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Ageing limits stemness and tumorigenesis by reprogramming iron homeostasis

Nature volume 637, pages 184–194 (2025)Cite this article

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A Publisher Correction to this article was published on 22 May 2025

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Abstract

Ageing is associated with a decline in the number and fitness of adult stem cells1,2. Ageing-associated loss of stemness is posited to suppress tumorigenesis3,4, but this hypothesis has not been tested in vivo. Here we use physiologically aged autochthonous genetically engineered5,6 mouse models and primary cells5,6 to demonstrate that ageing suppresses lung cancer initiation and progression by degrading the stemness of the alveolar cell of origin. This phenotype is underpinned by the ageing-associated induction of the transcription factor NUPR1 and its downstream target lipocalin-2 in the cell of origin in mice and humans, which leads to functional iron insufficiency in the aged cells. Genetic inactivation of the NUPR1–lipocalin-2 axis or iron supplementation rescues stemness and promotes the tumorigenic potential of aged alveolar cells. Conversely, targeting the NUPR1–lipocalin-2 axis is detrimental to young alveolar cells through ferroptosis induction. Ageing-associated DNA hypomethylation at specific enhancer sites is associated with increased NUPR1 expression, which is recapitulated in young alveolar cells through DNA methylation inhibition. We uncover that ageing drives functional iron insufficiency that leads to loss of stemness and tumorigenesis but promotes resistance to ferroptosis. These findings have implications for the therapeutic modulation of cellular iron homeostasis in regenerative medicine and in cancer prevention. Furthermore, our findings are consistent with a model whereby most human cancers initiate at a young age, thereby highlighting the importance of directing cancer prevention efforts towards young individuals.

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Fig. 1: Aged AT2 cells exhibit reduced cell-intrinsic potential for regeneration and tumorigenesis.
Fig. 2: Induction of Nupr1 suppresses tumorigenesis in the aged lung by disrupting iron homeostasis.
Fig. 3: NUPR1 drives functional iron insufficiency and ferroptosis resistance in aged AT2 cells through lipocalin-2.
Fig. 4: DNA hypomethylation underpins ageing-associated induction of the NUPR1–lipocalin-2 axis.
Fig. 5: Ageing-induced changes in AT2 cells and LUAD are conserved in humans and in adult stem cells outside the lung.

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Data availability

The following datasets are available from the Gene Expression Omnibus (GEO): mouse lung AT2 cell chromatin immunoprecipitation (GSE158205)37; scRNA-seq data of normal AT2 and LUAD cells from aged and young mice (GSE277408); scRNA-seq of AT2 cells harbouring Nupr1 and Dnmt1 knockdown by shRNAs (accession numbers GSE277203 and GSE277210, respectively); bulk mRNA-seq of lung tumour cells with Nupr1 knockout by sgRNAs (GSE277076), ex vivo transformed tumour cells (GSE277241) and DNMT1 inhibitor-treated AT2 cells (GSE277061) from young and aged mice; and DNA methylome sequencing data (SE276548). Pyrosequencing data are available from Figshare (https://figshare.com/s/9e382098b24c29b04fa6)85. Source data are provided with this paper.

Code availability

The following scripts are available at GitHub: analysis scripts for TCGA analysis (https://github.com/bioinfoDZ/AgingLUAD); scripts for methylome data pre-processing (https://github.com/zyqfrog10/DNAmethylAnalysis) and scripts for single-cell and bulk mRNA-seq and DMC analyses (https://github.com/ewonglab/LUAD-aging-project).

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Acknowledgements

We thank L. Jones, T. Papagiannakopoulos and C. M. Rudin for comments on the manuscript; K. Birsoy, M. Conrad, A.-K. Hadjantonakis, D. Huangfu, A. Koff, L. Parada, C. Sawyers, L. Studer, A. Wahida, M. Winslow and members of the Tammela Laboratory for discussions; M. Conrad for advice on experiments involving ferroptosis; S. Persad and D. Pe’er for help with the SEACells algorithm; D. Buenocore, J. Silber, R. Spencer and K. Ventura for help with TMA generation; A. Chavez Perez and S. Lowe for help with senescence-associated β-galactosidase and p16 staining; R. Gardner for FACS support; K. Manova for histology support; E. Chan and E. Rosiek for help with image analyses and quantification; J. Simundza for manuscript editing; A. Alonso for low-input DNA methyl-seq analysis; C. Cobbs, N. Mohibullah and A. Viale for next-generation sequencing; J. Agnis, J. Lisanti and C. M. Rudin for primary patient lung tissue; H. Alcorn and O. Grbovic-Huezo for laboratory management; J. Chan, M. Gregory, G. Hartmann, A. Hudson, H. Styers, C. Sussman and S. Torborg for help with experiments; and staff at the Genetic Resources Core Facility, Johns Hopkins University School of Medicine (RRID: SCR_018669) and R. Ashworth and J. Totey, in particular, for their assistance with pyrosequencing. This work was supported by a Mark Foundation for Cancer Research Emerging Leader Award (to T. Tammela), the Go2 Foundation for Lung Cancer (to T. Tammela and M.J.B.) and by the NIH/NCI Cancer Center Support Grant P30-CA008748 (to MSKCC). X.Z. received support from a training award from New York Stem Cell Science NYSTEM (C32559GG) from the Center for Stem Cell Biology at MSKCC and from a grant from the Druckenmiller Center for Lung Cancer Research at MSKCC. S.J. received support from the Hope Funds for Cancer Research. R.C. is supported by The Alan and Sandra Gerry Foundation. E.S.W. is supported by a NHMRC Investigator Grant (GNT2009309), an ARC Discovery Project (DP200100250) and a Snow Medical Fellowship. T. Tammela was supported by Josie Robertson, American Cancer Society, Rita Allen, and V Foundation Scholarships. We acknowledge the use of the Integrated Genomics Operation Core (funded by Cycle for Survival, and the Marie-Josée and Henry R. Kravis Center for Molecular Oncology), the Flow Cytometry, the Laboratory of Comparative Pathology, and Histology Core Facilities at Sloan Kettering Institute, funded by CCSG P30-CA008748. We also acknowledge The Victor Chang Cardiac Research Institute Innovation Centre (funded by the New South Wales Government Ministry of Health). Illustrations in Figs. 1e–g, 2i, 3j, 4g and 5h and Extended Data Figs. 9g and 10c were created using BioRender (https://www.biorender.com).

