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Scalable technologies to sequence the transcriptomes and epigenomes of single cells are transforming our understanding of cell types and cell states. The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative Cell Census Network (BICCN) is applying these technologies at unprecedented scale to map the cell types in the mammalian brain. In an effort to increase data FAIRness (Findable, Accessible, Interoperable, Reusable), the NIH has established repositories to make data generated by the BICCN and related BRAIN Initiative projects accessible to the broader research community. Here, we describe the Neuroscience Multi-Omic Archive (NeMO Archive; nemoarchive.org), which serves as the primary repository for genomics data from the BRAIN Initiative. Working closely with other BRAIN Initiative researchers, we have organized these data into a continually expanding, curated repository, which contains transcriptomic and epigenomic data from over 50 million brain cells, including single-cell genomic data from all of the major regions of the adult and prenatal human and mouse brains, as well as substantial single-cell genomic data from non-human primates. We make available several tools for accessing these data, including a searchable web portal, a cloud-computing interface for large-scale data processing (implemented on Terra, terra.bio), and a visualization and analysis platform, NeMO Analytics (nemoanalytics.org).

Genetic risk for complex traits is strongly enriched in non-coding genomic regions involved in gene regulation, especially enhancers. However, we lack adequate tools to connect the characteristics of these disruptions to genetic risk. Here, we propose RWAS (Regulome Wide Association Study), a new application of the MAGMA software package to identify the characteristics of enhancers that contribute to genetic risk for disease. RWAS involves three steps: (i) assign genotyped SNPs to cell type- or tissue-specific regulatory features (e.g., enhancers); (ii) test associations of each regulatory feature with a trait of interest for which genome-wide association study (GWAS) summary statistics are available; (iii) perform enhancer-set enrichment analyses to identify quantitative or categorical features of regulatory elements that are associated with the trait. These steps are implemented as a novel application of MAGMA, a tool originally developed for gene-based GWAS analyses. Applying RWAS to interrogate genetic risk for schizophrenia, we discovered a class of risk-associated AT-rich enhancers that are active in the developing brain and harbor binding sites for multiple transcription factors with neurodevelopmental functions. RWAS utilizes open-source software, and we provide a comprehensive collection of annotations for tissue-specific enhancer locations and features, including their evolutionary conservation, AT content, and co-localization with binding sites for hundreds of TFs. RWAS will enable researchers to characterize properties of regulatory elements associated with any trait of interest for which GWAS summary statistics are available.

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1–3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas—containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities—is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis., The authors describe an integrated atlas of the diverse cell types in the mouse primary motor cortex.

BACKGROUND: Of the 108 Schizophrenia (SZ) risk-loci discovered through genome-wide association studies (GWAS), 96 are not altering the sequence of any protein. Evidence linking non-coding risk-SNPs and genes may be established using expression quantitative trait loci (eQTL). However, other approaches such allelic expression quantitative trait loci (aeQTL) also may be of use.METHODS: We applied both the eQTL and aeQTL analysis to a biobank of deeply sequenced RNA from 680 dorso-lateral pre-frontal cortex (DLPFC) samples. For each of 340 genes proximal to the SZ risk-SNPs, we asked how much SNP-genotype affected total expression (eQTL), as well as how much the expression ratio between the two alleles differed from 1:1 as a consequence of the risk-SNP genotype (aeQTL).RESULTS: We analyzed overlap with comparable eQTL-findings: 16 of the 30 risk-SNPs known to have gene-level eQTL also had gene-level aeQTL effects. 6 of 21 risk-SNPs with known splice-eQTL had exon-aeQTL effects. 12 novel potential risk genes were identified with the aeQTL approach, while 55 tested SNP-pairs were found as eQTL but not aeQTL. Of the tested 108 loci we could find at least one gene to be associated with 21 of the risk-SNPs using gene-level aeQTL, and with an additional 18 risk-SNPs using exon-level aeQTL.CONCLUSION: Our results suggest that the aeQTL strategy complements the eQTL approach to susceptibility gene identification.

