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Mouse Aging Cell Atlas Analysis Reveals Global and Cell Type Specific Aging Signatures

aging cell atlas mice

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#1 Engadin

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Posted 03 January 2020 - 11:22 PM







F U L L   T E X T   S O U R C E :   bioRXiv






Aging is associated with complex molecular and cellular processes that are poorly understood. Here we leveraged the Tabula Muris Senis single-cell RNA-seq dataset35 to systematically characterize gene expression changes during aging across diverse cell types in mouse. We identified aging-dependent genes in 76 tissue-cell types from 23 tissues and characterized both shared and tissue-cell-specific aging behaviors. We found that the aging-related genes shared by multiple tissue-cell types change their expression congruently in the same direction during aging in most tissue-cell types, suggesting a coordinated global aging behavior at the organismal level. We integrated the aging-related genes to construct a cell-wise aging score that allowed us to investigate the aging status of different cell types from a transcriptomic perspective. Overall, our analysis provides one of the most comprehensive and systematic characterization of the molecular signatures of aging across diverse tissue-cell types in a mammalian system.
Aging leads to the functional decline of major organs across the organism and is the main risk factor for many diseases, including cancer, cardiovascular disease, and neurodegeneration21, 26. Past studies have highlighted different hallmarks of the aging process, including genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication3, 21, 27, 41. However, the primary root of aging remains unclear, and the underlying molecular mechanisms are yet to be fully understood.
To gain a better insight into the mammalian aging process at the organismal level, the Tabula Muris Consortium, which we are members of, created a single cell transcriptomic dataset called Tabula Muris Senis (TMS)35. TMS is one of the largest expert-curated single-cell RNA sequencing (scRNA-seq) datasets, containing 529,823 cells from 23 tissues and organs of male and female mice (Mus musculus). The cells were collected from mice of diverse ages, making this data a tremendous opportunity to study the genetic basis of aging across different tissues and cell types. The TMS data is organized into scRNA-seq expression of different tissue-cell type combinations (e.g., B cells in spleen) via expert annotation and clustering.
The original TMS paper focused primarily on the cell-centric effects of aging, aiming to characterize changes in cell-type composition within different tissues. Here we provide a systematic gene-centric study of gene expression changes occurring during aging across different cell types. The cell-centric and gene-centric perspectives are complementary, as the gene expression can change within the same cell type during aging, even if the cell type composition in the tissue does not vary over time.
Our analysis focused on the TMS FACS data (acquired by cell sorting in microtiter well plates followed by Smart-seq2 library preparation34) because it has more comprehensive coverage of tissues and cell types (Supplementary Figures 1A-B) and is more sensitive at quantifying levels of gene expression. As shown in Figure 1A, the FACS data was collected from 16 C57BL/6JN mice (10 males, 6 females) with ages ranging from 3 months (20-year-old human equivalent) to 24 months (70-year-old human equivalent). It contains 120 cell types from 23 tissues (Supplementary Figures 1A-B), totalling 164,311 cells. We also used the TMS droplet data (derived from microfluidic droplets), for those tissues for which the data was available, to further validate our findings on an additional dataset generated by a different method.
Figure 1.
Analysis overview.
A: Sample description. The data was collected from 16 C57BL/6JN mice (10 males, 6 females) with ages ranging from 3 months (20-year-old human equivalent) to 24 months (70-year-old human equivalent). B: Significant aging-dependent genes in all 76 tissue-cell types. The left panels show the number of aging genes (discoveries) for each tissue-cell type, broken down into the number of up-regulated genes (orange) and the number of down-regulated genes (blue), with the numbers on the right showing the ratio (up/down). The higher number of the two bars is shown in a solid color, and the lower one is shown in a transparent color. We can see most tissue-cell types (62/76) have more down-regulated aging genes. The right panels show the number of cells sequenced for each tissue-cell type.
