26 results on '"Emma Garren"'
Search Results
2. Single-cell and single-nucleus RNA-seq uncovers shared and distinct axes of variation in dorsal LGN neurons in mice, non-human primates, and humans
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Trygve E Bakken, Cindy TJ van Velthoven, Vilas Menon, Rebecca D Hodge, Zizhen Yao, Thuc Nghi Nguyen, Lucas T Graybuck, Gregory D Horwitz, Darren Bertagnolli, Jeff Goldy, Anna Marie Yanny, Emma Garren, Sheana Parry, Tamara Casper, Soraya I Shehata, Eliza R Barkan, Aaron Szafer, Boaz P Levi, Nick Dee, Kimberly A Smith, Susan M Sunkin, Amy Bernard, John Phillips, Michael J Hawrylycz, Christof Koch, Gabe J Murphy, Ed Lein, Hongkui Zeng, and Bosiljka Tasic
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maccaca nemestrina ,macaca fascicularis ,single-cell RNA-seq ,species comparison ,lateral geniculate nucleus ,Medicine ,Science ,Biology (General) ,QH301-705.5 - Abstract
Abundant evidence supports the presence of at least three distinct types of thalamocortical (TC) neurons in the primate dorsal lateral geniculate nucleus (dLGN) of the thalamus, the brain region that conveys visual information from the retina to the primary visual cortex (V1). Different types of TC neurons in mice, humans, and macaques have distinct morphologies, distinct connectivity patterns, and convey different aspects of visual information to the cortex. To investigate the molecular underpinnings of these cell types, and how these relate to differences in dLGN between human, macaque, and mice, we profiled gene expression in single nuclei and cells using RNA-sequencing. These efforts identified four distinct types of TC neurons in the primate dLGN: magnocellular (M) neurons, parvocellular (P) neurons, and two types of koniocellular (K) neurons. Despite extensively documented morphological and physiological differences between M and P neurons, we identified few genes with significant differential expression between transcriptomic cell types corresponding to these two neuronal populations. Likewise, the dominant feature of TC neurons of the adult mouse dLGN is high transcriptomic similarity, with an axis of heterogeneity that aligns with core vs. shell portions of mouse dLGN. Together, these data show that transcriptomic differences between principal cell types in the mature mammalian dLGN are subtle relative to the observed differences in morphology and cortical projection targets. Finally, alignment of transcriptome profiles across species highlights expanded diversity of GABAergic neurons in primate versus mouse dLGN and homologous types of TC neurons in primates that are distinct from TC neurons in mouse.
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- 2021
- Full Text
- View/download PDF
3. Author Correction: Cell segmentation-free inference of cell types from in situ transcriptomics data
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Jeongbin Park, Wonyl Choi, Sebastian Tiesmeyer, Brian Long, Lars E. Borm, Emma Garren, Thuc Nghi Nguyen, Bosiljka Tasic, Simone Codeluppi, Tobias Graf, Matthias Schlesner, Oliver Stegle, Roland Eils, and Naveed Ishaque
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Science - Published
- 2021
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4. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types.
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Trygve E Bakken, Rebecca D Hodge, Jeremy A Miller, Zizhen Yao, Thuc Nghi Nguyen, Brian Aevermann, Eliza Barkan, Darren Bertagnolli, Tamara Casper, Nick Dee, Emma Garren, Jeff Goldy, Lucas T Graybuck, Matthew Kroll, Roger S Lasken, Kanan Lathia, Sheana Parry, Christine Rimorin, Richard H Scheuermann, Nicholas J Schork, Soraya I Shehata, Michael Tieu, John W Phillips, Amy Bernard, Kimberly A Smith, Hongkui Zeng, Ed S Lein, and Bosiljka Tasic
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Medicine ,Science - Abstract
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.
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- 2018
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5. Exquisite light sensitivity of Drosophila melanogaster cryptochrome.
