Oh, J., Lee, Y. D. & Wagers, A. J. Stem cell aging: mechanisms, regulators and therapeutic opportunities. Nat. Med. 20, 870–880 (2014).
Google Scholar
Schultz, M. B. & Sinclair, D. A. When stem cells grow old: phenotypes and mechanisms of stem cell aging. Development 143, 3–14 (2016).
Google Scholar
Rozhok, A. I. & DeGregori, J. The evolution of lifespan and age-dependent cancer risk. Trends Cancer 2, 552–560 (2016).
Google Scholar
Rozhok, A. & DeGregori, J. A generalized theory of age-dependent carcinogenesis. eLife 8, e39950 (2019).
Google Scholar
Jackson, E. L. et al. The differential effects of mutant p53 alleles on advanced murine lung cancer. Cancer Res. 65, 10280–10288 (2005).
Google Scholar
Jackson, E. L. et al. Analysis of lung tumor initiation and progression using conditional expression of oncogenic K-ras. Genes Dev. 15, 3243–3248 (2001).
Google Scholar
Cancer Statistics (National Cancer Institute, 2021); https://www.cancer.gov/about-cancer/understanding/statistics.
White, A. C. & Lowry, W. E. Refining the role for adult stem cells as cancer cells of origin. Trends Cell Biol. 25, 11–20 (2015).
Google Scholar
Tomasetti, C., Li, L. & Vogelstein, B. Stem cell divisions, somatic mutations, cancer etiology, and cancer prevention. Science 355, 1330–1334 (2017).
Google Scholar
Tomasetti, C. et al. Role of stem-cell divisions in cancer risk. Nature 548, E13–E14 (2017).
Google Scholar
Boyle, M., Wong, C., Rocha, M. & Jones, D. L. Decline in self-renewal factors contributes to aging of the stem cell niche in the Drosophila testis. Cell Stem Cell 1, 470–478 (2007).
Google Scholar
Pentinmikko, N. et al. Notum produced by Paneth cells attenuates regeneration of aged intestinal epithelium. Nature 571, 398–402 (2019).
Google Scholar
Lopez-Otin, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of aging. Cell 153, 1194–1217 (2013).
Google Scholar
Singh, P. P., Demmitt, B. A., Nath, R. D. & Brunet, A. The genetics of aging: a vertebrate perspective. Cell 177, 200–220 (2019).
Google Scholar
Booth, L. N. & Brunet, A. The aging epigenome. Mol. Cell 62, 728–744 (2016).
Google Scholar
Michalak, E. M., Burr, M. L., Bannister, A. J. & Dawson, M. A. The roles of DNA, RNA and histone methylation in ageing and cancer. Nat. Rev. Mol. Cell Biol. 20, 573–589 (2019).
Google Scholar
Seale, K., Horvath, S., Teschendorff, A., Eynon, N. & Voisin, S. Making sense of the ageing methylome. Nat. Rev. Genet. 23, 585–605 (2022).
Google Scholar
Bocklandt, S. et al. Epigenetic predictor of age. PLoS ONE 6, e14821 (2011).
Google Scholar
Field, A. E. et al. DNA methylation clocks in aging: categories, causes, and consequences. Mol. Cell 71, 882–895 (2018).
Google Scholar
Fane, M. & Weeraratna, A. T. How the ageing microenvironment influences tumour progression. Nat. Rev. Cancer 20, 89–106 (2020).
Google Scholar
Balducci, L. & Ershler, W. B. Cancer and ageing: a nexus at several levels. Nat. Rev. Cancer 5, 655–662 (2005).
Google Scholar
Liu, B. et al. Lung cancer in young adults aged 35 years or younger: a full-scale analysis and review. J. Cancer 10, 3553–3559 (2019).
Google Scholar
Marjanovic, N. D. et al. Emergence of a high-plasticity cell state during lung cancer evolution. Cancer Cell 38, 229–246.e13 (2020).
Google Scholar
Basil, M. C. et al. The cellular and physiological basis for lung repair and regeneration: past, present, and future. Cell Stem Cell 26, 482–502 (2020).
Google Scholar
Sutherland, K. D. et al. Multiple cells-of-origin of mutant K-Ras-induced mouse lung adenocarcinoma. Proc. Natl Acad. Sci. USA 111, 4952–4957 (2014).