We investigated the comprehensive expression signatures of aging across tissues and cell types in the mouse. We performed systematic differential gene expression (DGE) analysis to identify aging-related genes in 76 different tissue-cell type combinations across 23 tissues (Figure 1B). In addition, we characterized both shared and tissue-cell-specific aging signatures. Our study identified global aging genes, namely genes whose expression varies substantially with age in most (>50%) of the tissue-cell types. Interestingly, the gene expression changes are highly concordant across tissue-cell types and exhibit strong bimodality, i.e., a gene tends to be either down-regulated during aging in most of the tissue-cell types or up-regulated across the board. We leveraged this coordinated dynamic to construct an aging score based on global aging genes. This aging score informs the biological age (as opposed to the chronological age) and is correlated with the cell turnover rate of different tissue-cell types from a transcriptomic perspective. The aging score reflects the aging-related genomic changes and captures different information from the commonly-used DNA methylation features12, 14. In addition, our work investigates the biological age at the cell-type level, distinguishing itself from virtually all previous works which focus on predicting the individual-wise biological age11, 12, 14, 32, 33. Overall, our analysis highlights the power of scRNA-seq in studying aging and provides a comprehensive catalog of aging-related gene signatures across diverse tissue-cell types.
Identification of aging-related genes
We performed differential gene expression analysis to identify aging-related genes for 76 tissue-cell types with a sufficient sample size; each tissue-cell type is required to have more than 100 cells in both young (3m) and old (18m, 24m) age groups. For each tissue-cell type, we tested if the expression of each gene was significantly related to aging using a linear model treating age as a numerical variable while controlling for sex. We applied an FDR threshold of 0.01 and an age coefficient threshold of 0.005 (corresponding to ~10% fold change). For details, please refer to the differential gene expression analysis subsection in Methods.
As shown in Figure 1B, the number of significant age-dependent genes per tissue-cell type ranges from hundreds to thousands. Moreover, tissue-cell types with a higher number of cells (right panel in Figure 1B) tend to have more discoveries, likely due to their higher detection power. Interestingly, 62 out of 76 tissue-cell types have more down-regulated aging genes than up-regulated aging genes, indicating a general decrease in gene expression over aging. Cells from older mice (18m, 24m) were actually sequenced deeper, yet they still had much fewer expressed genes and lower expression in the expressed genes (Supplementary Figure 1C). This suggests that the decreasing expression phenomenon is genuine and unlikely to be confounded by sequencing depth or size factor normalization.
In contrast to the overall trend of decreasing expression, many immune cells have a higher number of up-regulated genes during aging, including B cells, T cells, as well as liver endothelial cells of the hepatic sinusoid, which are known to have the ability to become activated in response to diverse inflammatory stimuli20. This observation is particularly interesting given the strong link between the aging process and the immune system, as suggested by previous literature27, 35.
Tissue-cell level global aging markers
We visualized all discovered aging genes (significant in ≥ 1 tissue-cell type) in Figure 2A, where the color indicates the number of genes. The x-axis shows the proportion of tissue-cell types (out of 76 tissue-cell types) where the gene is significantly related to aging, while the y-axis shows the proportion of tissue-cell types where the gene is up-regulated. The visualization makes it clear that there are more down-regulated aging genes than up-regulated aging genes, consistent with the number of up/down-regulated discoveries, as shown in Figures 1B. Moreover, perhaps more strikingly, a bimodal pattern is apparent for aging-dependent genes. Genes tend to have a consistent direction of change during aging across different tissue-cell types—the expression either increases across most of the tissue-cell types or it decreases across the board. A similar bimodality was also observed recently in mouse brain cells44.
Figure 2.
Tissue-cell level global aging genes.
