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.

Unravelling the genetic basis of Schizophrenia.

Neuronal development is a highly regulated mechanism that is central to organismal function in animals. In humans, disruptions to this process can lead to a range of neurodevelopmental phenotypes, including Schizophrenia (SCZ). SCZ has a significant genetic component, whereby an individual with an SCZ affected family member is eight times more likely to develop the disease than someone with no family history of SCZ. By examining a combination of genomic, transcriptomic and epigenomic datasets, large-scale 'omics' studies aim to delineate the relationship between genetic variation and abnormal cellular activity in the SCZ brain. Herein, we provide a brief overview of some of the key omics methods currently being used in SCZ research, including RNA-seq, the assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and high-throughput chromosome conformation capture (3C) approaches (e.g., Hi-C), as well as single-cell/nuclei iterations of these methods. We also discuss how these techniques are being employed to further our understanding of the genetic basis of SCZ, and to identify associated molecular pathways, biomarkers, and candidate drug targets.

Activity-Dependent Transcriptional Program in NGN2+ Neurons Enriched for Genetic Risk for Brain-Related Disorders.

BACKGROUND: Converging evidence from large-scale genetic and postmortem studies highlights the role of aberrant neurotransmission and genetic regulation in brain-related disorders. However, identifying neuronal activity-regulated transcriptional programs in the human brain and understanding how changes contribute to disease remain challenging. METHODS: To better understand how the activity-dependent regulome contributes to risk for brain-related disorders, we profiled the transcriptomic and epigenomic changes following neuronal depolarization in human induced pluripotent stem cell-derived glutamatergic neurons (NGN2) from 6 patients with schizophrenia and 5 control participants. RESULTS: Multiomic data integration associated global patterns of chromatin accessibility with gene expression and identified enhancer-promoter interactions in glutamatergic neurons. Within 1 hour of potassium chloride-induced depolarization, independent of diagnosis, glutamatergic neurons displayed substantial activity-dependent changes in the expression of genes regulating synaptic function. Depolarization-induced changes in the regulome revealed significant heritability enrichment for schizophrenia and Parkinson's disease, adding to mounting evidence that sequence variation within activation-dependent regulatory elements contributes to the genetic risk for brain-related disorders. Gene coexpression network analysis elucidated interactions among activity-dependent and disease-associated genes and pointed to a key driver (NAV3) that interacted with multiple genes involved in axon guidance. CONCLUSIONS: Overall, we demonstrated that deciphering the activity-dependent regulome in glutamatergic neurons reveals novel targets for advanced diagnosis and therapy.

Long-read RNA-seq atlas of novel microglia isoforms elucidates disease-associated genetic regulation of splicing.

Microglia, the innate immune cells of the central nervous system, have been genetically implicated in multiple neurodegenerative diseases. We previously mapped the genetic regulation of gene expression and mRNA splicing in human microglia, identifying several loci where common genetic variants in microglia-specific regulatory elements explain disease risk loci identified by GWAS. However, identifying genetic effects on splicing has been challenging due to the use of short sequencing reads to identify causal isoforms. Here we present the isoform-centric microglia genomic atlas (isoMiGA) which leverages the power of long-read RNA-seq to identify 35,879 novel microglia isoforms. We show that the novel microglia isoforms are involved in stimulation response and brain region specificity. We then quantified the expression of both known and novel isoforms in a multi-ethnic meta-analysis of 555 human microglia short-read RNA-seq samples from 391 donors, the largest to date, and found associations with genetic risk loci in Alzheimer's disease and Parkinson's disease. We nominate several loci that may act through complex changes in isoform and splice site usage.

Convergence of the dysregulated regulome in schizophrenia with polygenic risk and evolutionarily constrained enhancers.

Enhancers play an essential role in the etiology of schizophrenia; however, the dysregulation of enhancer activity and its impact on the regulome in schizophrenia remains understudied. To address this gap in our knowledge, we assessed enhancer and gene expression in 1,382 brain samples comprising cases with schizophrenia and unaffected controls. Dysregulation of enhancer expression was concordant with changes in gene expression, and was more closely associated with schizophrenia polygenic risk, suggesting that enhancer dysregulation is proximal to the genetic etiology of the disease. Modeling the shared variance of cis-coordinated genes and enhancers revealed a gene regulatory program that was highly associated with genetic vulnerability to schizophrenia. By integrating coordinated factors with evolutionary constraints, we found that enhancers acquired during human evolution are more likely to regulate genes that are implicated in neuropsychiatric disorders and, thus, hold potential as therapeutic targets. Our analysis provides a systematic view of regulome dysregulation in schizophrenia and highlights its convergence with schizophrenia polygenic risk and human-gained enhancers.

The neuronal chromatin landscape in adult schizophrenia brains is linked to early fetal development.

