Identification of genomic enhancers through spatial integration of single-cell transcriptomics and epigenomics [10X_scRNAseq]
Ontology highlight
ABSTRACT:
ORGANISM(S): Drosophila melanogaster
PROVIDER: GSE141589 | GEO | 2020/04/08
REPOSITORIES: GEO
Similar Datasets
Project description:Enhancers act as docking stations in the genome to which transcription factors bind, tightly regulating gene expression. Although single-cell technologies allow measuring chromatin accessibility or gene expression in each individual cell, exploiting both layers towards bona fide gene regulatory networks and enhancers is still a challenge. Here, we independently generate comprehensive single-cell transcriptome and epigenome atlases of the Drosophila eye-antennal disc and propose the spatial integration of single-cell RNA-seq and single-cell ATAC-seq data using a virtual latent space that mimics the spatial organization of the 2D tissue. To validate spatially predicted enhancers, we use a large collection of enhancer-reporter lines and identify ~85% of enhancers for which chromatin accessibility and enhancer activity patterns agree; while the remaining ~15% are ubiquitously accessible regions bound by the pioneer transcription factor Grainyhead, for which accessibility and activity do not correlate. Next, we infer linear and non-linear enhancer-to-target gene relationships in the virtual space, and find that genes are mostly regulated by multiple -and in some cases, redundant - enhancers. In addition, using cell-type specific enhancers learned from the scATAC-seq data, we deconvolute the cell-type specific effects of chromatin accessibility QTLs from a panel of 50 bulk ATAC-seq profiles from Drosophila inbred lines. Finally, we identify Prospero as a key transcription factor driving neuronal photoreceptor differentiation through the binding of a GGG recognition motif. In summary, we provide a comprehensive spatial characterization of gene regulation in a 2D tissue, which can be explored via SCope (http://scope.aertslab.org/#/Bravo_et_al_EyeAntennalDisc) and the UCSC Genome browser (http://genome.ucsc.edu/s/cbravo/Bravo_et_al_EyeAntennalDisc).
2020-04-08 | GSE141580 | GEO
Project description:Enhancers act as docking stations in the genome to which transcription factors bind, tightly regulating gene expression. Although single-cell technologies allow measuring chromatin accessibility or gene expression in each individual cell, exploiting both layers towards bona fide gene regulatory networks and enhancers is still a challenge. Here, we independently generate comprehensive single-cell transcriptome and epigenome atlases of the Drosophila eye-antennal disc and propose the spatial integration of single-cell RNA-seq and single-cell ATAC-seq data using a virtual latent space that mimics the spatial organization of the 2D tissue. To validate spatially predicted enhancers, we use a large collection of enhancer-reporter lines and identify ~85% of enhancers for which chromatin accessibility and enhancer activity patterns agree; while the remaining ~15% are ubiquitously accessible regions bound by the pioneer transcription factor Grainyhead, for which accessibility and activity do not correlate. Next, we infer linear and non-linear enhancer-to-target gene relationships in the virtual space, and find that genes are mostly regulated by multiple -and in some cases, redundant - enhancers. In addition, using cell-type specific enhancers learned from the scATAC-seq data, we deconvolute the cell-type specific effects of chromatin accessibility QTLs from a panel of 50 bulk ATAC-seq profiles from Drosophila inbred lines. Finally, we identify Prospero as a key transcription factor driving neuronal photoreceptor differentiation through the binding of a GGG recognition motif. In summary, we provide a comprehensive spatial characterization of gene regulation in a 2D tissue, which can be explored via SCope (http://scope.aertslab.org/#/Bravo_et_al_EyeAntennalDisc) and the UCSC Genome browser (http://genome.ucsc.edu/s/cbravo/Bravo_et_al_EyeAntennalDisc).
2020-04-08 | GSE141584 | GEO
Project description:Enhancers act as docking stations in the genome to which transcription factors bind, tightly regulating gene expression. Although single-cell technologies allow measuring chromatin accessibility or gene expression in each individual cell, exploiting both layers towards bona fide gene regulatory networks and enhancers is still a challenge. Here, we independently generate comprehensive single-cell transcriptome and epigenome atlases of the Drosophila eye-antennal disc and propose the spatial integration of single-cell RNA-seq and single-cell ATAC-seq data using a virtual latent space that mimics the spatial organization of the 2D tissue. To validate spatially predicted enhancers, we use a large collection of enhancer-reporter lines and identify ~85% of enhancers for which chromatin accessibility and enhancer activity patterns agree; while the remaining ~15% are ubiquitously accessible regions bound by the pioneer transcription factor Grainyhead, for which accessibility and activity do not correlate. Next, we infer linear and non-linear enhancer-to-target gene relationships in the virtual space, and find that genes are mostly regulated by multiple -and in some cases, redundant - enhancers. In addition, using cell-type specific enhancers learned from the scATAC-seq data, we deconvolute the cell-type specific effects of chromatin accessibility QTLs from a panel of 50 bulk ATAC-seq profiles from Drosophila inbred lines. Finally, we identify Prospero as a key transcription factor driving neuronal photoreceptor differentiation through the binding of a GGG recognition motif. In summary, we provide a comprehensive spatial characterization of gene regulation in a 2D tissue, which can be explored via SCope (http://scope.aertslab.org/#/Bravo_et_al_EyeAntennalDisc) and the UCSC Genome browser (http://genome.ucsc.edu/s/cbravo/Bravo_et_al_EyeAntennalDisc).
2020-04-08 | GSE141573 | GEO
Project description:Enhancers act as docking stations in the genome to which transcription factors bind, tightly regulating gene expression. Although single-cell technologies allow measuring chromatin accessibility or gene expression in each individual cell, exploiting both layers towards bona fide gene regulatory networks and enhancers is still a challenge. Here, we independently generate comprehensive single-cell transcriptome and epigenome atlases of the Drosophila eye-antennal disc and propose the spatial integration of single-cell RNA-seq and single-cell ATAC-seq data using a virtual latent space that mimics the spatial organization of the 2D tissue. To validate spatially predicted enhancers, we use a large collection of enhancer-reporter lines and identify ~85% of enhancers for which chromatin accessibility and enhancer activity patterns agree; while the remaining ~15% are ubiquitously accessible regions bound by the pioneer transcription factor Grainyhead, for which accessibility and activity do not correlate. Next, we infer linear and non-linear enhancer-to-target gene relationships in the virtual space, and find that genes are mostly regulated by multiple -and in some cases, redundant - enhancers. In addition, using cell-type specific enhancers learned from the scATAC-seq data, we deconvolute the cell-type specific effects of chromatin accessibility QTLs from a panel of 50 bulk ATAC-seq profiles from Drosophila inbred lines. Finally, we identify Prospero as a key transcription factor driving neuronal photoreceptor differentiation through the binding of a GGG recognition motif. In summary, we provide a comprehensive spatial characterization of gene regulation in a 2D tissue, which can be explored via SCope (http://scope.aertslab.org/#/Bravo_et_al_EyeAntennalDisc) and the UCSC Genome browser (http://genome.ucsc.edu/s/cbravo/Bravo_et_al_EyeAntennalDisc).
2020-04-08 | GSE141586 | 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 | GSE236256 | 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 | 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 | GSE218465 | 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 | GSE236127 | GEO