A: Tissue-cell level aging related genes. The color indicates the number of genes. The x-axis shows the proportion of tissue-cell types (out of all 76 tissue-cell types) where the gene is significantly related to aging, while the y-axis shows the proportion of tissue-cell types where the gene is up-regulated. B: Heatmap of age coefficient for all global aging genes. C: Heatmap of age coefficient for the top 20 global aging genes. For Panels B-C, blue/red represents down-/up-regulation. Also, 20 times the age coefficient roughly corresponds to the log fold change from young (3m) to old (24m). D: Top pathways associated with the global aging genes. The negative z-score (blue) means the pathway is predicted to be down-regulated and the positive z-score (red) means the opposite. The ratio represents the proportion of pathway genes that are also global aging genes.
We define global aging genes to be genes whose expression changes significantly with age in more than half of tissue-cell types. The bimodality pattern is especially striking in these genes. We identified 292 global aging genes in total (Supplementary Table 1), among which 93 are consistently up-regulated (in >80% of tissue-cell types) and 169 are consistently down-regulated (in >80% tissue-cell types). Only 30 global aging genes have an inconsistent direction of regulation in different tissue-cell types (up-regulated in 20%-80% tissue-cell types). We also used a heatmap to visualize all global aging genes in Figure 2B, where the pattern of the consistent direction of change becomes more apparent (see a larger version in Supplementary Figure 2).
We visualized the 20 top global aging genes in Figure 2C; these are genes that showed the most substantial change during aging. Many of these have been previously shown to be highly relevant to aging. For example, the down-regulation of Lars2 has been shown to result in decreased mitochondrial activity and increase the lifespan for C. elegans19. On the other hand, Jund is a proto-oncogene known to protect cells against oxidative stress and its knockout may cause a shortened lifespan in mice18. Moreover, we also found Rpl13a, a key component of the GAIT (gamma interferon-activated inhibitor of translation) complex which mediates interferon-gamma-induced transcript-selective translation inhibition in inflammation processes23, to be up-regulated. As a negative regulator of inflammatory proteins, Rpl13a contributes to the resolution phase of the inflammatory response, ensuring that the inflamed tissues are completely restored back to normal tissues, which also contributes to preventing cancerous growth of the injured cells caused by prolonged expression of inflammatory genes45.
Many of the top global aging genes were also identified in previous studies or correspond to important aging-related biological processes. For example, Cat, Grn, Gpx2, Apoe were identified as aging-related genes in mouse in a previous study39 (Supplementary Figure 3A). App, Ctnnb1, Mapk1, Rac1, Arf1, Junb are related to senescence, a hallmark of aging3 (Supplementary Figure 3B). Many other global aging genes correspond to transcription factors (Supplementary Figure 4A), eukaryotic initiation factors (Eif genes, Supplementary Figure 4B), and ribosomal protein genes (Rpl/Rps genes, Supplementary Figure 5). Additional annotations are provided in Supplementary Figure 6.
Next, we performed a gene pathway analysis on the entire set of 292 global aging genes using the Ingenuity Pathway Analysis software (IPA)16. As shown in Figure 2D, the global aging genes are enriched for biological processes, including protein synthesis, apoptosis, cell cycle and tissue development and function. In general, we observed decreased activities of these pathways. Of note is the implication of the mTOR pathway, highlighted in Supplementary Figure 7, which is known to be related to aging13, 30, 42. mTOR and translation initiation go hand in hand17 and it is therefore not surprising that we also observe a significant correlation of aging and translation. mTOR down-regulation has been shown to promote longevity38 and it is interesting that when comparing old with young mice the global aging genes we identify point towards a collective down-regulation of this pathway.
Aging score based on global aging genes
Following the analysis of global aging genes, we next leveraged these marker genes to characterize the holistic aging status of individual cells. We use the global aging genes to define an aging score for each cell to explore how the aging process varies between different tissue-cell types in the same animal. More precisely, the aging score for a cell is defined to be the average expression of the up-regulated global aging genes (up-regulated in >80% tissue-cell types) minus the average expression of the down-regulated global aging genes (down-regulated in >80% tissue-cell types), while adjusting for the background gene expression level. For each tissue-cell type, we calculate the aging score by averaging over all cells from the tissue-cell type, and regressing out sex and the chronological age. See the aging score subsection in Methods for more details.


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