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Mitochondria-enriched hematopoietic stem cells exhibit elevated self-renewal capabilities, thriving within the context of aged bone marrow

Nature Aging volume 5, pages 831–847 (2025)Cite this article

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Abstract

The aging of hematopoietic stem cells (HSCs) substantially alters their characteristics. Mitochondria, essential for cellular metabolism, play a crucial role, and their dysfunction is a hallmark of aging-induced changes. The impact of mitochondrial mass on aged HSCs remains incompletely understood. Here we demonstrate that HSCs with high mitochondrial mass during aging are not merely cells that have accumulated damaged mitochondria and become exhausted. In addition, these HSCs retain a high regenerative capacity and remain in the aging bone marrow. Furthermore, we identified GPR183 as a distinct marker characterizing aged HSCs through single-cell analysis. HSCs marked by GPR183 were also enriched in aged HSCs with high mitochondrial mass, possessing a high capacity of self-renewal. These insights deepen understanding of HSC aging and provide additional perspectives on the assessment of aged HSCs, underscoring the importance of mitochondrial dynamics in aging.

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Fig. 1: Aged HSCs with a high mitochondrial mass retain robust stemness characteristics.
Fig. 2: Aged mito-Dendra2 High HSCs demonstrate a higher self-renewal capacity and a lower tendency toward differentiation than aged mito-Dendra2 Low HSCs.
Fig. 3: Single-cell multiome analysis revealed that aged mito-Dendra2 High HSCs were characterized with HSC signature and OXPHOS genes.
Fig. 4: Aged mito-Dendra2 High HSCs were characterized with higher ATP production with no increase of cellular and mitochondrial ROS.
Fig. 5: Gpr183 was identified as a marker gene specific to a subset of aged mito-Dendra2 High HSCs.
Fig. 6: Gpr183 was identified as a marker gene specific to a subset of aged mito-Dendra2 High HSCs.
Fig. 7: GPR183high aged HSCs maintain high autophagy capacity and preserve their properties well during BMT and long-term ex vivo culture.

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Data availability

The raw data for the multiome and bulk RNA-seq analyses in this study have been deposited in the Gene Expression Omnibus database under accession numbers GSE262784 and GSE262396. Any other data reported in this paper are available from the lead contact upon reasonable request.

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Acknowledgements

The authors thank the CSI FACS facility for technical assistance and the NUS MD2 Vivarium for mouse husbandry. This research is supported by the Singapore Translational Research Investigator Award from the National Medical Research Council in Singapore (NMRC/STaR 18 may-0004 to T. Suda); JSPS Grant-in-Aid for Scientific Research (S) (18H05284 and 26221309 to T. Suda); the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (CIFMS) (2024-12M-3-017 to T. Suda); the National Natural Science Foundation of China (W2441024 to T. Suda); JSPS Grant-in-Aid for Challenging Research (Exploratory) (JP21K19514 to T.M.); JSPS Grant-in-Aid for Scientific Research (C) (24K11298 to T.M.); the Daiwa Securities Health Foundation (T.M.); the Novartis Foundation (Japan) for the Promotion of Science (T.M.); the SENSHIN Medical Research Foundation (T.M.); the Takeda Science Foundation (T.M.); the Naito Foundation (T.M.); and the Open Fund-Young Individual Research Grant (OF-YIRG) from the National Medical Research Council in Singapore (OFYIRG21nov-0020 to H.T.).

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Author notes

  1. These authors contributed equally: Haruhito Totani, Takayoshi Matsumura.

  2. These authors jointly supervised this work: Takayoshi Matsumura, Toshio Suda.

Authors and Affiliations

  1. Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore

    Haruhito Totani, Takayoshi Matsumura, Rui Yokomori, Yuji Takihara, Chong Yang, Lee Hui Chua, Atsushi Watanabe, Takaomi Sanda & Toshio Suda

  2. Division of Cardiovascular and Genetic Research, Center for Molecular Medicine, Jichi Medical University, Tochigi, Japan

    Takayoshi Matsumura

  3. International Research Center of Medical Sciences, Kumamoto University, Kumamoto, Japan

    Terumasa Umemoto & Toshio Suda

  4. State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China

    Chong Yang & Toshio Suda

  5. Department of Hematology and Oncology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan

    Takaomi Sanda

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Contributions

H.T., T.M. and T. Suda conceived and designed the experiments. H.T., T.M., T.U., Y.T., C.Y., L.H.C., A.W. and T. Sanda performed the experiments. T.M. and R.Y. analyzed bioinformatics data. H.T. and T.M. wrote the manuscript. T.M. and T. Suda supervised the research. All authors reviewed and approved the manuscript. H.T. and T.M. contributed equally to this work.

