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Single-cell parallel analysis of DNA damage and transcriptome reveals selective genome vulnerability

Nature Methods volume 22, pages 962–972 (2025)Cite this article

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Abstract

Maintenance of genome integrity is paramount to molecular programs in multicellular organisms. Throughout the lifespan, various endogenous and environmental factors pose persistent threats to the genome, which can result in DNA damage. Understanding the functional consequences of DNA damage requires investigating their preferred genomic distributions and influences on gene regulatory programs. However, such analysis is hindered by both the complex cell-type compositions within organs and the high background levels due to the stochasticity of damage formation. To address these challenges, we developed Paired-Damage-seq for joint analysis of oxidative and single-stranded DNA damage with gene expression in single cells. We applied this approach to cultured HeLa cells and the mouse brain as a proof of concept. Our results indicated the associations between damage formation and epigenetic changes. The distribution of oxidative DNA damage hotspots exhibits cell-type-specific patterns; this selective genome vulnerability, in turn, can predict cell types and dysregulated molecular programs that contribute to disease risks.

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Fig. 1: Joint profiling of oxidative and single-stranded DNA damage with transcriptome in single cells.
Fig. 2: Distribution of oxidative DNA damage hotspots in HeLa cells.
Fig. 3: Cell-type-resolved DNA damage landscapes of the mouse brain.
Fig. 4: Cell-type-specific DNA damage hotspots associated with diverse cellular functions.

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Data availability

Raw sequencing and processed data generated in this study are available from NCBI Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) under the accession number GSE268567. Other external datasets were downloaded from the GEO with the following accession numbers: AP-seq (GSE121005), CLAPS-seq (GSE181312), Paired-seq and Paired-Tag (GSE152020), Droplet Paired-Tag (GSE224560), snATAC-seq and Paired-Tag of aging mouse brain (GSE187332), CoTECH (GSE158435), ENCODE (https://www.encodeproject.org/) with the following accession numbers: HeLa DNase-seq (ENCFF977IGB), HeLa H3K4me3 chromatin immunoprecipitation with sequencing (ChIP–seq) (ENCFF578NOK), HeLa H3K36me3 ChIP–seq (ENCFF248WXB), HeLa H3K4me1 ChIP–seq (ENCFF360CQR), HeLa H3K9me3 ChIP–seq (ENCFF712ATO), HeLa H3K27ac ChIP–seq (ENCFF392EDT), HeLa H3K27me3 ChIP–seq (ENCFF512TQI) and HeLa 18-state model chromatin states (ENCSR098REA); 4DN Data Portal (https://data.4dnucleome.org/)) with the following accession numbers: HeLa cell Hi-C (4DNFIBMVFFOF), mouse cortex Hi-C (4DNFIB16WAKX); Mouse Brain scRNA-seq (https://portal.brain-map.org/atlases-and-data/rnaseq) and the 10x Genomics website (https://10xgenomics.com/). Source data are provided with this paper.

Code availability

Custom scripts used for analyzing Paired-Damage-seq datasets are available from GitHub (https://github.com/czhulab/Paired-Damage-seq).

References

  1. Schumacher, B., Pothof, J., Vijg, J. & Hoeijmakers, J. H. J. The central role of DNA damage in the ageing process. Nature 592, 695–703 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Consortium, E. P. et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 583, 699–710 (2020).

    Article  Google Scholar 

  3. Sancar, A., Lindsey-Boltz, L. A., Unsal-Kacmaz, K. & Linn, S. Molecular mechanisms of mammalian DNA repair and the DNA damage checkpoints. Annu. Rev. Biochem. 73, 39–85 (2004).

    Article  CAS  PubMed  Google Scholar 

  4. Feinberg, A. P. Phenotypic plasticity and the epigenetics of human disease. Nature 447, 433–440 (2007).

    Article  CAS  PubMed  Google Scholar 

  5. Dabin, J., Fortuny, A. & Polo, S. E. Epigenome maintenance in response to DNA damage. Mol. Cell 62, 712–727 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Oberdoerffer, P. et al. SIRT1 redistribution on chromatin promotes genomic stability but alters gene expression during aging. Cell 135, 907–918 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Yang, J. H. et al. Loss of epigenetic information as a cause of mammalian aging. Cell 186, 305–326 e327 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Lu, Y. R., Tian, X. & Sinclair, D. A. The information theory of aging. Nat. Aging 3, 1486–1499 (2023).

