Somatic mutation in single human neurons tracks developmental and transcriptional history

Somatic variants in epilepsy - advancing gene discovery and disease mechanisms

In the past ten years, there has been increasing recognition that cells can acquire genetic variants during cortical development that can give rise to brain malformations as well as nonlesional focal epilepsy. These often brain tissue-specific, de novo variants can result in highly variable phenotypes based on the burden of a variant in specific tissues and cells. By discovering these variants, shared pathophysiological mechanisms are being revealed between clinically distinct disorders. Beyond informing disease mechanisms, mosaic variants also offer a powerful research tool to trace cellular lineages, to study the roles of specialized cell types in disease presentation, and to establish the cell-type specific genomic consequences of a variant.

Tools and Concepts for Interrogating and Defining Cellular Identity

Defining the mechanisms that generate specialized cell types and coordinate their functions is critical for understanding organ development and renewal. New tools and discoveries are challenging and refining our definitions of a cell type. A rapidly growing toolkit for single-cell analyses has expanded the number of markers that can be assigned to a cell simultaneously, revealing heterogeneity within cell types that were previously regarded as homogeneous populations. Additionally, cell types defined by specific molecular markers can exhibit distinct, context-dependent functions; for example, between tissues in homeostasis and those responding to damage. Here we review the current technologies used to identify and characterize cells, and we discuss how experimental and pathological perturbations are adding increasing complexity to our definitions of cell identity.

Deletion of Topoisomerase 1 in excitatory neurons causes genomic instability and early onset neurodegeneration

Topoisomerase 1 (TOP1) relieves torsional stress in DNA during transcription and facilitates the expression of long (>100 kb) genes, many of which are important for neuronal functions. To evaluate how loss of Top1 affected neurons in vivo, we conditionally deleted (cKO) Top1 in postmitotic excitatory neurons in the mouse cerebral cortex and hippocampus. Top1 cKO neurons develop properly, but then show biased transcriptional downregulation of long genes, signs of DNA damage, neuroinflammation, increased poly(ADP-ribose) polymerase-1 (PARP1) activity, single-cell somatic mutations, and ultimately degeneration. Supplementation of nicotinamide adenine dinucleotide (NAD⁺) with nicotinamide riboside partially blocked neurodegeneration, and increased the lifespan of Top1 cKO mice by 30%. A reduction of p53 also partially rescued cortical neuron loss. While neurodegeneration was partially rescued, behavioral decline was not prevented. These data indicate that reducing neuronal loss is not sufficient to limit behavioral decline when TOP1 function is disrupted.

Topoisomerase 1 (TOP1) relieves DNA torsional stress during transcription and facilitates the expression of long neuronal genes. Here we show that deletion of Top1 in excitatory neurons leads to early onset neurodegeneration that is partially dependent on p53/PARP1 activation and NAD⁺ depletion.

Single-cell omics in ageing: a young and growing field

Organismal ageing results from interlinked molecular changes in multiple organs over time. The study of ageing at the molecular level is complicated by varying decay characteristics and kinetics-both between and within organs-driven by intrinsic and extracellular factors. Emerging single-cell omics methods allow for molecular and spatial profiling of cells, and probing of regulatory states and cell-fate determination, thus providing promising tools for unravelling the heterogeneous process of ageing and making it amenable to intervention. These new strategies are enabled by advances in genomic, epigenomic and transcriptomic technologies. Combined with methods for proteome and metabolome analysis, single-cell techniques provide multidimensional, integrated data with unprecedented detail and throughput. Here, we provide an overview of the current state, and perspectives on the future, of this emerging field. We discuss how single-cell approaches can advance understanding of mechanisms underlying organismal ageing and aid in the development of interventions for ageing and ageing-associated diseases.

Emerging Frontiers in the Study of Molecular Evolution

A collection of the editors of Journal of Molecular Evolution have gotten together to pose a set of key challenges and future directions for the field of molecular evolution. Topics include challenges and new directions in prebiotic chemistry and the RNA world, reconstruction of early cellular genomes and proteins, macromolecular and functional evolution, evolutionary cell biology, genome evolution, molecular evolutionary ecology, viral phylodynamics, theoretical population genomics, somatic cell molecular evolution, and directed evolution. While our effort is not meant to be exhaustive, it reflects research questions and problems in the field of molecular evolution that are exciting to our editors.

Somatic mutation that affects transcription factor binding upstream of CD55 in the temporal cortex of a late-onset Alzheimer disease patient

Alzheimer’s disease (AD) is the most common neurodegenerative disease worldwide. Familial cases suggest genetic components; however, monogenetic causes are few, and the vast majority of incidences have unknown cause. Sequencing efforts have focused on germline mutations, but improved technology has opened up for studies on somatic mutations in affected brain tissue samples. Here we use ultra-deep sequencing on brain and blood from early-onset AD (EOAD) and late-onset AD (LOAD) patients and non-AD individuals (n = 16). In total, 2.86 Mb of genomic regions, previously associated with AD, were targeted included 28 genes and upstream and downstream regulatory regions. Tailored downstream bioinformatics filtering identified 11 somatic single nucleotide variants in the temporal cortex in AD patients and none in the controls. One variant was validated to be present at 0.4% allele frequency in temporal cortex of a LOAD patient. This variant was predicted to affect transcription factor binding sites upstream of the CD55 gene, contributing to AD pathogenesis by affecting the complement system. Our results suggest that future studies targeting larger portions of the genome for somatic mutation analysis are important to obtain an increased understanding for the molecular basis of both EOAD and LOAD.

Temporal changes in the gene expression heterogeneity during brain development and aging

Cells in largely non-mitotic tissues such as the brain are prone to stochastic (epi-)genetic alterations that may cause increased variability between cells and individuals over time. Although increased inter-individual heterogeneity in gene expression was previously reported, whether this process starts during development or if it is restricted to the aging period has not yet been studied. The regulatory dynamics and functional significance of putative aging-related heterogeneity are also unknown. Here we address these by a meta-analysis of 19 transcriptome datasets from three independent studies, covering diverse human brain regions. We observed a significant increase in inter-individual heterogeneity during aging (20 + years) compared to postnatal development (0 to 20 years). Increased heterogeneity during aging was consistent among different brain regions at the gene level and associated with lifespan regulation and neuronal functions. Overall, our results show that increased expression heterogeneity is a characteristic of aging human brain, and may influence aging-related changes in brain functions.