Author information

Authors and Affiliations

  1. Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Xueqian Zhuang, Simon Joost, Zhuxuan Li, Melissa Blum, Klavdija Krause, Selena Ding, Matthew J. Bott & Tuomas Tammela

  2. Victor Chang Cardiac Research Institute, Sydney, New South Wales, Australia

    Qing Wang, David T. Humphreys, Yuna Landais & Emily S. Wong

  3. Institute for Clinical and Translational Research, Albert Einstein College of Medicine, New York, NY, USA

    Alexander Ferrena & Deyou Zheng

  4. Weill Cornell Graduate School of Medical Science, Weill Cornell Medicine, New York, NY, USA

    Zhuxuan Li

  5. Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Yingqian Zhan & Richard P. Koche

  6. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Yang Zhao

  7. Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Ronan Chaligne

  8. Wellcome–MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK

    Joo-Hyeon Lee

  9. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK

    Joo-Hyeon Lee

  10. Laboratory of Comparative Pathology, Weill Cornell Medicine, Memorial Sloan Kettering Cancer Center and Rockefeller University, New York, NY, USA

    Sebastian E. Carrasco

  11. Pathology Core Facility, Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Umeshkumar K. Bhanot

  12. Institute of Biotechnology, HiLIFE, University of Helsinki, Helsinki, Finland

    Pekka Katajisto

  13. Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden

    Pekka Katajisto

  14. Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland

    Pekka Katajisto

  15. David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Yadira M. Soto-Feliciano

  16. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Yadira M. Soto-Feliciano

  17. Institute for NanoBioTechnology, Department of Oncology–Cancer Genetics and Epigenetics, Johns Hopkins University, Baltimore, MD, USA

    Thomas Pisanic

  18. Department of Pathology and Cell Biology, Columbia University College of Physicians and Surgeons, New York, NY, USA

    Tiffany Thomas

  19. Departments of Genetics, Neurology, and Neuroscience, Albert Einstein College of Medicine, New York, NY, USA

    Deyou Zheng

  20. School of Biotechnology and Biomolecular Sciences, UNSW, Sydney, New South Wales, Australia

    Emily S. Wong

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  1. Xueqian Zhuang
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  2. Qing Wang
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Contributions

X.Z. and T. Tammela conceived and designed the study and wrote the manuscript. X.Z., E.S.W. and T. Tammela interpreted the data. E.S.W., Q.W., S.J., M.B. and S.D. contributed to the writing of the manuscript. X.Z., M.B., S.D., K.K. and Z.L. performed experiments and data analyses. Q.W., S.J., A.F., D.T.H., Y. Zhan., R.P.K. and E.S.W. performed computational analyses. Q.W. and S.J. performed transcriptomics analyses. Y. Zhan., R.P.K., Q.W. and D.T.H. performed DNA methylation analyses. A.F. performed analyses of human TCGA data. S.J., R.P.K., D.Z. and E.S.W. supervised computational analyses and interpreted data. R.C. supervised scRNA-seq. J.-H.L. contributed mouse and human alveolar organoid culture methodology. S.E.C. performed pathology review of mouse lung tumours. U.K.B. constructed TMAs and performed pathology review of human lung and lung adenocarcinoma tissue. M.J.B. contributed primary human lung tissue. T. Thomas performed iron measurements by ICP-MS. Y. Zhao and T.P. designed and performed pyrosequencing. P.K. contributed expertise on experiments involving the small intestine, and Y.M.S.-F. provided expertise in and performed epigenomics analyses. M.J.B., E.S.W. and T. Tammela obtained funding. All of the authors approved the final manuscript.

Corresponding author

Correspondence to Tuomas Tammela.

Ethics declarations

Competing interests

T. Tammela is a scientific advisor with equity interests in Lime Therapeutics. His spouse is an employee of and has equity in Recursion Pharmaceuticals. The Tammela Laboratory receives funding from Ono Pharma unrelated to this work. E.S.W. has equity in and her spouse is a co-founder of and equity holder in Gertrude Biomedical Pty. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks David Feldser, Raphaël Rodriguez and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Ageing suppresses lung tumorigenesis by degrading the stemness of the cell of origin.