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Pooja Vinayak, Jamie Coupar, S Emile Hughes, Preeya Fozdar, Jack Kilby, Emma Garren, Taishi Yoshii, and Jay Hirsh
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Genetics ,QH426-470 - Abstract
Drosophila melanogaster shows exquisite light sensitivity for modulation of circadian functions in vivo, yet the activities of the Drosophila circadian photopigment cryptochrome (CRY) have only been observed at high light levels. We studied intensity/duration parameters for light pulse induced circadian phase shifts under dim light conditions in vivo. Flies show far greater light sensitivity than previously appreciated, and show a surprising sensitivity increase with pulse duration, implying a process of photic integration active up to at least 6 hours. The CRY target timeless (TIM) shows dim light dependent degradation in circadian pacemaker neurons that parallels phase shift amplitude, indicating that integration occurs at this step, with the strongest effect in a single identified pacemaker neuron. Our findings indicate that CRY compensates for limited light sensitivity in vivo by photon integration over extraordinarily long times, and point to select circadian pacemaker neurons as having important roles.
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- 2013
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6. Author Correction: Cell segmentation-free inference of cell types from in situ transcriptomics data
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Naveed Ishaque, Tobias Graf, Lars E. Borm, Sebastian Tiesmeyer, Matthias Schlesner, Bosiljka Tasic, Oliver Stegle, Roland Eils, Wonyl Choi, Simone Codeluppi, Emma Garren, Brian Long, Thuc Nghi Nguyen, and Jeongbin Park
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In situ ,Transcriptome ,Cell type ,Multidisciplinary ,Computer science ,Science ,General Physics and Astronomy ,Cell segmentation ,Inference ,General Chemistry ,Computational biology ,General Biochemistry, Genetics and Molecular Biology - Published
- 2021
7. Author response: Single-cell and single-nucleus RNA-seq uncovers shared and distinct axes of variation in dorsal LGN neurons in mice, non-human primates, and humans
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Amy Bernard, Aaron Szafer, Nick Dee, Michael Hawrylycz, Susan M. Sunkin, Ed S. Lein, Rebecca D. Hodge, Soraya I. Shehata, John W. Phillips, Gregory D. Horwitz, Emma Garren, Jeff Goldy, Christof Koch, Eliza Barkan, Zizhen Yao, Thuc Nghi Nguyen, Kimberly A. Smith, Sheana Parry, Lucas T. Graybuck, Anna Marie Yanny, Tamara Casper, Darren Bertagnolli, Hongkui Zeng, Bosiljka Tasic, Cindy T. J. van Velthoven, Boaz P. Levi, Trygve E. Bakken, Vilas Menon, and Gabe J. Murphy
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Dorsum ,Variation (linguistics) ,medicine.anatomical_structure ,Cell ,medicine ,RNA-Seq ,Biology ,Nucleus ,Cell biology - Published
- 2021
8. Classification of electrophysiological and morphological neuron types in the mouse visual cortex
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David Sandman, Brian Lee, Michael Hawrylycz, Sara Kebede, Tom Egdorf, David Reid, Rob Young, Nivretta Thatra, Stefan Mihalas, David Feng, John W. Phillips, Rebecca de Frates, DiJon Hill, Cliff Slaughterbeck, Samuel R Josephsen, Tamara Casper, Xiaoxiao Liu, Hanchuan Peng, Peter Chong, Colin Farrell, Zhi Zhou, Sheana Parry, Jed Perkins, Brian Long, Susan M. Sunkin, Matthew Kroll, Krissy Brouner, Melissa Gorham, Aaron Szafer, Wayne Wakeman, Hong Gu, Marissa Garwood, Daniel Park, Kristen Hadley, Michael S. Fisher, Lydia Potekhina, Ed Lein, Alice Mukora, Hongkui Zeng, Nick Dee, Aaron Oldre, Lindsay Ng, Thomas Braun, Grace Williams, Tracy Lemon, Julie A. Harris, Medea McGraw, Nadezhda Dotson, Philip R. Nicovich, Amanda Gary, Rusty Mann, Alex M. Henry, Caroline Habel, Samuel Dingman, Katherine E. Link, Nathalie Gaudreault, Gilberto J. Soler-Llavina, Thuc Nghi Nguyen, Nicole Blesie, Bosiljka Tasic, Lydia Ng, Christine Cuhaciyan, Tim Jarsky, Keith B. Godfrey, Costas A. Anastassiou, Kirsten Crichton, Josef Sulc, Martin Schroedter, Dan Castelli, Miranda Robertson, Amy Bernard, Lisa Kim, Songlin Ding, Alyse Doperalski, Nathan W. Gouwens, Herman Tung, Tsega Desta, Corinne Teeter, James Harrington, Jonathan T. Ting, Kris Bickley, Anton Arkhipov, Kiet Ngo, Changkyu Lee, Jim Berg, Agata Budzillo, Emma Garren, Tanya L. Daigle, Christof Koch, Rachel A. Dalley, Eliza Barkan, Staci A. Sorensen, Gabe J. Murphy, Shiella Caldejon, and Naz Taskin
