Brain-derived neurotrophic factor (Bdnf) transcription is controlled by several promoters, which drive expression of multiple transcripts encoding an identical protein. We previously reported that BDNF derived from promoters I and II is highly expressed in hypothalamus and is critical for regulating aggression in male mice. Here we report that BDNF loss from these promoters causes reduced sexual receptivity and impaired maternal care in female mice, which is concomitant with decreased oxytocin (Oxt) expression during development. We identify a novel link between BDNF signaling, oxytocin, and maternal behavior by demonstrating that ablation of TrkB selectively in OXT neurons partially recapitulates maternal care impairments observed in BDNF-deficient females. Using translating ribosome affinity purification and RNA-sequencing we define a molecular profile for OXT neurons and delineate how BDNF signaling impacts gene pathways critical for structural and functional plasticity. Our findings highlight BDNF as a modulator of sexually-dimorphic hypothalamic circuits that govern female-typical behaviors.

Publicly available genomic data are a valuable resource for studying normal human variation and disease, but these data are often not well labeled or annotated. The lack of phenotype information for public genomic data severely limits their utility for addressing targeted biological questions. We develop an in silico phenotyping approach for predicting critical missing annotation directly from genomic measurements using well-annotated genomic and phenotypic data produced by consortia like TCGA and GTEx as training data. We apply in silico phenotyping to a set of 70 000 RNA-seq samples we recently processed on a common pipeline as part of the recount2 project. We use gene expression data to build and evaluate predictors for both biological phenotypes (sex, tissue, sample source) and experimental conditions (sequencing strategy). We demonstrate how these predictions can be used to study cross-sample properties of public genomic data, select genomic projects with specific characteristics, and perform downstream analyses using predicted phenotypes. The methods to perform phenotype prediction are available in the phenopredict R package and the predictions for recount2 are available from the recount R package. With data and phenotype information available for 70,000 human samples, expression data is available for use on a scale that was not previously feasible.

RegulonDB (http://regulondb.ccg.unam.mx/) is the primary reference database of the best-known regulatory network of any free-living organism, that of Escherichia coli K-12. The major conceptual change since 3 years ago is an expanded biological context so that transcriptional regulation is now part of a unit that initiates with the signal and continues with the signal transduction to the core of regulation, modifying expression of the affected target genes responsible for the response. We call these genetic sensory response units, or Gensor Units. We have initiated their high-level curation, with graphic maps and superreactions with links to other databases. Additional connectivity uses expandable submaps. RegulonDB has summaries for every transcription factor (TF) and TF-binding sites with internal symmetry. Several DNA-binding motifs and their sizes have been redefined and relocated. In addition to data from the literature, we have incorporated our own information on transcription start sites (TSSs) and transcriptional units (TUs), obtained by using high-throughput whole-genome sequencing technologies. A new portable drawing tool for genomic features is also now available, as well as new ways to download the data, including web services, files for several relational database manager systems and text files including BioPAX format.

Differential expression analysis of RNA sequencing (RNA-seq) data typically relies on reconstructing transcripts or counting reads that overlap known gene structures. We previously introduced an intermediate statistical approach called differentially expressed region (DER) finder that seeks to identify contiguous regions of the genome showing differential expression signal at single base resolution without relying on existing annotation or potentially inaccurate transcript assembly.We present the derfinder software that improves our annotation-agnostic approach to RNA-seq analysis by: (i) implementing a computationally efficient bump-hunting approach to identify DERs that permits genome-scale analyses in a large number of samples, (ii) introducing a flexible statistical modeling framework, including multi-group and time-course analyses and (iii) introducing a new set of data visualizations for expressed region analysis. We apply this approach to public RNA-seq data from the Genotype-Tissue Expression (GTEx) project and BrainSpan project to show that derfinder permits the analysis of hundreds of samples at base resolution in R, identifies expression outside of known gene boundaries and can be used to visualize expressed regions at base-resolution. In simulations, our base resolution approaches enable discovery in the presence of incomplete annotation and is nearly as powerful as feature-level methods when the annotation is complete.derfinder analysis using expressed region-level and single base-level approaches provides a compromise between full transcript reconstruction and feature-level analysis. The package is available from Bioconductor at www.bioconductor.org/packages/derfinder.

