A Multimodal Atlas of Human Brain Cell Types
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ABSTRACT: "A multimodal atlas of human brain cell types" includes sample data targeting two cell types that show species differences between mouse and human. First, it includes a detailed transcriptomic, morphological, and electrophysiological characterization of cell types in layer 1 of human middle temporal gyrus, focusing primarily on different inhibitory cell types. This project also includes gene expression data collected from nuclei in layer 5 of human fronto-insula, with a goal of identifying transcriptomic signatures of Von Economo neurons. Control samples collected as part of the same experiment are also included in the data set. This study was conducted as part of a collaboration between the Allen Institute for Brain Science, the University of Szeged, and the J. Craig Venter Institute. Collaborators request that publications resulting from these data cite their original publication: Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type (PMID: 30150662).
REPOSITORIES: dbGaP
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Project description:During development, the human brain orchestrates the generation of hundreds of diverse cell types, and recent transcriptomic atlases of the developing human cortex provide new opportunities to identify novel regulators of cell fate specification. We have conducted an integrated meta-analysis of seven recently published single-cell transcriptomic profiles of the developing human cortex, generating a meta-atlas of 225 meta-modules. By tracking the activity of these meta-modules throughout development and comparing this to datasets from the adult human as well as the developing and adult mouse, we identified modules with potential roles in the transition from neurogenesis to gliogenesis and neuronal subtype maturation. The cell type and developmental state at which these modules exist was validated in primary samples from the developing human brain. Of particular interest, we identified and validated a module associated with both developing human deep layer neurons and human adult deep layer neuronal subtypes expressing the terminal differentiation marker FEZF2. Though FEZF2 is neither a member of nor co-expressed with our deep layer-associated gene module, almost half of module genes are putative FEZF2 targets, including TSHZ3, a transcription factor associated with neurodevelopmental disorders such as autism. Knockdown experiments in human cortical organoid models confirm that both FEZF2 and TSHZ3 are necessary for deep layer neuron specification. Importantly, we observe that subtle manipulations of these transcription factors result in slight changes in module activity, but together yield strong differences in cell fate. Our analyses therefore reveal a gene network that required to generate deep layer neuronal subtypes, demonstrating the ability of our meta-atlas to engender further mechanistic analyses of cortical fate specification. We anticipate this resource being useful for the study of co-expression programs across diverse biological questions related to the developing neocortex.
2023-09-12 | GSE242779 | GEO
Project description:A multimodal atlas of human brain cell types
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Project description:A multimodal atlas of human brain cell types
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Project description:Illustrating the cellular architecture of the mammalian brain is critical to understanding its diverse functions and complex animal behaviors. With support from NIMH and NINDS as part of the BRAIN Initiative Cell Census Network (BICCN), the Center for Epigenomics of the Mouse Brain Atlas (CEMBA) applied single nucleus methylation sequencing in 118 (45 in the current release) brain functional regions across the whole adult male P56 C57BL/6J mouse brain. In each major brain regions, we identified distinct cell clusters and formed them in a hierarchical taxonomy. These clusters include known primary brain cell types and possible sub-types. We use these data to identify the epigenomic characteristics and to define specific regulatory elements for each cell cluster. We integrate the single-cell methylome with single-cell gene expression and chromatin accessibility profiles from the same brain region to further identify potential enhancers and their corresponding genes for several neuronal cell types. By brain region spacial comparisons, we found spatial specificity among excitatory neuronal clusters within the same cortical layer. We identify brain-region-specific transcription factors that may regulate cortical region development of excitatory neurons. These data define the landscape of cell type and spatial heterogeneity in the mouse brain and the underlining regulatory epigenomic basis.
