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The mammalian brain consists of millions to billions of cells that are organized into many cell types with specific spatial distribution patterns and structural and functional properties1–3. Here we report a comprehensive and high-resolution transcriptomic and spatial cell-type atlas for the whole adult mouse brain. The cell-type atlas was created by combining a single-cell RNA-sequencing (scRNA-seq) dataset of around 7 million cells profiled (approximately 4.0 million cells passing quality control), and a spatial transcriptomic dataset of approximately 4.3 million cells using multiplexed error-robust fluorescence in situ hybridization (MERFISH). The atlas is hierarchically organized into 4 nested levels of classification: 34 classes, 338 subclasses, 1,201 supertypes and 5,322 clusters. We present an online platform, Allen Brain Cell Atlas, to visualize the mouse whole-brain cell-type atlas along with the single-cell RNA-sequencing and MERFISH datasets. We systematically analysed the neuronal and non-neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell-type organization in different brain regions—in particular, a dichotomy between the dorsal and ventral parts of the brain. The dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. Our study also uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types. Finally, we found that transcription factors are major determinants of cell-type classification and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole mouse brain transcriptomic and spatial cell-type atlas establishes a benchmark reference atlas and a foundational resource for integrative investigations of cellular and circuit function, development and evolution of the mammalian brain.

The brain controls nearly all bodily functions via spinal projecting neurons (SPNs) that carry command signals from the brain to the spinal cord. However, a comprehensive molecular characterization of brain-wide SPNs is still lacking. Here we transcriptionally profiled a total of 65,002 SPNs, identified 76 region-specific SPN types, and mapped these types into a companion atlas of the whole mouse brain1. This taxonomy reveals a three-component organization of SPNs: (1) molecularly homogeneous excitatory SPNs from the cortex, red nucleus and cerebellum with somatotopic spinal terminations suitable for point-to-point communication; (2) heterogeneous populations in the reticular formation with broad spinal termination patterns, suitable for relaying commands related to the activities of the entire spinal cord; and (3) modulatory neurons expressing slow-acting neurotransmitters and/or neuropeptides in the hypothalamus, midbrain and reticular formation for ‘gain setting’ of brain–spinal signals. In addition, this atlas revealed a LIM homeobox transcription factor code that parcellates the reticulospinal neurons into five molecularly distinct and spatially segregated populations. Finally, we found transcriptional signatures of a subset of SPNs with large soma size and correlated these with fast-firing electrophysiological properties. Together, this study establishes a comprehensive taxonomy of brain-wide SPNs and provides insight into the functional organization of SPNs in mediating brain control of bodily functions.

In mammalian brains, millions to billions of cells form complex interaction networks to enable a wide range of functions. The enormous diversity and intricate organization of cells have impeded our understanding of the molecular and cellular basis of brain function. Recent advances in spatially resolved single-cell transcriptomics have enabled systematic mapping of the spatial organization of molecularly defined cell types in complex tissues1–3, including several brain regions (for example, refs. 1–11). However, a comprehensive cell atlas of the whole brain is still missing. Here we imaged a panel of more than 1,100 genes in approximately 10 million cells across the entire adult mouse brains using multiplexed error-robust fluorescence in situ hybridization12and performed spatially resolved, single-cell expression profiling at the whole-transcriptome scale by integrating multiplexed error-robust fluorescence in situ hybridization and single-cell RNA sequencing data. Using this approach, we generated a comprehensive cell atlas of more than 5,000 transcriptionally distinct cell clusters, belonging to more than 300 major cell types, in the whole mouse brain with high molecular and spatial resolution. Registration of this atlas to the mouse brain common coordinate framework allowed systematic quantifications of the cell-type composition and organization in individual brain regions. We further identified spatial modules characterized by distinct cell-type compositions and spatial gradients featuring gradual changes of cells. Finally, this high-resolution spatial map of cells, each with a transcriptome-wide expression profile, allowed us to infer cell-type-specific interactions between hundreds of cell-type pairs and predict molecular (ligand–receptor) basis and functional implications of these cell–cell interactions. These results provide rich insights into the molecular and cellular architecture of the brain and a foundation for functional investigations of neural circuits and their dysfunction in health and disease.

