1. Fig. 2 CD4 T cell subset composition in aging.
CD4 T cell subset composition in aging. (A) t-SNE projection of all 24,007 CD4 T cells presenting seven different subsets, identified via shared nearest neighbor modularity optimization-based clustering algorithm, followed by merging of similar clusters. (B) Heatmap of gene expression z scores across cells. All CD4 T cells were grouped by subset and age (horizontal bars). Genes shown were up-regulated significantly [ln(fold change) > 0.4, combined P < 10−3] in at least one subset compared to all other cells. Genes were ordered by significance and associated with the subset with higher detection rates. (C) Violin plots showing the expression (z score) of selected canonical marker genes across all seven subsets. (D) Representative pie charts showing the percentage of cells belonging to each of the seven subsets in a young mouse and an old mouse. (E) Enrichment was computed as the log odds ratio between the frequency of each subset in old versus young mice, across all pairs. Naïve subsets were enriched in young mice [Naïve: log(median ratio) = −0.27, P = 0.03 and Naïve_Isg15: log(median ratio) = −0.23, P = 0.03]. rTregs subset was equally distributed [log(median ratio) = 0.02, P = 0.89]. Four subsets were enriched in every old mouse: TEM [log(median ratio) = 0.51, P = 0.03], aTregs [log(median ratio) = 1, P = 0.03], exhausted [log(median ratio) = 1.32, P = 0.03], and cytotoxic [log(median ratio) = 1.46, P = 0.03] subsets. Mann-Whitney U test. (F) A heatmap showing Spearman correlation coefficient (rho) between the frequency of each subset and cytokine concentration (in micrograms per milliliter) measured in serum of old mice. Because of the limited number of mice, we considered as meaningful only strong correlations (absolute Spearman correlation coefficient > 0.6).
Yehezqel Elyahu et al. Sci Adv 2019;5:eaaw8330
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