Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Multi-omic profiling of single nuclei uncovers regulatory diversity of brain cell types and diseases

ABSTRACT: Most existing single-cell techniques can only make one type of molecular measurements. Although computational approaches have been developed to integrate single-cell datasets, their efficacy still needs to be determined with reference to authentic single-cell multi-omic profiles. To address this challenge, we devised single-nucleus methylCytosine, Chromatin accessibility and Transcriptome sequencing (snmC2T-seq) and applied the approach to post-mortem human frontal cortex tissue. We developed a computational framework to evaluate the quality of finely defined cell types using multi-modal information and validated the efficacy of computational multi-omic integration methods. Correlation analysis in individual cells revealed gene groups showing distinct relations between methylation and expression. Integration of snmC2T-seq with other multi- and single- modal datasets enabled joint analyses of the methylome, chromatin accessibility, transcriptome, and chromatin architecture for 63 human cortical cell types. We reconstructed the regulatory lineage of these cortical cell types and found pronounced cell-type-specific enrichment of disease risks for neuropsychiatric traits, predicting causal cell types that can be targeted for treatment.

ORGANISM(S): Homo sapiens

PROVIDER: GSE140493 | GEO | 2019/12/12

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Quartet Methylomics

Project description:The Quartet Project aims to provide resources for QC of multiple types of omic technologies and the effective integration of diverse datasets from various scenarios. Large quantities of multi-omics materials, datasets, and best practices for their QC utilities were developed for whole process QC of large-scale, multi-center, and longitudinal multi-omics profiling.

2023-09-04 | GSE241900 | GEO

The Quartet Data Portal: integration of community-wide resources for multiomics quality control (Proteomics)

Project description:The Project aims to provide resources for QC of multiple types of omic technologies and effective integration of diverse datasets from various scenarios. Large quantities of multi-omics materials, datasets, and best practice for their QC utilities were developed for whole process QC of large scale, multi-center, and longitudinal multi-omics profiling.

2023-09-01 | PXD045065 |

SnapHiC-D: a computational pipeline to identify differential chromatin contacts from single cell Hi-C data

Project description:Single cell Hi-C (scHi-C) has been used to map genome organization in complex tissues. However, computational tools to detect dynamic chromatin contacts from scHi-C datasets in development and through disease pathogenesis are still lacking. Here, we present SnapHiC-D, a computational pipeline to identify differential chromatin contacts (DCCs) between two scHi-C datasets. Compared to methods designed for bulk Hi-C data, SnapHiC-D detects DCCs with high sensitivity and accuracy. We used SnapHiC-D to identify cell-type-specific chromatin contacts at 10 kilobase resolution in mouse hippocampal and human prefrontal cortical tissues, and demonstrated that DCCs detected in the cortical and hippocampal cell types are generally correlated with cell-type-specific gene expression patterns and epigenomic features. SnapHiC-D is freely available at https://github.com/HuMingLab/SnapHiC-D.

2023-08-24 | GSE210585 | GEO

Single-cell multi-omic integration compares and contrasts features of brain cell identity

Project description:Single-cell multi-omic integration compares and contrasts features of brain cell identity

| PRJNA523430 | ENA

Multi-omics gene regulatory network analysis of resting and stimulated PBMCs [PBMC isolate scRNA-seq]

Project description:Cells require coordinated control over gene expression changes when responding to environmental stimuli. Recent advancements in single-cell technologies have enabled studies of regulatory dynamics associated with cellular perturbation response in immune cells. However, associating these changes to underlying differences in epigenetic regulation through integration of multi-omic data has not been achieved at single-cell resolution. Here, we apply single-cell ATAC-seq (scATAC-seq) and scRNA-seq to assay chromatin accessibility and gene expression in resting and stimulated human blood cells. Collectively, we generate ~91,000 high-coverage single-cell profiles, allowing us to probe the cis-regulatory landscape of immunological response across cell types, stimuli and time. Advancing tools to integrate multiomic data, we connect distal cis-regulatory elements to genes, identifying key regulatory hubs associated with immune signaling and stress response. Subsequently, we design FigR - a method to infer gene regulatory networks (GRNs) to identify candidate TF regulators across single cells. Importantly, construction of a GRN using stimulus response data identifies TF regulatory activity associated with genetic variants in inflammatory diseases. Lastly, we assess the time-dependent regulatory dynamics of immune stimulation, where we find that cells alter chromatin accessibility prior to productive gene expression, a process occurring at time scales of minutes. Extending this concept of chromatin-mediated priming, and incorporating regulatory relationships defined by FigR, we detect a rare subpopulation of monocytes marked by a primed chromatin state predictive of enhanced immunological response. Overall, we build upon computational tools for GRN construction and demonstrate the utility of GRNs for analysis of multi-omic single cell data.

2022-06-20 | GSE178430 | GEO

Multi-omics gene regulatory network analysis of resting and stimulated PBMCs [stimulation scRNA-seq]

2022-06-20 | GSE178429 | GEO

Multi-omics gene regulatory network analysis of resting and stimulated PBMCs [scATAC-seq]

2022-06-20 | GSE178428 | GEO

Single-cell multi-omic velocity infers dynamic and decoupled gene regulation

Project description:Single-cell multi-omic datasets, in which multiple molecular modalities are profiled within the same cell, provide a unique opportunity to discover the interplay between cellular epigenomic and transcriptomic changes. To realize this potential, we developed MultiVelo, a mechanistic model of gene expression that extends the popular RNA velocity framework by incorporating epigenomic data. MultiVelo uses a probabilistic latent variable model to estimate the switch time and rate parameters of gene regulation, providing a quantitative summary of the temporal relationship between epigenomic and transcriptomic changes. Fitting MultiVelo on single-cell multi-omic datasets revealed two distinct mechanisms of regulation by chromatin accessibility, quantified the degree of concordance or discordance between transcriptomic and epigenomic states within each cell, and inferred the lengths of time lags between transcriptomic and epigenomic changes.

2022-08-01 | GSE209878 | GEO

Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses

Project description:The integration of multi-omic data sets can provide unique information about molecular processes in a cell. Despite the development of many tools to extract information from such data sets, there are limited strategies to systematically extract mechanistic hypotheses from them. We here present COSMOS (Causal Oriented Search of Multi-Omic Space), a method that integrates cell signaling pathways, transcriptional, and metabolics data sets. COSMOS leverages extensive prior knowledge of interactions between biomolecules with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS can provide mechanistic explanations for experimental observations across multiple omic data sets. We applied COSMOS to a dataset comprising transcriptomic, phosphoproteomic, and metabolomic data from nine renal cell carcinoma patients comparing healthy non affected kidney tissue and kidney cancer. We used COSMOS to generate novel hypotheses such as the impact of CDK7 on nucleoside metabolism and its influence on citrulline production, that we validated experimentally. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omic studies.

2021-02-02 | PXD018218 | Pride

Multiomic profiling of in vitro models of endothelial-to-mesenchymal transition reveals endothelial cell subtype is a major determinant of fidelity to observed states in atherosclerosis

Project description:Endothelial-to-mesenchymal transition (EndMT) is an example of endothelial cell (EC) heterogeneity which is commonly modeled in vitro to better understand driving mechanisms of disease proceses, including atherosclerosis. We used multi-modal single nucleus RNA sequencing (snRNA-seq) and ATAC sequencing (snATAC-seq) to analyze the diversity of ECs in vitro, divergent EC responses to known EndMT perturbations, and the ability of in vitro EC known EndMT models to recapitulate the in vivo 'omic profiles of cells observed in atherosclerosis.

2023-05-03 | GSE228428 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data