Alzheimer's disease (AD) is the most common neurodegenerative disorder, affecting over 30 million people worldwide(Brookmeyer et al., 2007). AD is characterized by progressive amyloid-β deposition, neurofibrillary tangle formation, synaptic dysfunction, and neuronal loss, and ultimately leads to cognitive dysfunctions and dementia(Huang and Mucke, 2012). Molecular genetic analyses have identified rare autosomal dominant mutations in three genes, the amyloid precursor protein gene (APP) and the presenilin genes 1 and 2 (PSEN1 and PSEN2), in early-onset familial AD (Cacace et al., 2016). Also, the ε4 allele of the Apolipoprotein E (APOE) gene is a strong common genetic risk factor for late-onset AD(van der Lee et al., 2018). However, late-onset AD is genetically complex and heterogeneous, with 60%–80% heritability (Gatz et al., 2006). Although the largest genome-wide association studies (GWAS) of AD have identified 75 risk loci (Bellenguez et al., 2022), these only account for a minor component of AD risk.

Somatic mutations are postzygotic genetic mutations that are caused by the failure of accurate DNA-damage-repair during DNA replication and transcription processes (Verheijen et al., 2018). Increasing evidence shows that somatic mutations occur in the normal human brain during development and aging (Bae et al., 2018; Lodato et al., 2018; Lodato et al., 2015), and neuronal diversity caused by somatic mutations has been implicated as a cause of neurodegenerative diseases (Lodato et al., 2018; Poduri et al., 2013). During normal human brain development, all neuronal stem cells (NSCs) and progenitor cells (NPCs) undergo billions of cell divisions. Somatic mutations accumulate at an estimated rate of about 5.1 single nucleotide variants (SNVs) per day per progenitor cell during neurogenesis (Bae et al., 2018). Somatic mutations that occur in this process may confer phenotypes to subsets of neurons leading to dysfunction of cortical circuits (Poduri et al., 2013). Meanwhile, the fully developed human brain contains ∼ 80 billion neurons, and each neuron accumulates postmitotic somatic mutations with increasing age at a rate of ∼20-40 SNVs per year per neuron (Lodato et al., 2018; Miller et al., 2022). Because neurons have a long life span, postmitotic somatic mutations could accumulate in neurons with aging, and ultimately result in huge amounts in elderly individuals. Somatic mutations that occur at an earlier stage of neurogenesis (i.e., NSCs and NPCs) will generate more mutated offspring cells and will result in higher frequency of mutated brain cells. Post mitotic somatic mutation occurs in mature neurons and is not shared between the other neuron cells. Although a single postmitotic somatic SNV (sSNV) might only affect one neuron, the massive accumulation of postmitotic somatic mutations in neurons across the brain might jointly contribute to neurodegenerative diseases (Lodato et al., 2018; Lodato and Walsh, 2019).

Somatic mutations in the human brain have been found to be associated with neuropsychiatric disorders (Kim et al., 2021; Miller et al., 2021; Rodin et al., 2021). Bulk sequencing of AD brain tissues yielded inconsistent results about the roles of somatic mutations in the pathogenesis of AD. Several whole-exome (WES) and targeted sequencing studies of bulk brain tissues suggested a potential role of somatic mutations in the pathogenesis of AD (Bushman et al., 2015; Helgadottir et al., 2019; Lee et al., 2018; Park et al., 2019), but not identified in other studies (Abascal et al., 2021; Beck et al., 2004; Min et al., 2021; Nicolas et al., 2018; Parcerisas et al., 2014). GWAS studies have shown that AD-associated variants mainly lie in non-coding regions (Wightman et al., 2020), indicating that the roles of somatic mutations in non-coding or regulatory regions should not be neglected (Helgadottir et al., 2019). Thus, our recent research evaluated the genome-wide burden of somatic mutations within the brains of AD and neurotypical control individuals using whole-genome sequencing (WGS) bulk data and found no difference in the average numbers of sSNVs between AD and control groups (Min et al., 2021). Given that bulk sequencing has limitations when the somatic mutations are only present in a small proportion of cells as in the case of the post-mitotic mutations that are present in the single neuron, single-cell whole-genome sequencing (scWGS) may offer deeper insight into the genome-wide somatic mutational landscape at the single-cell level (Gawad et al., 2016). Recently, Miller et al. performed scWGS in 319 neurons from AD (N = 9) and control (N = 20) individuals and found an increased burden and unique mutation signature of sSNVs in AD neurons (Miller et al., 2022). The potential roles of these sSNVs remain unknown and the genome-wide landscape of sSNVs in AD neurons still needs to be validated in independent samples.

Currently, the most frequently used single-cell genome amplification method, multiple displacement amplification (MDA) (Dean et al., 2002), is hampered by the considerable amplification bias. To address this issue, Dong et al. developed single-cell multiple displacement amplification (SCMDA) and a single-cell-variant caller (SCcaller) that can greatly improve the capability of detecting somatic mutations in single-cell genomes (Dong et al., 2017). Here, we used SCMDA and SCcaller to analyze somatic mutations in 96 human neurons from the prefrontal cortex (PFC) of 8 AD and 8 normal control individuals (6 neurons per individual). We aim to quantify and characterize the genome-wide somatic mutations (sSNVs and sIndels) in individual neurons from AD patients. We first conducted a quantitative comparison of somatic mutation counts per neuron to compare the genome-wide mutational burden in AD and controls. Moreover, we performed an annotation category-based burden test to detect annotation categories that have excess somatic mutations in AD patients versus controls. Then, we performed mutational signature analysis to reveal the potential mutagenesis processes of somatic mutations in neurons. Finally, we prioritized functional somatic mutations with in silico analysis and carried out functional enrichment analysis for functional somatic mutations to get a deeper insight into the role of somatic mutations underlying the pathogenesis of AD.