Neurons with Complex Karyotypes Are Rare in Aged Human Neocortex

A subset of human neocortical neurons harbors complex karyotypes wherein megabase-scale copy-number variants (CNVs) alter allelic diversity. Divergent levels of neurons with complex karyotypes (CNV neurons) are reported in different individuals, yet genome-wide and familial studies implicitly assume a single brain genome when assessing the genetic risk architecture of neurological disease. We assembled a brain CNV atlas using a robust computational approach applied to a new dataset (>800 neurons from 5 neurotypical individuals) and to published data from 10 additional neurotypical individuals. The atlas reveals that the frequency of neocortical neurons with complex karyotypes varies widely among individuals, but this variability is not readily accounted for by tissue quality or CNV detection approach. Rather, the age of the individual is anti-correlated with CNV neuron frequency. Fewer CNV neurons are observed in aged individuals than in young individuals.

RETrace: simultaneous retrospective lineage tracing and methylation profiling of single cells

Retrospective lineage tracing harnesses naturally occurring mutations in cells to elucidate single cell development. Common single-cell phylogenetic fate mapping methods have utilized highly mutable microsatellite loci found within the human genome. Such methods were limited by the introduction of in vitro noise through polymerase slippage inherent in DNA amplification, which we characterized to be approximately 10–100× higher than the in vivo replication mutation rate. Here, we present RETrace, a method for simultaneously capturing both microsatellites and methylation-informative cytosines to characterize both lineage and cell type, respectively, from the same single cell. An important unique feature of RETrace was the introduction of linear amplification of microsatellites in order to reduce in vitro amplification noise. We further coupled microsatellite capture with single-cell reduced representation bisulfite sequencing (scRRBS), to measure the CpG methylation status on the same cell for cell type inference. When compared to existing retrospective lineage tracing methods, RETrace achieved higher accuracy (88% triplet accuracy from an ex vivo HCT116 tree) at a higher cell division resolution (lowering the required number of cell division difference between single cells by approximately 100 divisions). Simultaneously, RETrace demonstrated the ability to capture on average 150,000 unique CpGs per single cell in order to accurately determine cell type. We further formulated additional developments that would allow high-resolution mapping on microsatellite-stable cells or tissues with RETrace. Overall, we present RETrace as a foundation for multi-omics lineage mapping and cell typing of single cells.

The multiplex model of the genetics of Alzheimer's disease

Genes play a strong role in Alzheimer's disease (AD), with late-onset AD showing heritability of 58-79% and early-onset AD showing over 90%. Genetic association provides a robust platform to build our understanding of the etiology of this complex disease. Over 50 loci are now implicated for AD, suggesting that AD is a disease of multiple components, as supported by pathway analyses (immunity, endocytosis, cholesterol transport, ubiquitination, amyloid-β and tau processing). Over 50% of late-onset AD heritability has been captured, allowing researchers to calculate the accumulation of AD genetic risk through polygenic risk scores. A polygenic risk score predicts disease with up to 90% accuracy and is an exciting tool in our research armory that could allow selection of those with high polygenic risk scores for clinical trials and precision medicine. It could also allow cellular modelling of the combined risk. Here we propose the multiplex model as a new perspective from which to understand AD. The multiplex model reflects the combination of some, or all, of these model components (genetic and environmental), in a tissue-specific manner, to trigger or sustain a disease cascade, which ultimately results in the cell and synaptic loss observed in AD.

Inherited Eye Diseases with Retinal Manifestations through the Eyes of Homeobox Genes

Retinal development is under the coordinated control of overlapping networks of signaling pathways and transcription factors. The paper was conceived as a review of the data and ideas that have been formed to date on homeobox genes mutations that lead to the disruption of eye organogenesis and result in inherited eye/retinal diseases. Many of these diseases are part of the same clinical spectrum and have high genetic heterogeneity with already identified associated genes. We summarize the known key regulators of eye development, with a focus on the homeobox genes associated with monogenic eye diseases showing retinal manifestations. Recent advances in the field of genetics and high-throughput next-generation sequencing technologies, including single-cell transcriptome analysis have allowed for deepening of knowledge of the genetic basis of inherited retinal diseases (IRDs), as well as improve their diagnostics. We highlight some promising avenues of research involving molecular-genetic and cell-technology approaches that can be effective for IRDs therapy. The most promising neuroprotective strategies are aimed at mobilizing the endogenous cellular reserve of the retina.

CellTagging: combinatorial indexing to simultaneously map lineage and identity at single-cell resolution

Single-cell technologies are offering unparalleled insight into complex biology, revealing the behavior of rare cell populations that are masked in bulk population analyses. One current limitation of single-cell approaches is that lineage relationships are typically lost as a result of cell processing. We recently established a method, CellTagging, permitting the parallel capture of lineage information and cell identity via a combinatorial cell indexing approach. CellTagging integrates with high-throughput single-cell RNA sequencing, where sequential rounds of cell labeling enable the construction of multi-level lineage trees. Here, we provide a detailed protocol to (i) generate complex plasmid and lentivirus CellTag libraries for labeling of cells; (ii) sequentially CellTag cells over the course of a biological process; (iii) profile single-cell transcriptomes via high-throughput droplet-based platforms; and (iv) generate a CellTag expression matrix, followed by clone calling and lineage reconstruction. This lentiviral-labeling approach can be deployed in any organism or in vitro culture system that is amenable to viral transduction to simultaneously profile lineage and identity at single-cell resolution.

