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Single-cell analysis of human thymus and peripheral blood unveils the dynamics of T cell development and aging

Nature Aging volume 5, pages 2494–2513 (2025)Cite this article

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Abstract

Age-related thymic involution increases vulnerability to cancers and infection in older adults, yet the driving mechanisms and its impact on peripheral T cells remain unclear. Using single-cell sequencing, we here analyzed 387,762 cells from human thymus and peripheral blood of young and aged individuals. Within thymus, we found aging reduced T-lineage potential in early thymic progenitors but increased innate lymphocyte lineage potential. Aged thymus were enriched in mature T cells with low SOX4 expression and inflammatory profiles but depleted of thymic epithelial cells and expression of tissue-restricted antigens. In the periphery, we identified transcriptional features of T cell aging and established a naive T cell-based model for immune age prediction. Furthermore, we identified CD38 as a marker of recent thymic emigrants. Finally, single-cell T cell receptor (TCR) repertoire sequencing identified shifts in TCR repertoire diversity within memory/effector T cells and expanded virus-specific T cells during aging. Collectively, our data offer insights into human thymic involution and peripheral T cell aging and could inform strategies to restore compromised T cell immunity.

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Fig. 1: Single-cell transcriptome revealed age-related dynamics of human thymocytes.
Fig. 2: T-lineage potentials of ETPs altered with age.
Fig. 3: Conventional T cell development in involuting thymus.
Fig. 4: The functional decay of thymic stromal cells with age.
Fig. 5: Age-related changes in peripheral T cells.
Fig. 6: Characterization of RTEs and identification of CD38 as marker of RTEs.
Fig. 7: TCR diversity in thymus and the periphery.

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Data availability

scRNA-seq, scTCR-seq and CITE-seq datasets generated in this study have been deposited in NCBI GEO with the accession code GSE231906. Source data are provided within this paper. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.

Code availability

Computer code used in this study has been deposited to the Github repository (https://github.com/dengyj-2019/thymus_and_periphery_aging) for public access.

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Acknowledgements

We thank members of the flow cytometry core laboratory and histological core laboratory at West China Hospital, Sichuan University for technical help. This study was supported by grants from the Chongqing International Institute for Immunology (2020YJC01), National Natural Science Foundation of China (82025002, 32230036, 82394411, 82394410, 82170113 and 81670327), the 1.3.5 Project of disciplines of excellence (ZYGD22005, RHM24203 and ZYGD20010) and National Clinical Research Center for Geriatrics (Z20201001 and Z2021JC009), West China Hospital, Sichuan University.

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  1. These authors contributed equally: Yujun Deng, Zhengcan Peng, Kang Ming, Xiaona Qiao, Bin Ye.

Authors and Affiliations

  1. Center for Immunology and Hematology, Department of Biotherapy and Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China

    Yujun Deng, Zhengcan Peng, Kang Ming, Xiaona Qiao, Bin Ye, Yan Liu, Hong Chen, Hui Yu, Liangbin Lin, Xinkai Zou, Keyue Wang, Pengbo Guan, Huiyuan Zhang & Hongbo Hu

  2. Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China

    Haiyue Wang, Peng Yang, Yu Zhang, Yi Xie & Jia Hu

  3. Department of Cardiac Surgery and Center for Immune Ageing and Rejuvenation, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China

    Kun Zhou, Quanwei Huang, Wei Guo, Xiaolong Chen, Xiaowen Wang & Hongbo Hu

  4. National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China

    Birong Dong

  5. Institute of Immunology PLA & Department of Immunology, College of Basic Medicine, Army Medical University (Third Military Medical University), Chongqing, China

    Yuzhang Wu

  6. Chongqing International Institute for Immunology, Chongqing, China

    Yuzhang Wu & Hongbo Hu

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  1. Yujun Deng
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Contributions

Y.D. performed the majority of data analysis; Z.P., K.M., X.Q., B.Y. and Y.L. processed the samples and performed the biological experiments, with help from L.L., H.Y., H.C., X.Z., K.W. and P.G.; P.Y., H.W., Y.Z., Y.X., K.Z., Q.H. and W.G. collected and validated human samples; B.D., X.C., X.W. and J.H. supervised human sample collection; Y.D., Z.P., K.M., X.Q., B.Y. and Y.L. prepared the figures and tables; and H.Z., Y.W. and H.H. conceived of the project and wrote the paper.