Summary: GWAS have identified 108 schizophrenia risk loci, but risk mechanisms for individual loci are largely unknown. Using developmental, genetic, and illness-based RNA sequencing expression analysis in human brain, we characterized the human brain transcriptome around these loci and found enrichment for developmentally regulated genes with novel examples of shifting isoform usage across pre- and post-natal life. Across the genome, we found widespread expression quantitative trait loci (eQTLs), including many with transcript specificity and previously unannotated sequence that were independently replicated. We leveraged this general eQTL database to show that 48.1% of risk variants for schizophrenia associated with nearby expression. We lastly found 237 genes significantly differentially expressed between patients and controls which replicated in an independent dataset, implicated synaptic processes and were strongly regulated in early development. These findings together offer genetic- and diagnosis-related targets for better modeling schizophrenia risk. This publicly-available resource is available at: http://eqtl.brainseq.org/phase1

Genetic and genomic studies suggest an important role for transcriptional regulatory changes in brain diseases, but roles for specific transcription factors (TFs) remain poorly understood. We integrated human brain-specific DNase I footprinting and TF-gene co-expression to reconstruct a transcriptional regulatory network (TRN) model for the human brain, predicting the brain-specific binding sites and target genes for 741 TFs. We used this model to predict core TFs involved in psychiatric and neurodegenerative diseases. Our results suggest that disease-related transcriptomic and genetic changes converge on small sets of disease-specific regulators, with distinct networks underlying neurodegenerative vs. psychiatric diseases. Core TFs were frequently implicated in a disease through multiple mechanisms, including differential expression of their target genes, disruption of their binding sites by disease-associated SNPs, and associations of the genetic loci encoding these TFs with disease risk. We validated our model’s predictions through systematic comparison to publicly available ChIP-seq and TF perturbation studies and through experimental studies in primary human neural stem cells. Combined genetic and transcriptional evidence supports roles for neuronal and microglia-enriched, MEF2C-regulated networks in Alzheimer’s disease; an oligodendrocyte-enriched, SREBF1-regulated network in schizophrenia; and a neural stem cell and astrocyte-enriched, POU3F2-regulated network in bipolar disorder. We provide our models of brain-specific TF binding sites and target genes as a resource for network analysis of brain diseases.

Defining the environmental context in which genes enhance disease susceptibility can provide insight into the pathogenesis of complex disorders. We report that the intra-uterine environment modulates the association of schizophrenia with genomic risk (in this study, genome-wide association study–derived polygenic risk scores (PRSs)). In independent samples from the United States, Italy, and Germany, the liability of schizophrenia explained by PRS is more than five times greater in the presence of early-life complications (ELCs) compared with their absence. Patients with ELC histories have significantly higher PRS than patients without ELC histories, which is confirmed in additional samples from Germany and Japan. The gene set composed of schizophrenia loci that interact with ELCs is highly expressed in placenta, is differentially expressed in placentae from complicated in comparison with normal pregnancies, and is differentially upregulated in placentae from male compared with female offspring. Pathway analyses reveal that genes driving the PRS-ELC interaction are involved in cellular stress response; genes that do not drive such interaction implicate orthogonal biological processes (for example, synaptic function). We conclude that a subset of the most significant genetic variants associated with schizophrenia converge on a developmental trajectory sensitive to events that affect the placental response to stress, which may offer insights into sex biases and primary prevention.

Defining the environmental context in which genes enhance susceptibility can provide insight into the pathogenesis of complex disorders, like schizophrenia. Here we show that the intrauterine and perinatal environment modulates the association of schizophrenia with genomic risk, as measured with polygenic risk scores (PRS) based primarily on GWAS significant variants. Genomic risk interacts with intrauterine and perinatal complications (Early Life Complications, ELCs) in each of three independent samples from USA, Italy and Germany (overall n= 1693, p= 6e-05). In each sample, the liability of schizophrenia explained by PRS is nominally more than five times greater in the presence of a history of ELCs compared with its absence. In each sample, patients with positive ELC histories have higher PRS than patients without ELCs, which is further confirmed in two additional patient samples from Germany and Japan (overall n=2038, p= 1e-04). The gene set based on the schizophrenia loci interacting with ELCs is highly expressed in multiple placental compartments and dynamically regulated in placenta from complicated in comparison with normal pregnancies. The same genes are differentially up-regulated in placentae from male compared with female offspring. The interaction between genomic risk and ELCs is mainly driven by GWAS significant loci enriched for genes highly expressed in the various placenta samples. Molecular pathway analyses based on the genes not driving this interaction reflect previous analyses about schizophrenia risk-genes, while genes highly and differentially expressed in placentae implicate an orthogonal biology involving cellular stress. These results suggest that the most significant genetic variants detected by current schizophrenia GWAS contribute to risk in part by converging on a developmental trajectory sensitive to events affecting placentation, which may underlie the male preponderance of schizophrenia and offer new insights into primary prevention.

