Mouse aging cell atlas analysis reveals global and cell type-specific aging signatures
- Department of Electrical Engineering, Stanford University, United States
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, United States
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, United States
- Chan-Zuckerberg Biohub, United States
- Department of Biomedical Data Science, Stanford University, United States
Figures
Figure 1 with 6 supplements
Analysis overview.
(A) Sample description. The TMS 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 …
-
Figure 1—source data 1
Source data for Figure 1.
- https://cdn.elifesciences.org/articles/62293/elife-62293-fig1-data1-v2.xlsx
Figure 1—figure supplement 1
Covariates for the TMS FACS data and the TMS droplet data.
(A) Number of expressed genes per cell (CDR) for the TMS FACS data. (B) Number of expressed genes per cell (CDR) for the TMS droplet data. (C) Number of read counts per cell for the TMS FACS data. (D…
Figure 1—figure supplement 2
Number of significantly age-dependent genes for each tissue-cell type, from DGE analyses performed using subsets of mice in the TMS FACS data.
(A) Using only male mice. (B) Using only 3 m and 18 m male mice. (C) Using only female mice, which are either 3 m or 18 m. We can see that most tissue-cell types in panel A have significantly more …
Figure 1—figure supplement 3
Number of significantly age-dependent genes for each tissue-cell type, from DGE analyses performed using subsets of mice in the TMS droplet data.
(A) Using all mice. (B) Using only male mice. (C) Using only female mice. We can see that there are more downregulated aging-related genes in all three panels. The comparison between panels B and C …
Figure 1—figure supplement 4
Aging trajectory of aging-related genes in the TMS FACS data.
For each aging-related tissue-cell-gene tuple (defined as the gene significantly related to aging in a tissue-cell type), we computed its aging trajectory by first computing the mean expression of …
Figure 1—figure supplement 5
Aging trajectory of aging-related genes in the TMS droplet data.
We computed and clustered the aging trajectories for genes in the TMS droplet data similar to Figure 1—figure supplement 4. As shown in panel A, according to the elbow method there are k = 3 …
Figure 1—figure supplement 6
Correlations between the age coefficients estimated with and without CDR correction.
The bar plots show the correlation between age coefficients estimated with and without CDR correction, across all the genes, for each tissue-cell type separately. The average correlation, across all …
Figure 2 with 4 supplements
Tissue-cell level global aging genes (GAGs).
(A) Tissue-cell level aging-related genes with color indicating the number of the genes. The x-axis shows the weighted proportion of tissue-cell types (out of all 76 tissue-cell types) where the …
-
Figure 2—source data 1
Source data for Figure 2.
- https://cdn.elifesciences.org/articles/62293/elife-62293-fig2-data1-v2.xlsx
Figure 2—figure supplement 1
Comparison between aging-related genes discovered in the TMS FACS data and other gene sets, including the mouse aging genes and human aging genes in the GenAge database (Tacutu et al., 2018), senescence genes as provided by the IPA software (Krämer et al., 2014), transcription factors, eukaryotic initiation factors, and ribosomal protein genes (Rpl/Rps genes).
The figure axes are the same as Figure 2A. We observe significant overlap between the FACS aging-related genes and all six gene sets, as quantified by Fisher’s exact tests with p-values provided in …
Figure 2—figure supplement 2
Overlap between the TMS FACS aging-related genes and the GenAge aging markers.
The figure shows, for each tissue-cell type, the proportion of aging-related genes that are also shared by the GenAge human aging genes (blue) and the GenAge mouse aging genes (orange). No …
Figure 2—figure supplement 3
Additional pathway enrichment analysis results.
(A) Top GO biological pathways for the TMS droplet GAGs. (B) Top 20 KEGG pathways for the TMS droplet GAGs. (C) Top GO biological pathways for the TMS FACS GAGs selected with different significance …
Figure 2—figure supplement 4
KEGG pathways and IPA pathways for the 330 TMS FACS GAGs.
(A) Top 20 KEGG pathways for the GAGs. (B) Top IPA pathways for the GAGs. The ratio represents the proportion of pathway genes that are also GAGs. (C) Regulation prediction for mTOR pathway by IPA. …
Figure 3 with 8 supplements
GAG score.
