NeuronNeurosciencePeer-reviewed journal article
Longitudinal single-cell transcriptional dynamics throughout neurodegeneration in SCA1
Leon TejwaniiD, Neal G. RavindraiD, Chang‐Woo LeeiD, Yubao ChengiD, Billy Nguyen, Kimberly P. LuttikiD, Luhan NiiD, Shupei Zhang, Logan M MorrisoniD, John T. GioncoiD, Yangfei XiangiD, Jennifer YooniD, Hannah J. RoiD, Fatema Haidery, Rosalie M. GrijalvaiD, Eunwoo Bae, Kristen KimiD, Regina T. MartuscelloiD, Harry T. OrriD, Huda Yahya ZoghbiiD, Hayley S. McLoughliniD, Laura P.W. RanumiD, Vikram G. ShakkottaiiD, Phyllis L. FaustiD, Siyuan S. WangiD, , Janghoo LimiD
Abstract
Neurodegeneration is a protracted process involving progressive changes in myriad cell types that ultimately result in neuronal death. Changes in vulnerable neuronal populations are highly influenced by concomitant changes in surrounding cells, complicating experimental approaches to interrogate the simultaneous events that underlie neurodegeneration. To dissect how individual cell types within a heterogeneous tissue contribute to the pathogenesis and progression of a neurodegenerative disorder, we performed longitudinal single-nucleus RNA sequencing of the mouse and human spinocerebellar ataxia type 1 (SCA1) cerebellum, establishing continuous dynamic trajectories of each population. Furthermore, we defined the precise transcriptional changes that precede loss of Purkinje cells and identified early oligodendroglial impairments that can profoundly impact cerebellar function. Finally, we applied a deep learning method to accurately predict disease state and identify drivers of disease. Together, this work uncovers new roles for diverse cerebellar cell types in SCA1 and provides a generalizable analysis framework for studying neurodegeneration.
- organism: mouse
- organism: human
- assay: scRNA-seq
- research focus: trajectory inference
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 37
- FWCI
- 3.11
- Field percentile
- top 6%
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