Non-coding variants increase risk of neuropsychiatric disease. However, our understanding of the cell-type specific role of the non-coding genome in disease is incomplete. We performed population scale (N=1,393) chromatin accessibility profiling of neurons and non-neurons from two neocortical brain regions: the anterior cingulate cortex and dorsolateral prefrontal cortex. Across both regions, we observed notable differences in neuronal chromatin accessibility between schizophrenia cases and controls. A per-sample disease pseudotime was positively associated with genetic liability for schizophrenia. Organizing chromatin into cis- and trans-regulatory domains, identified a prominent neuronal trans-regulatory domain (TRD1) active in immature glutamatergic neurons during fetal development. Polygenic risk score analysis using genetic variants within chromatin accessibility of TRD1 successfully predicted susceptibility to schizophrenia in the Million Veteran Program cohort. Overall, we present the most extensive resource to date of chromatin accessibility in the human cortex, yielding insights into the cell-type specific etiology of schizophrenia.

DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.

BACKGROUND: Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models. METHOD: To address these challenges, we developed DeepGAMI, an interpretable neural network model to improve genotype-phenotype prediction from multimodal data. DeepGAMI leverages functional genomic information, such as eQTLs and gene regulation, to guide neural network connections. Additionally, it includes an auxiliary learning layer for cross-modal imputation allowing the imputation of latent features of missing modalities and thus predicting phenotypes from a single modality. Finally, DeepGAMI uses integrated gradient to prioritize multimodal features for various phenotypes. RESULTS: We applied DeepGAMI to several multimodal datasets including genotype and bulk and cell-type gene expression data in brain diseases, and gene expression and electrophysiology data of mouse neuronal cells. Using cross-validation and independent validation, DeepGAMI outperformed existing methods for classifying disease types, and cellular and clinical phenotypes, even using single modalities (e.g., AUC score of 0.79 for Schizophrenia and 0.73 for cognitive impairment in Alzheimer's disease). CONCLUSION: We demonstrated that DeepGAMI improves phenotype prediction and prioritizes phenotypic features and networks in multiple multimodal datasets in complex brains and brain diseases. Also, it prioritized disease-associated variants, genes, and regulatory networks linked to different phenotypes, providing novel insights into the interpretation of gene regulatory mechanisms. DeepGAMI is open-source and available for general use.

Lineage specific 3D genome structure in the adult human brain and neurodevelopmental changes in the chromatin interactome.

The human brain is a complex organ comprised of distinct cell types, and the contribution of the 3D genome to lineage specific gene expression remains poorly understood. To decipher cell type specific genome architecture, and characterize fine scale changes in the chromatin interactome across neural development, we compared the 3D genome of the human fetal cortical plate to that of neurons and glia isolated from the adult prefrontal cortex. We found that neurons have weaker genome compartmentalization compared to glia, but stronger TADs, which emerge during fetal development. Furthermore, relative to glia, the neuronal genome shifts more strongly towards repressive compartments. Neurons have differential TAD boundaries that are proximal to active promoters involved in neurodevelopmental processes. CRISPRi on CNTNAP2 in hIPSC-derived neurons reveals that transcriptional inactivation correlates with loss of insulation at the differential boundary. Finally, re-wiring of chromatin loops during neural development is associated with transcriptional and functional changes. Importantly, differential loops in the fetal cortex are associated with autism GWAS loci, suggesting a neuropsychiatric disease mechanism affecting the chromatin interactome. Furthermore, neural development involves gaining enhancer-promoter loops that upregulate genes that control synaptic activity. Altogether, our study provides multi-scale insights on the 3D genome in the human brain.

The neuronal chromatin landscape in adult schizophrenia brains is linked to early fetal development.

Non-coding variants increase risk of neuropsychiatric disease. However, our understanding of the cell-type specific role of the non-coding genome in disease is incomplete. We performed population scale (N=1,393) chromatin accessibility profiling of neurons and non-neurons from two neocortical brain regions: the anterior cingulate cortex and dorsolateral prefrontal cortex. Across both regions, we observed notable differences in neuronal chromatin accessibility between schizophrenia cases and controls. A per-sample disease pseudotime was positively associated with genetic liability for schizophrenia. Organizing chromatin into cis- and trans-regulatory domains, identified a prominent neuronal trans-regulatory domain (TRD1) active in immature glutamatergic neurons during fetal development. Polygenic risk score analysis using genetic variants within chromatin accessibility of TRD1 successfully predicted susceptibility to schizophrenia in the Million Veteran Program cohort. Overall, we present the most extensive resource to date of chromatin accessibility in the human cortex, yielding insights into the cell-type specific etiology of schizophrenia.

Multi-omic profiling of the developing human cerebral cortex at the single-cell level.

