Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Somatic genomic changes in single Alzheimer’s disease neurons

Abstract

Dementia in Alzheimer’s disease progresses alongside neurodegeneration1,2,3,4, but the specific events that cause neuronal dysfunction and death remain poorly understood. During normal ageing, neurons progressively accumulate somatic mutations5 at rates similar to those of dividing cells6,7 which suggests that genetic factors, environmental exposures or disease states might influence this accumulation5. Here we analysed single-cell whole-genome sequencing data from 319 neurons from the prefrontal cortex and hippocampus of individuals with Alzheimer’s disease and neurotypical control individuals. We found that somatic DNA alterations increase in individuals with Alzheimer’s disease, with distinct molecular patterns. Normal neurons accumulate mutations primarily in an age-related pattern (signature A), which closely resembles ‘clock-like’ mutational signatures that have been previously described in healthy and cancerous cells6,7,8,9,10. In neurons affected by Alzheimer’s disease, additional DNA alterations are driven by distinct processes (signature C) that highlight C>A and other specific nucleotide changes. These changes potentially implicate nucleotide oxidation4,11, which we show is increased in Alzheimer’s-disease-affected neurons in situ. Expressed genes exhibit signature-specific damage, and mutations show a transcriptional strand bias, which suggests that transcription-coupled nucleotide excision repair has a role in the generation of mutations. The alterations in Alzheimer’s disease affect coding exons and are predicted to create dysfunctional genetic knockout cells and proteostatic stress. Our results suggest that known pathogenic mechanisms in Alzheimer’s disease may lead to genomic damage to neurons that can progressively impair function. The aberrant accumulation of DNA alterations in neurodegeneration provides insight into the cascade of molecular and cellular events that occurs in the development of Alzheimer’s disease.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Somatic mutations in single neurons in control individuals and individuals with AD.
Fig. 2: Somatic mutational signatures and patterns in AD neurons by MDA.
Fig. 3: Profile of somatic mutations in single AD neurons by PTA.
Fig. 4: Model of the role of somatic mutations in AD pathogenesis.

Data availability

scWGS data have been deposited in the NIH Alzheimer’s disease genomic data repository, NIAGADS, under accession number NG00121. The data are available under controlled-use conditions established by the tissue banks and institutional review boards (see Methods), and can be obtained by qualified investigators at https://www.niagads.org/. Gene transcripts per million (TPM) data (V8) of GTEx samples were downloaded from https://www.gtexportal.org/home/datasetsSource data are provided with this paper.

Code availability

Custom Bash and R scripts used in this study are publicly available at https://gitlab.aleelab.net/august/ad-single-cell.git.

References

  1. Selkoe, D. J. & Hardy, J. The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol. Med. 8, 595–608 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. Hyman, B. T. et al. National Institute on Aging–Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease. Alzheimers Dement. 8, 1–13 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  3. Braak, H. & Braak, E. Staging of Alzheimer’s disease-related neurofibrillary changes. Neurobiol. Aging 16, 271–278 (1995).

    CAS  PubMed  Article  Google Scholar 

  4. Gabbita, S. P., Lovell, M. A. & Markesbery, W. R. Increased nuclear DNA oxidation in the brain in Alzheimer’s disease. J. Neurochem. 71, 2034–2040 (1998).

    CAS  PubMed  Article  Google Scholar 

  5. Lodato, M. A. et al. Aging and neurodegeneration are associated with increased mutations in single human neurons. Science 359, 555–559 (2018).

    ADS  CAS  PubMed  Article  Google Scholar 

  6. Blokzijl, F. et al. Tissue-specific mutation accumulation in human adult stem cells during life. Nature 538, 260–264 (2016).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. Osorio, F. G. et al. Somatic mutations reveal lineage relationships and age-related mutagenesis in human hematopoiesis. Cell Rep. 25, 2308–2316 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. Alexandrov, L. B. et al. Clock-like mutational processes in human somatic cells. Nat. Genet. 47, 1402–1407 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  11. Lu, T. et al. REST and stress resistance in ageing and Alzheimer’s disease. Nature 507, 448–454 (2014).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. Genovese, G. et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med. 371, 2477–2487 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  13. Martincorena, I. et al. Tumor evolution. High burden and pervasive positive selection of somatic mutations in normal human skin. Science 348, 880–886 (2015).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Martincorena, I. et al. Somatic mutant clones colonize the human esophagus with age. Science 362, 911–917 (2018).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. Lodato, M. A. et al. Somatic mutation in single human neurons tracks developmental and transcriptional history. Science 350, 94–98 (2015).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. Hazen, J. L. et al. The complete genome sequences, unique mutational spectra, and developmental potency of adult neurons revealed by cloning. Neuron 89, 1223–1236 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. Bhagwat, A. S. et al. Strand-biased cytosine deamination at the replication fork causes cytosine to thymine mutations in Escherichia coli. Proc. Natl Acad. Sci. USA 113, 2176–2181 (2016).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. Kucab, J. E. et al. A compendium of mutational signatures of environmental agents. Cell 177, 821–836 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. Sala Frigerio, C. et al. On the identification of low allele frequency mosaic mutations in the brains of Alzheimer’s disease patients. Alzheimers Dement. 11, 1265–1276 (2015).