Corresponding authors

Correspondence to Jia Hu, Yuzhang Wu, Huiyuan Zhang or Hongbo Hu.

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Extended data

Extended Data Fig. 1 The dynamic changes of thymocytes with aging.

(a-b) Uniform and manifold approximation and Projection (UMAP) visualization of thymocyte clusters (a), based on the differentially expressed genes (DEGs) visualized in violin plots (b). Colors represent the annotation of cell identity. (c-d) Representative flow cytometry data of CD45+ and CD45− cells in thymus across age groups (c). Statistics of the percentages of CD45+ and CD45− cells in live cells, along with the total cell number in thymi across Groups I to III (d) (n = 9 for Group III; n = 7 for Groups I and II). P values (from left to right): ns, P = 0.8451; **P = 0.0025; **P = 0.0094; ns, P = 0.8428; **P = 0.0025; **P = 0.0096; ****P < 0.0001; ****P < 0.0001; ns, P = 0.5519; **P = 0.0020; ***P = 0.0003; ns, P = 0.8186. (e) Representative flow cytometry data of T, CD19+ B and CD33+ myeloid cells in thymus across age groups. All cells were firstly gated on CD45+ cells. T cells are divided into double negative (DN), double positive (DP), CD4 single positive (SP) and CD8 SP cells, gated on CD45+CD19−CD33− cells. (f) Statistics of cell numbers of DN, DP, CD4 SP, CD8 SP, CD19+ B cells and CD33+ myeloid cells in CD45+ cells in thymi across Groups I to III (n = 9 for Group III; n = 7 for Groups I and II). P values (row-wise: from left to right): ***P = 0.0002; ****P < 0.0001; ns, P = 0.6188; ****P < 0.0001; ****P < 0.0001; ns, P = 0.5985; ****P < 0.0001; ****P < 0.0001; ns, P = 0.4760; ***P = 0.0003; ****P < 0.0001; ns, P = 0.6310; **P = 0.0015; ***P = 0.0005; ns, P = 0.9676; *P = 0.0183; **P = 0.0081; ns, P = 0.9814. (g-h) Flow cytometry gating strategies (g) and representative data (h) of NK cells, ILC in thymi across Groups I to III. All cells are gated on Lin−CD45+ cells. NK, CD56+; ILC1, CD56−CD161+ CD117−CRTH2−; ILC2, CD56−CD161+ CRTH2+; ILC3, CD56−CD161+ CD117+CRTH2−. (i) Statistics of the percentages of ILCs and NK in Lin−CD45+ cells in thymus with aging (n = 7 for Group II; n = 9 for Groups I and III). P values (from left to right): ns, P = 0.5107; ***P = 0.0006; *P = 0.0178; ns, P = 0.9917; *P = 0.0302; ns, P = 0.0570; ns, P = 0.5592; ***P = 0.0003; **P = 0.0084; ns, P = 0.3243; *P = 0.0143; ns, P = 0.3498; ns, P = 0.5336; ***P = 0.0008; *P = 0.0203. All data represent biological replicates. Data are presented as mean ± standard error of mean (s.e.m.). Statistical analysis was performed using Bonferroni-adjusted two-tailed Wilcoxon rank sum test (b) or One-way ANOVA with Tukey’s multiple comparisons test (d, f, i).

Source data

Extended Data Fig. 2 Reduced T cell differentiation lineage potentials in aged human thymus.

