伏隔核
生物
转录组
前额叶皮质
神经科学
神经退行性变
基因表达
中棘神经元
电池类型
基因
多巴胺
遗传学
纹状体
内科学
细胞
医学
疾病
认知
作者
Edwin J. C. G. van den Oord,Lin Xie,Min Zhao,Karolina A. Åberg,Shaunna L. Clark
摘要
Abstract Gene expression studies offer promising opportunities to better understand the processes underlying alcohol use disorder (AUD). As cell types differ in their function, gene expression profiles will typically vary across cell types. When studying bulk tissue, failure to account for this cellular diversity has a detrimental impact on the ability to detect disease associations. We therefore assayed the transcriptomes of 32,531 individual nuclei extracted from the nucleus accumbens (NAc) of nine donors with AUD and nine controls (72% male). Our study identified 17 clearly delineated cell types. We detected 26 transcriptome‐wide significant differentially expressed genes (DEGs) that mainly involved medium spiny neurons with both D1‐type and D2‐type dopamine receptors, microglia (MGL) and oligodendrocytes. A higher than expected number of DEGs replicated in an existing single nucleus gene expression study of alcohol dependence in the prefrontal cortex (enrichment ratio 1.91, p value 0.019) with two genes remaining significant after a Bonferroni correction. Our most compelling result involved CD53 in MGL that replicated in the same cell type in the prefrontal cortex and was previously implicated in studies of DNA methylation, bulk gene expression and genetic variants. Several DEGs were previously reported to be associated with AUD (e.g., PER1 and MGAT5 ). The DEGs for MSN.3 seemed involved in neurodegeneration, disruption of circadian rhythms, alterations in glucose metabolism and changes in synaptic plasticity. For MGL, the DEGs implicated neuroinflammation and immune‐related processes and for OLI, disruptions in myelination. This identification of the specific cell‐types from which the association signals originate will be key for designing proper follow‐up experiments and, eventually, novel clinical interventions.
科研通智能强力驱动
Strongly Powered by AbleSci AI