34 results found
Mucha M, Skrzypiec AE, Kolenchery JB, et al., 2023, miR-483-5p offsets functional and behavioural effects of stress in male mice through synapse-targeted repression of Pgap2 in the basolateral amygdala., Nat Commun, Vol: 14
Severe psychological trauma triggers genetic, biochemical and morphological changes in amygdala neurons, which underpin the development of stress-induced behavioural abnormalities, such as high levels of anxiety. miRNAs are small, non-coding RNA fragments that orchestrate complex neuronal responses by simultaneous transcriptional/translational repression of multiple target genes. Here we show that miR-483-5p in the amygdala of male mice counterbalances the structural, functional and behavioural consequences of stress to promote a reduction in anxiety-like behaviour. Upon stress, miR-483-5p is upregulated in the synaptic compartment of amygdala neurons and directly represses three stress-associated genes: Pgap2, Gpx3 and Macf1. Upregulation of miR-483-5p leads to selective contraction of distal parts of the dendritic arbour and conversion of immature filopodia into mature, mushroom-like dendritic spines. Consistent with its role in reducing the stress response, upregulation of miR-483-5p in the basolateral amygdala produces a reduction in anxiety-like behaviour. Stress-induced neuromorphological and behavioural effects of miR-483-5p can be recapitulated by shRNA mediated suppression of Pgap2 and prevented by simultaneous overexpression of miR-483-5p-resistant Pgap2. Our results demonstrate that miR-483-5p is sufficient to confer a reduction in anxiety-like behaviour and point to miR-483-5p-mediated repression of Pgap2 as a critical cellular event offsetting the functional and behavioural consequences of psychological stress.
Schilder B, Murphy A, Skene N, 2023, The rworkflows suite: automated continuous integration for quality checking, documentation website creation, and containerised deployment of R packages, Publisher: Research Square
Reproducibility is essential to the progress of research, yet achieving it remains elusive even in computational fields. Continuous Integration (CI) platforms offer a powerful way to launch automated workflows to check and document code, but often require considerable time, effort, and technical expertise to setup. We therefore developed the rworkflows suite to make robust CI workflows easy and freely accessible to all R package developers (https://github.com/neurogenomics/rworkflows). rworkflows consists of 1) a CRAN/Bioconductor-compatible R package template, 2) an R package to quickly implement a standardised workflow, and 3) a centrally maintained GitHub Action. Each time it is triggered by a push to a GitHub repository, it automatically creates virtual machines across multiple OS, installs all dependencies, runs code checks, builds/deploys a documentation website, and builds/deploys version-controlled containers with a built-in RStudio interface. Additional analyses demonstrate that >50% of all R packages are only available via GitHub, highlighting the need for accessible solutions. Thus, rworkflows greatly reduces the barriers to implementing robust and reproducible best practices.
Murphy AE, Skene NG, 2022, A balanced measure shows superior performance of pseudobulk methods in single-cell RNA-sequencing analysis., Nat Commun, Vol: 13
Wahedi A, Soondram C, Murphy AE, et al., 2022, Transcriptomic analyses reveal neuronal specificity of Leigh syndrome associated genes, JOURNAL OF INHERITED METABOLIC DISEASE, ISSN: 0141-8955
Choi S, Schilder B, Abbasova L, et al., 2022, EpiCompare: R package for the comparison and quality control of epigenomic peak files, Publisher: Cold Spring Harbor Laboratory
Summary EpiCompare combines a variety of downstream analysis tools to compare, quality control and benchmark different epigenomic datasets. The package requires minimal input from users, can be run with just one line of code and provides all results of the analysis in a single interactive HTML report. EpiCompare thus enables downstream analysis of multiple epigenomic datasets in a simple, effective and user-friendly manner.Availability and Implementation EpiCompare is available on Bioconductor (≥ v3.15):https://bioconductor.org/packages/release/bioc/html/EpiCompare.htmlAll source code is publically available via GitHub:https://github.com/neurogenomics/EpiCompareDocumentation websitehttps://neurogenomics.github.io/EpiCompareEpiCompare DockerHub repository:https://hub.docker.com/repository/docker/neurogenomicslab/epicompareCompeting Interest StatementThe authors have declared no competing interest.