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Correspondence to Takayoshi Matsumura or Toshio Suda.

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Nature Aging thanks Simón Méndez-Ferrer, Danica Chen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Flow cytometry gating strategy for HSCs.

(a) Gating Strategy for HSCs in flow cytometry. FSC, forward scatter; FSC-A, FSC-aria; FSC-H, FSC-height; FSC-W, FSC-width; SSC, side scatter; SSC-A, SSC-aria; SSC-H, SSC-height; SSC-W, SSC-width; PI, propidium iodide; Live cells, PI− cells; Lin− cells, lineage− (CD4−CD8−B220−Gr1−CD11b−Ter119−NK1.1−CD127−) cells; LS−K, Lin−Sca1−c-Kit+; LSK, Lin−Sca1+c-Kit+; MPP, CD150−CD48+ LSK; ST-HSC, CD150−CD48− LSK; SLAM (LT-HSC), CD150+CD48− LSK; ESLAM, EPCR+ SLAM-HSC. (b) Gating Strategy for classical HSCs in flow cytometry. LT-HSC, CD34−CD135− LSK; ST-HSC, CD34+CD135− LSK; MPP, CD34+CD135+ LSK; CMP, CD34+CD16/32− LS−K; GMP, CD34+CD16/32+ LS−K; MEP, CD34−CD16/32− LS−K.

Extended Data Fig. 2 Expression of mito-Dendra2 in HSCs and analysis of their phenotype during bone marrow transplantation.

(a) Mitochondrial mass in young and aged HSPCs. Representative histogram of mito-Dendra2 for ST-HSC, MPP, LS−K, and Lin−. (b) Mitochondrial DNA copy number in mito-Dendra2 Low and High HSCs (P = 0.0118, n = 6). (c) EPCR expression in aged ESLAM-HSCs by mitochondrial mass. Relative MFI of EPCR (P = 0.0001, n = 6). (d) Complete blood count post-BMT (n = 6, 8 for Low and High groups). P-values by week: white blood cell - 4 (0.0015), 8 (0.4187), 12 ( < 0.0001), 16 (0.0529); red blood cell - 4 (0.8886), 8 (0.8816), 12 (0.0300), 16 (0.8269); hemoglobin - 4 (0.4995), 8 (0.3988), 12 (0.0037), 16 (0.8101); hematocrit - 4 (0.9067), 8 (0.4799), 12 (0.0092), 16 (0.4611); platelet - 4 (0.0672), 8 (0.8458), 12 (0.5581), 16 (0.6478). (e-g) Proportion of mito-Dendra2+ cell in LSK and LS−K fractions at 16 weeks post-BMT (Low: n = 6, High: n = 8). (e) HSPCs (Extended Data Fig. 1a). P-values: ST-HSC (0.1639), MPP (0.5754), LSK (0.1102), LS−K (0.5728), Lin− (0.5853). (f) HSPCs (Extended Data Fig. 1b). P-values: ST-HSC (CD34+CD135−) (0.2781), MPP (CD34+CD135+) (0.4255), CMP (0.6850), GMP (0.5686), MEP (0.5608). (g) MPP2 (CD150+CD48− ESLAM-HSCs). P = 0.1084. (h) mito-Dendra2 expression in donor-derived cells in the HSC at 16 weeks post-BMT. Due to the lower number of donor-positive cells in the HSC fraction originating from mito-Dendra2 Low cells, the sample size is smaller compared to that of mito-Dendra2 High cells (n = 3, 7 for mito-Dendra2 Low and High). P = 0.1510. (i) Proportion of donor-derived cells post-BMT in young mice (ref. 26; n = 5, 4 for Young-Low and Young-High). P-values: SLAM (0.0374), ST-HSC (0.1513), MPP (0.0975). Note: All charts show mean ± SD. Statistical analysis: two-tailed Student’s t-test (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; ns = not significant).