(a) Survival of aged vs. young KP LUAD mice post-tumor initiation (n = 13 young and 7 aged mice) (Source Data). (b) Representative images of LUAD tumors in aged vs. young KPT (4 and 8 weeks) or KP (12 weeks) mice. At 4 and 8 weeks post-tumor initiation, LUAD cells were visualized by tdTomato immunohistochemistry. Scale bar: 200 µm. (c) Percentage of single tdTomato+ cells (singletons) per total number of tdTomato+ lesions at 2 weeks post-tumor initiation using adeno-SPC-Cre in aged vs. young KPT mice. N = 5 young and 3 aged mice. Representative images of early LUAD lesions, visualized by tdTomato fluorescence, in aged vs. young KPT mice is shown on the right. Arrowheads point to singletons. Scale bar: 50 µm. (d) Quantification of adeno-SPC-Cre transduction efficiency. Shown on the left is the total number of transduced AT2 cells, defined by co-expression of fluorescent reporter and the AT2 cell surface markers in Supplementary Fig. 2, and quantified by flow cytometry. Successful transduction is defined by the switch from tdTomato to GFP fluorescence in aged and young Rosa26mTmG mice following intratracheal administration of adeno-SPC-Cre (n = 6 young and 5 aged mice). Shown on the right is the area density of tdTomato+ AT2 (SPC+) cells in tissue sections obtained from aged vs. young Rosa26LSL-tdTomato mice 3 days after intratracheal administration of adeno-SPC-Cre (n = 4 mice per group). (e) Quantification of tumor burden in 3, 6, 9, 12, 18, and 24 months-old KP mice at 12 weeks following tumor initiation with adeno-SPC-Cre (n = 5, 6, 6, 5 and 5 mice per condition). (f) Schematic summary of the Eml4-Alk LUAD model (image representing chromosomal rearrangement is reproduced from Maddalo et al26). Lungs of aged (104-130 weeks old) and young (12-16 weeks old) wild-type C57BL/6 mice were intratracheally transduced with adeno-sgEml4-sgAlk-Cas9 virus, which induces an Eml4-Alk gene fusion via an intra-chromosomal inversion in chromosome 17. (g-h) Tumor number (g, n = 19 young and 8 aged mice) and sizes (h, n = 231 and 184 tumors from young and aged mice, respectively) of Elm4-Alk fusion driven-LUAD in aged vs young C57BL6/J mice. (i) PCR assay used to detect Eml4-Alk inversion (top panel) and quantification of the inversion rate using the PCR assay in aged and young mice (bottom panel, n = 4 mice per condition). Note that the PCR primers specifically amplify the fusion gene. Raw gel image is shown in Supplementary Fig. 1. (j) Quantification of AT2 cells in aged and young mouse lungs. AT2 cells (red arrowheads) identified by surfactant protein-C (SPC) immunohistochemistry (n = 3 aged and 6 young mice). Scale bar; 50 µm. (k) FACS strategy for isolating mouse AT2 cells. AT2 cells are identified as DAPI-/Lineage (CD45, CD11b, CD11c, F4/80 and Ter119)-/EpCAM+/MHCII+ (AT2). Gating strategy of AT2 cells is shown in Supplementary Fig. 2. Validation of AT2 cell purity was performed by qPCR for the AT2 marker gene Sftpc. Note lack of detectable Sftpc expression in non-AT2 lineage-negative cells (EpCAM- and other EpCAM+) (n = 3 biological replicates). (l) Quantification of the percentage of AT2 cells in the total EpCAM+ epithelial cell pool in young (12-16 weeks old), middle-aged (52 weeks old), and aged (104-130 weeks old) wild-type C57BL/6 mice (n = 5 mice per condition). (m) Representative images of 6 young and 6 aged primary and 5 young and 4 aged secondary alveolar organoids. Scale bar: 100 µm. (n) Representative images (left) and quantification (right) of senescent cells identified by C12RG fluorescence-based detection of senescence-associated β-galactosidase activity. Note that C12RG positive senescent cells (arrowheads) do not express the AT2 cell marker SPC (n = 7 young and 6 aged mice). Scale bar: 50 µm. Y: young, A: aged. Mean with SD is shown in (c-e), (g), (i-l) and (n). Median and 25th and 75th percentiles are showed by dashed lines in (h). Log rank test was used in (a). Two-tailed Mann-Whitney test was used in (c), (i) and (j). Two-tailed Student’s t-test was used in (d), (g-h), (k) and (n). One-way ANOVA was used in (e) and (l).

Source Data

 Extended Data Fig. 2 Ageing suppresses the proliferation of LUAD cells and delays LUAD progression.

