Revealing the brain's molecular architecture

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.

Genetics of human brain development

Brain development in humans is achieved through precise spatiotemporal genetic control, the mechanisms of which remain largely elusive. Recently, integration of technological advances in human stem cell-based modelling with genome editing has emerged as a powerful platform to establish causative links between genotypes and phenotypes directly in the human system. Here, we review our current knowledge of complex genetic regulation of each key step of human brain development through the lens of evolutionary specialization and neurodevelopmental disorders and highlight the use of human stem cell-derived 2D cultures and 3D brain organoids to investigate human-enriched features and disease mechanisms. We also discuss opportunities and challenges of integrating new technologies to reveal the genetic architecture of human brain development and disorders.

How spatial omics approaches can be used to map the biological impacts of stress in psychiatric disorders: a perspective, overview and technical guide

Exposure to significant levels of stress and trauma throughout life is a leading risk factor for the development of major psychiatric disorders. Despite this, we do not have a comprehensive understanding of the mechanisms that explain how stress raises psychiatric disorder risk. Stress in humans is complex and produces variable molecular outcomes depending on the stress type, timing, and duration. Deciphering how stress increases disorder risk has consequently been challenging to address with the traditional single-target experimental approaches primarily utilized to date. Importantly, the molecular processes that occur following stress are not fully understood but are needed to find novel treatment targets. Sequencing-based omics technologies, allowing for an unbiased investigation of physiological changes induced by stress, are rapidly accelerating our knowledge of the molecular sequelae of stress at a single-cell resolution. Spatial multi-omics technologies are now also emerging, allowing for simultaneous analysis of functional molecular layers, from epigenome to proteome, with anatomical context. The technology has immense potential to transform our understanding of how disorders develop, which we believe will significantly propel our understanding of how specific risk factors, such as stress, contribute to disease course. Here, we provide our perspective of how we believe these technologies will transform our understanding of the neurobiology of stress, and also provided a technical guide to assist molecular psychiatry and stress researchers who wish to implement spatial omics approaches in their own research. Finally, we identify potential future directions using multi-omics technology in stress research.

Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases

Identification of therapeutic targets from genome-wide association studies (GWAS) requires insights into downstream functional consequences. We harmonized 8,613 RNA-sequencing samples from 14 brain datasets to create the MetaBrain resource and performed cis- and trans-expression quantitative trait locus (eQTL) meta-analyses in multiple brain region- and ancestry-specific datasets (n ≤ 2,759). Many of the 16,169 cortex cis-eQTLs were tissue-dependent when compared with blood cis-eQTLs. We inferred brain cell types for 3,549 cis-eQTLs by interaction analysis. We prioritized 186 cis-eQTLs for 31 brain-related traits using Mendelian randomization and co-localization including 40 cis-eQTLs with an inferred cell type, such as a neuron-specific cis-eQTL (CYP24A1) for multiple sclerosis. We further describe 737 trans-eQTLs for 526 unique variants and 108 unique genes. We used brain-specific gene-co-regulation networks to link GWAS loci and prioritize additional genes for five central nervous system diseases. This study represents a valuable resource for post-GWAS research on central nervous system diseases.

Feasibility and application of polygenic score analysis to the morphology of human-induced pluripotent stem cells

Genome-wide association studies have identified thousands of significant associations between genetic variants and complex traits. Inferring biological insights from these associations has been challenging. One approach attempted has been to examine the effects of individual variants in cellular models. Here, I demonstrate the feasibility of examining the aggregate effect of many variants on cellular phenotypes. I examine the effects of polygenic scores for cross-psychiatric disorder risk, schizophrenia, body mass index and height on cellular morphology, using 1.5 million induced pluripotent stem cells (iPSC) from 60 European-ancestry donors from the Human iPSC Initiative dataset. I show that measuring multiple cells per donor provides sufficient power for polygenic score analyses, and that cross-psychiatric disorder risk is associated with cell area (p = 0.004). Combined with emerging methods of high-throughput iPSC phenotyping, cellular polygenic scoring is a promising method for understanding potential biological effects of the polygenic component of complex traits.

