Complex mosaic structural variations in human fetal brains

Advances in single-cell DNA sequencing enable insights into human somatic mosaicism

DNA sequencing from bulk or clonal human tissues has shown that genetic mosaicism is common and contributes to both cancer and non-cancerous disorders. However, single-cell resolution is required to understand the full genetic heterogeneity that exists within a tissue and the mechanisms that lead to somatic mosaicism. Single-cell DNA-sequencing technologies have traditionally trailed behind those of single-cell transcriptomics and epigenomics, largely because most applications require whole-genome amplification before costly whole-genome sequencing. Now, recent technological and computational advances are enabling the use of single-cell DNA sequencing to tackle previously intractable problems, such as delineating the genetic landscape of tissues with complex clonal patterns, of samples where cellular material is scarce and of non-cycling, postmitotic cells. Single-cell genomes are also revealing the mutational patterns that arise from biological processes or disease states, and have made it possible to track cell lineage in human tissues. These advances in our understanding of tissue biology and our ability to identify disease mechanisms will ultimately transform how disease is diagnosed and monitored.

Somatic Mosaicism in Brain Disorders

Research efforts over the past decade have defined the genetic landscape of somatic variation in the brain. Neurons accumulate somatic mutations from development through aging with potentially profound functional consequences. Recent studies have revealed the contribution of somatic mosaicism to various brain disorders including focal epilepsy, neuropsychiatric disease, and neurodegeneration. One notable finding is that the effect of somatic mosaicism on clinical outcomes can vary depending on contextual factors, such as the developmental origin of a variant or the number and type of cells affected. In this review, we highlight current knowledge regarding the role of somatic mosaicism in brain disorders and how biological context can mediate phenotypes. First, we identify the origins of brain somatic variation throughout the lifespan of an individual. Second, we explore recent discoveries that suggest somatic mosaicism contributes to various brain disorders. Finally, we discuss neuropathological associations of brain mosaicism in different biological contexts and potential clinical utility.

Detection and analysis of complex structural variation in human genomes across populations and in brains of donors with psychiatric disorders

Complex structural variations (cxSVs) are often overlooked in genome analyses due to detection challenges. We developed ARC-SV, a probabilistic and machine-learning-based method that enables accurate detection and reconstruction of cxSVs from standard datasets. By applying ARC-SV across 4,262 genomes representing all continental populations, we identified cxSVs as a significant source of natural human genetic variation. Rare cxSVs have a propensity to occur in neural genes and loci that underwent rapid human-specific evolution, including those regulating corticogenesis. By performing single-nucleus multiomics in postmortem brains, we discovered cxSVs associated with differential gene expression and chromatin accessibility across various brain regions and cell types. Additionally, cxSVs detected in brains of psychiatric cases are enriched for linkage with psychiatric GWAS risk alleles detected in the same brains. Furthermore, our analysis revealed significantly decreased brain-region- and cell-type-specific expression of cxSV genes, specifically for psychiatric cases, implicating cxSVs in the molecular etiology of major neuropsychiatric disorders.

Single-cell somatic copy number variants in brain using different amplification methods and reference genomes

The presence of somatic mutations, including copy number variants (CNVs), in the brain is well recognized. Comprehensive study requires single-cell whole genome amplification, with several methods available, prior to sequencing. Here we compare PicoPLEX with two recent adaptations of multiple displacement amplification (MDA): primary template-directed amplification (PTA) and droplet MDA, across 93 human brain cortical nuclei. We demonstrate different properties for each, with PTA providing the broadest amplification, PicoPLEX the most even, and distinct chimeric profiles. Furthermore, we perform CNV calling on two brains with multiple system atrophy and one control brain using different reference genomes. We find that 20.6% of brain cells have at least one Mb-scale CNV, with some supported by bulk sequencing or single-cells from other brain regions. Our study highlights the importance of selecting whole genome amplification method and reference genome for CNV calling, while supporting the existence of somatic CNVs in healthy and diseased human brain.