    Article  PubMed  Google Scholar 

  9. Qian, M. X. et al. Acetylation-mediated proteasomal degradation of core histones during DNA repair and spermatogenesis. Cell 153, 1012–1024 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Wang, S., Meyer, D. H. & Schumacher, B. Inheritance of paternal DNA damage by histone-mediated repair restriction. Nature 613, 365–374 (2023).

    Article  CAS  PubMed  Google Scholar 

  11. Wu, X. & Zhang, Y. TET-mediated active DNA demethylation: mechanism, function and beyond. Nat. Rev. Genet. 18, 517–534 (2017).

    Article  CAS  PubMed  Google Scholar 

  12. Wu, W. et al. Neuronal enhancers are hotspots for DNA single-strand break repair. Nature 593, 440–444 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Reid, D. A. et al. Incorporation of a nucleoside analog maps genome repair sites in postmitotic human neurons. Science 372, 91–94 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Luquette, L. J. et al. Single-cell genome sequencing of human neurons identifies somatic point mutation and indel enrichment in regulatory elements. Nat. Genet. 54, 1564–1571 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Wang, D. et al. Active DNA demethylation promotes cell fate specification and the DNA damage response. Science 378, 983–989 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Tubbs, A. & Nussenzweig, A. Endogenous DNA damage as a source of genomic instability in cancer. Cell 168, 644–656 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Poetsch, A. R. The genomics of oxidative DNA damage, repair, and resulting mutagenesis. Comput. Struct. Biotechnol. J. 18, 207–219 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Krokan, H. E. & Bjoras, M. Base excision repair. Cold Spring Harb. Perspect. Biol. 5, a012583 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Ding, Y., Fleming, A. M. & Burrows, C. J. Sequencing the mouse genome for the oxidatively modified base 8-oxo-7,8-dihydroguanine by OG-seq. J. Am. Chem. Soc. 139, 2569–2572 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Wu, J., McKeague, M. & Sturla, S. J. Nucleotide-resolution genome-wide mapping of oxidative DNA Damage by Click-Code-seq. J. Am. Chem. Soc. 140, 9783–9787 (2018).

    Article  CAS  PubMed  Google Scholar 

  21. Poetsch, A. R., Boulton, S. J. & Luscombe, N. M. Genomic landscape of oxidative DNA damage and repair reveals regioselective protection from mutagenesis. Genome Biol. 19, 215 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Amente, S. et al. Genome-wide mapping of 8-oxo-7,8-dihydro-2’-deoxyguanosine reveals accumulation of oxidatively-generated damage at DNA replication origins within transcribed long genes of mammalian cells. Nucleic Acids Res. 47, 221–236 (2019).

    Article  CAS  PubMed  Google Scholar 

  23. Liu, Z. J., Martinez Cuesta, S., van Delft, P. & Balasubramanian, S. Sequencing abasic sites in DNA at single-nucleotide resolution. Nat. Chem. 11, 629–637 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Fang, Y. & Zou, P. Genome-wide mapping of oxidative DNA damage via engineering of 8-oxoguanine DNA glycosylase. Biochemistry 59, 85–89 (2020).

    Article  CAS  PubMed  Google Scholar 

  25. Cao, B. et al. Nick-seq for single-nucleotide resolution genomic maps of DNA modifications and damage. Nucleic Acids Res. 48, 6715–6725 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Gorini, F. et al. The genomic landscape of 8-oxodG reveals enrichment at specific inherently fragile promoters. Nucleic Acids Res. 48, 4309–4324 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zhu, C. et al. Joint profiling of histone modifications and transcriptome in single cells from mouse brain. Nat. Methods 18, 283–292 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Xiao, S., Fleming, A. M. & Burrows, C. J. Sequencing for oxidative DNA damage at single-nucleotide resolution with click-code-seq v2.0. Chem. Commun. 59, 8997–9000 (2023).

