Atlas of the aging mouse brain reveals white matter as vulnerable foci
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Abstract
Summary: Aging is the key risk factor for cognitive decline, yet the molecular changes underlying brain aging remain poorly understood. Here, we conducted spatiotemporal RNA-seq of the mouse brain, profiling 1,076 samples from 15 regions across 7 ages and 2 rejuvenation interventions. Our analysis identified a brain-wide gene signature of aging in glial cells, which exhibited spatially defined changes in magnitude. By integrating spatial and single-nucleus transcriptomics, we found that glia aging was particularly accelerated in white matter compared to cortical regions, while specialized neuronal populations showed region-specific expression changes. Rejuvenation interventions, including young plasma injection and dietary restriction, exhibited distinct effects on gene expression in specific brain regions. Furthermore, we discovered differential gene expression patterns associated with three human neurodegenerative diseases, highlighting the importance of regional aging as a potential modulator of disease. Our findings identify molecular foci of brain aging, providing a foundation to target age-related cognitive decline.
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TL;DR
This study maps spatiotemporal transcriptomic aging across the mouse brain and finds that white matter tracts, especially the corpus callosum, are foci of accelerated aging. A common 82-gene aging signature is largely driven by glial and endothelial cells, while region-specific neuronal signatures and disease-associated genes show regional vulnerability.
Key findings
- Across 847 bulk, 16,277 spatial spot, and 81,616 single-cell/nuclei transcriptomes from 15 brain regions and 7 ages, aging effects were highly region- and time-dependent; the entorhinal cortex had only 13 detectable DEGs, whereas white matter-rich regions such as corpus callosum, cerebellum, and caudate putamen were most affected.
- An 82-gene common aging score (CAS) was defined from genes changing in ≥10 of 14 regions; the corpus callosum had the fastest CAS velocity, while cortical areas and olfactory bulb ranked last at approximately one-third of that velocity.
- CAS increases were most pronounced in microglia, followed by mature oligodendrocytes, brain endothelial cells, astrocytes, and OPCs; microglia from cerebellum and striatum showed greater CAS increases than those from hippocampus and cortex.
- Region-specific aging signatures were largely encoded by neuronal subpopulations, and human GWAS genes for multiple sclerosis, Alzheimer’s disease, and Parkinson’s disease showed distinct region-selective enrichment patterns.
Methods
- Bulk RNA-seq was performed on 15 punched brain regions from left/right hemispheres of C57BL/6JN mice across ages 3–28 months, yielding 847 single-region transcriptomes after QC; spatial transcriptomics was performed on coronal sections from an independent male cohort at 6, 18, and 21 months.
- Single-nuclei RNA-seq was performed on anterior hippocampus and caudate putamen from the same bulk-seq cohort at 3 and 21 months, and public scRNA-seq datasets were reanalyzed.
- Analyses included DESeq2 differential expression, Spearman age correlation, WGCNA, VISION-based gene signature scores, linear models for CAS velocity, and MAST for single-cell differential expression.
Limitations
- The paper states that it cannot predict whether the directionality of disease-associated gene regulation has a biological consequence, that the substantia nigra was not quantified, and that spatial-seq and single-cell CAS analyses treat spots/cells as replicates rather than accounting for biological replicates.
- The paper notes that requiring significance in at least two pairwise comparisons selects against genes differentially expressed very late in life, and that very low aNSC numbers at 28 months complicate robust CAS calculations.
- Evident from the text: findings are from mouse brain and rely on bulk tissue punches, spatial spots, and nuclei/cell suspensions, with limited direct human validation.
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- Findingown data§ Spatiotemporal quantification of age-related gene expression across the mouse brain
The corpus callosum-derived signature demarcated fiber tracts throughout the brain.
“Notably, the corpus callosum-derived signature demarcated fiber tracts throughout the brain, indicating that the sampled transcriptome of this region could be a proxy for white matter tracts in general (Figure S3A).”
Quote in the paperSubstantial claim - Findingown data§ Spatiotemporal quantification of age-related gene expression across the mouse brain
Region-enriched gene signature scores were distinctly elevated in areas corresponding to anatomical structures annotated in the Allen brain reference atlas.
“Signature scores were distinctly elevated in areas corresponding to the anatomical structures annotated in the Allen brain reference atlas (Figure S3A) (Lein et al., 2007).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Lein et al., 2007 source
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- Findingown data§ Spatiotemporal quantification of age-related gene expression across the mouse brain
Within individual regions, samples segregated transcriptionally by age.
“However, within individual regions, samples segregated transcriptionally by age.”
Quote in the paperSubstantial claim - Findingown data§ Region identity is linked to expression dynamics during aging
There is substantial region-dependence in the magnitude and timing of C4b expression.
“For instance, we found substantial region-dependence in the magnitude and timing of C4b expression (Figure 1E), a complement component and major schizophrenia risk factor (Sekar et al., 2016) that is robustly up-regulated in aged mice (Hahn et al., 2020) and models of neurodegeneration (Zhou et al., 2020).”
Quote in the paperSubstantial claim0 of 3 sources checked - Findingown data§ Neuronal transcripts encode region-specific expression patterns
Expression of Chrm3 dropped significantly in the caudate putamen, reflecting down-regulation specifically in D2 MSNs.
“For instance, expression of muscarinic acetylcholine receptor gene Chrm3 dropped significantly in the caudate putamen, reflecting down-regulation of this gene specifically in D2 MSNs (Figure 5I,J).”
Quote in the paperNot a substantial claim
- Findingown data§ A minimal gene set forms a common fingerprint of brain aging
There is a gradient of CAS velocities across regions, with the three cortical areas and the olfactory bulb ranking last, at approximately one-third of the velocity of the corpus callosum, the fastest region.
“Our analysis revealed a gradient of velocities across regions, with the three cortical areas and the olfactory bulb ranking last, at approximately one-third of the velocity of the corpus callosum, the 'fastest' region (Figure 2G).”
Quote in the paperSubstantial claim - Findingown data§ Region identity is linked to expression dynamics during aging
The general trend across regions indicated an increase of DEGs over time that plateaued around 21 months.
“The general trend across regions indicated an increase of DEGs over time that plateaued around 21 months (Figure 1F,G), yet individual regions varied profoundly with respect to their total number of DEGs and the trajectory of DEG accumulation (Figure 1F and Table S2).”
Quote in the paperNot a substantial claim - Findingown data§ Fiber tracts are foci of accelerated brain aging
Calculating CAS for each Spatial-seq spot identified a distinct, spatially-confined increase of the score along white matter tracts, including the corpus callosum, fimbria and internal capsule.
“Calculating CAS for each Spatial-seq spot identified a distinct, spatially-confined increase of the score along the white matter tracts, including not only the corpus callosum but also other fiber tract sub-structures such as the fimbria and internal capsule (Figure 2N,O).”
Quote in the paperSubstantial claim - Findingown data§ Neuronal transcripts encode region-specific expression patterns
This down-regulation of mitochondrial processes was not detected in cell types from the hippocampus or the SVZ.
“This was not detected in cell types from the hippocampus or the SVZ (Figure S11F).”
Quote in the paperSubstantial claim - Findingown data§ A minimal gene set forms a common fingerprint of brain aging
The regions' differing CAS velocities resulted in increased per-animal variance, indicating that the transcriptional state of this gene set becomes profoundly desynchronized across the brain.
“Yet, the regions' differing velocities resulted in increased per-animal variance, indicating that the transcriptional state of this gene set becomes profoundly desynchronized across the brain.”
Quote in the paperSubstantial claim - Findingown data§ Region identity is linked to expression dynamics during aging
In 10 regions, at least one module with increased expression over time was enriched for microglia- and inflammation-related genes.
“Interestingly, we discovered in 10 regions at least one module with increased expression over time that was enriched for microglia- and inflammation-related genes (Fig 1I,J).”
Quote in the paperSubstantial claim - Findingown data§ Region identity is linked to expression dynamics during aging
Regions differed in the number of age-correlated genes, confirming that the effect size of age depends on the region.
“Not only did the regions differ in the number of age-correlated genes, confirming that the effect size of age depends on the region, but also the corpus callosum and cerebellum were the most impacted, while the entorhinal cortex remained largely unaffected (Figure 1H).”
Quote in the paperSubstantial claim - Findingown data§ Neuronal transcripts encode region-specific expression patterns
The caudate putamen-specific signature was enriched for down-regulated mitochondrial processes and up-regulated cell adhesion and lipid binding functions.
“Notably, signature genes appeared to be functionally connected, as exemplified by the caudate putamen-specific signature which was enriched for down-regulated mitochondrial processes and up-regulated cell adhesion and lipid binding functions (Figure 5E and Table S8).”
Quote in the paperNot a substantial claim - Findingown data§ A minimal gene set forms a common fingerprint of brain aging
The 82 genes were strongly enriched for up-regulated genes with immune-modulatory functions, including MHC I-mediated antigen presentation, interferon-response, cell adhesion and complement cascade, as well as regulators of mouse microglia activity including Cd22, Trem2 and Tyrobp.
“These were strongly enriched for up-regulated genes with immune-modulatory functions (Table S5), including MHC I-mediated antigen presentation, interferon-response, cell adhesion and complement cascade, as well as regulators of mouse microglia activity (Figure 2C) including Cd22 (Pluvinage et al., 2019), Trem2 and Tyrobp (Yeh et al., 2017).”
Quote in the paperSubstantial claim0 of 2 sources checked - Findingown data§ Fiber tracts are foci of accelerated brain aging
There was a more pronounced regulation of DEGs in the white matter cluster compared to the cortex cluster, including several of the 82 CAS genes such as Trem2.
“We confirmed a more pronounced regulation of DEGs in the white matter cluster (equivalent to the dissected corpus callosum region) compared to the cortex cluster (equivalent to the motor cortex region), including several of the 82 CAS genes (Figure 2L, Table S7) such as Trem2 (Figure 2M).”
Quote in the paperSubstantial claim - Findingown data§ A minimal gene set forms a common fingerprint of brain aging
Between 3 and 21 months, there was moderate but significant acceleration of CAS in females compared to males.
“Interestingly, when we examined the CAS trajectories for the interval between 3 and 21 months, we observed moderate but significant acceleration of the CAS in females compared to males (Figure 2I, S4C,D).”
Quote in the paperSubstantial claim - Findingown data§ Heterogeneous velocity of CAS is encoded by glial transcripts
Aging can induce expression of genes that are not detected at young age.
“Of note, this analysis also demonstrated that aging can induce expression of genes that are not detected at young age.”
Quote in the paperNot a substantial claim - Findingown data§ Transcriptional aging of microglia is region-dependent
The same trend was detectable on the level of individual CAS genes, like Trem2.
“The same trend was detectable on the level of individual CAS genes, like Trem2 (Figure 4C).”
Quote in the paperNot a substantial claim - Findingown data§ Neuronal transcripts encode region-specific expression patterns
Most region-specific scores increased with age predominantly in the region on which they were based.
“As exemplified by the specific signature of the caudate putamen - a region marked by intertwined gray and white matter structures - we found that most region-specific scores increased with age predominantly in the region on which they were based (Figure 5B-D and Figure S10A,B).”
