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A metabolic atlas of blood cells in young and aged mice identifies uridine as a metabolite to rejuvenate aged hematopoietic stem cells

Nature Aging (2024)Cite this article

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Abstract

Aging of hematopoietic stem cells (HSCs) is accompanied by impaired self-renewal ability, myeloid skewing, immunodeficiencies and increased susceptibility to malignancies. Although previous studies highlighted the pivotal roles of individual metabolites in hematopoiesis, comprehensive and high-resolution metabolomic profiles of different hematopoietic cells across ages are still lacking. In this study, we created a metabolome atlas of different blood cells across ages in mice. We reveal here that purine, pyrimidine and retinol metabolism are enriched in young hematopoietic stem and progenitor cells (HSPCs), whereas glutamate and sphingolipid metabolism are concentrated in aged HSPCs. Through metabolic screening, we identified uridine as a potential regulator to rejuvenate aged HSPCs. Mechanistically, uridine treatment upregulates the FoxO signaling pathway and enhances self-renewal while suppressing inflammation in aged HSCs. Finally, we constructed an open-source platform for public easy access and metabolomic analysis in blood cells. Collectively, we provide a resource for metabolic studies in hematopoiesis that can contribute to future anti-aging metabolite screening.

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Fig. 1: Metabolome of different blood cell types during aging.
Fig. 2: Identification of cell-specific metabolic signatures.
Fig. 3: Correlation analysis among metabolites and genes based on our metabolite data and transcriptomic data.
Fig. 4: Identification of aging-related metabolites.
Fig. 5: Screening of metabolites identified uridine as the regulator to rejuvenate aged HSPCs.
Fig. 6: Uridine improved function of aged HSCs in vivo.
Fig. 7: Uridine treatment rejuvenated aged HSPCs by enhancing self-renewal while suppressing inflammation.

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Data availability

The data used in this study have been placed into Figshare (https://doi.org/10.6084/m9.figshare.22708162.v1) and the Gene Expression Omnibus (GSE240731). Public online databases (KEGG (https://www.kegg.jp/) and HMDB (https://hmdb.ca/)) were applied to match metabolites based on the exact mass data (m/z). All data supporting the findings of this study are available within the article or from the corresponding authors upon reasonable request.

Code availability

The code used in this study has been placed into Figshare (https://doi.org/10.6084/m9.figshare.22708162.v1).

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Acknowledgements

This work was supported by grants from the National Key Research and Development Program of China (2022YFA1103500 to P.Q.); the National Natural Science Foundation of China (82130003 to H.H., 82270221 to J.Y., 82200255 to M.Z., 82222003 to P.Q., 92268117 to P.Q. and 82161138028 to P.Q.); the Key R&D Project of the Zhejiang Provincial Science and Technology Department (2021C03010 to H.H.); the Key R&D Program of Zhejiang Province (2024SSYS0023 to H.H. and 2024SSYS0024 to P.Q.); the Research Center of Prevention and Treatment of Senescence Syndrome, Zhejiang University School of Medicine (2022020002 to P.Q.); the Zhejiang Provincial Natural Science Foundation of China (Z24H080001 to P.Q. and LQ22H080005 to M.Z.); the Department of Science and Technology of Zhejiang Province (2023R01012 to P.Q.); and the Fundamental Research Funds for the Central Universities (226-2024-00007 to P.Q.). We are grateful for the technical assistance provided by B. Xu from Liangzhu Laboratory, Zhejiang University, and for the support and help from members of the core facility platform of Zhejiang University School of Medicine and the Laboratory Animal Center of Zhejiang University. The authors acknowledge BioRender (https://www.biorender.com/), as flow diagrams in this study were created with the BioRender platform.