(a) Airspace size of aged and young mice before and after hyperoxia-induced lung injury measured by mean cord length (MCL) measurement (n = 4 mice per group). Shown on the right are representative images of alveoli from young and aged animals pre- and post-hyperoxia injury (28 days). Scale bar: 50 µm. (b) Transformation efficiency of aged and young KP-Cas9 AT2 cells, calculated as the ratio of GFP+ transformed organoids/non-transformed alveolar organoids from the same animal (n = 8 biological replicates). (c) Quantification of lentiviral transduction efficiency of aged and young AT2 cells in the ex vivo transformation assay. AT2 cells were isolated from both aged and young KP mice and transduced with lenti-PGK-GFP. Transduction efficiency was defined as the percentage of GFP+ AT2 cells over total number of AT2 cells (n = 3 biological replicates). (d) Quantification of lentiviral transduction efficiency of aged and young AT2 cells in vivo. Aged and young Rosa26mTmG mice were transduced by Lenti-PGK-Cre and transduction efficiency was defined by the total number of GFP+ AT2 cells (GFP+/MHCII+/EpCAM+/lineage-/DAPI-), where Cre recombinase converts the tdTomato fluorescence to GFP fluorescence (n = 3 mice). (e) Quantification of KP LUAD tumor size in aged vs. young mice at 4, 8, 12, and 17 weeks post-tumor initiation. Number of cancer cells per tumor nodule was quantified in KPT mice using tdTomato to visualize cancer cells at the 4- and 8-week time points; tumor size was quantified in KP mice from HE stained sections for at the 12- and 17-week time points. N = 109, 60, 371, and 358 tumors from young mice and n = 95, 66, 121, and 144 tumors from aged mice at 4, 8, 12, and 17 weeks. (f) Quantification of proliferating LUAD cells at different stages of tumor development. The proportion of Ki67 positive cells of total lung cancer cells (identified by endogenous reporter alleles or SPC immunofluorescence) was calculated and normalized to the mean of young tumors at the corresponding time point. N = 55, 62, 127, and 306 tumors from young mice and n = 32, 62, 25, and 237 tumors from aged mice at 4, 8, 12, and 17 weeks. (g-i) Quantification of senescent cancer cells in young and aged KP LUAD tumors at 12 weeks post-tumor initiation. The senescent cells were identified by p16 (g-h) (n = 8 young and 6 aged mice) or C12RG staining (i) (n = 11 and 9 tumors from young and aged KP tumor-bearing mice, respectively). Representative images of p16 staining are shown in (g). Scale bar: 50 µm. (j) Quantification of cleaved caspase-3 (c-Casp3) positive LUAD tumors, defined as tumors with ≥ 1 c-Casp3+ cell (n = 4 mice). (k-l) Histopathological grading of KP LUAD tumors in aged vs. young mice at 12 and 17 weeks post-tumor initiation. Representative images of the grading by an automated deep neural network (Aiforia Technologies) are shown (k). Proportion of G1-G4 tumor area per total tumor area. Proportion in each mouse was calculated individually and mean is shown in (l, n = 10 young and 5 aged mice for 12 week time point and n = 10 young and 10 aged mice for 17 week time point, respectively). Scale bar: 1 mm. Y: young, A: aged. Mean with SEM is shown in (e) and mean with SD is shown in (a-c) and (h-j). Median and 25th and 75th percentiles are shown by dashed lines in (f). One-way ANOVA was used in (a). Two-tailed Student’s t test was used in (b), (e-f), (h-j) and (l). Two-tailed Mann-Whitney test was used in (c-d).

Source Data

Extended Data Fig. 3 Ageing delays the molecular progression of LUAD in vivo and in vitro.

(a) Uniform manifold approximation projection (UMAP) embedding of LUAD single-cell transcriptomes isolated from young and aged KP tumors labeled based on previously defined transcriptionally distinct subsets23, at 4, 12, and 17 weeks post-tumor initiation. The numbers indicate order of cell state progression23. WT AT2: wild-type AT2 cells, the cell of origin (“0”). (b) MetaCell neighborhoods70, groups of similar cells representing discrete cell states, of 4-week-old KP LUAD tumors projected onto the UMAP introduced in (a). Circle size corresponds to the number of cells, while color signifies the fraction of young and aged cells forming a MetaCell neighborhood (blue: enriched in young; red: enriched in aged). Note enrichment of young cancer cells at the high-plasticity cell state (HPCS, dashed blue oval). Validation of higher proportion of young cancer cells in HPCS by immunofluorescence for the HPCS marker integrin α2 (green) at 4 weeks post-tumor initiation. tdTomato (red) marks cancer cells. N = 54 aged and 125 young tumors. Scale bar: 50 µm. Arrowheads indicate integrin α2 positive cells. (c) Quantification of KRT8-high tdTomato+ LUAD cells at 4 weeks post-tumor induction (n = 99 aged and 91 young tumors). Scale bar: 50 µm. Arrowheads point to KRT8-high cells. (d) MetaCell analysis of KP LUAD tumors at 17 weeks post-tumor initiation. Note enrichment of young cancer cells at the endoderm-like state (dashed blue circle), which is validated by immunohistochemical staining for the endoderm-like state marker HNF4α. Inset shows nuclear HNF4α immunolabeling in neoplastic cells (brown) in the tumors. The proportion of tumors containing >5% HNF4α+ cells per the total numbers of tumors is shown. N = 10 young and 11 aged tumor-bearing mice. Scale bar: 100 µm. (e) Schematic summary of experiment evaluating molecular progression of KP LUAD cells in vitro. Briefly, AT2 cells were isolated from aged vs. young KP-Cas9 mice and transformed ex vivo by lentiviral Cre recombinase (P0, as shown in Fig. 1g). Bulk mRNA sequencing (RNA-seq) was performed over eight serial passages of tumor spheres (P1-P8). Non-KP alveolar organoids from both aged and young AT2 cells are also included. (f) Projection of bulk RNA-seq data from ex vivo transformed young and aged AT2 cells in diffusion pseudotime space. Upper panel: samples colored according to diffusion pseudotime using untransformed AT2 cells as the starting point (0). Lower panels: samples colored based on age (blue: young; aged: red). Note enrichment of young tumor spheres at later pseudotime points (blue dashed oval), whereas aged spheres are enriched at the midway point (dashed red circle). (g) Boxplots showing diffusion pseudotime distribution of young (blue) and aged (red) ex vivo transformed cells stratified according to passage (P). Individual samples are represented by data dots (p = 0.0014, Wilcoxon ranked sum test). N = 5 young and 5 aged biological ex vivo transformed tumor sphere cell lines. Maxima and minima are indicated by the whiskers; 25th and 75th percentiles are shown by the boundaries of the box; median is shown as the center line in the box. (h) Heatmap showing the mean expression of alveolar epithelial lineage markers [AT2 marker genes (Sftpb, Sftpc, Lyz1 and Lyz2) and AT1 marker (Ager)]in young and aged ex vivo transformed cells stratified according to passage. The colormap shows the log2-fold change compared to sample mean for each gene harmonized for young and aged samples. (i) Heatmap displaying the MAST DEG coefficient of the top 25 most consistently upregulated and downregulated genes across normal and tumor cell types. The top 25 upregulated and top 25 downregulated genes with aging were selected based on (i) significance in normal AT2 with FDR < 0.1; (ii) similar gene expression change trend across AT2 cells and all LUAD cell states; (iii) the sum of absolute DEG coefficient across AT2 cells and all LUAD cell states. The color bar indicates the MAST coefficient value. The genes are ordered from most downregulated (dark blue) to the most upregulated (dark red) in aged across the x-axis. The uniform manifold approximation and projection (UMAP) plot of the single-cell transcriptomics showing wildtype AT2 cells (cluster 0) and five LUAD cell states (AT2-like, AT1-like, high-plasticity cell state, HPCS, endoderm-like and ribosome-high, cluster 1-5) that molecularly define LUAD progression is shown in Extended Data Fig. 3a. Note that the expression of the DEGs in the wildtype AT2 is shown at the top row and the five LUAD cell clusters follow. The DEGs were ranked based on a two-part generalized linear statistical model where they were identified in all tumor cells with batch, sex, cell type, and cellular detection rate added as covariates. Subsequently, the aging-related DEGs in the normal AT2 cells and in all five LUAD cell states were identified separately with batch, sex, and cellular detection rate included in the model as covariates. Y: young, A: aged. Median and 25th and 75th percentiles are shown in (b-c). Mean with SD is shown in (d). Two-tailed Student’s t-test was used in (b-d).