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0301 basic medicine ,Genetically modified mouse ,Cell type ,Patch-Clamp Techniques ,Databases, Factual ,Action Potentials ,Datasets as Topic ,Mice, Transgenic ,Biology ,Article ,Neuron types ,Mice ,03 medical and health sciences ,chemistry.chemical_compound ,0302 clinical medicine ,Genes, Reporter ,Biocytin ,medicine ,Animals ,Cell shape ,Cell Shape ,Visual Cortex ,Neurons ,General Neuroscience ,Laboratory mouse ,Electrophysiology ,030104 developmental biology ,Visual cortex ,medicine.anatomical_structure ,chemistry ,Transcriptome ,Neuroscience ,030217 neurology & neurosurgery - Abstract
Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To systematically profile morpho-electric properties of mammalian neurons, we established a single-cell characterization pipeline using standardized patch-clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly accessible online database, the Allen Cell Types Database, to display these datasets. Intrinsic physiological properties were measured from 1,938 neurons from the adult laboratory mouse visual cortex, morphological properties were measured from 461 reconstructed neurons, and 452 neurons had both measurements available. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We established a taxonomy of morphologically and electrophysiologically defined cell types for this region of the cortex, with 17 electrophysiological types, 38 morphological types and 46 morpho-electric types. There was good correspondence with previously defined transcriptomic cell types and subclasses using the same transgenic mouse lines.
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- 2019
9. Single-cell and single-nucleus RNA-seq uncovers shared and distinct axes of variation in dorsal LGN neurons in mice, non-human primates, and humans
- Author
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Ed S. Lein, Vilas Menon, John W. Phillips, Sheana Parry, Jeff Goldy, Lucas T. Graybuck, Cindy T. J. van Velthoven, Michael Hawrylycz, Kimberly A. Smith, Susan M. Sunkin, Amy Bernard, Christof Koch, Zizhen Yao, Aaron Szafer, Nick Dee, Bosiljka Tasic, Anna Marie Yanny, Rebecca D. Hodge, Hongkui Zeng, Darren Bertagnolli, Tamara Casper, Boaz P. Levi, Trygve E. Bakken, Thuc Nghi Nguyen, Gabe J. Murphy, Eliza Barkan, Emma Garren, Soraya I. Shehata, and Gregory D. Horwitz
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Cell type ,Mouse ,QH301-705.5 ,species comparison ,Science ,macaca fascicularis ,Thalamus ,Lateral geniculate nucleus ,Macaque ,General Biochemistry, Genetics and Molecular Biology ,Mice ,lateral geniculate nucleus ,Parvocellular cell ,biology.animal ,medicine ,Animals ,Humans ,Visual Pathways ,RNA-Seq ,Biology (General) ,Visual Cortex ,Cell Nucleus ,Neurons ,single-cell RNA-seq ,General Immunology and Microbiology ,biology ,General Neuroscience ,Gene Expression Profiling ,Geniculate Bodies ,General Medicine ,Koniocellular cell ,Visual cortex ,medicine.anatomical_structure ,nervous system ,maccaca nemestrina ,Medicine ,Macaca ,Other ,Single-Cell Analysis ,Neuroscience ,Nucleus ,Research Article ,Human - Abstract
Abundant evidence supports the presence of at least three distinct types of thalamocortical (TC) neurons in the primate dorsal lateral geniculate nucleus (dLGN) of the thalamus, the brain region that conveys visual information from the retina to the primary visual cortex (V1). Different types of TC neurons in mice, humans, and macaques have distinct morphologies, distinct connectivity patterns, and convey different aspects of visual information to the cortex. To investigate the molecular underpinnings of these cell types, and how these relate to differences in dLGN between human, macaque, and mice, we profiled gene expression in single nuclei and cells using RNA-sequencing. These efforts identified four distinct types of TC neurons in the primate dLGN: magnocellular (M) neurons, parvocellular (P) neurons, and two types of koniocellular (K) neurons. Despite extensively documented morphological and physiological differences between M and P neurons, we identified few genes with significant differential expression between transcriptomic cell types corresponding to these two neuronal populations. Likewise, the dominant feature of TC neurons of the adult mouse dLGN is high transcriptomic similarity, with an axis of heterogeneity that aligns with core vs. shell portions of mouse dLGN. Together, these data show that transcriptomic differences between principal cell types in the mature mammalian dLGN are subtle relative to the observed differences in morphology and cortical projection targets. Finally, alignment of transcriptome profiles across species highlights expanded diversity of GABAergic neurons in primate versus mouse dLGN and homologous types of TC neurons in primates that are distinct from TC neurons in mouse.