Perinatal Reduction of Genetically Aberrant Neurons from Human Cerebral Cortex

Since human neurons are postmitotic and long-lived, the regulation of their genomic content is crucial. Normal neuronal function is uniquely dependent on gene dosage, with diverse genome copy number alterations (CNA) associated with neurodevelopmental and neuropsychiatric conditions 1-3 . In this study, we evaluated the landscape of CNA arising in normal human brains, focusing on prenatal and perinatal ages. We surveyed ∼5,897 CNA in >1,200 single neurons from human postmortem brain of individuals without a neurological diagnosis, ranging in age from gestational week (GW) 14 to 90 years old. Using Tn5-based single-cell whole-genome amplification (scWGA) and informatic advances to validate CNAs in single neurons, we determined that a striking proportion of neurons (up to 45%) in human prenatal cortex showed aberrant genomes, characterized by large-scale CNAs in multiple chromosomes, which reduces significantly during the perinatal period (p<0.1). Furthermore, we identified micronuclei in the developing cortex, reflecting genetic instability reminiscent of that described in early embryonic development 4-6 . The scale of CNA appeared to alter the trajectory of neuronal elimination, as subchromosomal CNAs were more slowly eliminated, over the course of a lifetime. CNAs were depleted for dosage-sensitive genes and genes involved in neurodevelopmental disorders (p<.05), and thus represent genomic quality control mechanisms that eliminate selectively those neurons with CNA involving critical genes. Perinatal elimination of defective neuronal genomes may in turn reflect a developmental landmark essential for normal cognitive function.

Detection of mosaic and population-level structural variants with Sniffles2

Calling structural variations (SVs) is technically challenging, but using long reads remains the most accurate way to identify complex genomic alterations. Here we present Sniffles2, which improves over current methods by implementing a repeat aware clustering coupled with a fast consensus sequence and coverage-adaptive filtering. Sniffles2 is 11.8 times faster and 29% more accurate than state-of-the-art SV callers across different coverages (5-50×), sequencing technologies (ONT and HiFi) and SV types. Furthermore, Sniffles2 solves the problem of family-level to population-level SV calling to produce fully genotyped VCF files. Across 11 probands, we accurately identified causative SVs around MECP2, including highly complex alleles with three overlapping SVs. Sniffles2 also enables the detection of mosaic SVs in bulk long-read data. As a result, we identified multiple mosaic SVs in brain tissue from a patient with multiple system atrophy. The identified SV showed a remarkable diversity within the cingulate cortex, impacting both genes involved in neuron function and repetitive elements.

Impact and characterization of serial structural variations across humans and great apes

Modern sequencing technology enables the systematic detection of complex structural variation (SV) across genomes. However, extensive DNA rearrangements arising through a series of mutations, a phenomenon we refer to as serial SV (sSV), remain underexplored, posing a challenge for SV discovery. Here, we present NAHRwhals ( https://github.com/WHops/NAHRwhals ), a method to infer repeat-mediated series of SVs in long-read genomic assemblies. Applying NAHRwhals to haplotype-resolved human genomes from 28 individuals reveals 37 sSV loci of various length and complexity. These sSVs explain otherwise cryptic variation in medically relevant regions such as the TPSAB1 gene, 8p23.1, 22q11 and Sotos syndrome regions. Comparisons with great ape assemblies indicate that most human sSVs formed recently, after the human-ape split, and involved non-repeat-mediated processes in addition to non-allelic homologous recombination. NAHRwhals reliably discovers and characterizes sSVs at scale and independent of species, uncovering their genomic abundance and suggesting broader implications for disease.

Building the brain mosaic: an expanded view

The complexity of the brain is closely tied to its nature as a genetic mosaic, wherein each cell is distinguished by a unique constellation of somatic variants that contribute to functional and phenotypic diversity. Postzygotic variation arising during neurogenesis is recognized as a key contributor to brain mosaicism; however, recent advances have broadened our understanding to include sources of neural genomic diversity that develop throughout the entire lifespan, from embryogenesis through aging. Moving beyond the traditional confines of neurodevelopment, in this review, we delve into the complex mechanisms that enable various origins of brain mosaicism.

Comprehensive assessment of long-read sequencing platforms and calling algorithms for detection of copy number variation

Copy number variations (CNVs) play pivotal roles in disease susceptibility and have been intensively investigated in human disease studies. Long-read sequencing technologies offer opportunities for comprehensive structural variation (SV) detection, and numerous methodologies have been developed recently. Consequently, there is a pressing need to assess these methods and aid researchers in selecting appropriate techniques for CNV detection using long-read sequencing. Hence, we conducted an evaluation of eight CNV calling methods across 22 datasets from nine publicly available samples and 15 simulated datasets, covering multiple sequencing platforms. The overall performance of CNV callers varied substantially and was influenced by the input dataset type, sequencing depth, and CNV type, among others. Specifically, the PacBio CCS sequencing platform outperformed PacBio CLR and Nanopore platforms regarding CNV detection recall rates. A sequencing depth of 10x demonstrated the capability to identify 85% of the CNVs detected in a 50x dataset. Moreover, deletions were more generally detectable than duplications. Among the eight benchmarked methods, cuteSV, Delly, pbsv, and Sniffles2 demonstrated superior accuracy, while SVIM exhibited high recall rates.

