Browsing by Subject "Cognitive ageing"

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    Defining the causes of sporadic Parkinson's disease in the global Parkinson's genetics program (GP2)
    (Springer Nature, 2023-09-12) Towns, Clodagh; Richer, Madeleine; Jasaityte, Simona; Stafford, Eleanor J.; Joubert, Julie; Antar, Tarek; Martinez-Carrasco, Alejandro; Makarious, Mary B.; Casey, Bradford; Vitale, Dan; Levine, Kristin; Leonard, Hampton; Pantazis, Caroline B.; Screven, Laurel A.; Hernandez, Dena G.; Wegel, Claire E.; Solle, Justin; Nalls, Mike A.; Blauwendraat, Cornelis; Singleton, Andrew B.; Tan, Manuela M. X.; Iwaki, Hirotaka; Morris, Huw R.; Global Parkinson’s Genetics Program (GP2); Medical and Molecular Genetics, School of Medicine
    The Global Parkinson’s Genetics Program (GP2) will genotype over 150,000 participants from around the world, and integrate genetic and clinical data for use in large-scale analyses to dramatically expand our understanding of the genetic architecture of PD. This report details the workflow for cohort integration into the complex arm of GP2, and together with our outline of the monogenic hub in a companion paper, provides a generalizable blueprint for establishing large scale collaborative research consortia.
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    Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing
    (Nature Publishing Group, 2020-10-22) Johnson, Travis S.; Xiang, Shunian; Helm, Bryan R.; Abrams, Zachary B.; Neidecker, Peter; Machiraju, Raghu; Zhang, Yan; Huang, Kun; Zhang, Jie; Medicine, School of Medicine
    Single-cell RNA sequencing (scRNA-seq) resolves heterogenous cell populations in tissues and helps to reveal single-cell level function and dynamics. In neuroscience, the rarity of brain tissue is the bottleneck for such study. Evidence shows that, mouse and human share similar cell type gene markers. We hypothesized that the scRNA-seq data of mouse brain tissue can be used to complete human data to infer cell type composition in human samples. Here, we supplement cell type information of human scRNA-seq data, with mouse. The resulted data were used to infer the spatial cellular composition of 3702 human brain samples from Allen Human Brain Atlas. We then mapped the cell types back to corresponding brain regions. Most cell types were localized to the correct regions. We also compare the mapping results to those derived from neuronal nuclei locations. They were consistent after accounting for changes in neural connectivity between regions. Furthermore, we applied this approach on Alzheimer’s brain data and successfully captured cell pattern changes in AD brains. We believe this integrative approach can solve the sample rarity issue in the neuroscience.