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
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
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/datasets. Source 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
Selkoe, D. J. & Hardy, J. The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol. Med. 8, 595–608 (2016).
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).
Braak, H. & Braak, E. Staging of Alzheimer’s disease-related neurofibrillary changes. Neurobiol. Aging 16, 271–278 (1995).
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).
Lodato, M. A. et al. Aging and neurodegeneration are associated with increased mutations in single human neurons. Science 359, 555–559 (2018).
Blokzijl, F. et al. Tissue-specific mutation accumulation in human adult stem cells during life. Nature 538, 260–264 (2016).
Osorio, F. G. et al. Somatic mutations reveal lineage relationships and age-related mutagenesis in human hematopoiesis. Cell Rep. 25, 2308–2316 (2018).
Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).
Alexandrov, L. B. et al. Clock-like mutational processes in human somatic cells. Nat. Genet. 47, 1402–1407 (2015).
Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020).
Lu, T. et al. REST and stress resistance in ageing and Alzheimer’s disease. Nature 507, 448–454 (2014).
Genovese, G. et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med. 371, 2477–2487 (2014).
Martincorena, I. et al. Tumor evolution. High burden and pervasive positive selection of somatic mutations in normal human skin. Science 348, 880–886 (2015).
Martincorena, I. et al. Somatic mutant clones colonize the human esophagus with age. Science 362, 911–917 (2018).
Lodato, M. A. et al. Somatic mutation in single human neurons tracks developmental and transcriptional history. Science 350, 94–98 (2015).
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).
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).
Kucab, J. E. et al. A compendium of mutational signatures of environmental agents. Cell 177, 821–836 (2019).
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).
Abascal, F. et al. Somatic mutation landscapes at single-molecule resolution. Nature 593, 405–410 (2021).
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).
Leng, K. et al. Molecular characterization of selectively vulnerable neurons in Alzheimer’s disease. Nat. Neurosci. 24, 276–287 (2021).
Bohrson, C. L. et al. Linked-read analysis identifies mutations in single-cell DNA-sequencing data. Nat. Genet. 51, 749–754 (2019).
Petljak, M. et al. Characterizing mutational signatures in human cancer cell lines reveals episodic APOBEC mutagenesis. Cell 176, 1282–1294 (2019).
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).
Madabhushi, R. et al. Activity-induced DNA breaks govern the expression of neuronal early-response genes. Cell 161, 1592–1605 (2015).
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).
Lee, M. H. et al. Somatic APP gene recombination in Alzheimer’s disease and normal neurons. Nature 563, 639–645 (2018).
Kim, J. et al. APP gene copy number changes reflect exogenous contamination. Nature 584, E20–E28 (2020).
Jager, M. et al. Deficiency of nucleotide excision repair is associated with mutational signature observed in cancer. Genome Res. 29, 1067–1077 (2019).
Mecocci, P., MacGarvey, U. & Beal, M. F. Oxidative damage to mitochondrial DNA is increased in Alzheimer’s disease. Ann. Neurol. 36, 747–751 (1994).
Chun, H. et al. Severe reactive astrocytes precipitate pathological hallmarks of Alzheimer’s disease via H2O2− production. Nat. Neurosci. 23, 1555–1566 (2020).
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).
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).
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).
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).
Gate, D. et al. Clonally expanded CD8 T cells patrol the cerebrospinal fluid in Alzheimer’s disease. Nature 577, 399–404 (2020).
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).
Crabtree, G. R. Our fragile intellect. Part I. Trends Genet. 29, 1–3 (2013).
Fragola, G. et al. Deletion of topoisomerase 1 in excitatory neurons causes genomic instability and early onset neurodegeneration. Nat. Commun. 11, 1962 (2020).
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).
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).
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).
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).
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).
Khurana, V. et al. A neuroprotective role for the DNA damage checkpoint in tauopathy. Aging Cell 11, 360–362 (2012).
Sakofsky, C. J. et al. Repair of multiple simultaneous double-strand breaks causes bursts of genome-wide clustered hypermutation. PLoS Biol. 17, e3000464 (2019).
Mandrekar-Colucci, S. & Landreth, G. E. Microglia and inflammation in Alzheimer’s disease. CNS Neurol. Disord. Drug Targets 9, 156–167 (2010).
Rottkamp, C. A. et al. Redox-active iron mediates amyloid-beta toxicity. Free Radic. Biol. Med. 30, 447–450 (2001).
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).
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).
Evrony, G. D. et al. Single-neuron sequencing analysis of L1 retrotransposition and somatic mutation in the human brain. Cell 151, 483–496 (2012).
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
Fan, J. et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat. Methods 13, 241–244 (2016).
Dong, X. et al. Accurate identification of single-nucleotide variants in whole-genome-amplified single cells. Nat. Methods 14, 491–493 (2017).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
Keogh, M. J. et al. High prevalence of focal and multi-focal somatic genetic variants in the human brain. Nat. Commun. 9, 4257 (2018).
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).
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).
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).
Baslan, T. et al. Genome-wide copy number analysis of single cells. Nat. Protoc. 7, 1024–1041 (2012).
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).
Blokzijl, F., Janssen, R., van Boxtel, R. & Cuppen, E. MutationalPatterns: comprehensive genome-wide analysis of mutational processes. Genome Med. 10, 33 (2018).
Kim, J. et al. Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors. Nat. Genet. 48, 600–606 (2016).
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
Consortium, G. T. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
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).
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).
Green, P. et al. Transcription-associated mutational asymmetry in mammalian evolution. Nat. Genet. 33, 514–517 (2003).
Polak, P. & Arndt, P. F. Transcription induces strand-specific mutations at the 5′ end of human genes. Genome Res. 18, 1216–1223 (2008).
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).
Coppede, F. & Migliore, L. DNA damage and repair in Alzheimer’s disease. Curr. Alzheimer Res. 6, 36–47 (2009).
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).
Franco, I. et al. Somatic mutagenesis in satellite cells associates with human skeletal muscle aging. Nat. Commun. 9, 800 (2018).
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).
Lee-Six, H. et al. The landscape of somatic mutation in normal colorectal epithelial cells. Nature 574, 532–537 (2019).
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).
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).
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
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
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.
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.
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). a–d, 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. e–h, 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.
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.
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.
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.
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.
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. c–f, 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.
Supplementary information
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
About this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41586-022-04640-1
This article is cited by
-
The interaction between ageing and Alzheimer's disease: insights from the hallmarks of ageing
Translational Neurodegeneration (2024)
-
Genetic variation across and within individuals
Nature Reviews Genetics (2024)
-
Alzheimer’s disease, aging, and cannabidiol treatment: a promising path to promote brain health and delay aging
Molecular Biology Reports (2024)
-
Somatic mutations in aging and disease
GeroScience (2024)
-
Disease-Associated Neurotoxic Astrocyte Markers in Alzheimer Disease Based on Integrative Single-Nucleus RNA Sequencing
Cellular and Molecular Neurobiology (2024)
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.