Cytosine DNA methylation is essential in brain development and is implicated in various neurological disorders. Understanding DNA methylation diversity across the entire brain in a spatial context is fundamental for a complete molecular atlas of brain cell types and their gene regulatory landscapes. Here we used single-nucleus methylome sequencing (snmC-seq3) and multi-omic sequencing (snm3C-seq)1technologies to generate 301,626 methylomes and 176,003 chromatin conformation–methylome joint profiles from 117 dissected regions throughout the adult mouse brain. Using iterative clustering and integrating with companion whole-brain transcriptome and chromatin accessibility datasets, we constructed a methylation-based cell taxonomy with 4,673 cell groups and 274 cross-modality-annotated subclasses. We identified 2.6 million differentially methylated regions across the genome that represent potential gene regulation elements. Notably, we observed spatial cytosine methylation patterns on both genes and regulatory elements in cell types within and across brain regions. Brain-wide spatial transcriptomics data validated the association of spatial epigenetic diversity with transcription and improved the anatomical mapping of our epigenetic datasets. Furthermore, chromatin conformation diversities occurred in important neuronal genes and were highly associated with DNA methylation and transcription changes. Brain-wide cell-type comparisons enabled the construction of regulatory networks that incorporate transcription factors, regulatory elements and their potential downstream gene targets. Finally, intragenic DNA methylation and chromatin conformation patterns predicted alternative gene isoform expression observed in a whole-brain SMART-seq2dataset. Our study establishes a brain-wide, single-cell DNA methylome and 3D multi-omic atlas and provides a valuable resource for comprehending the cellular–spatial and regulatory genome diversity of the mouse brain.

Recent advances in single-cell technologies have led to the discovery of thousands of brain cell types; however, our understanding of the gene regulatory programs in these cell types is far from complete1–4. Here we report a comprehensive atlas of candidate cis-regulatory DNA elements (cCREs) in the adult mouse brain, generated by analysing chromatin accessibility in 2.3 million individual brain cells from 117 anatomical dissections. The atlas includes approximately 1 million cCREs and their chromatin accessibility across 1,482 distinct brain cell populations, adding over 446,000 cCREs to the most recent such annotation in the mouse genome. The mouse brain cCREs are moderately conserved in the human brain. The mouse-specific cCREs—specifically, those identified from a subset of cortical excitatory neurons—are strongly enriched for transposable elements, suggesting a potential role for transposable elements in the emergence of new regulatory programs and neuronal diversity. Finally, we infer the gene regulatory networks in over 260 subclasses of mouse brain cells and develop deep-learning models to predict the activities of gene regulatory elements in different brain cell types from the DNA sequence alone. Our results provide a resource for the analysis of cell-type-specific gene regulation programs in both mouse and human brains.

Single-cell analyses parse the brain’s billions of neurons into thousands of ‘cell-type’ clusters residing in different brain structures1. Many cell types mediate their functions through targeted long-distance projections allowing interactions between specific cell types. Here we used epi-retro-seq2to link single-cell epigenomes and cell types to long-distance projections for 33,034 neurons dissected from 32 different regions projecting to 24 different targets (225 source-to-target combinations) across the whole mouse brain. We highlight uses of these data for interrogating principles relating projection types to transcriptomics and epigenomics, and for addressing hypotheses about cell types and connections related to genetics. We provide an overall synthesis with 926 statistical comparisons of discriminability of neurons projecting to each target for every source. We integrate this dataset into the larger BRAIN Initiative Cell Census Network atlas, composed of millions of neurons, to link projection cell types to consensus clusters. Integration with spatial transcriptomics further assigns projection-enriched clusters to smaller source regions than the original dissections. We exemplify this by presenting in-depth analyses of projection neurons from the hypothalamus, thalamus, hindbrain, amygdala and midbrain to provide insights into properties of those cell types, including differentially expressed genes, their associated cis-regulatory elements and transcription-factor-binding motifs, and neurotransmitter use.

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1–3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas—containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities—is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits.

