Diverse somatic genomic alterations in single neurons in chronic traumatic encephalopathy

Abstract

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease that is linked to exposure to repetitive head impacts (RHI), yet little is known about its pathogenesis. Applying two single-cell whole-genome sequencing methods to hundreds of neurons from prefrontal cortex of 15 individuals with CTE, and 4 with RHI without CTE, revealed increased somatic single-nucleotide variants in CTE, resembling a pattern previously reported in Alzheimer's disease (AD). Furthermore, we discovered remarkably high burdens of somatic small insertions and deletions in a subset of CTE individuals, resembling a known pattern, ID4, also found in AD. Our results suggest that neurons in CTE experience stereotyped mutational processes shared with AD; the absence of similar changes in RHI neurons without CTE suggests that CTE involves mechanisms beyond RHI alone.

Fig. 1.. Study design and somatic SNV burden in CTE neurons.

(A) Cohort design: neurotypical control, repetitive head impacts (RHI), chronic traumatic encephalopathy (CTE), and Alzheimer’s disease (AD) postmortem human brain tissue. Illustrations of CTE and AD show characteristic tau pathology patterns (in brown). The number of cases included in this study are shown for each clinical condition. (B) Single-cell whole-genome sequencing (WGS) experimental approach. Nuclei are isolated from postmortem human brain tissue and subjected to fluorescence-activated nuclear sorting for the neuronal nuclear marker NeuN. Nuclei are sorted one-per-well, lysed, and subjected to primary template amplification (PTA) and multiplexed end-tagging amplification of complementary strands (META-CS). Amplified genomic DNA is then assayed using WGS to identify somatic mutations. PTA data is used to determine somatic mutation burden, while META-CS data is used to identify strand-related signatures. (C) Increased somatic SNV burden in CTE (red) brains compared to RHI (light blue) brains and neurotypical controls (dark blue). Somatic SNV burden (from each neuron as a point) estimated by SCAN2 is fitted against age by clinical conditions using a LME model (neurotypical control: dark blue; RHI: light blue, P = 0.752; CTE: red, P = 0.005). (D) CTE neurons show a significant excess of somatic SNV burden (P = 3.3 × 10⁻⁶, two-tailed Wilcoxon test) while RHI neurons show no significant difference compared to neurotypical controls (P = 0.583, two-tailed Wilcoxon test). (E) Excess somatic SNVs in each CTE case ordered by increasing age. The dashed blue line shows SNVs attributable to age (zero excess). (F) Circos plot showing the PTA somatic SNV density distribution of CTE cases across the whole genome. Each CTE case is depicted by color in a circular track. Cases are ordered by increasing age same as (E) from the inner track to the outer track.

Fig. 2.. Somatic SNV mutational signatures in CTE neurons.

(A, B) Somatic SNV burden separated by contributions from Signatures A (A) and C (B) in CTE (red) and neurotypical controls (dark blue) neurons. These signatures decompose somatic SNV burden into age- and disease-specific effects. Their contributions are fitted against age by clinical conditions using a LME model (A, Signature A, CTE: P = 0.088; B, Signature C, CTE: P = 7.7 × 10⁻⁴). (C) Ratios of each signature’s total contribution to age-related contribution in neurotypical control, CTE, and AD neurons. Age-contributed SNVs of each signature are obtained from the LME model in neurotypical controls. A ratio > 1 indicates a higher contribution from the signature compared to age-matched controls. Bars in each box plot from top to bottom show the first, second (median), and third quartile; whiskers extend 1.5 interquartile range (IQR). (D) Relative contribution of Signatures A and C in CTE and AD after adjusting for age. Based on the ratios shown in (C), the relative contribution of each signature is calculated by removing the median ratio of neurotypical controls (representing age effect) from the median ratio of each disease. (E) Mutational spectra of neurotypical control, CTE, and AD neurons are fitted to the COSMIC SBS database of cancer mutational signatures. Residual mutational patterns for CTE and AD are obtained by subtracting mutation profiles of age-matched controls from those of CTE and AD to show disease-specific contributions.

Fig. 3.. Somatic Indel burden and mutational signatures in CTE neurons.

