Brain Transcriptome Databases: A User's Guide

Subregion-specific transcriptomic profiling of rat brain reveals sex-distinct gene expression impacted by adolescent stress

Stress during adolescence clearly impacts brain development and function. Sex differences in adolescent stress-induced or exacerbated emotional and metabolic vulnerabilities could be due to sex-distinct gene expression in hypothalamic, limbic, and prefrontal brain regions. However, adolescent stress-induced whole-genome expression changes in key subregions of these brain regions were unclear. In this study, female and male adolescent Sprague Dawley rats received one-hour restraint stress daily from postnatal day (PD) 32 to PD44. Corticosterone levels, body weights, food intake, body composition, and circulating adiposity and sex hormones were measured. On PD44, brain and blood samples were collected. Using RNA-sequencing, sex-specific differences in stress-induced differentially expressed (DE) genes were identified in subregions of the hypothalamus, limbic system, and prefrontal cortex. Canonical pathways reflected well-known sex-distinct maladies and diseases, substantiating the therapeutic potential of the DE genes found in the current study. Thus, we proposed specific sex distinct, adolescent stress-induced transcriptional changes found in the current study as examples of the molecular bases for sex differences witnessed in stress induced or exacerbated emotional and metabolic disorders. Future behavioral studies and single-cell studies are warranted to test the implications of the DE genes identified in this study in sex-distinct stress-induced susceptibilities.

BrainTACO: an explorable multi-scale multi-modal brain transcriptomic and connectivity data resource

Exploring the relationships between genes and brain circuitry can be accelerated by joint analysis of heterogeneous datasets from 3D imaging data, anatomical data, as well as brain networks at varying scales, resolutions, and modalities. Generating an integrated view, beyond the individual resources' original purpose, requires the fusion of these data to a common space, and a visualization that bridges the gap across scales. However, despite ever expanding datasets, few platforms for integration and exploration of this heterogeneous data exist. To this end, we present the BrainTACO (Brain Transcriptomic And Connectivity Data) resource, a selection of heterogeneous, and multi-scale neurobiological data spatially mapped onto a common, hierarchical reference space, combined via a holistic data integration scheme. To access BrainTACO, we extended BrainTrawler, a web-based visual analytics framework for spatial neurobiological data, with comparative visualizations of multiple resources. This enables gene expression dissection of brain networks with, to the best of our knowledge, an unprecedented coverage and allows for the identification of potential genetic drivers of connectivity in both mice and humans that may contribute to the discovery of dysconnectivity phenotypes. Hence, BrainTACO reduces the need for time-consuming manual data aggregation often required for computational analyses in script-based toolboxes, and supports neuroscientists by directly leveraging the data instead of preparing it.

MousiPLIER: A Mouse Pathway-Level Information Extractor Model

High-throughput gene expression profiling measures individual gene expression across conditions. However, genes are regulated in complex networks, not as individual entities, limiting the interpretability of gene expression data. Machine learning models that incorporate prior biological knowledge are a powerful tool to extract meaningful biology from gene expression data. Pathway-level information extractor (PLIER) is an unsupervised machine learning method that defines biological pathways by leveraging the vast amount of published transcriptomic data. PLIER converts gene expression data into known pathway gene sets, termed latent variables (LVs), to substantially reduce data dimensionality and improve interpretability. In the current study, we trained the first mouse PLIER model on 190,111 mouse brain RNA-sequencing samples, the greatest amount of training data ever used by PLIER. We then validated the mousiPLIER approach in a study of microglia and astrocyte gene expression across mouse brain aging. mousiPLIER identified biological pathways that are significantly associated with aging, including one latent variable (LV41) corresponding to striatal signal. To gain further insight into the genes contained in LV41, we performed k-means clustering on the training data to identify studies that respond strongly to LV41. We found that the variable was relevant to striatum and aging across the scientific literature. Finally, we built a Web server (http://mousiplier.greenelab.com/) for users to easily explore the learned latent variables. Taken together, this study defines mousiPLIER as a method to uncover meaningful biological processes in mouse brain transcriptomic studies.

Neurogenetic underpinnings of nicotine use severity: Integrating the brain transcriptomes and GWAS variants via network approaches

Our study focused on human brain transcriptomes and the genetic risks of cigarettes per day (CPD) to investigate the neurogenetic mechanisms of individual variation in nicotine use severity. We constructed whole-brain and intramodular region-specific coexpression networks using BrainSpan's transcriptomes, and the genomewide association studies identified risk variants of CPD, confirmed the associations between CPD and each gene set in the region-specific subnetworks using an independent dataset, and conducted bioinformatic analyses. Eight brain-region-specific coexpression subnetworks were identified in association with CPD: amygdala, hippocampus, medial prefrontal cortex (MPFC), orbitofrontal cortex (OPFC), dorsolateral prefrontal cortex, striatum, mediodorsal nucleus of the thalamus (MDTHAL), and primary motor cortex (M1C). Each gene set in the eight subnetworks was associated with CPD. We also identified three hub proteins encoded by GRIN2A in the amygdala, PMCA2 in the hippocampus, MPFC, OPFC, striatum, and MDTHAL, and SV2B in M1C. Intriguingly, the pancreatic secretion pathway appeared in all the significant protein interaction subnetworks, suggesting pleiotropic effects between cigarette smoking and pancreatic diseases. The three hub proteins and genes are implicated in stress response, drug memory, calcium homeostasis, and inhibitory control. These findings provide novel evidence of the neurogenetic underpinnings of smoking severity.

Relationship between hippocampal gene expression and cognitive performance differences in visual discrimination learning task of male rats

Learning to discriminate between environmental visual stimuli is essential to make right decisions and guide appropriate behaviors. Moreover, impairments in visual discrimination learning are observed in several neuropsychiatric disorders. Visual discrimination learning requires perception and memory processing, in which the hippocampus critically involved. To understand the molecular mechanisms underpinning hippocampus function in visual discrimination learning, we examined the hippocampal gene expression profiles of Sprague-Dawley rats with different cognitive performance (high cognition group vs. low cognition group) in the modified visual discrimination learning task, using high-throughput RNA sequencing technology. Compared with the low cognition group, bioinformatics analysis indicated that 319 genes were differentially expressed in the high cognition group with statistical significance, of which 253 genes were down-regulated and 66 genes were up-regulated. The functional enrichment analysis showed that protein translation and energy metabolism were up-regulated pathways, while transforming growth factor beta receptor signaling pathway, bone morphogenetic protein signaling pathway, apoptosis, inflammation response, transport, and glycosaminoglycan metabolism were down-regulated pathways, which were related to good cognitive performance in the visual discrimination learning task. Taken together, our finding reveals the differential gene expression and enrichment biological pathways related to cognitive performance differences in visual discrimination learning of rats, which provides us direct insight into the molecular mechanisms of hippocampus function in visual discrimination learning and may contribute to developing potential treatment strategies for neuropsychiatric disorders accompanied with cognitive impairments.