    PubMed  Article  Google Scholar 

  20. Abascal, F. et al. Somatic mutation landscapes at single-molecule resolution. Nature 593, 405–410 (2021).

    ADS  CAS  PubMed  Article  Google Scholar 

  21. Fu, H. et al. A tau homeostasis signature is linked with the cellular and regional vulnerability of excitatory neurons to tau pathology. Nat. Neurosci. 22, 47–56 (2019).

    CAS  PubMed  Article  Google Scholar 

  22. Leng, K. et al. Molecular characterization of selectively vulnerable neurons in Alzheimer’s disease. Nat. Neurosci. 24, 276–287 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. Bohrson, C. L. et al. Linked-read analysis identifies mutations in single-cell DNA-sequencing data. Nat. Genet. 51, 749–754 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. Petljak, M. et al. Characterizing mutational signatures in human cancer cell lines reveals episodic APOBEC mutagenesis. Cell 176, 1282–1294 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. Xing, D., Tan, L., Chang, C.-H., Li, H. & Xie, X. S. Accurate SNV detection in single cells by transposon-based whole-genome amplification of complementary strands. Proc. Natl Acad. Sci. USA 118, e2013106118 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Madabhushi, R. et al. Activity-induced DNA breaks govern the expression of neuronal early-response genes. Cell 161, 1592–1605 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. Min, S. et al. Absence of coding somatic single nucleotide variants within well-known candidate genes in late-onset sporadic Alzheimer’s disease based on the analysis of multi-omics data. Neurobiol. Aging 108, 207–209 (2021).

    CAS  PubMed  Article  Google Scholar 

  28. Lee, M. H. et al. Somatic APP gene recombination in Alzheimer’s disease and normal neurons. Nature 563, 639–645 (2018).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. Kim, J. et al. APP gene copy number changes reflect exogenous contamination. Nature 584, E20–E28 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. Jager, M. et al. Deficiency of nucleotide excision repair is associated with mutational signature observed in cancer. Genome Res. 29, 1067–1077 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. Mecocci, P., MacGarvey, U. & Beal, M. F. Oxidative damage to mitochondrial DNA is increased in Alzheimer’s disease. Ann. Neurol. 36, 747–751 (1994).

    CAS  PubMed  Article  Google Scholar 

  32. Chun, H. et al. Severe reactive astrocytes precipitate pathological hallmarks of Alzheimer’s disease via H2O2 production. Nat. Neurosci. 23, 1555–1566 (2020).

    CAS  PubMed  Article  Google Scholar 

  33. Pao, P. C. et al. HDAC1 modulates OGG1-initiated oxidative DNA damage repair in the aging brain and Alzheimer’s disease. Nat. Commun. 11, 2484 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. Nouspikel, T. & Hanawalt, P. C. Terminally differentiated human neurons repair transcribed genes but display attenuated global DNA repair and modulation of repair gene expression. Mol. Cell. Biol. 20, 1562–1570 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. Seplyarskiy, V. B. et al. Error-prone bypass of DNA lesions during lagging-strand replication is a common source of germline and cancer mutations. Nat. Genet. 51, 36–41 (2019).

    CAS  PubMed  Article  Google Scholar 

  36. Huang, J. C., Svoboda, D. L., Reardon, J. T. & Sancar, A. Human nucleotide excision nuclease removes thymine dimers from DNA by incising the 22nd phosphodiester bond 5′ and the 6th phosphodiester bond 3′ to the photodimer. Proc. Natl Acad. Sci. USA 89, 3664–3668 (1992).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Gate, D. et al. Clonally expanded CD8 T cells patrol the cerebrospinal fluid in Alzheimer’s disease. Nature 577, 399–404 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. Soheili-Nezhad, S., van der Linden, R. J., Olde Rikkert, M., Sprooten, E. & Poelmans, G. Long genes are more frequently affected by somatic mutations and show reduced expression in Alzheimer’s disease: Implications for disease etiology. Alzheimers Dement. 17, 489–499 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  39. Crabtree, G. R. Our fragile intellect. Part I. Trends Genet. 29, 1–3 (2013).