Human transposon insertion profiling by sequencing (TIPseq) to map LINE-1 insertions in single cells

Long interspersed element-1 (LINE-1, L1) sequences, which comprise about 17% of human genome, are the product of one of the most active types of mobile DNAs in modern humans. LINE-1 insertion alleles can cause inherited and de novo genetic diseases, and LINE-1-encoded proteins are highly expressed in some cancers. Genome-wide LINE-1 mapping in single cells could be useful for defining somatic and germline retrotransposition rates, and for enabling studies to characterize tumour heterogeneity, relate insertions to transcriptional and epigenetic effects at the cellular level, or describe cellular phylogenies in development. Our laboratories have reported a genome-wide LINE-1 insertion site mapping method for bulk DNA, named transposon insertion profiling by sequencing (TIPseq). There have been significant barriers applying LINE-1 mapping to single cells, owing to the chimeric artefacts and features of repetitive sequences. Here, we optimize a modified TIPseq protocol and show its utility for LINE-1 mapping in single lymphoblastoid cells. Results from single-cell TIPseq experiments compare well to known LINE-1 insertions found by whole-genome sequencing and TIPseq on bulk DNA. Among the several approaches we tested, whole-genome amplification by multiple displacement amplification followed by restriction enzyme digestion, vectorette ligation and LINE-1-targeted PCR had the best assay performance. This article is part of a discussion meeting issue 'Crossroads between transposons and gene regulation'.

Proteome-Scale Mapping of Perturbed Proteostasis in Living Cells

Proteinopathies are degenerative diseases in which specific proteins adopt deleterious conformations, leading to the dysfunction and demise of distinct cell types. They comprise some of the most significant diseases of aging-from Alzheimer's disease to Parkinson's disease to type 2 diabetes-for which not a single disease-modifying or preventative strategy exists. Here, we survey approaches in tractable cellular and organismal models that bring us toward a more complete understanding of the molecular consequences of protein misfolding. These include proteome-scale profiling of genetic modifiers, as well as transcriptional and proteome changes. We describe assays that can capture protein interactomes in situ and distinct protein conformational states. A picture of cellular drivers and responders to proteotoxicity emerges from this work, distinguishing general alterations of proteostasis from cellular events that are deeply tied to the intrinsic function of the misfolding protein. These distinctions have consequences for the understanding and treatment of proteinopathies.

Natural and pathogenic protein sequence variation affecting prion-like domains within and across human proteomes

BACKGROUND

Impaired proteostatic regulation of proteins with prion-like domains (PrLDs) is associated with a variety of human diseases including neurodegenerative disorders, myopathies, and certain forms of cancer. For many of these disorders, current models suggest a prion-like molecular mechanism of disease, whereby proteins aggregate and spread to neighboring cells in an infectious manner. The development of prion prediction algorithms has facilitated the large-scale identification of PrLDs among "reference" proteomes for various organisms. However, the degree to which intraspecies protein sequence diversity influences predicted prion propensity has not been systematically examined.

RESULTS

Here, we explore protein sequence variation introduced at genetic, post-transcriptional, and post-translational levels, and its influence on predicted aggregation propensity for human PrLDs. We find that sequence variation is relatively common among PrLDs and in some cases can result in relatively large differences in predicted prion propensity. Sequence variation introduced at the post-transcriptional level (via alternative splicing) also commonly affects predicted aggregation propensity, often by direct inclusion or exclusion of a PrLD. Finally, analysis of a database of sequence variants associated with human disease reveals a number of mutations within PrLDs that are predicted to increase prion propensity.

CONCLUSIONS

Our analyses expand the list of candidate human PrLDs, quantitatively estimate the effects of sequence variation on the aggregation propensity of PrLDs, and suggest the involvement of prion-like mechanisms in additional human diseases.

Accurate detection of mosaic variants in sequencing data without matched controls

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 (SNVs) and indels, achieving a multifold increase in specificity compared to existing algorithms. Using single-cell sequencing and targeted sequencing, we validated 80–90% of the mosaic SNVs and 60–80% 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.

Identification of differentially expressed genes by single-cell transcriptional profiling of umbilical cord and synovial fluid mesenchymal stem cells

The purpose of this study was to measure the heterogeneity in human umbilical cord–derived mesenchymal stem cells (hUC‐MSCs) and human synovial fluid–derived mesenchymal stem cells (hSF‐MSCs) by single‐cell RNA‐sequencing (scRNA‐seq). Using Chromium™ technology, scRNA‐seq was performed on hUC‐MSCs and hSF‐MSCs from samples that passed our quality control checks. In order to identify subgroups and activated pathways, several bioinformatics tools were used to analyse the transcriptomic profiles, including clustering, principle components analysis (PCA), t‐Distributed Stochastic Neighbor Embedding (t‐SNE), gene set enrichment analysis, as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. scRNA‐seq was performed on the two sample sets. In total, there were 104 761 163 reads for the hUC‐MSCs and 6 577 715 for the hSF‐MSCs, with >60% mapping rate. Based on PCA and t‐SNE analyses, we identified 11 subsets within hUC‐MSCs and seven subsets within hSF‐MSCs. Gene set enrichment analysis determined that there were 533, 57, 32, 44, 10, 319, 731, 1037, 90, 25 and 230 differentially expressed genes (DEGs) in the 11 subsets of hUC‐MSCs and 204, 577, 30, 577, 16, 57 and 35 DEGs in the seven subsets of hSF‐MSCs. scRNA‐seq was not only able to identify subpopulations of hUC‐MSCs and hSF‐MSCs within the sample sets, but also provided a digital transcript count of hUC‐MSCs and hSF‐MSCs within a single patient. scRNA‐seq analysis may elucidate some of the biological characteristics of MSCs and allow for a better understanding of the multi‐directional differentiation, immunomodulatory properties and tissue repair capabilities of MSCs.