(A) Tissue-cell GAG score effects with 95% confidence intervals. The color represents the functional category of the tissue-cell type. (B and C) Effects of cell functional categories (panel B) and …
-
Figure 3—source data 1
Source data for Figure 3.
- https://cdn.elifesciences.org/articles/62293/elife-62293-fig3-data1-v2.xlsx
Figure 3—figure supplement 1
Additional validations of the GAG score.
(A–D) Correlation between the GAG scores computed using different scoring rules. The original GAG score was computed using genes that are significantly related to aging in >50% of weighted …
Figure 3—figure supplement 2
Additional validations of the GAG score.
(A) Histogram of mean log expression of genes used for computing the GAG score. As shown in the figure, there are no genes with very high expression levels, indicating that the GAG score is unlikely …
Figure 3—figure supplement 3
Distribution of GAG scores across cells; stratified by tissue-cell types and grouped by mice’s chronological age.
Most tissue-cell types have lower GAG scores for young cells and higher GAG scores for old cells. However, as shown by the GAG score distribution plots in panel A, we found four tissue-cell types …
Figure 3—figure supplement 4
Tissue-cell GAG score effects for the four validation data sets.
(A) TMS droplet data. (B) Data (Kimmel et al., 2019). (C) The bulk data (Schaum et al., 2020). (D) Data (Kowalczyk et al., 2015).
Figure 3—figure supplement 5
Tissue-level analysis as validation.
(A) Number of discoveries for each tissue. The left panels show the number of aging genes (discoveries) for each tissue, broken down into the number of upregulated genes (orange), and the number of …
Figure 3—figure supplement 6
Number of discoveries for each tissue.
(A) TMS droplet data. (B) The bulk data. The left panels show the number of aging genes (discoveries) for each tissue, broken down into the number of upregulated genes (orange) and the number of …
Figure 3—figure supplement 7
Tissue-cell validations.
(A) Tissue-cell level aging-related genes for the TMS droplet data with color indicating the number of genes. (B) Tissue-level aging-related genes for the TMS droplet data with color indicating the …
Figure 3—figure supplement 8
Tissue-level GAG score effects.
(A) TMS droplet data. (B) The bulk data. The results are consistent with the result in the FACS data as shown in Figure 3—figure supplement 5C, with the immune tissues (spleen, white blood cells, …
Figure 4 with 3 supplements
Functional-category-specific genes.
(A) Age coefficients, in the unit of log fold change per month, for functional-category-specific genes. Both genes in the x-axis and tissue-cell types in the y-axis are ordered by functional …
-
Figure 4—source data 1
Source data for Figure 4.
- https://cdn.elifesciences.org/articles/62293/elife-62293-fig4-data1-v2.xlsx
Figure 4—figure supplement 1
Age coefficients of top functional-category-specific genes.
Top 10 genes with the most significant p-values were selected for each category, ordered from left to right as the endothelial, epithelial, immune, stem/progenitor, stromal, and parenchymal …
Figure 4—figure supplement 2
Two examples of functional-category-specific genes.
(A) C2cd4b which is parenchymal-specific. (B) Gsn which is stromal-specific. Estimated age coefficients are provided for each tissue-cell type with 95% confidence intervals. Within-set and …
Figure 4—figure supplement 3
Cell type-specific genes.
(A) Number of cell type-specific genes. (B) GO biological pathways for the cell type-specific genes, with color representing the negative log10 FDR.
Additional files
-
Supplementary file 1
Summary of tissues and cell types for TMS FACS data, TMS droplet data, and the bulk data.
- https://cdn.elifesciences.org/articles/62293/elife-62293-supp1-v2.xlsx
-
Supplementary file 2
Significantly aging-related genes in each tissue-cell type for TMS FACS data and TMS droplet data.
- https://cdn.elifesciences.org/articles/62293/elife-62293-supp2-v2.xlsx
-
Supplementary file 3
Gene sets identified in the study, including GAGs and category-specific genes, for TMS FACS data and TMS droplet data.
- https://cdn.elifesciences.org/articles/62293/elife-62293-supp3-v2.xlsx
-
Transparent reporting form
- https://cdn.elifesciences.org/articles/62293/elife-62293-transrepform-v2.pdf