Somatic Indel burdens were identified from PTA scWGS data, with strand-related analysis inferred from mutational signatures extracted from META-CS scWGS data. (A) Increased somatic Indel burden in CTE (red) brains compared to RHI (light blue) brains and neurotypical controls (dark blue). Somatic Indel burden (from each neuron as a triangle) estimated by SCAN2 is fitted against age by clinical conditions using a LME model (neurotypical control: dark blue; RHI: light blue, P = 0.814; CTE: red, P = 0.002). (B) CTE neurons show a significant excess of somatic Indel burden (P = 2.8 × 10⁻⁸, two-tailed Wilcoxon test) while RHI neurons show no significant difference (P = 0.375, two-tailed Wilcoxon test) compared to neurotypical controls. Data is mean ± standard error. The dashed blue line shows Indels attributable to age (zero excess). (C) Comparison of all types of Indels across age-matched controls, RHI, and CTE. Data is mean burden per cell. Asterisk denotes significant changes in certain types of Indels when compared to age-matched controls (P < 0.05, two-tailed Wilcoxon test). 2 to 4 bp deletions are significantly more abundant in CTE neurons but not in RHI neurons. (D) Double-stranded (ds) and single-stranded (ss) Indel signatures in neurotypical controls and CTE extracted from META-CS data. (E) Absolute (top) and relative (bottom) contribution of dsIndel and ssIndel signatures in PTA-profiled neurotypical control and CTE neurons. The pair of dsIndel and ssIndel signatures used for decomposition is determined by the clinical condition of PTA neurons. We observe a pronounced ssIndel contribution in neurons from certain CTE cases (cases are indicated by brackets). (F) Decomposition of dsIndel and ssIndel signatures to COSMIC ID database, where we see a pronounced presence of ID4 specific to CTE.

Fig. 4.. Somatic ID4-like deletions in certain CTE individuals.

(A, B) Excess somatic Indels in each CTE (A) and AD (B) case ordered by increasing age. CTE and AD cases are separated into either High-Indel group (High-Indel CTE: red, High-Indel AD: dark green) or Low-Indel group (Low-Indel CTE: yellow, Low-Indel AD: light green) based on whether they have a significant excess of Indels (> 50). Data is mean ± standard error. The dashed blue line shows Indels attributable to age (zero excess). (C) Principal component analysis (PCA) clustering of Indels from each case across neurotypical controls, CTE, and AD. The first two dimensions are used for visualization with the percentage of variance explained shown for each dimension. Cases with < 15 Indels are filtered out. (D, E) Comparison of proportions of 2 to 4 bp deletions in dsIndels (D) and ssIndels (E) across age-matched controls, Low-Indel CTE, and High-Indel CTE. Each triangle represents an individual from META-CS data. High-Indel CTE showed a significantly higher proportion in both dsIndels and ssIndels (P = 1.7 × 10⁻⁵, two-tailed Wilcoxon test), whereas Low-Indel CTE showed marginally significant difference in dsIndels (P = 0.014, two-tailed Wilcoxon test) compared to age-matched controls. Data is mean ± standard deviation. (F) Relative contribution of ID4 to dsIndels and ssIndels of each case from META-CS data shown as a heatmap. Case IDs are colored by their group assignment (age-matched control: dark blue, Low-Indel CTE: yellow, High-Indel CTE: red).

Fig. 5.. Enrichment analysis of somatic SNVs and Indels in CTE neurons.

(A–D) Somatic SNV (A, B) and Indel (C, D) density as a function of gene expression and chromatin accessibility levels. Neuronal transcriptional profiles were characterized from snRNA-seq data sequenced in this study. Neuronal accessibility profiles were obtained from snATAC-seq data in Ganz et al.(23). Genes and open chromatin regions are separated into 10 (for SNV) or 5 (for Indel) equally sized groups with increasing levels indicating increasing expression or accessibility. Observed density of each expression or accessibility group is obtained by overlapping the original mutation call sets with regions of each group. Expected density is calculated from 1000 permutations of mutation call sets overlapped with regions of each group. Enrichment ratio is calculated by observed / expected density for each permutation, and mean ratio over 1000 permutations is used to construct the trend line by linear model (error bar indicates standard deviation). Pearson correlation coefficient (R) and two-tailed p-value (P) are shown. obs.: observed, exp.: expected. (E, F) Gene ontology (GO) analysis of genes where somatic SNVs (E) and Indels (F) are located. GO terms with FDR-adjusted P < 0.01 in both CTE and control are reported.

Fig. 6.. Proposed mechanisms of mutagenesis for somatic SNVs and Indels in CTE.

An elevated level of DNA damage in CTE neuron in part caused by ROS leads to the accumulation of both somatic SNVs and Indels. For SNVs, oxidative damage frequently results in 8-oxoguanine on one strand which becomes a double-stranded C>A mutation when NER and other pathways fail to repair it. For Indels, genome-embedded ribonucleotides are removed by TOP1-mediated activities during which a cleavage on one strand with the TNT motif leads to strand realignment and consequently a 2 bp single-stranded deletion. A small portion of the single-stranded deletions may become double-stranded deletions potentially through other endogenous processes. The accumulation of such DNA damage may eventually lead to neurodegeneration and cell death.