Source Data

Extended Data Fig. 4 Nupr1 differentially regulates lung cancer growth in aged vs young mice.

(a) Plots of gene set enrichment analysis (GSEA) showing the enrichment of gene sets linked to iron metabolism in age-related signatures introduced in Extended Data Fig. 3i and Supplementary Table 2 ordered according to fold change. Red indicates enrichment in aged, blue enrichment in young. (b) Violin plots of showing the expression of Nupr1 in young and aged AT2 cells and LUAD cell states. Two-tailed Wilcoxon rank-sum tests on single-cell gene expression were performed for statistical significance. (c-d) Quantification of Nupr1 mRNA level (red dots) by in situ hybridization in AT2 cells (c, n = 1126 and 425 AT2 cells from young and aged animals, respectively) and LUAD cells at 12 weeks post-tumor initiation (d, n = 32 and 12 tumors from young and aged tumor-bearing mice at 12 weeks post-tumor initiation, respectively). Surfactant protein-C (Sftpc) mRNA (green) was used to mark AT2 and cancer cells. Scale bar: 20 µm. (e) Ex vivo transformation of AT2 cells isolated from aged and young KP-Cas9 mice with the indicated lentiviral vectors (Fig. 2c) delivering Cre + control sgRNA or two independent sgRNAs targeting Nupr1 (n = 4 biological replicates). Representative images of transformed tumor spheres are shown on the right. Scale bar: 100 µm. (f) Ex vivo transformation of AT2 cells isolated from aged and young KP-RIK mice, with or without treatment with the NUPR1 inhibitor ZZW-115 (2 µM) (n = 3 biological replicates). (g-h) Quantification of tumor size (g, n = 87, 40, 146, and 121 tumors for young-sgControl, young-sgNupr1, aged-sgControl, and aged-sgNupr1, respectively) and proportion of proliferating (Ki67+) tumor cells per total tumor cells (h, n = 71, 133, 40, and 93 tumors for young-sgControl, young-sgNupr1, aged-sgControl, and aged-sgNupr1) in the experiment described in Fig. 2c. (i) Ex vivo transformation of AT2 cells isolated from aged and young KP-Cas9 mice with the indicated lentiviral vectors (Fig. 2c) delivering Cre + control sgRNA or two independent sgRNAs targeting Nupr1, with (grey shading) or without DFO treatment (n = 6 biological replicates except n = 5 in the first and third group). (j) Ex vivo transformation of AT2 cells isolated from aged and young KP-RIK mice by lentiviral PGK-Cre with or without treatment with ZZW-115 (2 µM) and deferoxamine (DFO, 2 µM) (n = 4 biological replicates). (k) Ex vivo transformation of AT2 cells isolated from aged and young KP mice and stimulated with transferrin (50 µg ml−1; n = 3, 3, 4, 4 biological replicates, left to right). (l) Ex vivo transformation of AT2 cells isolated from aged and young KP-RIK mice by lentiviral PGK-Cre with or without stimulation with 50 µM ferric ammonium citrate (FAC) (n = 5 biological replicates). (m) Ex vivo transformation of AT2 cells isolated from aged and young KP-Cas9 mice using lentiviral vectors delivering Cre recombinase and two sgRNAs targeting Nupr1 or a control sgRNA with or without transferrin supplementation (50 µg ml−1; n = 4 biological replicates). Mean with SD is shown in (e-f) and (i-m). Mean with SEM is shown in (g). Median and 25th and 75th percentiles are shown by dashed lines in (c-d) and (h). Two-tailed Student’s t test was used in (c-d). One-way ANOVA was used in (e-m).