Quote in the paperSubstantial claim - Findingown data§ Fiber tracts are foci of accelerated brain aging
In the cortex, there was only a mild increase of CAS with age.
“In the cortex, however, we observed only a mild increase of CAS with age.”
Quote in the paperSubstantial claim - Findingown data§ A minimal gene set forms a common fingerprint of brain aging
The median CAS across all regions associated with the animals' chronological age.
“The median CAS across all regions associated with the animals' chronological age (Figure S4B).”
Quote in the paperNot a substantial claim - Findingown data§ A minimal gene set forms a common fingerprint of brain aging
The CAS at baseline did not predict a region's CAS velocity.
“Of note, the CAS at baseline (i.e. the offset of the linear fit) did not predict a region's CAS velocity (Figure 2F).”
Quote in the paperNot a substantial claim - Findingown data§ Neuronal transcripts encode region-specific expression patterns
Except for the thalamus, pons and SVZ, a given region's signature velocity outperformed those of all other regions.
“Except for the thalamus, pons and SVZ, a given region's signature velocity outperformed those of all other regions.”
Quote in the paperSubstantial claim - Findingown data§ Neuronal transcripts encode region-specific expression patterns
The number of region-specific DEGs varies greatly.
“We found that the number of region-specific DEGs varies greatly (Figure 5A), which we utilized to build aging signatures for each region before calculating the respective score across all other regions (Figure 5B,C, Figure S10A,B).”
Quote in the paperSubstantial claim - Findingown data§ Heterogeneous velocity of CAS is encoded by glial transcripts
The highest baseline CAS was observed in microglia.
“We observed the highest baseline CAS in microglia, in line with several of the CAS genes being known immune-response genes (Figure 2B,C).”
Quote in the paperSubstantial claim - Findingown data§ Region identity is linked to expression dynamics during aging
The entorhinal cortex appeared largely refractory to the effects of age, with only 13 detectable DEGs in total.
“In contrast, the transcriptome of the entorhinal cortex appeared largely refractory to the effects of age altogether, with only 13 detectable DEGs in total (Figure 1F,G).”
Quote in the paperSubstantial claim - Findingcitation§ Heterogeneous velocity of CAS is encoded by glial transcripts
In an independent scRNA-seq dataset from dissected SVZ of young and old male mice, the CAS increase was most pronounced in microglia, consistent with the authors' data.
“We validated our findings in an independent dataset, using publicly available scRNA-seq data from dissected SVZ of young and old male mice (Dulken et al., 2019) (Figure S8A). Though generated using a different cohort, region and method, the CAS increase was most pronounced in microglia which is consistent with our data (Figure S8B-G).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Dulken et al., 2019 source
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- Findingown data§ Aging results in region-specific expression changes of genes associated with human diseases
The visual and motor cortex regions formed the second cluster.
“The visual and motor cortex regions formed the second cluster,”
Quote in the paperNot a substantial claim - Findingown data§ Neuronal transcripts encode region-specific expression patterns
There was a distinct increase of the caudate putamen-specific signature in D1 and D2 MSNs which was not seen with signatures from other regions.
“We found a distinct increase of the caudate putamen-specific signature in D1 and D2 MSNs which was not seen with signatures from other regions (Figure 5H).”
Quote in the paperNot a substantial claim - Findingown data§ Region identity is linked to expression dynamics during aging
Individual regions varied profoundly with respect to their total number of DEGs and the trajectory of DEG accumulation.
“The general trend across regions indicated an increase of DEGs over time that plateaued around 21 months (Figure 1F,G), yet individual regions varied profoundly with respect to their total number of DEGs and the trajectory of DEG accumulation (Figure 1F and Table S2).”
Quote in the paperSubstantial claim - Findingown data§ Neuronal transcripts encode region-specific expression patterns
Onecut2 exhibited selective regulation in the corpus callosum and caudate putamen, namely D1 and D2 MSNs.
“Interestingly, Onecut2 , a member of the same transcription factor family, exhibited selective regulation in the corpus callosum and caudate putamen, namely D1 and D2 MSNs (Figure S11C,D).”
Quote in the paperNot a substantial claim - Findingown data§ Heterogeneous velocity of CAS is encoded by glial transcripts
H2-Q7 expression only became detectable in BECs with old age.
“Similarly, expression of H2-Q7 only became detectable in BECs with old age (Figure S7F).”
Quote in the paperNot a substantial claim - Findingown data§ A minimal gene set forms a common fingerprint of brain aging
82 genes were differentially regulated in 10 or more regions.
“While the vast majority of DEGs appeared to change only in three or less regions, indicative of region-selective expression patterns, we found 82 genes that were differentially regulated in 10 or more regions (Figure 2A,B; Table S2).”
Quote in the paperSubstantial claim - Findingown data§ Discussion
Key genetic risk genes are differentially expressed in a region-specific manner.
“We demonstrate that key genetic risk genes are differentially expressed in a region-specific manner”
Quote in the paperSubstantial claim - Findingown data§ Bulk-seq Common Aging Score (CAS) calculation and CAS velocity comparison
The analysis identified 82 genes that were marked as DEG in at least 10 out of 14 regions.
“This led to the identification of 82 genes that were marked as DEG in at least 10 out of 14 regions (15 regions minus the posterior hippocampus region).”
Quote in the paperSubstantial claim - Findingown data§ Main Figures
A common gene signature detects accelerated aging in white matter tracts.
“A common gene signature detects accelerated aging in white matter tracts”
Quote in the paperSubstantial claim - Findingown data§ Discussion
White matter and white matter-rich regions emerge as the most transcriptionally impacted areas during aging.
“Across analyses, white matter and white matter-rich regions emerge as the most transcriptionally impacted areas during aging.”
Quote in the paperSubstantial claim - Findingown data§ Discussion
The study reports a comprehensive spatiotemporal map of gene expression across the mouse brain throughout the adult lifespan, consisting of 847 bulk, 16,277 spatial spot and 81,616 single-cell transcriptomes.
“We report here a comprehensive spatiotemporal map of gene expression across the mouse brain throughout the adult lifespan, consisting of 847 bulk, 16,277 spatial spot and 81,616 singlecell transcriptomes - supported by several publicly available spatial- and scRNA-seq datasets.”
Quote in the paperSubstantial claim - Findingown data§ Figure S3 Collected brain regions reflect transcriptomes of anatomically distinct structures
The activated neural stem cell (aNSC)-derived marker gene signature shows a significant drop in score in the olfactory bulb, SVZ and both anterior and posterior hippocampus regions.
“A significant drop in the signature score is notable in olfactory bulb, SVZ and both anterior and posterior hippocampus regions.”
Quote in the paperNot a substantial claim - Findingown data§ Main Figures
Transcriptional aging of microglia depends on their region of origin.
“CAS analysis reveals that transcriptional aging of microglia depends on their region of origin.”
Quote in the paperSubstantial claim - Findingnone given§ Main Figures
Brain regions exhibit distinct transcriptional patterns of aging independent of anatomical proximity.
“Brain regions exhibit distinct transcriptional patterns of aging independent of anatomical proximity”
Quote in the paperSubstantial claim - Findingown data§ Figure S3 Collected brain regions reflect transcriptomes of anatomically distinct structures
The activated neural stem cell (aNSC)-derived marker gene signature exhibits its highest baseline expression in olfactory bulb and subventricular zone (SVZ) derived samples.
“Signature exhibits the highest baseline expression in the olfactory bulb and subventricular zone (SVZ) derived samples.”
Quote in the paperNot a substantial claim - Findingown data§ Main Figures
Aging in glia and endothelial cells is the major contributor to CAS increase.
“Aging in glia and endothelial cells is the major contributor to CAS increase”
Quote in the paperSubstantial claim - Findingown data§ Main Figures
Granule cells exhibit a distinct increase with age exclusively for the anterior hippocampus-specific signature.
“Granule cells are highlighted, as they exhibit a distinct increase with age exclusively for the anterior hippocampus-specific signature.”
Quote in the paperNot a substantial claim - Findingnone given§ Main Figures
Region-specific expression shifts are encoded by neuronal transcripts.
“Region-specific expression shifts are encoded by neuronal transcripts”
Quote in the paperNot a substantial claim - Findingown data§ Aging results in region-specific expression changes of genes associated with human diseases
Genetic risk factors linked to three major neurological and neurodegenerative diseases are affected by age in a region-selective manner.
“Taken together, our data demonstrate that genetic risk factors linked to three major neurological and neurodegenerative diseases are affected by age in a region-selective manner.”
Quote in the paperSubstantial claim - Findingown data§ Main Figures
D1 and D2 medium spiny neuron populations exhibit a distinct increase with age exclusively for the caudate putamen-specific signature.
“D1 and D2 Medium spiny neuron populations (MSN) are highlighted, as they exhibit a distinct increase with age exclusively for the caudate putamenspecific signature.”
Quote in the paperNot a substantial claim - Findingown data§ Discussion
Gene expression shifts during aging fall into two general categories.
“Gene expression shifts during aging fall into two general categories:”
Quote in the paperSubstantial claim - Findingown data§ Discussion
Aging affects regional transcriptomes with widely varying magnitude and timing, including the expression of risk genes for neurodegenerative disorders.
“We find that aging affects regional transcriptomes with widely varying magnitude and timing, including the expression of risk genes for neurodegenerative disorders.”
Quote in the paperSubstantial claim - Findingown data§ Discussion
A dozen gene sets have region-specific activity encoded in specialized neuronal subpopulations.
“and a dozen gene sets with region-specific activity encoded in specialized neuronal subpopulations.”
Quote in the paperNot a substantial claim - Findingown data§ Spatial-seq CAS calculation
In Spatial-seq, age-related changes in CAS exceed the intra-replicate CAS differences.
“We therefore visualized CAS in Spatial-seq for each replicate, to demonstrate that age-related changes in CAS supersede the intra-replicate CAS differences.”
Quote in the paperNot a substantial claim - Findingown data§ Discussion
A common aging signature increased in microglia with region-dependent effect size.
“While we established that a common aging signature increased in microglia with region-dependent effect size”
Quote in the paperSubstantial claim - Findingown data§ Discussion
A single set of 82 genes reflects shared aging dynamics of glia cells with spatially-defined magnitude.
“a single set of 82 genes that reflects shared aging dynamics of glia cells with spatially-defined magnitude”
Quote in the paperSubstantial claim - Findingown data§ Discussion
AD risk genes including Apoe, Ms4a6d, Plcg2 and Gab2 are dysregulated with aging in the mouse choroid plexus, corpus callosum and pons.
“highlighted by AD risk genes including Apoe , Ms4a6d , Plcg2 and Gab2 which are dysregulated with aging in the mouse choroid plexus, corpus callosum and pons with aging.”
Quote in the paperSubstantial claim - Findingown data§ Aging results in region-specific expression changes of genes associated with human diseases
AD GWAS hit genes Apoe, Ms4a6d, Plcg2 and Gab2 were part of DEGs upregulated in a small cluster of three regions: the choroid plexus, corpus callosum, and pons.