    Article  CAS  Google Scholar 

  29. Liang, Y. et al. DNA Damage Atlas: an atlas of DNA damage and repair. Nucleic Acids Res. 52, D1218–D1226 (2024).

    Article  CAS  PubMed  Google Scholar 

  30. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    Article  CAS  PubMed  Google Scholar 

  31. Preissl, S., Gaulton, K. J. & Ren, B. Characterizing cis-regulatory elements using single-cell epigenomics. Nat. Rev. Genet. https://doi.org/10.1038/s41576-022-00509-1 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Vandereyken, K., Sifrim, A., Thienpont, B. & Voet, T. Methods and applications for single-cell and spatial multi-omics. Nat. Rev. Genet. 24, 494–515 (2023).

    Article  CAS  PubMed  Google Scholar 

  33. Williams, J. S. & Kunkel, T. A. Ribonucleotides in DNA: origins, repair and consequences. DNA Repair 19, 27–37 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Riedl, J., Fleming, A. M. & Burrows, C. J. Sequencing of DNA lesions facilitated by site-specific excision via base excision repair DNA glycosylases yielding ligatable gaps. J. Am. Chem. Soc. 138, 491–494 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Shu, X. et al. Genome-wide mapping reveals that deoxyuridine is enriched in the human centromeric DNA. Nat. Chem. Biol. 14, 680–687 (2018).

    Article  CAS  PubMed  Google Scholar 

  36. Mulqueen, R. M. et al. Highly scalable generation of DNA methylation profiles in single cells. Nat. Biotechnol. 36, 428–431 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Zhu, C. et al. An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome. Nat. Struct. Mol. Biol. 26, 1063–1070 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Xie, Y. et al. Droplet-based single-cell joint profiling of histone modifications and transcriptomes. Nat. Struct. Mol. Biol. 30, 1428–1433 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Xiong, H., Luo, Y., Wang, Q., Yu, X. & He, A. Single-cell joint detection of chromatin occupancy and transcriptome enables higher-dimensional epigenomic reconstructions. Nat. Methods 18, 652–660 (2021).

    Article  CAS  PubMed  Google Scholar 

  42. Bloom, J. D. Estimating the frequency of multiplets in single-cell RNA sequencing from cell-mixing experiments. PeerJ 6, e5578 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  43. An, J. et al. Genome-wide analysis of 8-oxo-7,8-dihydro-2’-deoxyguanosine at single-nucleotide resolution unveils reduced occurrence of oxidative damage at G-quadruplex sites. Nucleic Acids Res. 49, 12252–12267 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Chen, Q. & Ames, B. N. Senescence-like growth arrest induced by hydrogen peroxide in human diploid fibroblast F65 cells. Proc. Natl Acad. Sci. USA 91, 4130–4134 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Coluzzi, E., Leone, S. & Sgura, A. Oxidative stress induces telomere dysfunction and senescence by replication fork arrest. Cells https://doi.org/10.3390/cells8010019 (2019).

  46. Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E. & Swarup, V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep. Methods 3, 100498 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Schuster-Bockler, B. & Lehner, B. Chromatin organization is a major influence on regional mutation rates in human cancer cells. Nature 488, 504–507 (2012).

    Article  PubMed  Google Scholar 

  48. Fousteri, M. & Mullenders, L. H. Transcription-coupled nucleotide excision repair in mammalian cells: molecular mechanisms and biological effects. Cell Res. 18, 73–84 (2008).

