A High-Resolution Enhancer Atlas of the Developing Telencephalon
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA183699 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR630869.fastq.gz | Fastqsanger.gz | |||
| SRR630870.fastq.gz | Fastqsanger.gz | |||
| SRR630871.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 3 of 3 |
Similar Datasets
Project description:The mammalian telencephalon plays critical roles in cognition, motor function, and emotion. While many of the genes required for its development have been identified, the distant‐acting regulatory sequences orchestrating their in vivo expression are mostly unknown. Here we describe a digital atlas of in vivo enhancers active in subregions of the developing telencephalon. We identified over 4,600 candidate embryonic forebrain enhancers and studied the in vivo activity of 329 of these sequences in transgenic mouse embryos. We generated serial sets of histological brain sections for 145 reproducible forebrain enhancers, resulting in a publicly accessible web‐based enhancer atlas comprising over 33,000 sections. We show how this large collection of annotated telencephalon enhancers can be used to study the regulatory architecture of individual genes, to examine the sequence motif content of enhancers, and to drive targeted reporter or effector protein expression in experimental applications. Furthermore, we used epigenomic analysis of human and mouse cortex tissue to directly compare the genome‐wide enhancer architecture in these species. This atlas provides a primary resource for investigating gene regulatory mechanisms of telencephalon development and enables studies of the role of distant‐acting enhancers in neurodevelopmental disorders. Examination of p300 binding in mouse embryonic stage 11.5 forebrain, mouse postnatal (P0) cortex tissue and human fetal (gestational week 20) cortex
2013-01-29 | E-GEOD-42881 | biostudies-arrayexpress
Project description:The mammalian telencephalon plays critical roles in cognition, motor function, and emotion. While many of the genes required for its development have been identified, the distant‐acting regulatory sequences orchestrating their in vivo expression are mostly unknown. Here we describe a digital atlas of in vivo enhancers active in subregions of the developing telencephalon. We identified over 4,600 candidate embryonic forebrain enhancers and studied the in vivo activity of 329 of these sequences in transgenic mouse embryos. We generated serial sets of histological brain sections for 145 reproducible forebrain enhancers, resulting in a publicly accessible web‐based enhancer atlas comprising over 33,000 sections. We show how this large collection of annotated telencephalon enhancers can be used to study the regulatory architecture of individual genes, to examine the sequence motif content of enhancers, and to drive targeted reporter or effector protein expression in experimental applications. Furthermore, we used epigenomic analysis of human and mouse cortex tissue to directly compare the genome‐wide enhancer architecture in these species. This atlas provides a primary resource for investigating gene regulatory mechanisms of telencephalon development and enables studies of the role of distant‐acting enhancers in neurodevelopmental disorders.
2013-01-29 | GSE42881 | GEO
Project description:The mammalian telencephalon plays critical roles in cognition, motor function, and emotion. Though many of the genes required for its development have been identified, the distant-acting regulatory sequences orchestrating their in vivo expression are mostly unknown. Here, we describe a digital atlas of in vivo enhancers active in subregions of the developing telencephalon. We identified more than 4,600 candidate embryonic forebrain enhancers and studied the in vivo activity of 329 of these sequences in transgenic mouse embryos. We generated serial sets of histological brain sections for 145 reproducible forebrain enhancers, resulting in a publicly accessible web-based data collection comprising more than 32,000 sections. We also used epigenomic analysis of human and mouse cortex tissue to directly compare the genome-wide enhancer architecture in these species. These data provide a primary resource for investigating gene regulatory mechanisms of telencephalon development and enable studies of the role of distant-acting enhancers in neurodevelopmental disorders.
| S-EPMC3660042 | biostudies-literature
Project description:A Chromatin Accessibility Atlas of the Developing Human Telencephalon
| PRJNA627850 | ENA
Project description:A chromatin accessibility atlas of the developing human telencephalon
| PRJNA634163 | ENA
Project description:A chromatin accessibility atlas of the developing human telencephalon
| PRJNA634164 | ENA
Project description:Gene expression differs between cell types and regions within complex tissues such as the developing brain. To discover regulatory elements underlying this specificity, we generated genome-wide maps of chromatin accessibility in nine anatomically-defined regions of the developing human telencephalon. Additionally, we defined the histone modification landscape of the prefrontal cortex and generated chromatin accessibility maps of its upper and deep layers. We predicted a subset of open chromatin regions (18%) that are most likely to be active enhancers, many of which are dynamic with 26% differing between early and late mid-gestation and 28% present in only one brain region. These predicted regulatory elements (pREs) are enriched proximal to genes with expression differences across developmental stages, regions, and cortical laminae; they harbor distinct sequence motifs that suggest potential upstream regulators. We leveraged this atlas to predict and validate novel regulatory elements of genes that control cortex laminar identity and genes associated with autism spectrum disorder (ASD). These include enhancers proximal to FEZF2 and BCL11A that were validated in mouse, and an enhancer of ASD gene SLC6A1 containing two functional de novo mutations in individuals with ASD whose enhancer function we validated via CRISPRa. These applications demonstrate the utility of this atlas for decoding neurodevelopmental gene regulation in health and disease.
2020-04-25 | GSE149268 | GEO
Project description:A single-cell transcriptome atlas of the zebrafish telencephalon
| PRJNA875033 | ENA
Project description:Combinations of transcription factors govern the identity of cell types, which is reflected by genomic enhancer codes. We utilized deep learning to characterize these enhancer codes and devised three novel metrics to compare cell types in the telencephalon between mammals and birds. To this end, we generated single-cell multiome and spatially-resolved transcriptomics data of the chicken telencephalon. Enhancer codes of orthologous non-neuronal and GABAergic cell types show a high degree of similarity across vertebrates, while excitatory neurons of the mammalian neocortex and avian pallium exhibit varying degrees of similarity. Enhancer codes of avian mesopallial neurons are most similar to those of mammalian deep layer neurons. With this study, we present generally applicable deep learning approaches to characterize and compare cell types solely based on genomic sequences.
2024-08-24 | GSE275097 | GEO
Project description:Combinations of transcription factors govern the identity of cell types, which is reflected by genomic enhancer codes. We utilized deep learning to characterize these enhancer codes and devised three novel metrics to compare cell types in the telencephalon between mammals and birds. To this end, we generated single-cell multiome and spatially-resolved transcriptomics data of the chicken telencephalon. Enhancer codes of orthologous non-neuronal and GABAergic cell types show a high degree of similarity across vertebrates, while excitatory neurons of the mammalian neocortex and avian pallium exhibit varying degrees of similarity. Enhancer codes of avian mesopallial neurons are most similar to those of mammalian deep layer neurons. With this study, we present generally applicable deep learning approaches to characterize and compare cell types solely based on genomic sequences.
2024-06-14 | GSE262321 | GEO