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Single-nucleus transcriptome analysis reveals cell-type-specific molecular signatures across reward circuitry in the human brain.
- Source :
-
Neuron . Oct2021, Vol. 109 Issue 19, p3088-3088. 1p. - Publication Year :
- 2021
-
Abstract
- Single-cell gene expression technologies are powerful tools to study cell types in the human brain, but efforts have largely focused on cortical brain regions. We therefore created a single-nucleus RNA-sequencing resource of 70,615 high-quality nuclei to generate a molecular taxonomy of cell types across five human brain regions that serve as key nodes of the human brain reward circuitry: nucleus accumbens, amygdala, subgenual anterior cingulate cortex, hippocampus, and dorsolateral prefrontal cortex. We first identified novel subpopulations of interneurons and medium spiny neurons (MSNs) in the nucleus accumbens and further characterized robust GABAergic inhibitory cell populations in the amygdala. Joint analyses across the 107 reported cell classes revealed cell-type substructure and unique patterns of transcriptomic dynamics. We identified discrete subpopulations of D1- and D2-expressing MSNs in the nucleus accumbens to which we mapped cell-type-specific enrichment for genetic risk associated with both psychiatric disease and addiction. [Display omitted] • snRNA sequencing (70,615 nuclei) of 5 human brain regions with roles in reward • Characterization of the transcriptomic architecture across 107 cell classes • Genetic risk association for substance use phenotypes in specific cell populations • Interactive app for each brain region to explore genes of interest in cell classes For this NeuroResource, >100 cell classes from five brain regions in the human reward circuitry are characterized by single-nucleus RNA sequencing, with interactive apps made available. The molecular relationships across this circuitry are described, and genetic risk for various psychiatric and substance use phenotypes is quantified across all cell classes. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08966273
- Volume :
- 109
- Issue :
- 19
- Database :
- Academic Search Index
- Journal :
- Neuron
- Publication Type :
- Academic Journal
- Accession number :
- 152846371
- Full Text :
- https://doi.org/10.1016/j.neuron.2021.09.001