    CAS  PubMed  Article  Google Scholar 

  40. Fragola, G. et al. Deletion of topoisomerase 1 in excitatory neurons causes genomic instability and early onset neurodegeneration. Nat. Commun. 11, 1962 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Gonzalez-Pena, V. et al. Accurate genomic variant detection in single cells with primary template-directed amplification. Proc. Natl Acad. Sci. USA 118, e2024176118 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. Luquette, L. J. et al. Ultraspecific somatic SNV and indel detection in single neurons using primary template-directed amplification. Preprint at bioRxiv https://doi.org/10.1101/2021.04.30.442032 (2021).

  43. Kaur, U. et al. Reactive oxygen species, redox signaling and neuroinflammation in Alzheimer’s disease: the NF-κB connection. Curr. Top. Med. Chem. 15, 446–457 (2015).

    CAS  PubMed  Article  Google Scholar 

  44. Butterfield, D. A., Castegna, A., Lauderback, C. M. & Drake, J. Evidence that amyloid beta-peptide-induced lipid peroxidation and its sequelae in Alzheimer’s disease brain contribute to neuronal death. Neurobiol. Aging 23, 655–664 (2002).

    PubMed  Article  Google Scholar 

  45. David, D. C. et al. Proteomic and functional analyses reveal a mitochondrial dysfunction in P301L tau transgenic mice. J. Biol. Chem. 280, 23802–23814 (2005).

    CAS  PubMed  Article  Google Scholar 

  46. Khurana, V. et al. A neuroprotective role for the DNA damage checkpoint in tauopathy. Aging Cell 11, 360–362 (2012).

    CAS  PubMed  Article  Google Scholar 

  47. Sakofsky, C. J. et al. Repair of multiple simultaneous double-strand breaks causes bursts of genome-wide clustered hypermutation. PLoS Biol. 17, e3000464 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. Mandrekar-Colucci, S. & Landreth, G. E. Microglia and inflammation in Alzheimer’s disease. CNS Neurol. Disord. Drug Targets 9, 156–167 (2010).

    CAS  PubMed  Article  Google Scholar 

  49. Rottkamp, C. A. et al. Redox-active iron mediates amyloid-beta toxicity. Free Radic. Biol. Med. 30, 447–450 (2001).

    CAS  PubMed  Article  Google Scholar 

  50. Huang, A. Y. et al. Parallel RNA and DNA analysis after deep sequencing (PRDD-seq) reveals cell type-specific lineage patterns in human brain. Proc. Natl Acad. Sci. USA 117, 13886–13895 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. Dean, F. B., Nelson, J. R., Giesler, T. L. & Lasken, R. S. Rapid amplification of plasmid and phage DNA using Phi 29 DNA polymerase and multiply-primed rolling circle amplification. Genome Res. 11, 1095–1099 (2001).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. Evrony, G. D. et al. Single-neuron sequencing analysis of L1 retrotransposition and somatic mutation in the human brain. Cell 151, 483–496 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. Fan, J. et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat. Methods 13, 241–244 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. Dong, X. et al. Accurate identification of single-nucleotide variants in whole-genome-amplified single cells. Nat. Methods 14, 491–493 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. Keogh, M. J. et al. High prevalence of focal and multi-focal somatic genetic variants in the human brain. Nat. Commun. 9, 4257 (2018).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  59. Park, J. S. et al. Brain somatic mutations observed in Alzheimer’s disease associated with aging and dysregulation of tau phosphorylation. Nat. Commun. 10, 3090 (2019).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  60. Luquette, L. J., Bohrson, C. L., Sherman, M. A. & Park, P. J. Identification of somatic mutations in single cell DNA-seq using a spatial model of allelic imbalance. Nat. Commun. 10, 3908 (2019).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  61. Cai, X. et al. Single-cell, genome-wide sequencing identifies clonal somatic copy-number variation in the human brain. Cell Rep. 8, 1280–1289 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. Baslan, T. et al. Genome-wide copy number analysis of single cells. Nat. Protoc. 7, 1024–1041 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. Alexandrov, L. B., Nik-Zainal, S., Wedge, D. C., Campbell, P. J. & Stratton, M. R. Deciphering signatures of mutational processes operative in human cancer. Cell Rep. 3, 246–259 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  64. Blokzijl, F., Janssen, R., van Boxtel, R. & Cuppen, E. MutationalPatterns: comprehensive genome-wide analysis of mutational processes. Genome Med. 10, 33 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  65. Kim, J. et al. Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors. Nat. Genet. 48, 600–606 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article  Google Scholar 

  67. Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).