Functional Characterization of Genetic Variant Effects on Expression

Thousands of common genetic variants in the human population have been associated with disease risk and phenotypic variation by genome-wide association studies (GWAS). However, the majority of GWAS variants fall into noncoding regions of the genome, complicating our understanding of their regulatory functions, and few molecular mechanisms of GWAS variant effects have been clearly elucidated. Here, we set out to review genetic variant effects, focusing on expression quantitative trait loci (eQTLs), including their utility in interpreting GWAS variant mechanisms. We discuss the interrelated challenges and opportunities for eQTL analysis, covering determining causal variants, elucidating molecular mechanisms of action, and understanding context variability. Addressing these questions can enable better functional characterization of disease-associated loci and provide insights into fundamental biological questions of the noncoding genetic regulatory code and its control of gene expression. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Functional genomics data: privacy risk assessment and technological mitigation

The generation of functional genomics data by next-generation sequencing has increased greatly in the past decade. Broad sharing of these data is essential for research advancement but poses notable privacy challenges, some of which are analogous to those that occur when sharing genetic variant data. However, there are also unique privacy challenges that arise from cryptic information leakage during the processing and summarization of functional genomics data from raw reads to derived quantities, such as gene expression values. Here, we review these challenges and present potential solutions for mitigating privacy risks while allowing broad data dissemination and analysis.

Privacy-preserving genotype imputation with fully homomorphic encryption

Genotype imputation is the inference of unknown genotypes using known population structure observed in large genomic datasets; it can further our understanding of phenotype-genotype relationships and is useful for QTL mapping and GWASs. However, the compute-intensive nature of genotype imputation can overwhelm local servers for computation and storage. Hence, many researchers are moving toward using cloud services, raising privacy concerns. We address these concerns by developing an efficient, privacy-preserving algorithm called p-Impute. Our method uses homomorphic encryption, allowing calculations on ciphertext, thereby avoiding the decryption of private genotypes in the cloud. It is similar to k-nearest neighbor approaches, inferring missing genotypes in a genomic block based on the SNP genotypes of genetically related individuals in the same block. Our results demonstrate accuracy in agreement with the state-of-the-art plaintext solutions. Moreover, p-Impute is scalable to real-world applications as its memory and time requirements increase linearly with the increasing number of samples. p-Impute is freely available for download here: https://doi.org/10.5281/zenodo.5542001.

Cell type-specific chromatin accessibility analysis in the mouse and human brain

The Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) is becoming popular in the neuroscience field where chromatin regulation is thought to be involved in neurodevelopment, activity-dependent gene regulation, hormonal and environmental responses, and pathophysiology of neuropsychiatric disorders. The advantages of using ATAC-seq include a small amount of material needed, fast protocol, and the ability to capture a range of gene regulatory elements with a single assay. With increasing interest in chromatin research, it is an imperative to have feasible, reliable assays that are compatible with a range of neuroscience study designs. Here we tested three protocols for neuronal chromatin accessibility analysis, including a varying brain tissue freezing method followed by fluorescence-activated nuclei sorting (FANS) and ATAC-seq. Our study shows that the cryopreservation method impacts the number of open chromatin regions identified from frozen brain tissue using ATAC-seq. However, we show that all protocols generate consistent and robust data and enable the identification of functional regulatory elements in neuronal cells. Our study implies that the broad biological interpretation of chromatin accessibility data is not significantly affected by the freezing condition. We also reveal additional challenges of doing chromatin analysis on post-mortem human brain tissue. Overall, ATAC-seq coupled with FANS is a powerful method to capture cell-type-specific chromatin accessibility information in mouse and human brain. Our study provides alternative brain preservation methods that generate high-quality ATAC-seq data while fitting in different study designs, and further encourages the use of this method to uncover the role of epigenetic (dys)regulation in the brain.

