Abstract
Neuroinflammation underpins neurodegeneration and clinical progression in multiple sclerosis (MS), but knowledge of processes linking these disease mechanisms remains incomplete. Here we investigated somatic single-nucleotide variants (sSNVs) in the genomes of 106 single neurons from post-mortem brain tissue of ten MS cases and 16 controls to determine whether somatic mutagenesis is involved. We observed an increase of 43.9 sSNVs per year in neurons from chronic MS lesions, a 2.5 times faster rate than in neurons from normal-appearing MS and control tissues. This difference was equivalent to 1,291 excess sSNVs in lesion neurons at 70 years of age compared to controls. We performed mutational signature analysis to investigate mechanisms underlying neuronal sSNVs and identified a signature characteristic of lesions with a strong, age-associated contribution to sSNV counts. This research suggests that neuroinflammation is mutagenic in the MS brain, potentially contributing to disease progression.
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Data availability
Data (excluding DNA sequencing data) pertaining to this work are available in Supplementary Tables 1–3 and through a public repository (https://doi.org/10.5281/zenodo.14325479)57. Controlled access to DNA sequencing data is required under the terms and conditions of the local ethics committees underpinning this study as well as for a data transfer agreement, which is compatible with the material transfer agreements between The Florey Institute of Neuroscience and Mental Health and the brain tissue biobanks from which tissues were obtained for this research. Data pertaining to this work can be accessed by contacting the corresponding author. Any initial requests for data access will be responded to within 4 weeks. External single PTA neuron control data used in this study is available as supplementary information in a separate publication17. Human reference genome GRCh37 is available from https://console.cloud.google.com/storage/browser/gcp-public-data–broad-references.
Code availability
All analyses were performed with freely available software as described in Methods. Custom code is freely available at https://doi.org/10.5281/zenodo.14325479 (ref. 57).
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Acknowledgements
We sincerely thank the individuals and families who donated MS and control tissues for making this scientific research possible. We thank A. Harding, A. Affleck, D. Davies, D. Gveric, F. Hinton and G. Pavey for their assistance with tissue characterization and procurement. Our thanks go to V. Jameson and M. Sakkas from the University of Melbourne (UoM) Flow Cytometry Core Facility and UoM Research Computing Services. We thank L. Ngyuen for assistance with figure illustrations. MS and control tissues used in this research were made available by the MSABB, the UKMSPDTB and the VBB. The MSABB is supported by MS Australia, the University of Sydney and the Brain and Mind Centre, the NSW Office for Health and Medical Research and Royal Prince Alfred Hospital Sydney, and has also received support from a number of trusts and foundations, including the Trish MS Research Foundation, Levy Foundation, Collier Charitable Fund, Medical Advances Without Animals Trust and the FIL Foundation. The UKMSPDTB is funded by The Multiple Sclerosis Society, a registered charity 207495, and samples and associated clinical and neuropathological data were supplied through Imperial College London (UK). This work was funded by MS Australia in the form of an incubator grant (16-015 to J.P.R.), project grants (17-0208 and 22-0111 to J.P.R.) and fellowship support (23-SF-0034 to J.P.R.). Other funding was provided by The Bethlehem Griffiths Research Foundation (BGRF1801 to J.P.R.), The Rebecca L. Cooper Medical Research Foundation (10665 to J.P.R.), The National MS Society (PP-1606-24404 to J.P.R.) and The National Health and Medical Research Council of Australia (Ideas Grant 1184640 to J.P.R. and Investigator Grant 1175775 to T.J.K.). This work also received support from the China National GeneBank.
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A.M., S.L. and J.P.R. conceived and designed the study. M.B., C.M. and J.P.R. acquired the biological samples. S.J., B.Y., I.H., W.T., W.I.A.S., Y.S., B.W. and J.P.R. performed biological sample experiments and data generation. A.M., S.J., S.L. and J.P.R. conducted data analysis and interpretation. A.M. and S.L. performed statistical analysis. A.M., M.B., T.J.K., S.L. and J.P.R. acquired funding. J.P.R. was responsible for project administration. B.Y., S.L. and J.P.R. supervised the project. A.M., S.J., S.L. and J.P.R. wrote the original draft of the paper; all authors reviewed and edited the final version.
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B.Y., I.H., W.T., W.I.A.S. and B.W. are employees of BGI-Australia. M.B. has received institutional support for research or speaking from Alexion, Biogen, BMS, Merck, Novartis, Roche and Sanofi Genzyme. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Extended experimental workflow.
Characterised frozen brain tissue was provided by brain banks, for MS cases, as chronic demyelinated lesion or normal-appearing (NA) tissue blocks. If lesion blocks were large (that is potentially containing lesion and MS NA tissue) further characterisation of tissue blocks was performed using the myelin stain, luxol fast blue, and oil red-O stain for immunological activity. Frozen tissue was dounce homogenised and filtered prior to anti-NeuN antibody staining and fluorescence activated nuclei sorting (see Methods, Extended data Fig. 2 for the gating strategy). Single neuronal nuclei were subjected to cold alkaline lysis18 and then whole genome amplification (WGA) using PTA19. Single nucleus WGA samples were subjected to multiplex PCR QC15, and QC pass samples were run on a Tapestation (Agilent) prior to whole genome sequencing (WGS).
