Integrating spatial and single-nucleus transcriptomic data elucidates microglial-specific responses in female cynomolgus macaques with depressive-like behaviors.
Jing WuYifan LiYu HuangLanxiang LiuHanping ZhangCorina NagyXunmin TanKe ChengYiyun LiuJuncai PuHaiyang WangQingyuan WuSeth W PerryGustavo TureckiMa-Li WongJulio LicinioPeng ZhengPeng XiePublished in: Nature neuroscience (2023)
Major depressive disorder represents a serious public health challenge worldwide; however, the underlying cellular and molecular mechanisms are mostly unknown. Here, we profile the dorsolateral prefrontal cortex of female cynomolgus macaques with social stress-associated depressive-like behaviors using single-nucleus RNA-sequencing and spatial transcriptomics. We find gene expression changes associated with depressive-like behaviors mostly in microglia, and we report a pro-inflammatory microglia subpopulation enriched in the depressive-like condition. Single-nucleus RNA-sequencing data result in the identification of six enriched gene modules associated with depressive-like behaviors, and these modules are further resolved by spatial transcriptomics. Gene modules associated with huddle and sit alone behaviors are expressed in neurons and oligodendrocytes of the superficial cortical layer, while gene modules associated with locomotion and amicable behaviors are enriched in microglia and astrocytes in mid-to-deep cortical layers. The depressive-like behavior associated microglia subpopulation is enriched in deep cortical layers. In summary, our findings show cell-type and cortical layer-specific gene expression changes and identify one microglia subpopulation associated with depressive-like behaviors in female non-human primates.
Keyphrases
- bipolar disorder
- major depressive disorder
- gene expression
- single cell
- stress induced
- inflammatory response
- public health
- prefrontal cortex
- neuropathic pain
- dna methylation
- genome wide
- healthcare
- copy number
- spinal cord
- endothelial cells
- lipopolysaccharide induced
- electronic health record
- big data
- machine learning
- working memory
- data analysis
- induced pluripotent stem cells
- genome wide analysis