The long non-coding RNA SPRIGHTLY and its binding partner PTBP1 regulate exon 5 skipping of SMYD3 transcripts in group 4 medulloblastomas

Background

Although some of the regulatory genes, signaling pathways, and gene regulatory networks altered in medulloblastomas (MB) are known, the roles of non-coding RNAs, particularly long non-coding RNAs (lncRNAs), are poorly described. Here we report that the lncRNA SPRIGHTLY (SPRY4-IT1) gene is upregulated in group 4 medulloblastoma (G4 MB).

Methods

SPRIGHTLY expression was assessed in MB subgroup patient-derived xenografts, cell lines, and patient samples. The effect of SPRIGHTLY hemizygous deletion on proliferation, invasion, apoptosis, and colony formation were assessed in vitro and on tumor growth in vivo. dChIRP pull-down assays were used to assess SPRIGHTLY-binding partners, confirmed by immunoprecipitation. SMYD3 ΔE5 transcripts were examined in cell lines and publicly available RNA-seq data. Pathway analysis was performed by phospho-kinase profiling and RNA-seq.

Results

CRISPR/Cas9 deletion of SPRIGHTLY reduced cell viability and invasion and increased apoptosis in G4 MB cell lines in vitro. SPRIGHTLY hemizygous-deleted G4 MB cells injected into mouse cerebellums produced smaller tumors than those derived from parental cells expressing both copies of SPRIGHTLY. SPRIGHTLY lncRNA bound to the intronic region of the SMYD3 pre-mRNA transcript. SPRIGHTLY also interacted with PTPB1 protein to regulate SMYD3 exon skipping to produce an aberrant protein. SPRIGHTLY-driven SMYD3 regulation enhanced the expression of EGFR pathway genes in G4 MB cell lines and activated cell coagulation/hemostasis-related gene expression, suggesting a novel oncogenic role in G4 MB.

Conclusions

These results demonstrate the importance of SPRIGHTLY lncRNA as a promoter of G4 MB and the role of the SPRIGHTLY-SMYD3-PTPB1 axis as an important oncogenic regulator in MB.

Non-coding regulatory elements: Potential roles in disease and the case of epilepsy

Non-coding DNA (ncDNA) refers to the portion of the genome that does not code for proteins and accounts for the greatest physical proportion of the human genome. ncDNA includes sequences that are transcribed into RNA molecules, such as ribosomal RNAs (rRNAs), microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and un-transcribed sequences that have regulatory functions, including gene promoters and enhancers. Variation in non-coding regions of the genome have an established role in human disease, with growing evidence from many areas, including several cancers, Parkinson's disease and autism. Here, we review the features and functions of the regulatory elements that are present in the non-coding genome and the role that these regions have in human disease. We then review the existing research in epilepsy and emphasise the potential value of further exploring non-coding regulatory elements in epilepsy. In addition, we outline the most widely used techniques for recognising regulatory elements throughout the genome, current methodologies for investigating variation and the main challenges associated with research in the field of non-coding DNA.

Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics

Broad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify "druggable" targets. Using our meta-analytic data set (N = 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.

Molecular mechanism involved in epithelial to mesenchymal transition

Epithelial to mesenchymal transition (EMT) is a biological process that plays an important role during embryonic development. During this process, the epithelial cells lose their polarity and acquire mesenchymal properties. In addition to embryonic development, EMT is also well-known to participate in tissue repair, inflammation, fibrosis, and tumor metastasis. In the present review, we address the basics of epithelial to mesenchymal transition during both development and disease conditions and emphasize the role of various transcription factors and miRNAs involved in the process.

