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Single-cell transcriptomic profiling of the aging mouse brain

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 (accessible online at https://portals.broadinstitute.org/single_cell/study/aging-mouse-brain) provide a resource for the neuroscience community that will facilitate additional discoveries directed towards understanding and modifying the aging process.

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Fig. 1: Identification of cell types.
Fig. 2: Aging-related population shifts and changes in gene expression.
Fig. 3: Validation of shared and cell-type-specific aging-related gene expression changes.
Fig. 4: Validation of bidirectional aging-related gene expression changes.
Fig. 5: Aging-related changes in the expression of ribosomal protein genes.
Fig. 6: Aging-related changes in cellular pathways and processes.
Fig. 7: Aging-related changes in intercellular communication.

Data availability

The raw single-cell RNA sequencing data are available through NCBI’s Gene Expression Omnibus (GEO) under the accession number GSE129788. The processed datasets can be readily viewed, explored and downloaded through our web-based interactive viewers at https://portals.broadinstitute.org/single_cell/study/aging-mouse-brain and http://shiny.baderlab.org/AgingMouseBrain/.

Code availability

The code used to perform analysis of the sequencing data was an adaptation of standard R packages, such as Seurat and MAST, as described in the Methods section. The code used for the ligand–receptor interaction analyses is available on GitHub at https://github.com/BaderLab/AgingMouseBrainCCInx. More detailed information is available upon request.

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Acknowledgments

We acknowledge T. Okino and his team at Ono Pharmaceuticals for fruitful discussions and useful suggestions during the progress of this work. We are grateful to F. Rapino, N. Rodriguez-Muela, K. Pfaff, A. Freeman and J. LaLonde for their helpful advice in different aspects of our work and/or for reviewing the manuscript. We also thank the stuff members of the Harvard Bauer Core Facility, the Harvard Center for Biological Imaging and the Harvard Stem Cell and Regenerative Biology Histology Core for their technical advice and assistance. The work was supported by Ono Pharmaceutical Co., Ltd (L.L.R.), the Stanley Center for Psychiatric Research and the Klarman Cell Observatory (J.Z.L). The funders had no role in the study design, experiments performed, data collection, data analysis and interpretation, or preparation of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

M.X., S.L.L., S.M.B. and L.L.R. conceived the study. M.X., S.L.L., S.M.B., J.Z.L. and L.L.R. designed the study. M.X., X.A., D.D. and L.N. performed the scRNA-seq experiments. S.L.L., S.K.S. and J.Z.L. processed the scRNA-seq data. M.X. and S.L.L. analyzed the scRNA-seq data. B.T.I. and G.D.B created the ligand–receptor interaction network. M.X. and V.L.B. performed the bulk RNA-seq experiments. S.L.L and V.L.B. processed the bulk RNA-seq data. M.X. and S.L.L. analyzed the bulk RNA-seq data. M.X. and Z.N. performed the flow cytometry experiments. M.X. performed the qRT-PCR experiments. M.X. and B.A.M. performed the RNAscope ISH experiments. M.X. and C.O. performed the IHC experiments. R.I. and G.D.B. provided the cell–cell interaction dataset. M.X., S.L.L., S.S.L., A.R., G.D.B. and J.Z.L. supervised aspects of the study. L.L.R. supervised the whole study. J.Z.L. and L.L.R. secured funding. M.X. wrote the original draft of the manuscript. S.L.L., S.M.B. and L.L.R. edited the manuscript. All authors reviewed the manuscript and approved its submission.

Corresponding authors

Correspondence to Methodios Ximerakis or Lee L. Rubin.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Mark Cookson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Supplementary information

Supplementary Figs. 1–18.

Reporting Summary

Supplementary Table 1

List of abbreviations for all cell types and subtypes.

Supplementary Table 2

Transcriptional profile of each cell type and subtype by age. This file contains 9 worksheet tabs (1 for the whole brain, 1 for all cell types, 6 for the cell classes and 1 for the additional neuronal subtypes). Each tab contains the gene expression profile of the corresponding population by age in the form of transcripts per million (TPM). ‘Y’ prefix indicates expression in young brains and ‘O’ in old brains.

Supplementary Table 3

List of most discriminating genes per cell type. These cell-type marker genes were calculated by aggregating all young and old cells identified as belonging to a certain cell type and comparing them to all the other brain cells. Each cell type has its own worksheet tab within the file with data showing thus: gene name (Gene); original P value (pval); FDR-adjusted P value (padj); the fold change expression (avg_diff); the fraction of cells transcribing the corresponding gene in the cell type of interest (pct.1); and the fraction of cells transcribing the gene in all the remaining brain cell types (pct.2).