Author information

Author notes

  1. These authors contributed equally: Xiangjun Zeng, Ce Shi, Yingli Han, Kejia Hu, Xiaoqing Li, Cong Wei.

Authors and Affiliations

  1. Bone Marrow Transplantation Center of the First Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou, China

    Xiangjun Zeng, Ce Shi, Yingli Han, Kejia Hu, Xiaoqing Li, Cong Wei, Jiazhen Cui, Simao Huang, Yulin Xu, Meng Zhang, Wei Shan, Qian Luo, Jian Yu, Xia Li, Pengxu Qian & He Huang

  2. Institute of Hematology, Zhejiang University, Hangzhou, China

    Xiangjun Zeng, Ce Shi, Yingli Han, Kejia Hu, Xiaoqing Li, Cong Wei, Jiazhen Cui, Simao Huang, Yulin Xu, Meng Zhang, Wei Shan, Qian Luo, Jian Yu, Xia Li, Pengxu Qian & He Huang

  3. Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China

    Xiangjun Zeng, Ce Shi, Yingli Han, Kejia Hu, Xiaoqing Li, Cong Wei, Jiazhen Cui, Simao Huang, Yulin Xu, Meng Zhang, Wei Shan, Qian Luo, Jian Yu, Xia Li, Pengxu Qian & He Huang

  4. Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China

    Lijuan Ding

  5. Shanghai Tissuebank Biotechnology Co. Ltd., Shanghai, China

    Zhongzheng Zheng

  6. Center for Stem Cell and Regenerative Medicine and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Pengxu Qian

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Contributions

H.H., P.Q. and X.Z. conceived the study. X.Z., C.S., Y.H., K.H., Xiaoqing Li and C.W. curated the data. X.Z., C.S., Y.H., K.H., Xiaoqing Li and C.W. analyzed the data. H.H. and P.Q. determined the methodology. H.H., Q.P. and J.Y. acquired funding. X.Z. and C.S. wrote the original draft. C.S., C.W., L.D., J.C., S.H., K.H., Y.X., M.Z., W.S., Q.L., J.Y., Y.H., Z.Z., Xia Li, P.Q. and H.H. reviewed and edited the manuscript.

Corresponding authors

Correspondence to Xia Li, Pengxu Qian or He Huang.

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Nature Aging thanks Toshio Suda and the other, anonymous, reviewer(s) for their contributions to the peer review of this work.

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Extended data

Extended Data Fig. 1 Metabolome of different blood cell types during aging.

(a–d) Flow cytometry gating strategy. (a) cKit+ cells enriched by immunomagnetic sorting were used for further FACS. After being discriminated from debris by gating on forward scatter (FSC) and side scatter (SSC), LSK cells, CLPs, CMPs, GMPs and MEPs were collected from BMs. (b) Megakaryocytes, macrophages, dendritic cells, monocytes and granulocytes were collected from BM cells. (c) CD19− cells enriched by immunomagnetic sorting in spleen were used for further FACS. CD4 T cells, CD8 T cells and NK cells were collected by FACS. (d) Erythrocytes were collected from PB cells. (e) Bar graphs showing the count of metabolites detected in each cell type in aged mice. (f) Violin plot showing the relative abundance of detected metabolites in different cell types in aged mice. (g) Pie chart showing the superclass of the detected metabolites in aged mice. (h) T-SNE plot showing the clustering result of different cell types in aged mice.

Source data

Extended Data Fig. 2 Quantitative analysis of glycolysis-related metabolites in different cell types.

Graphs show mean ± SEM, with statistical significance determined by one-way ANOVA followed by Dunnett’s multiple comparison test (n = 4 per group, 6–8 weeks, C57BL/6J female mice).

Source data

Extended Data Fig. 3 Identification of cell-specific metabolic signatures.

(a) Wind-rose plots showing the count of cell-specific metabolic signatures in different cell types. (b-o) Representative KEGG analysis results of cell-specific metabolic signatures in CLPs, CMPs, GMPs, MEPs, B cells, CD4 T cells, CD8 T cells, NK cells, megakaryocytes, macrophages, dendritic cells, monocytes, granulocytes and erythrocytes.

Source data

Extended Data Fig. 4 Enrichment analysis of aging-related metabolites.

(a–ab) Representative KEGG analysis results of differential metabolites during aging in CLPs (a,b), CMPs (c,d), B cells (e,j), GMPs (f,g), macrophages (h,i), dendritic cells (k,l), CD4 T cells (m,n), erythrocytes (o,t), MEPs (p,q), CD8 T cells (r,s), granulocytes (u,v), megakaryocytes (w,x), monocytes (y,z) and NK cells (aa,ab).