SummaryGWAS have identified 108 loci that confer risk for schizophrenia, but risk mechanisms for individual loci are largely unknown. Using developmental, genetic, and illness-based RNA sequencing expression analysis, we characterized the human brain transcriptome around these loci and found enrichment for developmentally regulated genes with novel examples of shifting isoform usage across pre- and post-natal life. We found widespread expression quantitative trait loci (eQTLs), including many with transcript specificity and previously unannotated sequence that were independently replicated. We leveraged this eQTL database to show that 48.1% of risk variants for schizophrenia associated with nearby expression. Within patients and controls, we implemented a novel algorithm for RNA quality adjustment, and identified 237 genes significantly associated with diagnosis that replicated in an independent case-control dataset. These genes implicated synaptic processes and were strongly regulated in early development (p < 10-20). These data offer new targets for modeling schizophrenia risk in cellular systems.

The human prefrontal cortex (PFC), a mastermind of the brain, is one of the last brain regions to mature. To investigate the role of epigenetics in the development of PFC, we examined DNA methylation in ∼14,500 genes at ∼27,000 CpG loci focused on 5′ promoter regions in 108 subjects range in age from fetal to elderly. DNA methylation in the PFC shows unique temporal patterns across life. The fastest changes occur during the prenatal period, slow down markedly after birth and continue to slow further with aging. At the genome level, the transition from fetal to postnatal life is typified by a reversal of direction, from demethylation prenatally to increased methylation postnatally. DNA methylation is strongly associated with genotypic variants and correlates with expression of a subset of genes, including genes involved in brain development and in de novo DNA methylation. Our results indicate that promoter DNA methylation in the human PFC is a highly dynamic process modified by genetic variance and regulating gene transcription. Additional discovery is made possible with a stand-alone application, BrainCloudMethyl.

Previous investigations have combined transcriptional and genetic analyses in human cell lines1-3, but few have applied these techniques to human neural tissue4-8. To gain a global molecular perspective on the role of the human genome in cortical development, function and ageing, we explore the temporal dynamics and genetic control of transcription in human prefrontal cortex in an extensive series of post-mortem brains from fetal development through ageing. We discover a wave of gene expression changes occurring during fetal development which are reversed in early postnatal life. One half-century later in life, this pattern of reversals is mirrored in ageing and in neurodegeneration. Although we identify thousands of robust associations of individual genetic polymorphisms with gene expression, we also demonstrate that there is no association between the total extent of genetic differences between subjects and the global similarity of their transcriptional profiles. Hence, the human genome produces a consistent molecular architecture in the prefrontal cortex, despite millions of genetic differences across individuals and races. To enable further discovery, this entire data set is freely available (from Gene Expression Omnibus: accession GSE30272; and dbGaP: accession phs000417.v1.p1) and can also be interrogated via a biologist-friendly stand-alone application (http://www.libd.org/braincloud).