[Sperm Mosaic Variants and Their Influence on the Offspring]

Genomic mosaicism arising from mosaic variants is a phenomenon that describes the presence of a cell or cell populations with different genome compositions from the germline cells of an individual. It comprises all types of genetic variants. A large proportion of childhood genetic disorders are defined as being de novo, meaning that the disease-causing mutations are only detected in the proband, not in any of the parents. Population studies show that 80% of the de novo mutations arise from the paternal haplotype, that is, from paternal sperm mosaicism. This review provides a summary of the types and detection strategies of sperm mosaicism. In addition, it provides discussions on how recent studies demonstrated that genomic mosaic mutations in parents, especially those in the paternal sperms, could be inherited by the offspring and cause childhood disorders. According to the previous findings of the author's research team, sperm mosaicism derived from early embryogenesis and primordial germ cell stages can explain 5% to 20% of the de novo mutations related to clinical phenotypes and can serve as an important predictor of both rare and complex disorders. Sperm mosaicism shows great potential for clinical genetic diagnosis and consultations. Based on the published literature, the author suggests that, large-scale screening for de novo sperm mosaic mutations and population-based genetic screening should be conducted in future studies, which will greatly enhance the risk assessment in the offspring and effectively improve the genetic health at the population level. Implementation of direct sperm detection for de novo mutations will significantly increase the efficiency of the stratification of patient cohorts and improve recurrence risk assessment for future births. Future research in the field should be focused on the impact of environmental and lifestyle factors on the health of the offspring through sperms and their modeling of mutation signatures. In addition, targeted in vitro modeling of sperm mutations will also be a promising direction.

Single-cell somatic copy number variants in brain using different amplification methods and reference genomes

The presence of somatic mutations, including copy number variants (CNVs), in the brain is well recognized. Comprehensive study requires single-cell whole genome amplification, with several methods available, prior to sequencing. We compared PicoPLEX with two recent adaptations of multiple displacement amplification (MDA): primary template-directed amplification (PTA) and droplet MDA, across 93 human brain cortical nuclei. We demonstrated different properties for each, with PTA providing the broadest amplification, PicoPLEX the most even, and distinct chimeric profiles. Furthermore, we performed CNV calling on two brains with multiple system atrophy and one control brain using different reference genomes. We found that 38% of brain cells have at least one Mb-scale CNV, with some supported by bulk sequencing or single-cells from other brain regions. Our study highlights the importance of selecting whole genome amplification method and reference genome for CNV calling, while supporting the existence of somatic CNVs in healthy and diseased human brain.

Genomic variant benchmark: if you cannot measure it, you cannot improve it

Genomic benchmark datasets are essential to driving the field of genomics and bioinformatics. They provide a snapshot of the performances of sequencing technologies and analytical methods and highlight future challenges. However, they depend on sequencing technology, reference genome, and available benchmarking methods. Thus, creating a genomic benchmark dataset is laborious and highly challenging, often involving multiple sequencing technologies, different variant calling tools, and laborious manual curation. In this review, we discuss the available benchmark datasets and their utility. Additionally, we focus on the most recent benchmark of genes with medical relevance and challenging genomic complexity.

Applications of advanced technologies for detecting genomic structural variation

Chromosomal structural variation (SV) encompasses a heterogenous class of genetic variants that exerts strong influences on human health and disease. Despite their importance, many structural variants (SVs) have remained poorly characterized at even a basic level, a discrepancy predicated upon the technical limitations of prior genomic assays. However, recent advances in genomic technology can identify and localize SVs accurately, opening new questions regarding SV risk factors and their impacts in humans. Here, we first define and classify human SVs and their generative mechanisms, highlighting characteristics leveraged by various SV assays. We next examine the first-ever gapless assembly of the human genome and the technical process of assembling it, which required third-generation sequencing technologies to resolve structurally complex loci. The new portions of that "telomere-to-telomere" and subsequent pangenome assemblies highlight aspects of SV biology likely to develop in the near-term. We consider the strengths and limitations of the most promising new SV technologies and when they or longstanding approaches are best suited to meeting salient goals in the study of human SV in population-scale genomics research, clinical, and public health contexts. It is a watershed time in our understanding of human SV when new approaches are expected to fundamentally change genomic applications.