Source Data

Extended Data Fig. 5 NUPR1–lipocalin-2 axis reprograms iron metabolism in AT2 cells and LUAD cells.

(a) Measurement of labile iron by Biotracker FerroFarRed dye in KP-RIK LUAD cells following ex vivo transformation of AT2 cells by lentiviral PGK-Cre with or without stimulation with 0.1 µM 2,5-dihydroxybenzoic acid (2,5-DHBA) (n = 3 biological replicates). (b) Quantification of tumor spheres in (a, n = 3 biological replicates). (c) Normalized total cellular iron content measured by ICP-MS in LUAD cells derived from the ex vivo transformation experiment shown in Extended Data Fig. 4i (n = 5, 10, 4, 6, 5, 10, 4, and 6 biological replicates per condition). (d) Normalized labile iron levels measured by Biotracker FerroFarRed dye in LUAD cells derived from the ex vivo transformation experiment shown in Extended Data Fig. 4i (n = 9, 15, 3, 6, 8, 15, 3, and 7 biological replicates). Note that same data in groups without DFO treatment is shown in Fig. 2g. (e-f) Measurement of cellular iron in primary AT2 cells by ICP-MS (total iron, e, n = 4 biological replicates) and staining of BioTracker FerroFarRed dye (labile iron, f, n = 8 biological replicates). (g) Nupr1 mRNA in cultured KP LUAD cells transduced with lentiviral control shRNA (shRenilla) or shRNAs targeting Nupr1 (n = 3 technical replicates from one representative experiment, repeated twice). (h) Alveolar organoid formation by aged vs. young primary mouse AT2 cells stimulated with 50 µg ml−1 iron-loaded mouse transferrin or vehicle control (n = 4, 4, 4, and 3 biological replicates). (i) Alveolar organoid formation by aged vs. young primary AT2 cells in the presence of vehicle control, ZZW-115 (1 µM), or ZZW-115 + 2 µM DFO (n = 4, 4, 4, 4, 4 and 3 biological replicates, left to right). Representative images of alveolar organoids are shown on the right. Scale bar: 100 µm. (j) Violin plots showing expression of Lcn2 in young and aged AT2 cells and LUAD cell states. Two-sided Wilcoxon rank-sum tests on single-cell gene expression vectors were performed for significance testing. (k-m) Lcn2 mRNA level in transformed AT2 cells from the experiment in Extended Data Fig. 4e (k, n = 3 technical replicates), Extended Data Fig. 4f (l, n = 3 technical replicates), and normal alveolar organoids treated with or without NUPR1 inhibitor ZZW-115 (1 µM; m, n = 3 technical replicates). (n) Expression of Nupr1 and Lcn2 in AT2 cells expressing shRNAs targeting Nupr1 or Renilla control in vivo. Gene expression was assessed by single-cell mRNA sequencing of GFP+ AT2 cells isolated from the experiment in Fig. 2i. Differential expression was assessed using two-tailed quasi-likelihood F-tests. **, p < 0.01; ***, p < 0.001. Numerical p value is shown in Supplementary Table 18. Y: young, A: aged. Mean with SD is shown in (a-i) and (k-m). Two-tailed Student’s t-test was used in (a), (d) and (f). One-way ANOVA was used in (b-c), (g-i) and (k-m). Two-tailed Mann-Whitney test was used in (e).

Source Data

Extended Data Fig. 6 Activation of NUPR1–lipocalin-2 axis leads to ferroptosis resistance in aged LUAD cells.

(a) Ex vivo transformation of AT2 cells isolated from aged and young KP mice with lentiviral PGK-Cre + shRNAs targeting the indicated genes (n = 3 biological replicates). Data are shown as fold change compared to shRenilla control. (b) Measurement of labile iron in LUAD cells derived from Fig. 3e; data is shown normalized to control sgRNA. N = 9, 15, 5, 8, 8, 15, 5, and 10 biological replicates from left to right, respectively. Note that data points of sgControl and sgNupr1 without Lcn2 cDNA is the same as in Fig. 2g. (c) Ex vivo transformation of AT2 cells isolated from aged vs. young KP-RIK mice using lentiviral PGK-Cre, with or without 100 ng ml−1 recombinant mouse lipocalin-2 and/or 2 µM ZZW-115 (n = 4, 4, 4, 3, 4 and 4 biological replicates, left to right). (d) Measurement of labile iron by Biotracker FerroFarRed dye in KP LUAD cells derived from ex vivo transformation of aged and young AT2 cells treated with anti-lipocation-2 neutralizing antibody (5 µg ml−1, n = 6 biological replicates). (e) Ex vivo transformation of AT2 cells isolated from aged vs. young KP-Cas9 mice with PGK-Cre and control sgRNA or sgRNAs targeting Lcn2, with or without treatment of anti-lipocation-2 neutralizing antibody (5 µg ml−1, n = 4 biological replicates). (f) Validation of shRNA targeting Gpx4 by quantitative PCR (n = 3 technical replicates per condition from a representative experiment, repeated twice). (g) Ex vivo transformation of aged and young AT2 cells of KP mice with shRNA targeting Gpx4 (n = 6 biological replicates). Data are shown as fold change compared to shRenilla control. (h) Ex vivo transformation of AT2 cells isolated from aged vs. young KP mice with PGK-Cre, with or without NUPR1 inhibitor ZZW-115 (2 µM) and ferroptosis inhibitor liproxstatin-1 (5 µM). N = 4 biological replicates. (i) Ex vivo transformation of AT2 cells isolated from aged vs. young KP mice using lentiviral PGK-Cre, with or without mouse transferrin or ferroptosis inducer RSL-3 (n = 3 biological replicates). Mean with SD is shown in (a-i). Two-tailed Student’s t test was used in (a), (d) and (f-g). One-way ANOVA was used in (b-c), (e) and (h-i).