    Article  CAS  PubMed  Google Scholar 

  49. Roadmap Epigenomics, C. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Article  Google Scholar 

  50. Milano, L., Gautam, A. & Caldecott, K. W. DNA damage and transcription stress. Mol. Cell 84, 70–79 (2024).

    Article  CAS  PubMed  Google Scholar 

  51. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Khristich, A. N. & Mirkin, S. M. On the wrong DNA track: Molecular mechanisms of repeat-mediated genome instability. J. Biol. Chem. 295, 4134–4170 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Fleming, A. M. & Burrows, C. J. Oxidative stress-mediated epigenetic regulation by G-quadruplexes. NAR Cancer 3, zcab038 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Fleming, A. M., Ding, Y. & Burrows, C. J. Oxidative DNA damage is epigenetic by regulating gene transcription via base excision repair. Proc. Natl Acad. Sci. USA 114, 2604–2609 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Fleming, A. M., Zhu, J., Ding, Y. & Burrows, C. J. 8-Oxo-7,8-dihydroguanine in the context of a gene promoter G-quadruplex is an on-off switch for transcription. ACS Chem. Biol. 12, 2417–2426 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Fleming, A. M. & Burrows, C. J. Interplay of guanine oxidation and G-quadruplex folding in gene promoters. J. Am. Chem. Soc. 142, 1115–1136 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Liu, Y. et al. Multi-omic measurements of heterogeneity in HeLa cells across laboratories. Nat. Biotechnol. 37, 314–322 (2019).

    Article  CAS  PubMed  Google Scholar 

  59. Persad, S. et al. SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data. Nat. Biotechnol. 41, 1746–1757 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Yao, Z. et al. A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex. Nature 598, 103–110 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 e3529 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Li, P. W., Li, J., Timmerman, S. L., Krushel, L. A. & Martin, S. L. The dicistronic RNA from the mouse LINE-1 retrotransposon contains an internal ribosome entry site upstream of each ORF: implications for retrotransposition. Nucleic Acids Res. 34, 853–864 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Zhang, Y. et al. Single-cell epigenome analysis reveals age-associated decay of heterochromatin domains in excitatory neurons in the mouse brain. Cell Res. 32, 1008–1021 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014 e1022 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Zhang, K., Zemke, N. R., Armand, E. J. & Ren, B. A fast, scalable and versatile tool for analysis of single-cell omics data. Nat. Methods 21, 217–227 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Li, Y. E. et al. An atlas of gene regulatory elements in adult mouse cerebrum. Nature 598, 129–136 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Heins, N. et al. Glial cells generate neurons: the role of the transcription factor Pax6. Nat. Neurosci. 5, 308–315 (2002).

    Article  CAS  PubMed  Google Scholar 

  69. Lee, B. T. et al. The UCSC Genome Browser database: 2022 update. Nucleic Acids Res. 50, D1115–D1122 (2022).

    Article  CAS  PubMed  Google Scholar 

  70. Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Szebeni, A. et al. Elevated DNA oxidation and DNA repair enzyme expression in brain white matter in major depressive disorder. Int. J. Neuropsychopharmacol. 20, 363–373 (2017).

    CAS  PubMed  Google Scholar 

  72. Chou, V. et al. INPP5D regulates inflammasome activation in human microglia. Nat. Commun. 14, 7552 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Bellenguez, C. et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat. Genet. 54, 412–436 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Mingard, C., Wu, J., McKeague, M. & Sturla, S. J. Next-generation DNA damage sequencing. Chem. Soc. Rev. 49, 7354–7377 (2020).

    Article  CAS  PubMed  Google Scholar 

  75. Amente, S. et al. Genome-wide mapping of genomic DNA damage: methods and implications. Cell. Mol. Life Sci. 78, 6745–6762 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Zhu, Q., Niu, Y., Gundry, M. & Zong, C. Single-cell damagenome profiling unveils vulnerable genes and functional pathways in human genome toward DNA damage. Sci. Adv. https://doi.org/10.1126/sciadv.abf3329 (2021).

  77. Dileep, V. et al. Neuronal DNA double-strand breaks lead to genome structural variations and 3D genome disruption in neurodegeneration. Cell 186, 4404–4421 e4420 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Xiong, X. et al. Epigenomic dissection of Alzheimer’s disease pinpoints causal variants and reveals epigenome erosion. Cell 186, 4422–4437 e4421 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat. Biotechnol. 42, 293–304 (2024).

