Atlas of Mouse Brain Aging Finds Biggest Changes in White Matter

How does gene expression in the brain change with age? The answer remains murky, because the field has lacked a systematic, brain-wide survey of alterations across the lifespan. Researchers led by Tony Wyss-Coray at Stanford University, Palo Alto, California, set out to create such a resource for mouse brain, profiling 15 regions at seven ages. In an April 17 bioRxiv preprint, now in press at Cell, they identified a set of 82 genes that changed over time in all brain regions examined. This common aging signature was expressed most strongly by white-matter glia, hinting that these cells might drive decline. The authors also identified region-specific expression changes, which tended to be in neurons. Notably, some brain regions accumulated many changes with time, while others were relatively insensitive to aging. “The brain does not age uniformly,” Wyss-Coray told Alzforum.

  • White-matter glial cells seem to drive overall brain aging in mice.
  • Neurons have unique aging signatures in each region.
  • Anti-aging interventions affect brain gene expression in different ways.

Wyss-Coray stressed the importance of the wide, unbiased net the study cast. “The classic hypothesis-driven approach has often misled us. Now we have the tools to look globally across the whole brain, and see what changes,” he said. He believes foundational studies such as this will help researchers pinpoint the best targets for maintaining brain health and slowing neurodegenerative disease.

Others agreed. “The detailed transcriptomic map offers a valuable resource for understanding the regional impact of aging on brain function,” Berislav Zlokovic and Ruslan Rust at the University of Southern California, Los Angeles, wrote to Alzforum. “The resource can be used, for instance, in evaluating novel rejuvenation strategies and to quantify their spatiotemporal impact on the brain at the molecular level.”

Varied Aging Rates. A Common Aging Score (CAS) changes quickest in white-matter-rich regions (corpus callosum, cerebellum), at an intermediate rate in subcortical structures (anterior hippocampus), and slowest in cortex (motor, entorhinal). [Courtesy of Hahn et al., bioRxiv.]

Previous surveys of brain aging in mice were limited, profiling only a few brain regions and ages (Lee et al., 2000; Zahn et al., 2007; Tabula Muris Consortium, 2020). To broaden the picture, first author Oliver Hahn isolated tissue from 12 gray-matter regions, comprising motor, visual, and entorhinal cortex, anterior and posterior hippocampus, hypothalamus, thalamus, caudate putamen, pons, medulla, cerebellum, and the olfactory bulb. He also isolated tissue from the corpus callosum white-matter tract, the choroid plexus, and the subventricular zone where neurons are born. The authors used a total of 59 mice, or around eight at each age: 3, 12, 15, 18, 21, 26, and 28 months.

Bulk RNA-Seq of each region revealed expression changes in the set of 82 common aging genes. The set was characterized by a boost in the expression of immune genes, including those involved in antigen presentation, the complement cascade, and the interferon response, paired with a drop in genes that regulate protein quality control. This common aging score (CAS) advanced most rapidly in regions rich in white matter: the corpus callosum, cerebellum, and caudate putamen. Aging progressed at an intermediate rate in subcortical regions such as the hippocampus, thalamus, and hypothalamus, and most slowly in cortical regions and the olfactory bulb. Notably, the CAS changed faster in female than male mice, particularly in the hypothalamus.

Following up with spatial transcriptomics and single-cell RNA-Seq, the authors found the CAS signature was most highly expressed in microglia, followed by oligodendrocytes, endothelial cells, astrocytes, and oligodendrocyte precursor cells. Comparing microglia isolated from the cerebellum, caudate putamen, hippocampus, and cortex, the authors confirmed that those from the white-matter-rich regions, i.e., cerebellum and caudate putamen, aged the fastest. Intriguingly, previous work on human brain also found prominent age-related changes in glial cells (Jan 2017 news).

Neuronal genes, on the other hand, changed regionally, not globally, with age. For example, the acetylcholine receptor CHRM3 waned in medium spiny neurons of the caudate putamen, while the transcription factor ONECUT1 rose in dentate gyrus granule cells in the hippocampus. The reason for these regional differences is unclear.

Disease-linked genes showed unexpected patterns of change. Several Alzheimer’s genes, including APOE, MS4A6D, and PLCG2, changed most dramatically with age in the choroid plexus, corpus callosum, and pons, regions not usually associated with AD. “The region-specific differential regulation of such genes could be an additional factor modulating disease risk,” the authors noted.