- Published
- 2020
10. Regional, layer, and cell-class specific connectivity of the mouse default mode network
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Ali Williford, Nile Graddis, Karla E. Hirokawa, Wayne Wakeman, Stefan Mihalas, Philip R. Nicovich, Thuc Nghi Nguyen, Olivia Fong, Adam Liska, Phillip Bohn, Anh Ho, Lydia Ng, Emma Garren, Boaz P. Levi, Kimberly A. Smith, Nick Dee, Julie A. Harris, David Feng, Alex M. Henry, Cindy T. J. van Velthoven, Peter A. Groblewski, Alessandro Gozzi, Jennifer D. Whitesell, Hongkui Zeng, Bosiljka Tasic, Maitham Naeemi, Joseph E. Knox, Leonard Kuan, and Ludovico Coletta
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Resting state functional magnetic resonance imaging ,Cell type ,Then test ,medicine.anatomical_structure ,Retrosplenial cortex ,Cell ,medicine ,Neuron ,Biology ,Neuroscience ,Multiple disorders ,human activities ,Default mode network - Abstract
The evolutionarily conserved default mode network (DMN) is characterized by temporally correlated activity between brain regions during resting states. The DMN has emerged as a selectively vulnerable network in multiple disorders, so understanding its anatomical composition will provide fundamental insight into how its function is impacted by disease. Reproducible rodent analogs of the human DMN offer an opportunity to investigate the underlying brain regions and structural connectivity (SC) with high spatial and cell type resolution. Here, we performed systematic analyses using mouse resting state functional magnetic resonance imaging to identify the DMN and whole brain axonal tracing data, co-registered to the 3D Allen Mouse Common Coordinate Framework reference atlas. We identified the specific, predominantly cortical, brain regions comprising the mouse DMN and report preferential SC between these regions. Next, at the cell class level, we report that cortical layer (L) 2/3 neurons in DMN regions project almost exclusively to other DMN regions, whereas L5 neurons project to targets both in and out of the DMN. We then test the hypothesis that in- and out-DMN projection patterns originate from distinct L5 neuron sub-classes using an intersectional viral tracing strategy to label all the axons from neurons defined by a single target. In the ventral retrosplenial cortex, a core DMN region, we found two L5 projection types related to the DMN and mapped them to unique transcriptomically-defined cell types. Together, our results provide a multi-scale description of the anatomical correlates of the mouse DMN.