Full-length SMART-seq1single-cell RNA sequencing can be used to measure gene expression at isoform resolution, making possible the identification of specific isoform markers for different cell types. Used in conjunction with spatial RNA capture and gene-tagging methods, this enables the inference of spatially resolved isoform expression for different cell types. Here, in a comprehensive analysis of 6,160 mouse primary motor cortex cells assayed with SMART-seq, 280,327 cells assayed with MERFISH2and 94,162 cells assayed with 10x Genomics sequencing3, we find examples of isoform specificity in cell types—including isoform shifts between cell types that are masked in gene-level analysis—as well as examples of transcriptional regulation. Additionally, we show that isoform specificity helps to refine cell types, and that a multi-platform analysis of single-cell transcriptomic data leveraging multiple measurements provides a comprehensive atlas of transcription in the mouse primary motor cortex that improves on the possibilities offered by any single technology.

A mammalian brain is composed of numerous cell types organized in an intricate manner to form functional neural circuits. Single-cell RNA sequencing allows systematic identification of cell types based on their gene expression profiles and has revealed many distinct cell populations in the brain1,2. Single-cell epigenomic profiling3,4further provides information on gene-regulatory signatures of different cell types. Understanding how different cell types contribute to brain function, however, requires knowledge of their spatial organization and connectivity, which is not preserved in sequencing-based methods that involve cell dissociation. Here we used a single-cell transcriptome-imaging method, multiplexed error-robust fluorescence in situ hybridization (MERFISH)5, to generate a molecularly defined and spatially resolved cell atlas of the mouse primary motor cortex. We profiled approximately 300,000 cells in the mouse primary motor cortex and its adjacent areas, identified 95 neuronal and non-neuronal cell clusters, and revealed a complex spatial map in which not only excitatory but also most inhibitory neuronal clusters adopted laminar organizations. Intratelencephalic neurons formed a largely continuous gradient along the cortical depth axis, in which the gene expression of individual cells correlated with their cortical depths. Furthermore, we integrated MERFISH with retrograde labelling to probe projection targets of neurons of the mouse primary motor cortex and found that their cortical projections formed a complex network in which individual neuronal clusters project to multiple target regions and individual target regions receive inputs from multiple neuronal clusters.

The neocortex is disproportionately expanded in human compared with mouse1,2, both in its total volume relative to subcortical structures and in the proportion occupied by supragranular layers composed of neurons that selectively make connections within the neocortex and with other telencephalic structures. Single-cell transcriptomic analyses of human and mouse neocortex show an increased diversity of glutamatergic neuron types in supragranular layers in human neocortex and pronounced gradients as a function of cortical depth3. Here, to probe the functional and anatomical correlates of this transcriptomic diversity, we developed a robust platform combining patch clamp recording, biocytin staining and single-cell RNA-sequencing (Patch-seq) to examine neurosurgically resected human tissues. We demonstrate a strong correspondence between morphological, physiological and transcriptomic phenotypes of five human glutamatergic supragranular neuron types. These were enriched in but not restricted to layers, with one type varying continuously in all phenotypes across layers 2 and 3. The deep portion of layer 3 contained highly distinctive cell types, two of which express a neurofilament protein that labels long-range projection neurons in primates that are selectively depleted in Alzheimer’s disease4,5. Together, these results demonstrate the explanatory power of transcriptomic cell-type classification, provide a structural underpinning for increased complexity of cortical function in humans, and implicate discrete transcriptomic neuron types as selectively vulnerable in disease.

The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch–seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

While opioids are powerful analgesics, side effects including addiction and death through respiratory depression limit their clinical utility. Interrogating how opioids signal through distinct neuron types to either relieve pain or induce side effects may allow for development of more targeted pain medications. Although opioids bind the mu, kappa, and delta opioid receptors, the mu opioid receptor (MOR) is known to mediate both addictive and analgesic effects of clinically used and illicit opioid drugs, and we therefore focus on its distribution across neuronal circuits. Specifically, MORs are known to be expressed across the cerebral cortex, including in multiple areas involved in pain perception. To compare the effect of opioids across cortical regions mediating the sensory-discriminative and affective-motivational dimensions of pain, we resolved the MOR expression pattern in the primary somatosensory and anterior cingulate cortices using scRNAseq data from cortical samples prepared by SMART-seq technology. Using intersectional mouse genetic strategies based on marker genes derived from scRNA-seq data the spatial distribution, connectivity, and dynamic profiles of MOR-expressing subpopulations across cortical regions and layers were dissected using anatomical, viral tracing, and calcium imaging experiments. With specific focus on cell types conserved in humans, these experiments reveal the molecular architecture of opioid-sensitive cortical neurons and their contributions to opioid-induced behaviors. This work is supported by grants from the NIH (R01DA054583, G.S., H.K.Z., F30DA057839, N.E.O., F32DA057779, J.K.N), and the McKnight Endowment Fund for Neuroscience (G.S.).

