expression of LGR5 and AXIN2 and a corresponding expression gradient of the liver progenitor marker TBX3, indicates a predominant pericentral source of hepatocyte regeneration under steady-state conditions in humans. Conversely, the periportal expression of NOTCH and EPCAM at the border to the biliary tree as source of regeneration under injury conditions. Overall our data provide a deep understanding of molecular control of the zonal organisation in the human liver.
2018-09-13 | GSE105127 | GEO
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.
2023-09-07 | GSE236123 | GEO
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.
2023-09-07 | GSE236121 | GEO
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.
2023-09-07 | GSE218471 | GEO
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.
2023-09-07 | GSE218470 | GEO
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.
2023-09-07 | GSE218469 | GEO