Journal of NeuroscienceGenomicsPeer-reviewed journal article
Single-nucleus RNA-seq reveals dysregulation of striatal cell identity due to Huntington’s disease mutations
Sonia MalaiyaiD, Marcia Cortes-GutierreziD, Brian R. HerbiD, Sydney R. CoffeyiD, Samuel R.W. Legg, Jeffrey P. CantleiD, Carlo ColantuoniiD, Jeffrey B. CarrolliD, Seth A. AmentiD
Abstract
Huntingtons disease (HD) is a dominantly inherited neurodegenerative disorder caused by a trinucleotide expansion in exon 1 of the huntingtin (Htt) gene. Cell death in HD occurs primarily in striatal medium spiny neurons (MSNs), but the involvement of specific MSN subtypes and of other striatal cell types remains poorly understood. To gain insight into cell type-specific disease processes, we studied the nuclear transcriptomes of 4,524 cells from the striatum of a genetically precise knock-in mouse model of the HD mutation, HttQ175/+, and from wildtype controls. We used 14-15-month-old mice, a time point roughly equivalent to an early stage of symptomatic human disease. Cell type distributions indicated selective loss of D2 MSNs and increased microglia in aged HttQ175/+ mice. Thousands of differentially expressed genes were distributed across most striatal cell types, including transcriptional changes in glial populations that are not apparent from RNA-seq of bulk tissue. Reconstruction of cell typespecific transcriptional networks revealed a striking pattern of bidirectional dysregulation for many cell type-specific genes. Typically, these genes were repressed in their primary cell type, yet de-repressed in other striatal cell types. Integration with existing epigenomic and transcriptomic data suggest that partial loss-of-function of the Polycomb Repressive Complex 2 (PRC2) may underlie many of these transcriptional changes, leading to deficits in the maintenance of cell identity across virtually all cell types in the adult striatum.
- organism: mouse
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: experimental assay
- Citations
- 73
- FWCI
- 6.22
- Field percentile
- top 2%
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