Cortical neurons exhibit extreme diversity in gene expression as well as in morphological and electrophysiological properties1,2. Most existing neural taxonomies are based on either transcriptomic3,4or morpho-electric5,6criteria, as it has been technically challenging to study both aspects of neuronal diversity in the same set of cells7. Here we used Patch-seq8to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of more than 1,300 neurons in adult mouse primary motor cortex, providing a morpho-electric annotation of almost all transcriptomically defined neural cell types. We found that, although broad families of transcriptomic types (those expressing Vip, Pvalb, Sstand so on) had distinct and essentially non-overlapping morpho-electric phenotypes, individual transcriptomic types within the same family were not well separated in the morpho-electric space. Instead, there was a continuum of variability in morphology and electrophysiology, with neighbouring transcriptomic cell types showing similar morpho-electric features, often without clear boundaries between them. Our results suggest that neuronal types in the neocortex do not always form discrete entities. Instead, neurons form a hierarchy that consists of distinct non-overlapping branches at the level of families, but can form continuous and correlated transcriptomic and morpho-electrical landscapes within families.

Elucidating the cellular architecture of the human cerebral cortex is central to understanding our cognitive abilities and susceptibility to disease. Here we used single-nucleus RNA-sequencing analysis to perform a comprehensive study of cell types in the middle temporal gyrus of human cortex. We identified a highly diverse set of excitatory and inhibitory neuron types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to similar mouse cortex single-cell RNA-sequencing datasets revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of properties of human cell types. Despite this general conservation, we also found extensive differences between homologous human and mouse cell types, including marked alterations in proportions, laminar distributions, gene expression and morphology. These species-specific features emphasize the importance of directly studying human brain. RNA-sequencing analysis of cells in the human cortex enabled identification of diverse cell types, revealing well-conserved architecture and homologous cell types as well as extensive differences when compared with datasets covering the analogous region of the mouse brain.

The neocortex contains a multitude of cell types that are segregated into layers and functionally distinct areas. To investigate the diversity of cell types across the mouse neocortex, here we analysed 23,822 cells from two areas at distant poles of the mouse neocortex: the primary visual cortex and the anterior lateral motor cortex. We define 133 transcriptomic cell types by deep, single-cell RNA sequencing. Nearly all types of GABA (γ-aminobutyric acid)-containing neurons are shared across both areas, whereas most types of glutamatergic neurons were found in one of the two areas. By combining single-cell RNA sequencing and retrograde labelling, we match transcriptomic types of glutamatergic neurons to their long-range projection specificity. Our study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex.

Activity in the motor cortex predicts movements, seconds before they are initiated. This preparatory activity has been observed across cortical layers, including in descending pyramidal tract neurons in layer 5. A key question is how preparatory activity is maintained without causing movement, and is ultimately converted to a motor command to trigger appropriate movements. Here, using single-cell transcriptional profiling and axonal reconstructions, we identify two types of pyramidal tract neuron. Both types project to several targets in the basal ganglia and brainstem. One type projects to thalamic regions that connect back to motor cortex; populations of these neurons produced early preparatory activity that persisted until the movement was initiated. The second type projects to motor centres in the medulla and mainly produced late preparatory activity and motor commands. These results indicate that two types of motor cortex output neurons have specialized roles in motor control.

