Atlas of the Aging Mouse Brain
A single-cell transcriptomic atlas of the aging mouse brain reveals coordinated and cell-type specific aging signatures across multiple cell populations. Catalogues of gene expression profiles, top marker genes, aging-related genes, pathways and ligand-receptor interactions are reported.
Citation
Methodios Ximerakis, Scott L. Lipnick, Brendan T. Innes, Sean K. Simmons, Xian Adiconis, Danielle Dionne, Brittany A. Mayweather, Lan Nguyen, Zachary Niziolek, Ceren Ozek, Vincent L. Butty, Ruth Isserlin, Sean M. Buchanan, Stuart S. Levine, Aviv Regev, Gary D. Bader, Joshua Z. Levin, Lee L. Rubin. Single-cell transcriptomic profiling of the aging mouse brain. Nature Neuroscience 2019. DOI: https://doi.org/10.1038/s41593-019-0491-3
Correspondence to M.X. (methodios_ximerakis@harvard.edu) or L.L.R. (lee_rubin@harvard.edu)
Abstract
The mammalian brain is complex, with multiple cell types performing a variety of diverse functions, but exactly how each cell type is affected in aging remains largely unknown. Here, we performed a single-cell transcriptomic analysis of young and old mouse brains. We provide comprehensive datasets of aging-related genes, pathways and ligand-receptor interactions in nearly all brain cell types. Our analysis identified gene signatures that vary in a coordinated manner across cell types and gene sets that are regulated in a cell-type specific manner, even at times in opposite directions. These data reveal that aging, rather than inducing a universal program, drives a distinct transcriptional course in each cell population, and they highlight key molecular processes, including ribosome biogenesis, underlying brain aging. Overall, these large-scale datasets provide a resource for the neuroscience community that will facilitate additional discoveries directed towards understanding and modifying the aging process.
Additional portals for data exploration
Advanced interactive data viewer: http://shiny.baderlab.org/AgingMouseBrain/AgingMouseBrain_SCV/
Intercellular communication network: http://shiny.baderlab.org/AgingMouseBrain/AgingMouseBrain_CCInx/
Raw data and codes availability
GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE129788
GitHub: https://github.com/BaderLab/AgingMouseBrainCCInx
Preprint on bioRxiv
DOI: https://www.biorxiv.org/content/10.1101/440032v1
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UPDATE: 2023-03-09
Follow-up study in Nature Aging
DOI: https://www.nature.com/articles/s43587-023-00373-6
Preprint
DOI: https://www.biorxiv.org/content/10.1101/2022.01.27.477911v1
Portals
Interactive data viewer: https://singlecell.broadinstitute.org/single_cell/study/SCP2011/aging-mouse-brain-parabiosis
Advanced interactive data viewer: https://rubinlab.connect.hms.harvard.edu/parabiosis/
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NOTES FOR DATA EXPLORATION
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Cell type abbreviations by Cell class
> Oligodendrocyte lineage (and OEG):
OPC: Oligodendrocyte precursor cells
OLG: Oligodendrocytes
OEG: Olfactory ensheathing glia
> Astrocyte lineage (and NSC):
NSC: Neural stem cells
ARP: Astrocyte-restricted precursors
ASC: Astrocytes
> Neuronal lineage:
NRP: Neuronal-restricted precursors
NEUR_immature: immature Neurons
NEUR_mature: mature Neurons
NendC: Neuroendocrine cells
> Ependymal cells:
EPC: Ependymocytes
HypEPC: Hypendymal cells
TNC: Tanycytes
CPC: Choroid plexus epithelial cells
> Vasculature cells:
EC: Endothelial cells
PC: Pericytes
Hb-VC: Hemoglobin-expressing vascular cells
VSMC: Vascular smooth muscle cells
VLMC: Vascular and leptomeningeal cells
ABC: Arachnoid barrier cells
> Immune cells:
MG: Microglia
MNC: Monocytes
MAC: Macrophages
DC: Dendritic cells
NEUT: Neutrophils
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