Brain-derived neurotrophic factor (BDNF) signals through its cognate receptor tropomyosin receptor kinase B (TrkB) to promote the function of several classes of inhibitory interneurons. We previously reported that loss of BDNF-TrkB signaling in cortistatin (Cort)-expressing interneurons leads to behavioral hyperactivity and spontaneous seizures in mice. We performed bulk RNA sequencing (RNA-seq) from the cortex of mice with disruption of BDNF-TrkB signaling in cortistatin interneurons, and identified differential expression of genes important for excitatory neuron function. Using translating ribosome affinity purification and RNA-seq, we define a molecular profile for Cort-expressing inhibitory neurons and subsequently compare the translatome of normal and TrkB-depleted Cort neurons, revealing alterations in calcium signaling and axon development. Several of the genes enriched in Cort neurons and differentially expressed in TrkB-depleted neurons are also implicated in autism and epilepsy. Our findings highlight TrkB-dependent molecular pathways as critical for the maturation of inhibitory interneurons and support the hypothesis that loss of BDNF signaling in Cort interneurons leads to altered excitatory/inhibitory balance.

Genome-wide association studies have generated an increasing number of common genetic variants associated with neurological and psychiatric disease risk. An improved understanding of the genetic control of gene expression in human brain is vital considering this is the likely modus operandum for many causal variants. However, human brain sampling complexities limit the explanatory power of brain-related expression quantitative trait loci (eQTL) and allele-specific expression (ASE) signals. We address this, using paired genomic and transcriptomic data from putamen and substantia nigra from 117 human brains, interrogating regulation at different RNA processing stages and uncovering novel transcripts. We identify disease-relevant regulatory loci, find that splicing eQTLs are enriched for regulatory information of neuron-specific genes, that ASEs provide cell-specific regulatory information with evidence for cellular specificity, and that incomplete annotation of the brain transcriptome limits interpretation of risk loci for neuropsychiatric disease. This resource of regulatory data is accessible through our web server, http://braineacv2.inf.um.es/.

Down syndrome cell adhesion molecule, or DSCAM, has been implicated in many neurodevelopmental processes including axon guidance, dendrite arborization, and synapse formation. Here we show that DSCAM plays an important role in regulating the morphogenesis of cortical pyramidal neurons in the mouse. We report that DSCAM expression is developmentally regulated and localizes to synaptic plasma membranes during a time of robust cortical dendrite arborization and spine formation. Analysis of mice that carry a spontaneous mutation in DSCAM (DSCAM(del17)) revealed gross morphological changes in brain size and shape in addition to subtle changes in cortical organization, volume, and lamination. Early postnatal mutant mice displayed a transient decrease in cortical thickness, but these reductions could not be attributed to changes in neuron production or cell death. DSCAM(del17) mutants showed temporary impairments in the branching of layer V pyramidal neuron dendrites at P10 and P17 that recovered to normal by adulthood. Defects in DSCAM(del17) dendrite branching correlated with a temporal increase in apical branch spine density and lasting changes in spine morphology. At P15 and P42, mutant mice displayed a decrease in the percentage of large, stable spines and an increase in the percentage of small, immature spines. Together, our findings suggest that DSCAM contributes to pyramidal neuron morphogenesis by regulating dendrite arborization and spine formation during cortical circuit development.

RNA sequencing offers advantages over other quantification methods for microRNA (miRNA), yet numerous biases make reliable quantification challenging. Previous evaluations of these biases have focused on adapter ligation bias with limited evaluation of reverse transcription bias or amplification bias. Furthermore, evaluations of the quantification of isomiRs (miRNA isoforms) or the influence of starting amount on performance have been very limited. No study had yet evaluated the quantification of isomiRs of altered length or compared the consistency of results derived from multiple moderate starting inputs. We therefore evaluated quantifications of miRNA and isomiRs using four library preparation kits, with various starting amounts, as well as quantifications following removal of duplicate reads using unique molecular identifiers (UMIs) to mitigate reverse transcription and amplification biases.

Understanding the molecular profile of every human cell type is essential for understanding its role in normal physiology and disease. Technological advancements in DNA sequencing, mass spectrometry, and computational methods allow us to carry out multiomics analyses although such approaches are not routine yet. Human umbilical vein endothelial cells (HUVECs) are a widely used model system to study pathological and physiological processes associated with the cardiovascular system. In this study, next-generation sequencing and high-resolution mass spectrometry to profile the transcriptome and proteome of primary HUVECs is employed. Analysis of 145 million paired-end reads from next-generation sequencing confirmed expression of 12 186 protein-coding genes (FPKM ≥0.1), 439 novel long non-coding RNAs, and revealed 6089 novel isoforms that were not annotated in GENCODE. Proteomics analysis identifies 6477 proteins including confirmation of N-termini for 1091 proteins, isoforms for 149 proteins, and 1034 phosphosites. A database search to specifically identify other post-translational modifications provide evidence for a number of modification sites on 117 proteins which include ubiquitylation, lysine acetylation, and mono-, di- and tri-methylation events. Evidence for 11 "missing proteins," which are proteins for which there was insufficient or no protein level evidence, is provided. Peptides supporting missing protein and novel events are validated by comparison of MS/MS fragmentation patterns with synthetic peptides. Finally, 245 variant peptides derived from 207 expressed proteins in addition to alternate translational start sites for seven proteins and evidence for novel proteoforms for five proteins resulting from alternative splicing are identified. Overall, it is believed that the integrated approach employed in this study is widely applicable to study any primary cell type for deeper molecular characterization.