Source Data

Extended Data Fig. 7 Ageing changes DNA methylation patterns in primary AT2 cells and LUAD cells.

(a) Density plot showing Enzymatic Methyl-Sequencing (EM-Seq) coverage in primary AT2 cell samples. (b) Density plot of the methylation proportion in AT2 cell samples. (c) Heatmap showing unsupervised clustering of aged vs. young AT2 cells based on DNA methylation profiles. (d) Number of differentially methylated cytosines (DMCs) in aged vs. young AT2 cells based on location of CpG residues (n = 4 young and 4 age mice). (e) Density plot showing EM-Seq coverage in primary KP LUAD cell samples. (f) Density plot of the methylation proportion of KP LUAD samples. (g) Heatmap showing unsupervised clustering of aged vs. young KP LUAD cells based on DNA methylation profiles. (h) Number of differentially methylated cytosines (DMCs) in aged vs. young KP LUAD cells based on location of CpG residues (n = 5 young and 4 age mice). The central line within each boxplot in (d) and (h) represents the median (50th percentile) and the boundaries of the box indicate 25th and 75th percentiles. The whiskers extend to the minimum and maximum values within 1.5 times the interquartile range (IQR) from the first and third quartiles, respectively. Points outside the whiskers are considered outliers and are shown as individual dots. Two-sided Wilcoxon rank-sum test was performed in (d) and (h).

Extended Data Fig. 8 Ageing-associated DNA methylation changes correlate with gene expression alterations that emerge with aging.

(a) The number of DMCs at sites marked by distinct histone modifications in AT2 cells. The height of the bars indicates higher methylation level in young (blue) or higher methylation in aged (red). The total number of DMCs that were detected at least in one sample is shown. (b) The mean differential methylation change of AT2 DMCs at histone marks at Lcn2 and Nupr1. Grey means no DMC was detected. The color bar indicates the methylation change. Purple-blue color indicates demethylation in aged compared to young mice. Results of individual DMCs are shown Supplementary Table 8. (c) Genome browser tracks of CpGs in the gene body of Nupr1 and Lcn2 in aged vs. young AT2 cells, measured by EM-Seq. Green indicates differential methylation between aged (red) and young (blue): green bars below zero indicate CpG hypomethylation in aged AT2 cells. (d) Pearson correlation between DMCs and DEGs in aged vs. young AT2 cells in each category of histone modification (FDR < 0.05). The regression line (blue) and its 95% confidence interval (light grey) were plotted using a linear model. Pearson correlation test was used for testing statistical significance.

Extended Data Fig. 9 Suppression of DNA methylation in young AT2 cells and LUAD cells recapitulates ageing-associated changes in gene expression.

(a) Quantification of 5-methylcytosine (5-mC) immunofluorescence in lungs of young mice administered the DNMT1 inhibitor GSK3685032 for 8 days or vehicle controls (n = 4 mice). AT2 cells were identified as SPC+ cells. (b) Scatter plot showing the correlation between the fold change of DEGs from the signature shared by AT2 cells and LUAD cells (Extended Data Fig. 3i; Supplementary Table 2) in AT2 cells isolated from young mice administered DNMT1-i or vehicle (x-axis) vs. aged or young vehicle-treated mice (y-axis). Pearson correlation was calculated. The regression line (blue) and its 95% confidence interval (light grey) were plotted using a linear model. (c) Quantification of Nupr1 mRNA molecules in Sftpc+ AT2 cells in young mice administered vehicle (blue) or DNMT1-i (orange) for 8 days, compared to aged mice that received vehicle (red) (n = 353, 438, and 429 AT2 cells per condition, respectively). Scale bar: 20 µm. (d) Ex vivo transformation of AT2 cells from aged and young KP mice with or without DNMT1 inhibition (n = 3 biological replicates). (e-f) Expression of Nupr1 and Lcn2 in KP LUAD cell lines with or without DNMT1 inhibition (n = 4 independent KP lines). The mRNA levels are normalized to the housekeeping gene Gapdh and further normalized to vehicle control. Note that data of (e) was plotted as a heatmap in Fig. 4f. (g) Experimental strategy for transcriptomic profiling of AT2 cells following shRNA-mediated knockdown of Dnmt1 compared to Renilla control in vivo. (h) Validation of shRNA targeting Dnmt1 by quantitative PCR (n = 3 technical replicates from one representative experiment, repeated twice). (i) Quantification of 5-mC immunofluorescence in AT2 cells of young Rosa26mTmG mice transduced with lentiviral PGK-Cre plus shRNAs targeting Dnmt1 or Renilla control (n = 4 and 7 mice). Given that successful delivery of the shRNAs is marked by switch of endogenous fluorescence from tdTomato to GFP, 5-mC only in the GFP+ AT2 cells was quantified. (j) Contour plot showing correlation between the fold change (FC) of DEGs in AT2 cells isolated from young mice administered shDNMT1s or control shRNA (shRenilla) (x-axis) compared to aged vs. young AT2 cells expressing shRenilla control (y-axis). Grayscale represents the probability distribution. Pearson correlation was calculated. The percentage of genes in each quadrant is significantly different from random (binomial test p < 2.2 ×10−16). The regression line (blue) was plotted using a linear model. (k) Scatter plot showing the correlation between the fold change of DEGs from the signature shared by AT2 cells and LUAD cells (Extended Data Fig. 3i; Supplementary Table 2) in AT2 cells expressing shDnmt1 or shRenilla (x-axis) isolated from young mice vs. aged or young shRenilla-treated mice (y-axis). Pearson correlation was calculated. The regression line (blue) and its 95% confidence interval (light grey) were plotted using a linear model. (l) Expression of Nupr1 in KP LUAD cell lines expressing shRNAs targeting Dnmt1 or Renilla control (n = 4 independent KP lines). The mRNA level is normalized to the housekeeping gene Gapdh and further normalized to shRenilla. Mean with SD is shown in (a), (d-f), (h-i) and (l). Mean with SEM is shown in (c). Two-tailed Student’s t test was used in (a) and (i). One-way ANOVA was used in (c-d) and (h). Two-tailed paired Student’s t test was used in (e-f) and (l). Pearson correlation test was used in (b) and (k). The illustration in (g) was created using BioRender (https://biorender.com).

