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Nadiya V. Shapovalova

Bio: Nadiya V. Shapovalova is an academic researcher from Allen Institute for Brain Science. The author has contributed to research in topics: Cell type & Neocortex. The author has an hindex of 16, co-authored 20 publications receiving 8720 citations.
Topics: Cell type, Neocortex, Biology, Human brain, Enhancer

Papers
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Journal ArticleDOI
Ed S. Lein1, Michael Hawrylycz1, Nancy Ao2, Mikael Ayres1, Amy Bensinger1, Amy Bernard1, Andrew F. Boe1, Mark S. Boguski3, Mark S. Boguski1, Kevin S. Brockway1, Emi J. Byrnes1, Lin Chen1, Li Chen2, Tsuey-Ming Chen2, Mei Chi Chin1, Jimmy Chong1, Brian E. Crook1, Aneta Czaplinska2, Chinh Dang1, Suvro Datta1, Nick Dee1, Aimee L. Desaki1, Tsega Desta1, Ellen Diep1, Tim A. Dolbeare1, Matthew J. Donelan1, Hong-Wei Dong1, Jennifer G. Dougherty1, Ben J. Duncan1, Amanda Ebbert1, Gregor Eichele4, Lili K. Estin1, Casey Faber1, Benjamin A.C. Facer1, Rick Fields2, Shanna R. Fischer1, Tim P. Fliss1, Cliff Frensley1, Sabrina N. Gates1, Katie J. Glattfelder1, Kevin R. Halverson1, Matthew R. Hart1, John G. Hohmann1, Maureen P. Howell1, Darren P. Jeung1, Rebecca A. Johnson1, Patrick T. Karr1, Reena Kawal1, Jolene Kidney1, Rachel H. Knapik1, Chihchau L. Kuan1, James H. Lake1, Annabel R. Laramee1, Kirk D. Larsen1, Christopher Lau1, Tracy Lemon1, Agnes J. Liang2, Ying Liu2, Lon T. Luong1, Jesse Michaels1, Judith J. Morgan1, Rebecca J. Morgan1, Marty Mortrud1, Nerick Mosqueda1, Lydia Ng1, Randy Ng1, Geralyn J. Orta1, Caroline C. Overly1, Tu H. Pak1, Sheana Parry1, Sayan Dev Pathak1, Owen C. Pearson1, Ralph B. Puchalski1, Zackery L. Riley1, Hannah R. Rockett1, Stephen A. Rowland1, Joshua J. Royall1, Marcos J. Ruiz2, Nadia R. Sarno1, Katherine Schaffnit1, Nadiya V. Shapovalova1, Taz Sivisay1, Clifford R. Slaughterbeck1, Simon Smith1, Kimberly A. Smith1, Bryan I. Smith1, Andy J. Sodt1, Nick N. Stewart1, Kenda-Ruth Stumpf1, Susan M. Sunkin1, Madhavi Sutram1, Angelene Tam2, Carey D. Teemer1, Christina Thaller2, Carol L. Thompson1, Lee R. Varnam1, Axel Visel5, Axel Visel4, Ray M. Whitlock1, Paul Wohnoutka1, Crissa K. Wolkey1, Victoria Y. Wong1, Matthew J.A. Wood2, Murat B. Yaylaoglu2, Rob Young1, Brian L. Youngstrom1, Xu Feng Yuan1, Bin Zhang2, Theresa A. Zwingman1, Allan R. Jones1 
11 Jan 2007-Nature
TL;DR: An anatomically comprehensive digital atlas containing the expression patterns of ∼20,000 genes in the adult mouse brain is described, providing an open, primary data resource for a wide variety of further studies concerning brain organization and function.
Abstract: Molecular approaches to understanding the functional circuitry of the nervous system promise new insights into the relationship between genes, brain and behaviour. The cellular diversity of the brain necessitates a cellular resolution approach towards understanding the functional genomics of the nervous system. We describe here an anatomically comprehensive digital atlas containing the expression patterns of approximately 20,000 genes in the adult mouse brain. Data were generated using automated high-throughput procedures for in situ hybridization and data acquisition, and are publicly accessible online. Newly developed image-based informatics tools allow global genome-scale structural analysis and cross-correlation, as well as identification of regionally enriched genes. Unbiased fine-resolution analysis has identified highly specific cellular markers as well as extensive evidence of cellular heterogeneity not evident in classical neuroanatomical atlases. This highly standardized atlas provides an open, primary data resource for a wide variety of further studies concerning brain organization and function.

4,944 citations

Journal ArticleDOI
TL;DR: This work constructed a cellular taxonomy of one cortical region, primary visual cortex, in adult mice on the basis of single-cell RNA sequencing and identified 49 transcriptomic cell types, including 23 GABAergic, 19 glutamatergic and 7 non-neuronal types.
Abstract: 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.

1,388 citations

Journal ArticleDOI
31 Oct 2018-Nature
TL;DR: This study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex and identifies 133 transcriptomic types of glutamatergic neurons to their long-range projection specificity.
Abstract: 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.