Supplementary Table 4

List of most discriminating genes per neuronal subtype. These cell-type marker genes were calculated by aggregating all young and old cells identified as belonging to a certain major (CHOL, DOPA, GABA, GLUT) or minor (e.g., GABA_1, GLUT_1) neurotransmitter-expressing neuronal subtype and comparing them to all the other cells within the neuronal lineage class. Each major/minor neuronal subtype has its own worksheet tab within the file with data showing thus: gene name (Gene); original P value (pval); the fold change expression (avg_diff); the fraction of cells transcribing the corresponding gene in the subtype of interest (pct.1); and the fraction of cells transcribing the gene in all the remaining cell types/subtypes within the neuronal lineage class (pct.2).

Supplementary Table 5

Cell counts of each cell type and subtype by age and animal. This file contains 8 worksheet tabs (1 for all cell types, 6 for the cell classes and 1 for the additional neuronal subtypes). Each tab contains cell counts split out by cell type/subtype/state (Cell_type), animal of origin (Animal_name), animal age (Animal_type), total number of cells (Total_cells) and the fraction of cells of the specific cell type/subtype/state within the animal of origin (Fraction_per_brain).

Supplementary Table 6

Differential gene expression data between young and old cell types. Each cell type has its own worksheet tab within the file with data as determined by MAST analysis showing thus: gene name (Gene); original P value (pval); FDR-adjusted P value (padj); log fold change from young to old (logFC_Young_to_Old); folds change from young to old, based on the LogFC values (FC_Young_to_Old); folds change from young to old, based on the TPM values (TPM_based_FC_Young_to_Old); gene expression in the form of transcripts per million (TPM); standard deviation for TPM by cell (SD); fraction of cells that are positive for the gene (Percent_Pos_Cells); and fraction of animals with cells expressing the specified gene (Percentage_of_animals_positive_for_gene).

Supplementary Table 7

Differential gene expression data between young and old neuronal subtypes. Each neuronal subtype has its own worksheet tab within the file with data as determined by MAST analysis showing thus: gene name (Gene); original P value (pval); FDR-adjusted P value (padj); log fold change from young to old (logFC_Young_to_Old); folds change from young to old, based on the LogFC values (FC_Young_to_Old); folds change from young to old, based on the TPM values (TPM_based_FC_Young_to_Old); gene expression in the form of transcripts per million (TPM); standard deviation for TPM by cell (SD); fraction of cells that are positive for the gene (Percent_Pos_Cells); and fraction of animals with cells expressing the specified gene (Percentage_of_animals_positive_for_gene).

Supplementary Table 8

Matrix of aging-related genes across cell types and neuronal subtypes. This file contains logFC values for all the identified aging-related genes (FDR < 0.05 and FC > 10%), as determined by MAST analysis, across 15 cell types and 3 major neurotransmitter-expressing neuronal subtypes. Positive values indicate upregulation with aging (in red), while negative values indicate downregulation with aging (in blue).

Supplementary Table 9

List of aging-related pathways and processes by cell type and neuronal subtype. Two rounds of gene set enrichment analysis (GSEA) were performed, using either preranked genes lists with all genes transcribed (‘all genes’) or without the highly abundant mitochondrially encoding genes and ribosomal protein genes (‘no mito & ribo’). Each round has its own worksheet tab within the file with data showing thus: the cell-type identity (Population); the original GSEA term (GSEA term); the direction with aging (Aging direction); the size of gene set (Size); the enrichment score (ES); the normalized enrichment score (NES); the nominal P value (NOM p-val) and the FDR q value (FDR q-val). Only significant GSEA terms are listed (GSEA statistics; P < 0.05, with cutoff q < 0.25). To improve interpretation, GSEA terms overrepresenting the same pathway were grouped together (Node/Pathway), while nodes/pathways belonging to similar biological processes were also grouped together (Process) for easier navigation/exploration (see details in Methods).

Supplementary Table 10

Matrix of aging-related pathways and processes across cell types and neuronal subtypes. This file contains NES values for all GSEA terms (from both rounds of pathway analysis) that were significantly associated with aging (GSEA statistics; P < 0.05, with cutoff q < 0.25) across 18 cell types and 3 major neurotransmitter-expressing neuronal subtypes. Positive values indicate upregulation with aging (in red), while negative values indicate downregulation with aging (in blue).

Supplementary Table 11

List of primers sequences used in qRT-PCR.

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Ximerakis, M., Lipnick, S.L., Innes, B.T. et al. Single-cell transcriptomic profiling of the aging mouse brain. Nat Neurosci 22, 1696–1708 (2019). https://doi.org/10.1038/s41593-019-0491-3

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