Human Pluripotent Stem-Cell-Derived Models as a Missing Link in Drug Discovery and Development

Human pluripotent stem cells (hPSCs), including human embryonic stem cells (hESCs) and human-induced pluripotent stem cells (hiPSCs), have the potential to accelerate the drug discovery and development process. In this review, by analyzing each stage of the drug discovery and development process, we identified the active role of hPSC-derived in vitro models in phenotypic screening, target-based screening, target validation, toxicology evaluation, precision medicine, clinical trial in a dish, and post-clinical studies. Patient-derived or genome-edited PSCs can generate valid in vitro models for dissecting disease mechanisms, discovering novel drug targets, screening drug candidates, and preclinically and post-clinically evaluating drug safety and efficacy. With the advances in modern biotechnologies and developmental biology, hPSC-derived in vitro models will hopefully improve the cost-effectiveness and the success rate of drug discovery and development.

Data Sanitization to Reduce Private Information Leakage from Functional Genomics

The generation of functional genomics datasets is surging, because they provide insight into gene regulation and organismal phenotypes (e.g., genes upregulated in cancer). The intent behind functional genomics experiments is not necessarily to study genetic variants, yet they pose privacy concerns due to their use of next-generation sequencing. Moreover, there is a great incentive to broadly share raw reads for better statistical power and general research reproducibility. Thus, we need new modes of sharing beyond traditional controlled-access models. Here, we develop a data-sanitization procedure allowing raw functional genomics reads to be shared while minimizing privacy leakage, enabling principled privacy-utility trade-offs. Our protocol works with traditional Illumina-based assays and newer technologies such as 10x single-cell RNA sequencing. It involves quantifying the privacy leakage in reads by statistically linking study participants to known individuals. We carried out these linkages using data from highly accurate reference genomes and more realistic environmental samples.

Molecular and computational approaches to map regulatory elements in 3D chromatin structure

Epigenetic marks do not change the sequence of DNA but affect gene expression in a cell-type specific manner by altering the activities of regulatory elements. Development of new molecular biology assays, sequencing technologies, and computational approaches enables us to profile the human epigenome in three-dimensional structure genome-wide. Here we describe various molecular biology techniques and bioinformatic tools that have been developed to measure the activities of regulatory elements and their chromatin interactions. Moreover, we list currently available three-dimensional epigenomic data sets that are generated in various human cell types and tissues to assist in the design and analysis of research projects.

Characterization of mitochondrial health from human peripheral blood mononuclear cells to cerebral organoids derived from induced pluripotent stem cells

Mitochondrial health plays a crucial role in human brain development and diseases. However, the evaluation of mitochondrial health in the brain is not incorporated into clinical practice due to ethical and logistical concerns. As a result, the development of targeted mitochondrial therapeutics remains a significant challenge due to the lack of appropriate patient-derived brain tissues. To address these unmet needs, we developed cerebral organoids (COs) from induced pluripotent stem cells (iPSCs) derived from human peripheral blood mononuclear cells (PBMCs) and monitored mitochondrial health from the primary, reprogrammed and differentiated stages. Our results show preserved mitochondrial genetics, function and treatment responses across PBMCs to iPSCs to COs, and measurable neuronal activity in the COs. We expect our approach will serve as a model for more widespread evaluation of mitochondrial health relevant to a wide range of human diseases using readily accessible patient peripheral (PBMCs) and stem-cell derived brain tissue samples.

Special Article: Translational Science Update. Pharmacological Implications of Emerging Schizophrenia Genetics: Can the Bridge From 'Genomics' to 'Therapeutics' be Defined and Traversed?

Recent schizophrenia genome-wide association studies (GWAS) have identified genomic variants of common and rare frequency, significantly associated with schizophrenia. While numerous functional genomics efforts are ongoing to elucidate the biological effects of schizophrenia risk variants, a consideration of their therapeutic implications is timely and imperative, for patients as well as for an iterative effect on elucidating the underlying biology and pathophysiology of illness. The current article reviews efforts to translate emerging schizophrenia genomics into novel approaches to target discovery and therapeutic intervention. Though the path from 'genetic risk to therapy' is far from straightforward, there are provocative early possibilities that harbor the promise of treatment based on causation rather than phenomenology, as well as 'precision psychiatry,' a basis for stratifying patients to enable more precise and effective, personalized therapy.