Background— Cardiac electromechanical dyssynchrony causes regional disparities in workload, oxygen consumption, and myocardial perfusion within the left ventricle. We hypothesized that such dyssynchrony also induces region-specific alterations in the myocardial transcriptome that are corrected by cardiac resynchronization therapy (CRT). Methods and Results— Adult dogs underwent left bundle branch ablation and right atrial pacing at 200 bpm for either 6 weeks (dyssynchronous heart failure, n=12) or 3 weeks, followed by 3 weeks of resynchronization by biventricular pacing at the same pacing rate (CRT, n=10). Control animals without left bundle branch block were not paced (n=13). At 6 weeks, RNA was isolated from the anterior and lateral left ventricular (LV) walls and hybridized onto canine-specific 44K microarrays. Echocardiographically, CRT led to a significant decrease in the dyssynchrony index, while dyssynchronous heart failure and CRT animals had a comparable degree of LV dysfunction. In dyssynchronous heart failure, changes in gene expression were primarily observed in the anterior LV, resulting in increased regional heterogeneity of gene expression within the LV. Dyssynchrony-induced expression changes in 1050 transcripts were reversed by CRT to levels of nonpaced hearts (false discovery rate Conclusions— Our results demonstrate a profound effect of electromechanical dyssynchrony on the regional cardiac transcriptome, causing gene expression changes primarily in the anterior LV wall. CRT corrected the alterations in gene expression in the anterior wall, supporting a global effect of biventricular pacing on the ventricular transcriptome that extends beyond the pacing site in the lateral wall.

The molecular basis of complex neuropsychiatric disorders most likely involves many genes. In recent years, specific genetic variations influencing risk for schizophrenia and other neuropsychiatric disorders have been reported. We have used custom DNA microarrays and qPCR to investigate the expression of putative schizophrenia susceptibility genes and related genes of interest in the normal human brain. Expression of 31 genes was measured in Brodmann's area 10 (BA10) in the prefrontal cortex of 72 postmortem brain samples spanning half a century of human aging (18-67 years), each without history of neuropsychiatric illness, neurological disease, or drug abuse. Examination of expression across age allowed the identification of genes whose expression patterns correlate with age, as well as genes that share common expression patterns and that possibly participate in common cellular mechanisms related to the emergence of schizophrenia in early adult life. The expression of GRM3 and RGS4 decreased across the entire age range surveyed, while that of PRODH and DARPP-32 was shown to increase with age. NRG1, ERBB3, and NGFR show expression changes during the years of greatest risk for the development of schizophrenia. Expression of FEZ1, GAD1, and RGS4 showed especially high correlation with one another, in addition to the strongest mean levels of absolute correlation with all other genes studied here. All microarray data are available at NCBI's Gene Expression Omnibus: GEO Series accession number GSE11546 (http://www.ncbi.nlm.nih.gov/geo) [corrected]

Receptor tyrosine kinases (RTK) and the tumor suppressor PTEN co-regulate oncogenic cell signaling pathways. How these interactions influence gene transcription is inadequately understood. We used expression microarrays to investigate the effects of PTEN on gene expression changes caused by activating c-Met in human glioblastoma cells. c-Met activation by scatter factor/hepatocyte growth factor (SF/HGF) altered the expression of 27-fold more genes in PTEN-null U-373MG cells than in PTEN homozygous primary normal human astrocytes (523 vs 19 genes). Restoring wt-PTEN in U-373MG cells dramatically altered patterns of c-Met regulated gene expression. This effect was varied depending on the specific gene in question. PTEN reduced the number of c-Met regulated transcripts from 931 to 502, decreased the relative number of genes upregulated by c-Met from 46 to 25%, and increased the relative number of downregulated genes from 54 to 75%. PTEN and c-Met co-regulated many genes involved in cell growth regulation such as oncogenes, growth factors, transcription factors, and constituents of the ubiquitin pathway. c-Met activation in PTEN-null (but not PTEN reconstituted) cells led to upregulation of the EGFR agonist TGFalpha and subsequently to EGFR activation. Using PTEN mutants, we found that PTEN's transcriptional effects were either lipid-phosphatase dependent, protein-phosphatase dependent, or phosphatase-independent. These results show that PTEN has critical and mechanistically complex effects on RTK-regulated gene transcription. These findings expand our understanding of tumor promoter/suppressor inter-relationships and downstream transcriptional effects of PTEN loss and c-Met overexpression in malignant gliomas.