Hu D, Abbasova L, Schilder B, et al., 2022, CUT&Tag recovers up to half of ENCODE ChIP-seq peaks, Publisher: Cold Spring Harbor Laboratory
Techniques for genome-wide epigenetic profiling have been undergoing rapid development toward recovery of high quality data from bulk and single cell samples. DNA-protein interactions have traditionally been profiled via chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq), which has become the gold standard for studying histone modifications or transcription factor binding. Cleavage Under Targets & Tagmentation (CUT&Tag) is a promising new technique, which enables profiling of such interactions in situ at high sensitivity and is adaptable to single cell applications. However thorough evaluation and benchmarking against established ChIP-seq datasets are still lacking. Here we comprehensively benchmarked CUT&Tag for H3K27ac and H3K27me3 against published ChIP-seq profiles from ENCODE in K562 cells. Across a total of 30 new and 6 published CUT&Tag datasets we found that no experiment recovers more than 50% of known ENCODE peaks, regardless of the histone mark. We tested peak callers MACS2 and SEACR, identifying optimal peak calling parameters. Balancing both precision and recall of known ENCODE peaks, SEACR without retention of duplicates showed the best performance. We found that reducing PCR cycles during library preparation lowered duplication rates at the expense of ENCODE peak recovery. Despite the moderate ENCODE peak recovery, peaks identified by CUT&Tag represent the strongest ENCODE peaks and show the same functional and biological enrichments as ChIP-seq peaks identified by ENCODE. Our workflow systematically evaluates the merits of methodological adjustments and will facilitate future efforts to apply CUT&Tag in human tissues and single cells.
Murphy AE, Skene NG, 2022, A balanced measure shows superior performance of pseudobulk methods over mixed models and pseudoreplication approaches in single-cell RNA-sequencing analysis
<jats:title>Summary</jats:title><jats:p>Recently, Zimmerman<jats:italic>et al</jats:italic>.,<jats:sup>1</jats:sup>highlighted the importance of accounting for the dependence between cells from the same individual when conducting differential expression analysis on single-cell RNA-sequencing data. Their work proved the inadequacy of pseudoreplication approaches for such analysis – This was an important step forward that was conclusively proven by them. A hierarchical single-cell expression simulation approach (<jats:underline>hierarchicell</jats:underline>) was developed by Zimmerman<jats:italic>et al</jats:italic>.,<jats:sup>1</jats:sup>to generate non-differentially expressed genes upon which performance was evaluated using the type 1 error rate; the proportion of non-differentially expressed genes indicated as differentially expressed by a model. However, evaluating such models on their type 1 or type 2 error rate in isolation is insufficient to determine their true performance – for example, a method with low type 1 error may have a high type 2 error rate. Moreover, because no seed was set for the pseudo-random number generator used in hierarchicell, the different methods evaluated by Zimmerman<jats:italic>et al</jats:italic>. were done so on different simulated datasets. Here, we corrected these issues, reran the author’s analysis and found pseudobulk methods outperformed mixed models.</jats:p><jats:sec><jats:title>Contact</jats:title><jats:p>Alan Murphy:<jats:email>email@example.com</jats:email>, Nathan Skene:<jats:email>firstname.lastname@example.org</jats:email></jats:p></jats:sec><jats:sec><jats:title>Code availability</jats:title><jats:p>The modified version of hierarchicell which returns all error metrics, uses the same simulated data across approaches and has ch
Andrews B, Murphy AE, Stofella M, et al., 2022, Multidimensional Dynamics of the Proteome in the Neurodegenerative and Aging Mammalian Brain, MOLECULAR & CELLULAR PROTEOMICS, Vol: 21