Source data

Extended Data Fig. 3 Single cell multiome analysis classified HSCs into four clusters.

(a) A heatmap of multiome analysis of young and aged mito-Dendra2 HSCs, showing the top differentially expressed genes in each cluster. The x-axis represents individual cells and clusters, and the y-axis represents differentially expressed genes. (b) Uniform manifold approximation and projection (UMAP) visualization of HSCs from young and aged mito-Dendra2 mice. scRNA-seq and scATAC-seq data were integrated with Weighted Nearest Neighbor (WNN) analysis. UMAP based on WNN analysis (top), scRNA-seq data only (middle), and scATAC-seq data only (bottom) were shown. In the right panels, young and aged mito-Dendra2 Low and High HSCs were depicted separately. The top leftmost panel is identical to Fig. 3b.

Extended Data Fig. 4 Single cell multiome analysis revealed that aged mito-Dendra2 High HSCs were characterized with HSC signature and oxidative phosphorylation genes.

(a) Violin plots showing differences in Surface Marker Overlap (SuMo) scores (upper) and HSC scores (lower) of young and aged mito-Dendra2 Low and High HSCs in each cluster. In the upper panel, P = 0, 2.99×10−209, 1.04×10−70, and 7.10×10−25 for Clusters 1, 2,3 and 4 (one-way Welch’s ANOVA). *P = 0, 0, 3.26×10−13, and 1.00×10−11 between aged mito-Dendra2 Low and High HSCs for Clusters 1, 2,3 and 4 (Games-Howell post hoc test). In the lower panel, P = 0, 1.90×10−172, 3.83×10−77, and 1.39×10−19 for Clusters 1, 2,3 and 4 (one-way Welch’s ANOVA). *P = 0, 3.77×10−14, 2.42×10−13, and 3.93×10−8 between aged mito-Dendra2 Low and High HSCs for Clusters 1, 2,3 and 4 (Games-Howell post hoc test). (b) Violin plot showing myeloid-biased (left) and lymphoid-biased (right) score. P = 0 and 0 (one-way Welch’s ANOVA). *P = 2.05×10−12 and 1.69×10−12 (Games-Howell post hoc test). (c) Violin plots showing differences in mRNA expression levels of ribosomal genes between aged mito-Dendra2 Low and High HSCs in clusters 1 (left) and 2 (right). *P = 1.13×10−261, 9.30×10−125, 1.93×10−264, 6.27×10−115, 9.12×10−227, 1.31×10−85, 1.62×10−273 and 2.65×10−150 from left to right, calculated by the Wilcoxon Rank Sum test, and adjusted based on Bonferroni correction with all genes using the Seurat package in R in a two-sided manner. (d) Gene set variation analysis (GSVA) using MitoCarta gene sets between aged mito-Dendra2 Low and High HSCs. Each dot represents one gene set. Two-sided adjusted P-values were calculated using the limma package. (e) Violin plots showing differences in mRNA expression levels of selected iron homeostasis genes between aged mito-Dendra2 Low and High HSCs. *P = 5.18×10−84 and 2.48×10−81 from left to right, calculated by the Wilcoxon Rank Sum test, and adjusted based on Bonferroni correction with all genes using the Seurat package in R in a two-sided manner.

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Extended Data Fig. 5 scRNA-seq and bulk RNA-seq revealed genes specific to aged HSCs.