Functional genomics links genetic origins to pathophysiology in neurodegenerative and neuropsychiatric disease

Neurodegenerative and neuropsychiatric disorders are pervasive and debilitating conditions characterized by diverse clinical syndromes and comorbidities, whose origins are as complex and heterogeneous as their associated phenotypes. Risk for these disorders involves substantial genetic liability, which has fueled large-scale genetic studies that have led to a flood of discoveries. In turn, these discoveries have exposed substantial gaps in our knowledge with regards to the complicated genetic architecture of each disorder and the substantial amount of genetic overlap among disorders, which implies some degree of shared pathophysiology underlying these clinically distinct, multifactorial disorders. Understanding the role of specific genetic variants will involve resolving the connections between molecular pathways, heterogeneous cell types, specific circuits and disease pathogenesis at the tissue and patient level. We consider the current known genetic basis of these disorders and highlight the utility of molecular systems approaches that establish the function of genetic variation in the context of specific neurobiological networks, cell-types, and life stages. Beyond expanding our knowledge of disease mechanisms, understanding these relationships provides promise for early detection and potential therapeutic interventions.

New gene discoveries highlight functional convergence in autism and related neurodevelopmental disorders

Over the last two years, remarkable gene discovery efforts have implicated disruption of pathways involving gene regulatory functions and neuronal processes in autism spectrum disorder (ASD), and more broadly defined neurodevelopmental disorders (NDDs). Functional studies in the developing brain and across cell types demonstrate that the spatiotemporal expression patterns of many of these genes coalesce on subnetworks with distinct developmental trajectories. Here, we review the convergent biological processes derived from gene discovery and functional genomics in ASD and NDD from 2018-2020. We further probe the mechanistic insights that suggest these frequently perturbed pathways are interconnected and, ultimately, converge on specific functional deficits in human neurodevelopment.

In vitro modeling for inherited neurological diseases using induced pluripotent stem cells: from 2D to organoid

Stem cells are characterized by self-renewal and by their ability to differentiate into cells of various organs. With massive progress in 2D and 3D cell culture techniques, in vitro generation of various types of such organoids from patient-derived stem cells is now possible. As in vitro differentiation protocols are usually made to resemble human developmental processes, organogenesis of patient-derived stem cells can provide key information regarding a range of developmental diseases. Human stem cell-based in vitro modeling as opposed to using animal models can particularly benefit the evaluation of neurological diseases because of significant differences in structure and developmental processes between the human and the animal brain. This review focuses on stem cell-based in vitro modeling of neurodevelopmental disorders, more specifically, the fundamentals and technical advancements in monolayer neuron and brain organoid cultures. Furthermore, we discuss the drawbacks of the conventional culture method and explore the advanced, cutting edge 3D organoid models for several neurodevelopmental diseases, including genetic diseases such as Down syndrome, Rett syndrome, and Miller-Dieker syndrome, as well as brain malformations like macrocephaly and microcephaly. Finally, we discuss the limitations of the current organoid techniques and some potential solutions that pave the way for accurate modeling of neurological disorders in a dish.

Use of the epigenetic toolbox to contextualize common variants associated with schizophrenia risk

Schizophrenia is a debilitating psychiatric disorder with a complex genetic architecture and limited understanding of its neuropathology, reflected by the lack of diagnostic measures and effective pharmacological treatments. Geneticists have recently identified more than 145 risk loci comprising hundreds of common variants of small effect sizes, most of which lie in noncoding genomic regions. This review will discuss how the epigenetic toolbox can be applied to contextualize genetic findings in schizophrenia. Progress in next-generation sequencing, along with increasing methodological complexity, has led to the compilation of genome-wide maps of DNA methylation, histone modifications, RNA expression, and more. Integration of chromatin conformation datasets is one of the latest efforts in deciphering schizophrenia risk, allowing the identification of genes in contact with regulatory variants across 100s of kilobases. Large-scale multiomics studies will facilitate the prioritization of putative causal risk variants and gene networks that contribute to schizophrenia etiology, informing clinical diagnostics and treatment downstream.
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Informing disease modelling with brain-relevant functional genomic annotations