Murphy A, Schilder BM, Skene N, 2021, MungeSumstats: a Bioconductor package for the standardisation and quality control of many GWAS summary statistics, Bioinformatics, Vol: 37, Pages: 4593-4596, ISSN: 1367-4803
Motivation:Genome-wide association studies (GWAS) summary statistics have popularised and accelerated genetic research. However, a lack of standardisation of the file formats used has proven problematic when running secondary analysis tools or performing meta-analysis studies.Results:To address this issue, we have developed MungeSumstats, a Bioconductor R package for the standardisation and quality control of GWAS summary statistics. MungeSumstats can handle the most common summary statistic formats, including variant call format (VCF) producing a reformatted, standardised, tabular summary statistic file, VCF or R native data object.Availability:MungeSumstats is available on Bioconductor (v 3.13) and can also be found on Github at: https://neurogenomics.github.io/MungeSumstats
Khozoie C, Fancy N, Marjaneh MM, et al., 2021, scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data
<jats:title>Abstract</jats:title><jats:p>Advances in single-cell RNA-sequencing technology over the last decade have enabled exponential increases in throughput: datasets with over a million cells are becoming commonplace. The burgeoning scale of data generation, combined with the proliferation of alternative analysis methods, led us to develop the scFlow toolkit and the nf-core/scflow pipeline for reproducible, efficient, and scalable analyses of single-cell and single-nuclei RNA-sequencing data. The scFlow toolkit provides a higher level of abstraction on top of popular single-cell packages within an R ecosystem, while the nf-core/scflow Nextflow pipeline is built within the nf-core framework to enable compute infrastructure-independent deployment across all institutions and research facilities. Here we present our flexible pipeline, which leverages the advantages of containerization and the potential of Cloud computing for easy orchestration and scaling of the analysis of large case/control datasets by even non-expert users. We demonstrate the functionality of the analysis pipeline from sparse-matrix quality control through to insight discovery with examples of analysis of four recently published public datasets and describe the extensibility of scFlow as a modular, open-source tool for single-cell and single nuclei bioinformatic analyses.</jats:p>
Månberg A, Skene N, Sanders F, et al., 2021, Altered perivascular fibroblast activity precedes ALS disease onset, Nature Medicine, Vol: 27, Pages: 640-646, ISSN: 1078-8956
Apart from well-defined factors in neuronal cells1, only a few reports consider that the variability of sporadic amyotrophic lateral sclerosis (ALS) progression can depend on less-defined contributions from glia2,3 and blood vessels4. In this study we use an expression-weighted cell-type enrichment method to infer cell activity in spinal cord samples from patients with sporadic ALS and mouse models of this disease. Here we report that patients with sporadic ALS present cell activity patterns consistent with two mouse models in which enrichments of vascular cell genes preceded microglial response. Notably, during the presymptomatic stage, perivascular fibroblast cells showed the strongest gene enrichments, and their marker proteins SPP1 and COL6A1 accumulated in enlarged perivascular spaces in patients with sporadic ALS. Moreover, in plasma of 574 patients with ALS from four independent cohorts, increased levels of SPP1 at disease diagnosis repeatedly predicted shorter survival with stronger effect than the established risk factors of bulbar onset or neurofilament levels in cerebrospinal fluid. We propose that the activity of the recently discovered perivascular fibroblast can predict survival of patients with ALS and provide a new conceptual framework to re-evaluate definitions of ALS etiology.