“They were part of DEGs that are upregulated in a small cluster of three regions: the choroid plexus, corpus callosum, and pons”
Quote in the paperSubstantial claim - Findingown data§ Aging results in region-specific expression changes of genes associated with human diseases
PD-related genes, like Ip6k2, were not concentrated within a cluster of regions but rather distributed across the choroid plexus, cerebellum, SVZ, and visual cortex with limited overlap.
“In contrast, PD-related genes, like the neuroprotective gene Ip6k2 (Nagpal et al., 2022) (Figure 6I), were not concentrated within a cluster of regions but rather distributed across the choroid plexus, cerebellum, SVZ, and visual cortex with limited overlap (Figure 6C).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Nagpal et al., 2022 source
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- Premisecitation§ Region identity is linked to expression dynamics during aging
The composition of major cell types remains almost constant throughout the aging mouse brain.
“Notably, recent single-cell studies revealed that the composition of major cell types remains almost constant throughout the aging mouse brain (Ximerakis et al., 2019), thus the expression dynamics observed in bulk are unlikely to be driven primarily by shifts in cell type abundance.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ximerakis et al., 2019 source
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- Premisecitation§ Introduction
Comprehensive studies of mouse brain aging covering more than a few regions and at high temporal resolution do not exist to the authors' knowledge.
“Alternatively, expression profiling in model organisms like M. musculus enables quantitative data with minimal confounding factors, but comprehensive studies covering more than a few regions and at high temporal resolution (Hargis and Blalock, 2017; Lee et al., 2000; Tabula Muris Consortium, 2020; Zahn et al., 2007) do - to our knowledge - not exist.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites Hargis and Blalock, 2017
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- cites Lee et al., 2000 source
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- cites Tabula Muris Consortium, 2020
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- Interpretationown data§ Spatiotemporal quantification of age-related gene expression across the mouse brain
The sampled transcriptome of the corpus callosum could be a proxy for white matter tracts in general.
“Notably, the corpus callosum-derived signature demarcated fiber tracts throughout the brain, indicating that the sampled transcriptome of this region could be a proxy for white matter tracts in general (Figure S3A).”
Quote in the paperSubstantial claim - Interpretationcitation§ Region identity is linked to expression dynamics during aging
The expression dynamics observed in bulk are unlikely to be driven primarily by shifts in cell type abundance.
“Notably, recent single-cell studies revealed that the composition of major cell types remains almost constant throughout the aging mouse brain (Ximerakis et al., 2019), thus the expression dynamics observed in bulk are unlikely to be driven primarily by shifts in cell type abundance.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ximerakis et al., 2019 source
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- Premisecitation§ Region identity is linked to expression dynamics during aging
RNA-seq permits quantitative comparisons of aging rates between organs and cell types based on timing and effect size of gene expression shifts.
“RNA-seq permits quantitative comparisons of aging rates between organs and cell types (Schaum et al., 2020; Tabula Muris Consortium, 2020) based on timing and effect size of gene expression shifts.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Schaum et al., 2020 source
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- cites Tabula Muris Consortium, 2020
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- Premisecitation§ Introduction
Changes in gene expression can be a readout of cellular deterioration and molecular processes accompanying aging, permitting quantitative comparisons of aging rates between tissues and cell types.
“In contrast, changes in gene expression can be a readout of cellular deterioration and molecular processes accompanying aging, permitting quantitative comparisons of aging rates between tissues (Schaum et al., 2020) and cell types (Buckley et al., 2022).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Schaum et al., 2020 source
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- cites Buckley et al., 2022
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- Premisecitation§ Introduction
Expression profiling during human brain aging is challenging because postmortem tissue can take hours to days to stabilize.
“Expression profiling during human brain aging is particularly challenging since it can take hours to days before postmortem tissue is stabilized (Melé et al., 2015; Soreq et al., 2017; Trabzuni et al., 2011).”
Quote in the paperSubstantial claim0 of 3 sources checked - Study design and conductnone given§ Spatiotemporal quantification of age-related gene expression across the mouse brain
The tissue isolation and bulk RNA-seq workflow yielded high-quality transcriptomes that robustly captured a region's gene expression profile across a cohort of mice.
“In summary, our tissue isolation and bulk-seq workflow yielded high-quality transcriptomes that robustly captured a region's gene expression profile across a cohort of mice.”
Quote in the paperSubstantial claim - Interpretationown data§ Neuronal transcripts encode region-specific expression patterns
The specific down-regulation of mitochondrial processes in aged striatum could help to explain previous observations demonstrating selective vulnerability to mitochondrial toxins and stresses in the striatum of old animals.
“This specific down-regulation of mitochondrial processes in aged striatum could help to explain previous observations demonstrating selective vulnerability to mitochondrial toxins and stresses in the striatum of old animals (Brouillet et al., 1993; Patki et al., 2009).”
Quote in the paperNot a substantial claim0 of 2 sources checked - Interpretationown data§ A minimal gene set forms a common fingerprint of brain aging
The desynchronization of the transcriptional state of the CAS gene set appeared to be independent of the regions' anatomical location.
“This appeared to be independent of the regions' anatomical location, as the fast-aging corpus callosum stretches between the slow-aging cortical areas and hippocampus (Figure 2H).”
Quote in the paperSubstantial claim - Interpretationown data§ Neuronal transcripts encode region-specific expression patterns
The authors discovered extensive region-specific transcriptional signatures of aging that are largely encoded by expression shifts in distinct neuronal subpopulations reflective of a region's specialization.
“In conclusion, we discovered extensive region-specific transcriptional signatures of aging that are largely encoded by expression shifts in distinct neuronal subpopulations reflective of a region's specialization.”
Quote in the paperSubstantial claim - Premisecitation§ Aging results in region-specific expression changes of genes associated with human diseases
Transcriptional shifts (e.g. of Cbln2) in cortical areas can occur far away from the actual lesions.
“This supports recent evidence indicating transcriptional shifts (e.g. of Cbln2 , Figure 6G) in cortical areas that can occur far away from the actual lesions (Kihara et al., 2022)”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Kihara et al., 2022 source
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- Interpretationown data§ Region identity is linked to expression dynamics during aging
The effect size of expression shifts during brain aging is strikingly region- and time-dependent.
“Together, these results reveal that the effect size of expression shifts during brain aging are strikingly region- and time-dependent, highlighting the necessity for region-resolved quantification and analysis.”
Quote in the paperSubstantial claim - Interpretationown data§ Aging results in region-specific expression changes of genes associated with human diseases
The region-specific regulation of AD GWAS hit genes could be a potential modulator of disease presentation.
“suggesting a different region-specific regulation of these genes as a potential modulator of disease presentation.”
Quote in the paperSubstantial claim - Study design and conductown data§ Brain region dissection
Perfusing the animal before isolating and freezing the whole brain in under 5 minutes rapidly stabilizes the tissue and RNA.
“We found success in perfusing the animal before isolating and freezing the whole brain in under 5 minutes, thus rapidly stabilizing the tissue and RNA.”
Quote in the paperSubstantial claim - Study design and conductreasoning§ Bulk-seq differential expression
The criterion requiring significance in at least 2 pairwise comparisons tends to select against genes that are differentially expressed very late in life.
“We recognize that this tends to select against genes that are differentially expressed very late in life (i.e. 3 months vs 28 months).”
Quote in the paperSubstantial claim - Interpretationown data§ Aging results in region-specific expression changes of genes associated with human diseases
The pattern of PD-related gene expression indicates a disperse regulation of disease-associated genes among the regions studied.
“This pattern indicates a disperse regulation of disease-associated genes among the regions studied”
Quote in the paperSubstantial claim - Interpretationcitation§ Discussion
The strong activation of immune- and inflammation-related genes, as well as differential expression of remyelination regulators like Gpr17, suggest that the homeostasis of white matter is compromised at old age.
“The strong activation of immune- and inflammation-related genes, as well as differential expression of remyelination regulators like Gpr17 (Dziedzic et al., 2020; Rivera et al., 2021), suggest that the homeostasis of this region is compromised at old age.”
Quote in the paperSubstantial claim0 of 2 sources checked - Premisecitation§ Aging results in region-specific expression changes of genes associated with human diseases
GWAS hits for AD include genes Apoe, Ms4a6d, Plcg2 and Gab2.
“GWAS hits for AD included genes that are currently under intense mechanistic investigation, including Apoe , Ms4a6d , Plcg2 and Gab2 (Reiman et al., 2007) (Figure 6B,H).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Reiman et al., 2007 source
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- Interpretationown data§ Discussion
The impacts of aging on brain function are region specific.
“Our findings strongly support the notion that the impacts of aging on brain function are region specific”
Quote in the paperSubstantial claim - Interpretationreasoning§ Aging results in region-specific expression changes of genes associated with human diseases
The region-specific differential regulation of disease risk genes could be an additional factor modulating disease risk.
“we consider that the region-specific differential regulation of such genes could be an additional factor modulating disease risk.”
Quote in the paperSubstantial claim - Interpretationreasoning§ Discussion
The region-specific differential expression of key genetic risk genes locally amplifies or attenuates their impact on disease pathways.
“thus locally amplifying, or attenuating their impact on disease pathways.”
Quote in the paperSubstantial claim - Interpretationown data§ Discussion
Aging may drive dysfunction in brain regions that are not predominantly affected and studied by classical pathological hallmarks.
“our findings also suggest that aging may drive dysfunction in brain regions that are not predominantly affected and studied by classical pathological hallmarks”
Quote in the paperSubstantial claim - Interpretationown data§ Discussion
Region identity is a profound modulator of gene expression dynamics in the context of aging and neurodegeneration.
“Our results emphasize the importance of region identity as a profound modulator of gene expression dynamics in the context of aging and neurodegeneration.”
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Findingown data§ Spatiotemporal quantification of age-related gene expression across the mouse brainUMAP visualization separated samples by region, but not by sex or age.
“Visualization in uniform manifold approximation and projection (UMAP) space separated samples by region (Figure 1C), but not sex or age, which concurred with deterministic shared-nearest-neighbors graph clustering and hierarchical clustering (Figure S2A-E).”
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Findingown data§ Heterogeneous velocity of CAS is encoded by glial transcriptsThere was a profound increase of CAS in aNSCs, though the very low number of cells at 28 months complicates robust calculations of CAS at this age.
“There was also a profound increase of CAS in aNSCs, though the very low number of cells at 28 months (less than 50 per animal) complicates robust calculations of CAS at this age.”
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Findingown data§ Transcriptional aging of microglia is region-dependentIn aged microglia from the Tabula Muris consortium, CAS increased significantly in all four regions (cerebellum, striatum, hippocampus, cortex), with greater magnitude in cerebellum and striatum, followed by hippocampus and cortex.
“Indeed, as predicted by our bulk-seq results, the CAS in aged microglia increased in all four regions significantly, though with greater magnitude in the cerebellum and striatum, followed by the hippocampus and cortex, respectively (Figure 4B).”