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- 2020
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11. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation
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Susan M. Sunkin, Qingzhong Ren, Michael Tieu, Fahimeh Baftizadeh, Kimberly A. Smith, Boaz P. Levi, Kanan Lathia, Olivia Fong, James Gray, Lucas T. Graybuck, Jeff Goldy, Bosiljka Tasic, Christine Rimorin, Thuc Nghi Nguyen, Kirsten Crichton, Josef Sulc, Songlin Ding, Darren Bertagnolli, Zizhen Yao, Hongkui Zeng, Delissa McMillen, Cindy T. J. van Velthoven, Katelyn Ward, Alexandra Glandon, Thanh Pham, Herman Tung, Amy Torkelson, Nick Dee, Nadiya V. Shapovalova, Stephanie Mok, Emma Garren, Matthew Kroll, Tamara Casper, Adriana E. Sedeno-Cortes, and Daniel Hirschstein
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Transcriptome ,Glutamatergic ,Cell type ,Cellular composition ,Spatial distribution pattern ,Biology ,Hippocampal formation ,GABAergic neuron ,Neuroscience ,Neuron types - Abstract
SUMMARYThe isocortex and hippocampal formation are two major structures in the mammalian brain that play critical roles in perception, cognition, emotion and learning. Both structures contain multiple regions, for many of which the cellular composition is still poorly understood. In this study, we used two complementary single-cell RNA-sequencing approaches, SMART-Seq and 10x, to profile ∼1.2 million cells covering all regions in the adult mouse isocortex and hippocampal formation, and derived a cell type taxonomy comprising 379 transcriptomic types. The completeness of coverage enabled us to define gene expression variations across the entire spatial landscape without significant gaps. We found that cell types are organized in a hierarchical manner and exhibit varying degrees of discrete or continuous relatedness with each other. Such molecular relationships correlate strongly with the spatial distribution patterns of the cell types, which can be region-specific, or shared across multiple regions, or part of one or more gradients along with other cell types. Glutamatergic neuron types have much greater diversity than GABAergic neuron types, both molecularly and spatially, and they define regional identities as well as inter-region relationships. For example, we found that glutamatergic cell types between the isocortex and hippocampal formation are highly distinct from each other yet possess shared molecular signatures and corresponding layer specificities, indicating their homologous relationships. Overall, our study establishes a molecular architecture of the mammalian isocortex and hippocampal formation for the first time, and begins to shed light on its underlying relationship with the development, evolution, connectivity and function of these two brain structures.
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- 2020
12. Single molecule FISH v1
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Thuc Nguyen and Emma Garren
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Chemistry ,Zoology ,%22">Fish - Abstract
Updated This protocol describes multiround hybrization of directly-conjugated FISH probes for single molecule RNA detection. Thin tissue sections (10-μm) are placed onto silanized coverslips (24x50) that fit onto an ASI imaging chamber. A SecureSeal chamber is placed around the sections, which act as a reaction chamber and imaging chamber.
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- 2020
13. Regional, Layer, and Cell-Type-Specific Connectivity of the Mouse Default Mode Network
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Phillip Bohn, Lydia Ng, Maitham Naeemi, Thuc Nghi Nguyen, Karla E. Hirokawa, Stefan Mihalas, Ali Williford, Kimberly A. Smith, Leonard Kuan, Joseph E. Knox, Nick Dee, Hongkui Zeng, Julie A. Harris, Ludovico Coletta, Alex M. Henry, Peter A. Groblewski, Olivia Fong, Adam Liska, Nile Graddis, Anh Ho, David Feng, Cindy T. J. van Velthoven, Wayne Wakeman, Jennifer D. Whitesell, Bosiljka Tasic, Boaz P. Levi, Alessandro Gozzi, Philip R. Nicovich, and Emma Garren
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0301 basic medicine ,retrosplenial cortex ,Single cell transcriptomics ,Cell type specific ,Population ,Biology ,single cell transcriptomics ,Axonal tracing ,Article ,projection neuron types ,Mice ,03 medical and health sciences ,0302 clinical medicine ,Retrosplenial cortex ,Connectome ,medicine ,Animals ,DMN ,Layer (object-oriented design) ,education ,Default mode network ,axonal projections ,Neurons ,education.field_of_study ,medicine.diagnostic_test ,General Neuroscience ,Brain ,Default Mode Network ,Magnetic Resonance Imaging ,030104 developmental biology ,connectivity ,cortical connectome ,Nerve Net ,Functional magnetic resonance imaging ,viral tracer ,human activities ,Neuroscience ,030217 neurology & neurosurgery - Abstract
Summary The evolutionarily conserved default mode network (DMN) is a distributed set of brain regions coactivated during resting states that is vulnerable to brain disorders. How disease affects the DMN is unknown, but detailed anatomical descriptions could provide clues. Mice offer an opportunity to investigate structural connectivity of the DMN across spatial scales with cell-type resolution. We co-registered maps from functional magnetic resonance imaging and axonal tracing experiments into the 3D Allen mouse brain reference atlas. We find that the mouse DMN consists of preferentially interconnected cortical regions. As a population, DMN layer 2/3 (L2/3) neurons project almost exclusively to other DMN regions, whereas L5 neurons project in and out of the DMN. In the retrosplenial cortex, a core DMN region, we identify two L5 projection types differentiated by in- or out-DMN targets, laminar position, and gene expression. These results provide a multi-scale description of the anatomical correlates of the mouse DMN., Graphical Abstract, Highlights • Mouse resting-state default mode network anatomy described at high resolution in 3D • Systematic axon tracing shows cortical DMN regions are preferentially interconnected • Layer 2/3 DMN neurons project mostly in the DMN; layer 5 neurons project in and out • Retrosplenial cortex contains distinct types of in- and out-DMN projection neurons, The default mode network is vulnerable to brain disorders, but details of its anatomy and connectivity are coarse. Whitesell et al. use modern neuroanatomical tools in the mouse, including whole-brain imaging and viral tracing, to provide high-resolution anatomical descriptions and identify cell type correlates of this conserved brain network.