Google Scholar
Maddalo, D. et al. In vivo engineering of oncogenic chromosomal rearrangements with the CRISPR/Cas9 system. Nature 516, 423–427 (2014).
Google Scholar
Altemeier, W. A., Hung, C. F. & Matute-Bello, G. in Acute Lung Injury and Repair: Scientific Fundamentals and Methods (eds Schnapp, L. M. & Feghali-Bostwick, C.) 5–23 (Springer International Publishing, 2017).
Lee, J.-H. et al. Lung stem cell differentiation in mice directed by endothelial cells via a BMP4–NFATc1–thrombospondin-1 axis. Cell 156, 440–455 (2014).
Google Scholar
Rowbotham, S. P. et al. Age-associated H3K9me2 loss alters the regenerative equilibrium between murine lung alveolar and bronchiolar progenitors. Dev. Cell 58, 2974–2991.e6 (2023).
Google Scholar
Winslow, M. M. et al. Suppression of lung adenocarcinoma progression by Nkx2-1. Nature 473, 101–104 (2011).
Google Scholar
Strunz, M. et al. Alveolar regeneration through a Krt8+ transitional stem cell state that persists in human lung fibrosis. Nat. Commun. 11, 3559 (2020).
Google Scholar
Liu, J. et al. NUPR1 is a critical repressor of ferroptosis. Nat. Commun. 12, 647 (2021).
Google Scholar
Liu, S. & Costa, M. The role of NUPR1 in response to stress and cancer development. Toxicol. Appl. Pharmacol. 454, 116244 (2022).
Google Scholar
Huang, C., Santofimia-Castano, P. & Iovanna, J. NUPR1: a critical regulator of the antioxidant system. Cancers 13, 3670 (2021).
Google Scholar
Tammela, T. et al. A Wnt-producing niche drives proliferative potential and progression in lung adenocarcinoma. Nature 545, 355–359 (2017).
Google Scholar
Lan, W. et al. ZZW-115-dependent inhibition of NUPR1 nuclear translocation sensitizes cancer cells to genotoxic agents. JCI Insight 5, e138117 (2020).
Google Scholar
Little, D. R. et al. Differential chromatin binding of the lung lineage transcription factor NKX2-1 resolves opposing murine alveolar cell fates in vivo. Nat. Commun. 12, 2509 (2021).
Google Scholar
Pappalardi, M. B. et al. Discovery of a first-in-class reversible DNMT1-selective inhibitor with improved tolerability and efficacy in acute myeloid leukemia. Nat. Cancer 2, 1002–1017 (2021).
Google Scholar
The Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543–550 (2014).
Google Scholar
Tabula Muris, C. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590–595 (2020).
Google Scholar
Ge, Y. et al. The aging skin microenvironment dictates stem cell behavior. Proc. Natl Acad. Sci. USA 117, 5339–5350 (2020).
Google Scholar
Liu, Z. et al. Immunosenescence: molecular mechanisms and diseases. Signal Transduct. Target. Ther. 8, 200 (2023).
Google Scholar
Gomes, A. P. et al. Age-induced accumulation of methylmalonic acid promotes tumour progression. Nature 585, 283–287 (2020).
Google Scholar
Lee, J. J. et al. Tracing oncogene rearrangements in the mutational history of lung adenocarcinoma. Cell 177, 1842–1857.e21 (2019).
Google Scholar
Jiang, X., Stockwell, B. R. & Conrad, M. Ferroptosis: mechanisms, biology and role in disease. Nat. Rev. Mol. Cell Biol. 22, 266–282 (2021).
Google Scholar
Rodriguez, R., Schreiber, S. L. & Conrad, M. Persister cancer cells: iron addiction and vulnerability to ferroptosis. Mol. Cell 82, 728–740 (2022).
Google Scholar
Signer, R. A. & Morrison, S. J. Mechanisms that regulate stem cell aging and life span. Cell Stem Cell 12, 152–165 (2013).
Google Scholar
Kobayashi, Y. et al. Persistence of a regeneration-associated, transitional alveolar epithelial cell state in pulmonary fibrosis. Nat. Cell Biol. 22, 934–946 (2020).
Google Scholar
Alvarez, S. W. et al. NFS1 undergoes positive selection in lung tumours and protects cells from ferroptosis. Nature 551, 639–643 (2017).
Google Scholar
Muller, S. et al. CD44 regulates epigenetic plasticity by mediating iron endocytosis. Nat. Chem. 12, 929–938 (2020).