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Extended Data Fig. 10 Identification of ageing-associated differentially methylated CpGs in Nupr1 distal enhancers.

(a) Strategy of the selection of candidate distal Nupr1enhancers, including methylation status by EM-Seq, proximity to Nupr1 transcription start site (TSS), overlap with transcription factor (TF) binding sites known to regulate Nupr133, enhancer-associated histone markers86, and chromatin accessibility by single-cell ATAC-seq37. (b) Genomic distribution of the 11 candidate enhancers. (c) CRISPR interference (CRISPRi)-mediated functional testing of the candidate enhancers. (d) Nupr1 quantitative PCR in KP LUAD cells expressing sgRNAs targeting the candidate enhancers (Supplementary Table 11, n = 9 data point from 3 biologically independent cell lines, each transduced with 3 different sgRNAs target the same enhancer). (e) Sequence context of differentially methylated CpGs in the high-confidence enhancers (E1, E7 and E8) and sgRNAs targeting these enhancers in CRISPRi assay. Mean with SD is shown in (d). Kruskal-Wallis test was used in (d). The illustration in (c) was created using BioRender (https://biorender.com).

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Extended Data Fig. 11 Hypomethylation of specific CpGs in enhancers induces expression of Nupr1.

(a) Genome browser tracks showing differentially methylated CpGs at the high-confidence Nupr1 enhancers (E1, E7 and E8) in aged vs. young AT2 cells, measured by EM-Seq. Green indicates differential methylation between aged (red) and young (blue): green bars below zero indicate CpG hypomethylation in aged AT2 cells. (b) Correlation between hypomethylation at the five CpGs at Nupr1 enhancers hypomethylated with aging and Nupr1 gene expression with or without DNMT1 inhibition. Pearson correlation was calculated. The regression lines were calculated using a linear model. Note that the same data is summarized as a heatmap in Fig. 4f. Pearson correlation test was used for testing statistical significance.

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Extended Data Fig. 12 Ageing-induced induction of  NUPR1–lipocalin-2 axis is conserved in human lung and mouse intestine.

(a) Quantification of AT2 cell density in healthy lungs of aged (>70 years old) and young or middle-aged (<50 years old) humans (n = 23 young/middle-aged and 25 aged patients, respectively). The proportion of HTII-280 positive AT2 cells (orange) per total lung cell number is shown. (b) Quantification of lipocalin-2 protein level in normal lung tissues of aged and young or middle-aged patients (n = 14 young/middle-aged cases vs. 15 aged cases). The intensity of lipocalin-2 (red) immunofluorescence was quantified only in cells expressing the AT2 cell marker SPC (green). A. U.: arbitrary units. (c) Quantification of Lcn2 mRNA (number of green dots) in aged vs. young mouse intestinal stem cells marked by the expression of Lgr5 (n = 291 Lgr5+ cells in the aged and n = 289 Lgr5+ cells in the young). (d) Quantification of organoid formation by intestinal crypts isolated from aged vs. young mice, with or without liproxstatin-1 and RSL-3 (n = 4 technical repeats per condition, a representative experiment repeated 3 times is shown). Scale bar: 20 µm in (a-c). Y: young; A: aged. Mean with SD is shown in (a-b) and (d). Mean with SEM is shown in (c). Two-tailed Student’s t test was used in (a-d).

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This file contains descriptions for Supplementary Tables 1–20 (Supplementary Tables supplied separately), Supplementary Figs. 1–11, Supplementary Discussion and Supplementary References.

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Zhuang, X., Wang, Q., Joost, S. et al. Ageing limits stemness and tumorigenesis by reprogramming iron homeostasis. Nature 637, 184–194 (2025). https://doi.org/10.1038/s41586-024-08285-0

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