    Article  CAS  PubMed  Google Scholar 

  80. Bai, D. et al. Simultaneous single-cell analysis of 5mC and 5hmC with SIMPLE-seq. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02148-9 (2024).

    Article  PubMed  Google Scholar 

  81. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Krueger, F. Trim galore: v0.6.10 - add default decompression path. Zenodo https://doi.org/10.5281/zenodo.5127898 (2023).

  83. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  84. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Ramirez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Leland McInnes, J. H., Saul, N. & Großberger, L. UMAP: uniform nanifold approximation and projection. J. Open Source Softw. 3, 861 (2018).

    Article  Google Scholar 

  89. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Howard, D. M. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat. Neurosci. 22, 343–352 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Duncan, L. E. et al. Largest GWAS of PTSD (N = 20 070) yields genetic overlap with schizophrenia and sex differences in heritability. Mol. Psychiatry 23, 666–673 (2018).

    Article  CAS  PubMed  Google Scholar 

  92. Luciano, M. et al. Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism. Nat. Genet. 50, 6–11 (2018).

    Article  CAS  PubMed  Google Scholar 

  93. Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429 e1419 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. van Rheenen, W. et al. Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis. Nat. Genet. 48, 1043–1048 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Watson, H. J. et al. Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa. Nat. Genet. 51, 1207–1214 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Fernandez de la Cruz, L. et al. Suicide in obsessive-compulsive disorder: a population-based study of 36 788 Swedish patients. Mol. Psychiatry 22, 1626–1632 (2017).

    Article  CAS  PubMed  Google Scholar 

  99. Paternoster, L. et al. Multi-ancestry genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis. Nat. Genet. 47, 1449–1456 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 551, 92–94 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Malik, R. et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat. Genet. 50, 524–537 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank QB3 MacroLab for the Tn5 and protein A-Tn5 enzymes. We thank A. Nussenzweig, Y. Zhao, Q. Gan and Z. Ying for thoughtful discussions related to this work. C.Z. is supported by Weill Cornell Medicine and New York Genome Center startup funds, National Institutes of Health (NIH)/National Institute of General Medical Sciences (grant no. DP2GM154011), NIH/National Human Genome Research Institute (grant nos. R00HG011483 and RM1HG011014) and the MacMillan Center for the Study of the Noncoding Cancer Genome at the New York Genome Center.

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Author notes

  1. These authors contributed equally: Dongsheng Bai, Zhenkun Cao.

Authors and Affiliations

  1. New York Genome Center, New York, NY, USA

    Dongsheng Bai, Nivedita Attada & Chenxu Zhu

  2. Physiology, Biophysics and Systems Biology Graduate Program, Weill Cornell Medicine, New York, NY, USA

    Zhenkun Cao

  3. Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA

    Zhenkun Cao & Chenxu Zhu

  4. Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Zhenkun Cao & Jinghui Song

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  1. Dongsheng Bai
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  2. Zhenkun Cao
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  3. Nivedita Attada
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  4. Jinghui Song
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  5. Chenxu Zhu
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Contributions

D.B., Z.C. and C.Z. conceived the study. D.B. developed the Paired-Damage-seq protocol and generated the data with the help from N.A. D.B., Z.C. and C.Z. analyzed the data. D.B., J.S. and C.Z. wrote the manuscript and discussed it with all authors. C.Z. supervised the study.

Corresponding author

Correspondence to Chenxu Zhu.

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Competing interests

C.Z. and D.B. are listed as inventors of a provisional patent application related to the methods developed in this study. The remaining authors declare no competing interests.

