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Multiregion transcriptomic profiling of the primate brain reveals signatures of aging and the social environment

Nature Neuroscience volume 25, pages 1714–1723 (2022)Cite this article

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Abstract

Aging is accompanied by a host of social and biological changes that correlate with behavior, cognitive health and susceptibility to neurodegenerative disease. To understand trajectories of brain aging in a primate, we generated a multiregion bulk (N = 527 samples) and single-nucleus (N = 24 samples) brain transcriptional dataset encompassing 15 brain regions and both sexes in a unique population of free-ranging, behaviorally phenotyped rhesus macaques. We demonstrate that age-related changes in the level and variance of gene expression occur in genes associated with neural functions and neurological diseases, including Alzheimer’s disease. Further, we show that higher social status in females is associated with younger relative transcriptional ages, providing a link between the social environment and aging in the brain. Our findings lend insight into biological mechanisms underlying brain aging in a nonhuman primate model of human behavior, cognition and health.

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Fig. 1: Experimental design and global expression patterns.
Fig. 2: Age broadly influences mean transcription levels across the brain.
Fig. 3: Parallel age-associated transcriptional signatures between macaques and humans.
Fig. 4: Age-associated changes in variance of transcription across the brain.
Fig. 5: Age alters both brain cell proportions and cell-type-specific expression.
Fig. 6: Social status signatures of the aging transcriptome.

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Data availability

The data generated in this study can be downloaded in raw and processed forms from the NCBI Gene Expression Omnibus under series accession number GSE179330.

Datasets/databases used in this study are publicly available and include the rhesus macaque Mmul_10 reference assembly (NCBI RefSeq accession GCF_003339765.1), Ensembl release 101 (http://aug2020.archive.ensembl.org), KEGG release 100.0 (https://www.genome.jp/kegg), GTEx analysis V8 (dbGaP accession phs000424.v8.p2) and the Alzheimer’s Knowledge Portal AMP-AD meta-analysis (https://doi.org/10.7303/syn11914606).

All requests for biological material should be directed to the CBRU (cbru@pennmedicine.upenn.edu). Requests will be reviewed by the Scientific Review Committee and granted depending on availability, proposed use, investigator funding and other considerations.

Code availability

All code for this study is accessible through the following GitHub repositories: https://github.com/CayoBiobankResearchUnit/brain_transcriptome_aging_bulk (bulk RNA-seq analysis), https://github.com/CayoBiobankResearchUnit/brain_transcriptome_aging_sc (single-nucleus RNA-seq analysis), https://github.com/bbi-lab/bbi-dmux (sci-RNA-seq3 data demultiplexing) and https://github.com/bbi-lab/bbi-sci (sci-RNA-seq3 data preprocessing).

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Acknowledgements

We thank those who make our research possible, particularly the CPRC and the Cayo Santiago Field Station staff, without whom we would not be able to study this amazing system. We also thank the staff of the Sabana Seca Field Station for assistance with sample collection and J. Cao, A. Lea, N. Simons, R. Campbell, J. Tung, I. Schneider-Crease and the University of Washington Basic Biology of Aging training group for valuable feedback at various stages throughout the project. Funding for this research was provided by the National Institutes of Health (R01-MH118203 to M.L.P., U01-MH121260 to N.S.-M., M.L.P., and J. Shendure, R01-MH096875 to M.L.P., R01-AG060931 to N.S.-M., L.J.N.B., and J.P.H., R00-AG051764 to N.S.-M., R01-NS097537 to J.M.N., R35-GM124827 to M.A.W., K99-AG075241 to K.L.C. and P40-OD012217 to M.I.M.), the National Science Foundation (BCS-1800558 to J.P.H. and BCS-1752393 to A.R.D.) and a pilot grant to N.S.-M. from the Brotman Baty Institute. K.L.C. was supported by National Institutes of Health fellowship T32-AG000057 during this research.