Quote in the paperSubstantial claim- The authors state this as an assumption: Microglia from the Tabula Muris consortium collected from cerebellum, striatum, hippocampus and cortex are sufficient equivalents to the authors' cerebellum, caudate putamen, anterior hippocampus and motor/visual cortex regions.
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Findingown data§ A minimal gene set forms a common fingerprint of brain agingOther white matter-rich areas such as the caudate putamen also exhibited high CAS velocities, while the hippocampus, thalamus and hypothalamus ranked slightly below average.
“Other white matter-rich areas such as the caudate putamen also exhibited high velocities, while the hippocampus, thalamus and hypothalamus - some of the most investigated regions in mouse and human brain aging research (Hargis and Blalock, 2017) - ranked slightly below average.”
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Findingown data§ Neuronal transcripts encode region-specific expression patternsSeveral signature genes like Fgf16, S100a10 and Fabp4 were mapped to distinct cell populations, suggesting that bulk tissue can capture the expression dynamics of specific cell subsets.
“We were able to map several signature genes like Fgf16 , S100a10 and Fabp4 (Figure S10C-E) to distinct cell populations (Figure 5G, STAR Methods and Table S9) suggesting that bulk tissue can indeed capture the expression dynamics of specific cell subsets.”
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Findingown data§ Region identity is linked to expression dynamics during agingThe visual cortex showed a steady increase of DEGs until late age, while the motor cortex exhibited significant perturbation at 12 months with little increase until a sudden jump at 21 months.
“For instance, the visual cortex showed a steady increase of DEGs until late age, while the transcriptome of the motor cortex already exhibited significant perturbation at 12 months, but there was little increase until a sudden jump at 21 months (Figure 1F,G).”
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Findingown data§ Aging results in region-specific expression changes of genes associated with human diseasesThe visual and motor cortex regions formed the second cluster, exhibiting even numbers of up- and down-regulated MS genes.
“The visual and motor cortex regions formed the second cluster, exhibiting even numbers of upand down-regulated MS genes.”
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Findingown data§ Neuronal transcripts encode region-specific expression patternsDentate gyrus granule cells of the hippocampus exhibited a distinct increase of the hippocampus-specific signature, and granule cell-specific regulation of several signature genes such as Unc5d and Onecut1 was found.
“In comparison, dentate gyrus granule cells of the hippocampus exhibited a distinct increase of the hippocampusspecific signature (Figure 5K), and we found granule cell-specific regulation of several signature genes such as axon-guidance receptor Unc5d (Yamagishi et al., 2011) as well as transcription factor Onecut1 (Figure 5L,M, Figure S11A,B).”
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Findingcitation§ Region identity is linked to expression dynamics during agingA small, common set of differentially regulated genes, including neuroinflammatory markers Fcgr2b, Ctss, Cst7, was found in modules across regions, suggesting the presence of a minimal group of coregulated genes changing throughout the brain.
“Consistent with these findings, we found a small, common set of differentially regulated genes, including neuroinflammatory markers Fcgr2b , Ctss , Cst7 (Chen et al., 2020) in modules across regions, suggesting the presence of a minimal group of coregulated genes changing throughout the brain.”
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Findingown data§ Aging results in region-specific expression changes of genes associated with human diseasesMS genes showed significant associations with DEGs from nine different regions that fell into two clusters, indicating two disparate subsets.
“MS genes, for instance, showed significant associations with DEGs from nine different regions that fell into two clusters, indicating two disparate subsets.”
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Findingown data§ Region identity is linked to expression dynamics during agingThe corpus callosum and cerebellum were the most impacted by age-correlated gene expression changes, while the entorhinal cortex remained largely unaffected.
“Not only did the regions differ in the number of age-correlated genes, confirming that the effect size of age depends on the region, but also the corpus callosum and cerebellum were the most impacted, while the entorhinal cortex remained largely unaffected (Figure 1H).”
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Findingown data§ Fiber tracts are foci of accelerated brain agingCAS velocities calculated via bulk-seq and those calculated via spatial-seq data were well-correlated, confirming vastly differing aging velocities between proximal regions in situ.
“In general, CAS velocities calculated via bulk-seq and those calculated via spatial-seq data were well-correlated (Figure 2P), confirming vastly differing aging velocities between proximal regions in-situ .”
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Findingown data§ Heterogeneous velocity of CAS is encoded by glial transcriptsC4b was mostly expressed in astrocytes at young age, but its expression became detectable and increased foremost with age in mature oligodendrocytes.
“For instance, C4b was mostly expressed in astrocytes at young age, however its expression became detectable and increased foremost with age in mature oligodendrocytes (Figure S6E).”
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Findingown data§ Region identity is linked to expression dynamics during agingThe number of age-related WGCNA modules differed between regions, with seven or more modules in the corpus callosum, cerebellum and motor cortex, and no age-related modules in the entorhinal cortex.
“In line with the above results, we found seven or more modules in the corpus callosum, cerebellum and motor cortex, whereas we detected no agerelated modules in the entorhinal cortex (Table S4).”
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Findingown data§ Heterogeneous velocity of CAS is encoded by glial transcriptsCAS showed a statistically significant increase in all cell types, with the most accentuated increase in microglia, followed by mature oligodendrocytes, BECs, astrocytes and OPCs.
“While the CAS showed a statistically significant increase in all cell types (Figure 3B), including a mild elevation in several neuronal populations, the most accentuated increase was observed in microglia (Figure 3C), followed by mature oligodendrocytes, brain endothelial cells (BECs), astrocytes and oligodendrocyte progenitor cells (OPCs).”
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Findingown data§ Transcriptional aging of microglia is region-dependentThere was no detectable CAS difference among microglia at young age across the striatum, hippocampus and cortex, while cerebellum-derived microglia exhibited a slightly higher CAS at baseline.
“Notably, there was no detectable CAS difference among microglia at young age across the striatum, hippocampus and cortex, while the cerebellumderived microglia exhibited a slightly higher CAS at baseline.”
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Findingcitation§ A minimal gene set forms a common fingerprint of brain agingOf the only 7 down-regulated genes in the 82-gene set, protein homeostasis genes Dnajb1, Hsph1 and Ahsa1, as well as collagen synthesis gene P4ha1, were found, which is in line with perturbed protein quality control mechanisms as a hallmark of aging.
“Interestingly, of the only 7 down-regulated genes in this set, we found protein homeostasis genes Dnajb1 , Hsph1 and Ahsa1 , as well as collagen synthesis gene P4ha1 , which is in line with perturbed protein quality control mechanisms as a hallmark of aging (López-Otín et al., 2013) (Figure 2B).”
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Findingown data§ Neuronal transcripts encode region-specific expression patternsThere was a significant down-regulation of several mitochondria-related genes in the caudate putamen, including several members of the electron transport chain, which could be indicative of impaired mitochondrial function.
“We observed a significant down-regulation of several mitochondria-related genes in the caudate putamen, including several members of the electron transport chain, which could be indicative of impaired mitochondrial function (Figure 5E).”
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Findingown data§ Aging results in region-specific expression changes of genes associated with human diseasesOne cluster consisted of several regions, including the corpus callosum and cerebellum, that up-regulated a shared set of inflammation-related genes such as Stat3, Ly86 and Irf8, all of which were also part of the CAS.
“One cluster consisted of several regions, including the corpus callosum and cerebellum, that up-regulated a shared set of inflammation-related genes such as Stat3 , Ly86 and Irf8 , all of which were also part of the CAS (Figure 6A).”
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Findingown data§ A minimal gene set forms a common fingerprint of brain agingThe hypothalamus exhibited the most pronounced acceleration of CAS in females, with the thalamus and corpus callosum indicating a similar trend.
“In particular, the hypothalamus exhibited the most pronounced acceleration in females (Figure S4D), with the thalamus and corpus callosum indicating a similar trend.”
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Findingown data§ Aging results in region-specific expression changes of genes associated with human diseasesEach of the three disease-associated gene sets (AD, PD, MS) exhibited a different enrichment pattern and a varying number of associated genes.
“Interestingly, each of the three disease-associated gene sets exhibited a different enrichment pattern (Figure 6A-C) and a varying number of associated genes (Figure 6D-F).”
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Findingown data§ Heterogeneous velocity of CAS is encoded by glial transcriptsSingle-nuclei sequencing yielded all major cell types, with no evidence for a shift in cell type composition with age or sex.
“Singlenuclei sequencing (nuc-seq) yielded all major cell types, with no evidence for a shift in cell type composition with age or sex.”
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Findingown data§ Neuronal transcripts encode region-specific expression patternsIn the caudate putamen, there was a global, gradual downregulation of all genes coding for mitochondria-related proteins, as well as a significant drop in scores for a corresponding mitochondrial signature in aged D2 MSNs, mature oligodendrocytes, and astrocytes.
“We identified in this region a global, gradual downregulation of all genes coding for mitochondria-related proteins (Figure S11E), as well as a significant drop in scores for a corresponding mitochondrial signature in aged D2 MSNs, mature oligodendrocytes, and astrocytes (Figure S11F).”
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Findingown data§ Region identity is linked to expression dynamics during agingThe regions with the most profound and earliest shifts in gene expression were the white matter-rich caudate putamen, cerebellum and corpus callosum, with the corpus callosum showing a tenfold increase in the number of DEGs between 12 and 18 months.
“Notably, the regions with the most profound and earliest shifts in gene expression were the white matter-rich caudate putamen, cerebellum and corpus callosum, the latter showing a tenfold increase in the number of DEGs between 12 and 18 months.”
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Findingown data§ Bulk-seq GWAS gene enrichmentThe authors observed several white matter-related effects in their dataset.
“as we had observed several white matter-related effects in our dataset.”
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Findingown data§ Spatial-seq differential expression analysis and comparison with Bulk-seq dataFor both the white matter/corpus callosum and cortex/motor cortex regions, there is significant overlap between the DEGs found in the Bulk-seq and Spatial-seq datasets (Fisher's exact test, P < 0.05).
“For both regions, we confirmed significant overlap between the DEGs found in Bulk- and Spatial-seq dataset (Fisher's exact test, P Value < 0.05).”
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Findingown data§ DiscussionCertain brain regions are selectively vulnerable to aging, with the white matter fiber tracts exhibiting a particular sensitivity.
“Our data reveal that certain brain regions are selectively vulnerable to aging, with the white matter fiber tracts exhibiting a particular sensitivity.”
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Findingown data§ Spatial-seq mapping, embedding, clustering and region identificationTranscriptional clustering of the spatial spot transcriptomes yielded 29 clusters.
“Transcriptional clustering yielded 29 clusters”
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Findingown data§ Bulk-seq region-specific aging signature identification and velocity comparisonFewer than 20 unique DEGs were found for the entorhinal cortex, which the authors considered too small to construct a signature.
“Further, there were less than 20 unique DEGs found for the entorhinal cortex, which we considered too small to construct a signature.”
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Study design and conductown data§ Spatiotemporal quantification of age-related gene expression across the mouse brainThe study achieved robust tissue sampling with high RNA quality while minimizing perfusion artifacts, as indicated by consistent RNA yields across samples from the same region, median RNA integrity numbers of 9.45 out of 10, and a negligible fraction of reads mapping to hemoglobin genes.