The authors applied this aging atlas to examine the effects of two known anti-aging interventions: caloric restriction and injections of plasma from younger mice (Jan 2017 news; May 2022 news). Surprisingly, these interventions had very different effects on brain gene expression. Caloric restriction altered circadian rhythms in glial cells, but did not affect the CAS. Young plasma, on the other hand, directly reversed age-related changes in the cortex, caudate putamen, hypothalamus, and SVZ, lowering the CAS.

The authors are now repeating the study in human brain, with the goal of profiling 100 regions in young to old samples.—Madolyn Bowman Rogers

Comments

  1. Both this excellent new study, and our previous study of aging with human brain samples (Soreq et al., 2017), find that the most accentuated increased expression is in microglial transcripts. This is followed by major changes in transcripts specific for oligodendrocytes, brain endothelial cells (BECs) and astrocytes. Changes in neuronal transcripts are more region-specific and subtle as compared to changes in glial transcripts, which is reassuring.

    Our study found that the largest decrease is in transcripts specific for oligodendrocytes. Using imaging, we see the greatest decrease in oligodendrocytes in the low-density tiles (likely white matter/fiber tracts), so again it’s reassuring that fiber tracts also come up as foci of accelerated brain aging in mice.

    References:

    Soreq L, UK Brain Expression Consortium, North American Brain Expression Consortium, Rose J, Soreq E, Hardy J, Trabzuni D, Cookson MR, Smith C, Ryten M, Patani R, Ule J. Major Shifts in Glial Regional Identity Are a Transcriptional Hallmark of Human Brain Aging. Cell Rep. 2017 Jan 10;18(2):557-570. PubMed.

    View all comments by Jernej Ule

  2. In this article, the authors conducted spatiotemporal RNA-Seq of the mouse brain, profiling 1,076 samples from 15 regions across seven ages and two rejuvenation interventions. This is a very thorough study using the most advanced techniques available today, and it offers a plethora of results.

    In our studies of rhesus monkeys normal brain aging, which started 25 years ago, we were surprised to find that white-matter changes occur before the gray-matter changes. We compared young, middle-aged, and old monkeys using behavioral studies, electrophysiology, electron microscopy, immunohistochemistry, and biochemistry, including gene expression. Molecular techniques have evolved considerably since then, and more information is becoming available regarding mechanisms of normal brain aging and potential interventions to slow down deterioration. 

    For a review of the rhesus monkey results see Hinman and Abraham, 2007.

    In the aged monkey corpus callosum, we found activation of microglia and astrocytes (Sloane et al., 1999; Sloane et al., 2000) and breakdown of myelin proteins involving calpain-1 and the complement system (Sloane et al., 2003; Hinman et al., 2004; Duce et al., 2006; Hinman et al., 2008). Finally, using Affymetrix gene chip analysis, we identified genes that are upregulated and downregulated in the aged white matter. The genes that changed the most were in the inflammation and immunity categories. For more details, see (Sloane et al., 1999; Sloane et al., 2000; Sloane et al., 2003; Hinman et al., 2004; Hinman et al., 2006; Duce et al., 2006; Duce et al., 2008; Hinman et al., 2008; Chen et al., 2013).

    It would be interesting to compare the age-dependent differentially expressed genes in the inbred mouse strain with those in the rhesus monkeys with a broader genetic background and, thus, more similar to humans.

    References:

    Hinman JD, Abraham CR. What's behind the decline? The role of white matter in brain aging. Neurochem Res. 2007 Dec;32(12):2023-31. Epub 2007 Apr 20 PubMed.

    Sloane JA, Hollander W, Moss MB, Rosene DL, Abraham CR. Increased microglial activation and protein nitration in white matter of the aging monkey. Neurobiol Aging. 1999 Jul-Aug;20(4):395-405. PubMed.

    Sloane JA, Hollander W, Rosene DL, Moss MB, Kemper T, Abraham CR. Astrocytic hypertrophy and altered GFAP degradation with age in subcortical white matter of the rhesus monkey. Brain Res. 2000 Apr 17;862(1-2):1-10. PubMed.

    Sloane JA, Hinman JD, Lubonia M, Hollander W, Abraham CR. Age-dependent myelin degeneration and proteolysis of oligodendrocyte proteins is associated with the activation of calpain-1 in the rhesus monkey. J Neurochem. 2003 Jan;84(1):157-68. PubMed.

    Hinman JD, Duce JA, Siman RA, Hollander W, Abraham CR. Activation of calpain-1 in myelin and microglia in the white matter of the aged rhesus monkey. J Neurochem. 2004 Apr;89(2):430-41. PubMed.