The GTEx Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health (https://commonfund.nih.gov/GTEx). Additional funds were provided by the NCI, NHGRI, NHLBI, NIDA, NIMH and NINDS. Donors were enrolled at Biospecimen Source Sites funded by NCI\Leidos Biomedical Research, Inc., subcontracts to the National Disease Research Interchange (10XS170), Roswell Park Cancer Institute (10XS171) and Science Care, Inc. (X10S172). The Laboratory, Data Analysis and Coordinating Center (LDACC) was funded through a contract (HHSN268201000029C) to The Broad Institute, Inc. Biorepository operations were funded through a Leidos Biomedical Research, Inc., subcontract to Van Andel Research Institute (10ST1035). Additional data repository and project management were provided by Leidos Biomedical Research. (HHSN261200800001E). The Brain Bank was supported by supplements to University of Miami grant DA006227. Statistical Methods development grants were made to the University of Geneva (MH090941 and MH101814), the University of Chicago (MH090951, MH090937, MH101825 and MH101820), the University of North Carolina Chapel Hill (MH090936), North Carolina State University (MH101819), Harvard University (MH090948), Stanford University (MH101782), Washington University (MH101810) and the University of Pennsylvania (MH101822). The datasets used for the analyses described in this manuscript were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/gap through dbGaP accession number phs000424.v8.p2.

The results published here are in whole or in part based on data obtained from the AD Knowledge Portal (https://adknowledgeportal.org/). MayoRNAseq data were provided by the following sources: The Mayo Clinic Alzheimer’s Disease Genetic Studies, led by N. Taner and S. G. Younkin, Mayo Clinic, Jacksonville, FL, using samples from the Mayo Clinic Study of Aging, the Mayo Clinic Alzheimer’s Disease Research Center and the Mayo Clinic Brain Bank. Data collection was supported through funding by NIA grants P50 AG016574, R01 AG032990, U01 AG046139, R01 AG018023, U01 AG006576, U01 AG006786, R01 AG025711, R01 AG017216 and R01 AG003949, NINDS grant R01 NS080820, CurePSP Foundation and support from Mayo Foundation. Study data include samples collected through the Sun Health Research Institute Brain and Body Donation Program of Sun City, AZ. The Brain and Body Donation Program is supported by the NINDS (U24 NS072026 National Brain and Tissue Resource for Parkinson’s Disease and Related Disorders), the NIA (P30 AG19610 Arizona Alzheimer’s Disease Core Center), the Arizona Department of Health Services (contract 211002, Arizona Alzheimer’s Research Center), the Arizona Biomedical Research Commission (contracts 4001, 0011, 05-901 and 1001 to the Arizona Parkinson’s Disease Consortium) and the Michael J. Fox Foundation for Parkinson’s Research. MSBB data were generated from postmortem brain tissue collected through the Mount Sinai VA Medical Center Brain Bank and were provided by E. Schadt from Mount Sinai School of Medicine. ROSMAP data were provided by the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL. Data collection was supported through funding by NIA grants P30AG10161 (ROS), R01AG15819 (ROSMAP; genomics and RNA-seq), R01AG17917 (MAP), R01AG30146, R01AG36042 (5hC methylation and ATACseq), RC2AG036547 (H3K9Ac), R01AG36836 (RNA-seq), R01AG48015 (monocyte RNA-seq) RF1AG57473 (single-nucleus RNA-seq), U01AG32984 (genomics and whole-exome sequencing), U01AG46152 (ROSMAP AMP-AD and targeted proteomics), U01AG46161 (TMT proteomics), U01AG61356 (whole-genome sequencing, targeted proteomics and ROSMAP AMP-AD), the Illinois Department of Public Health (ROSMAP) and the Translational Genomics Research Institute (genomics). Additional phenotypic data can be requested at www.radc.rush.edu.

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Author notes

  1. These authors contributed equally: Kenneth L. Chiou, Alex R. DeCasien.

Authors and Affiliations

  1. Center for Evolution and Medicine, Arizona State University, Tempe, AZ, USA

    Kenneth L. Chiou, Melissa A. Wilson & Noah Snyder-Mackler

  2. School of Life Sciences, Arizona State University, Tempe, AZ, USA

    Kenneth L. Chiou, Katherina P. Rees, Josué E. Negrón-Del Valle, Melissa A. Wilson, Jason M. Newbern & Noah Snyder-Mackler