The human brain is thought to have the greatest complexity of gene expression of any region of the body, reflecting the diverse functions of neurons and glia. Studies of gene expression in the human brain may yield fundamental information about the phenotype of brain cells in different stages of development, in different brain regions, and in different physiological and pathological states. As the human genome project nears completion, several technological advances allow the analysis of thousands of expressed genes in a small brain sample. This review describes available sources of human brain material, and several high throughput techniques used to measure the expression of thousands of genes. These techniques include expressed sequence tag (EST) sequencing of cDNA libraries; differential display; subtractive hybridization; serial analysis of gene expression (SAGE); and the emerging technology of high density DNA microarrays. Measurement of gene expression with microarrays and other technologies has potential applications in the study of human brain diseases, including cognitive disorders for which animal models are typically not available. Gene expression measurements may be used to identify genes that are abnormally regulated as a secondary consequence of a disease state, or to identify the response of brain cells to pharmacological treatments.

Genome-wide profiling of multiple histone methylations in olfactory cells: further implications for cellular susceptibility to oxidative stress in schizophrenia

The identification of mutations in the transcriptional repressor methyl-CpG-binding protein 2 (MECP2) gene in Rett Syndrome (RTT) suggests that an inappropriate release of transcriptional silencing may give rise to RTT neuropathology. Despite this progress, the molecular basis of RTT neuropathogenesis remains unclear. Using multiple cDNA microarray technologies, subtractive hybridization, and conventional biochemistry, we generated comprehensive gene expression profiles of postmortem brain tissue from RTT patients and matched controls. Many glial transcripts involved in known neuropathological mechanisms were found to have increased expression in RTT brain, while decreases were observed in the expression of multiple neuron-specific mRNAs. Dramatic and consistent decreases in transcripts encoding presynaptic markers indicated a specific deficit in presynaptic development. Employing multiple clustering algorithms, it was possible to accurately segregate RTT from control brain tissue samples based solely on gene expression profile. Although previously achieved in cancers, our results constitute the first report of human disease classification using gene expression profiling in a complex tissue source such as brain.

A complete system containing high density microarrays on glass slides and all necessary experimental reagents has been developed. Utilizing the TSATM (Tyramide Signal Amplification) technology, the system offers significantly improved sensitivity which reduces sample requirement by 10-50 fold and uses fluorescence-based detection. MICROMAXTM Microarray System I spotted with 2,400 known human genes provides the opportunity to monitor gene expression between two comparative cell lines or tissue samples. In addition to following global changes in gene expression patterns in basic research, the system also offers the potential of detecting changes in gene expression for diagnostic applications. We have used the system to analyze gene expression in human postmortem brains. Samples of parietal cortex of patients with Rett syndrome, an autism-related disease that affects females exclusively, were compared to age-matched tissue samples from normal controls. Several genes were up- or down-regulated in the diseased samples. These studies highlight the potential of the MICROMAXTM system to measure gene expression in complex biological samples.

Background By studying genome-wide expression patterns in healthy and diseased tissues across a wide range of pathophysiological conditions, DNA microarrays have revealed unique insights into complex diseases. However, the high-dimensionality of microarray data makes interpretation of heterogeneous gene expression studies inherently difficult. Results Using a large-scale analysis of more than 40 microarray studies encompassing ~2400 mammalian tissue samples, we identified a common theme across heterogeneous microarray studies evident by a robust genome-wide inverse regulation of metabolic and cell signaling pathways: We found that upregulation of cell signaling pathways was invariably accompanied by downregulation of cell metabolic transcriptional activity (and vice versa). Several findings suggest that this characteristic gene expression pattern represents a new principle of mammalian transcriptional regulation. First, this coordinated transcriptional pattern occurred in a wide variety of physiological and pathophysiological conditions and was identified across all 20 human and animal tissue types examined. Second, the differences in metabolic gene expression predicted the magnitude of differences for signaling and all other pathways, i.e. tissue samples with similar expression levels of metabolic transcripts did not show any differences in gene expression for all other pathways. Third, this transcriptional pattern predicted a profound effect on the proteome, evident by differences in structure, stability and post-translational modifications of proteins belonging to signaling and metabolic pathways, respectively. Conclusions Our data suggest that in a wide range of physiological and pathophysiological conditions, gene expression changes exhibit a recurring pattern along a transcriptional axis, characterized by an inverse regulation of major metabolic and cell signaling pathways. Given its widespread occurrence and its predicted effects on protein structure, protein stability and post-translational modifications, we propose a new principle for transcriptional regulation in mammalian biology.