bioRxivNeurosciencePreprint
Tourette disorder features pervasive neuronal and glial transcriptional remodeling in the dorsolateral prefrontal cortex
Philip J. MoosiD, Caterina BrancaiD, Teresa S. Musci, Giulia BraccagniiD, Easton R. van LuikiD, Marco BortolatoiD
Abstract
Tourette disorder (TD) is a neurodevelopmental condition with a robust genetic basis, characterized by multiple motor and vocal tics. Tics arise from dysfunction within cortico-striatal-thalamo-cortical circuits, with pathological evidence largely implicating striatal interneuron deficits and microglial activation. Recently, the largest genome-wide association study (GWAS) meta-analysis of TD identified polygenic risk enrichment in Brodmann area 9 (BA9), corresponding to the dorsolateral prefrontal cortex (DLPFC), a region implicated in executive control and tic suppression. However, the molecular landscape of BA9 in TD remains unexplored. Here, we performed single-nucleus RNA sequencing of postmortem BA9 from five males with TD and five matched controls, yielding 72,340 nuclei across neuronal and glial populations. While cell-type proportions were preserved, transcriptional remodeling was pervasive. In particular, biosynthetic and translational programs were upregulated across microglia, interneurons, oligodendrocytes, and superficial- and middle-layer excitatory neurons. Most cell types showed enrichment for glucocorticoid-responsive and immediate early gene modules consistent with stress-associated transcriptional activation. Finally, cross-regional comparison with striatal datasets revealed conservation of microglial and oligodendrocyte programs. These findings point to extensive transcriptional reprogramming in the DLPFC of individuals with TD, characterized by stress-associated activation, most strongly in oligodendrocytes and neurons.
- organism: human
- assay: scRNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: cell type annotation
- research focus: spatial domain detection
- approach: statistical model
- Citations
- 0
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