Artificial intelligence for precision medicine in neurodevelopmental disorders

The ambition of precision medicine is to design and optimize the pathway for diagnosis, therapeutic intervention, and prognosis by using large multidimensional biological datasets that capture individual variability in genes, function and environment. This offers clinicians the opportunity to more carefully tailor early interventions- whether treatment or preventative in nature-to each individual patient. Taking advantage of high performance computer capabilities, artificial intelligence (AI) algorithms can now achieve reasonable success in predicting risk in certain cancers and cardiovascular disease from available multidimensional clinical and biological data. In contrast, less progress has been made with the neurodevelopmental disorders, which include intellectual disability (ID), autism spectrum disorder (ASD), epilepsy and broader neurodevelopmental disorders. Much hope is pinned on the opportunity to quantify risk from patterns of genomic variation, including the functional characterization of genes and variants, but this ambition is confounded by phenotypic and etiologic heterogeneity, along with the rare and variable penetrant nature of the underlying risk variants identified so far. Structural and functional brain imaging and neuropsychological and neurophysiological markers may provide further dimensionality, but often require more development to achieve sensitivity for diagnosis. Herein, therefore, lies a precision medicine conundrum: can artificial intelligence offer a breakthrough in predicting risks and prognosis for neurodevelopmental disorders? In this review we will examine these complexities, and consider some of the strategies whereby artificial intelligence may overcome them.

Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data

BACKGROUND

Systematic interrogation of single-nucleotide variants (SNVs) is one of the most promising approaches to delineate the cellular heterogeneity and phylogenetic relationships at the single-cell level. While SNV detection from abundant single-cell RNA sequencing (scRNA-seq) data is applicable and cost-effective in identifying expressed variants, inferring sub-clones, and deciphering genotype-phenotype linkages, there is a lack of computational methods specifically developed for SNV calling in scRNA-seq. Although variant callers for bulk RNA-seq have been sporadically used in scRNA-seq, the performances of different tools have not been assessed.

RESULTS

Here, we perform a systematic comparison of seven tools including SAMtools, the GATK pipeline, CTAT, FreeBayes, MuTect2, Strelka2, and VarScan2, using both simulation and scRNA-seq datasets, and identify multiple elements influencing their performance. While the specificities are generally high, with sensitivities exceeding 90% for most tools when calling homozygous SNVs in high-confident coding regions with sufficient read depths, such sensitivities dramatically decrease when calling SNVs with low read depths, low variant allele frequencies, or in specific genomic contexts. SAMtools shows the highest sensitivity in most cases especially with low supporting reads, despite the relatively low specificity in introns or high-identity regions. Strelka2 shows consistently good performance when sufficient supporting reads are provided, while FreeBayes shows good performance in the cases of high variant allele frequencies.

CONCLUSIONS

We recommend SAMtools, Strelka2, FreeBayes, or CTAT, depending on the specific conditions of usage. Our study provides the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data.

Somatic Mutations in Vascular Malformations of Hereditary Hemorrhagic Telangiectasia Result in Bi-allelic Loss of ENG or ACVRL1

Hereditary hemorrhagic telangiectasia (HHT) is a Mendelian disease characterized by vascular malformations (VMs) including visceral arteriovenous malformations and mucosal telangiectasia. HHT is caused by loss-of-function (LoF) mutations in one of three genes, ENG, ACVRL1, or SMAD4, and is inherited as an autosomal-dominant condition. Intriguingly, the constitutional mutation causing HHT is present throughout the body, yet the multiple VMs in individuals with HHT occur focally, rather than manifesting as a systemic vascular defect. This disconnect between genotype and phenotype suggests that a local event is necessary for the development of VMs. We investigated the hypothesis that local somatic mutations seed the formation HHT-related telangiectasia in a genetic two-hit mechanism. We identified low-frequency somatic mutations in 9/19 telangiectasia through the use of next-generation sequencing. We established phase for seven of nine samples, which confirms that the germline and somatic mutations in all seven samples exist in trans configuration; this is consistent with a genetic two-hit mechanism. These combined data suggest that bi-allelic loss of ENG or ACVRL1 may be a required event in the development of telangiectasia, and that rather than haploinsufficiency, VMs in HHT are caused by a Knudsonian two-hit mechanism.

Clonal copy-number mosaicism in autoreactive T lymphocytes in diabetic NOD mice

Concordance for type 1 diabetes (T1D) is far from 100% in monozygotic twins and in inbred nonobese diabetic (NOD) mice, despite genetic identity and shared environment during incidence peak years. This points to stochastic determinants, such as postzygotic mutations (PZMs) in the expanding antigen-specific autoreactive T cell lineages, by analogy to their role in the expanding tumor lineage in cancer. Using comparative genomic hybridization of DNA from pancreatic lymph-node memory CD4⁺ T cells of 25 diabetic NOD mice, we found lymphocyte-exclusive mosaic somatic copy-number aberrations (CNAs) with highly nonrandom independent involvement of the same gene(s) across different mice, some with an autoimmunity association (e.g., Ilf3 and Dgka). We confirmed genes of interest using the gold standard approach for CNA quantification, multiplex ligation-dependent probe amplification (MLPA), as an independent method. As controls, we examined lymphocytes expanded during normal host defense (17 NOD and BALB/c mice infected with Leishmania major parasite). Here, CNAs found were fewer and significantly smaller compared to those in autoreactive cells (P = 0.0019). We determined a low T cell clonality for our samples suggesting a prethymic formation of these CNAs. In this study, we describe a novel, unexplored phenomenon of a potential causal contribution of PZMs in autoreactive T cells in T1D pathogenesis. We expect that exploration of point mutations and studies in human T cells will enable the further delineation of driver genes to target for functional studies. Our findings challenge the classical notions of autoimmunity and open conceptual avenues toward individualized prevention and therapeutics.

Unravelling cellular relationships during development and regeneration using genetic lineage tracing

Tracking the progeny of single cells is necessary for building lineage trees that recapitulate processes such as embryonic development and stem cell differentiation. In classical lineage tracing experiments, cells are fluorescently labelled to allow identification by microscopy of a limited number of cell clones. To track a larger number of clones in complex tissues, fluorescent proteins are now replaced by heritable DNA barcodes that are read using next-generation sequencing. In prospective lineage tracing, unique DNA barcodes are introduced into single cells through genetic manipulation (using, for example, Cre-mediated recombination or CRISPR-Cas9-mediated editing) and tracked over time. Alternatively, in retrospective lineage tracing, naturally occurring somatic mutations can be used as endogenous DNA barcodes. Finally, single-cell mRNA-sequencing datasets that capture different cell states within a developmental or differentiation trajectory can be used to recapitulate lineages. In this Review, we discuss methods for prospective or retrospective lineage tracing and demonstrate how trajectory reconstruction algorithms can be applied to single-cell mRNA-sequencing datasets to infer developmental or differentiation tracks. We discuss how these approaches are used to understand cell fate during embryogenesis, cell differentiation and tissue regeneration.