  3. Department of Psychology, University of Washington, Seattle, WA, USA

    Kenneth L. Chiou, Arianne Mercer & Noah Snyder-Mackler

  4. Nathan Shock Center of Excellence in the Basic Biology of Aging, University of Washington, Seattle, WA, USA

    Kenneth L. Chiou & Noah Snyder-Mackler

  5. Department of Anthropology, New York University, New York, NY, USA

    Alex R. DeCasien, Connor J. Whalen, Susan C. Antón & James P. Higham

  6. New York Consortium in Evolutionary Primatology, New York, NY, USA

    Alex R. DeCasien, Susan C. Antón & James P. Higham

  7. Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA

    Camille Testard, Sébastien Tremblay, Michael J. Montague & Michael L. Platt

  8. Brotman Baty Institute for Precision Medicine, Seattle, WA, USA

    Cailyn H. Spurrell, Aishwarya A. Gogate, Hannah A. Pliner, Lea M. Starita & Jay Shendure

  9. Seattle Children’s Research Institute, Seattle, WA, USA

    Aishwarya A. Gogate

  10. School of Science, Engineering, & Environment, University of Salford, Salford, UK

    Mareike C. Janiak

  11. Caribbean Primate Research Center, University of Puerto Rico, San Juan, PR, USA

    Samuel E. Bauman Surratt, Nicole R. Compo, Angelina V. Ruiz-Lambides & Melween I. Martínez

  12. Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX, USA

    Olga González

  13. Department of Sociology and Anthropology, Metropolitan State University of Denver, Denver, CO, USA

    Michala K. Stock

  14. Department of Anthropology and Archaeology, University of Calgary, Calgary, Alberta, Canada

    Amanda D. Melin

  15. Department of Medical Genetics, University of Calgary, Calgary, Alberta, Canada

    Amanda D. Melin

  16. Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada

    Amanda D. Melin

  17. Department of Molecular Biomedical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, NC, USA

    Christopher S. Walker

  18. Stem Cell and Brain Research Institute, Université Lyon, Lyon, France

    Jérôme Sallet

  19. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Lea M. Starita & Jay Shendure

  20. Howard Hughes Medical Institute, Seattle, WA, USA

    Jay Shendure

  21. Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA

    Jay Shendure

  22. Centre for Research in Animal Behaviour, University of Exeter, Exeter, UK

    Lauren J. N. Brent

  23. Marketing Department, University of Pennsylvania, Philadelphia, PA, USA

    Michael L. Platt

  24. Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA

    Michael L. Platt

  25. Center for Studies in Demography & Ecology, University of Washington, Seattle, WA, USA

    Noah Snyder-Mackler

  26. ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, USA

    Noah Snyder-Mackler

  27. School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA

    Noah Snyder-Mackler

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  1. Kenneth L. Chiou
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  13. Samuel E. Bauman Surratt
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Consortia

Cayo Biobank Research Unit

  • Susan C. Antón
  • , Lauren J. N. Brent
  • , James P. Higham
  • , Melween I. Martínez
  • , Amanda D. Melin
  • , Michael J. Montague
  • , Michael L. Platt
  • , Jérôme Sallet
  •  & Noah Snyder-Mackler

Contributions

N.S.-M., M.L.P., M.J.M., J.P.H., L.J.N.B., K.L.C. and A.R.D. conceptualized the research. N.S.-M., J. Shendure, M.L.P., M.J.M., L.M.S. and K.L.C. conceptualized the single-cell sequencing studies. M.J.M., N.S.-M., K.L.C., A.R.D., O.G., N.R.C., S.E.B.S., M.C.J., C.J.W. and S.T. collected brain tissue, facilitated by M.I.M., A.V.R.-L., J. Sallet, C.S.W., S.C.A., M.K.S., A.D.M., J.P.H., M.L.P. and CBRU. M.A.W. contributed data. K.L.C., A.R.D., A.M. and C.H.S. performed genomic lab work. J.E.N.-D.V. collected behavioral data using a protocol designed by L.J.N.B. K.L.C., A.R.D., C.H.S., A.A.G. and H.A.P. performed genomic analysis with input from N.S.-M. and J. Shendure. C.T. and L.J.N.B. performed behavioral analysis. K.P.R. and J.M.N. performed protein experiments and analysis. K.L.C., A.R.D. and N.S.-M. wrote the manuscript. All authors reviewed and revised the manuscript. Full membership of the CBRU: S. C. Antón, L. J. N. Brent, J. P. Higham, M. I. Martínez, A. D. Melin, M. J. Montague, M. L. Platt, J. Sallet and N. Snyder-Mackler

Corresponding authors

Correspondence to Kenneth L. Chiou, Alex R. DeCasien or Noah Snyder-Mackler.