Publicly available ex vivo transcriptomics datasets to explore CNS physiology and neurodegeneration: state of the art and perspectives

The central nervous system (CNS) is characterized by an intricate composition of diverse cell types, including neurons and glia cells (astrocytes, oligodendrocytes, and microglia), whose functions may differ along time, between sexes and upon pathology. The advancements in high-throughput transcriptomics are providing fundamental insights on cell phenotypes, so that molecular codes and instructions are ever more described for CNS physiology and neurodegeneration. To facilitate the search of relevant information, this review provides an overview of key CNS transcriptomics studies ranging from CNS development to ageing and from physiology to pathology as defined for five neurodegenerative disorders and their relative animal models, with a focus on molecular descriptions whose raw data were publicly available. Accurate phenotypic descriptions of cellular states correlate with functional changes and this knowledge may support research devoted to the development of therapeutic strategies supporting CNS repair and function.

MousiPLIER: A Mouse Pathway-Level Information Extractor Model

High throughput gene expression profiling is a powerful approach to generate hypotheses on the underlying causes of biological function and disease. Yet this approach is limited by its ability to infer underlying biological pathways and burden of testing tens of thousands of individual genes. Machine learning models that incorporate prior biological knowledge are necessary to extract meaningful pathways and generate rational hypothesis from the vast amount of gene expression data generated to date. We adopted an unsupervised machine learning method, Pathway-level information extractor (PLIER), to train the first mouse PLIER model on 190,111 mouse brain RNA-sequencing samples, the greatest amount of training data ever used by PLIER. mousiPLER converted gene expression data into a latent variables that align to known pathway or cell maker gene sets, substantially reducing data dimensionality and improving interpretability. To determine the utility of mousiPLIER, we applied it to a mouse brain aging study of microglia and astrocyte transcriptomic profiling. We found a specific set of latent variables that are significantly associated with aging, including one latent variable (LV41) corresponding to striatal signal. We next performed k-means clustering on the training data to identify studies that respond strongly to LV41, finding that the variable is relevant to striatum and aging across the scientific literature. Finally, we built a web server (http://mousiplier.greenelab.com/) for users to easily explore the learned latent variables. Taken together this study provides proof of concept that mousiPLIER can uncover meaningful biological processes in mouse transcriptomic studies.

Toward Best Practices for Imaging Transcriptomics of the Human Brain

Modern brainwide transcriptional atlases provide unprecedented opportunities for investigating the molecular correlates of brain organization, as quantified using noninvasive neuroimaging. However, integrating neuroimaging data with transcriptomic measures is not straightforward, and careful consideration is required to make valid inferences. In this article, we review recent work exploring how various methodological choices affect 3 main phases of imaging transcriptomic analyses, including 1) processing of transcriptional atlas data; 2) relating transcriptional measures to independently derived neuroimaging phenotypes; and 3) evaluating the functional implications of identified associations through gene enrichment analyses. Our aim is to facilitate the development of standardized and reproducible approaches for this rapidly growing field. We identify sources of methodological variability, key choices that can affect findings, and considerations for mitigating false positive and/or spurious results. Finally, we provide an overview of freely available open-source toolboxes implementing current best-practice procedures across all 3 analysis phases.

BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids

Our machine-learning framework, brain and organoid manifold alignment (BOMA), first performs a global alignment of developmental gene expression data between brains and organoids. It then applies manifold learning to locally refine the alignment, revealing conserved and specific developmental trajectories across brains and organoids. Using BOMA, we found that human cortical organoids better align with certain brain cortical regions than with other non-cortical regions, implying organoid-preserved developmental gene expression programs specific to brain regions. Additionally, our alignment of non-human primate and human brains reveals highly conserved gene expression around birth. Also, we integrated and analyzed developmental single-cell RNA sequencing (scRNA-seq) data of human brains and organoids, showing conserved and specific cell trajectories and clusters. Further identification of expressed genes of such clusters and enrichment analyses reveal brain- or organoid-specific developmental functions and pathways. Finally, we experimentally validated important specific expressed genes through the use of immunofluorescence. BOMA is open-source available as a web tool for community use.

Network pharmacology and bioinformatics approach reveals the therapeutic mechanism of action of curcumin in Alzheimer disease

Curcumin is a natural anti-inflammatory and antioxidant substance which plays a major role in reducing the amyloid plaques formation, which is the major cause of Alzheimer's disease (AD). Consequently, a methodical approach was used to select the potential protein targets of curcumin in AD through network pharmacology. In this study, through integrative methods, AD targets of curcumin through SwissTargetPrediction database, STITCH database, BindingDB, PharmMapper, Therapeutic Target Database (TTD), Online Mendelian Inheritance in Man (OMIM) database were predicted followed by gene enrichment analysis, network construction, network topology, and docking studies. Gene ontology analysis facilitated identification of a list of possible AD targets of curcumin (74 targets genes). The correlation of the obtained targets with AD was analysed by using gene ontology (GO) pathway enrichment analyses and Kyoto Encyclopaedia of Genes and Genomes (KEGG). We have incorporated the applied network pharmacological approach to identify key genes. Furthermore, we have performed molecular docking for analysing the mechanism of curcumin. In order to validate the temporospatial expression of key genes in human central nervous system (CNS), we searched the Human Brain Transcriptome (HBT) dataset. We identified top five key genes namely, PPARγ, MAPK1, STAT3, KDR and APP. Further validated the expression profiling of these key genes in publicly available brain data expression profile databases. In context to a valuable addition in the treatment of AD, this study is concluded with novel insights into the therapeutic mechanisms of curcumin, will ease the treatment of AD with the clinical application of curcumin.

Regional gene expression patterns are associated with task-specific brain activation during reward and emotion processing measured with functional MRI

The exploration of the spatial relationship between gene expression profiles and task-evoked response patterns known to be altered in neuropsychiatric disorders, for example depression, can guide the development of more targeted therapies. Here, we estimated the correlation between human transcriptome data and two different brain activation maps measured with functional magnetic resonance imaging (fMRI) in healthy subjects. Whole-brain activation patterns evoked during an emotional face recognition task were associated with topological mRNA expression of genes involved in cellular transport. In contrast, fMRI activation patterns related to the acceptance of monetary rewards were associated with genes implicated in cellular localization processes, metabolism, translation, and synapse regulation. An overlap of these genes with risk genes from major depressive disorder genome-wide association studies revealed the involvement of the master regulators TCF4 and PAX6 in emotion and reward processing. Overall, the identification of stable relationships between spatial gene expression profiles and fMRI data may reshape the prospects for imaging transcriptomics studies.

Microfluidics for Neuronal Cell and Circuit Engineering

The widespread adoption of microfluidic devices among the neuroscience and neurobiology communities has enabled addressing a broad range of questions at the molecular, cellular, circuit, and system levels. Here, we review biomedical engineering approaches that harness the power of microfluidics for bottom-up generation of neuronal cell types and for the assembly and analysis of neural circuits. Microfluidics-based approaches are instrumental to generate the knowledge necessary for the derivation of diverse neuronal cell types from human pluripotent stem cells, as they enable the isolation and subsequent examination of individual neurons of interest. Moreover, microfluidic devices allow to engineer neural circuits with specific orientations and directionality by providing control over neuronal cell polarity and permitting the isolation of axons in individual microchannels. Similarly, the use of microfluidic chips enables the construction not only of 2D but also of 3D brain, retinal, and peripheral nervous system model circuits. Such brain-on-a-chip and organoid-on-a-chip technologies are promising platforms for studying these organs as they closely recapitulate some aspects of in vivo biological processes. Microfluidic 3D neuronal models, together with 2D in vitro systems, are widely used in many applications ranging from drug development and toxicology studies to neurological disease modeling and personalized medicine. Altogether, microfluidics provide researchers with powerful systems that complement and partially replace animal models.