The past decade has seen a surge in the number of disease/trait-associated variants, largely because of the union of studies to share genetic data and the availability of electronic health records from large cohorts for research use. Variant discovery for neurological and neuropsychiatric genome-wide association studies, including schizophrenia, Parkinson's disease and Alzheimer's disease, has greatly benefitted; however, the translation of these genetic association results to interpretable biological mechanisms and models is lagging. Interpreting disease-associated variants requires knowledge of gene regulatory mechanisms and computational tools that permit integration of this knowledge with genome-wide association study results. Here, we summarize key conceptual advances in the generation of brain-relevant functional genomic annotations and amongst tools that allow integration of these annotations with association summary statistics, which together provide a new and exciting opportunity to identify disease-relevant genes, pathways and cell types in silico. We discuss the opportunities and challenges associated with these developments and conclude with our perspective on future advances in annotation generation, tool development and the union of the two.

Neurobiological functions of transcriptional enhancers

Transcriptional enhancers are regulatory DNA elements that underlie the specificity and dynamic patterns of gene expression. Over the past decade, large-scale functional genomics projects have driven transformative progress in our understanding of enhancers. These data have relevance for identifying mechanisms of gene regulation in the CNS, elucidating the function of non-coding regulatory sequences in neurobiology and linking sequence variation within enhancers to genetic risk for neurological and psychiatric disorders. However, the sheer volume and complexity of genomic data presents a challenge to interpreting enhancer function in normal and pathogenic neurobiological processes. Here, to advance the application of genome-scale enhancer data, we offer a primer on current models of enhancer function in the CNS, we review how enhancers regulate gene expression across the neuronal lifespan, and we suggest how emerging findings regarding the role of non-coding sequence variation offer opportunities for understanding brain disorders and developing new technologies for neuroscience.

Suicide and suicide risk

Although recent years have seen large decreases in the overall global rate of suicide fatalities, this trend is not reflected everywhere. Suicide and suicidal behaviour continue to present key challenges for public policy and health services, with increasing suicide deaths in some countries such as the USA. The development of suicide risk is complex, involving contributions from biological (including genetics), psychological (such as certain personality traits), clinical (such as comorbid psychiatric illness), social and environmental factors. The involvement of multiple risk factors in conveying risk of suicide means that determining an individual's risk of suicide is challenging. Improving risk assessment, for example, by using computer testing and genetic screening, is an area of ongoing research. Prevention is key to reduce the number of suicide deaths and prevention efforts include universal, selective and indicated interventions, although these interventions are often delivered in combination. These interventions, combined with psychological (such as cognitive behavioural therapy, caring contacts and safety planning) and pharmacological treatments (for example, clozapine and ketamine) along with coordinated social and public health initiatives, should continue to improve the management of individuals who are suicidal and decrease suicide-associated morbidity.

Multiscale Neuroscience of Psychiatric Disorders

The human brain comprises a multiscale network with multiple levels of organization. Neurons with dendritic and axonal connections form the microscale fabric of brain circuitry, and macroscale brain regions and white matter connections form the infrastructure for system-level brain communication and information integration. In this review, we discuss the emerging trend of multiscale neuroscience, the multidisciplinary field that brings together data from these different levels of nervous system organization to form a better understanding of between-scale relationships of brain structure, function, and behavior in health and disease. We provide a broad overview of this developing field and discuss recent findings of exemplary multiscale neuroscience studies that illustrate the importance of studying cross-scale interactions among the genetic, molecular, cellular, and macroscale levels of brain circuitry and connectivity and behavior. We particularly consider a central, overarching goal of these multiscale neuroscience studies of human brain connectivity: to obtain insight into how disease-related alterations at one level of organization may underlie alterations observed at other scales of brain network organization in mental disorders. We conclude by discussing the current limitations, challenges, and future directions of the field.