SVDSS: structural variation discovery in hard-to-call genomic regions using sample-specific strings from accurate long reads

Structural variants (SVs) account for a large amount of sequence variability across genomes and play an important role in human genomics and precision medicine. Despite intense efforts over the years, the discovery of SVs in individuals remains challenging due to the diploid and highly repetitive structure of the human genome, and by the presence of SVs that vastly exceed sequencing read lengths. However, the recent introduction of low-error long-read sequencing technologies such as PacBio HiFi may finally enable these barriers to be overcome. Here we present SV discovery with sample-specific strings (SVDSS)-a method for discovery of SVs from long-read sequencing technologies (for example, PacBio HiFi) that combines and effectively leverages mapping-free, mapping-based and assembly-based methodologies for overall superior SV discovery performance. Our experiments on several human samples show that SVDSS outperforms state-of-the-art mapping-based methods for discovery of insertion and deletion SVs in PacBio HiFi reads and achieves notable improvements in calling SVs in repetitive regions of the genome.

Somatic CNV Detection by Single-Cell Whole-Genome Sequencing in Postmortem Human Brain

The evidence for a role of somatic mutations, including copy-number variants (CNVs), in neurodegeneration has increased in the last decade. However, the understanding of the types and origins of these mutations, and their exact contributions to disease onset and progression, is still in its infancy. The use of single-cell (or nuclear) whole-genome sequencing (scWGS) has emerged as a powerful tool to answer these questions. In the present chapter, we provide laboratory and bioinformatic protocols used successfully in our lab to detect megabase-scale CNVs in single cells from multiple system atrophy (MSA) human postmortem brains, using immunolabeling prior to selection of nuclei for whole-genome amplification (WGA). We also present an unpublished comparison of scWGS generated from the same control substantia nigra (SN) sample, using the latest versions of popular WGA chemistries, MDA and PicoPLEX. We have used this protocol to focus on brain cell types most relevant to synucleinopathies (dopaminergic [DA] neurons in Parkinson's disease [PD] and oligodendrocytes in MSA), but it can be applied to any tissue and/or cell type with appropriate markers.

Somatic mosaicism in the diseased brain

It is hard to believe that all the cells of a human brain share identical genomes. Indeed, single cell genetic studies have demonstrated intercellular genomic variability in the normal and diseased brain. Moreover, there is a growing amount of evidence on the contribution of somatic mosaicism (the presence of genetically different cell populations in the same individual/tissue) to the etiology of brain diseases. However, brain-specific genomic variations are generally overlooked during the research of genetic defects associated with a brain disease. Accordingly, a review of brain-specific somatic mosaicism in disease context seems to be required. Here, we overview gene mutations, copy number variations and chromosome abnormalities (aneuploidy, deletions, duplications and supernumerary rearranged chromosomes) detected in the neural/neuronal cells of the diseased brain. Additionally, chromosome instability in non-cancerous brain diseases is addressed. Finally, theoretical analysis of possible mechanisms for neurodevelopmental and neurodegenerative disorders indicates that a genetic background for formation of somatic (chromosomal) mosaicism in the brain is likely to exist. In total, somatic mosaicism affecting the central nervous system seems to be a mechanism of brain diseases.

Extrachromosomal circular DNA: Current status and future prospects

Extrachromosomal circular DNA (eccDNA) is a double-stranded DNA molecule found in various organisms, including humans. In the past few decades, the research on eccDNA has mainly focused on cancers and their associated diseases. Advancements in modern omics technologies have reinvigorated research on eccDNA and shed light on the role of these molecules in a range of diseases and normal cell phenotypes. In this review, we first summarize the formation of eccDNA and its modes of action in eukaryotic cells. We then outline eccDNA as a disease biomarker and reveal its regulatory mechanism. We finally discuss the future prospects of eccDNA, including basic research and clinical application. Thus, with the deepening of understanding and exploration of eccDNAs, they hold great promise in future biomedical research and clinical translational application.

Analysis of somatic mutations in 131 human brains reveals aging-associated hypermutability

We analyzed 131 human brains (44 neurotypical, 19 with Tourette syndrome, 9 with schizophrenia, and 59 with autism) for somatic mutations after whole genome sequencing to a depth of more than 200×. Typically, brains had 20 to 60 detectable single-nucleotide mutations, but ~6% of brains harbored hundreds of somatic mutations. Hypermutability was associated with age and damaging mutations in genes implicated in cancers and, in some brains, reflected in vivo clonal expansions. Somatic duplications, likely arising during development, were found in ~5% of normal and diseased brains, reflecting background mutagenesis. Brains with autism were associated with mutations creating putative transcription factor binding motifs in enhancer-like regions in the developing brain. The top-ranked affected motifs corresponded to MEIS (myeloid ectopic viral integration site) transcription factors, suggesting a potential link between their involvement in gene regulation and autism.