Modeling spatial, developmental, physiological, and topological constraints on human brain connectivity

The complex connectivity of nervous systems is thought to have been shaped by competitive selection pressures to minimize wiring costs and support adaptive function. Accordingly, recent modeling work indicates that stochastic processes, shaped by putative trade-offs between the cost and value of each connection, can successfully reproduce many topological properties of macroscale human connectomes measured with diffusion magnetic resonance imaging. Here, we derive a new formalism that more accurately captures the competing pressures of wiring cost minimization and topological complexity. We further show that model performance can be improved by accounting for developmental changes in brain geometry and associated wiring costs, and by using interregional transcriptional or microstructural similarity rather than topological wiring rules. However, all models struggled to capture topographical (i.e., spatial) network properties. Our findings highlight an important role for genetics in shaping macroscale brain connectivity and indicate that stochastic models offer an incomplete account of connectome organization.

Neuron-Specific Deletion of Scrib in Mice Leads to Neuroanatomical and Locomotor Deficits

Scribble (Scrib) is a conserved polarity protein acting as a scaffold involved in multiple cellular and developmental processes. Recent evidence from our group indicates that Scrib is also essential for brain development as early global deletion of Scrib in the dorsal telencephalon induced cortical thickness reduction and alteration of interhemispheric connectivity. In addition, Scrib conditional knockout (cKO) mice have behavioral deficits such as locomotor activity impairment and memory alterations. Given Scrib broad expression in multiple cell types in the brain, we decided to determine the neuronal contribution of Scrib for these phenotypes. In the present study, we further investigate the function of Scrib specifically in excitatory neurons on the forebrain formation and the control of locomotor behavior. To do so, we generated a novel neuronal glutamatergic specific Scrib cKO mouse line called Nex-Scrib^−/− cKO. Remarkably, cortical layering and commissures were impaired in these mice and reproduced to some extent the previously described phenotype in global Scrib cKO. In addition and in contrast to our previous results using Emx1-Scrib^−/− cKO, the Nex-Scrib^−/− cKO mutant mice exhibited significantly reduced locomotion. Altogether, the novel cKO model described in this study further highlights an essential role for Scrib in forebrain development and locomotor behavior.

DNA damage contributes to neurotoxic inflammation in Aicardi-Goutières syndrome astrocytes

Aberrant induction of type I IFN is a hallmark of the inherited encephalopathy Aicardi-Goutières syndrome (AGS), but the mechanisms triggering disease in the human central nervous system (CNS) remain elusive. Here, we generated human models of AGS using genetically modified and patient-derived pluripotent stem cells harboring TREX1 or RNASEH2B loss-of-function alleles. Genome-wide transcriptomic analysis reveals that spontaneous proinflammatory activation in AGS astrocytes initiates signaling cascades impacting multiple CNS cell subsets analyzed at the single-cell level. We identify accumulating DNA damage, with elevated R-loop and micronuclei formation, as a driver of STING- and NLRP3-related inflammatory responses leading to the secretion of neurotoxic mediators. Importantly, pharmacological inhibition of proapoptotic or inflammatory cascades in AGS astrocytes prevents neurotoxicity without apparent impact on their increased type I IFN responses. Together, our work identifies DNA damage as a major driver of neurotoxic inflammation in AGS astrocytes, suggests a role for AGS gene products in R-loop homeostasis, and identifies common denominators of disease that can be targeted to prevent astrocyte-mediated neurotoxicity in AGS.

Where the genome meets the connectome: Understanding how genes shape human brain connectivity

The integration of modern neuroimaging methods with genetically informative designs and data can shed light on the molecular mechanisms underlying the structural and functional organization of the human connectome. Here, we review studies that have investigated the genetic basis of human brain network structure and function through three complementary frameworks: (1) the quantification of phenotypic heritability through classical twin designs; (2) the identification of specific DNA variants linked to phenotypic variation through association and related studies; and (3) the analysis of correlations between spatial variations in imaging phenotypes and gene expression profiles through the integration of neuroimaging and transcriptional atlas data. We consider the basic foundations, strengths, limitations, and discoveries associated with each approach. We present converging evidence to indicate that anatomical connectivity is under stronger genetic influence than functional connectivity and that genetic influences are not uniformly distributed throughout the brain, with phenotypic variation in certain regions and connections being under stronger genetic control than others. We also consider how the combination of imaging and genetics can be used to understand the ways in which genes may drive brain dysfunction in different clinical disorders.

Imaging Transcriptomics of Brain Disorders

Noninvasive neuroimaging is a powerful tool for quantifying diverse aspects of brain structure and function in vivo, and it has been used extensively to map the neural changes associated with various brain disorders. However, most neuroimaging techniques offer only indirect measures of underlying pathological mechanisms. The recent development of anatomically comprehensive gene expression atlases has opened new opportunities for studying the transcriptional correlates of noninvasively measured neural phenotypes, offering a rich framework for evaluating pathophysiological hypotheses and putative mechanisms. Here, we provide an overview of some fundamental methods in imaging transcriptomics and outline their application to understanding brain disorders of neurodevelopment, adulthood, and neurodegeneration. Converging evidence indicates that spatial variations in gene expression are linked to normative changes in brain structure during age-related maturation and neurodegeneration that are in part associated with cell-specific gene expression markers of gene expression. Transcriptional correlates of disorder-related neuroimaging phenotypes are also linked to transcriptionally dysregulated genes identified in ex vivo analyses of patient brains. Modeling studies demonstrate that spatial patterns of gene expression are involved in regional vulnerability to neurodegeneration and the spread of disease across the brain. This growing body of work supports the utility of transcriptional atlases in testing hypotheses about the molecular mechanism driving disease-related changes in macroscopic neuroimaging phenotypes.

Transposable elements and their KZFP controllers are drivers of transcriptional innovation in the developing human brain

Transposable elements (TEs) account for more than 50% of the human genome and many have been co-opted throughout evolution to provide regulatory functions for gene expression networks. Several lines of evidence suggest that these networks are fine-tuned by the largest family of TE controllers, the KRAB-containing zinc finger proteins (KZFPs). One tissue permissive for TE transcriptional activation (termed "transposcription") is the adult human brain, however comprehensive studies on the extent of this process and its potential contribution to human brain development are lacking. To elucidate the spatiotemporal transposcriptome of the developing human brain, we have analyzed two independent RNA-seq data sets encompassing 16 brain regions from eight weeks postconception into adulthood. We reveal a distinct KZFP:TE transcriptional profile defining the late prenatal to early postnatal transition, and the spatiotemporal and cell type-specific activation of TE-derived alternative promoters driving the expression of neurogenesis-associated genes. Long-read sequencing confirmed these TE-driven isoforms as significant contributors to neurogenic transcripts. We also show experimentally that a co-opted antisense L2 element drives temporal protein relocalization away from the endoplasmic reticulum, suggestive of novel TE dependent protein function in primate evolution. This work highlights the widespread dynamic nature of the spatiotemporal KZFP:TE transcriptome and its importance throughout TE mediated genome innovation and neurotypical human brain development. To facilitate interactive exploration of these spatiotemporal gene and TE expression dynamics, we provide the "Brain TExplorer" web application freely accessible for the community.