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- 2020
- Full Text
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14. A Taxonomy of Transcriptomic Cell Types Across the Isocortex and Hippocampal Formation
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James Gray, Adriana E. Sedeno-Cortes, Michael Tieu, Songlin Ding, Michael Hawrylycz, Herman Tung, Olivia Fong, Matthew Kroll, Stephanie Mok, Zizhen Yao, Darren Bertagnolli, Fahimeh Baftizadeh, Thanh Pham, Delissa McMillen, Thuc Nghi Nguyen, Hongkui Zeng, Tamara Casper, Katelyn Ward, Emma Garren, Kimberly A. Smith, Qingzhong Ren, Christine Rimorin, Jeff Goldy, Alexandra Glandon, Kanan Lathia, Lucas T. Graybuck, Amy Torkelson, Nick Dee, Nadiya V. Shapovalova, Susan M. Sunkin, Daniel Hirschstein, Bosiljka Tasic, Kirsten Crichton, Josef Sulc, Boaz P. Levi, and Cindy T. J. van Velthoven
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Transcriptome ,Cell type ,Glutamatergic ,Neocortex ,medicine.anatomical_structure ,Taxonomy (general) ,medicine ,Hippocampus ,Hippocampal formation ,Biology ,Neuroscience ,Function (biology) - Abstract
The isocortex and hippocampal formation are two major structures in the mammalian brain that play critical roles in perception, cognition, emotion and learning. Using single-cell RNA-sequencing approaches, we profiled ~1.2 million cells covering all regions in the adult mouse isocortex and hippocampal formation. The cell types are organized hierarchically and exhibit varying degrees of discrete or continuous variations. Such molecular relationships correlate strongly with the spatial distribution patterns of the cell types, which can be region-specific, shared across multiple regions, or part of one or more gradients. Glutamatergic neuron types display much greater diversity than GABAergic neuron types, both molecularly and spatially, and define regional identities as well as inter-region relationships. Our study establishes a molecular architecture of the mammalian isocortex and hippocampal formation for the first time, and begins to shed light on its underlying relationship with the development, evolution, connectivity and function of these two brain structures.
- Published
- 2020
15. Cell segmentation-free inference of cell types from in situ transcriptomics data
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Oliver Stegle, Lars E. Borm, Sebastian Tiesmeyer, Wonyl Choi, Jeongbin Park, Emma Garren, Matthias Schlesner, Naveed Ishaque, Thuc Nghi Nguyen, Brian Long, Tobias Graf, Roland Eils, Simone Codeluppi, and Bosiljka Tasic
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In situ ,Cell type ,medicine.diagnostic_test ,Computer science ,Cell ,Cell segmentation ,Inference ,Computational biology ,Brain tissue ,Transcriptome ,medicine.anatomical_structure ,Visual cortex ,medicine ,Fluorescence in situ hybridization - Abstract
Summary Multiplexed fluorescence in situ hybridization techniques have enabled cell class or type identification by mRNA quantification in situ . However, inaccurate cell segmentation can result in incomplete cell-type and tissue characterization. Here, we present a robust segmentation-free computational framework, applicable to a variety of in situ transcriptomics platforms, called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM). SSAM assumes that spatial distribution of mRNAs relates to organization of higher complexity structures (e.g. cells or tissue layers) and performs de novo cell-type and tissue domain identification. Optionally, SSAM can also integrate prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. SSAM outperforms segmentation-based results, demonstrating that segmentation of cells is not required for inferring cell-type signatures, cell-type organization or tissue domains.