    Article  Google Scholar 

  68. Consortium, G. T. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    Article  Google Scholar 

  69. 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).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  70. Young, M. D., Wakefield, M. J., Smyth, G. K. & Oshlack, A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 11, R14 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  71. Green, P. et al. Transcription-associated mutational asymmetry in mammalian evolution. Nat. Genet. 33, 514–517 (2003).

    CAS  PubMed  Article  Google Scholar 

  72. Polak, P. & Arndt, P. F. Transcription induces strand-specific mutations at the 5′ end of human genes. Genome Res. 18, 1216–1223 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  73. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  74. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  75. Coppede, F. & Migliore, L. DNA damage and repair in Alzheimer’s disease. Curr. Alzheimer Res. 6, 36–47 (2009).

    CAS  PubMed  Article  Google Scholar 

  76. Hoang, M. L. et al. Genome-wide quantification of rare somatic mutations in normal human tissues using massively parallel sequencing. Proc. Natl Acad. Sci. USA 113, 9846–9851 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  77. Franco, I. et al. Somatic mutagenesis in satellite cells associates with human skeletal muscle aging. Nat. Commun. 9, 800 (2018).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  78. Zhang, L. et al. Single-cell whole-genome sequencing reveals the functional landscape of somatic mutations in B lymphocytes across the human lifespan. Proc. Natl Acad. Sci. USA 116, 9014–9019 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  79. Lee-Six, H. et al. The landscape of somatic mutation in normal colorectal epithelial cells. Nature 574, 532–537 (2019).

    ADS  CAS  PubMed  Article  Google Scholar 

  80. Franco, I. et al. Whole genome DNA sequencing provides an atlas of somatic mutagenesis in healthy human cells and identifies a tumor-prone cell type. Genome Biol. 20, 285 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  81. Brunet, J. P., Tamayo, P., Golub, T. R. & Mesirov, J. P. Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl Acad. Sci. USA 101, 4164–4169 (2004).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

We thank R. Mathieu and L. Cheemalamarri at the Boston Children’s Hospital and Harvard Stem Cell Institute Flow Cytometry Research Facility, R. S. Hill, the Research Computing group at Harvard Medical School and the Boston Children’s Hospital Intellectual and Developmental Disabilities Research Center (IDDRC) Molecular Genetics Core for assistance. We thank C. L. Bohrson for mutational signature discussions. The brain and nuclei in Fig. 1 were illustrated by A. Lai with input from the authors, and Fig. 4 was illustrated by K. Probst (Xavier Studio) with input from the authors. Human tissue was obtained from the Massachusetts Alzheimer’s Disease Research Center (1P30AG062421-01) and the NIH Neurobiobank at the University of Maryland, and we thank the donors and families for their contributions, and J. Gonzalez and P. Dooley for assistance with tissue procurement. This work was supported by K08 AG065502 (M.B.M.); T32 HL007627 (M.B.M.); the Brigham and Women’s Hospital Program for Interdisciplinary Neuroscience through a gift from L. and T. Rand (M.B.M.); the donors of the Alzheimer’s Disease Research program of the BrightFocus Foundation A20201292F (M.B.M.); the Doris Duke Charitable Foundation Clinical Scientist Development Award 2021183 (M.B.M.); T32 GM007753 (E.A.M.); T15 LM007098 (E.A.M.); R00 AG054748 (M.A.L.); K01 AG051791 (E.A.L.); the Suh Kyungbae Foundation (E.A.L.), DP2 AG072437 (E.A.L.); R01 NS032457-20S1 (C.A.W.); R01 AG070921 (C.A.W. and E.A.L.); the F-Prime Foundation (C.A.W.); and the Allen Discovery Center program, a Paul G. Allen Frontiers Group advised program of the Paul G. Allen Family Foundation (C.A.W. and E.A.L.). C.A.W. is an Investigator of the Howard Hughes Medical Institute.

