Project description:Brain development and function depend on the precise regulation of gene expression. However, our understanding of the complexity and dynamics of the transcriptome of the human brain is incomplete. Here we report the generation and analysis of exon-level transcriptome and associated genotyping data, representing males and females of different ethnicities, from multiple brain regions and neocortical areas of developing and adult post-mortem human brains. We found that 86 per cent of the genes analysed were expressed, and that 90 per cent of these were differentially regulated at the whole-transcript or exon level across brain regions and/or time. The majority of these spatio-temporal differences were detected before birth, with subsequent increases in the similarity among regional transcriptomes. The transcriptome is organized into distinct co-expression networks, and shows sex-biased gene expression and exon usage. We also profiled trajectories of genes associated with neurobiological categories and diseases, and identified associations between single nucleotide polymorphisms and gene expression. This study provides a comprehensive data set on the human brain transcriptome and insights into the transcriptional foundations of human neurodevelopment.
Project description:Aging is associated with gradual deterioration of physiological and biochemical functions, including cognitive decline. Transcriptome profiling of brain samples from individuals of varying ages has identified the whole-transcriptome changes that underlie age-associated cognitive declines. In this review, we discuss transcriptome-based research on human brain aging performed by using microarray and RNA sequencing analyses. Overall, decreased synaptic function and increased immune function are prevalent in most regions of the aged brain. Age-associated gene expression changes are also cell dependent and region dependent and are affected by genotype. In addition, the transcriptome changes that occur during brain aging include different splicing events, intersample heterogeneity, and altered levels of various types of noncoding RNAs. Establishing transcriptome-based hallmarks of human brain aging will improve the understanding of cognitive aging and neurodegenerative diseases and eventually lead to interventions that delay or prevent brain aging.
Project description:The enormous complexity of the human brain ultimately derives from a finite set of molecular instructions encoded in the human genome. These instructions can be directly studied by exploring the organization of the brain's transcriptome through systematic analysis of gene coexpression relationships. We analyzed gene coexpression relationships in microarray data generated from specific human brain regions and identified modules of coexpressed genes that correspond to neurons, oligodendrocytes, astrocytes and microglia. These modules provide an initial description of the transcriptional programs that distinguish the major cell classes of the human brain and indicate that cell type-specific information can be obtained from whole brain tissue without isolating homogeneous populations of cells. Other modules corresponded to additional cell types, organelles, synaptic function, gender differences and the subventricular neurogenic niche. We found that subventricular zone astrocytes, which are thought to function as neural stem cells in adults, have a distinct gene expression pattern relative to protoplasmic astrocytes. Our findings provide a new foundation for neurogenetic inquiries by revealing a robust and previously unrecognized organization to the human brain transcriptome.
Project description:Brain mural cells, including pericytes and vascular smooth muscle cells, are important for vascular development, blood-brain barrier function, and neurovascular coupling, but the molecular characteristics of human brain mural cells are incompletely characterized. Single cell RNA-sequencing (scRNA-seq) is increasingly being applied to assess cellular diversity in the human brain, but the scarcity of mural cells in whole brain samples has limited their molecular profiling. Here, we leverage the combined power of multiple independent human brain scRNA-seq datasets to build a transcriptomic database of human brain mural cells. We use this combined dataset to determine human-mouse species differences in mural cell transcriptomes, culture-induced dedifferentiation of human brain pericytes, and human mural cell organotypicity, with several key findings validated by RNA fluorescence in situ hybridization. Together, this work improves knowledge regarding the molecular constituents of human brain mural cells, serves as a resource for hypothesis generation in understanding brain mural cell function, and will facilitate comparative evaluation of animal and in vitro models.
Project description:BackgroundIdentifying biological pathways that vary across the age spectrum can provide insight into fundamental mechanisms that impact disease and frailty in the elderly. Few methodological approaches offer the means to explore this question on as broad a scale as gene expression profiling. Here, we have evaluated mRNA expression profiles as a function of age in two populations; one consisting of 191 individuals with ages-at-death ranging from 65-100 years and with post-mortem brain mRNA measurements of 13,216 genes and a second with 1240 individuals ages 15-94 and lymphocyte mRNA estimates for 18,519 genes.Principal findingsAmong negatively correlated transcripts, an enrichment of mitochondrial genes was evident in both populations, providing a replication of previous studies indicating this as a common signature of aging. Sample differences were prominent, the most significant being a decrease in expression of genes involved in translation in lymphocytes and an increase in genes involved in transcription in brain, suggesting that apart from energy metabolism other basic cell processes are affected by age but in a tissue-specific manner. In assessing genomic architecture, intron/exon sequence length ratios were larger among negatively regulated genes in both samples, suggesting that a decrease in the expression of non-compact genes may also be a general effect of aging. Variance in gene expression itself has been theorized to change with age due to accumulation of somatic mutations and/or increasingly heterogeneous environmental exposures, but we found no evidence for such a trend here.SignificanceResults affirm that deteriorating mitochondrial gene expression is a common theme in senescence, but also highlight novel pathways and features of gene architecture that may be important for understanding the molecular consequences of aging.
Project description:Alcohol dependence is a heterogeneous psychiatric disorder characterized by high genetic heritability and neuroadaptations occurring from repeated drug exposure. Through an integrated systems approach we observed consistent differences in transcriptome organization within postmortem human brain tissue associated with the lifetime consumption of alcohol. Molecular networks, determined using high-throughput RNA sequencing, for drinking behavior were dominated by neurophysiological targets and signaling mechanisms of alcohol. The systematic structure of gene sets demonstrates a novel alliance of multiple ion channels, and related processes, underlying lifetime alcohol consumption. Coordinate expression of these transcripts was enriched for genome-wide association signals in alcohol dependence and a meta-analysis of alcohol self-administration in mice. Further dissection of genes within alcohol consumption networks revealed the potential interaction of alternatively spliced transcripts. For example, expression of a human-specific isoform of the voltage-gated sodium channel subunit SCN4B was significantly correlated to lifetime alcohol consumption. Overall, our work demonstrates novel convergent evidence for biological networks related to excessive alcohol consumption, which may prove fundamentally important in the development of pharmacotherapies for alcohol dependence.
