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 

If problems are encountered with the loading and visualization of data, please try a different browser software.

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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