Robust differences in cortical cell type proportions across healthy human aging inferred through cross-dataset transcriptome analyses.


Journal

Neurobiology of aging
ISSN: 1558-1497
Titre abrégé: Neurobiol Aging
Pays: United States
ID NLM: 8100437

Informations de publication

Date de publication:
05 2023
Historique:
received: 20 05 2022
revised: 22 01 2023
accepted: 24 01 2023
pubmed: 26 2 2023
medline: 25 3 2023
entrez: 25 2 2023
Statut: ppublish

Résumé

Age-related declines in cognitive function are driven by cell type-specific changes in the brain. However, it remains challenging to study cellular differences associated with healthy aging as traditional approaches scale poorly to the sample sizes needed to capture aging and cellular heterogeneity. Here, we employed cellular deconvolution to estimate relative cell type proportions using frontal cortex bulk gene expression from individuals without psychiatric conditions or brain pathologies. Our analyses comprised 8 datasets and 6 cohorts (1142 subjects and 1429 samples) with ages of death spanning 15-90 years. We found aging associated with profound differences in cellular proportions, with the largest changes reflecting fewer somatostatin- and vasoactive intestinal peptide-expressing interneurons, more astrocytes and other non-neuronal cells, and a suggestive "U-shaped" quadratic relationship for microglia. Cell type associations with age were markedly robust across bulk-and single nucleus datasets. Altogether, we present a comprehensive account of proportional differences in cortical cell types associated with healthy aging.

Identifiants

pubmed: 36841202
pii: S0197-4580(23)00021-0
doi: 10.1016/j.neurobiolaging.2023.01.013
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

49-61

Subventions

Organisme : CIHR
ID : NGN-171423
Pays : Canada
Organisme : CIHR
ID : PJT-175254
Pays : Canada

Informations de copyright

Copyright © 2023 Elsevier Inc. All rights reserved.

Auteurs

Yuxiao Chen (Y)

The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.

Emma Hunter (E)

The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.

Keon Arbabi (K)

The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.

Alex Guet-McCreight (A)

The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.

Micaela Consens (M)

The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.

Daniel Felsky (D)

The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.

Etienne Sibille (E)

Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Toronto, Ontario, Canada; Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada.

Shreejoy J Tripathy (SJ)

The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Physiology, University of Toronto, Toronto, Ontario, Canada. Electronic address: Shreejoy.Tripathy@camh.ca.

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Classifications MeSH