Single-cell genomics & regulatory networks for 388 human brains
Ontology highlight
ABSTRACT: Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.
Project description:Using an optimized data-independent acquisition approach on trapped ion mobility spectrometry, we measured proteins, post-translational modifications and variant peptides in single cells and single nuclei, which provided insights into heterogeneity of cell-state and signaling dependencies at the single cell level and the molecular impact of an epigenetic drug at the level of a single organelle.
Project description:Single cell mRNA-seq (3' UMI counting) experiments of the sperm and vegetative nuclei from the Arabidopsis pollen to investigate the heterogeneity of those cell types.
Project description:Identification of cell types in the interphase between muscle and tendon by Visium Spatial Transcriptomics of four human semitendinous muscle-tendon biopsies. Cell types identified by single nuclei RNA seq on similar tissue were localized in situ with the use of Spatial Transcriptomics.
Project description:Cell types in the human retina are highly heterogeneous with their abundance varies by several orders of magnitude. To decipher the complexity of gene expression and regulation of the human retinal cell types, we generated a multi-omics single-cell atlas of the adult human retina, including over 250K nuclei for single-nuclei RNA-seq and 150K nuclei for single-nuclei ATAC-seq. Over 60 cell subtypes have been identified based on their transcriptomic profiles, reaching a sensitivity of 0.01%. Integrative analysis of this single-cell multi-omics dataset identified gene regulatory elements across the genome for each cell subtype. In addition, when combined with other data modalities, such as eQTL, potential causal variants can be identified through fine mapping. Taken together, this new dataset represents the most comprehensive single-cell multi-omics profiling for the human retina that enables in-depth molecular characterization of most cell subtypes.
Project description:We report a single-nuclei RNA-seq (snRNA-seq) transcriptomic study on human neural retinal tissue to identify transcriptome profile for individual cell types. Six retina samples from three healthy donors were profiled and RNA-seq data with high quality was obtained for 5873 single nuclei. All seven major retinal cell types were observed from the dataset and marker genes for each cell type were identified by differential gene express analysis.
Project description:To identify LPS activated cell types in the NTS and AP we region we used single-nuclei RNA seq comparing baseline cell types and cell types labeled using the TRAP2 mouse line crossed to the INTACT mouse line after labeling during LPS treatment.
Project description:In this study we performed single-cell RNA sequencing and single-nuclei ATAC-seq of E12.5- E14.5 mouse cerebella to reveal molecular and epigenetic features of various cerebellar cell types or cell states.