bioRxivNeurosciencePreprint
Increased vulnerability of Purkinje cells in the posterior cerebellum of SCA1 mice is associated with molecular and cellular alterations related to disease pathology
Katherine A. HameliD, Francisco Emmanuel Labrada-MoncadaiD, Carrie A. Sheeler, Juao‐Guilherme S RosaiD, Stephen GilliatiD, Ying ZhangiD, Marija CvetanovićiD
Abstract
Regional heterogeneity of neurons and glia is a key feature of the brain, yet the effect of disease on heterogeneity and its relationship with selective neuronal vulnerability remains poorly understood. Using region-specific RNA sequencing, we identified a large number of differentially expressed genes (DEGs) across distinct regions of the cerebellar cortex, supporting the notable intrinsic regional transcriptional heterogeneity of the healthy cerebellum. Further, we used fiber photometry to identify regional physiological differences in the activity of Purkinje cells (PCs) during self-motivated, unrestrained walking and non-walking states. In the inherited cerebellar neurodegenerative disease Spinocerebellar ataxia type 1 (SCA1), patients exhibit preferential degeneration of the posterior cerebellum, suggesting regionally selective vulnerability. We demonstrated that in a mouse model of SCA1 the Purkinje cells and glia residing in the posterior vermis of cerebellum also undergo earlier and more severe pathology. Intriguingly, the intrinsic transcriptional heterogeneity of anterior and posterior cerebellum seen in healthy mice was diminished in SCA1 mice. This disruption was also demonstrated via fiber photometry, where we found notable impacts in PC activity in the posterior cerebellum as well as loss of regional differences in PC activity during self-motivated, unrestrained walking, and non-walking states in SCA1 mice. Our findings indicate regionally distinct mechanisms of pathogenesis across cerebellar regions that result in reduced intracerebellar heterogeneity.
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 5
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