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Nature Methods thanks Andrew Adey and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Lei Tang, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 Validation of Paired-Damage-seq.

a, Dotplots showing the relative enrichments of model DNA sequences treated with different buffer conditions. Nickases Nt.AlwI and Nt.BstNBI were used to generate SSBs as positive control; technical replicates n = 3 for all conditions. b, Line plots showing the normalized DNA signal enrichments of ATAC-seq, non-targeting tagmentation control and Paired-Damage-seq DNA signal on DHSs (DNase I hypersensitive sites) of HeLa cells. c, Barplots showing the relative DNA damage levels (normalized by spike-in mouse 3T3 cells) in HeLa cells labeled with different enzyme combinations; technical replicates n = 3 for all combinations. d, Scatter plot showing the correlation between detected DNA damage reads densities and the numbers of Nt.BbvCI cutting sites per 10k-bp non-overlapping bins for control nuclei. e, Scatter plots showing the fraction of RNA reads mapped to human and mouse reference genome for each cell barcode in the species-mixing experiment. Barcodes with less than 75% reads from the same species were identified as mixed cells. f, Scatter plots showing the Pearson’s correlation coefficient of Paired-Damage-seq RNA dataset with in-house generated nucleus RNA-seq from HeLa cells. g, Scatter plots showing the Spearman’s correlation coefficients of pair-wise correlations between bulk and aggregated single-cell Paired-Damage-seq DNA dataset, AP-seq (AP-sites) and CLAPS-seq (8-Oxoguanine) datasets from HeLa cells. ATAC-seq and non-targeting tagmentation control are also shown for comparisons.

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Extended Data Fig. 2 Distribution of oxidative DNA damage hotspots.

a, Heatmaps showing the reads densities on DNA damage hotspots from cells with treatment of varying concentrations of H2O2. b, Experiment design of H2O2 treatment on HeLa cells. c, Uniform manifold approximation and projection (UMAP) embedding showing single cells based on Paired-Damage-seq DNA profiles. Each dot represents an individual nucleus profiled by Paired-Damage-seq and is colored according to the treatment conditions. d, Weighted gene co-expression network analysis (hdWGCNA) dendrograms for the co-expression networks constructed. e, Module eigengene as a function of pseudotime for the representative co-expression module M1 (with decreased expression levels) and M3 (with increased expression levels). The solid lines represent LOESS (locally estimated scatterplot smoothing) regression fits, with the shaded areas indicating the 95% confidence intervals. f, The enriched GO Terms for co-expression module M1 and M3. g, Violin plots showing the average detected signal levels in compartments A and B (RPKM, in 250-kb non-overlapping bins) for DNA damage, non-targeting tagmentation control and ATAC-seq signals of HeLa cells; for all box plots, hinges were drawn from the 25th to 75th percentiles, with the middle line denoting the median, whiskers denoting a maximum 2× the interquartile range and outliers indicated with dots; n = 5,495 (Compartment A) and 4,593 (Compartment B). h, Line plots showing the DNA damage signals around genic regions of genes with different expression levels. i, Barplots showing the numbers of DNA damage peaks in control and H2O2 treated HeLa cells. j, Barplots showing the relative enrichments of DNA damage peaks of control and H2O2 treated HeLa cells in different genomic regions. k, Upset plots showing the intersection size of damage peaks in untreated and H2O2 treated HeLa cells. The non-targeting tagmentation control is also shown for comparison.

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Extended Data Fig. 3 Accumulation of DNA damage induced by oxidative stress.

a, Relative enrichment of DNA damage peaks in control and 48-hr post H2O2 treatment HeLa cells in different short tandem repeat (STR) subfamilies. b, Barplots showing the relative enrichment of conserved and induced DNA damage peaks in different genomic regions. c, Line plots showing the DNA damage levels (RPKM) on conserved and induced peaks in HeLa cells of different treatments. d, Line plots showing the DNA damage levels (RPKM) on simple repeats, Z-form DNA and putative G-quadruplex sequences in control and 48-hr post H2O2 treatment HeLa cells. e, Line plots showing DNA damage signals, ATAC-seq signals and RNA-seq signals around endogenous retroviruses (ERV) long terminal repeats (LTR) regions in compartment A and compartment B of control and 48-hr post H2O2 treatment HeLa cells.