    Hinman JD, Peters A, Cabral H, Rosene DL, Hollander W, Rasband MN, Abraham CR. Age-related molecular reorganization at the node of Ranvier. J Comp Neurol. 2006 Apr 1;495(4):351-62. PubMed.

    Duce JA, Hollander W, Jaffe R, Abraham CR. Activation of early components of complement targets myelin and oligodendrocytes in the aged rhesus monkey brain. Neurobiol Aging. 2006 Apr;27(4):633-44. Epub 2005 Jun 29 PubMed.

    Duce JA, Podvin S, Hollander W, Kipling D, Rosene DL, Abraham CR. Gene profile analysis implicates Klotho as an important contributor to aging changes in brain white matter of the rhesus monkey. Glia. 2008 Jan 1;56(1):106-17. PubMed.

    Hinman JD, Chen CD, Oh SY, Hollander W, Abraham CR. Age-dependent accumulation of ubiquitinated 2',3'-cyclic nucleotide 3'-phosphodiesterase in myelin lipid rafts. Glia. 2008 Jan 1;56(1):118-33. PubMed.

    Zeldich E, Chen CD, Avila R, Medicetty S, Abraham CR. The Anti-Aging Protein Klotho Enhances Remyelination Following Cuprizone-Induced Demyelination. J Mol Neurosci. 2015 Oct;57(2):185-96. Epub 2015 Jun 12 PubMed.

    View all comments by Carmela Abraham

  3. In recent years, several studies have been conducted to investigate the changes in gene expression associated with aging in both human and mouse brains (Colantuoni et al., 2011; Ham et al., 2020; Lee et al., 2000; Zahn et al., 2007). This new work by Hahn et al. represents the most comprehensive analysis to date, exploring bulk, spatial, and single-cell transcriptome changes in more than 1,000 mouse samples across 15 brain regions, seven ages, and two rejuvenation interventions.

    Initially, the researchers identified a cluster comprising 82 genes that consistently exhibited changes throughout the aging process; this gene cluster was subsequently used to establish a common aging score (CAS) for estimating the rate of age-dependent change in each brain region.

    Interestingly, the study found that white-matter-rich regions were particularly susceptible to aging, with glial and brain endothelial cells making significant contributions to these changes, as revealed by single-cell RNA sequencing. Some of the age-dependent expression changes of genes were associated with human diseases, such as Alzheimer’s or Parkinson’s.

    The authors also investigated the effects of two rejuvenation interventions: young plasma injection and dietary restriction. These interventions had distinct effects on gene expression in specific brain regions, further highlighting the complexity of the aging process.

    The detailed transcriptomic map offers a valuable resource for understanding the regional impact of aging on brain function. The resource can be used, for instance, in evaluating novel rejuvenation strategies and to quantify their spatiotemporal impact on the brain at the molecular level. However, it will be important to understand whether comparable heterogenous expression patterns are also present at the protein level and if the underlying regional changes are conserved in human brain samples.

    Interestingly, in addition to glial cells, brain endothelial cells demonstrated notable transcriptomic changes associated with aging indicated by elevated CAS. These findings are particularly interesting since recent studies have found regional changes in brain vascular atlases related to brain injury and neurodegenerative diseases, such as AD (Winkler et al., 2022; Yang et al., 2022; Vanlandewijck et al., 2018; Sun et al., 2023). With the accumulating evidence highlighting early vascular involvement in AD, it is crucial to look in more detail into the regional roles of vascular and perivascular cells, along with related pathways. These cells could be future targets for the development of therapeutic drugs (Bosworth et al., 2023). 

    References:

    Colantuoni C, Lipska BK, Ye T, Hyde TM, Tao R, Leek JT, Colantuoni EA, Elkahloun AG, Herman MM, Weinberger DR, Kleinman JE. Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature. 2011 Oct 27;478(7370):519-23. PubMed.

    Ham S, Lee SV. Advances in transcriptome analysis of human brain aging. Exp Mol Med. 2020 Nov;52(11):1787-1797. Epub 2020 Nov 26 PubMed.

    Lee CK, Weindruch R, Prolla TA. Gene-expression profile of the ageing brain in mice. Nat Genet. 2000 Jul;25(3):294-7. PubMed.

    Zahn JM, Poosala S, Owen AB, Ingram DK, Lustig A, Carter A, Weeraratna AT, Taub DD, Gorospe M, Mazan-Mamczarz K, Lakatta EG, Boheler KR, Xu X, Mattson MP, Falco G, Ko MS, Schlessinger D, Firman J, Kummerfeld SK, Wood WH, Zonderman AB, Kim SK, Becker KG. AGEMAP: a gene expression database for aging in mice. PLoS Genet. 2007 Nov;3(11):e201. PubMed.