Unveiling the neuroimaging-genetic intersections in the human brain

PURPOSE OF REVIEW

The prevalence of new public datasets of brain-wide and single-cell transcriptome data has created new opportunities to link neuroimaging findings with genetic data. The aim of this study is to present the different methodological approaches that have been used to combine this data.

RECENT FINDINGS

Drawing from various sources of open access data, several studies have been able to correlate neuroimaging maps with spatial distribution of brain expression. These efforts have enabled researchers to identify functional annotations of related genes, identify specific cell types related to brain phenotypes, study the expression of genes across life span and highlight the importance of selected brain genes in disease genetic networks.

SUMMARY

New transcriptome datasets and methodological approaches complement current neuroimaging work and will be crucial to improve our understanding of the biological mechanism that underlies many neurological conditions.

Ciclesonide activates glucocorticoid signaling in neonatal rat lung but does not trigger adverse effects in the cortex and cerebellum

Synthetic glucocorticoids (sGCs) such as dexamethasone (DEX), while used to mitigate inflammation and disease progression in premature infants with severe bronchopulmonary dysplasia (BPD), are also associated with significant adverse neurologic effects such as reductions in myelination and abnormalities in neuroanatomical development. Ciclesonide (CIC) is a sGC prodrug approved for asthma treatment that exhibits limited systemic side effects. Carboxylesterases enriched in the lower airways convert CIC to the glucocorticoid receptor (GR) agonist des-CIC. We therefore examined whether CIC would likewise activate GR in neonatal lung but have limited adverse extra-pulmonary effects, particularly in the developing brain. Neonatal rats were administered subcutaneous injections of CIC, DEX or vehicle from postnatal days 1-5 (PND1-PND5). Systemic effects linked to DEX exposure, including reduced body and brain weight, were not observed in CIC treated neonates. Furthermore, CIC did not trigger the long-lasting reduction in myelin basic protein expression in the cerebral cortex nor cerebellar size caused by neonatal DEX exposure. Conversely, DEX and CIC were both effective at inducing the expression of select GR target genes in neonatal lung, including those implicated in lung-protective and anti-inflammatory effects. Thus, CIC is a promising, novel candidate drug to treat or prevent BPD in neonates given its activation of GR in neonatal lung and limited adverse neurodevelopmental effects. Furthermore, since sGCs such as DEX administered to pregnant women in pre-term labor can adversely affect fetal brain development, the neurological-sparing properties of CIC, make it an attractive alternative for DEX to treat pregnant women severely ill with respiratory illness, such as with asthma exacerbations or COVID-19 infections.

Interfacing Seurat with the R tidy universe

Motivation

Seurat is one of the most popular software suites for the analysis of single-cell RNA sequencing data. Considering the popularity of the tidyverse ecosystem, which offers a large set of data display, query, manipulation, integration and visualization utilities, a great opportunity exists to interface the Seurat object with the tidyverse. This interface gives the large data science community of tidyverse users the possibility to operate with familiar grammar.

Results

To provide Seurat with a tidyverse-oriented interface without compromising efficiency, we developed tidyseurat, a lightweight adapter to the tidyverse. Tidyseurat displays cell information as a tibble abstraction, allowing intuitively interfacing Seurat with dplyr, tidyr, ggplot2 and plotly packages powering efficient data manipulation, integration and visualization. Iterative analyses on data subsets are enabled by interfacing with the popular nest-map framework.

Availability and implementation

The software is freely available at cran.r-project.org/web/packages/tidyseurat and github.com/stemangiola/tidyseurat.

Supplementary information

Supplementary data are available at Bioinformatics online.

Employing core regulatory circuits to define cell identity

The interplay between extrinsic signaling and downstream gene networks controls the establishment of cell identity during development and its maintenance in adult life. Advances in next-generation sequencing and single-cell technologies have revealed additional layers of complexity in cell identity. Here, we review our current understanding of transcription factor (TF) networks as key determinants of cell identity. We discuss the concept of the core regulatory circuit as a set of TFs and interacting factors that together define the gene expression profile of the cell. We propose the core regulatory circuit as a comprehensive conceptual framework for defining cellular identity and discuss its connections to cell function in different contexts.

Uncovering Statistical Links Between Gene Expression and Structural Connectivity Patterns in the Mouse Brain

Finding links between genes and structural connectivity is of the utmost importance for unravelling the underlying mechanism of the brain connectome. In this study we identify links between the gene expression and the axonal projection density in the mouse brain, by applying a modified version of the Linked ICA method to volumetric data from the Allen Institute for Brain Science for identifying independent sources of information that link both modalities at the voxel level. We performed separate analyses on sets of projections from the visual cortex, the caudoputamen and the midbrain reticular nucleus, and we determined those brain areas, injections and genes that were most involved in independent components that link both gene expression and projection density data, while we validated their biological context through enrichment analysis. We identified representative and literature-validated cortico-midbrain and cortico-striatal projections, whose gene subsets were enriched with annotations for neuronal and synaptic function and related developmental and metabolic processes. The results were highly reproducible when including all available projections, as well as consistent with factorisations obtained using the Dictionary Learning and Sparse Coding technique. Hence, Linked ICA yielded reproducible independent components that were preserved under increasing data variance. Taken together, we have developed and validated a novel paradigm for linking gene expression and structural projection patterns in the mouse mesoconnectome, which can power future studies aiming to relate genes to brain function.

Alternatively Splicing Interactomes Identify Novel Isoform-Specific Partners for NSD2

Nuclear receptor SET domain protein (NSD2) plays a fundamental role in the pathogenesis of Wolf-Hirschhorn Syndrome (WHS) and is overexpressed in multiple human myelomas, but its protein-protein interaction (PPI) patterns, particularly at the isoform/exon levels, are poorly understood. We explored the subcellular localizations of four representative NSD2 transcripts with immunofluorescence microscopy. Next, we used label-free quantification to perform immunoprecipitation mass spectrometry (IP-MS) analyses of the transcripts. Using the interaction partners for each transcript detected in the IP-MS results, we identified 890 isoform-specific PPI partners (83% are novel). These PPI networks were further divided into four categories of the exon-specific interactome. In these exon-specific PPI partners, two genes, RPL10 and HSPA8, were successfully confirmed by co-immunoprecipitation and Western blotting. RPL10 primarily interacted with Isoforms 1, 3, and 5, and HSPA8 interacted with all four isoforms, respectively. Using our extended NSD2 protein interactions, we constructed an isoform-level PPI landscape for NSD2 to serve as reference interactome data for NSD2 spliceosome-level studies. Furthermore, the RNA splicing processes supported by these isoform partners shed light on the diverse roles NSD2 plays in WHS and myeloma development. We also validated the interactions using Western blotting, RPL10, and the three NSD2 (Isoform 1, 3, and 5). Our results expand gene-level NSD2 PPI networks and provide a basis for the treatment of NSD2-related developmental diseases.