(a) Dot plots of DEGs among DN cells clusters described in Fig. 2a. Colors represent the scaled expression and dot sizes encode the proportion of gene-expressing cells. (b-c) UMAP visualizations of Monocle3 derived differentiation trajectory of DN cells. Colors indicate cell identity (b) or pseudotime (c). (d) Dot plot shows the transcription levels of CD3, CD4 and CD8 among DN cell subsets and DP cells. Dot colors represent the scaled expression and dot sizes encode the proportion of gene-expressing cells. (e) Heatmaps indicate the age-associated genes (adjusted P values < 0.05, fold change > 1.25) in DN-Q1 (top) and DN-Q2 (bottom) cells. (f) Violin plots of DEGs in each lineage engaged in STEMNET analysis. (g) Statistics of the proportion of T cells differentiated from thymic progenitor cells including Thy1 (CD45+CD34+CD7−CD1a−) and Thy2a (CD45+CD34+CD7+CD1a−CD44+CD2−) sorted from thymi across different age groups in artificial thymic organoid (ATO) (n = 5 for Group I; n = 3 for Group II; n = 4 for Group III). P values (row-wise: from left to right): P = 0.0726; *P = 0.0333; ns, P = 0.9686; P = 0.1394; P = 0.0983; ns, P = 0.9992; ns, P = 0.2285; ns, P = 0.1830; ns, P > 0.9999; ns, P = 0.1562; *P = 0.0259;ns, P = 0.6752; P = 0.0603; **P = 0.0039; ns, P = 0.3886; ns, P = 0.3410; ns, P = 0.1512; ns, P = 0.9180. Data are presented as mean ± s.e.m. (h) Representative flow cytometry data of differentiation results of CD45+CD34+ thymic progenitor cells including Thy1 and Thy2a sorted from thymi across age groups under T cell differentiation conditions, which are derived from 4 independent experiments. All data represent biological replicates. Statistical analysis was performed using Bonferroni-adjusted two-tailed Wilcoxon rank sum test (a, d, e, f) or One-way ANOVA with Tukey’s multiple comparisons test (g).

Source data

Extended Data Fig. 3 Aged-related genes and pathways in DP to SP cells.

(a) Violin plots show the DEGs of intrathymic conventional CD8 T cells, CD8ααT and nature killer T cells (NKTs) described in Fig. 3c. (b) The proportions of thymocytes from DP to SP stages change with aging. (c) Heatmaps display the age-related genes (adjusted P values < 0.05, fold change > 1.1) of DP-P (left) and DP-Q (right) cells in different age groups. (d) Dot plots show the age-related pathways (adjusted P values < 0.05) identified by Single Sample Gene Set Enrichment Analysis (ssGSEA), in DP-P (top) and DP-Q (bottom) cells across age groups. Dot colors represent the scaled enrichment score while dot sizes encode -log10 adjusted P values. Annotation colors represent the age group information. (e) Heatmaps display the age-related genes (adjusted P values < 0.05, fold change > 1.25) of mature CD4 (left) and CD8 (right) T cells in different age groups. All data represent biological replicates. Statistical analysis was performed using Bonferroni-adjusted two-tailed Wilcoxon rank sum test (a, c, d, e).

Extended Data Fig. 4 The functional decay of thymic stromal cell with age.

(a) Dot plot shows the representative DEGs of all thymic stromal populations described in Fig. 4a. Dot colors represent the scaled expression and dot sizes encode the proportion of gene-expressing cells. Bonferroni-adjusted two-tailed Wilcoxon rank sum test. (b) The proportions of thymic stromal cell populations excluding sorted EpCAM+ cells in each age group. (c) Bar plot filled by donor information, indicates that the cells of each thymic epithelial cell (TEC) subset in each age group are derived from multiple donors. (d) The distribution of TEC subsets in each age group. (e) Heatmap shows the systematic changes of age-associated genes (Bonferroni-adjusted two-tailed Wilcoxon rank sum test, adjusted P values < 0.05, fold change > 1.5) in TEC subsets, which are clustered into 5 gene modules via hierarchical clustering method. Heatmap tile colors represent fold change. Annotation tile colors represent the cell identity or the age group information. Significant genes (left) and related pathways (Benjamini-Hochberg adjusted hypergeometric test, adjusted P < 0.05) enriched by over representative analysis (ORA) (right) are colored according to the related gene module. (f) Dot plot shows the expression of tissue-restricted antigens (TRAs) from different tissues in different TEC clusters. Dot colors represent the scaled TRA scores, and dot sizes encode the proportion of TRA-expressing cells. (g) Bar plot shows the proportions of skeletal muscle-restricted genes regulated by transcription factors (TFs) highly expressed in TEC-myo (adjusted P values < 0.05, fold change > 1.5). (h) The scatter plot illustrates the Pearson correlation between the expression levels of the top 5 TFs, as shown in Extended Data Fig. 4g, and the expression of skeletal muscle-restricted antigens in TEC-myo. P values were calculated via two-tailed t-test. (i) Violin plots visualize the expression of TFs positively correlated with the expression of skeletal muscle-restricted antigens, as indicated in Extended Data Fig. 4h, in TEC-myo during aging (83 cells for Group I; 138 cells for Group II; 57 cells for Group III). Benjamini-Hochberg adjusted two-tailed Wilcoxon rank sum test. Data are presented as mean ± s.e.m. P values (from left to right): ****P < 0.0001; ****P < 0.0001; ****P < 0.0001; ns, P = 0.311; ****P < 0.0001; ****P < 0.0001; **P = 0.005; ***P = 0.0001; P = 0.0768; ns, P = 0.892; ***P = 0.0003; **P = 0.0014. All data represent biological replicates.