Thrupp N, Frigerio CS, Wolfs L, et al., 2020, Single-nucleus RNA-seq is not suitable for detection of microglial activation genes in humans, Cell Reports, Vol: 32, Pages: 1-13, ISSN: 2211-1247
Single-nucleus RNA sequencing (snRNA-seq) is used as an alternative to single-cell RNA-seq, as it allows transcriptomic profiling of frozen tissue. However, it is unclear whether snRNA-seq is able to detect cellular state in human tissue. Indeed, snRNA-seq analyses of human brain samples have failed to detect a consistent microglial activation signature in Alzheimer’s disease. Our comparison of microglia from single cells and single nuclei of four human subjects reveals that, although most genes show similar relative abundances in cells and nuclei, a small population of genes (∼1%) is depleted in nuclei compared to whole cells. This population is enriched for genes previously implicated in microglial activation, including APOE, CST3, SPP1, and CD74, comprising 18% of previously identified microglial-disease-associated genes. Given the low sensitivity of snRNA-seq to detect many activation genes, we conclude that snRNA-seq is not suited for detecting cellular activation in microglia in human disease.
Byrne EM, Zhu Z, Qi T, et al., 2020, Conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders, MOLECULAR PSYCHIATRY, Vol: 26, Pages: 2070-2081, ISSN: 1359-4184
- Author Web Link
- Citations: 27
Bryois J, Skene NG, Hansen TF, et al., 2020, Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson's disease, Nature Genetics, Vol: 52, Pages: 482-493, ISSN: 1061-4036
Genome-wide association studies have discovered hundreds of loci associated with complex brain disorders, but it remains unclear in which cell types these loci are active. Here we integrate genome-wide association study results with single-cell transcriptomic data from the entire mouse nervous system to systematically identify cell types underlying brain complex traits. We show that psychiatric disorders are predominantly associated with projecting excitatory and inhibitory neurons. Neurological diseases were associated with different cell types, which is consistent with other lines of evidence. Notably, Parkinson's disease was genetically associated not only with cholinergic and monoaminergic neurons (which include dopaminergic neurons) but also with enteric neurons and oligodendrocytes. Using post-mortem brain transcriptomic data, we confirmed alterations in these cells, even at the earliest stages of disease progression. Our study provides an important framework for understanding the cellular basis of complex brain maladies, and reveals an unexpected role of oligodendrocytes in Parkinson's disease.
Thrupp N, Frigerio CS, Wolfs L, et al., 2020, Single nucleus sequencing fails to detect microglial activation in human tissue
<jats:title>Abstract</jats:title><jats:p>Single nucleus RNA-Seq (snRNA-Seq) methods are used as an alternative to single cell RNA-Seq methods, as they allow transcriptomic profiling of frozen tissue. However, it is unclear whether snRNA-Seq is able to detect cellular state in human tissue. Indeed, snRNA-Seq analyses of human brain samples have failed to detect a consistent microglial activation signature in Alzheimer’s Disease. A comparison of microglia from single cells and single nuclei of four human subjects reveals that ~1% of genes is depleted in nuclei compared to whole cells. This small population contains 18% of genes previously implicated in microglial activation, including <jats:italic>APOE, CST3, FTL, SPP1</jats:italic>, and <jats:italic>CD74</jats:italic>. We confirm our findings across multiple previous single nucleus and single cell studies. Given the low sensitivity of snRNA-Seq to this population of activation genes, we conclude that snRNA-Seq is not suited to detecting cellular activation in microglia in human disease.</jats:p>
Qian X, Harris KD, Hauling T, et al., 2020, Probabilistic cell typing enables fine mapping of closely related cell types in situ, Nature Methods, Vol: 17, Pages: 101-106, ISSN: 1548-7091
Understanding the function of a tissue requires knowing the spatial organization of its constituent cell types. In the cerebral cortex, single-cell RNA sequencing (scRNA-seq) has revealed the genome-wide expression patterns that define its many, closely related neuronal types, but cannot reveal their spatial arrangement. Here we introduce probabilistic cell typing by in situ sequencing (pciSeq), an approach that leverages previous scRNA-seq classification to identify cell types using multiplexed in situ RNA detection. We applied this method by mapping the inhibitory neurons of mouse hippocampal area CA1, for which ground truth is available from extensive previous work identifying their laminar organization. Our method identified these neuronal classes in a spatial arrangement matching ground truth, and further identified multiple classes of isocortical pyramidal cell in a pattern matching their known organization. This method will allow identifying the spatial organization of closely related cell types across the brain and other tissues.