Flexibility sustains epithelial tissue homeostasis

Epithelia surround our bodies and line most of our organs. Intrinsic homeostatic mechanisms replenish and repair these tissues in the face of wear and tear, wounds, and even the presence of accumulating mutations. Recent advances in cell biology, genetics, and live-imaging techniques have revealed that epithelial homeostasis represents an intrinsically flexible process at the level of individual epithelial cells. This homeostatic flexibility has important implications for how we think about the more dramatic cell plasticity that is frequently thought to be associated with pathological settings. In this review, we will focus on key emerging mechanisms and processes of epithelial homeostasis and elaborate on the known molecular mechanisms of epithelial cell interactions to illuminate how epithelia are maintained throughout an organism's lifetime.

Behavioral analyses of animal models of intellectual and developmental disabilities

Intellectual and developmental disabilities (IDDs) are a common group of disorders that frequently share overlapping symptoms, including cognitive deficits, altered attention, seizures, impaired social interactions, and anxiety. The causes of these disorders are varied ranging from early prenatal/postnatal insults to genetic variants that either cause or are associated with an increased likelihood of an IDD. As many of the symptoms observed in individuals with IDDs are a manifestation of altered nervous system function resulting in altered behaviors, it should not be surprising that the field is very dependent upon in vivo model systems. This special issue of Neurobiology of Learning and Memory is focused on the methods and approaches that are being used to model and understand these disorders in mammals. While surveys by the Pew Foundation continue to find a high degree of confidence/trust in scientists by the public, several recent studies have documented issues with reproducibility in scientific publications. This special issue includes both primary research articles and review articles in which careful attention has been made to transparently report methods and use rigorous approaches to ensure reproducibility. Although there have been and will continue to be remarkable advances for treatment of subset of IDDs, it is clear that this field is still in its early stages. There is no doubt that the strategies being used to model IDDs will continue to evolve. We hope this special issue will support this evolution so that we can maintain the trust of the public and elected officials, and continue developing evidence-based approaches to new therapeutics.

Intellectual and developmental disabilities research centers: Fifty years of scientific accomplishments

Progress in addressing the origins of intellectual and developmental disabilities accelerated with the establishment 50 years ago of the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health and associated Intellectual and Developmental Disabilities Research Centers. Investigators at these Centers have made seminal contributions to understanding human brain and behavioral development and defining mechanisms and treatments of disorders of the developing brain. ANN NEUROL 2019;86:332-343.

Identification of somatic mutations in single cell DNA-seq using a spatial model of allelic imbalance

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.

Deciphering Brain Complexity Using Single-cell Sequencing

The human brain contains billions of highly differentiated and interconnected cells that form intricate neural networks and collectively control the physical activities and high-level cognitive functions, such as memory, decision-making, and social behavior. Big data is required to decipher the complexity of cell types, as well as connectivity and functions of the brain. The newly developed single-cell sequencing technology, which provides a comprehensive landscape of brain cell type diversity by profiling the transcriptome, genome, and/or epigenome of individual cells, has contributed substantially to revealing the complexity and dynamics of the brain and providing new insights into brain development and brain-related disorders. In this review, we first introduce the progresses in both experimental and computational methods of single-cell sequencing technology. Applications of single-cell sequencing-based technologies in brain research, including cell type classification, brain development, and brain disease mechanisms, are then elucidated by representative studies. Lastly, we provided our perspectives into the challenges and future developments in the field of single-cell sequencing. In summary, this mini review aims to provide an overview of how big data generated from single-cell sequencing have empowered the advancements in neuroscience and shed light on the complex problems in understanding brain functions and diseases.

Integrative single-cell analysis

The recent maturation of single-cell RNA sequencing (scRNA-seq) technologies has coincided with transformative new methods to profile genetic, epigenetic, spatial, proteomic and lineage information in individual cells. This provides unique opportunities, alongside computational challenges, for integrative methods that can jointly learn across multiple types of data. Integrated analysis can discover relationships across cellular modalities, learn a holistic representation of the cell state, and enable the pooling of data sets produced across individuals and technologies. In this Review, we discuss the recent advances in the collection and integration of different data types at single-cell resolution with a focus on the integration of gene expression data with other types of single-cell measurement.

PaSD-qc: quality control for single cell whole-genome sequencing data using power spectral density estimation

Single cell whole-genome sequencing (scWGS) is providing novel insights into the nature of genetic heterogeneity in normal and diseased cells. However, the whole-genome amplification process required for scWGS introduces biases into the resulting sequencing that can confound downstream analysis. Here, we present a statistical method, with an accompanying package PaSD-qc (Power Spectral Density-qc), that evaluates the properties and quality of single cell libraries. It uses a modified power spectral density to assess amplification uniformity, amplicon size distribution, autocovariance and inter-sample consistency as well as to identify chromosomes with aberrant read-density profiles due either to copy alterations or poor amplification. These metrics provide a standard way to compare the quality of single cell samples as well as yield information necessary to improve variant calling strategies. We demonstrate the usefulness of this tool in comparing the properties of scWGS protocols, identifying potential chromosomal copy number variation, determining chromosomal and subchromosomal regions of poor amplification, and selecting high-quality libraries from low-coverage data for deep sequencing. The software is available free and open-source at https://github.com/parklab/PaSDqc.