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Extended Data Fig. 5 Quantification of multiple immunofluorescence staining validates the age-related changes of interaction between stromal cells and T cell progenitors.

(a) Quantitative ImageJ-based immunofluorescence analysis of age-related changes in the proportions of DLL4+CD205+ cortical TECs (cTECs), JAG1+PDGFRα+ fibroblasts (FBs) and NOTCH1+CD34+CD45+ T-progenitor cells (b) (n = 3 for all Groups). P values (from left to right): P = 0.6425; P = 0.3535; P = 0.8385; P = 0.8466; P = 0.1221; P = 0.2472; P = 0.8851; P = 0.0791; *P = 0.0438. (b-c) Spatial immunofluorescence quantitative analysis of the proportion of cTEC-T- progenitor cell (b) or FB-T-progenitor cell (c) interaction pairs across age groups, which is defined as nuclei centroids ≤15 μm apart, n = 3. (d-e) Representative flow cytometry plots (top) and quantitative analysis (bottom) of CD3+TCRβ+ T cell (d) or CD3−CD161+ ILC (e) populations derived from Thy1 progenitor cells of Groups I and III, following differentiation in the JAG1-ATO system (n = 3 for all Groups). P values (from left to right): P = 0.2802; **P = 0.0083. All data represent biological replicates. Data are presented as mean ± s.e.m. Statistical analysis was performed using One-way ANOVA with Tukey’s multiple comparisons test (a) and two-tailed t-test (d, e).

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Extended Data Fig. 6 The age-related changed in the peripheral T cells.

(a) UMAP visualization of peripheral blood mononuclear cell (PBMC) clusters. Colors indicate the annotation of cell identity. (b) Heatmap showing the DEGs of PBMC clusters derived from Extended Data Fig. 6a. (c) The proportions of cell clusters of PBMCs changed with aging. (d) The DEGs of peripheral T cell clusters derived from Fig. 5a. Dot colors represent the scaled expression and dot sizes encode the proportion of gene-expressing cells. (e) Pearson correlation between the chronological and predicted age (based on peripheral pan-T cells) in the training cohorts (left, n = 394 samples) or external healthy validation cohorts (right, n = 81 samples). All data represent biological replicates. Statistical analysis was performed using Bonferroni-adjusted two-tailed Wilcoxon rank sum test (b, d) or two-tailed t-test (e).

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Extended Data Fig. 7 Identification of human Recent Thymic Emigrants (RTEs).

(a-b) Partition-based graph abstraction (PAGA) results show the differentiation/development trajectory of peripheral CD4 (a) and CD8 (b) T cells, with edge width reflects the strength of connectivity between cell clusters, and node colors denote the cell identity annotation. Dot colors represent cell identity. (c-d) Spearman correlation among CD4 (c) and CD8 (d) intrathymic mature SOX4hi/lo SP, peripheral RTEs and naive T cells. (e-f) DEGs among CD4 (e) and CD8 (f) intrathymic mature SOX4hi/lo SP, peripheral RTEs and naive T cells. Bonferroni-adjusted two-tailed Wilcoxon rank sum test. All data represent biological replicates.