Failure of Glial Cell-Line Derived Neurotrophic Factor (GDNF) in Clinical Trials Orchestrated By Reduced NR4A2 (NURR1) Transcription Factor in Parkinson's Disease. A Systematic Review

Parkinson’s disease (PD) is one of the most common neurodegenerative maladies with unforeseen complex pathologies. While this neurodegenerative disorder’s neuropathology is reasonably well known, its etiology remains a mystery, making it challenging to aim therapy. Glial cell-line derived neurotrophic factor (GDNF) remains an auspicious therapeutic molecule for treating PD. Neurotrophic factor derived from glial cell lines is effective in rodents and nonhuman primates, but clinical findings have been equivocal. Laborious exertions have been made over the past few decades to improve and assess GDNF in treating PD (clinical studies). Definitive clinical trials have, however, failed to demonstrate a survival advantage. Consequently, there seemed to be a doubt as to whether GDNF has merit in the potential treatment of PD. The purpose of this cutting edge review is to speculate as to why the clinical trials have failed to meet the primary endpoint. We introduce a hypothesis, “Failure of GDNF in clinical trials succumbed by nuclear receptor-related factor 1 (Nurr1) shortfall.” We demonstrate how Nurr1 binds to GDNF to induce dopaminergic neuron synthesis. Due to its undisputable neuro-protection aptitude, we display Nurr1 (also called Nr4a2) as a promising therapeutic target for PD.

A Common Space Approach to Comparative Neuroscience

Comparative neuroscience is entering the era of big data. New high-throughput methods and data-sharing initiatives have resulted in the availability of large, digital data sets containing many types of data from ever more species. Here, we present a framework for exploiting the new possibilities offered. The multimodality of the data allows vertical translations, which are comparisons of different aspects of brain organization within a single species and across scales. Horizontal translations compare particular aspects of brain organization across species, often by building abstract feature spaces. Combining vertical and horizontal translations allows for more sophisticated comparisons, including relating principles of brain organization across species by contrasting horizontal translations, and for making formal predictions of unobtainable data based on observed results in a model species.

The Spatial and Cell-Type Distribution of SARS-CoV-2 Receptor ACE2 in the Human and Mouse Brains

By engaging angiotensin-converting enzyme 2 (ACE2 or Ace2), the novel pathogenic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) invades host cells and affects many organs, including the brain. However, the distribution of ACE2 in the brain is still obscure. Here, we investigated the ACE2 expression in the brain by analyzing data from publicly available brain transcriptome databases. According to our spatial distribution analysis, ACE2 was relatively highly expressed in some brain locations, such as the choroid plexus and paraventricular nuclei of the thalamus. According to cell-type distribution analysis, nuclear expression of ACE2 was found in many neurons (both excitatory and inhibitory neurons) and some non-neuron cells (mainly astrocytes, oligodendrocytes, and endothelial cells) in the human middle temporal gyrus and posterior cingulate cortex. A few ACE2-expressing nuclei were found in a hippocampal dataset, and none were detected in the prefrontal cortex. Except for the additional high expression of Ace2 in the olfactory bulb areas for spatial distribution as well as in the pericytes and endothelial cells for cell-type distribution, the distribution of Ace2 in the mouse brain was similar to that in the human brain. Thus, our results reveal an outline of ACE2/Ace2 distribution in the human and mouse brains, which indicates that the brain infection of SARS-CoV-2 may be capable of inducing central nervous system symptoms in coronavirus disease 2019 (COVID-19) patients. Potential species differences should be considered when using mouse models to study the neurological effects of SARS-CoV-2 infection.

A Picture Worth a Thousand Molecules-Integrative Technologies for Mapping Subcellular Molecular Organization and Plasticity in Developing Circuits

A key challenge in developmental neuroscience is identifying the local regulatory mechanisms that control neurite and synaptic refinement over large brain volumes. Innovative molecular techniques and high-resolution imaging tools are beginning to reshape our view of how local protein translation in subcellular compartments drives axonal, dendritic, and synaptic development and plasticity. Here we review recent progress in three areas of neurite and synaptic study in situ-compartment-specific transcriptomics/translatomics, targeted proteomics, and super-resolution imaging analysis of synaptic organization and development. We discuss synergies between sequencing and imaging techniques for the discovery and validation of local molecular signaling mechanisms regulating synaptic development, plasticity, and maintenance in circuits.

PsychENCODE and beyond: transcriptomics and epigenomics of brain development and organoids

Crucial decisions involving cell fate and connectivity that shape the distinctive development of the human brain occur in the embryonic and fetal stages-stages that are difficult to access and investigate in humans. The last decade has seen an impressive increase in resources-from atlases and databases to biological models-that is progressively lifting the curtain on this critical period. In this review, we describe the current state of genomic, transcriptomic, and epigenomic datasets charting the development of normal human brain with a particular focus on recent single-cell technologies. We discuss the emergence of brain organoids generated from pluripotent stem cells as a model to compensate for the limited availability of fetal tissue. Indeed, comparisons of neural lineages, transcriptional dynamics, and noncoding element activity between fetal brain and organoids have helped identify gene regulatory networks functioning at early stages of brain development. Altogether, we argue that large multi-omics investigations have pushed brain development into the "big data" era, and that current and future transversal approaches needed to leverage both fetal brain and organoid resources promise to answer major questions of brain biology and psychiatry.

Master Regulators and Cofactors of Human Neuronal Cell Fate Specification Identified by CRISPR Gene Activation Screens

Technologies to reprogram cell-type specification have revolutionized the fields of regenerative medicine and disease modeling. Currently, the selection of fate-determining factors for cell reprogramming applications is typically a laborious and low-throughput process. Therefore, we use high-throughput pooled CRISPR activation (CRISPRa) screens to systematically map human neuronal cell fate regulators. We utilize deactivated Cas9 (dCas9)-based gene activation to target 1,496 putative transcription factors (TFs) in the human genome. Using a reporter of neuronal commitment, we profile the neurogenic activity of these factors in human pluripotent stem cells (PSCs), leading to a curated set of pro-neuronal factors. Activation of pairs of TFs reveals neuronal cofactors, including E2F7, RUNX3, and LHX8, that improve conversion efficiency, subtype specificity, and maturation of neuronal cell types. Finally, using multiplexed gene regulation with orthogonal CRISPR systems, we demonstrate improved neuronal differentiation with concurrent activation and repression of target genes, underscoring the power of CRISPR-based gene regulation for programming complex cellular phenotypes.

Endogenous Acetylcholine and Its Modulation of Cortical Microcircuits to Enhance Cognition

Acetylcholine regulates the cerebral cortex to sharpen sensory perception and enhance attentional focus. The cellular and circuit mechanisms of this cholinergic modulation are under active investigation in sensory and prefrontal cortex, but the universality of these mechanisms across the cerebral cortex is not clear. Anatomical maps suggest that the sensory and prefrontal cortices receive distinct cholinergic projections and have subtle differences in the expression of cholinergic receptors and the metabolic enzyme acetylcholinesterase. First, we briefly review this anatomical literature and the recent progress in the field. Next, we discuss in detail the electrophysiological effects of cholinergic receptor subtypes and the cell and circuit consequences of their stimulation by endogenous acetylcholine as established by recent optogenetic work. Finally, we explore the behavioral ramifications of in vivo manipulations of endogenous acetylcholine. We find broader similarities than we expected between the cholinergic regulation of sensory and prefrontal cortex, but there are some differences and some gaps in knowledge. In visual, auditory, and somatosensory cortex, the cell and circuit mechanisms of cholinergic sharpening of sensory perception have been probed in vivo with calcium imaging and optogenetic experiments to simultaneously test mechanism and measure the consequences of manipulation. By contrast, ascertaining the links between attentional performance and cholinergic modulation of specific prefrontal microcircuits is more complicated due to the nature of the required tasks. However, ex vivo optogenetic manipulations point to differences in the cholinergic modulation of sensory and prefrontal cortex. Understanding how and where acetylcholine acts within the cerebral cortex to shape cognition is essential to pinpoint novel treatment targets for the perceptual and attention deficits found in multiple psychiatric and neurological disorders.

