Genetic regulation of gene expression in human brain cell types
Ontology highlight
ABSTRACT:
PROVIDER: EGAS00001006345 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Genotyping 323 Europeans in order to perform cis-eQTL analysis in 9 tissue cell types. Identification of regulatory modules across genes and tissues on the basis of correlated SNPs in expression associated patterns. Integration of cis-eQTLs regulatory modules with known 200 IBD loci.
2018-04-28 | E-MTAB-6666 | biostudies-arrayexpress
Project description:Transcriptome in 6 immune cell types and 3 colonic biopsis from 323 individuals in order to perform cis-eQTL analysis. Identification of regulatory modules across genes and tissues on the basis of correlated SNPs in expression associated patterns (EAPs). Integration of cis-eQTLs regulatory modules with known 200 IBD loci.
2018-04-28 | E-MTAB-6667 | biostudies-arrayexpress
Project description:<p>"A multimodal atlas of human brain cell types" includes sample data targeting two cell types that show species differences between mouse and human. First, it includes a detailed transcriptomic, morphological, and electrophysiological characterization of cell types in layer 1 of human middle temporal gyrus, focusing primarily on different inhibitory cell types. This project also includes gene expression data collected from nuclei in layer 5 of human fronto-insula, with a goal of identifying transcriptomic signatures of Von Economo neurons. Control samples collected as part of the same experiment are also included in the data set.</p> <p>This study was conducted as part of a collaboration between the Allen Institute for Brain Science, the University of Szeged, and the J. Craig Venter Institute. Collaborators request that publications resulting from these data cite their original publication: Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type (PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/30150662" target="_blank">30150662</a>).</p>
| phs001791 | dbGaP
Project description:The dynamically organized chromatin complexes often involve multiplex chromatin interactions and sometimes chromatin-associated RNA (caRNA). Chromatin complex compositions change during cellular differentiation and aging, and are expected to be highly heterogeneous among terminally differentiated single cells. Here we introduce the Multi-Nucleic Acid Interaction Mapping in Single Cell (MUSIC) technique for concurrent profiling of multiplex chromatin interactions, gene expression, and RNA-chromatin associations within individual nuclei. Applied to 14 human frontal cortex samples from elderly donors, MUSIC delineates diverse cortical cell types and states. We observed the nuclei exhibiting fewer short-range chromatin interactions are correlated with an “older” transcriptomic signature and with Alzheimer’s pathology. Furthermore, the cell type exhibiting chromatin contacts between cis expression quantitative trait loci (cis eQTLs) and a promoter tends to be the cell type where these cis eQTLs specifically affect their target gene’s expression. Additionally, the female cortical cells exhibit highly heterogeneous interactions between the XIST non-coding RNA and Chromosome X, along with diverse spatial organizations of the X chromosomes. MUSIC presents a potent tool for exploring chromatin architecture and transcription at cellular resolution in complex tissues.
2024-04-01 | GSE253754 | GEO
Project description:<p>While many genetic variants have been associated with risk for human diseases, how these variants affect gene expression in various cell types remains largely unknown. To address this gap, the DICE (Database of Immune Cell Expression, Expression quantitative trait loci (eQTLs) and Epigenomics) project was established. Considering all human immune cell types and conditions studied, we identified cis-eQTLs for a total of 12,254 unique genes, which represent 61% of all protein-coding genes expressed in these cell types. Strikingly, a large fraction (41%) of these genes showed a strong cis- association with genotype only in a single cell type. We also found that biological sex is associated with major differences in immune cell gene expression in a highly cell- specific manner. These datasets will help reveal the effects of disease risk-associated genetic polymorphisms on specific immune cell types, providing mechanistic insights into how they might influence pathogenesis (<a href="http://dice-database.org">http://dice-database.org</a>). </p>
| phs001703 | dbGaP
Project description:Genetic variants that impact gene regulation are important contributors to human phenotypic variation. For this reason, considerable efforts have been made to identify genetic associations with differences in mRNA levels of nearby genes, namely, cis expression quantitative trait loci (eQTLs). The phenotypic consequences of eQTLs are presumably due, in most cases, to their ultimate effects on protein expression levels. Yet, only few studies have quantified the impact of genetic variation on proteins levels directly. It remains unclear how faithfully eQTLs are reflected at the protein level, and whether there is a significant layer of cis regulatory variation acting primarily on translation or steady state protein levels. To address these questions, we measured ribosome occupancy by high-throughput sequencing, and relative protein levels by high-resolution quantitative mass spectrometry, in a panel of lymphoblastoid cell lines (LCLs) in which we had previously measured transcript expression using RNA sequencing. We then mapped genetic variants that are associated with changes in transcript expression (eQTLs), ribosome occupancy (rQTLs), or protein abundance (pQTLs). Most of the QTLs we detected are associated with transcript expression levels, with consequent effects on ribosome and protein levels. However, we found that eQTLs tend to have significantly reduced effect sizes on protein levels, suggesting that their potential impact on downstream phenotypes is often attenuated or buffered. Additionally, we confirmed the presence of a class of cis QTLs that specifically affect protein abundance with little or no effect on mRNA levels; most of these QTLs have little effect on ribosome occupancy, and hence may arise from differences in post-translational regulation.