Author information

Authors and Affiliations

Authors

Contributions

E.A.L., M.A.L., M.B.M. and C.A.W. conceived and designed the study. M.B.M., M.A.L., Z.Z. and S.L.K. performed single-neuron sorting and sequencing. A.Y.H. performed bioinformatic analysis with assistance from J.K. and E.A.M. L.R., S.C.R., S.L.K. and C.C.M. performed quality control experiments. B.T.H., M.P.F., D.H.O., M.B.M. and H.M.A. provided clinico-pathological analysis and selection of disease cases. J.S.Z. optimized and performed immunofluorescent imaging and quantification, and generated data shown in this manuscript. H.C.R. independently performed exploratory immunofluorescent staining. L.J.L. provided expertise in variant analysis and SCAN-SNV calling. J.E.N. contributed tissue procurement and ethics expertise. E.A.L., C.A.W. and M.A.L. supervised the study. M.B.M., A.Y.H., M.A.L., C.A.W. and E.A.L. wrote the manuscript.

Corresponding authors

Correspondence to Michael A. Lodato, Eunjung Alice Lee or Christopher A. Walsh.

Ethics declarations

Competing interests

C.A.W. is a paid consultant (cash, no equity) to Third Rock Ventures and Flagship Pioneering (cash, no equity) and is on the Clinical Advisory Board (cash and equity) of Maze Therapeutics. No research support is received. These companies did not fund and had no role in the conception or performance of this research project. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature thanks Young Seok Ju and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Filtering of LiRA-called sSNVs to minimize single-cell artefacts from MDA amplification.

a, Total pre-filtering LiRA-called sSNV per genome for control and AD single neurons. Single neuronal nuclei from prefrontal cortex (PFC) and hippocampal CA1 (HC) underwent scWGS (45X targeted average coverage). Genome-wide counts of sSNV were determined using linked-read analysis (LiRA). Per genome sSNV counts for all control and AD neurons are shown here, prior to signature-based filtering. b, Total pre-filtering LiRA-called sSNV per genome plotted against raw LiRA-called sSNVs, an intermediate metric in the LiRA calling pipeline prior to power ratio adjustment for genome coverage and false positive rate. c, Single neuron sSNV counts in relation to coverage evenness of genome sequencing. Total pre-filtering LiRA-called sSNV counts from single neuronal nuclei are shown in relation to median absolute pairwise difference (MAPD) scores for the coverage evenness of each cell. At very high MAPD scores (>2.0), sSNV counts increase with MAPD, raising concern for artefactual sSNV calls in these cells owing to uneven genome coverage. d, e, Using NMF mutational signature analysis, the sSNV contribution was determined for two signatures potentially representing single-cell amplification artefacts: SBS scE and SBS scF24. For signature, the mutation type frequency for each trinucleotide context is shown above the sSNV plot. SBS scF is composed of C>T changes, while SBS scE is characterized by a particular subset of C>T, GC>GT. Signature SBS scE showed elevation in cells with MAPD >2.0. Signature SBS scF shows a relationship between uneven amplification (high MAPD) and SBS scF, perhaps owing to allele dropout causing single strand lesions to be read as somatic mutations. A subset of AD neurons showed LiRA-called pre-filtering sSNV counts >20,000/neuron and substantial component of potential artefact signature SBS scE. These neurons may represent an agonal ‘ultramutated’ state, but were not included in subsequent analyses owing to the abundance of potential artefact signature SBS scE (see g). f, Schematic for potential generation of artefactual sSNV in scWGS owing to uneven coverage. The scWGS LiRA platform calls sSNVs that are linked by sequencing reads to heterozygous germline single nucleotide polymorphisms (SNPs) (left). A single-stranded lesion of DNA damage, such as oxidation or alkylation, is paired with an unmodified base on the opposite genomic strand, such that LiRA would not call a sSNV under conditions of sufficiently even sequencing coverage (middle). However, if severe non-uniformity in strand-specific amplification (strand dropout) occurred, the single-stranded DNA lesion (or a polymerase error on one strand) could be erroneously called as an sSNV (right). For this reason, severely uneven single-cell genome amplification could produce artefactual LiRA sSNV calls. g, Analysis pipeline for minimization of potential artefacts of single-cell genome amplification and sequencing. Using our observations and advances reported in Petljak et al.24, we developed a computational pipeline to generate a set of higher-confidence filtered sSNV calls. This pipeline uses SNP-phased SNVs called by linked-read analysis (LiRA), and applies 3 additional specific steps to the initial variant call set: 1) Removal of single neurons which display widely uneven genome amplification, as indicated by MAPD score >2.0, above which the number of sSNVs increases (see c), raising concern for false positive variant calls due to uneven genome coverage; 2) Removal of single neurons whose mutational profile is dominated by the potential artefact mutational signature SBS scE (see d); and 3) Removal from each neuron the contribution of variants from the potentially artefactual signatures SBS scE and SBS scF. These steps produce counts of higher-confidence filtered sSNVs from single neurons. Although mutational signatures SBS scE and SBS scF have been previously reported as a potential artefact of single-cell genome amplification, the signal does potentially carry biological information. However, in this study we exclude these variants so as to minimize the influence of potential artefactual sSNV calls, to focus our analysis on the higher-confidence filtered sSNVs.