“We achieved robust tissue sampling with high RNA quality while minimizing perfusion artifacts, as indicated by consistent RNA yields across samples from the same region (Figure S1B), median RNA integrity numbers of 9.45 out of 10 (Figure S1C) (Mueller), and a neglectable fraction of reads mapping to hemoglobin genes (Figure S1D).”
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Interpretationown data§ Spatiotemporal quantification of age-related gene expression across the mouse brainThe comparatively subtle effect of aging on gene expression highlights the necessity for precise isolation of brain tissue to avoid confounding cross-region contamination.
“The comparatively subtle effect of aging on gene expression highlights the necessity for precise isolation of brain tissue to avoid confounding crossregion contamination (Figure 1D and S2A).”
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Premisecitation§ IntroductionNeuroimaging studies using structural and functional MRI indicate that aging impacts the brain in a region-specific manner.
“Interestingly, neuroimaging studies using structural and functional magnetic resonance imaging (MRI) data indicate that aging impacts the brain in a region-specific manner (Feng et al., 2020; Pomponio et al., 2020).”
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- cites Pomponio et al., 2020
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Premisecitation§ IntroductionAging is the predominant risk factor for cognitive dysfunction and several neurodegenerative disorders, including Alzheimer's disease and Parkinson's disease.
“Aging is the predominant risk factor for cognitive dysfunction (Kalache and Gatti, 2003; Partridge et al., 2018) and several neurodegenerative disorders, including Alzheimer's disease (AD) and Parkinson's disease (PD) (Hou et al., 2019; López-Otín et al., 2013; Niccoli et al., 2017).”
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Premisecitation§ Spatiotemporal quantification of age-related gene expression across the mouse brainActivated neural stem cells are enriched in neurogenic areas at young age and their number declines in old individuals.
“These aNSCs are enriched in neurogenic areas at young age and their number declines in old individuals (Dulken et al., 2019).”
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Interpretationreasoning§ Neuronal transcripts encode region-specific expression patternsThe transcriptome of neuronal populations could be regulated selectively through age-related activity of specific transcription factors.
“This may indicate that the transcriptome of neuronal populations could be regulated selectively through age-related activity of specific transcription factors.”
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Interpretationown data§ Heterogeneous velocity of CAS is encoded by glial transcriptsRegion-to-region differences in CAS velocity predominantly reflect age effects in non-neuronal cell types, with microglia having the strongest contribution.
“Thus, the region-to-region differences in CAS velocity are predominantly reflecting age effects in non-neuronal cell types, with microglia having the strongest contribution.”
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Study design and conductown data§ Fiber tracts are foci of accelerated brain agingSpatial transcriptomics data demonstrated robust capture of the same regions across age groups and individuals, enabling comparison of DEGs found in bulk-seq with spatial-seq data.
“Our data demonstrated robust capture of the same regions across age groups and individuals (Figure S5G-L), thus enabling the comparison of DEGs found in bulk-seq with spatial-seq data (Table S7).”
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Interpretationown data§ A minimal gene set forms a common fingerprint of brain agingThe authors' data could advance understanding of sexual dimorphisms in the brain, including the higher age-specific risk of dementia among women and the dynamics of reproductive aging.
“Our data could advance the understanding of several sexual-dimorphisms observed in the brain, including the higher age-specific risk of dementia among women (Corrada et al., 2008) in general, and the dynamics of reproductive aging in particular, given the hypothalamus' critical role in regulating reproduction, development and metabolism (Mozhui et al., 2012).”
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- cites Mozhui et al., 2012
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Interpretationown data§ Neuronal transcripts encode region-specific expression patternsDozens to hundreds of genes in the brain are regulated in a region-specific or -selective manner, revealing highly compartmentalized effects of aging within a single organ.
“Thus, dozens to hundreds of genes in the brain are regulated in a region-specific or -selective manner, revealing highly compartmentalized effects of aging within a single organ.”
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Premisecitation§ Neuronal transcripts encode region-specific expression patternsOnecut transcription factors have only been recently investigated in the CNS as regulators of neuronal differentiation and their motif is a hotspot of DNA single-strand break repair in neurons.
“Onecut transcription factors have only been recently investigated in the CNS as regulators of neuronal differentiation and their motif is a hotspot of DNA single-strand break repair in neurons (Wu et al., 2021).”
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Premisecitation§ Region identity is linked to expression dynamics during agingHuman MRI and microarray studies demonstrate that the entorhinal cortex displays only mild alterations during cognitively normal human aging, whereas it frequently exhibits the first amyloid deposition in AD patients.
“This is in line with human MRI (Feng et al., 2020) and microarray (Berchtold et al., 2008) studies demonstrating that the entorhinal cortex displays only mild alterations during cognitively normal human aging, whereas it frequently exhibits the first amyloid deposition in AD patients (Van Hoesen et al., 1991).”
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- cites Berchtold et al., 2008 source
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- cites Van Hoesen et al., 1991
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Interpretationown data§ Region identity is linked to expression dynamics during agingThe results of three independent analyses (pairwise tests, age-correlation and WGCNA) were congruent, demonstrating that the observed effects of aging on the transcriptome are region specific.
“In summary, we found the results of three independent analyses (pairwise tests, age-correlation and WGCNA) congruent, demonstrating that the observed effects of aging on the transcriptome are region specific.”
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Interpretationown data§ Aging results in region-specific expression changes of genes associated with human diseasesThere is an intriguing possibility of similarities between the pathophysiology of inflammation and demyelination associated with MS and the accelerated aging observed in white matter-rich areas.
“This raises the intriguing possibility of similarities between the pathophysiology of inflammation and demyelination associated with MS and the accelerated aging observed in white matter-rich areas.”
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Premisecitation§ A minimal gene set forms a common fingerprint of brain agingHuman studies report more pronounced expression of immune-related genes in the hippocampus and cortex of aged women.
“These findings are in line with human studies reporting more pronounced expression of immune-related genes in the hippocampus and cortex of aged women (Berchtold et al., 2008; Yuan et al., 2012).”
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- cites Yuan et al., 2012
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Study design and conductnone given§ Bulk-seq quantification, quality controlThe quantification and smoothing of trajectories was solely used for visualization and was not the basis for any statistical testing in this study.
“This quantification and smoothing was solely used for visualization and was not the basis for any statistical testing in this study.”
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Premisecitation§ Gene signature generation and score calculationVISION z-normalizes signature scores with random gene signatures to account for global sample-level metrics such as total number of counts/UMIs, which can be affected by age.
“Notably, VISION znormalizes signature scores with random gene signatures to account for global sample-level metrics (such as total number of counts/UMIs, which can be affected by age (Pálovics et al., 2022)).”
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- cites Pálovics et al., 2022
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Study design and conductnone given§ Bulk-seq differential expressionP values were adjusted for multiple testing, and genes with an adjusted P value of less than 0.05 were determined to be statistically significant.
“P values were adjusted for multiple testing, and genes with an adjusted P value of less than 0.05 were determined to be statistically significant.”
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Study design and conductnone given§ Spatial-seq CAS calculationThe Spatial-seq CAS analysis does not account for biological replicates but treats each spot belonging to the same region as a replicate.
“We acknowledge that this analysis does not account for biological replicates but treats each spot belonging to the same region as replicate.”
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Study design and conductreasoning§ Bulk-seq Common Aging Score (CAS) calculation and CAS velocity comparisonOnly the anterior hippocampus region was included in the selection of cross-region DEGs to prevent a potential bias towards aging effects in the hippocampus.
“We included only the anterior hippocampus region in the selection of cross-region DEGs to prevent a potential bias towards aging effects in the hippocampus.”
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Study design and conductreasoning§ Bulk-seq differential expressionA gene was required to reach statistical significance after multiple testing correction in at least 2 pairwise comparisons to be called a differentially expressed gene (DEG).
“Finally, we required a gene to reach statistical significance (after multiple testing correction) in at least 2 pairwise comparisons (e.g. 3 months vs 18 months and 3 months vs 21 months) to be called a differentially expressed gene (DEG).”
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Study design and conductown data§ Bulk-seq preparation and sequencingRNA quality control was conducted using a Bioanalyzer for three randomly selected samples per brain region.
“Quality control of RNA was conducted using a Bioanalyzer (Agilent) at the Stanford Protein and Nucleic Acid Facility for three randomly selected samples per brain region.”
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Study design and conductown data§ Bulk-seq quantification, quality controlThe Seurat-based quality assessment was corroborated by loading and normalizing the count matrix using DEseq2 and conducting variance stabilizing transformation.
“We corroborated the Seurat-based quality assessment by loading and normalizing the count matrix using DEseq2 before conducting the built-in variance stabilizing transformation”
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Study design and conductown data§ Signature calculations in scRNA- and Nuc-seq dataThe scRNA-/Nuc-seq CAS analysis does not account for biological replicates but treats each cell belonging to the same cell type as a replicate.
“We acknowledge that this analysis does not account for biological replicates but treats each cell belonging to the same cell type as replicate.”
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Study design and conductnone given§ Gene signature generation and score calculationThe VISION analysis workflow is applicable for bulk, spatial and single-cell/-nuclei datasets.
“While VISION was originally intended for the analysis of signatures in single-cell data we found its analysis workflow applicable for bulk, spatial and single-cell/-nuclei datasets.”
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Study design and conductnone given§ Spatial-seq differential expression analysis and comparison with Bulk-seq dataClusters in the Spatial-seq data are comparably represented across all samples and age groups, making differential expression analysis across age groups feasible.
“Given comparable representation of clusters across all samples and age groups, we considered differential expression analysis across age groups feasible.”
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Study design and conductcitation§ DEG Gene Ontology functional enrichmentThe set of expressed genes, defined as passing the independent filtering criterion of DEseq2, was used as background for all functional enrichment analyses involving expression data.
“Unless stated otherwise, the set of expressed genes (defined as passing the independent filtering criterion of DEseq2 (Love et al., 2014)) was used as background for all functional enrichment analyses involving expression data.”
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Study design and conductown data§ Spatial-seq mapping, embedding, clustering and region identificationSpatial-seq clusters were annotated by comparing marker gene expression to in situ hybridization data from the Allen Mouse Brain Atlas and to visual landmarks from the H&E microscopy images.
“We compared the expression of marker genes to insitu hybridization (ISH) data from the Allen Mouse Brain Atlas(Lein et al., 2007) and visual landmarks from the H&E microscopy images”
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Study design and conductnone given§ Aging results in region-specific expression changes of genes associated with human diseasesThe substantia nigra, a major region where PD typically manifests, was not quantified in this study.
“Of note, the substantia nigra, a major region where PD typically manifests, was not quantified in our study.”
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Premisecitation§ DiscussionRejuvenation of hippocampal oligodendrogenesis in aged mice via injection of young cerebrospinal fluid improves long-term memory consolidation, demonstrating a causal role of compromised myelin on cognition.
“In line with this hypothesis, rejuvenation of hippocampal oligodendrogenesis in aged mice via injection of young cerebrospinal fluid (CSF) improves long-term memory consolidation, thus demonstrating a causal role of compromised myelin on cognition (Iram et al., 2022).”