    Winkler EA, Kim CN, Ross JM, Garcia JH, Gil E, Oh I, Chen LQ, Wu D, Catapano JS, Raygor K, Narsinh K, Kim H, Weinsheimer S, Cooke DL, Walcott BP, Lawton MT, Gupta N, Zlokovic BV, Chang EF, Abla AA, Lim DA, Nowakowski TJ. A single-cell atlas of the normal and malformed human brain vasculature. Science. 2022 Mar 4;375(6584):eabi7377. PubMed.

    Yang AC, Vest RT, Kern F, Lee DP, Agam M, Maat CA, Losada PM, Chen MB, Schaum N, Khoury N, Toland A, Calcuttawala K, Shin H, Pálovics R, Shin A, Wang EY, Luo J, Gate D, Schulz-Schaeffer WJ, Chu P, Siegenthaler JA, McNerney MW, Keller A, Wyss-Coray T. A human brain vascular atlas reveals diverse mediators of Alzheimer's risk. Nature. 2022 Mar;603(7903):885-892. Epub 2022 Feb 14 PubMed.

    Vanlandewijck M, He L, Mäe MA, Andrae J, Ando K, Del Gaudio F, Nahar K, Lebouvier T, Laviña B, Gouveia L, Sun Y, Raschperger E, Räsänen M, Zarb Y, Mochizuki N, Keller A, Lendahl U, Betsholtz C. A molecular atlas of cell types and zonation in the brain vasculature. Nature. 2018 Feb 14; PubMed.

    Sun N, Akay LA, Murdock MH, Park Y, Galiana-Melendez F, Bubnys A, Galani K, Mathys H, Jiang X, Ng AP, Bennett DA, Tsai LH, Kellis M. Single-nucleus multiregion transcriptomic analysis of brain vasculature in Alzheimer's disease. Nat Neurosci. 2023 Jun;26(6):970-982. PubMed. Correction.

    Bosworth A, Griffin C, Chakhoyan A, Sagare AP, Nelson AR, Wang Y, Kisler K, Montagne A, Clementel V, TCW J, Rust R, Coba M, Zlokovic B. Molecular signature and functional properties of human pluripotent stem cell-derived brain pericytes. 2023 Jun 28 10.1101/2023.06.26.546577 (version 1) bioRxiv.

    View all comments by Berislav Zlokovic View all comments by Ruslan Rust

  4. This very interesting resource defines a common RNA aging signature or “common aging score.” Strikingly, the score maps to regions rich in myelin and, on a cellular level, to glia, particularly microglia and myelinating oligodendrocytes.

    Together, these data point to myelin as one important contributor to normal brain aging. Thus, age-related myelin pathology needs to be considered when we study neurodegenerative diseases that have age as a major risk factor.

    View all comments by Mikael Simons

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References

News Citations

  1. Aging Causes “Identity Crisis” in Glia
  2. Consensus Reached: Dieting Monkeys Survive Longer
  3. Cerebrospinal Fluid from Youngsters Boosts Memory in Old Mice

Paper Citations

  1. Lee CK, Weindruch R, Prolla TA. Gene-expression profile of the ageing brain in mice. Nat Genet. 2000 Jul;25(3):294-7. PubMed.
  2. Zahn JM, Poosala S, Owen AB, Ingram DK, Lustig A, Carter A, Weeraratna AT, Taub DD, Gorospe M, Mazan-Mamczarz K, Lakatta EG, Boheler KR, Xu X, Mattson MP, Falco G, Ko MS, Schlessinger D, Firman J, Kummerfeld SK, Wood WH, Zonderman AB, Kim SK, Becker KG. AGEMAP: a gene expression database for aging in mice. PLoS Genet. 2007 Nov;3(11):e201. PubMed.
  3. Tabula Muris Consortium. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature. 2020 Jul;583(7817):590-595. Epub 2020 Jul 15 PubMed.

Further Reading

Primary Papers

  1. Hahn O, Foltz AG, Atkins M, Kedir B, Moran-Losada P, Guldner IH, Munson C, Kern F, Palovics R, Lu N, Kaur A, Hull J, Huguenard J, Groenke S, Lehallier B, Partridge L, Keller A, Wyss-Coray T. A spatiotemporal map of the aging mouse brain reveals white matter tracts as vulnerable foci. 2023 Apr 17 10.1101/2022.09.18.508419 (version 3) bioRxiv.

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