Source data

Extended Data Fig. 2 Single-cell variant calling identifies high-confidence sSNVs.

To assess the quality of the sSNVs identified from single-cell MDA-amplified WGS data, we compared their variant allele fractions in control and AD neurons to those of phaseable high-confidence heterozygous germline SNVs from the same neurons, shown for each base change type. The distributions between somatic and germline SNVs are comparable, indicating the validity of the somatic mutation calling method, as has been previously reported for the LiRA calling method5,23.

Source data

Extended Data Fig. 3 sSNVs in neurotypical control and AD neurons, normalized by evenness of genome amplification or LiRA caller power ratio.

To assess the sSNV, as determined by the variant calling approach used in this study, we plotted sSNV counts from MDA-amplified single neurons against age, including using sSNV counts that were normalized for two distinct measures of evenness of genome coverage, median absolute pairwise difference (MAPD) and coefficient of variation (CoV). We also normalized by the power ratio used in LiRA phasing-based sSNV detection (see Methods). ad, sSNVs per genome for neurotypical control neurons, with mixed-effects modelling trend lines for ageing. We observed a significant age-dependent increase of sSNV burden in each analysis, with the slope for human pyramidal neurons ranging from 16.4 sSNV/yr to 21.1 sSNV/yr, depending on the method of adjustment for genome coverage evenness. For analysis of PFC region cells alone, we observed a similar range of slopes by this analysis: 16.8 sSNV/yr to 21.3 sSNV/yr. eh, sSNVs in AD compared to neurotypical control neurons. Unadjusted for evenness (e, reproduced from Fig. 1h, AD neurons show a mean of 2672 (range 783-8990) sSNVs, an excess of 971 over controls (P = 6.5 × 10−5, linear mixed model). f, Normalized for MAPD, AD neurons show a mean of 1582 (range 33-8366) sSNVs, an excess of 480 over controls (P = 0.01, linear mixed model). g, Normalized for CoV, AD neurons show a mean of 2264 (range 68-8861) sSNVs, an excess of 831 over controls (P = 6.7 × 10−5, linear mixed model). h, Normalized for power ratio, AD neurons show a mean of 2015 (range 162-7892) sSNVs, an excess of 511 over controls (P = 7.2 × 10−3, linear mixed model). In each analysis, AD neurons showed a significantly greater number of sSNV compared to control neurons. Although some normalizations may result in reduced detection of biological differences in AD specimens, we observed that sSNV differences are retained even after normalization, supporting a sSNV difference between AD and control neurons.

Source data

Extended Data Fig. 4 Distribution of sSNVs in relation to gene position comparing AD and age-matched control neurons.

a, sSNVs per neuron across different categories of genomic regions, based on position relative to gene structure. b, Proportional distribution of sSNVs in AD and control cases across different categories of genomic regions. Upstream and downstream were defined as <1 kb genomic regions from the transcription start and end sites, respectively. Each proportion is normalized by the expected proportion after controlling for trinucleotide context of phaseable regions. c, Proportional distribution of sSNVs relative to gene transcript length. The proportions for control or AD sSNVs were normalized by the expected proportion after controlling for trinucleotide context of phaseable regions. For each set, mean ± SEM is shown. For b, c, P value is shown for the observation showing statistically significant difference between AD and control (two-tailed t-test). AD neurons show a trend of excess over controls in sSNVs in upstream positions (not surviving Bonferroni correction). Data in this figure were obtained by MDA amplification of single genomes of neurons.