Extended Data Fig. 8 Potential connection of intrathymic and peripheral T cells.

(a-c) Dot plots illustrate the age-related changes in TCR V/J gene segment proportions during T cell development. Proportions significantly changed (Benjamini-Hochberg adjusted tow-tailed Wilcoxon rank sum test, P < 0.05) in at least one of the T cell development stages are displayed, with significant differences highlighted in red boxes. (d) The Pielou’s evenness index of TCR repertoire of intrathymic (top, n = 18) and peripheral T cell populations (bottom, n = 22). Kruskal-Wallis test. Colors indicate cell populations. The box in the boxplot represents the interquartile range (IQR), with its lower and upper edges indicating the 25th and 75th percentiles, respectively. The medium value (50th percentile) is shown within the box, and the whiskers extend to the minimum and maximum values within 1.5 times the IQR of the quartiles. (e) TCR similarity score (TRSS) reflected the similarity in clonal distribution across two sets of cells. Colors and sizes represent TRSS. The numbers on the x-axis represent ages of individual samples. (f) GSEA comparison of intrathymic memory-like CD8 T cells and peripheral CD8 TCM (left) or CD8 TEM-K (GZMK+ CD8 effector memory, right). Permutation test. Absolute normalized enrichment score (NES) > 1, P values < 0.05 and adjusted P values < 0.25. (g) Dot plot shows TRSS of peripheral CD4 subsets and intrathymic memory-like CD4 T cells, colors and sizes represent TRSS. (h) Spearman correlation of peripheral CD4 subsets and intrathymic memory-like CD4 T cells. (i) Dot plots show TRSS of peripheral CD4 TCM (central memory), CD4 TEFF (effector), CD4 TEM (effector memory), CD4 CTL (cytotoxic T lymphocyte) and intrathymic memory-like CD4 T cells with age. Colors and sizes represent TRSS. (j) Spearman correlation of peripheral CD4 TCM, CD4 TEFF, CD4 TEM, CD4 CTL and intrathymic memory-like CD4 T cells with age. All data represent biological replicates.

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Supplementary information

Supplementary Information (download PDF )

Supplementary Fig. 1. Gating strategies used for flow cytometry.

Reporting Summary (download PDF )

Supplementary Table 1 (download XLSX )

Volunteer information for different experiments, including sex, age, tissues, cell type and etc.

Supplementary Table 2 (download XLSX )

Reagent and resource include flow antibody, chemicals, peptides, recombinant proteins,software and algorithms.

Supplementary Table 3 (download XLSX )

Cell number of major cell type across different age groups.

Supplementary Table 4 (download XLSX )

Top 30 maker genes of major cell cluster test by Wilcoxon rank-sum test with P value adjusted for multiple testing by Bonferroni method.

Supplementary Table 5 (download XLSX )

DEGs within a specific cell type across different age groups were test by Wilcoxon rank-sum test with P value adjusted for multiple testing by Bonferroni method.

Supplementary Table 6 (download XLSX )

Dynamic pathways (pathways matrices were calculated by ssGSEA algorithm) within a specific cell type across different age groups were test by Wilcoxon rank-sum test with P value adjusted for multiple testing by Bonferroni method.

Supplementary Table 7 (download XLSX )

Over representation analysis in age-related gene module. P values were adjusted for multiple testing by Benjamini-Hochberg method.

Supplementary Table 8 (download XLSX )

Crosstalk between TEC and ETP-TP cells, as well as FB and ETP-TP cells during aging, related to Fig. 4.

Supplementary Table 9 (download XLSX )

Pathways enriched by genes positively or negatively correlated with AgeDiff (predicted age - actual chronological age), calculated using GSEA with P values detected by permutation test.

Supplementary Table 10 (download XLSX )

List of nucleotide sequences of the plasmid MigR1 and the gene JAG1.

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Deng, Y., Peng, Z., Ming, K. et al. Single-cell analysis of human thymus and peripheral blood unveils the dynamics of T cell development and aging. Nat Aging 5, 2494–2513 (2025). https://doi.org/10.1038/s43587-025-00990-3

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