Project description:Measuring allelic RNA expression ratios is a powerful approach for detecting cis-acting regulatory variants, RNA editing, loss of heterozygosity in cancer, copy number variation, and allele-specific epigenetic gene silencing. Whole transcriptome RNA sequencing (RNA-Seq) has emerged as a genome-wide tool for identifying allelic expression imbalance (AEI), but numerous factors bias allelic RNA ratio measurements. Here, we compare RNA-Seq allelic ratios measured in nine different human brain regions with a highly sensitive and accurate SNaPshot measure of allelic RNA ratios, identifying factors affecting reliable allelic ratio measurement. Accounting for these factors, we subsequently surveyed the variability of RNA editing across brain regions and across individuals.We find that RNA-Seq allelic ratios from standard alignment methods correlate poorly with SNaPshot, but applying alternative alignment strategies and correcting for observed biases significantly improves correlations. Deploying these methods on a transcriptome-wide basis in nine brain regions from a single individual, we identified genes with AEI across all regions (SLC1A3, NHP2L1) and many others with region-specific AEI. In dorsolateral prefrontal cortex (DLPFC) tissues from 14 individuals, we found evidence for frequent regulatory variants affecting RNA expression in tens to hundreds of genes, depending on stringency for assigning AEI. Further, we find that the extent and variability of RNA editing is similar across brain regions and across individuals.These results identify critical factors affecting allelic ratios measured by RNA-Seq and provide a foundation for using this technology to screen allelic RNA expression on a transcriptome-wide basis. Using this technology as a screening tool reveals tens to hundreds of genes harboring frequent functional variants affecting RNA expression in the human brain. With respect to RNA editing, the similarities within and between individuals leads us to conclude that this post-transcriptional process is under heavy regulatory influence to maintain an optimal degree of editing for normal biological function.
Project description:BackgroundMouse models have allowed for the direct interrogation of genetic effects on molecular, physiological, and behavioral brain phenotypes. However, it is unknown to what extent neurological or psychiatric traits may be human- or primate-specific and therefore which components can be faithfully recapitulated in mouse models.ResultsWe compare conservation of co-expression in 116 independent data sets derived from human, mouse, and non-human primate representing more than 15,000 total samples. We observe greater changes occurring on the human lineage than mouse, and substantial regional variation that highlights cerebral cortex as the most diverged region. Glia, notably microglia, astrocytes, and oligodendrocytes are the most divergent cell type, three times more on average than neurons. We show that cis-regulatory sequence divergence explains a significant fraction of co-expression divergence. Moreover, protein coding sequence constraint parallels co-expression conservation, such that genes with loss of function intolerance are enriched in neuronal, rather than glial modules. We identify dozens of human neuropsychiatric and neurodegenerative disease risk genes, such as COMT, PSEN-1, LRRK2, SHANK3, and SNCA, with highly divergent co-expression between mouse and human and show that 3D human brain organoids recapitulate in vivo co-expression modules representing several human cell types.ConclusionsWe identify robust co-expression modules reflecting whole-brain and regional patterns of gene expression. Compared with those that represent basic metabolic processes, cell-type-specific modules, most prominently glial modules, are the most divergent between species. These data and analyses serve as a foundational resource to guide human disease modeling and its interpretation.
Project description:Alcoholism remains a prevalent health concern throughout the world. Previous studies have identified transcriptomic patterns in the brain associated with alcohol dependence in both humans and animal models. But none of these studies have systematically investigated expression within the unique cell types present in the brain. We utilized single nucleus RNA sequencing (snRNA-seq) to examine the transcriptomes of over 16 000 nuclei isolated from the prefrontal cortex of alcoholic and control individuals. Each nucleus was assigned to one of seven major cell types by unsupervised clustering. Cell type enrichment patterns varied greatly among neuroinflammatory-related genes, which are known to play roles in alcohol dependence and neurodegeneration. Differential expression analysis identified cell type-specific genes with altered expression in alcoholics. The largest number of differentially expressed genes (DEGs), including both protein-coding and non-coding, were detected in astrocytes, oligodendrocytes and microglia. To our knowledge, this is the first single cell transcriptome analysis of alcohol-associated gene expression in any species and the first such analysis in humans for any addictive substance. These findings greatly advance the understanding of transcriptomic changes in the brain of alcohol-dependent individuals.
Project description:The orchestration of brain function requires complex gene regulatory networks that are modulated, in part, by microRNAs (miRNAs). These noncoding RNAs associate with argonaute (Ago) proteins in order to direct posttranscriptional gene suppression via base pairing with target transcripts. In order to better understand how miRNAs contribute to human-specialized brain processes and neurological phenotypes, identifying their targets is of paramount importance. Here, we address the latter by profiling Ago2:RNA interactions using HITS-CLIP to generate a transcriptome-wide map of miRNA binding sites in human brain. We uncovered ∼ 7,000 stringent Ago2 binding sites that are highly enriched for conserved sequences corresponding to abundant brain miRNAs. This interactome points to functional miRNA:target pairs across >3,000 genes and represents a valuable resource for accelerating our understanding of miRNA functions in brain. We demonstrate the utility of this map for exploring clinically relevant miRNA binding sites that may facilitate the translation of genetic studies of complex neuropsychiatric diseases into therapeutics.