Source data

Extended Data Fig. 5 Somatic mutation trinucleotide context profiles and signature derivation in MDA-amplified single-neuron genomes.

a, Trinucleotide context somatic mutation profiles in AD and control neurons. Mutations called by LiRA are shown by base substitution change (bar colour), separated for each of the 16 possible trinucleotide contexts for each substitution (96 total trinucleotide contexts). For each brain region profiled, the aggregate is shown for AD cases, neurotypical controls, and the difference (residual of cases mutations minus control mutations). b, Signature metrics for de novo mutational signature derivation from neurons in this study. Using the frequency of sSNV mutations in their trinucleotide context for all control and AD neurons, we fitted mutational signatures with a NMF-based framework. We identified four signatures, N1-N4, that maximize the cophenetic of the decomposition81. c, sSNV mutational signatures evaluated in this study. We performed de novo mutational signature generation using NMF (MutationalPatterns and SignatureAnalyzer) on the set of scWGS data from single neurons from AD and neurotypical controls, which each produced 4 highly similar signatures by best fit. Previously published analysis of single neurons (Lodato et al.)5 during ageing produced 3 signatures: A, B, and C. A recently published study of cultured cells (Petljak et al.)24 identified signatures thought to represent artefacts of scWGS, including SBS scE and SBS scF. d, Variation between neurons of mutational signature contributions. We performed linear regression for signature contribution with respect to age and disease status. The residual signature contribution of each neuron for signature A and signature C is shown here, for each disease group. Also shown are the mean (bar) ± standard deviation (boxes), with the range (whisker lines). In addition to the neurotypical control and AD neurons reported in this manuscript, we also performed this analysis on previously reported single human neuron data for two NER-deficiency diseases: Cockayne syndrome (CS) and xeroderma pigmentosum (XP)5. Because only PFC was studied for CS and XP, only the control and AD neurons from PFC were used for this analysis. For each disease group, signature C showed a greater standard deviation than signature A; standard deviation ratios between signatures C and A are as follows: 1.2 (control), 1.2 (AD), 3.2 (CS), and 1.1 (XP). Data were obtained from MDA amplification of single neuron genomes. Boxplots show mean ± SD, with whiskers denoting minima and maxima.

Source data

Extended Data Fig. 6 COSMIC mutational signature contributions to single-neuron signatures and disease-related mutational patterns.

a, The set of trinucleotide contexts in single neuron signatures derived in the prior study (signatures A and C)5, along with single neuron signatures derived de novo from single AD and control neurons (signatures N4 and N2 derived using MutationalPatterns, and signatures W3 and W2 derived using SignatureAnalyzer) were analysed for contributions by COSMIC v3 single base substitution mutational signatures by NMF. The matching prior and de novo signatures show highly similar COSMIC signature contributions. b, The set of mutation trinucleotide contexts present in AD and control neuron genomes amplified by MDA, as well as the matrix of mutations obtained by subtracting control from AD (AD residual), were analysed for contributions by COSMIC signatures. Multiple COSMIC signatures identified here, many of which also contribute to signature C5, are associated with transcription-coupled nucleotide excision repair at particular damaged nucleotides with specific resultant base changes, including: SBS8 (guanine damage, C>A mutations), SBS22 (adenine damage, T>A mutations), SBS12 (adenine damage, T>C mutations), and SBS19 (guanine damage, C>T mutations). Other signatures have been associated with deficiencies of separate DNA repair processes: SBS6 (mismatch repair) and SBS30 (base excision repair). SBS5, associated with ageing, contributes significantly to the control and AD samples, but not to the AD residual mutations.

Source data

Extended Data Fig. 7 Immunofluorescent detection of nucleotide oxidation in neurons.

Immunofluorescence was performed on post-mortem human brain prefrontal cortex. NeuN (AF488) was used to label neurons and 8-oxoG (AF555) used to label oxidized guanine nucleotides. a, For each case sample, in a full microscopic field of up to 100 NeuN+ neurons, 8-oxoG signal was quantified per neuron. Here, each data point represents the 8-oxoG signal from one neuron, with mean and SEM shown in black for each case. Figure 2f shows mean 8-oxoG values of each case in relation to age and disease status. b, Representative microscopy images (turquoise or purple boxes) are shown for neurotypical control and AD samples from a. n = 100 total neurons examined (50 neurons each from two independent staining experiment batches per case). NeuN+ neurons are shown in green and 8-oxoG in greyscale or magenta. Scale bars represent 60 µm.