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Competing interests

J. Shendure is a scientific advisory board member, consultant and/or cofounder of Cajal Neuroscience, Guardant Health, Maze Therapeutics, Camp4 Therapeutics, Phase Genomics, Adaptive Biotechnologies, Scale Biosciences and Sixth Street Capital. M.L.P. is a scientific advisory board member, consultant and/or cofounder of Blue Horizons International, NeuroFlow, Amplio, Cogwear Technologies, Burgeon Labs and Ashurst Cognitive Health and receives research funding from AIIR Consulting, the SEB Group, Mars Inc., Slalom Inc., the Lefkort Family Research Foundation, Sisu Capital and Benjamin Franklin Technology Partners. All other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Sample and study design.

(a) Location of Cayo Santiago off the southeastern coast of Puerto Rico. (b) Age distribution of individuals sampled for the bulk tissue RNA sequencing dataset. Selected individuals span the natural adult age distribution and are evenly balanced between sexes. (c) Age distribution of individuals per brain region (excludes samples not passing laboratory or bioinformatic quality control). (d) Age distribution of females sampled for the single-nucleus RNA sequencing dataset. Selected individuals span the natural age distribution and are evenly balanced between two naturally occurring social groups (HH and KK) on Cayo Santiago. (e) Hazard rates from a previously published demographic model of Cayo Santiago macaques (ref. 2) demonstrate that individuals in the age range of our sample experience age-associated increases in mortality risk (N = 11,659 biologically independent animals). Error bars represent the mean hazard rate ± the standard error.

Extended Data Fig. 2 Global similarity of gene expression across brain regions.

(a) Dendrogram showing hierarchical clustering results on averaged expression for each brain region. (b) The same dendrogram visualized over 1000 bootstrap replicates in which libraries were randomly sampled with replacement before calculating average expression and repeating hierarchical clustering. (c) Dendrogram showing hierarchical clustering results on all libraries. Terminal branches are colored by brain region.

Extended Data Fig. 3 Comparison of variance partitioning of age between macaques and humans.

(a) Age explains similar proportions of variance in global gene expression across diverse brain regions between macaques from this study (left) and humans from the GTEx study (right). Median variance explained by age was 0.19% (interquartile range [IQR] = 0.04–0.51%) in this study (N = 527 biologically independent samples) and 0.26% (IQR = 0.07–0.82%) in the GTEx study (N = 2,642 biologically independent samples). Effects of technical covariates were first removed to facilitate this comparison. (b) Age explains similar proportions of variance in gene expression within individual brain regions. Median variance explained by age ranged from 0.6–6.4% across brain regions from this study (N = 36 biologically independent animals, left) and ranged from 0.3–4.2% across brain regions from the GTEx study (N = 382 biologically independent individuals, right). Box plots depict the median (center), and IQR (bounds of box), with whiskers extending to either the maxima/minima or to the median ±1.5×IQR, whichever is nearest.

Extended Data Fig. 4 Analysis of age-associated effects on gene expression levels.

(a) Quantile-quantile (QQ) plots of P values from initial efficient mixed model association (EMMA) tests show enrichment of low P values across nearly all brain regions tested (all EMMA tests are two-sided). (b) A multivariate adaptive shrinkage (MASH) approach substantially improves statistical power by leveraging shared patterns between tissue datasets, resulting in a greater number of genes passing a threshold (local false sign rate [LFSR] < 0.2) relative to a similar threshold applied to our EMMA results (false discovery rate [FDR] < 0.2). (c) The number of significant genes visualized over a range of LFSR thresholds shows extremely stable rank order of brain regions. (d) Whole-brain age-differentially expressed genes (wbaDEGs) cross-referenced with our gene trajectory results (Fig. 1d) demonstrate that the vast majority of wbaDEGs fall into four clusters, marked by asterisks (***). Most wbaDEGs decreasing in expression with age fall into trajectories associated with signaling-related functions, while most wbaDEGs increasing in expression with age fall into a trajectory associated with the immune response. In the bottom panel, the percentage of genes assigned to wbaDEGs is plotted on the y axis.

Extended Data Fig. 5 Similarities of age effects across brain regions.