1,184 citations

Journal ArticleDOI
10 Apr 2014-Nature
TL;DR: An anatomically comprehensive atlas of the mid-gestational human brain is described, including de novo reference atlases, in situ hybridization, ultra-high-resolution magnetic resonance imaging (MRI) and microarray analysis on highly discrete laser-microdissected brain regions.
Abstract: The anatomical and functional architecture of the human brain is mainly determined by prenatal transcriptional processes. We describe an anatomically comprehensive atlas of the mid-gestational human brain, including de novo reference atlases, in situ hybridization, ultra-high-resolution magnetic resonance imaging (MRI) and microarray analysis on highly discrete laser-microdissected brain regions. In developing cerebral cortex, transcriptional differences are found between different proliferative and post-mitotic layers, wherein laminar signatures reflect cellular composition and developmental processes. Cytoarchitectural differences between human and mouse have molecular correlates, including species differences in gene expression in subplate, although surprisingly we find minimal differences between the inner and outer subventricular zones even though the outer zone is expanded in humans. Both germinal and post-mitotic cortical layers exhibit fronto-temporal gradients, with particular enrichment in the frontal lobe. Finally, many neurodevelopmental disorder and human-evolution-related genes show patterned expression, potentially underlying unique features of human cortical formation. These data provide a rich, freely-accessible resource for understanding human brain development.

1,114 citations

Journal ArticleDOI
21 Aug 2019-Nature
TL;DR: 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.
Abstract: 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.

1,044 citations


Cited by
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Journal ArticleDOI
13 Jun 2019-Cell
TL;DR: A strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities.

7,892 citations

Journal ArticleDOI
TL;DR: A set of Cre reporter mice with strong, ubiquitous expression of fluorescent proteins of different spectra is generated and enables direct visualization of fine dendritic structures and axonal projections of the labeled neurons, which is useful in mapping neuronal circuitry, imaging and tracking specific cell populations in vivo.
Abstract: The Cre/lox system is widely used in mice to achieve cell-type-specific gene expression. However, a strong and universally responding system to express genes under Cre control is still lacking. We have generated a set of Cre reporter mice with strong, ubiquitous expression of fluorescent proteins of different spectra. The robust native fluorescence of these reporters enables direct visualization of fine dendritic structures and axonal projections of the labeled neurons, which is useful in mapping neuronal circuitry, imaging and tracking specific cell populations in vivo. Using these reporters and a high-throughput in situ hybridization platform, we are systematically profiling Cre-directed gene expression throughout the mouse brain in several Cre-driver lines, including new Cre lines targeting different cell types in the cortex. Our expression data are displayed in a public online database to help researchers assess the utility of various Cre-driver lines for cell-type-specific genetic manipulation.

5,365 citations

Journal ArticleDOI
TL;DR: These findings call into question the concept of a “glial” cell class as the gene profiles of astrocyte and oligodendrocytes are as dissimilar to each other as they are to neurons, for better understanding of neural development, function, and disease.
Abstract: Understanding the cell–cell interactions that control CNS development and function has long been limited by the lack of methods to cleanly separate neural cell types. Here we describe methods for the prospective isolation and purification of astrocytes, neurons, and oligodendrocytes from developing and mature mouse forebrain. We used FACS (fluorescent-activated cell sorting) to isolate astrocytes from transgenic mice that express enhanced green fluorescent protein (EGFP) under the control of an S100β promoter. Using Affymetrix GeneChip Arrays, we then created a transcriptome database of the expression levels of >20,000 genes by gene profiling these three main CNS neural cell types at various postnatal ages between postnatal day 1 (P1) and P30. This database provides a detailed global characterization and comparison of the genes expressed by acutely isolated astrocytes, neurons, and oligodendrocytes. We found that Aldh1L1 is a highly specific antigenic marker for astrocytes with a substantially broader pattern of astrocyte expression than the traditional astrocyte marker GFAP. Astrocytes were enriched in specific metabolic and lipid synthetic pathways, as well as the draper/Megf10 and Mertk/integrin αvβ5 phagocytic pathways suggesting that astrocytes are professional phagocytes. Our findings call into question the concept of a “glial” cell class as the gene profiles of astrocytes and oligodendrocytes are as dissimilar to each other as they are to neurons. This transcriptome database of acutely isolated purified astrocytes, neurons, and oligodendrocytes provides a resource to the neuroscience community by providing improved cell-type-specific markers and for better understanding of neural development, function, and disease.

2,838 citations

Journal ArticleDOI
06 Mar 2015-Science
TL;DR: Large-scale single-cell RNA sequencing is used to classify cells in the mouse somatosensory cortex and hippocampal CA1 region and found 47 molecularly distinct subclasses, comprising all known major cell types in the cortex.
Abstract: 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.

2,675 citations

Journal ArticleDOI
TL;DR: Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.
Abstract: The emerging diversity of single-cell RNA-seq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies, because biological and technical differences are interspersed. We present Harmony ( https://github.com/immunogenomics/harmony ), an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms while requiring fewer computational resources. Harmony enables the integration of ~106 cells on a personal computer. We apply Harmony to peripheral blood mononuclear cells from datasets with large experimental differences, five studies of pancreatic islet cells, mouse embryogenesis datasets and the integration of scRNA-seq with spatial transcriptomics data. Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.

2,459 citations