Population-scale genotyping of structural variation in the era of long-read sequencing

Population-scale studies of structural variation (SV) are growing rapidly worldwide with the development of long-read sequencing technology, yielding a considerable number of novel SVs and complete gap-closed genome assemblies. Herein, we highlight recent studies using a hybrid sequencing strategy and present the challenges toward large-scale genotyping for SVs due to the reference bias. Genotyping SVs at a population scale remains challenging, which severely impacts genotype-based population genetic studies or genome-wide association studies of complex diseases. We summarize academic efforts to improve genotype quality through linear or graph representations of reference and alternative alleles. Graph-based genotypers capable of integrating diverse genetic information are effectively applied to large and diverse cohorts, contributing to unbiased downstream analysis. Meanwhile, there is still an urgent need in this field for efficient tools to construct complex graphs and perform sequence-to-graph alignments.

All2: A tool for selecting mosaic mutations from comprehensive multi-cell comparisons

Accurate discovery of somatic mutations in a cell is a challenge that partially lays in immaturity of dedicated analytical approaches. Approaches comparing a cell's genome to a control bulk sample miss common mutations, while approaches to find such mutations from bulk suffer from low sensitivity. We developed a tool, All2, which enables accurate filtering of mutations in a cell without the need for data from bulk(s). It is based on pair-wise comparisons of all cells to each other where every call for base pair substitution and indel is classified as either a germline variant, mosaic mutation, or false positive. As All2 allows for considering dropped-out regions, it is applicable to whole genome and exome analysis of cloned and amplified cells. By applying the approach to a variety of available data, we showed that its application reduces false positives, enables sensitive discovery of high frequency mutations, and is indispensable for conducting high resolution cell lineage tracing.

Decoding and rejuvenating human ageing genomes: Lessons from mosaic chromosomal alterations

One of the most curious findings emerged from genome-wide studies over the last decade was that genetic mosaicism is a dominant feature of human ageing genomes. The clonal dominance of genetic mosaicism occurs preceding the physiological and physical ageing and associates with propensity for diseases including cancer, Alzheimer's disease, cardiovascular disease and diabetes. These findings are revolutionizing the ways biologists thinking about health and disease pathogenesis. Among all mosaic mutations in ageing genomes, mosaic chromosomal alterations (mCAs) have the most significant functional consequences because they can produce intercellular genomic variations simultaneously involving dozens to hundreds or even thousands genes, and therefore have most profound effects in human ageing and disease etiology. Here, we provide a comprehensive picture of the landscapes, causes, consequences and rejuvenation of mCAs at multiple scales, from cell to human population, by reviewing data from cytogenetic, genetic and genomic studies in cells, animal models (fly and mouse) and, more frequently, large-cohort populations. A detailed decoding of ageing genomes with a focus on mCAs may yield important insights into the genomic architecture of human ageing, accelerate the risk stratification of age-related diseases (particularly cancers) and development of novel targets and strategies for delaying or rejuvenating human (genome) ageing.

Chromosome Instability, Aging and Brain Diseases

Chromosome instability (CIN) has been repeatedly associated with aging and progeroid phenotypes. Moreover, brain-specific CIN seems to be an important element of pathogenic cascades leading to neurodegeneration in late adulthood. Alternatively, CIN and aneuploidy (chromosomal loss/gain) syndromes exhibit accelerated aging phenotypes. Molecularly, cellular senescence, which seems to be mediated by CIN and aneuploidy, is likely to contribute to brain aging in health and disease. However, there is no consensus about the occurrence of CIN in the aging brain. As a result, the role of CIN/somatic aneuploidy in normal and pathological brain aging is a matter of debate. Still, taking into account the effects of CIN on cellular homeostasis, the possibility of involvement in brain aging is highly likely. More importantly, the CIN contribution to neuronal cell death may be responsible for neurodegeneration and the aging-related deterioration of the brain. The loss of CIN-affected neurons probably underlies the contradiction between reports addressing ontogenetic changes of karyotypes within the aged brain. In future studies, the combination of single-cell visualization and whole-genome techniques with systems biology methods would certainly define the intrinsic role of CIN in the aging of the normal and diseased brain.