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Interpretationreasoning§ DiscussionThe impacts of aging on brain function potentially relate to the regional vulnerability across different diseases as well as the varied manifestations of neurodegeneration at the level of an individual.
“potentially relating to the regional vulnerability across different diseases as well as the varied manifestations of neurodegeneration at the level of an individual.”
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Study design and conductown data§ Bulk-seq GWAS gene enrichmentThe anterior hippocampus region was chosen as representative for the hippocampus, and the entorhinal cortex and olfactory bulb were excluded.
“We chose the anterior hippocampus region as representative for the hippocampus, and further excluded the entorhinal cortex (too few DEGs) and olfactory bulb.”
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Study design and conductnone given§ Animal husbandry and organ collectionAge groups and sexes were rotated through over the duration of the takedowns to average out the impact of takedown time.
“Age groups and sexes were rotated through over the duration of the takedowns to average out the impact of takedown time.”
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Study design and conductnone given§ Bulk-seq correlation of gene expression with ageGenes with Spearman's rho ≥ 0.5 or ≤ -0.5 and padj ≤ 0.05 were called significantly age-correlated in a given region.
“Genes with Spearman's rho ≥ 0.5 or ≤ -0.5, respectively, and padj ≤ 0.05 were called as significantly age-correlated in a given region.”
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Study design and conductown data§ Bulk-seq differential expressionDifferential expression analysis comparing samples from 3 months to each consecutive time point used sex as a covariate.
“We conducted differential expression analysis comparing samples from 3 months to each consecutive time point, using sex as covariate.”
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Interpretationnone given§ Spatial-seq differential expression analysis and comparison with Bulk-seq dataThe Spatial-seq white matter cluster is comparable to the bulk-seq corpus callosum region (high CAS velocity) and the Spatial-seq cortex cluster is comparable to the bulk-seq motor cortex region (low CAS velocity).
“We analyzed differential expression in the white matter and cortex cluster as we considered them comparable to the corpus callosum (high CAS velocity) and motor cortex (low CAS velocity) region from the bulk-seq dataset.”
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Premisereasoning§ Gene signature generation and score calculationVISION scores can be negative due to differences in baseline expression across regions or cell types and the z-normalization.
“We note that due to differences in baseline expression across regions or cell types as well as the znormalization mentioned above, VISION scores can be negative.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
Bulk RNA-seq data, even with regional dissection, could mask transcriptional changes with age that occur in sub-structures of regions, such as specific layers of the cortex.
“Bulk-seq data - even with the regional dissection conducted here - could mask transcriptional changes with age that occur in sub-structures of regions, such as specific layers of the cortex.”
The authors state this as an assumption.
Microglia from the Tabula Muris consortium collected from cerebellum, striatum, hippocampus and cortex are sufficient equivalents to the authors' cerebellum, caudate putamen, anterior hippocampus and motor/visual cortex regions.
“which we considered sufficient equivalents to the cerebellum, caudate putamen (both areas with high velocity), anterior hippocampus (medium velocity) and motor/visual cortex (low velocity) regions (compare Figure 2G).”
The paper relies on this without support.
Mouse brain aging findings are relevant to human brain aging and neurodegenerative disease.
“Our findings identify molecular foci of brain aging, providing a foundation to target age-related cognitive decline.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Previously, spatial transcriptomics technology has been utilized to create spatiotemporal maps of various life stages, including mouse brain, regenerating axolotl telencephalons, aging ovary, and brain.
“Previously, this technology has been utilized to create spatiotemporal maps of various life stages, including mouse brain, regenerating axolotl telencephalons, aging ovary, and brain.21”
From Spatial transcriptomic landscape unveils immunoglobin-associated senescence as a hallmark of aging (2024) · cites this paper as [59]
The Discussion (page not provided) reports a spatiotemporal map of gene expression across the mouse brain throughout adulthood, and “Fiber tracts are foci of accelerated brain aging” describes spatial transcriptomics across mouse ages. Our copy does not support the sentence’s other examples—regenerating axolotl telencephalons and aging ovary—as applications documented by this source. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe white matter is selectively vulnerable to the aging process.
“The white matter is selectively vulnerable to the aging process,91,92”
From Uncovering the signatures of aging and senescence in the human dorsolateral prefrontal cortex (2026) · cites this paper as [91]
For 66 of the 82 target genes, expression values from 809 samples matching to the miRNA samples were obtained using gene data from Hahn et al.
“Using the gene data from Hahn et al.3, we obtained for 66 of the target genes expression values from 809 samples matching to the miRNA samples.”
From A spatio-temporal brain miRNA expression atlas identifies sex-independent age-related microglial driven miR-155-5p increase (2025) · cites this paper as Hahn et al. [3]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Caudate putamen gene named differently in text and figure legend
MinorThe results name Fgf16 as a gene shown in Figure S10C-E, whereas the legend identifies the gene in panel C as Fgf17.
“We were able to map several signature genes like Fgf16 , S100a10 and Fabp4 (Figure S10C-E)”
“(C-G) Bulk expression across proximal brain regions of genes (C) Fgf17 , (D) S100a10 , (E) Fabp4 , (F) Cpa6”
Male cohort sizes disagree between figure and methods
MinorFigure 1A gives a maximum of five males per age, while the animal-husbandry methods state that five to six males were used for each of the five age groups from 3 through 21 months.
“Whole brains were collected from male (n = 3-5, 3-28 months) and female (n = 5, 3-21 months) C57BL/6JN mice.”
“5-6 male and 5 female mice were used for each 3, 12, 15, 18, and 21 months group”
Spatial-seq comparison cites an age that was not sampled
MinorFigure 2L describes spatial-seq expression ratios for 21 months relative to 3 months, but the spatial-seq cohort had no 3-month-old mice. The methods describe the spatial-seq differential-expression comparison as 21 versus 6 months.
“DEGs (Padj < 0.05) found in both datasets are shown, with their log2-transformed expression ratios (21 rel. to 3 months) in Bulk- and Spatial-seq data.”
“Differential gene expression of genes comparing Spatial-seq data from 6 months to 21 months was done using the 'DESeq2' algorithm implemented in Seurat on Spatial count data.”
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · see citing works
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458 · see citing works
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252 · see citing works
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453 · see citing works
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013) · Cell · doi:10.1016/j.cell.2013.05.039 · Cited by 14,982 · see citing works
Self-citationLeast-Squares Means: The R Package lsmeans
Lenth, R. (2016) · Journal of Statistical Software · doi:10.18637/jss.v069.i01 · Cited by 5,837 · see citing works
Genome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572 · see citing works
Ageing as a risk factor for neurodegenerative disease
Hou, Y., Dan, X., Babbar, M., Wei, Y., Hasselbalch, S. G., Croteau, D. L., & Bohr, V. A. (2019) · Nature Reviews Neurology · doi:10.1038/s41582-019-0244-7 · Cited by 3,310 · see citing works
GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor
Davis, S. R., & Meltzer, P. S. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm254 · Cited by 3,289 · see citing works
Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A. K., Slichter, C. K., Miller, H. W., McElrath, M. J., Prlic, M., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0844-5 · Cited by 3,159 · see citing works
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124 · see citing works
The RIN: an RNA integrity number for assigning integrity values to RNA measurements
Schroeder, A., Mueller, O., Stocker, S., Salowsky, R., Leiber, M., Gassmann, M., Lightfoot, S., Menzel, W., Granzow, M., & Ragg, T. (2006) · BMC Molecular Biology · doi:10.1186/1471-2199-7-3 · Cited by 2,623 · see citing works
Single-cell transcriptomic analysis of Alzheimer’s disease
Mathys, H., Davila-Velderrain, J., Peng, Z., Gao, F., Mohammadi, S., Young, J. Z., Menon, M., He, L., Abdurrob, F., Jiang, X., et al. (2019) · Nature · doi:10.1038/s41586-019-1195-2 · Cited by 2,568 · see citing works
Schizophrenia risk from complex variation of complement component 4
Sekar, A., Bialas, A. R., de Rivera, H., Davis, A., Hammond, T. R., Kamitaki, N., Tooley, K., Presumey, J., Baum, M., Van Doren, V., et al. (2016) · Nature · doi:10.1038/nature16549 · Cited by 2,394 · see citing works
Smart-seq2 for sensitive full-length transcriptome profiling in single cells
Picelli, S., Björklund, Å. K., Faridani, O. R., Sagasser, S., Winberg, G., & Sandberg, R. (2013) · Nature Methods · doi:10.1038/nmeth.2639 · Cited by 2,358 · see citing works
Zhang, Y., Sloan, S. A., Clarke, L. E., Caneda, C., Plaza, C. A., Blumenthal, P. D., Vogel, H., Steinberg, G. K., Edwards, M. S. B., Li, G., et al. (2016) · Neuron · doi:10.1016/j.neuron.2015.11.013 · Cited by 2,295 · see citing works
Spatio-temporal transcriptome of the human brain
Kang, H. J., Kawasawa, Y. I., Cheng, F., Zhu, Y., Xu, X., Li, M., Sousa, A. M. M., Pletikos, M., Meyer, K. A., Sedmak, G., et al. (2011) · Nature · doi:10.1038/nature10523 · Cited by 2,084 · see citing works
Active Ageing: A Policy Framework
Kalache, A., & Gatti, A. (2002) · The Aging Male · doi:10.1080/713604647 · Cited by 1,764 · see citing works
The ageing systemic milieu negatively regulates neurogenesis and cognitive function
Villeda, S. A., Luo, J., Mosher, K. I., Zou, B., Britschgi, M., Bieri, G., Stan, T. M., Fainberg, N., Ding, Z., Eggel, A., et al. (2011) · Nature · doi:10.1038/nature10357 · Cited by 1,763 · see citing works
Self-citationThree-dimensional intact-tissue sequencing of single-cell transcriptional states
Wang, X., Allen, W. E., Wright, M. A., Sylwestrak, E. L., Samusik, N., Vesuna, S., Evans, K., Liu, C., Ramakrishnan, C., Liu, J., et al. (2018) · Science · doi:10.1126/science.aat5691 · Cited by 1,620 · see citing works
Active Ageing: A Policy Framework
(2002) · The Aging Male · doi:10.1080/tam.5.1.1.37 · Cited by 1,567 · see citing works
Facing up to the global challenges of ageing
Partridge, L., Deelen, J., & Slagboom, P. E. (2018) · Nature · doi:10.1038/s41586-018-0457-8 · Cited by 1,475 · see citing works
Self-citationEleven grand challenges in single-cell data science
Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1926-6 · Cited by 1,327 · see citing works
The human transcriptome across tissues and individuals
Melé, M., Ferreira, P. G., Reverter, F., DeLuca, D. S., Monlong, J., Sammeth, M., Young, T. R., Goldmann, J. M., Pervouchine, D. D., Sullivan, T. J., et al. (2015) · Science · doi:10.1126/science.aaa0355 · Cited by 1,304 · see citing works
A single-cell transcriptomic atlas characterizes ageing tissues in the mouse
The Tabula Muris Consortium, Almanzar, N., Antony, J., Baghel, A. S., Bakerman, I., Bansal, I., Barres, B. A., Beachy, P. A., Berdnik, D., Bilen, B., et al. (2020) · Nature · doi:10.1038/s41586-020-2496-1 · Cited by 1,247 · see citing works
Self-citationNormal aging induces A1-like astrocyte reactivity
Clarke, L. E., Liddelow, S. A., Chakraborty, C., Münch, A. E., Heiman, M., & Barres, B. A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1800165115 · Cited by 1,237 · see citing works
Origin, fate and dynamics of macrophages at central nervous system interfaces
Goldmann, T., Wieghofer, P., Jordão, M. J. C., Prutek, F., Hagemeyer, N., Frenzel, K., Amann, L., Staszewski, O., Kierdorf, K., Krueger, M., et al. (2016) · Nature Immunology · doi:10.1038/ni.3423 · Cited by 1,209 · see citing works
Microglial brain region-dependent diversity and selective regional sensitivities to aging
Grabert, K., Michoel, T., Karavolos, M. H., Clohisey, S., Baillie, J. K., Stevens, M. P., Freeman, T. C., Summers, K. M., & McColl, B. W. (2016) · Nature Neuroscience · doi:10.1038/nn.4222 · Cited by 1,151 · see citing works
Zhou, Y., Song, W. M., Andhey, P. S., Swain, A., Levy, T., Miller, K. R., Poliani, P. L., Cominelli, M., Grover, S., Gilfillan, S., et al. (2020) · Nature Medicine · doi:10.1038/s41591-019-0695-9 · Cited by 1,150 · see citing works
Young blood reverses age-related impairments in cognitive function and synaptic plasticity in mice
Villeda, S. A., Plambeck, K. E., Middeldorp, J., Castellano, J. M., Mosher, K. I., Luo, J., Smith, L. K., Bieri, G., Lin, K., Berdnik, D., et al. (2014) · Nature Medicine · doi:10.1038/nm.3569 · Cited by 1,081 · see citing works
Self-citationGene-expression profile of the ageing brain in mice
Lee, C.-K., Weindruch, R., & Prolla, T. A. (2000) · Nature Genetics · doi:10.1038/77046 · Cited by 1,055 · see citing works
Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease
Chen, W.-T., Lu, A., Craessaerts, K., Pavie, B., Sala Frigerio, C., Corthout, N., Qian, X., Laláková, J., Kühnemund, M., Voytyuk, I., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.038 · Cited by 949 · cited 1× · see citing works
“Consistent with these findings, we found a small, common set of DEGs, including neuroinflammatory markers Fcgr2b , Ctss , and Cst7 38 in modules across regions, suggesting the presence of a minimal group of co-regulated genes changing throughout the brain.”
