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Transposable elements (TEs) are abundant in the human genome, and some are capable of generating new insertions through RNA intermediates. In cancer, the disruption of cellular mechanisms that normally suppress TE activity may facilitate mutagenic retrotranspositions. We performed single-nucleotide resolution analysis of TE insertions in 43 high-coverage whole-genome sequencing data sets from five cancer types. We identified 194 high-confidence somatic TE insertions, as well as thousands of polymorphic TE insertions in matched normal genomes. Somatic insertions were present in epithelial tumors but not in blood or brain cancers. Somatic L1 insertions tend to occur in genes that are commonly mutated in cancer, disrupt the expression of the target genes, and are biased toward regions of cancer-specific DNA hypomethylation, highlighting their potential impact in tumorigenesis.

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. Analyses of single-cell whole-genome sequencing data show that somatic mutations are increased in the brain of individuals with Alzheimer’s disease compared to neurotypical individuals, with a pattern of genomic damage distinct from that of normal ageing.

Neurons live for decades in a postmitotic state, their genomes susceptible to DNA damage. Here we survey the landscape of somatic single-nucleotide variants (SNVs) in the human brain. We identified thousands of somatic SNVs by single-cell sequencing of 36 neurons from the cerebral cortex of three normal individuals. Unlike germline and cancer SNVs, which are often caused by errors in DNA replication, neuronal mutations appear to reflect damage during active transcription. Somatic mutations create nested lineage trees, allowing them to be dated relative to developmental landmarks and revealing a polyclonal architecture of the human cerebral cortex. Thus, somatic mutations in the brain represent a durable and ongoing record of neuronal life history, from development through postmitotic function.

Recent advances in single cell technology have enabled dissection of cellular heterogeneity in great detail. However, analysis of single cell DNA sequencing data remains challenging due to bias and artifacts that arise during DNA extraction and whole-genome amplification, including allelic imbalance and dropout. Here, we present a framework for statistical estimation of allele-specific amplification imbalance at any given position in single cell whole-genome sequencing data by utilizing the allele frequencies of heterozygous single nucleotide polymorphisms in the neighborhood. The resulting allelic imbalance profile is critical for determining whether the variant allele fraction of an observed mutation is consistent with the expected fraction for a true variant. This method, implemented in SCAN-SNV (Single Cell ANalysis of SNVs), substantially improves the identification of somatic variants in single cells. Our allele balance framework is broadly applicable to genotype analysis of any variant type in any data that might exhibit allelic imbalance. Single cell whole-genome sequencing data harbors information about somatic genetic variation but is challenging to analyze. Here, the authors develop a spatial model to correct for allelic amplification imbalance and a somatic SNV genotyper SCAN-SNV for analyzing single cell DNA sequencing data.

Detection of mosaic mutations that arise in normal development is challenging, as such mutations are typically present in only a minute fraction of cells and there is no clear matched control for removing germline variants and systematic artifacts. We present MosaicForecast, a machine-learning method that leverages read-based phasing and read-level features to accurately detect mosaic single-nucleotide variants and indels, achieving a multifold increase in specificity compared with existing algorithms. Using single-cell sequencing and targeted sequencing, we validated 80–90% of the mosaic single-nucleotide variants and 60–80% of indels detected in human brain whole-genome sequencing data. Our method should help elucidate the contribution of mosaic somatic mutations to the origin and development of disease. MosaicForecast detects mosaic single-nucleotide variants and indels in human samples.

Background Genomic rearrangements exert a heavy influence on the molecular landscape of cancer. New analytical approaches integrating somatic structural variants (SSVs) with altered gene features represent a framework by which we can assign global significance to a core set of genes, analogous to established methods that identify genes non-randomly targeted by somatic mutation or copy number alteration. While recent studies have defined broad patterns of association involving gene transcription and nearby SSV breakpoints, global alterations in DNA methylation in the context of SSVs remain largely unexplored. Results By data integration of whole genome sequencing, RNA sequencing, and DNA methylation arrays from more than 1400 human cancers, we identify hundreds of genes and associated CpG islands (CGIs) for which the nearby presence of a somatic structural variant (SSV) breakpoint is recurrently associated with altered expression or DNA methylation, respectively, independently of copy number alterations. CGIs with SSV-associated increased methylation are predominantly promoter-associated, while CGIs with SSV-associated decreased methylation are enriched for gene body CGIs. Rearrangement of genomic regions normally having higher or lower methylation is often involved in SSV-associated CGI methylation alterations. Across cancers, the overall structural variation burden is associated with a global decrease in methylation, increased expression in methyltransferase genes and DNA damage response genes, and decreased immune cell infiltration. Conclusion Genomic rearrangement appears to have a major role in shaping the cancer DNA methylome, to be considered alongside commonly accepted mechanisms including histone modifications and disruption of DNA methyltransferases.