Google Scholar
Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).
Google Scholar
Madisen, L. et al. A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat. Neurosci. 13, 133–140 (2010).
Google Scholar
Muzumdar, M. D., Tasic, B., Miyamichi, K., Li, L. & Luo, L. A global double-fluorescent Cre reporter mouse. Genesis 45, 593–605 (2007).
Google Scholar
Dow, L. E. et al. Conditional reverse tet-transactivator mouse strains for the efficient induction of TRE-regulated transgenes in mice. PLoS ONE 9, e95236 (2014).
Google Scholar
Safran, M. et al. Mouse reporter strain for noninvasive bioluminescent imaging of cells that have undergone Cre-mediated recombination. Mol. Imaging 2, 297–302 (2003).
Google Scholar
Platt, R. J. et al. CRISPR–Cas9 knockin mice for genome editing and cancer modeling. Cell 159, 440–455 (2014).
Google Scholar
Hsia, C. C., Hyde, D. M., Ochs, M. & Weibel, E. R. An official research policy statement of the American Thoracic Society/European Respiratory Society: standards for quantitative assessment of lung structure. Am. J. Respir. Crit. Care Med. 181, 394–418 (2010).
Google Scholar
LaFave, L. M. et al. Epigenomic state transitions characterize tumor progression in mouse lung adenocarcinoma. Cancer Cell 38, 212–228.e13 (2020).
Google Scholar
Nikitin, A. Y. et al. Classification of proliferative pulmonary lesions of the mouse: recommendations of the mouse models of human cancers consortium. Cancer Res. 64, 2307–2316 (2004).
Google Scholar
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Google Scholar
Gonzalez, R. F., Allen, L., Gonzales, L., Ballard, P. L. & Dobbs, L. G. HTII-280, a biomarker specific to the apical plasma membrane of human lung alveolar type II cells. J. Histochem. Cytochem. 58, 891–901 (2010).
Google Scholar
Dost, A. F. M. et al. Organoids model transcriptional hallmarks of oncogenic KRAS activation in lung epithelial progenitor cells. Cell Stem Cell 27, 663–678.e8 (2020).
Google Scholar
Liu, Z. et al. Systematic comparison of 2A peptides for cloning multi-genes in a polycistronic vector. Sci. Rep. 7, 2193 (2017).
Google Scholar
Sánchez-Rivera, F. J. et al. Rapid modelling of cooperating genetic events in cancer through somatic genome editing. Nature 516, 428–431 (2014).
VanDussen, K. L., Sonnek, N. M. & Stappenbeck, T. S. L-WRN conditioned medium for gastrointestinal epithelial stem cell culture shows replicable batch-to-batch activity levels across multiple research teams. Stem Cell Res. 37, 101430 (2019).
Google Scholar
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Google Scholar
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).
Google Scholar
Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291.e9 (2019).
Google Scholar
Lun, A. T. L., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).
Google Scholar
Persad, S. et al. SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data. Nat. Biotechnol. 41, 1746–1757 (2023).
Google Scholar
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Google Scholar
Fang, Z., Liu, X. & Peltz, G. GSEApy: a comprehensive package for performing gene set enrichment analysis in Python. Bioinformatics https://doi.org/10.1093/bioinformatics/btac757 (2023).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2012).
Google Scholar
Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2014).
Google Scholar
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Google Scholar
Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for bisulfite-seq applications. Bioinformatics 27, 1571–1572 (2011).
Google Scholar
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. https://doi.org/10.14806/ej.17.1.200 (2011).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Google Scholar
Feng, H., Conneely, K. N. & Wu, H. A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data. Nucleic Acids Res. 42, e69 (2014).
Google Scholar
Park, Y. & Wu, H. Differential methylation analysis for BS-seq data under general experimental design. Bioinformatics 32, 1446–1453 (2016).
Google Scholar
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Google Scholar
Aran, D., Sirota, M. & Butte, A. J. Systematic pan-cancer analysis of tumour purity. Nat. Commun. 6, 8971 (2015).
Google Scholar
Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).
Google Scholar
Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).
Google Scholar
Zhuang, X. et al. Pyrosequencing data. Figshare https://figshare.com/s/9e382098b24c29b04fa6 (2024).
Calo, E. & Wysocka, J. Modification of enhancer chromatin: what, how, and why? Mol. Cell 49, 825–837 (2013).
Google Scholar