ResultA human brain vascular atlas reveals diverse mediators of Alzheimer’s risk
Yang, A. C., Vest, R. T., Kern, F., Lee, D. P., Agam, M., Maat, C. A., Losada, P. M., Chen, M. B., Schaum, N., Khoury, N., et al. (2022) · Nature · doi:10.1038/s41586-021-04369-3 · Cited by 797 · see citing works
Self-citationSingle-cell transcriptomic profiling of the aging mouse brain
Ximerakis, M., Lipnick, S. L., Innes, B. T., Simmons, S. K., Adiconis, X., Dionne, D., Mayweather, B. A., Nguyen, L., Niziolek, Z., Ozek, C., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0491-3 · Cited by 736 · see citing works
The aging astrocyte transcriptome from multiple regions of the mouse brain.
Boisvert, M. M., Erikson, G. A., Shokhirev, M. N., & Allen, N. J. (2018) · Cell Reports · doi:10.1016/j.celrep.2017.12.039 · Cited by 702 · see citing works
Temporal dynamics and genetic control of transcription in the human prefrontal cortex
Colantuoni, C., Lipska, B. K., Ye, T., Hyde, T. M., Tao, R., Leek, J. T., Colantuoni, E. A., Elkahloun, A. G., Herman, M. M., Weinberger, D. R., et al. (2011) · Nature · doi:10.1038/nature10524 · Cited by 685 · see citing works
Ageing hallmarks exhibit organ-specific temporal signatures
Schaum, N., Lehallier, B., Hahn, O., Pálovics, R., Hosseinzadeh, S., Lee, S. E., Sit, R., Lee, D. P., Losada, P. M., Zardeneta, M. E., et al. (2020) · Nature · doi:10.1038/s41586-020-2499-y · Cited by 671 · see citing works
Self-citationGene expression changes in the course of normal brain aging are sexually dimorphic
Berchtold, N. C., Cribbs, D. H., Coleman, P. D., Rogers, J., Head, E., Kim, R., Beach, T., Miller, C., Troncoso, J., Trojanowski, J. Q., et al. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0806883105 · Cited by 626 · see citing works
Dysregulation of brain and choroid plexus cell types in severe COVID-19
Yang, A. C., Kern, F., Losada, P. M., Agam, M. R., Maat, C. A., Schmartz, G. P., Fehlmann, T., Stein, J. A., Schaum, N., Lee, D. P., et al. (2021) · Nature · doi:10.1038/s41586-021-03710-0 · Cited by 620 · see citing works
Self-citationPomponio, R., Erus, G., Habes, M., Doshi, J., Srinivasan, D., Mamourian, E., Bashyam, V. M., Nasrallah, I., Satterthwaite, T., Fan, Y., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116450 · Cited by 576 · see citing works
A multimodal cell census and atlas of the mammalian primary motor cortex
BRAIN Initiative Cell Census Network (BICCN), BRAIN Initiative Cell Census Network (BICCN) Corresponding authors, Callaway, E. M., Dong, H.-W., Ecker, J. R., Hawrylycz, M. J., Huang, Z. J., Lein, E. S., Ngai, J., Osten, P., et al. (2021) · Nature · doi:10.1038/s41586-021-03950-0 · Cited by 522 · see citing works
Single-cell analysis reveals T cell infiltration in old neurogenic niches
Dulken, B. W., Buckley, M. T., Navarro Negredo, P., Saligrama, N., Cayrol, R., Leeman, D. S., George, B. M., Boutet, S. C., Hebestreit, K., Pluvinage, J. V., et al. (2019) · Nature · doi:10.1038/s41586-019-1362-5 · Cited by 521 · see citing works
Self-citationFrancesco, A. D., Germanio, C. D., Bernier, M., & de Cabo, R. (2018) · Science · doi:10.1126/science.aau2095 · Cited by 497 · see citing works
GAB2 Alleles Modify Alzheimer's Risk in APOE ɛ4 Carriers
Reiman, E. M., Webster, J. A., Myers, A. J., Hardy, J., Dunckley, T., Zismann, V. L., Joshipura, K. D., Pearson, J. V., Hu-Lince, D., Huentelman, M. J., et al. (2007) · Neuron · doi:10.1016/j.neuron.2007.05.022 · Cited by 487 · see citing works
Physiological blood–brain transport is impaired with age by a shift in transcytosis
Yang, A. C., Stevens, M. Y., Chen, M. B., Lee, D. P., Stähli, D., Gate, D., Contrepois, K., Chen, W., Iram, T., Zhang, L., et al. (2020) · Nature · doi:10.1038/s41586-020-2453-z · Cited by 477 · see citing works
Self-citationWhite matter aging drives microglial diversity
Safaiyan, S., Besson-Girard, S., Kaya, T., Cantuti-Castelvetri, L., Liu, L., Ji, H., Schifferer, M., Gouna, G., Usifo, F., Kannaiyan, N., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.01.027 · Cited by 477 · see citing works
Entorhinal cortex pathology in Alzheimer's disease
van Hoesen, G. W., Hyman, B. T., & Damasio, A. R. (1991) · Hippocampus · doi:10.1002/hipo.450010102 · Cited by 475 · see citing works
CD22 blockade restores homeostatic microglial phagocytosis in ageing brains
Pluvinage, J. V., Haney, M. S., Smith, B. A. H., Sun, J., Iram, T., Bonanno, L., Li, L., Lee, D. P., Morgens, D. W., Yang, A. C., et al. (2019) · Nature · doi:10.1038/s41586-019-1088-4 · Cited by 459 · see citing works
Self-citationTREM2, microglia, and neurodegenerative diseases
Yeh, F. L., Hansen, D. V., & Sheng, M. (2017) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2017.03.008 · Cited by 440 · see citing works
AGEMAP: A Gene Expression Database for Aging in Mice
Zahn, J. M., Poosala, S., Owen, A. B., Ingram, D. K., Lustig, A., Carter, A., Weeraratna, A. T., Taub, D. D., Gorospe, M., Mazan-Mamczarz, K., et al. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030201 · Cited by 431 · see citing works
Molecular and spatial signatures of mouse brain aging at single-cell resolution
Allen, W. E., Blosser, T. R., Sullivan, Z. A., Dulac, C., & Zhuang, X. (2023) · Cell · doi:10.1016/j.cell.2022.12.010 · Cited by 423 · see citing works
Exercise plasma boosts memory and dampens brain inflammation via clusterin
De Miguel, Z., Khoury, N., Betley, M. J., Lehallier, B., Willoughby, D., Olsson, N., Yang, A. C., Hahn, O., Lu, N., Vest, R. T., et al. (2021) · Nature · doi:10.1038/s41586-021-04183-x · Cited by 422 · see citing works
Self-citationMajor Shifts in Glial Regional Identity Are a Transcriptional Hallmark of Human Brain Aging
Soreq, L., Rose, J., Soreq, E., Hardy, J., Trabzuni, D., Cookson, M. R., Smith, C., Ryten, M., Patani, R., & Ule, J. (2017) · Cell Reports · doi:10.1016/j.celrep.2016.12.011 · Cited by 419 · see citing works
CIS is a potent checkpoint in NK cell–mediated tumor immunity
Delconte, R. B., Kolesnik, T. B., Dagley, L. F., Rautela, J., Shi, W., Putz, E. M., Stannard, K. A., Zhang, J., Teh, C. E., Firth, M., et al. (2016) · Nature Immunology · doi:10.1038/ni.3470 · Cited by 378 · see citing works
Age-dependent vulnerability of the striatum to the mitochondrial toxin 3-nitropropionic acid
Brouillet, E., Jenkins, B. G., Hyman, B. T., Ferrante, R. J., Kowall, N. W., Srivastava, R., Roy, D. S., Rosen, B. R., & Beal, M. F. (1993) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.1993.tb05859.x · Cited by 367 · see citing works
Circadian alignment of early onset caloric restriction promotes longevity in male C57BL/6J mice
Acosta-Rodríguez, V. A., Rijo‐Ferreira, F., Izumo, M., Xu, P., Wight‐Carter, M., Green, C. B., & Takahashi, J. S. (2022) · Science · doi:10.1126/science.abk0297 · Cited by 361 · see citing works
Direct Comparative Analyses of 10X Genomics Chromium and Smart-Seq2
Wang, X., He, Y., Zhang, Q., Ren, X., & Zhang, Z. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.02.005 · Cited by 316 · see citing works
Acosta-Rodríguez, V. A., de Groot, M. H. M., Rijo‐Ferreira, F., Green, C. B., & Takahashi, J. S. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2017.06.007 · Cited by 252 · see citing works
Functional interpretation of single cell similarity maps
DeTomaso, D., Jones, M. G., Subramaniam, M., Ashuach, T., Ye, C., & Yosef, N. (2019) · Nature Communications · doi:10.1038/s41467-019-12235-0 · Cited by 251 · see citing works
Aging of Cerebral White Matter
Liu, H., Yang, Y.-Y., Xia, Y., Zhu, W., Leak, R., Wei, Z., Wang, J.-Y., & Hu, X.-M. (2016) · Ageing Research Reviews · doi:10.1016/j.arr.2016.11.006 · Cited by 249 · see citing works
Spatial atlas of the mouse central nervous system at molecular resolution
Shi, H., He, Y., Zhou, Y., Huang, J., Maher, K., Wang, B., Tang, Z., Luo, S., Tan, P., Wu, M., et al. (2023) · Nature · doi:10.1038/s41586-023-06569-5 · Cited by 244 · see citing works
Trabzuni, D., Ryten, M., Walker, R., Smith, C., Imran, S., Ramasamy, A., Weale, M. E., & Hardy, J. (2011) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.2011.07432.x · Cited by 229 · see citing works
Cold-inducible proteins CIRP and RBM3, a unique couple with activities far beyond the cold
Zhu, X., Bührer, C., & Wellmann, S. M. (2016) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-016-2253-7 · Cited by 218 · see citing works
Young CSF restores oligodendrogenesis and memory in aged mice via Fgf17
Iram, T., Kern, F., Kaur, A., Myneni, S., Morningstar, A. R., Shin, H., García, M. Á., Yerra, L. P., Pálovics, R., Yang, A. C., et al. (2022) · Nature · doi:10.1038/s41586-022-04722-0 · Cited by 198 · see citing works