(a) Gene set enrichment analysis (GSEA) comparing young and aged LT-HSCs using 50 hallmark gene sets. The top 4 gene sets enriched in aged LT-HSCs are shown. Each solid bar represents one gene within the gene set. NES, normalized enrichment score; FDR, false discovery rate. (b) Violin plots showing mRNA expression levels of Kcnb2 and Clu in each cluster of young mito-Dendra2 Low (cyan), young mito-Dendra2 High (pink), aged mito-Dendra2 Low (blue), and aged mito-Dendra2 High HSCs (red). P = 0, 1.84×10−146, 9.94×10−67, and 2.00×10−38 (Kcnb2), and 5.76×10−100, 7.11×10−52, 8.86×10−25, and 5.37×10−11 (Clu) between aged mito-Dendra2 High HSCs and others from left to right, calculated by the Wilcoxon Rank Sum test and adjusted based on Bonferroni correction with all genes using the Seurat package in R. (c) Box plots of bulk RNA-seq showing mRNA expression levels of selected aged LT-HSC-specific genes in young (pink) and aged (cyan) LT-HSCs, ST-HSCs, and MPPs (n = 4). P = 4.30×10−3, 7.07×10−12, 1.78×10−100, 6.46×10−116, 3.07×10−86, and 0, calculated by the likelihood ratio test and adjusted by the Benjamini and Hochberg method using the DESeq2 package in R. The boxes show median values, 1st and 3rd quartiles. The whiskers extend to the most extreme data point which is no more than 1.5 times the interquartile range from the box.

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Extended Data Fig. 6 The relationship between mitochondria and GPR183.

(a) Relative GPR183 expression following SR-18292 treatment, with normalized MFI value (P = 0.0029, n = 6). (b) Relative mito-Dendra2 expression following NIBR-189 and/or 7α,25-DHC treatment, with normalized MFI values (n = 3). Adjusted P-value: NIBR-189−/7α,25-DHC− vs NIBR-189−/7α,25-DHC+ (0.2551), NIBR-189+/7α,25-DHC− vs NIBR-189+/7α,25-DHC+ (0.1046), NIBR-189−/7α,25-DHC− vs NIBR-189+/7α,25-DHC− (0.2310), NIBR-189−/7α,25-DHC+ vs NIBR-189+/7α,25-DHC+ (0.7146). Note: All charts show mean ± SD. Statistical analysis: (a) two-tailed Student’s t-test and (b) one-way ANOVA (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; ns = not significant).

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Extended Data Fig. 7 BMT for GPR183neg and GPR183high HSCs.

(a) Complete blood count post-BMT (n = 6, 7 for GPR183neg and GPR183high groups). P-values by week: white blood cell - 4 (0.0817), 8 (0.0987), 12 (0.6245), 16 (0.0755); red blood cell - 4 (0.5566), 8 (0.0567), 12 (0.3505), 16 (0.3763); hemoglobin - 4 (0.4078), 8 (0.1669), 12 (0.8170), 16 (0.6456); hematocrit - 4 (0.4519), 8 (0.0957), 12 (0.4468), 16 (0.3169); platelet - 4 (0.0269), 8 (0.6668), 12 (0.9417), 16 (0.9568). (b) Proportion of CD45.1+ cells in PB. GPR183low (n = 6, 7 for GPR183neg and GPR183high groups). P-values by week: total cells - 4 (0.0195), 8 (0.0153), 12 (0.0219), 16 (0.0290); granulocytes - 4 (0.0049), 8 (0.0139), 12 (0.0056), 16 (0.0086); B-cells - 4 (0.0659), 8 (0.0829), 12 (0.0937), 16 (0.0933); T-cells - 4 (0.8150), 8 (0.1236), 12 (0.1091), 16 (0.2046). (c-d) Proportion of CD45.1+ cell in LSK and LS−K fractions at 16 weeks post-BMT (n = 6, 7 for GPR183neg and GPR183high groups). (c) HSPCs (Extended Data Fig. 1a). P-values: ST-HSC (0.0365), MPP (0.0268), LSK (0.0229), LS−K (0.0301), Lin− (0.0374). (d) HSPCs (Extended Data Fig. 1b). P-values: ST-HSC (CD34+CD135−) (0.0256), MPP (CD34+CD135+) (0.0370), CMP (0.0250), GMP (0.0205), MEP (0.0385). Note: All charts show mean ± SD. Statistical analysis: two-tailed Student’s t-test (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; ns = not significant).

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Totani, H., Matsumura, T., Yokomori, R. et al. Mitochondria-enriched hematopoietic stem cells exhibit elevated self-renewal capabilities, thriving within the context of aged bone marrow. Nat Aging 5, 831–847 (2025). https://doi.org/10.1038/s43587-025-00828-y

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