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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.A transcriptomic cell-type atlas of the whole adult mouse brain with ~5,300 clusters built from single-cell and spatial transcriptomic datasets with more than eight million cells reveals remarkable cell type diversity across the brain and unique cell type characteristics of different brain regions. [ABSTRACT FROM AUTHOR]

The distinctive physiology of striatal medium spiny neurons (MSNs) underlies their ability to integrate sensory and motor input. In rodents, MSNs have a hyperpolarized resting potential and low input resistance. When activated, they have a delayed onset of spiking and regular spike rate. Here, we show that in the macaque putamen, latency to spike is reduced and spike rate adaptation is increased relative to mouse. We use whole-cell brain slice recordings and recover single-cell gene expression using Patch-seq to distinguish macaque MSN cell types. Species differences in the expression of ion channel genes including the calcium-activated chloride channel, ANO2, and an auxiliary subunit of the A-type potassium channel, DPP10, are correlated with species differences in spike rate adaptation and latency to the first spike, respectively. These surprising divergences in physiology better define the strengths and limitations of mouse models for understanding neuronal and circuit function in the primate basal ganglia. [Display omitted] • Patch-seq shows species differences between macaque and mouse putamen neurons • There is a reduced latency to first spike in macaque MSNs relative to mouse MSNs • Macaque MSNs show spike rate adaptation that is absent in mouse MSNs • Transcriptomic analysis of ion channel genes suggests potential molecular mechanisms Our study compares medium spiny neurons (MSNs) in macaque putamen tissue and mouse tissue using Patch-seq; this allows the comparison of electrophysiological, morphological, and transcriptomic data between macaque and mouse MSNs. The results show species differences in ion channel gene expression, spike rate adaptation, and latency to the first spike. [ABSTRACT FROM AUTHOR]

Large-scale analysis of single-cell gene expression has revealed transcriptomically defined cell subclasses present throughout the primate neocortex with gene expression profiles that differ depending upon neocortical region. Here, we test whether the interareal differences in gene expression translate to regional specializations in the physiology and morphology of infragranular glutamatergic neurons by performing Patch-seq experiments in brain slices from the temporal cortex (TCx) and motor cortex (MCx) of the macaque. We confirm that transcriptomically defined extratelencephalically projecting neurons of layer 5 (L5 ET neurons) include retrogradely labeled corticospinal neurons in the MCx and find multiple physiological properties and ion channel genes that distinguish L5 ET from non-ET neurons in both areas. Additionally, while infragranular ET and non-ET neurons retain distinct neuronal properties across multiple regions, there are regional morpho-electric and gene expression specializations in the L5 ET subclass, providing mechanistic insights into the specialized functional architecture of the primate neocortex. [Display omitted] • Transcriptomically defined L5 ET neurons project subcerebrally in macaques • Ion channel gene expression differentiates neuronal subclasses and neocortical areas • L5 ET versus non-ET neuron physiological differences are conserved across regions • Many cross-areal differences in L5 ET morpho-electric properties exist in macaques Using Patch-seq recordings of macaque infragranular pyramidal neurons, including retrogradely labeled corticospinal neurons, Dembrow et al. identify cell-type and areal differences in gene expression, morphology, and physiology. These data demonstrate that regional specializations in function and cytoarchitecture are accompanied by differences in the intrinsic properties of discrete cell types. [ABSTRACT FROM AUTHOR]

Transcriptomic profiling of complex tissues by single-nucleus RNA-sequencing (snRNA-seq) affords some advantages over single-cell RNA-sequencing (scRNA-seq). snRNA-seq provides less biased cellular coverage, does not appear to suffer cell isolation-based transcriptional artifacts, and can be applied to archived frozen specimens. We used well-matched snRNA-seq and scRNA-seq datasets from mouse visual cortex to compare cell type detection. Although more transcripts are detected in individual whole cells (~11,000 genes) than nuclei (~7,000 genes), we demonstrate that closely related neuronal cell types can be similarly discriminated with both methods if intronic sequences are included in snRNA-seq analysis. We estimate that the nuclear proportion of total cellular mRNA varies from 20% to over 50% for large and small pyramidal neurons, respectively. Together, these results illustrate the high information content of nuclear RNA for characterization of cellular diversity in brain tissues. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background High-throughput sequencing is gradually replacing microarrays as the preferred method for studying mRNA expression levels, providing nucleotide resolution and accurately measuring absolute expression levels of almost any transcript, known or novel. However, existing microarray data from clinical, pharmaceutical, and academic settings represent valuable and often underappreciated resources, and methods for assessing and improving the quality of these data are lacking. Results To quantitatively assess the quality of microarray probes, we directly compare RNA-Seq to Agilent microarrays by processing 231 unique samples from the Allen Human Brain Atlas using RNA-Seq. Both techniques provide highly consistent, highly reproducible gene expression measurements in adult human brain, with RNA-Seq slightly outperforming microarray results overall. We show that RNA-Seq can be used as ground truth to assess the reliability of most microarray probes, remove probes with off-target effects, and scale probe intensities to match the expression levels identified by RNA-Seq. These sequencing scaled microarray intensities (SSMIs) provide more reliable, quantitative estimates of absolute expression levels for many genes when compared with unscaled intensities. Finally, we validate this result in two human cell lines, showing that linear scaling factors can be applied across experiments using the same microarray platform. Conclusions Microarrays provide consistent, reproducible gene expression measurements, which are improved using RNA-Seq as ground truth. We expect that our strategy could be used to improve probe quality for many data sets from major existing repositories. [ABSTRACT FROM AUTHOR]

Localized accessibility of critical DNA sequences to the regulatory machinery is a key requirement for regulation of human genes. Here we describe a high-resolution, genome-scale approach for quantifying chromatin accessibility by measuring DNase I sensitivity as a continuous function of genome position using tiling DNA microarrays (DNase-array). We demonstrate this approach across 1% (∼30 Mb) of the human genome, wherein we localized 2,690 classical DNase I hypersensitive sites with high sensitivity and specificity, and also mapped larger-scale patterns of chromatin architecture. DNase I hypersensitive sites exhibit marked aggregation around transcriptional start sites (TSSs), though the majority mark nonpromoter functional elements. We also developed a computational approach for visualizing higher-order features of chromatin structure. This revealed that human chromatin organization is dominated by large (100–500 kb) 'superclusters' of DNase I hypersensitive sites, which encompass both gene-rich and gene-poor regions. DNase-array is a powerful and straightforward approach for systematic exposition of the cis-regulatory architecture of complex genomes. [ABSTRACT FROM AUTHOR]

Identification of functional, noncoding elements that regulate transcription in the context of complex genomes is a major goal of modern biology. Localization of functionality to specific sequences is a requirement for genetic and computational studies. Here, we describe a generic approach, quantitative chromatin profiling, that uses quantitative analysis of in vivo chromatin structure over entire gene loci to rapidly and precisely localize cis-regulatory sequences and other functional modalities encoded by DNase I hypersensitive sites. To demonstrate the accuracy of this approach, we analyzed ∼300 kilobases of human genome sequence from diverse gene loci and cleanly delineated functional elements corresponding to a spectrum of classical cis-regulatory activities including enhancers, promoters, locus control regions and insulators as well as novel elements. Systematic, high-throughput identification of functional elements coinciding with DNase I hypersensitive sites will substantially expand our knowledge of transcriptional regulation and should simplify the search for noncoding genetic variation with phenotypic consequences. [ABSTRACT FROM AUTHOR]