From Rare Copy Number Variants to Biological Processes in ADHD

OBJECTIVE

Attention deficit hyperactivity disorder (ADHD) is a highly heritable psychiatric disorder. The objective of this study was to define ADHD-associated candidate genes and their associated molecular modules and biological themes, based on the analysis of rare genetic variants.

METHODS

The authors combined data from 11 published copy number variation studies in 6,176 individuals with ADHD and 25,026 control subjects and prioritized genes by applying an integrative strategy based on criteria including recurrence in individuals with ADHD, absence in control subjects, complete coverage in copy number gains, and presence in the minimal region common to overlapping copy number variants (CNVs), as well as on protein-protein interactions and information from cross-species genotype-phenotype annotation.

RESULTS

The authors localized 2,241 eligible genes in the 1,532 reported CNVs, of which they classified 432 as high-priority ADHD candidate genes. The high-priority ADHD candidate genes were significantly coexpressed in the brain. A network of 66 genes was supported by ADHD-relevant phenotypes in the cross-species database. Four significantly interconnected protein modules were found among the high-priority ADHD genes. A total of 26 genes were observed across all applied bioinformatic methods. Lookup in the latest genome-wide association study for ADHD showed that among those 26 genes, POLR3C and RBFOX1 were also supported by common genetic variants.

CONCLUSIONS

Integration of a stringent filtering procedure in CNV studies with suitable bioinformatics approaches can identify ADHD candidate genes at increased levels of credibility. The authors' analytic pipeline provides additional insight into the molecular mechanisms underlying ADHD and allows prioritization of genes for functional validation in validated model organisms.

Genome-wide studies of time of day in the brain: Design and analysis

Transcriptome profiling at different times of day is powerful for studying circadian regulation in model organisms and humans. To date, 24 h profiles from many tissue types suggest that about half of all genes are circadian-expressed somewhere in the body. However, few of these studies focused on the brain. Thus, despite known links between circadian disruption and neurological disease, we have virtually no mechanistic understanding. In the coming decade, we expect more genome-wide studies of time of day in different brain diseases, regions, and cell types. We expect just as many different approaches to the design and analysis of these studies. This review considers key principles of circadian tran scriptomics, with the goal of maximizing utility and reproducibility of future studies in the nervous system.

Cytoskeletal regulation of synaptogenesis in a model of human fetal brain development

Excitatory synapse formation begins in mid-fetal gestation. However, due to our inability to image fetal synaptogenesis, the initial formation of synapses remains understudied. The recent development of human fetal brain spheroids provides access to this critical period of synapse formation. Using human neurons and brain spheroids, we address how altered actin regulation impacts the formation of excitatory synapses during fetal brain development. Prior to synapse formation, inhibition of RhoA kinase (ROCK) signaling promotes neurite elongation and branching. In addition to increasing neural complexity, ROCK inhibition increases the length of protrusions along the neurite, ultimately promoting excitatory synapse formation in human cortical brain spheroids. A corresponding increase in Rac1-driven actin polymerization drives this increase in excitatory synaptogenesis. Using STORM super-resolution microscopy, we demonstrate that actomyosin regulators, including the Rac1 regulator, α-PIX, and the RhoA regulator, p115-RhoGEF, localize to nascent excitatory synapses, where they preferentially localize to postsynaptic compartments. These results demonstrate that coordinated RhoGTPase activities underlie the initial formation of excitatory synapses and identify critical cytoskeletal regulators of early synaptogenic events.

Computational analysis and verification of molecular genetic targets for glioblastoma

BACKGROUND

Glioblastoma (GBM) is the most common malignant brain tumor with a poor prognosis. The initial treatment for high-grade gliomas is surgical excision. However, even with concomitant use of radiation or chemotherapy, patients are still prone to recurrence. The specific pathogenesis of GBM is still controversial.

METHODS

Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) between GBM and normal brain tissues were screened. P-value was obtained by Bayes test based on the limma package. Statistical significance was set as P-value <0.05 and |Fold change (FC)| > 0.2 (GSE90886); P-value <0.05 and |FC| > 1 (GSE116520, GSE103228). Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) network were performed. Hub genes were selected from miRNA target genes and DEGs. GBM and normal brain tissues were extracted to verify the expression.

RESULTS

A total of 100 DEGs were overlapped in both datasets. Analysis of pathways and process enrichment tests indicated that ion transport, positive regulation of macromolecule metabolic process, cell cycle, axon guidance were enriched in the GBM. Sixteen hub genes were identified. Hub genes ADARB1 and neuropilin 1 (NRP1) were significantly associated with overall survival (OS) and disease-free survival (DFS) (P<0.05). Eukaryotic translation termination factor 1 (ETF1) was associated with DFS (P<0.05).

CONCLUSIONS

DEGs and DEMs were found between GBM tumor tissues and normal brain tissues. These biomarkers may be used as targets for early diagnosis and specific treatment.

Identification of amygdala-expressed genes associated with autism spectrum disorder

BACKGROUND

Studies of individuals with autism spectrum disorder (ASD) have revealed a strong multigenic basis with the identification of hundreds of ASD susceptibility genes. ASD is characterized by social deficits and a range of other phenotypes, implicating complex genetics and involvement of a variety of brain regions. However, how mutations and mis-expression of select gene sets are associated with the behavioral components of ASD remains unknown. We reasoned that for genes to be associated with ASD core behaviors they must be: (1) expressed in brain regions relevant to ASD social behaviors and (2) expressed during the ASD susceptible window of brain development.

METHODS

Focusing on the amygdala, a brain region whose dysfunction has been highly implicated in the social component of ASD, we mined publicly available gene expression databases to identify ASD-susceptibility genes expressed during human and mouse amygdala development. We found that a large cohort of known ASD susceptibility genes is expressed in the developing human and mouse amygdala. We further performed analysis of single-nucleus RNA-seq (snRNA-seq) data from microdissected amygdala tissue from five ASD and five control human postmortem brains ranging in age from 4 to 20 years to elucidate cell type specificity of amygdala-expressed genes and their dysregulation in ASD.

RESULTS

Our analyses revealed that of the high-ranking ASD susceptibility genes, 80 are expressed in both human and mouse amygdala during fetal to early postnatal stages of development. Our human snRNA-seq analyses revealed cohorts of genes with altered expression in the ASD amygdala postnatally, especially within excitatory neurons, with dysregulated expression of seven genes predicted from our datamining pipeline.

LIMITATIONS

We were limited by the ages for which we were able to obtain human tissue; therefore, the results from our datamining pipeline approach will require validation, to the extent possible, in human tissue from earlier developmental stages.