Scales and mechanisms of somatic mutation rate variation across the human genome

Cancer genome sequencing has revealed that somatic mutation rates vary substantially across the human genome and at scales from megabase-sized domains to individual nucleotides. Here we review recent work that has both revealed the major mutation biases that operate across the genome and the molecular mechanisms that cause them. The default mutation rate landscape in mammalian genomes results in active genes having low mutation rates because of a combination of factors that increase DNA repair: early DNA replication, transcription, active chromatin modifications and accessible chromatin. Therefore, either an increase in the global mutation rate or a redistribution of mutations from inactive to active DNA can increase the rate at which consequential mutations are acquired in active genes. Several environmental carcinogens and intrinsic mechanisms operating in tumor cells likely cause cancer by this second mechanism: by specifically increasing the mutation rate in active regions of the genome.

Somatic Mutations and Clonal Hematopoiesis: Unexpected Potential New Drivers of Age-Related Cardiovascular Disease

Increasing evidence shows that conventional cardiovascular risk factors are incompletely predictive of cardiovascular disease, particularly in elderly individuals, suggesting that there may still be unidentified causal risk factors. Although the accumulation of somatic DNA mutations is a hallmark of aging, its relevance in cardiovascular disease or other age-related conditions has been, with the exception of cancer, largely unexplored. Here, we review recent clinical and preclinical studies that have identified acquired mutations in hematopoietic stem cells and subsequent clonal hematopoiesis as a new cardiovascular risk factor and a potential major driver of atherosclerosis. Understanding the mechanisms underlying the connection between somatic mutation-driven clonal hematopoiesis and cardiovascular disease will be highly relevant in the context of personalized medicine, as it may provide key information for the design of diagnostic, preventive, or therapeutic strategies tailored to the effects of specific somatic mutations.

Somatic mutation: The hidden genetics of brain malformations and focal epilepsies

Over the past decade there has been a substantial increase in genetic studies of brain malformations, fueled by the availability of improved technologies to study surgical tissue to address the hypothesis that focal lesions arise from focal, post-zygotic genetic disruptions. Traditional genetic studies of patients with malformations utilized leukocyte-derived DNA to search for germline variants, which are inherited or arise de novo in parental gametes. Recent studies have demonstrated somatic variants that arise post-zygotically also underlie brain malformations, and that somatic mutation explains a larger proportion of focal malformations than previously thought. We now know from studies of non-diseased individuals that somatic variation occurs routinely during cell division, including during early brain development when the rapid proliferation of neuronal precursor cells provides the ideal environment for somatic mutation to occur and somatic variants to accumulate. When confined to brain, pathogenic variants contribute to the "hidden genetics" of neurological diseases. With burgeoning novel high-throughput genetic technologies, somatic genetic variations are increasingly being recognized. Here we discuss accumulating evidence for the presence of somatic variants in normal brain tissue, review our current understanding of somatic variants in brain malformations associated with lesional epilepsy, and provide strategies to identify the potential contribution of somatic mutation to non-lesional epilepsies. We also discuss technologies that may improve detection of somatic variants in the future in these and other neurological conditions.

Mapping causal pathways from genetics to neuropsychiatric disorders using genome-wide imaging genetics: Current status and future directions

Imaging genetics aims to identify genetic variants associated with the structure and function of the human brain. Recently, collaborative consortia have been successful in this goal, identifying and replicating common genetic variants influencing gross human brain structure as measured through magnetic resonance imaging. In this review, we contextualize imaging genetic associations as one important link in understanding the causal chain from genetic variant to increased risk for neuropsychiatric disorders. We provide examples in other fields of how identifying genetic variant associations to disease and multiple phenotypes along the causal chain has revealed a mechanistic understanding of disease risk, with implications for how imaging genetics can be similarly applied. We discuss current findings in the imaging genetics research domain, including that common genetic variants can have a slightly larger effect on brain structure than on risk for disorders like schizophrenia, indicating a somewhat simpler genetic architecture. Also, gross brain structure measurements share a genetic basis with some, but not all, neuropsychiatric disorders, invalidating the previously held belief that they are broad endophenotypes, yet pinpointing brain regions likely involved in the pathology of specific disorders. Finally, we suggest that in order to build a more detailed mechanistic understanding of the effects of genetic variants on the brain, future directions in imaging genetics research will require observations of cellular and synaptic structure in specific brain regions beyond the resolution of magnetic resonance imaging. We expect that integrating genetic associations at biological levels from synapse to sulcus will reveal specific causal pathways impacting risk for neuropsychiatric disorders.

Recording development with single cell dynamic lineage tracing

Every animal grows from a single fertilized egg into an intricate network of cell types and organ systems. This process is captured in a lineage tree: a diagram of every cell's ancestry back to the founding zygote. Biologists have long sought to trace this cell lineage tree in individual organisms and have developed a variety of technologies to map the progeny of specific cells. However, there are billions to trillions of cells in complex organisms, and conventional approaches can only map a limited number of clonal populations per experiment. A new generation of tools that use molecular recording methods integrated with single cell profiling technologies may provide a solution. Here, we summarize recent breakthroughs in these technologies, outline experimental and computational challenges, and discuss biological questions that can be addressed using single cell dynamic lineage tracing.

Robust elimination of genome-damaged cells safeguards against brain somatic aneuploidy following Knl1 deletion

The brain is a genomic mosaic shaped by cellular responses to genome damage. Here, we manipulate somatic genome stability by conditional Knl1 deletion from embryonic mouse brain. KNL1 mutations cause microcephaly and KNL1 mediates the spindle assembly checkpoint, a safeguard against chromosome missegregation and aneuploidy. We find that following Knl1 deletion, segregation errors in mitotic neural progenitor cells give rise to DNA damage on the missegregated chromosomes. This triggers rapid p53 activation and robust apoptotic and microglial phagocytic responses that extensively eliminate cells with somatic genome damage, thus causing microcephaly. By leaving only karyotypically normal progenitors to continue dividing, these mechanisms provide a second safeguard against brain somatic aneuploidy. Without Knl1 or p53-dependent safeguards, genome-damaged cells are not cleared, alleviating microcephaly, but paradoxically leading to total pre-weaning lethality. Thus, mitotic genome damage activates robust responses to eliminate somatic mutant cells, which if left unpurged, can impact brain and organismal fitness.