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Project description:<p>Defining the number, proportion, or lineage of distinct cell types in the developing human brain is an important goal of modern brain research. We produced single cell transcriptomic profiles for 40,000 cells at mid-gestation to define deep expression profiles corresponding to all known major cell types at this developmental period and compare this with bulk tissue profiles. We identified multiple transcription factors (TFs) and co-factors expressed in specific cell types, including multiple new cell-type-specific relationships, providing an unprecedented resource for understanding human neocortical development and evolution. This includes the first single-cell characterization of human subplate neurons and subtypes of developing glutamatergic and GABAergic neurons. We also used these data to deconvolute single cell regulatory networks that connect regulatory elements and transcriptional drivers to single cell gene expression programs in the developing CNS. We characterized major developmental trajectories that tie cell cycle progression with early cell fate decisions during early neurogenesis. Remarkably, we found that differentiation occurs on a transcriptomic continuum, so that differentiating cells not only express the few key TFs that drive cell fates, but express broad, mixed cell-type transcriptomes prior to telophase. Finally, we mapped neuropsychiatric disease genes to specific cell types, implicating dysregulation of specific cell types in ASD, ID, and epilepsy, as the mechanistic underpinnings of several neurodevelopmental disorders. Together these results provide an extensive catalog of cell types in human neocortex and extend our understanding of early cortical development, human brain evolution and the cellular basis of neuropsychiatric disease.</p>
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Project description:<p>During development of the human brain, multiple cell types with diverse regional identities are generated. Here we report a system to generate early human brain forebrain and mid/hindbrain cell types from human embryonic stem cells (hESCs), and infer and experimentally confirm a lineage tree for the generation of these types based on single-cell RNA-Seq analysis. We engineered <i>SOX2<sup>Cit/+</sup></i> and <i>DCX<sup>Cit/Y</sup></i> hESC lines to target progenitors and neurons throughout neural differentiation for single-cell transcriptomic profiling, then identified discrete cell types consisting of both rostral (cortical) and caudal (mid/hindbrain) identities. Direct comparison of the cell types were made to primary tissues using gene expression atlases and fetal human brain single-cell gene expression data, and this established that the cell types resembled early human brain cell types, including preplate cells. From the single-cell transcriptomic data a Bayesian algorithm generated a unified lineage tree, and predicted novel regulatory transcription factors. The lineage tree highlighted a prominent bifurcation between cortical and mid/hindbrain cell types, confirmed by clonal analysis experiments. We demonstrated that cell types from either branch could preferentially be generated by manipulation of the canonical Wnt/beta-catenin pathway. In summary, we present an experimentally validated lineage tree that encompasses multiple brain regions, and our work sheds light on the molecular regulation of region-specific neural lineages during human brain development.</p>
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Project description:The mammalian brain consists of millions to billions of cells that are organized into numerous cell types with specific spatial distribution patterns and structural and functional properties. An essential step towards understanding brain function is to obtain a parts list, i.e., a catalog of cell types, of the brain. 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 based on the combination of two single-cell-level, whole-brain-scale datasets: a single-cell RNA-sequencing (scRNA-seq) dataset of ~7 million cells profiled (~4.0 million cells passing quality control), and a spatially resolved transcriptomic dataset of ~4.3 million cells using MERFISH. The atlas is hierarchically organized into four nested levels of classification: 34 classes, 338 subclasses, 1,201 supertypes and 5,322 clusters. We present a newly developed online platform, Allen Brain Cell (ABC) Atlas, to visualize the mouse whole brain cell type taxonomy and atlas along with the scRNA-seq and MERFISH data and metadata sets. We systematically analyzed the neuronal, non-neuronal, and immature 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. We also systematically characterized cell-type specific expression of neurotransmitters, neuropeptides, and transcription factors. The study uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types across the brain, suggesting they mediate myriad modes of intercellular communications. Finally, we found that transcription factors are major determinants of cell type classification in the adult mouse brain 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 deep and integrative investigations of cellular and circuit function, development, and evolution of the mammalian brain.
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Project description:The cerebellar cortex is a well-studied brain structure with diverse roles in motor learning, coordination, cognition, and autonomic regulation. Nonetheless, a complete inventory of cerebellar cell types is presently lacking. We used high-throughput transcriptional profiling to profile more than 600,000 nuclei and molecularly define cell types across individual lobules of the adult mouse cerebellum. Purkinje neurons showed considerable regional specialization, with the greatest diversity occurring in the posterior lobules. For multiple types of cerebellar interneurons, the molecular variation within each type was more continuous, rather than discrete. For the unipolar brush cells (UBCs)—an interneuron population previously subdivided into discrete populations—the continuous variation in gene expression was associated with a graded continuum of electrophysiological properties. Most surprisingly, we found that molecular layer interneurons (MLIs) were composed of two molecularly and functionally distinct types. Both show a continuum of morphological variation through the thickness of the molecular layer, but electrophysiological recordings revealed marked differences between the two types in spontaneous firing, excitability, and electrical coupling. Together, these findings provide the first comprehensive cellular atlas of the cerebellar cortex, and outline a methodological and conceptual framework for the integration of molecular, morphological, and physiological ontologies for defining brain cell types.
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