CONCLUSIONS

Our pipeline narrows down the number of amygdala-expressed genes possibly involved in the social pathophysiology of ASD. Our human single-nucleus gene expression analyses revealed that ASD is characterized by changes in gene expression in specific cell types in the early postnatal amygdala.

CNS microRNA profiles: a database for cell type enriched microRNA expression across the mouse central nervous system

microRNAs are short, noncoding RNAs that can regulate hundreds of targets and thus shape the expression landscape of a cell. Similar to mRNA, they often exhibit cell type enriched expression and serve to reinforce cellular identity. In tissue with high cellular complexity, such as the central nervous system (CNS), it is difficult to attribute microRNA changes to a particular cell type. To facilitate interpretation of microRNA studies in these tissues, we used previously generated data to develop a publicly accessible and user-friendly database to enable exploration of cell type enriched microRNA expression. We provide illustrations of how this database can be utilized as a reference as well as for hypothesis generation. First, we suggest a putative role for miR-21 in the microglial spinal injury response. Second, we highlight data indicating that differential microRNA expression, specifically miR-326, may in part explain regional differences in inflammatory cells. Finally, we show that miR-383 expression is enriched in cortical glutamatergic neurons, suggesting a unique role in these cells. These examples illustrate the database’s utility in guiding research towards unstudied regulators in the CNS. This novel resource will aid future research into microRNA-based regulatory mechanisms responsible for cellular phenotypes within the CNS.

Next-generation disease modeling with direct conversion: a new path to old neurons

Within just over a decade, human reprogramming-based disease modeling has developed from a rather outlandish idea into an essential part of disease research. While iPSCs are a valuable tool for modeling developmental and monogenetic disorders, their rejuvenated identity poses limitations for modeling age-associated diseases. Direct cell-type conversion of fibroblasts into induced neurons (iNs) circumvents rejuvenation and preserves hallmarks of cellular aging. iNs are thus advantageous for modeling diseases that possess strong age-related and epigenetic contributions and can complement iPSC-based strategies for disease modeling. In this review, we provide an overview of the state of the art of direct iN conversion and describe the key epigenetic, transcriptomic, and metabolic changes that occur in converting fibroblasts. Furthermore, we summarize new insights into this fascinating process, particularly focusing on the rapidly changing criteria used to define and characterize in vitro-born human neurons. Finally, we discuss the unique features that distinguish iNs from other reprogramming-based neuronal cell models and how iNs are relevant to disease modeling.

Glial mitochondrial function and dysfunction in health and neurodegeneration

Mitochondria play essential metabolic roles in neural cells. Mitochondrial dysfunction has profound effects on the brain. In primary mitochondrial diseases, mutations that impair specific oxidative phosphorylation (OXPHOS) proteins or OXPHOS assembly factors lead to isolated biochemical defects and a heterogeneous group of clinical phenotypes, including mitochondrial encephalopathies. A broader defect of OXPHOS function, due to mutations in proteins involved in mitochondrial DNA maintenance, mitochondrial biogenesis, or mitochondrial tRNAs can also underlie severe mitochondrial encephalopathies. While primary mitochondrial dysfunction causes rare genetic forms of neurological disorders, secondary mitochondrial dysfunction is involved in the pathophysiology of some of the most common neurodegenerative disorders, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. Many studies have investigated mitochondrial function and dysfunction in bulk central nervous system (CNS) tissue. However, the interpretation of these studies has been often complicated by the extreme cellular heterogeneity of the CNS, which includes many different types of neurons and glial cells. Because neurons are especially dependent on OXPHOS for ATP generation, mitochondrial dysfunction is thought to be directly involved in cell autonomous neuronal demise. Despite being metabolically more flexible than neurons, glial mitochondria also play an essential role in the function of the CNS, and have adapted specific metabolic and mitochondrial features to support their diversity of functions. This review analyzes our current understanding and the gaps in knowledge of mitochondrial properties of glia and how they affect neuronal functions, in health and disease.

Application of Computational Biology to Decode Brain Transcriptomes

The rapid development of high-throughput sequencing technologies has generated massive valuable brain transcriptome atlases, providing great opportunities for systematically investigating gene expression characteristics across various brain regions throughout a series of developmental stages. Recent studies have revealed that the transcriptional architecture is the key to interpreting the molecular mechanisms of brain complexity. However, our knowledge of brain transcriptional characteristics remains very limited. With the immense efforts to generate high-quality brain transcriptome atlases, new computational approaches to analyze these high-dimensional multivariate data are greatly needed. In this review, we summarize some public resources for brain transcriptome atlases and discuss the general computational pipelines that are commonly used in this field, which would aid in making new discoveries in brain development and disorders.

Profiling the unique protective properties of intracranial arterial endothelial cells

Cardiovascular disorders, like atherosclerosis and hypertension, are increasingly known to be associated with vascular cognitive impairment (VCI). In particular, intracranial atherosclerosis is one of the main causes of VCI, although plaque development occurs later in time and is structurally different compared to atherosclerosis in extracranial arteries. Recent data suggest that endothelial cells (ECs) that line the intracranial arteries may exert anti-atherosclerotic effects due to yet unidentified pathways. To gain insights into underlying mechanisms, we isolated post-mortem endothelial cells from both the intracranial basilar artery (BA) and the extracranial common carotid artery (CCA) from the same individual (total of 15 individuals) with laser capture microdissection. RNA sequencing revealed a distinct molecular signature of the two endothelial cell populations of which the most prominent ones were validated by means of qPCR. Our data reveal for the first time that intracranial artery ECs exert an immune quiescent phenotype. Secondly, genes known to be involved in the response of ECs to damage (inflammation, differentiation, adhesion, proliferation, permeability and oxidative stress) are differentially expressed in intracranial ECs compared to extracranial ECs. Finally, Desmoplakin (DSP) and Hop Homeobox (HOPX), two genes expressed at a higher level in intracranial ECs, and Sodium Voltage-Gated Channel Beta Subunit 3 (SCN3B), a gene expressed at a lower level in intracranial ECs compared to extracranial ECs, were shown to be responsive to shear stress and/or hypoxia. With our data we present a set of intracranial-specific endothelial genes that may contribute to its protective phenotype, thereby supporting proper perfusion and consequently may preserve cognitive function. Deciphering the molecular regulation of the vascular bed in the brain may lead to the identification of novel potential intervention strategies to halt vascular associated disorders, such as atherosclerosis and vascular cognitive dysfunction.

Single-Cell Applications of Next-Generation Sequencing

The single cell is considered the basic unit of biology, and the pursuit of understanding how heterogeneous populations of cells can functionally coexist in tissues, organisms, microbial ecosystems, and even cancer, makes them the subject of intense study. Next-generation sequencing (NGS) of RNA and DNA has opened a new frontier of (single)-cell biology. Hundreds to millions of cells now can be assayed in parallel, providing the molecular profile of each cell in its milieu inexpensively and in a manner that can be analyzed mathematically. The goal of this article is to provide a high-level overview of single-cell sequencing for the nonexpert and show how its applications are influencing both basic and applied clinical studies in embryology, developmental genetics, and cancer.