(a) Proportion of aDEGs with shared magnitude between brain regions. aDEGs share magnitude between regions when they share signs and their effect estimates are within a factor of 2 from one another. These conditions for sharing are more stringent than those in Fig. 2b, which does not require the latter criterion. For each pair of regions, genes are included if they were significant (LFSR < 0.2) in either region. (b) Upset plot highlights brain region combinations with the greatest number of aDEGs with shared signs. Note that among the top region combinations are single-region-specific aDEGs as well as wbaDEGs. (c) Proportion of aDEGs that exhibit (top) shared signs or (bottom) shared magnitudes across variable numbers of brain regions. These distributions reveal that subcortical regions sampled exhibited slightly broader sharing than cortical regions. For this analysis, genes are included if they were significant (LFSR < 0.2) in any region.

Extended Data Fig. 6 Cell-type marker genes and proportional changes with age.

(a) Expression of top 5 marker genes (according to the pseudo-R2 statistic) for 8 assigned cell types. (b) Age plotted against cell cluster percentages. Asterisks in cluster titles indicate a significant effect of age on cell cluster percentages based on a linear model (two-sided test, excitatory neurons 6: Bonferroni-adjusted P = 0.014; excitatory neurons 10: Bonferroni-adjusted P = 0.024).

Extended Data Fig. 7 Age-associated differences in gene expression within single cell types.

(a) QQ plot of EMMA tests on pseudobulk data show a strong enrichment of low P values (all EMMA tests are two-sided). (b) Comparison of model estimates from EMMA (FDR < 0.2) and MASH (LFSR < 0.2) show similar numbers of significant genes. (c). Cell-type aDEG counts (MASH) classified according to overlap with expressed genes and aDEGs from bulk RNA-seq analysis of the dlPFC. (d). Percentages of aDEGs overlapping with aDEGs from bulk RNA-seq analysis of the dlPFC (excluding genes not expressed in both modalities). (e) Rank order of significant genes per cell type are robust across a range of significance (LFSR) thresholds. (f) Upset plot shows rank order of cell type combinations with the greatest number of genes exhibiting significant age-associated differences in expression.

Extended Data Fig. 8 Cell-type deconvolution of bulk tissue RNA-seq data.

(a) After adjusting for cell type composition and repeating our EMMA and MASH models, similar numbers of genes per brain region pass our significance thresholds (LFSR < 0.2). (b) Standardized age effect estimates from analyses controlling and not controlling for cell-type composition are strongly positively correlated, but slopes < 1 in most brain regions suggest that some age effects identified in our bulk gene expression results are due to age-related changes in cell proportion. (c) The majority of aDEGs belonging to four clusters with pronounced age-associated directional changes (Fig. 1d) met our criteria (LFSR < 0.2) as aDEGs after controlling for heterogeneous tissue compositions using cell-type deconvolution (CD) analysis. Substantial fractions, however, did not, providing further evidence that some results at the bulk-tissue level are driven by age-related changes in cell proportion.

Extended Data Fig. 9 Exploratory analyses of single-cell-type pseudobulk data from the dlPFC.

UMAP plots of pseudobulk libraries reveal that the latent structure is driven primarily by (a) cell type and not (b) age. (c) Single-nucleus libraries were aggregated across all cell types to approximate bulk tissue RNA sequencing libraries. Age effects from pseudobulk dlPFC libraries were then compared to age effects from bulk dlPFC libraries, showing a strong positive correlation. Colors denote whether age effects were considered significant for both datasets (EMMA test uncorrected P < 0.05). Error bands represent the 95% confidence interval of linear model predictions. (d) Similarly, age prediction on pseudobulk libraries using the wbaDEGs model (see Methods) developed from analysis of bulk RNA-seq data shows a similar positive correlation (linear model statistics are presented) and accuracy to predictions on bulk tissue libraries. Error bands represent the 95% confidence interval of linear model predictions.

Supplementary information

Supplementary Information

Supplementary Figs. 1–17.

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Supplementary Tables 1–37.

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Chiou, K.L., DeCasien, A.R., Rees, K.P. et al. Multiregion transcriptomic profiling of the primate brain reveals signatures of aging and the social environment. Nat Neurosci 25, 1714–1723 (2022). https://doi.org/10.1038/s41593-022-01197-0

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