During human brain development, multiple signaling pathways generate diverse cell types with varied regional identities. Here, we integrate single-cell RNA sequencing and clonal analyses to reveal lineage trees and molecular signals underlying early forebrain and mid/hindbrain cell differentiation from human embryonic stem cells (hESCs). Clustering single-cell transcriptomic data identified 41 distinct populations of progenitor, neuronal, and non-neural cells across our differentiation time course. Comparisons with primary mouse and human gene expression data demonstrated rostral and caudal progenitor and neuronal identities from early brain development. Bayesian analyses inferred a unified cell-type lineage tree that bifurcates between cortical and mid/hindbrain cell types. Two methods of clonal analyses confirmed these findings and further revealed the importance of Wnt/β-catenin signaling in controlling this lineage decision. Together, these findings provide a rich transcriptome-based lineage map for studying human brain development and modeling developmental disorders.

Structured elements of RNA molecules are essential in, e.g., RNA stabilization, localization, and protein interaction, and their conservation across species suggests a common functional role. We computationally screened vertebrate genomes for conserved RNA structures (CRSs), leveraging structure-based, rather than sequence-based, alignments. After careful correction for sequence identity and GC content, we predict ∼516,000 human genomic regions containing CRSs. We find that a substantial fraction of human–mouse CRS regions (1) colocalize consistently with binding sites of the same RNA binding proteins (RBPs) or (2) are transcribed in corresponding tissues. Additionally, a CaptureSeq experiment revealed expression of many of our CRS regions in human fetal brain, including 662 novel ones. For selected human and mouse candidate pairs, qRT-PCR and in vitro RNA structure probing supported both shared expression and shared structure despite low abundance and low sequence identity. About 30,000 CRS regions are located near coding or long noncoding RNA genes or within enhancers. Structured (CRS overlapping) enhancer RNAs and extended 3′ ends have significantly increased expression levels over their nonstructured counterparts. Our findings of transcribed uncharacterized regulatory regions that contain CRSs support their RNA-mediated functionality.

Opioids are indispensable analgesics capable of producing dangerous side effects such as addiction and death through respiratory depression. Understanding the organization and function of neural circuits underlying opioid-induced phenotypes may reveal how to separate opioid-induced analgesia from unwanted side effects. Opioids elicit their effects through the mu, kappa, and delta opioid receptors expressed in neurons throughout the nervous system. In the cerebral cortex, opioids alter sensorimotor function, motivation, and affect, but the molecular identities, connectivity, and functional contributions of the neurons involved are unclear. Dissection of OR-expressing cells has been limited by the varied subcellular localization of ORs and the limited multiplexity of methods reliant on antibodies and probes. Single-cell RNA-sequencing (scRNAseq) has been used to investigate the cellular heterogeneity of the cortex, but the low sequencing depth of conventional high-throughput scRNAseq approaches limits reliable detection of lowly expressed transcripts, such as OR-encoding genes. To overcome this, we analyzed scRNAseq data from cortical samples prepared by SMART-seq technology, assessing ∼80,000 neurons from 15 cortical regions to characterize neurons expressing OR genes. Using the anterior cingulate cortex and primary somatosensory cortex as examples, we applied intersectional genetic approaches using marker genes from these data to determine the spatial distribution of OR-expressing subpopulations across cortical regions and layers. We interrogated the connectivity of OR-expressing cortical neuron populations and elucidated their contributions to opioid-induced phenotypes using Cre/Flp-dependent viral tools. Together, these experiments resolve the configuration of opioid-sensitive cortical neurons and reveal their contributions to opioid-induced behaviors. This work is supported by grants from the NIH (5R01DA054583, G.S., H.K.Z.), the Brain Research Foundation (G.S.), the McKnight Endowment Fund for Neuroscience (G.S.), the New York Stem Cell Foundation (G.S.), the Eshelman Institute for Innovation (G.S.).

Nervous systems are composed of various cell types, but the extent of cell type diversity is poorly understood. We constructed a cellular taxonomy of one cortical region, primary visual cortex, in adult mice on the basis of single-cell RNA sequencing. We identified 49 transcriptomic cell types, including 23 GABAergic, 19 glutamatergic and 7 non-neuronal types. We also analyzed cell type–specific mRNA processing and characterized genetic access to these transcriptomic types by many transgenic Cre lines. Finally, we found that some of our transcriptomic cell types displayed specific and differential electrophysiological and axon projection properties, thereby confirming that the single-cell transcriptomic signatures can be associated with specific cellular properties.