Self-citationCish actively silences TCR signaling in CD8+ T cells to maintain tumor tolerance
Palmer, D. C., Guittard, G. C., Franco, Z., Crompton, J. G., Eil, R. L., Patel, S. J., Ji, Y., van Panhuys, N., Klebanoff, C. A., Sukumar, M., et al. (2015) · The Journal of Experimental Medicine · doi:10.1084/jem.20150304 · Cited by 192 · see citing works
Buckley, M. T., Sun, E., George, B. M., Liu, L. A., Schaum, N., Xu, L., Reyes, J. M., Goodell, M. A., Weissman, I. L., Wyss‐Coray, T., et al. (2022) · Nature Aging · doi:10.1038/s43587-022-00335-4 · Cited by 189 · see citing works
Self-citationOrigins, Development, and Compartmentation of the Granule Cells of the Cerebellum
Consalez, G. G., Goldowitz, D., Casoni, F., & Hawkes, R. B. (2021) · Frontiers in Neural Circuits · doi:10.3389/fncir.2020.611841 · Cited by 167 · see citing works
Blood-to-brain communication in aging and rejuvenation
Bieri, G., Schroer, A. B., & Villeda, S. A. (2023) · Nature Neuroscience · doi:10.1038/s41593-022-01238-8 · Cited by 156 · see citing works
FLRT2 and FLRT3 act as repulsive guidance cues for Unc5‐positive neurons
Yamagishi, S., Hampel, F., Hata, K., del Toro, D., Schwark, M., Kvachnina, E., Bastmeyer, M., Yamashita, T., Tarabykin, V. S., Klein, R., et al. (2011) · The EMBO Journal · doi:10.1038/emboj.2011.189 · Cited by 153 · see citing works
Molecular hallmarks of heterochronic parabiosis at single-cell resolution
Pálovics, R., Keller, A., Schaum, N., Tan, W., Fehlmann, T., Borja, M., Kern, F., Bonanno, L., Calcuttawala, K., Webber, J., et al. (2022) · Nature · doi:10.1038/s41586-022-04461-2 · Cited by 153 · see citing works
Self-citationDiurnal transcriptome landscape of a multi-tissue response to time-restricted feeding in mammals
Deota, S., Lin, T. C., Chaix, A., Williams, A. E., Le, H. D., Calligaro, H., Ramasamy, R., Huang, L., & Panda, S. (2023) · Cell Metabolism · doi:10.1016/j.cmet.2022.12.006 · Cited by 139 · see citing works
Wahl, D., Solon‐Biet, S. M., Wang, Q., Wali, J. A., Pulpitel, T., Clark, X., Raubenheimer, D., Senior, A. M., Sinclair, D. A., Cooney, G. J., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.10.070 · Cited by 135 · see citing works
A nutritional memory effect counteracts the benefits of dietary restriction in old mice
Hahn, O., Drews, L. F., Nguyen, A., Tatsuta, T., Gkioni, L., Hendrich, O., Zhang, Q., Langer, T., Pletcher, S. D., Wakelam, M. J. O., et al. (2019) · Nature Metabolism · doi:10.1038/s42255-019-0121-0 · Cited by 118 · see citing works
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Advances in transcriptome analysis of human brain aging
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Rivera, A. D., Pieropan, F., Chacon‐De‐La‐Rocha, I., Lecca, D., Abbracchio, M. P., Azim, K., & Butt, A. M. (2021) · Aging Cell · doi:10.1111/acel.13335 · Cited by 61 · see citing works
A neuronal blood marker is associated with mortality in old age
Kaeser, S. A., Lehallier, B., Thinggaard, M., Häsler, L. M., Apel, A., Bergmann, C., Berdnik, D., Jeune, B., Christensen, K., Grönke, S., et al. (2021) · Nature Aging · doi:10.1038/s43587-021-00028-4 · Cited by 57 · see citing works
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Brain regions vulnerable and resistant to aging without Alzheimer’s disease
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Dziedzic, A., Miller, E., Saluk-Bijak, J., & Bijak, M. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21051852 · Cited by 38 · see citing works
Accelerated aging‐related transcriptome changes in the female prefrontal cortex
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Pituitary tumorigenesis in aging female C57BL/6J mice: A light and electron microscopic study
Schechter, J., Felicio, L. S., Nelson, J. F., & Finch, C. E. (1981) · The Anatomical Record · doi:10.1002/ar.1091990310 · Cited by 35 · see citing works
Ferreira, M., Francisco, S., Soares, A. R., Nobre, A., Pinheiro, M. M., Reis, A., Neto, S. C., Rodrigues, A. J., Sousa, N., Moura, G. R., et al. (2021) · Aging · doi:10.18632/aging.203379 · Cited by 34 · see citing works
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Shi, H., He, Y., Zhou, Y., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.20.496914 · Cited by 31 · see citing works
Kihara, Y., Zhu, Y., Jonnalagadda, D., Romanow, W., Palmer, C., Siddoway, B., Rivera, R., Dutta, R., Trapp, B. D., & Chun, J. (2022) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2022.918041 · Cited by 26 · see citing works
Nagpal, L., Kornberg, M. D., & Snyder, S. H. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2121946119 · Cited by 22 · see citing works
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Patki, G. (2009) · Frontiers in Aging Neuroscience · doi:10.3389/neuro.24.003.2009 · Cited by 20 · see citing works
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Wager‐Miller, J., Murphy, M. N., Shafique, H. S., et al. (2020) · Journal of Visualized Experiments · doi:10.3791/60474 · Cited by 20 · see citing works
CoolMPS for robust sequencing of single-nuclear RNAs captured by droplet-based method
Hahn, O., Fehlmann, T., Zhang, H., Munson, C. N., Vest, R. T., Borcherding, A., Liu, S. N., Villarosa, C., Drmanac, S., Drmanac, R., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1127 · Cited by 13 · see citing works
Self-citationPrevalence of dementia after age 90: Results from The 90+ Study
Corrada, M. M., Brookmeyer, R., Berlau, D., Paganini-Hill, A., & Kawas, C. H. (2008) · Neurology · doi:10.1212/01.wnl.0000310773.65918.cd · Cited by 11 · see citing works
Collection of Frozen Rodent Brain Regions for Downstream Analyses
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Sorted by citations
Spatial transcriptomic landscape unveils immunoglobin-associated senescence as a hallmark of aging
Ma, S., Ji, Z., Zhang, B., Geng, L., Cai, Y., Nie, C., Li, J., Zuo, Y., Sun, Y., Xu, G., et al. (2024) · Cell · doi:10.1016/j.cell.2024.10.019 · Cited by 162 · see citing works
Self-citationEngel, A., Wagner, V., Hahn, O., Foltz, A. G., Atkins, M., Beganovic, A., Guldner, I. H., Lu, N., Saksena, A., Fischer, U., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59860-6 · Cited by 18 · see citing works
Self-citationUncovering the signatures of aging and senescence in the human dorsolateral prefrontal cortex
Sloan, N. X., Mares, J., Daly, A. C., Grier, S., Haq, I., Jackson, C. A., Barretto, N., Casel, O., Kang, K., Khiste, S., et al. (2026) · Cell Genomics · doi:10.1016/j.xgen.2025.101127 · Cited by 2 · see citing works
- Authors
- Hahn, O., Foltz, A. G., Atkins, M., Kedir, B., Moran-Losada, P., Guldner, I. H., Munson, C., Kern, F., Pálovics, R., Lu, N., Zhang, H., Kaur, A., Hull, J., Huguenard, J. R., Grönke, S., Lehallier, B., Partridge, L., Keller, A., & Wyss-Coray, T.
- Affiliations
- Stanford Medicine (US)
- NYU Langone Health (US)
- Helmholtz Centre for Infection Research (DE)
- Max Planck Institute for Biology of Ageing (DE)
- Alkahest, Inc. (United States) (US)
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 186
- Issue
- 19
- Pages
- 4117-4133.e22
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC10528304
- OpenAlex
- W4385880069
- Semantic Scholar
- ddcf31304743bdc50ba04965e0b8b3bde2f0eb96
- Cited by
- 213
- References
- 92
- Citations by year
- 2026: 66 · 2025: 92 · 2024: 45 · 2023: 10
- FWCI
- 18.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 379 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Cell (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Michael J. Fox Foundation for Parkinson's Research (MJFF-021418, 125491594)
- Simons Foundation
- American Heart Association
- Cure Alzheimer's Fund
- Fondation Bertarelli
- European Commission (2007–2013, FP7 2007-2013, FP7/2007)
- Max-Planck-Gesellschaft
- Wu Tsai Neurosciences Institute, Stanford University
- National Institute on Aging (R01AG072255)
- National Institute of Mental Health (R01MH121075)
- FP7 Ideas: European Research Council (268739)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- brain aging, white matter, single-nucleus transcriptomics, dietary restriction, neurodegenerative diseases, age-related cognitive decline, mouse brain atlas
- MeSH terms
- Single-Cell Gene Expression Analysis, Aging, Animals, Brain, Humans, Solitary Nucleus, Gene Expression Profiling, Mice, Cognitive Dysfunction, White Matter
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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