Hill WD, Davies NM, Ritchie SJ, et al., 2019, Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income, NATURE COMMUNICATIONS, Vol: 10, ISSN: 2041-1723
- Author Web Link
- Citations: 53
Jansen IE, Savage JE, Watanabe K, et al., 2019, Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk, Nature Genetics, Vol: 51, Pages: 404-413, ISSN: 1061-4036
Alzheimer’s disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed a large genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (rg = 0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver, and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomization results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD.
Jansen PR, Watanabe K, Stringer S, et al., 2019, Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways, NATURE GENETICS, Vol: 51, Pages: 394-+, ISSN: 1061-4036
- Author Web Link
- Citations: 282
Coleman JRI, Bryois J, Gaspar HA, et al., 2019, Biological annotation of genetic loci associated with intelligence in a meta-analysis of 87,740 individuals, MOLECULAR PSYCHIATRY, Vol: 24, Pages: 182-197, ISSN: 1359-4184
- Author Web Link
- Citations: 27
Komiyama NH, van de Lagemaat LN, Stanford LE, et al., 2018, Synaptic combinatorial molecular mechanisms generate repertoires of innate and learned behavior
<jats:title>Abstract</jats:title><jats:p>Although molecular mechanisms underpinning specific behaviors have been described, whether there are mechanisms that orchestrate a behavioral repertoire is unknown. To test if the postsynaptic proteome of excitatory synapses could impart such a mechanism we conducted the largest genetic study of mammalian synapses yet undertaken. A repertoire of sixteen innate and learned behaviors was assessed from 290,850 measures in 55 lines of mutant mice carrying targeted mutations in the principal classes of postsynaptic proteins. Each innate and learned behavior used a different combination of proteins. These combinations were comprised of proteins that amplified or attenuated the magnitude of each behavioral response. All behaviors required proteins found in PSD95 supercomplexes. We show the vertebrate increase in proteome complexity drove an expansion in behavioral repertoires and generated susceptibility to a wide range of diseases. Our results reveal a molecular mechanism that generates a versatile and complex behavioral repertoire that is central to human behavioral disorders.</jats:p>
Nguyen HT, Dobbyn A, Charney AW, et al., 2018, Integrative analysis of rare variants and pathway information shows convergent results between immune pathways, drug targets and epilepsy genes
<jats:title>Abstract</jats:title><jats:p>Trio family and case-control studies of next-generation sequencing data have proven integral to understanding the contribution of rare inherited and<jats:italic>de novo</jats:italic>single-nucleotide variants to the genetic architecture of complex disease. Ideally, such studies should identify individual risk genes of moderate to large effect size to generate novel treatment hypotheses for further follow-up. However, due to insufficient power, gene set enrichment analyses have come to be relied upon for detecting differences between cases and controls, implicating sets of hundreds of genes rather than specific targets for further investigation. Here, we present a Bayesian statistical framework, termed gTADA, that integrates gene-set membership information with gene-level<jats:italic>de novo</jats:italic>and rare inherited case-control counts, to prioritize risk genes with excess rare variant burden within enriched gene sets. Applying gTADA to available whole-exome sequencing datasets for several neuropsychiatric conditions, we replicated previously reported gene set enrichments and identified novel risk genes. For epilepsy, gTADA prioritized 40 risk genes (posterior probabilities > 0.95), 6 of which replicate in an independent whole-genome sequencing study. In addition, 30/40 genes are novel genes. We found that epilepsy genes had high protein-protein interaction (PPI) network connectivity, and show specific expression during human brain development. Some of the top prioritized EPI genes were connected to a PPI subnetwork of immune genes and show specific expression in prenatal microglia. We also identified multiple enriched drug-target gene sets for EPI which included immunostimulants as well as known antiepileptics. Immune biology was supported specifically by case-control variants from familial epilepsies rather than do novo mutations in generalized encephalitic epilepsy.<