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Extended Data Fig. 4 Relationships between DNA damage levels and epigenome signature changes.

a and b, Scatter plots showing the relationships between changes in Paired-Damage-seq DNA levels and changes in ATAC-seq signals (RPKM, in 250-kb non-overlapping bins) in (a) 0-hr post H2O2 treatment, and (b) 6-hr post H2O2 treatment HeLa cells compared to control group. c and d, Scatter plots showing the correlation of changes in Paired-Damage-seq DNA levels and changes in H3K9me3 CUT&Tag signal (RPKM, in 250-kb non-overlapping bins) in (c) 0-hr post H2O2 treatment, and (d) 6-hr post H2O2 treatment HeLa cells compared to control group. Pearson correlation coefficients are also shown.

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Extended Data Fig. 5 Clustering of mouse cerebral cortex cells based on Paired-Damage-seq RNA profile.

a, Dot plots showing the expression of marker genes for each mouse brain cell type measured from Paired-Damage-seq RNA profiles. The size of the dots represents the fraction of cells positively detect the transcripts and the color of the dots represents the average levels. b, UMAP co-embedding of single nuclei transcriptomic profile from Paired-Damage-seq and reference snRNA-seq datasets on mouse motor cortex regions. c, Heatmap showing the overlap coefficients between cell type annotations based on Paired-Damage-seq RNA profiles and the previously published snRNA-seq dataset. d, Line plots showing the normalized DNA signal enrichments of ATAC-seq, non-targeting tagmentation control and Paired-Damage-seq DNA signals on ATAC-seq peak regions of mouse brain.

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Extended Data Fig. 6 Distribution of DNA damage signals on coding genes, LINE1 and ERV elements.

a, Line plots showing the DNA damage levels around genic regions of genes with different expression levels in each brain cell type, respectively. b, Violin plots showing the average detected signal levels in compartments A and B (RPKM, in 250-kb non-overlapping bins) for non-targeting tagmentation control and ATAC-seq of mouse brain; for all box plots, hinges were drawn from the 25th to 75th percentiles, with the middle line denoting the median, whiskers denoting a maximum 2× the interquartile range and outliers indicated with dots; n = 4,089 (compartment A), n = 5,095 (compartment B) elements. c, Line plots showing the DNA damage levels around long interspersed nuclear elements-1 (LINE1) and endogenous retroviruses (ERV) elements in compartment A and compartment B, respectively, for different brain cell types.

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Extended Data Fig. 7 Distribution of DNA damage signal on enhancers in the mouse brain.

a and b, Scatter plots showing the relationships between DNA damage levels and (a) H3K27ac levels (RPKM, in 250-kb non-overlapping bins) in compartment A, and (b) H3K9me3 levels (RPKM, in 250-kb non-overlapping bins) in compartment B for inhibitory neurons, oligodendrocytes and microglia cells. Pearson correlation coefficients are also shown. c, Heatmap showing the DNA damage signals over public H3K27ac peaks in different mouse brain cell types. d, Venn plots showing the overlaps between public ATAC-seq peaks (with and without H3K27ac peaks) and DNA damage peaks in different mouse brain cell types; P value, two-sided Fisher’s exact test.

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Extended Data Fig. 8 Relationships between DNA damage and epigenome erosion.

a, Barplots showing the numbers of cell-type specific DNA damage peaks that could be mapped to hg38. The mapped peaks (reproducible, <1 kb) were used for the GWAS trait enrichment analysis. b, Genome browser view of Tpcn1 locus in microglia cells. DNA damage peaks overlapped with ATAC-seq peaks decreased in aged mice are highlighted in light blue. Signals from inhibitory neurons are also shown as a control. c and d, Scatter plots showing the correlation of DNA damage levels (RPKM, in 100-kb non-overlapping bins) with changes in H3K9me3 levels (RPKM, in 100-kb non-overlapping bins) between 18-month and 3-month for (c) inhibitory neuron and (d) oligodendrocyte precursor cells. Only the genomic bins with the top 1% highest damage levels were shown. Genomic bins with ΔH3K9me3 < −0.2 are shown in red, > 0.2 are shown in blue. Pearson correlation coefficients are indicated.

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Bai, D., Cao, Z., Attada, N. et al. Single-cell parallel analysis of DNA damage and transcriptome reveals selective genome vulnerability. Nat Methods 22, 962–972 (2025). https://doi.org/10.1038/s41592-025-02632-3

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