Antibody class switch recombination (CSR) to IgG, IgA, or IgE is a hallmark of adaptive immunity, allowing antibody function diversification beyond IgM. CSR involves a deletion of the IgM/IgD constant region genes placing a new acceptor Constant gene, downstream of the VDJH exon. CSR depends on non-coding (CSRnc) transcription of donor Iμ and acceptor IH exons, located 5' upstream of each CH coding gene. Although, our knowledge of the role of CSRnc transcription has advanced greatly, its extension and importance in healthy and diseased humans is scarce. We analyzed CSRnc transcription in 70,603 publicly available RNA-seq samples, including GTEx, TCGA, and the Sequence Read Archive using recount2, an online resource consisting of normalized RNA-seq gene and exon counts, as well as, coverage BigWig files that can be programmatically accessed through R. CSRnc transcription was validated with a qRT-PCR assay for Iμ, Iγ3, and Iγ1 in humans in response to vaccination. We mapped IH transcription for the human IGH locus, including the less understood IGHD gene. CSRnc transcription was restricted to B cells and is widely distributed in normal adult tissues, but predominant in blood, spleen, MALT-containing tissues, visceral adipose tissue and some so-called "immune privileged" tissues. However, significant Iγ4 expression was found even in non-lymphoid fetal tissues. CSRnc expression in cancer tissues mimicked the expression of their normal counterparts, with notable pattern changes in some common cancer subsets. CSRnc transcription in tumors appears to result from tumor infiltration by B cells, since CSRnc transcription was not detected in corresponding tumor-derived immortal cell lines. Additionally, significantly increased Iδ transcription in ileal mucosa in Crohn's disease with ulceration was found. In conclusion, CSRnc transcription occurs in multiple anatomical locations beyond classical secondary lymphoid organs, representing a potentially useful marker of effector B cell responses in normal and pathological immune responses. The pattern of IH exon expression may reveal clues of the local immune response (i.e., cytokine milieu) in health and disease. This is a great example of how the public recount2 data can be used to further our understanding of transcription, including regions outside the known transcriptome.

Better understanding of the progression of neural stem cells (NSCs) in the developing cerebral cortex is important for modeling neurogenesis and defining the pathogenesis of neuropsychiatric disorders. Here, we use RNA sequencing, cell imaging, and lineage tracing of mouse and human in vitro NSCs and monkey brain sections to model the generation of cortical neuronal fates. We show that conserved signaling mechanisms regulate the acute transition from proliferative NSCs to committed glutamatergic excitatory neurons. As human telencephalic NSCs develop from pluripotency in vitro, they transition through organizer states that spatially pattern the cortex before generating glutamatergic precursor fates. NSCs derived from multiple human pluripotent lines vary in these early patterning states, leading differentially to dorsal or ventral telencephalic fates. This work furthers systematic analyses of the earliest patterning events that generate the major neuronal trajectories of the human telencephalon.

We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex. We identified extensive layer-enriched expression signatures and refined associations to previous laminar markers. We overlaid our laminar expression signatures on large-scale single nucleus RNA-sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions in which morphological architecture is not as well defined as cortical laminae. Last, we created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research ( http://research.libd.org/spatialLIBD ).

The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/ .

High-resolution and multiplexed imaging techniques are giving us an increasingly detailed observation of a biological system. However, sharing, exploring, and customizing the visualization of large multidimensional images can be a challenge. Here, we introduce Samui, a performant and interactive image visualization tool that runs completely in the web browser. Samui is specifically designed for fast image visualization and annotation and enables users to browse through large images and their selected features within seconds of receiving a link. We demonstrate the broad utility of Samui with images generated with two platforms: Vizgen MERFISH and 10x Genomics Visium Spatial Gene Expression. Samui along with example datasets is available at https://samuibrowser.com .