Source data

Extended Data Fig. 5 Screening of metabolites rejuvenating aged HSPCs.

(a–c) Absolute number of LT-HSCs, ST-HSCs and MPPs at different uridine concentrations when acting on aged HSPCs (n = 3 samples). (d, e) Percentage and absolute number of LSK cells at different guanosine concentrations when acting on aged HSPCs (n = 3 samples). (f–h) Absolute number of LSK cells after different metabolite interventions when acting on aged HSPCs (n = 3 samples). (i) Number of total colonies measured for aged HSPCs treated with guanosine in methylcellulose (n = 3 samples). (j) Number of total colonies measured for uncultured young and aged HSPCs in methylcellulose (n = 4 samples). (k, l) The percentage of Dcfh-da positive aged HSPCs (n = 3 samples). (m) Line graph of donor-derived CD45.2+ reconstitution in CD45.1+ mice receiving 1000 aged LSK cells post transplantation (vehicle group, 2 male and 2 female, 6–8 weeks C57BL/6 J; guanosine group, 1 male and 2 female, 6–8 weeks C57BL/6 J). (n, o) Absolute number of HSPCs and LT-HSCs at different concentrations of uridine when acting on young HSPCs (n = 3 samples). (p) In the in vitro uridine (200 μM) administration experiment, line graph of donor-derived CD45.2+ reconstitution in CD45.1+ mice receiving 1000 young HSPCs post transplantation (n = 6 mice per group, 3 male and 3 female, 6–8 weeks C57BL/6J). a, b, c, d, e, f, g, i, n, o, Graphs show mean ± SEM, with statistical significance determined by one-way ANOVA followed by Dunnett’s multiple comparison test. h, j, k, l, m, p, Graphs show mean ± SEM, with statistical significance determined by two-tail unpaired student’s t-test.

Source data

Extended Data Fig. 6 Uridine improved cellular function of aged HSCs in vivo.

(a) Absolute number of HSPCs in aged mice administered different concentrations of uridine orally (n = 6 mice in 0 and 0.25 mg/ml group, female, 18–20 months, C57BL/6 J; n = 5 mice in 0.05, 2.5, and 10 mg/ml group, female, 18–20 months, C57BL/6 J). (b) Number of total colonies measured for HSPCs from aged mice administered different concentrations of uridine orally (n = 6 mice in 0 and 0.25 mg/ml group, female, 18–20 months, C57BL/6 J; n = 5 mice in 0.05, 2.5, and 10 mg/ml group, female, 18–20 months, C57BL/6 J). (c–e) Concentration of creatinine, aspartate aminotransferase, and alanine aminotransferase in the serum from aged mice administered different concentrations of uridine orally (n = 5 mice per group, female, 18–20 months, C57BL/6 J). (f–k) Absolute number of LT-HSCs, ST-HSCs, MPPs, CMPs, GMPs, and MEPs in aged mice orally administered uridine (0.25 mg/ml) (n = 6 mice per group, female, 18–20 months, C57BL/6 J). a–e, Graphs show mean ± SEM, with statistical significance determined by one-way ANOVA followed by Dunnett’s multiple comparison test. f–k, Graphs show mean ± SEM, with statistical significance determined by two-tail unpaired student’s t-test.

Source data

Extended Data Fig. 7 Brief introduction of the open-source platform for analyzing metabolites across different blood cells and during aging.

The functions of the website including browsing experimental design and methods, performing dimensionality reduction analysis, retrieving the metabolic markers of specific cells in hematopoietic system, assessing the correlations between metabolites, retrieving the distribution of specific metabolites among different cell types, obtaining the differential metabolites in different cell types during aging and downloading the experimental data.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2.

Reporting Summary

Supplementary Data 1

Blood cell-type-specific metabolic signatures.

Supplementary Data 2

Aging-related metabolites in all blood cell types.

Supplementary Data 3

Differentially expressed genes in RNA sequencing.

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Zeng, X., Shi, C., Han, Y. et al. A metabolic atlas of blood cells in young and aged mice identifies uridine as a metabolite to rejuvenate aged hematopoietic stem cells. Nat Aging (2024). https://doi.org/10.1038/s43587-024-00669-1

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