The cellular complexity of the human brain is established via dynamic changes in gene expression throughout development that is mediated, in part, by the spatiotemporal activity of cis-regulatory elements (CREs). We simultaneously profiled gene expression and chromatin accessibility in 45,549 cortical nuclei across six broad developmental time points from fetus to adult. We identified cell type-specific domains in which chromatin accessibility is highly correlated with gene expression. Differentiation pseudotime trajectory analysis indicates that chromatin accessibility at CREs precedes transcription and that dynamic changes in chromatin structure play a critical role in neuronal lineage commitment. In addition, we mapped cell type-specific and temporally specific genetic loci implicated in neuropsychiatric traits, including schizophrenia and bipolar disorder. Together, our results describe the complex regulation of cell composition at critical stages in lineage determination and shed light on the impact of spatiotemporal alterations in gene expression on neuropsychiatric disease.

Multi-omic atlas of the parahippocampal gyrus in Alzheimer's disease.

Alzheimer's disease (AD) is the most common form of dementia worldwide, with a projection of 151 million cases by 2050. Previous genetic studies have identified three main genes associated with early-onset familial Alzheimer's disease, however this subtype accounts for less than 5% of total cases. Next-generation sequencing has been well established and holds great promise to assist in the development of novel therapeutics as well as biomarkers to prevent or slow the progression of this devastating disease. Here we present a public resource of functional genomic data from the parahippocampal gyrus of 201 postmortem control, mild cognitively impaired (MCI) and AD individuals from the Mount Sinai brain bank, of which whole-genome sequencing (WGS), and bulk RNA sequencing (RNA-seq) were previously published. The genomic data include bulk proteomics and DNA methylation, as well as cell-type-specific RNA-seq and assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) data. We have performed extensive preprocessing and quality control, allowing the research community to access and utilize this public resource available on the Synapse platform at https://doi.org/10.7303/syn51180043.2 .

Dopaminoceptive D1 and D2 neurons in ventral hippocampus arbitrate approach and avoidance in anxiety.

The hippocampus 1-7, as well as dopamine circuits 8-11, coordinate decision-making in anxiety-eliciting situations. Yet, little is known about how dopamine modulates hippocampal representations of emotionally-salient stimuli to inform appropriate resolution of approach versus avoidance conflicts. We here study dopaminoceptive neurons in mouse ventral hippocampus (vHipp), molecularly distinguished by their expression of dopamine D1 or D2 receptors. We show that these neurons are transcriptionally distinct and topographically organized across vHipp subfields and cell types. In the ventral subiculum where they are enriched, both D1 and D2 neurons are recruited during anxiogenic exploration, yet with distinct profiles related to investigation and behavioral selection. In turn, they mediate opposite approach/avoidance responses, and are differentially modulated by dopaminergic transmission in that region. Together, these results suggest that vHipp dopamine dynamics gate exploratory behaviors under contextual uncertainty, implicating dopaminoception in the complex computation engaged in vHipp to govern emotional states.

The stability of the myelinating oligodendrocyte transcriptome is regulated by the nuclear lamina.

Oligodendrocytes are specialized cells that insulate and support axons with their myelin membrane, allowing proper brain function. Here, we identify lamin A/C (LMNA/C) as essential for transcriptional and functional stability of myelinating oligodendrocytes. We show that LMNA/C levels increase with differentiation of progenitors and that loss of Lmna in differentiated oligodendrocytes profoundly alters their chromatin accessibility and transcriptional signature. Lmna deletion in myelinating glia is compatible with normal developmental myelination. However, altered chromatin accessibility is detected in fully differentiated oligodendrocytes together with increased expression of progenitor genes and decreased levels of lipid-related transcription factors and inner mitochondrial membrane transcripts. These changes are accompanied by altered brain metabolism, lower levels of myelin-related lipids, and altered mitochondrial structure in oligodendrocytes, thereby resulting in myelin thinning and the development of a progressively worsening motor phenotype. Overall, our data identify LMNA/C as essential for maintaining the transcriptional and functional stability of myelinating oligodendrocytes.

Interleukin-3 coordinates glial-peripheral immune crosstalk to incite multiple sclerosis.

Glial cells and central nervous system (CNS)-infiltrating leukocytes contribute to multiple sclerosis (MS). However, the networks that govern crosstalk among these ontologically distinct populations remain unclear. Here, we show that, in mice and humans, CNS-resident astrocytes and infiltrating CD44hiCD4+ T cells generated interleukin-3 (IL-3), while microglia and recruited myeloid cells expressed interleukin-3 receptor-ɑ (IL-3Rɑ). Astrocytic and T cell IL-3 elicited an immune migratory and chemotactic program by IL-3Rɑ+ myeloid cells that enhanced CNS immune cell infiltration, exacerbating MS and its preclinical model. Multiregional snRNA-seq of human CNS tissue revealed the appearance of IL3RA-expressing myeloid cells with chemotactic programming in MS plaques. IL3RA expression by plaque myeloid cells and IL-3 amount in the cerebrospinal fluid predicted myeloid and T cell abundance in the CNS and correlated with MS severity. Our findings establish IL-3:IL-3RA as a glial-peripheral immune network that prompts immune cell recruitment to the CNS and worsens MS.

Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamlet.

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet (DiseaseNeurogenomics.github.io/dreamlet) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.

ZBTB7A regulates MDD-specific chromatin signatures and astrocyte-mediated stress vulnerability in orbitofrontal cortex.

Hyperexcitability in the orbitofrontal cortex (OFC) is a key clinical feature of anhedonic domains of Major Depressive Disorder (MDD). However, the cellular and molecular substrates underlying this dysfunction remain unknown. Here, cell-population-specific chromatin accessibility profiling in human OFC unexpectedly mapped genetic risk for MDD exclusively to non-neuronal cells, and transcriptomic analyses revealed significant glial dysregulation in this region. Characterization of MDD-specific cis-regulatory elements identified ZBTB7A - a transcriptional regulator of astrocyte reactivity - as an important mediator of MDD-specific chromatin accessibility and gene expression. Genetic manipulations in mouse OFC demonstrated that astrocytic Zbtb7a is both necessary and sufficient to promote behavioral deficits, cell-type-specific transcriptional and chromatin profiles, and OFC neuronal hyperexcitability induced by chronic stress - a major risk factor for MDD. These data thus highlight a critical role for OFC astrocytes in stress vulnerability and pinpoint ZBTB7A as a key dysregulated factor in MDD that mediates maladaptive astrocytic functions driving OFC hyperexcitability.

Genetic regulation of cell-type specific chromatin accessibility shapes the etiology of brain diseases.

Nucleotide variants in cell type-specific gene regulatory elements in the human brain are major risk factors of human disease. We measured chromatin accessibility in sorted neurons and glia from 1,932 samples of human postmortem brain and identified 34,539 open chromatin regions with chromatin accessibility quantitative trait loci (caQTL). Only 10.4% of caQTL are shared between neurons and glia, supporting the cell type specificity of genetic regulation of the brain regulome. Incorporating allele specific chromatin accessibility improves statistical fine-mapping and refines molecular mechanisms underlying disease risk. Using massively parallel reporter assays in induced excitatory neurons, we screened 19,893 brain QTLs, identifying the functional impact of 476 regulatory variants. Combined, this comprehensive resource captures variation in the human brain regulome and provides novel insights into brain disease etiology.

Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamlet.

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet (DiseaseNeurogenomics.github.io/dreamlet) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data.

Sample-wise deconvolution methods have been developed to estimate cell-type proportions and gene expressions in bulk-tissue samples. However, the performance of these methods and their biological applications has not been evaluated, particularly on human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk-tissue RNAseq, single-cell/nuclei (sc/sn) RNAseq, and immunohistochemistry. A total of 1,130,767 nuclei/cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expression. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk-tissue or single-cell eQTLs alone. Differential gene expression associated with multiple phenotypes were also examined using the deconvoluted data. Our findings, which were replicated in bulk-tissue RNAseq and sc/snRNAseq data, provided new insights into the biological applications of deconvoluted data.

Genome-wide methylomic regulation of multiscale gene networks in Alzheimer's disease.

INTRODUCTION: Recent studies revealed the association of abnormal methylomic changes with Alzheimer's disease (AD) but there is a lack of systematic study of the impact of methylomic alterations over the molecular networks underlying AD. METHODS: We profiled genome-wide methylomic variations in the parahippocampal gyrus from 201 post mortem control, mild cognitive impaired, and AD brains. RESULTS: We identified 270 distinct differentially methylated regions (DMRs) associated with AD. We quantified the impact of these DMRs on each gene and each protein as well as gene and protein co-expression networks. DNA methylation had a profound impact on both AD-associated gene/protein modules and their key regulators. We further integrated the matched multi-omics data to show the impact of DNA methylation on chromatin accessibility, which further modulates gene and protein expression. DISCUSSION: The quantified impact of DNA methylation on gene and protein networks underlying AD identified potential upstream epigenetic regulators of AD. HIGHLIGHTS: A cohort of DNA methylation data in the parahippocampal gyrus was developed from 201 post mortem control, mild cognitive impaired, and Alzheimer's disease (AD) brains. Two hundred seventy distinct differentially methylated regions (DMRs) were found to be associated with AD compared to normal control. A metric was developed to quantify methylation impact on each gene and each protein. DNA methylation was found to have a profound impact on not only the AD-associated gene modules but also key regulators of the gene and protein networks. Key findings were validated in an independent multi-omics cohort in AD. The impact of DNA methylation on chromatin accessibility was also investigated by integrating the matched methylomic, epigenomic, transcriptomic, and proteomic data.