Source data

Extended Data Fig. 8 Features of somatic mutations in single neurons assessed by PTA.

a, Trinucleotide somatic mutation spectra of cells or bulk samples studied by various methods were compared. For PTA-amplified single neurons, the aggregate of mutations is shown for AD cases, age-matched neurotypical controls, and the residual (net increase of case mutations over control mutations). Mutational spectra from other methods include NanoSeq-studied bulk samples from AD or controls and META-CS single neuron data for double-stranded mutations or single-stranded DNA lesions. Mutations are shown by base substitution change (bar colour). Of note, single-stranded DNA lesions show a distinct profile from mutations detected by PTA, NanoSeq, and META-CS. b, The spectra of mutations detected in PTA-amplified neurons (AD, control, and AD residual) and from other published methods were analysed for contributions by COSMIC cancer signatures. Elements of COSMIC signatures identified in the AD residual mutation set, including SBS8, also contribute to signature C5. Of note, single-stranded DNA lesions show a distinct profile from mutations detected by PTA, NanoSeq, and META-CS. cf, sSNV detected using PTA in AD and neurotypical control neurons, normalized by evenness of genome amplification or LiRA caller power ratio. c, Total sSNVs per genome plotted against age (uncorrected, reproduced here from Fig. 3a for comparison). AD neurons show a mean of 1419 (range 514–2157) sSNVs, an excess of 196 over controls (P = 3.9 × 10−4, linear mixed model). d, MAPD-normalized sSNVs per genome, from which AD neurons show a mean of 1703 (range 814-2748) sSNVs, an excess of 453 over controls (P = 2.7 × 10−6, linear mixed model). e, CoV-normalized sSNVs per genome, from which AD neurons show a mean of 1440 (range 527-2255) sSNVs, an excess of 189 over controls (P = 5.3 × 10−4, linear mixed model). f, Power-normalized sSNVs per genome, from which AD neurons show a mean of 1423 (range 517–2166) sSNVs, an excess of 198 over controls (P = 3.8 × 10−3, linear mixed model). In each analysis, AD neurons showed a significantly greater number of sSNV compared to control neurons.

Source data

Extended Data Table 1 Studies of sSNV rates and signatures during ageing in various human cell types5,6,7,76,77,78,79,80

Supplementary information

Reporting Summary

Peer Review File

Supplementary Table 1

Sample information. The Sample Information tab contains detailed information for 26 individuals in present study. PMI = Post-Mortem Interval; SIDS = Sudden Infant Death Syndrome; MVA = Motor Vehicle Accident; HASCVD = Hypertensive Atherosclerotic Cardiovascular Disease; COPD = Chronic Obstructive Pulmonary Disease, RIN = RNA integrity number. The Library and Sequencing tabs contain information on each cell sequenced, for single-neuron genomes amplified with multiple displacement amplification (MDA) or primary template-directed amplification (PTA).

Supplementary Table 2

Sequencing statistics for WGS datasets. Tabs show the respective sequencing statistics for single-neuron genomes amplified with MDA or PTA.

Supplementary Table 3

sSNV candidates identified in each neuron. CC denotes Composite Coverage, an integer coverage-based quality metric for each putative sSNV22. Linked 1K Genomes SNP refers to the linked germline anchor site used to phase mutation calls. Orientation refers to whether the somatic alternate allele was on the same haplotype as the germline alternate allele (cis) or whether the two alternate alleles were on opposite haplotypes (trans). The two tabs show the respective characteristics for neuron genomes amplified with MDA or PTA.

Supplementary Table 4

sSNV counts per neuron. Mean sSNV count per gigabase pair (Gbp) estimates with lower bounds and upper bounds are provided. ‘Phaseable Mutations Identified’ reflects number of sSNV candidates passing the listed CC threshold. Estimated number of autosomal sSNVs was determined by multiplying the sSNV rate per Gbp by the size of the autosomal genome. Difference in number of identified phaseable mutations and estimated rates reflect ‘Power ratio’ extrapolation based on power analysis (see Methods). Filtering of estimated SNVs reflects removal of potential artefact signatures (see Methods). Median absolute pairwise difference (MAPD) and coefficient of variation (CoV) are measures of the unevenness of genome amplification.

Supplementary Table 5

Exonic sSNVs identified across datasets. Predicted functional effects are annotated.

Supplementary Table 6

Gene Ontology terms enriched for sSNVs.

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Miller, M.B., Huang, A.Y., Kim, J. et al. Somatic genomic changes in single Alzheimer’s disease neurons. Nature 604, 714–722 (2022). https://doi.org/10.1038/s41586-022-04640-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-022-04640-1

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing