The mammalian cerebral cortex supports cognitive functions such as sensorimotor integration, memory, and social behaviors. Normal brain function relies on a diverse set of differentiated cell types, including neurons, glia, and vasculature. Here, we have used large-scale single-cell RNA sequencing (RNA-seq) to classify cells in the mouse somatosensory cortex and hippocampal CA1 region. We found 47 molecularly distinct subclasses, comprising all known major cell types in the cortex. We identified numerous marker genes, which allowed alignment with known cell types, morphology, and location. We found a layer I interneuron expressing Pax6 and a distinct postmitotic oligodendrocyte subclass marked by Itpr2. Across the diversity of cortical cell types, transcription factors formed a complex, layered regulatory code, suggesting a mechanism for the maintenance of adult cell type identity.

Oligodendrocytes have been considered as a functionally homogenous population in the central nervous system (CNS). We performed single-cell RNA-Seq on 5072 cells of the oligodendrocyte lineage from ten regions of the mouse juvenile/adult CNS. Twelve populations were identified, representing a continuum from Pdgfra+ oligodendrocyte precursors (OPCs) to distinct mature oligodendrocytes. Initial stages of differentiation were similar across the juvenile CNS, whereas subsets of mature oligodendrocytes were enriched in specific regions in the adult brain. Newlyformed oligodendrocytes were found to be resident in the adult CNS and responsive to complex motor learning. A second Pdgfra+ population, distinct from OPCs, was found along vessels. Our study reveals the dynamics of oligodendrocyte differentiation and maturation, uncoupling them at a transcriptional level and highlighting oligodendrocyte heterogeneity in the CNS. *Correspondence to: sten.linnarsson@ki.se, goncalo.castelo-branco@ki.se. Additional Author notes: SM, AZ, HL, WDR, SL and GC-B designed the experiments. PE, EA, JH-L, TH, WDR, SL and GC-B, senior authors, obtained funding. SM, AZ, SC, HH, RAR, DG, MH, AMM, GLM, FR, HL, LX, EF performed experiments. LX, HL and WDR have priority of observation of the rapid differentiation of oligodendrocytes in the complex motor wheel paradigm. SM, AZ, DvB, AMF, GLM, PL analysed data. SM, AZ, SL and GC-B wrote the paper, with the assistance and proofreading of all authors. Oligodendrocytes ensheath axons in the CNS, allowing rapid saltatory conduction and providing metabolic support to neurons. While a largely homogeneous oligodendrocyte population is thought to execute these functions throughout the CNS (1), these cells were originally described as morphologically heterogeneous (2). It is thus unclear if oligodendrocytes become morphologically diversified during maturation through interactions within the local environment, or if there is intrinsic functional heterogeneity (3-5). We analyzed 5072 transcriptomes of single cells expressing markers from the oligodendrocyte lineage, isolated from ten distinct regions of the anterior-posterior and dorsal-ventral axis of the mouse juvenile and adult CNS (Fig. 1A and 1B). Biclustering analysis (6) ( Fig. S1B and S15), hierarchical clustering ( Fig. 1C) and differential expression analysis (Supporting File Supplementary Excel S1 and S2) led to the identification of thirteen distinct cell populations. t-Distributed Stochastic Neighbour Embedding (t-SNE) projection ( Fig. 2A) indicated a narrow differentiation path connecting OPCs and myelinforming oligodendrocytes, diversifying into six mature states, which was supported by pseudo-time analysis (Fig. S2A-B). Europe PMC Funders GroupOPCs co-expressed Pdgfra and Cspg4 (Figs. 2B, S1B and S10) and 10% co-expressed cell cycle genes ( Fig. S2E-F), consistent with a cell division turnover of 19 days in the juvenile cortex (7). Several genes identified in OPCs were previously associated with astrocytes/ radial glia (6) (Fabp7 an...

Intelligence is highly heritable 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.

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.

Neuroticism is an important risk factor for psychiatric traits, including depression, anxiety, and schizophrenia. At the time of analysis, previous genome-wide association studies (GWAS) reported 16 genomic loci associated to neuroticism. Here we conducted a large GWAS meta-analysis (n = 449,484) of neuroticism and identified 136 independent genome-wide significant loci (124 new at the time of analysis), which implicate 599 genes. Functional follow-up analyses showed enrichment in several brain regions and involvement of specific cell types, including dopaminergic neuroblasts (P = 3.49 × 10), medium spiny neurons (P = 4.23 × 10), and serotonergic neurons (P = 1.37 × 10). Gene set analyses implicated three specific pathways: neurogenesis (P = 4.43 × 10), behavioral response to cocaine processes (P = 1.84 × 10), and axon part (P = 5.26 × 10). We show that neuroticism's genetic signal partly originates in two genetically distinguishable subclusters ('depressed affect' and 'worry'), suggesting distinct causal mechanisms for subtypes of individuals. Mendelian randomization analysis showed unidirectional and bidirectional effects between neuroticism and multiple psychiatric traits. These results enhance neurobiological understanding of neuroticism and provide specific leads for functional follow-up experiments.

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 prior scRNA-seq classification to identify cell types using multiplexed in situ RNA detection. We applied this method by mapping the inhibitory neurons of hippocampal area CA1, for which ground truth is available from extensive prior work identifying their laminar organization. Our method identified these closely related classes in a spatial arrangement matching ground truth, and further identified multiple classes of isocortical pyramidal Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

SummaryStriatal 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.

BackgroundIntegrating rare variation from trio family and case–control studies has successfully implicated specific genes contributing to risk of neurodevelopmental disorders (NDDs) including autism spectrum disorders (ASD), intellectual disability (ID), developmental disorders (DDs), and epilepsy (EPI). For schizophrenia (SCZ), however, while sets of genes have been implicated through the study of rare variation, only two risk genes have been identified.MethodsWe used hierarchical Bayesian modeling of rare-variant genetic architecture to estimate mean effect sizes and risk-gene proportions, analyzing the largest available collection of whole exome sequence data for SCZ (1,077 trios, 6,699 cases, and 13,028 controls), and data for four NDDs (ASD, ID, DD, and EPI; total 10,792 trios, and 4,058 cases and controls).ResultsFor SCZ, we estimate there are 1,551 risk genes. There are more risk genes and they have weaker effects than for NDDs. We provide power analyses to predict the number of risk-gene discoveries as more data become available. We confirm and augment prior risk gene and gene set enrichment results for SCZ and NDDs. In particular, we detected 98 new DD risk genes at FDR < 0.05. Correlations of risk-gene posterior probabilities are high across four NDDs (ρ>0.55), but low between SCZ and the NDDs (ρ<0.3). An in-depth analysis of 288 NDD genes shows there is highly significant protein–protein interaction (PPI) network connectivity, and functionally distinct PPI subnetworks based on pathway enrichment, single-cell RNA-seq cell types, and multi-region developmental brain RNA-seq.ConclusionsWe have extended a pipeline used in ASD studies and applied it to infer rare genetic parameters for SCZ and four NDDs (https://github.com/hoangtn/extTADA). We find many new DD risk genes, supported by gene set enrichment and PPI network connectivity analyses. We find greater similarity among NDDs than between NDDs and SCZ. NDD gene subnetworks are implicated in postnatally expressed presynaptic and postsynaptic genes, and for transcriptional and post-transcriptional gene regulation in prenatal neural progenitor and stem cells.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-017-0497-y) contains supplementary material, which is available to authorized users.