The DOI matches its Crossref record; 87 of the 87 references it cites were found in a registry or an index.
Somatic genomic changes in single Alzheimer’s disease neurons
Open accessAbstract
Dementia in Alzheimer's disease progresses alongside neurodegeneration 1-4, but the specific events that cause neuronal dysfunction and death remain poorly understood. During normal ageing, neurons progressively accumulate somatic mutations 5 at rates similar to those of dividing cells 6,7 which suggests that genetic factors, environmental exposures or disease states might influence this accumulation 5. 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 cells 6-10. 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 oxidation 4,11, 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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“This is especially relevant when DNA damage impacts on stem cells, hampering their role in tissue renewal or leading to their exhaustion, which in turn promotes aging and increases susceptibility to age-related pathologies 6,7 .”
From Hallmarks of aging: An expanding universe (2023) · cites this paper as [7]
Experimental — accuracy not yet measuredIn the citing paper
“Previous studies have shown increased neuronal genomic mosaicism (including SNVs, copy number variations, and cDNA insertions in neurodegeneration)124–128.”
From Neuronal DNA double-strand breaks lead to genome structural variations and 3D genome disruption in neurodegeneration (2023) · cites this paper as [125]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Fitting Linear Mixed-Effects Models Using lme4
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895
lmerTest Package: Tests in Linear Mixed Effects Models
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017) · Journal of Statistical Software · doi:10.18637/jss.v082.i13 · Cited by 24,346
ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data
Wang, K., Li, M., & Hakonarson, H. H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq603 · Cited by 14,724
Signatures of mutational processes in human cancer
Initiative, A. P. C. G., Alexandrov, L. B., ICGC Breast Cancer Consortium, ICGC MMML-Seq Consortium, PedBrain, I., Nik-Zainal, S., Wedge, D. C., Aparicio, S. A. J. R., Behjati, S., Biankin, A. V., et al. (2013) · Nature · doi:10.1038/nature12477 · Cited by 9,660
Analysis of protein-coding genetic variation in 60,706 humans
Exome Aggregation Consortium, Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., O’Donnell-Luria, A. H., Ware, J. S., Hill, A. J., et al. (2016) · Nature · doi:10.1038/nature19057 · Cited by 9,587
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353
Gene ontology analysis for RNA-seq: accounting for selection bias
Young, M. D., Wakefield, M. J., Smyth, G. K., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-2-r14 · Cited by 7,107
Cited by
Sorted by citations
Hallmarks of aging: An expanding universe
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2023) · Cell · doi:10.1016/j.cell.2022.11.001 · Cited by 6,549
Methods and applications for single-cell and spatial multi-omics
Vandereyken, K., Sifrim, A., Thienpont, B., & Voet, T. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00580-2 · Cited by 1,244
Applications of single-cell RNA sequencing in drug discovery and development
de Sande, B. V., Lee, J. S., Mutasa-Gottgens, E., Naughton, B. J., Bacon, W., Manning, J. R., Wang, Y., Pollard, J. R., Mendez, M., Hill, J., et al. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00688-4 · Cited by 462
Alzheimer’s disease: insights into pathology, molecular mechanisms, and therapy
Zheng, Q., & Wang, X. (2024) · Protein & Cell · doi:10.1093/procel/pwae026 · Cited by 483
Redox regulation: mechanisms, biology and therapeutic targets in diseases
Li, B., Ming, H., Qin, S., Nice, E. C., Dong, J., Du, Z., & Huang, C. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-02095-6 · Cited by 389
Metadata
- Authors
- Miller, M. B., Huang, A. Y., Kim, J., Zhou, Z., Kirkham, S. L., Maury, E. A., Ziegenfuss, J. S., Reed, H. C., Neil, J. E., Rento, L., Ryu, S. C., Ma, C. C., Luquette, L. J., Ames, H. M., Oakley, D. H., Frosch, M. P., Hyman, B. T., Lodato, M. A., Lee, E. A., & Walsh, C. A.
- Affiliations
- Broad Institute (US)
- Boston Children's Hospital (US)
- University of Massachusetts Chan Medical School (US)
- Allegheny College (US)
- Harvard University (US)
- University of Maryland, Baltimore (US)
- Corresponding author
- Lodato, Michael A.
- Lee, Eunjung Alice
- Walsh, Christopher A.
- ORCID
- Venue
- Nature
- Published
- 2022-04-20
- Volume
- 604
- Issue
- 7907
- Pages
- 714-722
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC9357465
- OpenAlex
- W4224234680
- Cited by
- 223
- References
- 84
- Citations by year
- 2026: 41 · 2025: 52 · 2024: 57 · 2023: 53 · 2022: 20
- FWCI
- 28.33
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health (1t15lm007098-01)
- National Institute on Aging (K08AG065502, DP2AG072437, K01AG051791, R00AG054748, R01AG070921, P30AG062421)
- National Heart, Lung, and Blood Institute (T32HL007627)
- National Human Genome Research Institute (T32HG002295)
- National Institute of General Medical Sciences (T32GM007753)
- National Institute of Neurological Disorders and Stroke (R01NS032457)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (P50HD105351)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › DNA Repair Mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- Alzheimer's disease, somatic mutations, neuronal DNA damage, mutational signatures, nucleotide oxidation, transcription-coupled repair, prefrontal cortex, hippocampus, neurodegeneration, aging, single-cell whole-genome sequencing
- MeSH terms
- Whole Genome Sequencing, Aging, Alzheimer Disease, DNA, Exons, Hippocampus, Humans, Neurons, Nucleotides, Prefrontal Cortex, Genomics, Mutation Rate
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review