Cigarette smoking during pregnancy is a major public health concern. While there are well-described consequences in early child development, there is very little known about the effects of maternal smoking on human cortical biology during prenatal life. We therefore performed a genome-wide differential gene expression analysis using RNA sequencing (RNA-seq) on prenatal (N = 33; 16 smoking-exposed) as well as adult (N = 207; 57 active smokers) human postmortem prefrontal cortices. Smoking exposure during the prenatal period was directly associated with differential expression of 14 genes; in contrast, during adulthood, despite a much larger sample size, only two genes showed significant differential expression (FDR < 10%). Moreover, 1,315 genes showed significantly different exposure effects between maternal smoking during pregnancy and direct exposure in adulthood (FDR < 10%)-these differences were largely driven by prenatal differences that were enriched for pathways previously implicated in addiction and synaptic function. Furthermore, prenatal and age-dependent differentially expressed genes were enriched for genes implicated in non-syndromic autism spectrum disorder (ASD) and were differentially expressed as a set between patients with ASD and controls in postmortem cortical regions. These results underscore the enhanced sensitivity to the biological effect of smoking exposure in the developing brain and offer insight into how maternal smoking during pregnancy affects gene expression in the prenatal human cortex. They also begin to address the relationship between in utero exposure to smoking and the heightened risks for the subsequent development of neuropsychiatric disorders.

Genome-wide association studies have identified 108 schizophrenia risk loci, but biological 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 postnatal 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 general eQTL database to show that 48.1% of risk variants for schizophrenia associate 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 genetics- and diagnosis-related targets for better modeling of schizophrenia risk. This resource is publicly available at http://eqtl.brainseq.org/phase1 .

DNA methylation (DNAm) is a critical regulator of both development and cellular identity and shows unique patterns in neurons. To better characterize maturational changes in DNAm patterns in these cells, we profile the DNAm landscape at single-base resolution across the first two decades of human neocortical development in NeuN+ neurons using whole-genome bisulfite sequencing and compare them to non-neurons (primarily glia) and prenatal homogenate cortex.

We present recount3, a resource consisting of over 750,000 publicly available human and mouse RNA sequencing (RNA-seq) samples uniformly processed by our new Monorail analysis pipeline. To facilitate access to the data, we provide the recount3 and snapcount R/Bioconductor packages as well as complementary web resources. Using these tools, data can be downloaded as study-level summaries or queried for specific exon-exon junctions, genes, samples, or other features. Monorail can be used to process local and/or private data, allowing results to be directly compared to any study in recount3. Taken together, our tools help biologists maximize the utility of publicly available RNA-seq data, especially to improve their understanding of newly collected data. recount3 is available from http://rna.recount.bio .

Single-cell gene expression technologies are powerful tools to study cell types in the human brain, but efforts have largely focused on cortical brain regions. We therefore created a single-nucleus RNA-sequencing resource of 70,615 high-quality nuclei to generate a molecular taxonomy of cell types across five human brain regions that serve as key nodes of the human brain reward circuitry: nucleus accumbens, amygdala, subgenual anterior cingulate cortex, hippocampus, and dorsolateral prefrontal cortex. We first identified novel subpopulations of interneurons and medium spiny neurons (MSNs) in the nucleus accumbens and further characterized robust GABAergic inhibitory cell populations in the amygdala. Joint analyses across the 107 reported cell classes revealed cell-type substructure and unique patterns of transcriptomic dynamics. We identified discrete subpopulations of D1- and D2-expressing MSNs in the nucleus accumbens to which we mapped cell-type-specific enrichment for genetic risk associated with both psychiatric disease and addiction.

Dysregulation of RNA splicing contributes to both rare and complex diseases. RNA-sequencing data from human tissues has shown that this process can be inaccurate, resulting in the presence of novel introns detected at low frequency across samples and within an individual. To enable the full spectrum of intron use to be explored, we have developed IntroVerse, which offers an extensive catalogue on the splicing of 332,571 annotated introns and a linked set of 4,679,474 novel junctions covering 32,669 different genes. This dataset has been generated through the analysis of 17,510 human control RNA samples from 54 tissues provided by the Genotype-Tissue Expression Consortium. IntroVerse has two unique features: (i) it provides a complete catalogue of novel junctions and (ii) each novel junction has been assigned to a specific annotated intron. This unique, hierarchical structure offers multiple uses, including the identification of novel transcripts from known genes and their tissue-specific usage, and the assessment of background splicing noise for introns thought to be mis-spliced in disease states. IntroVerse provides a user-friendly web interface and is freely available at https://rytenlab.com/browser/app/introverse.