Extended Data Fig. 2 Neuronal nuclei FACS gating strategy.
The presented gating strategy pertains to chronic lesion tissue from MS10, a PPMS case with excess sSNVs in lesion vs. NA tissue (Table 1, Fig. 2c). (a) A proportion of chilled pre-filtered nuclear preparation was stained with Hoechst 33342 and run through the FACS machine to identify DNA-containing nuclei. (b) NeuN stained nuclei mapping to the same position were gated and their size measured using forward scatter (FSC-A) and side scatter (SSC-A). (c) Singlet nuclei were identified and were gated further to remove auto-fluorescent (AF) particles (d). (e) Resulting neuronal nuclei were separated further based on strength of NeuN staining. (f) Single neuronal nuclei from the highest staining proportion, NeuN + +(hi), were sorted for whole genome amplification and snWGS.
Extended Data Fig. 3 Purity assessment of neuronal nuclei.
(a) Droplet Digital PCR (ddPCR) was performed on total RNA/cDNA from pooled FACS sorted NeuN-positive (+) nuclei from MS chronic (CL) lesion tissue and matched normal appearing (NA) tissue from a single MS case and NeuNhi (high-expressing, ++) nuclei from cerebellum (Cb) tissue from a single control (b) FACS gating strategy is shown for NeuN/H33342 stained nuclei preparations (y-axis) from MS6 chronic lesion (CL), matched MS6 NA tissue and CT5 frontal lobe (FL) tissue. For each tissue sample, FACS gates are shown for NeuNhi and NeuNlow nuclei populations, which were sorted into six pools for RNA extraction, cDNA synthesis and ddPCR (c) ddPCR results for the astrocyte marker, ALDH1L1, showing elevated expression in pooled MS6 CL NeuNlow nuclei and low, baseline expression levels in other nuclei pools.
Extended Data Fig. 4 PTA QC metrics.
Single cell WGA QC metrics were computed for each of 73 PTA neurons: (a) SCAN2 power ratio (ratio of number of VAF-based sSNV calls to estimated genome-wide sSNV burden); (b) median absolute pairwise difference (MAPD) of estimated copy number in adjacent 50 kb bins; (c) coefficient of variation (CoV, ratio of standard deviation to mean) of estimated copy number in adjacent 50 kb bins; and (d) percentage of germline heterozygous SNPs with allele balance between 0.3 and 0.7. Each QC metric is plotted against the estimated genome-wide sSNV burden. (e) Residuals for sSNVs/neuron (actual values minus fitted values) from the linear mixed model using all 73 neurons (that is the fitted regression lines in Fig. 2a) were compared with post-mortem interval (PMI). The solid black line shows the estimated relationship between residuals and PMI from a linear mixed model (two-sided t-test for fixed effect with the Satterthwaite correction, p = 0.08). Two outlier neurons (x-axis, right end of the plot) from a control with a PMI of 50 hrs were found to be driving the suggestive association observed (see Results).
Extended Data Fig. 5 Neuronal sSNV counts and other information for controls.
Plot shows sSNV counts and the mean for individual PTA neurons from five control samples (CT1, 2, 4, 5, 6) ascertained for the current study (light blue dots) and 11 controls from an external dataset17 (purple dots). Control ID, sex (male (M), female (F)) and age-at-death in years are shown.
Extended Data Fig. 6 Mutational signature analysis diagnostics.
Forward stepwise regression was used to identify reference mutational signatures to be used for signature refitting to data from 106 PTA single neurons. (a) In the stepwise regression we computed the sum of squared error (SSE) of the reconstructed mutational profile after each step, to determine the optimal number of reference signatures. Stepwise regression was stopped after the improvement in SSE dropped below 2000, which resulted in the selection of 10 reference signatures. (b) Non-negative matrix factorization (NMF) rank survey diagnostics for mutational profiles from 106 PTA single neurons. Diagnostics were used to determine the optimal number of de novo signatures to extract, considering the trade-off between model complexity and fit. Rank 3 was chosen for de novo mutational signature extraction.
Extended Data Fig. 8 Refitting of de novo mutational signatures.
(a) Relative contributions of published mutational signatures (COSMIC SBS signatures and a PTA artifact-associated signature PTAerr), obtained by strict signature refitting with MutationalPatterns, to the de novo mutational signatures inferred from 106 PTA neurons. (b–d) The de novo mutational signatures MS sig 1-MS sig 3, each plotted alongside the published mutational signatures that were found to contribute to them. (e) The only published mutational signature, SBS42, which was identified as one of the 10 COSMIC signatures contributing to the overall sSNV mutational profile by stepwise regression, but not identified as contributing to one of the de novo signatures.
Supplementary information
Supplementary Tables 1–3
Sequence coverage statistics for bulk tissue and single PTA neurons (Supplementary Table 1), all SCAN2 calls (Supplementary Table 2) and sSNV data for single PTA neurons (Supplementary Table 3).
Supplementary Table 4
PCR and ddPCR oligonucleotide primer information.
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Motyer, A., Jackson, S., Yang, B. et al. Neuronal somatic mutations are increased in multiple sclerosis lesions. Nat Neurosci 28, 757–765 (2025). https://doi.org/10.1038/s41593-025-01895-5
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DOI: https://doi.org/10.1038/s41593-025-01895-5