The diverse progenitors that give rise to the human neocortex have been difficult to characterize because progenitors, particularly radial glia (RG), are rare and are defined by a combination of intracellular markers, position and morphology. To circumvent these problems, we developed Fixed and Recovered Intact Single-cell RNA (FRISCR), a method for profiling the transcriptomes of individual fixed, stained and sorted cells. Using FRISCR, we profiled primary human RG that constitute only 1% of the midgestation cortex and classified them as ventricular zone−enriched RG (vRG) that express ANXA1 and CRYAB, and outer subventricular zone−localized RG (oRG) that express HOPX. Our study identified vRG and oRG markers and molecular profiles, an essential step for understanding human neocortical progenitor development. FRISCR allows targeted single-cell profiling of any tissues that lack live-cell markers.

The structure and function of the human brain are highly stereotyped, implying a conserved molecular program responsible for its development, cellular structure and function. We applied a correlation-based metric called differential stability to assess reproducibility of gene expression patterning across 132 structures in six individual brains, revealing mesoscale genetic organization. The genes with the highest differential stability are highly biologically relevant, with enrichment for brain-related annotations, disease associations, drug targets and literature citations. Using genes with high differential stability, we identified 32 anatomically diverse and reproducible gene expression signatures, which represent distinct cell types, intracellular components and/or associations with neurodevelopmental and neurodegenerative disorders. Genes in neuron-associated compared to non-neuronal networks showed higher preservation between human and mouse; however, many diversely patterned genes displayed marked shifts in regulation between species. Finally, highly consistent transcriptional architecture in neocortex is correlated with resting state functional connectivity, suggesting a link between conserved gene expression and functionally relevant circuitry.

MyoD and NeuroD2, master regulators of myogenesis and neurogenesis, bind to a “shared” E-box sequence (CAGCTG) and a “private” sequence (CAGGTG or CAGATG, respectively). To determine whether private-site recognition is sufficient to confer lineage specification, we generated a MyoD mutant with the DNA-binding specificity of NeuroD2. This chimeric mutant gained binding to NeuroD2 private sites but maintained binding to a subset of MyoD-specific sites, activating part of both the muscle and neuronal programs. Sequence analysis revealed an enrichment for PBX/MEIS motifs at the subset of MyoD-specific sites bound by the chimera, and point mutations that prevent MyoD interaction with PBX/MEIS converted the chimera to a pure neurogenic factor. Therefore, redirecting MyoD binding from MyoD private sites to NeuroD2 private sites, despite preserved binding to the MyoD/NeuroD2 shared sites, is sufficient to change MyoD from a master regulator of myogenesis to a master regulator of neurogenesis.

Which cell types constitute brain circuits is a fundamental question, but establishing the correspondence across cellular data modalities is challenging. Bio-realistic models allow probing cause-and-effect and linking seemingly disparate modalities. Here, we introduce a computational optimization workflow to generate 9,200 single-neuron models with active conductances. These models are based on 230 in vitroelectrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that, in contrast to current belief, the generated models are robust representations of individual experiments and cortical cell types as defined via cellular electrophysiology or transcriptomics. Next, we show that differences in specific conductances predicted from the models reflect differences in gene expression supported by single-cell transcriptomics. The differences in model conductances, in turn, explain electrophysiological differences observed between the cortical subclasses. Our computational effort reconciles single-cell modalities that define cell types and enables causal relationships to be examined.

Genetic and epigenetic alterations are essential for the initiation and progression of human cancer. We previously reported that primary human medulloblastomas showed extensive cancer-specific CpG island DNA hypermethylation in critical developmental pathways. To determine whether genetically engineered mouse models (GEMMs) of medulloblastoma have comparable epigenetic changes, we assessed genome-wide DNA methylation in three mouse models of medulloblastoma. In contrast to human samples, very few loci with cancer-specific DNA hypermethylation were detected, and in almost all cases the degree of methylation was relatively modest compared with the dense hypermethylation in the human cancers. To determine if this finding was common to other GEMMs, we examined a Burkitt lymphoma and breast cancer model and did not detect promoter CpG island DNA hypermethylation, suggesting that human cancers and at least some GEMMs are fundamentally different with respect to this epigenetic modification. These findings provide an opportunity to both better understand the mechanism of aberrant DNA methylation in human cancer and construct better GEMMs to serve as preclinical platforms for therapy development.