2014-12-19 | PXD001406 | Pride
Project description:Multi-omic profiling of single nuclei uncovers regulatory diversity of brain cell types and diseases
| PRJNA589980 | ENA
Project description:Systematic characterization of how genetic variation modulates gene regulation in a cell type specific context is essential for understanding complex traits. To address this question, we profiled gene expression and chromatin state of cells from healthy retinae of 20 human donors with a single-cell multiomics approach, and performed genomic sequencing. sc-eQTLs, sc-caQTL, sc-ASCA and sc-ASE were mapped in major retinal cell types. By integrating these results, we identified and characterized regulatory elements and genetic variants effective on gene regulation in individual cell types. Most of the identified sc-eQTLs and sc-caQTLs exhibit cell type-specific effects. Interestingly, the cis-elements harboring genetic variants with cell type-specific effects tend to be accessible in multiple cell types. Lastly, we identified the enriched cell types, fine-mapped candidate causal variants and genes, and uncovered cell type-specific regulatory mechanism underlying GWAS loci.
2023-11-07 | GSE247157 | GEO
Project description:Single-nuclei RNA-Seq is widely employed to investigate cell types, especially of human brain and frozen samples. In contrast to single-cell approaches, many single-nuclei reads are purely intronic. Here, using microfluidics, PCR-based artifact removal, target enrichment, and long-read sequencing, we developed single-nuclei isoform RNA-sequencing (‘SnISOr-Seq’), and applied it to human adult frontal cortex. SnISOr-Seq dramatically increased the fraction of informative reads. We found that exons associated with autism exhibit coordinated and highly cell-type specific inclusion. We discovered two distinct combination patterns: first, those distinguishing neural cell types, enriched in TSS-exon, exon-polyA-site, and non-adjacent exon pairs. Second, those with multiple configurations within one cell type, enriched in adjacent exon pairs. Furthermore, adjacent exons are predominantly mutually-associated, while distant exons are frequently mutually-exclusive. Finally, we observed that human-specific exons are almost as tightly coordinated as conserved exons. SnISOr-Seq enables single-nuclei long-read isoform analysis in human brain, and in any frozen or hard-to-dissociate sample.
2022-01-16 | GSE178175 | GEO
Project description:Genetical genomics is a strategy for mapping gene expression variation to expression quantitative trait loci (eQTLs). We performed a genetical genomics experiment in four functionally distinct but developmentally closely related hematopoietic cell populations isolated from the BXD panel of recombinant inbred mouse strains. This analysis allowed us to analyze eQTL robustness/sensitivity across different cellular differentiation states. Although we identified a large number (365) of “static” eQTLs that were consistently active in all four cell types, we found a much larger number (1283) of “dynamic” eQTLs showing cell-type-dependence. Of these, 140, 45, 531, and 295 were preferentially active in stem, progenitor, erythroid and myeloid cells, respectively. A detailed investigation of those dynamic eQTLs showed that in many cases the eQTL specificity was associated with expression changes in the target gene. We found no evidence for target genes that were regulated by distinct eQTLs in different cell types, suggesting that large-scale changes within functional regulatory networks are uncommon. Our results demonstrate that heritable differences in gene expression are highly sensitive to the developmental stage of the cell population under study. Therefore, future genetical genomics studies should aim at studying multiple well-defined and highly-purified cell types in order to construct as comprehensive a picture of the changing functional regulatory relationships as possible.
2009-09-12 | GSE18067 | GEO