Improvements in single-cell whole-genome sequencing (scWGS) assays have enabled detailed characterization of somatic copy number alterations (CNAs) at the single-cell level. Yet, current computational methods are mostly designed for detecting chromosome-scale changes in cancer samples with low sequencing coverage. Here, we introduce HiScanner (High-resolution Single-Cell Allelic copy Number callER), which combines read depth, B-allele frequency, and haplotype phasing to identify CNAs with high resolution. In simulated data, HiScanner consistently outperforms state-of-the-art methods across various CNA types and sizes. When applied to high-coverage scWGS data from 65 cells across 11 neurotypical human brains, HiScanner shows a superior ability to detect smaller CNAs, uncovering distinct CNA patterns between neurons and oligodendrocytes. We also generated low-coverage scWGS data from 179 cells sampled from the same meningioma patient at two time points. For this serial dataset, integration of CNAs with point mutations revealed evolutionary trajectories of tumor cells. These findings show that HiScanner enables accurate characterization of frequency, clonality, and distribution of CNAs at the single-cell level in both non-neoplastic and neoplastic cells. HiScanner, a tool for identification of copy number alterations from single cell whole-genome sequencing data, uncovers cell-type-specific somatic mosaicism in human brain and offers a way to track clonal evolution at the single cell resolution.

DNA copy number variations (CNVs) play an important role in the pathogenesis and progression of cancer and confer susceptibility to a variety of human disorders. Array comparative genomic hybridization has been used widely to identify CNVs genome wide, but the next-generation sequencing technology provides an opportunity to characterize CNVs genome wide with unprecedented resolution. In this study, we developed an algorithm to detect CNVs from whole-genome sequencing data and applied it to a newly sequenced glioblastoma genome with a matched control. This read-depth algorithm, called BIC-seq, can accurately and efficiently identify CNVs via minimizing the Bayesian information criterion. Using BIC-seq, we identified hundreds of CNVs as small as 40 bp in the cancer genome sequenced at 10x coverage, whereas we could only detect large CNVs (> 15 kb) in the array comparative genomic hybridization profiles for the same genome. Eighty percent (14/16) of the small variants tested (110 bp to 14 kb) were experimentally validated by quantitative PCR, demonstrating high sensitivity and true positive rate of the algorithm. We also extended the algorithm to detect recurrent CNVs in multiple samples as well as deriving error bars for breakpoints using a Gibbs sampling approach. We propose this statistical approach as a principled yet practical and efficient method to estimate CNVs in whole-genome sequencing data.

Tan et al . use a multiplex screening method to systematically evaluate ∼500,000 drug pairs assembled from a collection of 1,000 FDA-approved or clinically tested compounds, and identify drugs that synergize to inhibit HIV replication. The systematic identification of effective drug combinations has been hindered by the unavailability of methods that can explore the large combinatorial search space of drug interactions. Here we present multiplex screening for interacting compounds (MuSIC), which expedites the comprehensive assessment of pairwise compound interactions. We examined ∼500,000 drug pairs from 1,000 US Food and Drug Administration (FDA)-approved or clinically tested drugs and identified drugs that synergize to inhibit HIV replication. Our analysis reveals an enrichment of anti-inflammatory drugs in drug combinations that synergize against HIV. As inflammation accompanies HIV infection, these findings indicate that inhibiting inflammation could curb HIV propagation. Multiple drug pairs identified in this study, including various glucocorticoids and nitazoxanide (NTZ), synergize by targeting different steps in the HIV life cycle. MuSIC can be applied to a wide variety of disease-relevant screens to facilitate efficient identification of compound combinations.

Most neurons that make up the human brain are postmitotic, living and functioning for a very long time without renewal (see the Perspective by Lee). Bae et al. examined the genomes of single neurons from the prenatal developing human brain. Both the type of mutation and the rates of accumulation changed between gastrulation and neurogenesis. These early mutations could be generating useful neuronal diversity or could predispose individuals to later dysfunction. Lodato et al. also found that neurons take on somatic mutations as they age by sequencing single neurons from subjects aged 4 months to 82 years. Somatic mutations accumulated with increasing age and accumulated faster in individuals affected by inborn errors in DNA repair. Postmitotic mutations might only affect one neuron, but the accumulated divergence of genomes across the brain could affect function. Science , this issue p. 550 , p. 555 ; see also p. 521 Brain mutations accumulate with age. It has long been hypothesized that aging and neurodegeneration are associated with somatic mutation in neurons; however, methodological hurdles have prevented testing this hypothesis directly. We used single-cell whole-genome sequencing to perform genome-wide somatic single-nucleotide variant (sSNV) identification on DNA from 159 single neurons from the prefrontal cortex and hippocampus of 15 normal individuals (aged 4 months to 82 years), as well as 9 individuals affected by early-onset neurodegeneration due to genetic disorders of DNA repair (Cockayne syndrome and xeroderma pigmentosum). sSNVs increased approximately linearly with age in both areas (with a higher rate in hippocampus) and were more abundant in neurodegenerative disease. The accumulation of somatic mutations with age—which we term genosenium—shows age-related, region-related, and disease-related molecular signatures and may be important in other human age-associated conditions.