Single-cell transcriptomics as a framework and roadmap for understanding the brain

A framework for interpreting and guiding experimental examination of the brain is essential for neuroscience. Recently, single-cell RNA sequencing and single-molecule fluorescent in situ hybridization have emerged as key technologies to generate such a framework at a single-cell resolution. These technologies provide a powerful complement for understanding gene expression in the brain: RNA sequencing enables genome-wide high-throughput quantification of gene expression, and in situ hybridization yields spatial registration of gene expression at a cellular resolution. Here, I discuss the insight that each of these technologies individually provide, and how they can be paired in principle and practice to resolve the cell-type-specific spatial organization of the brain. I further discuss the potential of cutting-edge spatial transcriptomics technologies that leverage the advantages of both techniques within the same assay, as well as how transcriptomic assays can be linked with higher-order features of brain structure and function. Such current and forthcoming transcriptomic technologies will have immense impact in generating an underlying logic of the nervous system, and will guide experiments and interpretations across molecular, cellular, circuit, and behavioural neuroscience.

Uncovering the Transcriptional Correlates of Hub Connectivity in Neural Networks

Connections in nervous systems are disproportionately concentrated on a small subset of neural elements that act as network hubs. Hubs have been found across different species and scales ranging from C. elegans to mouse, rat, cat, macaque, and human, suggesting a role for genetic influences. The recent availability of brain-wide gene expression atlases provides new opportunities for mapping the transcriptional correlates of large-scale network-level phenotypes. Here we review studies that use these atlases to investigate gene expression patterns associated with hub connectivity in neural networks and present evidence that some of these patterns are conserved across species and scales.

Effects of Neurotrophic Factors in Glial Cells in the Central Nervous System: Expression and Properties in Neurodegeneration and Injury

Astrocytes, oligodendrocytes, and microglia are abundant cell types found in the central nervous system and have been shown to play crucial roles in regulating both normal and disease states. An increasing amount of evidence points to the critical importance of glia in mediating neurodegeneration in Alzheimer’s and Parkinson’s diseases (AD, PD), and in ischemic stroke, where microglia are involved in initial tissue clearance, and astrocytes in the subsequent formation of a glial scar. The importance of these cells for neuronal survival has previously been studied in co-culture experiments and the search for neurotrophic factors (NTFs) initiated after finding that the addition of conditioned media from astrocyte cultures could support the survival of primary neurons in vitro. This led to the discovery of the potent dopamine neurotrophic factor, glial cell line-derived neurotrophic factor (GDNF). In this review, we focus on the relationship between glia and NTFs including neurotrophins, GDNF-family ligands, CNTF family, and CDNF/MANF-family proteins. We describe their expression in astrocytes, oligodendrocytes and their precursors (NG2-positive cells, OPCs), and microglia during development and in the adult brain. Furthermore, we review existing data on the glial phenotypes of NTF knockout mice and follow NTF expression patterns and their effects on glia in disease models such as AD, PD, stroke, and retinal degeneration.

Studying alcohol use disorder using Drosophila melanogaster in the era of 'Big Data'

Our understanding of the networks of genes and protein functions involved in Alcohol Use Disorder (AUD) remains incomplete, as do the mechanisms by which these networks lead to AUD phenotypes. The fruit fly (Drosophila melanogaster) is an efficient model for functional and mechanistic characterization of the genes involved in alcohol behavior. The fly offers many advantages as a model organism for investigating the molecular and cellular mechanisms of alcohol-related behaviors, and for understanding the underlying neural circuitry driving behaviors, such as locomotor stimulation, sedation, tolerance, and appetitive (reward) learning and memory. Fly researchers are able to use an extensive variety of tools for functional characterization of gene products. To understand how the fly can guide our understanding of AUD in the era of Big Data we will explore these tools, and review some of the gene networks identified in the fly through their use, including chromatin-remodeling, glial, cellular stress, and innate immunity genes. These networks hold great potential as translational drug targets, making it prudent to conduct further research into how these gene mechanisms are involved in alcohol behavior.

Using Openly Accessible Resources to Strengthen Causal Inference in Epigenetic Epidemiology of Neurodevelopment and Mental Health

The recent focus on the role of epigenetic mechanisms in mental health has led to several studies examining the association of epigenetic processes with psychiatric conditions and neurodevelopmental traits. Some studies suggest that epigenetic changes might be causal in the development of the psychiatric condition under investigation. However, other scenarios are possible, e.g., statistical confounding or reverse causation, making it particularly challenging to derive conclusions on causality. In the present review, we examine the evidence from human population studies for a possible role of epigenetic mechanisms in neurodevelopment and mental health and discuss methodological approaches on how to strengthen causal inference, including the need for replication, (quasi-)experimental approaches and Mendelian randomization. We signpost openly accessible resources (e.g., "MR-Base" "EWAS catalog" as well as tissue-specific methylation and gene expression databases) to aid the application of these approaches.

Transcriptome Profile in Hippocampus During Acute Inflammatory Response to Surgery: Toward Early Stage of PND

Perioperative neurocognitive disorders (PND) are common complications observed in surgical patients, but there are no effective treatments and the detailed mechanisms remain largely unknown. In this study, transcriptome analysis was performed to investigate the hippocampal changes after surgery and underlying molecular mechanisms of PND. Tibial fracture surgery was performed in 3–4 months old C57BL/6J mice to mimic human orthopedic surgery. We demonstrated that memory consolidation of the hippocampal-dependent trace-fear conditioning task was significantly impaired. By using ELISA, a significant elevated IL-6 was observed both in circulating system and central nervous system and peaked at 6 h post-surgery, but transiently returned to baseline thereafter. Hippocampus were collected at 6 h post-surgery then processed for RNA-Seq. A total of 268 genes were screened differentially expressed between the Surgery and Control group, including 170 up-regulated genes and 98 down-regulated genes. By functional enrichment analysis of differently expressed genes, several KEGG pathways involved in inflammatory mediator regulation of TRP channels, neuroactive ligand-receptor interaction and cholinergic synapse were overrepresented. Quantitative real-time PCR confirmed 15 dysregulated genes of interest. These results provide a comprehensive insight into global gene expression changes during the acute presence of hippocampal inflammation and a better understanding on early stage of PND.

Dynamic expression of genes associated with schizophrenia and bipolar disorder across development

Common genetic variation contributes a substantial proportion of risk for both schizophrenia and bipolar disorder. Furthermore, there is evidence of significant, but not complete, overlap in genetic risk between the two disorders. It has been hypothesised that genetic variants conferring risk for these disorders do so by influencing brain development, leading to the later emergence of symptoms. The comparative profile of risk gene expression for schizophrenia and bipolar disorder across development over different brain regions however remains unclear. Using genotypes derived from genome-wide associations studies of the largest available cohorts of patients and control subjects, we investigated whether genes enriched for schizophrenia and bipolar disorder association show a bias for expression across any of 13 developmental stages in prefrontal cortical and subcortical brain regions. We show that genetic association with schizophrenia is positively correlated with expression in the prefrontal cortex during early midfetal development and early infancy, and negatively correlated with expression during late childhood, which stabilises in adolescence. In contrast, risk-associated genes for bipolar disorder did not exhibit a bias towards expression at any prenatal stage, although the pattern of postnatal expression was similar to that of schizophrenia. These results highlight the dynamic expression of genes harbouring risk for schizophrenia and bipolar disorder across prefrontal cortex development and support the hypothesis that prenatal neurodevelopmental events are more strongly associated with schizophrenia than bipolar disorder.