Sox2-Dependent 3D Chromatin Interactomes in Transcription, Neural Stem Cell Proliferation and Neurodevelopmental Diseases

In our article, we asked whether Sox2, a transcription factor important in brain development and disease, is involved in gene regulation through its action on long-range interactions between promoters and distant enhancers. Our findings highlight that Sox2 shapes a genome-wide network of promoter-enhancer interactions, acting by direct binding to these elements. Sox2 loss affects the three-dimensional (3D) genome and decreases the activity of a subset of genes involved in Sox2-bound interactions. At least one of such downregulated genes, Socs3, is critical for long-term neural stem cell maintenance. These results point to the possibility of identifying a transcriptional network downstream to Sox2, and involved in neural stem cell maintenance. In addition, interacting Sox2-bound enhancers are often connected to genes which are relevant, in man, to neurodevelopmental disease; this may facilitate the detection of functionally relevant mutations in regulatory elements in man, contributing to neural disease.

Precision neuroradiology: mapping the nodes and networks that link genes to behaviour

What is the future of neuroradiology in the era of precision medicine? As with any big change, this transformation in medicine presents both challenges and opportunities, and to flourish in this new environment we will have to adapt. It is difficult to predict exactly how neuroradiology will evolve in this shifting landscape, but there will be changes in both what we image and what we do. In terms of imaging, we will need to move beyond simply imaging brain anatomy and toward imaging function, both at the molecular and circuit level. In terms of what we do, we will need to move from the periphery of the clinical enterprise toward its center, with a new emphasis on integrating imaging with genetic and clinical data to form a comprehensive picture of the patient that can be used to direct further testing and care.The payoff is that these changes will align neuroradiology with the emerging field of precision psychiatry, which promises to replace symptom-based diagnosis and trial-and-error treatment of psychiatric disorders with diagnoses based on quantifiable genetic, imaging, physiologic, and behavioural criteria and therapies targeted to the particular pathophysiology of individual patients. Here we review some of the recent developments in behavioural genetics and neuroscience that are laying the foundation for precision psychiatry. By no means comprehensive, our goal is to introduce some of the perspectives and techniques that are likely to be relevant to the precision neuroradiologist of the future.

Defining the Genetic, Genomic, Cellular, and Diagnostic Architectures of Psychiatric Disorders

Studies of the genetics of psychiatric disorders have become one of the most exciting and fast-moving areas in human genetics. A decade ago, there were few reproducible findings, and now there are hundreds. In this review, we focus on the findings that have illuminated the genetic architecture of psychiatric disorders and the challenges of using these findings to inform our understanding of pathophysiology. The evidence is now overwhelming that psychiatric disorders are "polygenic"-that many genetic loci contribute to risk. With the exception of a subset of those with ASD, few individuals with a psychiatric disorder have a single, deterministic genetic cause; rather, developing a psychiatric disorder is influenced by hundreds of different genetic variants, consistent with a polygenic model. As progressively larger studies have uncovered more about their genetic architecture, the need to elucidate additional architectures has become clear. Even if we were to have complete knowledge of the genetic architecture of a psychiatric disorder, full understanding requires deep knowledge of the functional genomic architecture-the implicated loci impact regulatory processes that influence gene expression and the functional coordination of genes that control biological processes. Following from this is cellular architecture: of all brain regions, cell types, and developmental stages, where and when are the functional architectures operative? Given that the genetic architectures of different psychiatric disorders often strongly overlap, we are challenged to re-evaluate and refine the diagnostic architectures of psychiatric disorders using fundamental genetic and neurobiological data.