Zhu F, Cizeron M, Qiu Z, et al., 2018, Architecture of the mouse brain synaptome, Neuron, Vol: 99, Pages: 781-799.e10, ISSN: 0896-6273
Synapses are found in vast numbers in the brain and contain complex proteomes. We developed genetic labeling and imaging methods to examine synaptic proteins in individual excitatory synapses across all regions of the mouse brain. Synapse catalogs were generated from the molecular and morphological features of a billion synapses. Each synapse subtype showed a unique anatomical distribution and each brain region showed a distinct signature of synapse subtypes. Whole brain synaptome cartography revealed spatial architecture from dendritic to global systems levels and previously unknown anatomical features. Synaptome mapping of circuits showed correspondence between synapse diversity and structural and functional connectomes. Behaviorally relevant patterns of neuronal activity trigger spatio-temporal postsynaptic responses sensitive to the structure of synaptome maps. Areas controlling higher cognitive function contain greatest synapse diversity and mutations causing cognitive disorders reorganized synaptome maps. Synaptome technology and resources have wide-ranging application in studies of the normal and diseased brain.
Munoz-Manchado AB, Gonzales CB, Zeisel A, et al., 2018, Diversity of interneurons in the dorsal striatum revealed by single-cell RNA sequencing and PatchSeq, Cell Reports, Vol: 24, Pages: 2179-2190.e1-e7, ISSN: 2211-1247
Striatal locally projecting neurons, or interneurons, act on nearby circuits and shape functional output to the rest of the basal ganglia. We performed single-cell RNA sequencing of striatal cells enriching for interneurons. We find seven discrete interneuron types, six of which are GABAergic. In addition to providing specific markers for the populations previously described, including those expressing Sst/Npy, Th, Npy without Sst, and Chat, we identify two small populations of cells expressing Cck with or without Vip. Surprisingly, the Pvalb-expressing cells do not constitute a discrete cluster but rather are part of a larger group of cells expressing Pthlh with a spatial gradient of Pvalb expression. Using PatchSeq, we show that Pthlh cells exhibit a continuum of electrophysiological properties correlated with expression of Pvalb. Furthermore, we find significant molecular differences that correlate with differences in electrophysiological properties between Pvalb-expressing cells of the striatum and those of the cortex.
Zeisel A, Hochgerner H, Lönnerberg P, et al., 2018, Molecular architecture of the mouse nervous system, Cell, Vol: 174, Pages: 999-1014.e22, ISSN: 0092-8674
The mammalian nervous system executes complex behaviors controlled by specialized, precisely positioned, and interacting cell types. Here, we used RNA sequencing of half a million single cells to create a detailed census of cell types in the mouse nervous system. We mapped cell types spatially and derived a hierarchical, data-driven taxonomy. Neurons were the most diverse and were grouped by developmental anatomical units and by the expression of neurotransmitters and neuropeptides. Neuronal diversity was driven by genes encoding cell identity, synaptic connectivity, neurotransmission, and membrane conductance. We discovered seven distinct, regionally restricted astrocyte types that obeyed developmental boundaries and correlated with the spatial distribution of key glutamate and glycine neurotransmitters. In contrast, oligodendrocytes showed a loss of regional identity followed by a secondary diversification. The resource presented here lays a solid foundation for understanding the molecular architecture of the mammalian nervous system and enables genetic manipulation of specific cell types.
Nagel M, Jansen PR, Stringer S, et al., 2018, Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways, NATURE GENETICS, Vol: 50, Pages: 920-+, ISSN: 1061-4036
- Author Web Link
- Citations: 283
Savage JE, Jansen PR, Stringer S, et al., 2018, Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence, Nature Genetics, Vol: 50, Pages: 912-919, ISSN: 1061-4036
Intelligence is highly heritable1 and a major determinant of human health and well-being. Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer’s disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.
Harris KD, Hochgerner H, Skene NG, et al., 2018, Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics, PLoS Biology, Vol: 16, ISSN: 1544-9173
Understanding any brain circuit will require a categorization of its constituent neurons. In hippocampal area CA1, at least 23 classes of GABAergic neuron have been proposed to date. However, this list may be incomplete; additionally, it is unclear whether discrete classes are sufficient to describe the diversity of cortical inhibitory neurons or whether continuous modes of variability are also required. We studied the transcriptomes of 3,663 CA1 inhibitory cells, revealing 10 major GABAergic groups that divided into 49 fine-scale clusters. All previously described and several novel cell classes were identified, with three previously described classes unexpectedly found to be identical. A division into discrete classes, however, was not sufficient to describe the diversity of these cells, as continuous variation also occurred between and within classes. Latent factor analysis revealed that a single continuous variable could predict the expression levels of several genes, which correlated similarly with it across multiple cell types. Analysis of the genes correlating with this variable suggested it reflects a range from metabolically highly active faster-spiking cells that proximally target pyramidal cells to slower-spiking cells targeting distal dendrites or interneurons. These results elucidate the complexity of inhibitory neurons in one of the simplest cortical structures and show that characterizing these cells requires continuous modes of variation as well as discrete cell classes.
Skene NG, Bryois JD, Bakken TE, et al., 2018, Genetic identification of brain cell types underlying schizophrenia, Nature Genetics, Vol: 50, Pages: 825-833, ISSN: 1061-4036
With few exceptions, the marked advances in knowledge about the genetic basis of schizophrenia have not converged on findings that can be confidently used for precise experimental modeling. By applying knowledge of the cellular taxonomy of the brain from single-cell RNA sequencing, we evaluated whether the genomic loci implicated in schizophrenia map onto specific brain cell types. We found that the common-variant genomic results consistently mapped to pyramidal cells, medium spiny neurons (MSNs) and certain interneurons, but far less consistently to embryonic, progenitor or glial cells. These enrichments were due to sets of genes that were specifically expressed in each of these cell types. We also found that many of the diverse gene sets previously associated with schizophrenia (genes involved in synaptic function, those encoding mRNAs that interact with FMRP, antipsychotic targets, etc.) generally implicated the same brain cell types. Our results suggest a parsimonious explanation: the common-variant genetic results for schizophrenia point at a limited set of neurons, and the gene sets point to the same cells. The genetic risk associated with MSNs did not overlap with that of glutamatergic pyramidal cells and interneurons, suggesting that different cell types have biologically distinct roles in schizophrenia.
Roy M, Sorokina O, Skene N, et al., 2018, Proteomic analysis of postsynaptic proteins in regions of the human neocortex, Nature Neuroscience, Vol: 21, Pages: 130-138, ISSN: 1097-6256
The postsynaptic proteome of excitatory synapses comprises ~1,000 highly conserved proteins that control the behavioral repertoire, and mutations disrupting their function cause >130 brain diseases. Here, we document the composition of postsynaptic proteomes in human neocortical regions and integrate it with genetic, functional and structural magnetic resonance imaging, positron emission tomography imaging, and behavioral data. Neocortical regions show signatures of expression of individual proteins, protein complexes, biochemical and metabolic pathways. We characterized the compositional signatures in brain regions involved with language, emotion and memory functions. Integrating large-scale GWAS with regional proteome data identifies the same cortical region for smoking behavior as found with fMRI data. The neocortical postsynaptic proteome data resource can be used to link genetics to brain imaging and behavior, and to study the role of postsynaptic proteins in localization of brain functions.
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