Resident Endothelial Progenitors Make Themselves at Home

Vascular endothelial cells adapt to their microenvironment and physiological demands to perform many essential functions. Recent studies (McDonald et al., 2018; Wakabayashi et al., 2018) suggest that quiescent endothelial stem/progenitor cells reside within blood vessels and are activated in response to injury, suggesting they can be harnessed for therapeutic applications.

Illegitimate and Repeated Genomic Integration of Cell-Free Chromatin in the Aetiology of Somatic Mosaicism, Ageing, Chronic Diseases and Cancer

Emerging evidence suggests that an individual is a complex mosaic of genetically divergent cells. Post-zygotic genomes of the same individual can differ from one another in the form of single nucleotide variations, copy number variations, insertions, deletions, inversions, translocations, other structural and chromosomal variations and footprints of transposable elements. High-throughput sequencing has led to increasing detection of mosaicism in healthy individuals which is related to ageing, neuro-degenerative disorders, diabetes mellitus, cardiovascular diseases and cancer. These age-related disorders are also known to be associated with significant increase in DNA damage and inflammation. Herein, we discuss a newly described phenomenon wherein the genome is under constant assault by illegitimate integration of cell-free chromatin (cfCh) particles that are released from the billions of cells that die in the body every day. We propose that such repeated genomic integration of cfCh followed by dsDNA breaks and repair by non-homologous-end-joining as well as physical damage to chromosomes occurring throughout life may lead to somatic/chromosomal mosaicism which would increase with age. We also discuss the recent finding that genomic integration of cfCh and the accompanying DNA damage is associated with marked activation of inflammatory cytokines. Thus, the triple pathologies of somatic mosaicism, DNA/chromosomal damage and inflammation brought about by a common mechanism of genomic integration of cfCh may help to provide an unifying model for the understanding of aetiologies of the inter-related conditions of ageing, degenerative disorders and cancer.

Using Cre-recombinase-driven Polylox barcoding for in vivo fate mapping in mice

Fate mapping is a powerful genetic tool for linking stem or progenitor cells with their progeny, and hence for defining cell lineages in vivo. The resolution of fate mapping depends on the numbers of distinct markers that are introduced in the beginning into stem or progenitor cells; ideally, numbers should be sufficiently large to allow the tracing of output from individual cells. Highly diverse genetic barcodes can serve this purpose. We recently developed an endogenous genetic barcoding system, termed Polylox. In Polylox, random DNA recombination can be induced by transient activity of Cre recombinase in a 2.1-kb-long artificial recombination substrate that has been introduced into a defined locus in mice (Rosa26^Polylox reporter mice). Here, we provide a step-by-step protocol for the use of Polylox, including barcode induction and estimation of induction efficiency, barcode retrieval with single-molecule real-time (SMRT) DNA sequencing followed by computational barcode identification, and the calculation of barcode-generation probabilities, which is key for estimations of single-cell labeling for a given number of stem cells. Thus, Polylox barcoding enables high-resolution fate mapping in essentially all tissues in mice for which inducible Cre driver lines are available. Alternative methods include ex vivo cell barcoding, inducible transposon insertion and CRISPR-Cas9-based barcoding; Polylox currently allows combining non-invasive and cell-type-specific labeling with high label diversity. The execution time of this protocol is ~2-3 weeks for experimental data generation and typically <2 d for computational Polylox decoding and downstream analysis.

Cellular labeling of endogenous retrovirus replication (CLEVR) reveals de novo insertions of the gypsy retrotransposable element in cell culture and in both neurons and glial cells of aging fruit flies

Evidence is rapidly mounting that transposable element (TE) expression and replication may impact biology more widely than previously thought. This includes potential effects on normal physiology of somatic tissues and dysfunctional impacts in diseases associated with aging, such as cancer and neurodegeneration. Investigation of the biological impact of mobile elements in somatic cells will be greatly facilitated by the use of donor elements that are engineered to report de novo events in vivo. In multicellular organisms, reporter constructs demonstrating engineered long interspersed nuclear element (LINE-1; L1) mobilization have been in use for quite some time, and strategies similar to L1 retrotransposition reporter assays have been developed to report replication of Ty1 elements in yeast and mouse intracisternal A particle (IAP) long terminal repeat (LTR) retrotransposons in cultivated cells. We describe a novel approach termed cellular labeling of endogenous retrovirus replication (CLEVR), which reports replication of the gypsy element within specific cells in vivo in Drosophila. The gypsy-CLEVR reporter reveals gypsy replication both in cell culture and in individual neurons and glial cells of the aging adult fly. We also demonstrate that the gypsy-CLEVR replication rate is increased when the short interfering RNA (siRNA) silencing system is genetically disrupted. This CLEVR strategy makes use of universally conserved features of retroviruses and should be widely applicable to other LTR retrotransposons, endogenous retroviruses (ERVs), and exogenous retroviruses.

The Pediatric Cell Atlas: Defining the Growth Phase of Human Development at Single-Cell Resolution

Single-cell gene expression analyses of mammalian tissues have uncovered profound stage-specific molecular regulatory phenomena that have changed the understanding of unique cell types and signaling pathways critical for lineage determination, morphogenesis, and growth. We discuss here the case for a Pediatric Cell Atlas as part of the Human Cell Atlas consortium to provide single-cell profiles and spatial characterization of gene expression across human tissues and organs. Such data will complement adult and developmentally focused HCA projects to provide a rich cytogenomic framework for understanding not only pediatric health and disease but also environmental and genetic impacts across the human lifespan.

Conbase: a software for unsupervised discovery of clonal somatic mutations in single cells through read phasing

Accurate variant calling and genotyping represent major limiting factors for downstream applications of single-cell genomics. Here, we report Conbase for the identification of somatic mutations in single-cell DNA sequencing data. Conbase leverages phased read data from multiple samples in a dataset to achieve increased confidence in somatic variant calls and genotype predictions. Comparing the performance of Conbase to three other methods, we find that Conbase performs best in terms of false discovery rate and specificity and provides superior robustness on simulated data, in vitro expanded fibroblasts and clonal lymphocyte populations isolated directly from a healthy human donor.