Rhabdomyosarcoma is the most common soft-tissue sarcoma in children. While cytogenetic abnormalities have been well characterized in this disease, aberrant epigenetic events such as DNA hypermethylation have not been described in genome-wide studies. We have analyzed the methylation status of 25,500 promoters in normal skeletal muscle, and in cell lines and tumor samples of embryonal and alveolar rhabdomyosarcoma from pediatric patients. We identified over 1,900 CpG islands that are hypermethylated in rhabdomyosarcomas relative to skeletal muscle. Genes involved in tissue development, differentiation, and oncogenesis such as DNAJA4, HES5, IRX1, BMP8A, GATA4, GATA6, ALX3, and P4HTMwere hypermethylated in both RMS cell lines and primary samples, implicating aberrant DNA methylation in the pathogenesis of rhabdomyosarcoma. Furthermore, cluster analysis revealed embryonal and alveolar subtypes had distinct DNA methylation patterns, with the alveolar subtype being enriched in DNA hypermethylation of polycomb target genes. These results suggest that DNA methylation signatures may aid in the diagnosis and risk stratification of pediatric rhabdomyosarcoma and help identify new targets for therapy.

A novel family of riboswitches, called SAM-IV, is the fourth distinct set of mRNA elements to be reported that regulate gene expression via direct sensing of S-adenosylmethionine (SAM or AdoMet). SAM-IV riboswitches share conserved nucleotide positions with the previously described SAM-I riboswitches, despite rearranged structures and nucleotide positions with family-specific nucleotide identities. Sequence analysis and molecular recognition experiments suggest that SAM-I and SAM-IV riboswitches share similar ligand binding sites, but have different scaffolds. Our findings support the view that RNA has considerable structural versatility and reveal that riboswitches exploit this potential to expand the scope of RNA in genetic regulation.

Subicular regions play important roles in spatial processing and many cognitive functions, and these are mainly attributed to the subiculum (Sub) rather than the prosubiculum (PS). Using single-cell RNA sequencing, we identify 27 transcriptomic cell types residing in sub-domains of the Sub and PS. Based on in situexpression of reliable transcriptomic markers, the precise boundaries of the Sub and PS are consistently defined along the dorsoventral axis. Using these borders to evaluate Cre-line specificity and tracer injections, we find bona fide Sub projections topographically to structures important for spatial processing and navigation. In contrast, the PS sends its outputs to widespread brain regions crucial for motivation, emotion, reward, stress, anxiety, and fear. The Sub and PS, respectively, dominate dorsal and ventral subicular regions and receive different afferents. These results reveal two molecularly and anatomically distinct circuits centered in the Sub and PS, respectively, providing a consistent explanation for historical data and a clearer foundation for future studies.

Motivation: The recent discoveries of large numbers of non-coding RNAs and computational advances in genome-scale RNA search create a need for tools for automatic, high quality identification and characterization of conserved RNA motifs that can be readily used for database search. Previous tools fall short of this goal. Results: CMfinder is a new tool to predict RNA motifs in unaligned sequences. It is an expectation maximization algorithm using covariance models for motif description, featuring novel integration of multiple techniques for effective search of motif space, and a Bayesian framework that blends mutual information-based and folding energy-based approaches to predict structure in a principled way. Extensive tests show that our method works well on datasets with either low or high sequence similarity, is robust to inclusion of lengthy extraneous flanking sequence and/or completely unrelated sequences, and is reasonably fast and scalable. In testing on 19 known ncRNA families, including some difficult cases with poor sequence conservation and large indels, our method demonstrates excellent average per-base-pair accuracy—79% compared with at most 60% for alternative methods. More importantly, the resulting probabilistic model can be directly used for homology search, allowing iterative refinement of structural models based on additional homologs. We have used this approach to obtain highly accurate covariance models of known RNA motifs based on small numbers of related sequences, which identified homologs in deeply-diverged species. Availability: Results and web server version are available at http://bio.cs.washington.edu/yzizhen/CMfinder/ Contact: yzizhen@cs.washington.edu Supplementary information: Supplementary technical details are available at http://bio.cs.washington.edu/yzizhen/CMfinder/