Entanglement of Genetics and Epigenetics in Parkinson's Disease

Parkinson disease (PD) is a common neurodegenerative disorder that progresses with age, with an increasing number of symptoms. Some of the efforts to understand PD progression have been focusing on the regulation of epigenetic mechanisms, that generally include small molecular modifications to the DNA and histones that are essential for regulating gene activity. Here, we have pointed out difficulties to untangle genetic and epigenetic mechanisms, and reviewed several studies that have aimed for untangling. Some of those have enabled more solid claims on independent roles for epigenetic mechanisms. Hereby, evidence that specific DNA hydroxymethylation, global hyperacetylation, and histone deacetylase (HDAC) dependent regulation of SNCA, one of the hallmark genes involved in PD, have become more prominent from the current perspective, than mechanisms that directly involve DNA methylation. In the absence of current epigenetic clinical targets to counteract PD progression, we also hypothesize how several mechanisms may affect local and global epigenetics in PD neurons, including inflammation, oxidative stress, autophagy and DNA repair mechanisms which may lead to future therapeutic targets.

Retrotransposon-induced mosaicism in the neural genome

Over the past decade, major discoveries in retrotransposon biology have depicted the neural genome as a dynamic structure during life. In particular, the retrotransposon LINE-1 (L1) has been shown to be transcribed and mobilized in the brain. Retrotransposition in the developing brain, as well as during adult neurogenesis, provides a milieu in which neural diversity can arise. Dysregulation of retrotransposon activity may also contribute to neurological disease. Here, we review recent reports of retrotransposon activity in the brain, and discuss the temporal nature of retrotransposition and its regulation in neural cells in response to stimuli. We also put forward hypotheses regarding the significance of retrotransposons for brain development and neurological function, and consider the potential implications of this phenomenon for neuropsychiatric and neurodegenerative conditions.

Linked-read analysis identifies mutations in single-cell DNA-sequencing data

Whole-genome sequencing of DNA from single cells has the potential to reshape our understanding of mutational heterogeneity in normal and diseased tissues. However, a major difficulty is distinguishing amplification artifacts from biologically derived somatic mutations. Here, we describe linked-read analysis (LiRA), a method that accurately identifies somatic single-nucleotide variants (sSNVs) by using read-level phasing with nearby germline heterozygous polymorphisms, thereby enabling the characterization of mutational signatures and estimation of somatic mutation rates in single cells.

On fitness: how do mutations shape the biology of cancer?

The theory of evolution by natural selection shapes our understanding of the living world. While natural selection has given rise to all the intricacies of life on the planet, those responsible for treating cancer have a darker view of adaptation and selection. Revolutionary changes in DNA sequencing technology have allowed us to survey the complexities that constitute the cancer genome, while advances in genetic engineering are allowing us to functionally interrogate these alterations. These approaches are providing new insights into how mutations influence cancer biology. It is possible that with time, this new knowledge will allow us to take control of the evolutionary processes that shape the disease, to develop more effective treatments.

Lineage Tracing in Humans Enabled by Mitochondrial Mutations and Single-Cell Genomics

Lineage tracing provides key insights into the fate of individual cells in complex organisms. Although effective genetic labeling approaches are available in model systems, in humans, most approaches require detection of nuclear somatic mutations, which have high error rates, limited scale, and do not capture cell state information. Here, we show that somatic mutations in mtDNA can be tracked by single-cell RNA or assay for transposase accessible chromatin (ATAC) sequencing. We leverage somatic mtDNA mutations as natural genetic barcodes and demonstrate their utility as highly accurate clonal markers to infer cellular relationships. We track native human cells both in vitro and in vivo and relate clonal dynamics to gene expression and chromatin accessibility. Our approach should allow clonal tracking at a 1,000-fold greater scale than with nuclear genome sequencing, with simultaneous information on cell state, opening the way to chart cellular dynamics in human health and disease.

The use of technical replication for detection of low-level somatic mutations in next-generation sequencing

Accurate genome-wide detection of somatic mutations with low variant allele frequency (VAF, <1%) has proven difficult, for which generalized, scalable methods are lacking. Herein, we describe a new computational method, called RePlow, that we developed to detect low-VAF somatic mutations based on simple, library-level replicates for next-generation sequencing on any platform. Through joint analysis of replicates, RePlow is able to remove prevailing background errors in next-generation sequencing analysis, facilitating remarkable improvement in the detection accuracy for low-VAF somatic mutations (up to ~99% reduction in false positives). The method is validated in independent cancer panel and brain tissue sequencing data. Our study suggests a new paradigm with which to exploit an overwhelming abundance of sequencing data for accurate variant detection.

Somatic mutations of low allele frequencies are often difficult to detect. Here, the authors develop RePlow, a computational method that leverages technical replication for detecting low-level somatic mutations using next-generation sequencing.

Beyond genome-wide significance: integrative approaches to the interpretation and extension of GWAS findings for alcohol use disorder

Alcohol use disorder (AUD) is a heritable complex behavior. Due to the highly polygenic nature of AUD, identifying genetic variants that comprise this heritable variation has proved to be challenging. With the exception of functional variants in alcohol metabolizing genes (e.g. ADH1B and ALDH2), few other candidate loci have been confidently linked to AUD. Genome-wide association studies (GWAS) of AUD and other alcohol-related phenotypes have either produced few hits with genome-wide significance or have failed to replicate on further study. These issues reinforce the complex nature of the genetic underpinnings for AUD and suggest that both GWAS studies with larger samples and additional analysis approaches that better harness the nominally significant loci in existing GWAS are needed. Here, we review approaches of interest in the post-GWAS era, including in silico functional analyses; functional partitioning of single nucleotide polymorphism heritability; aggregation of signal into genes and gene networks; and validation of identified loci, genes and gene networks in postmortem brain tissue and across species. These integrative approaches hold promise to illuminate our understanding of the biological basis of AUD; however, we recognize that the main challenge continues to be the extremely polygenic nature of AUD, which necessitates large samples to identify multiple loci associated with AUD liability.