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- 2019
16. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation
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Zizhen Yao, Olivia Fong, Thanh Pham, Katelyn Ward, James Gray, Susan M. Sunkin, Stephanie Mok, Hongkui Zeng, Songlin Ding, Boaz P. Levi, Qingzhong Ren, Daniel Hirschstein, Emma Garren, Nick Dee, Megan Chiang, Fahimeh Baftizadeh, Christine Rimorin, Kanan Lathia, Herman Tung, Cindy T. J. van Velthoven, Darren Bertagnolli, Nadiya V. Shapovalova, Lucas T. Graybuck, Jeff Goldy, Michael Tieu, Delissa McMillen, Kimberly A. Smith, Michael Hawrylycz, Bosiljka Tasic, Amy Torkelson, Kirsten Crichton, Josef Sulc, Alexandra Glandon, Nathan W. Gouwens, Thuc Nghi Nguyen, Tamara Casper, Matthew Kroll, Adriana E. Sedeno-Cortes, and Changkyu Lee
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Cell type ,Interneuron ,Glutamic Acid ,Hippocampus ,Mice, Transgenic ,Neocortex ,Hippocampal formation ,Biology ,Article ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,Glutamatergic ,0302 clinical medicine ,Cortex (anatomy) ,medicine ,Animals ,GABAergic Neurons ,030304 developmental biology ,0303 health sciences ,Subiculum ,Mice, Inbred C57BL ,medicine.anatomical_structure ,GABAergic ,Transcriptome ,Neuroscience ,030217 neurology & neurosurgery - Abstract
The isocortex and hippocampal formation (HPF) in the mammalian brain play critical roles in perception, cognition, emotion, and learning. We profiled ∼1.3 million cells covering the entire adult mouse isocortex and HPF and derived a transcriptomic cell-type taxonomy revealing a comprehensive repertoire of glutamatergic and GABAergic neuron types. Contrary to the traditional view of HPF as having a simpler cellular organization, we discover a complete set of glutamatergic types in HPF homologous to all major subclasses found in the six-layered isocortex, suggesting that HPF and the isocortex share a common circuit organization. We also identify large-scale continuous and graded variations of cell types along isocortical depth, across the isocortical sheet, and in multiple dimensions in hippocampus and subiculum. Overall, our study establishes a molecular architecture of the mammalian isocortex and hippocampal formation and begins to shed light on its underlying relationship with the development, evolution, connectivity, and function of these two brain structures.
- Published
- 2021
17. Conserved cell types with divergent features in human versus mouse cortex
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Elliot R. Thomsen, Ahmed Mahfouz, Saroja Somasundaram, Aaron Oldre, Bosiljka Tasic, Songlin Ding, Richard H. Scheuermann, Daniel Hirschstein, Thomas Höllt, Christine Rimorin, Thuc Nghi Nguyen, Jennie L. Close, John W. Phillips, Lydia Ng, Jeff Goldy, Darren Bertagnolli, Amy Bernard, Zizhen Yao, Boaz P. Levi, Trygve E. Bakken, Soraya I. Shehata, Susan M. Sunkin, Osnat Penn, Michael Tieu, Allison Beller, Boudewijn P. F. Lelieveldt, Jeffrey G. Ojemann, Shannon Reynolds, Michael Hawrylycz, Jeroen Eggermont, Medea McGraw, Ryder P. Gwinn, Sheana Parry, Kimberly A. Smith, Brian Long, Olivia Fong, Zoe Maltzer, Rafael Yuste, David Feng, Julie Nyhus, Rebecca D. Hodge, Ed Lein, Jeremy A. Miller, Brian D. Aevermann, Gerald Quon, Emma Garren, Christof Koch, Aaron Szafer, Nick Dee, Nadiya V. Shapovalova, Rachel A. Dalley, Tamara Casper, Mohamed Keshk, Nelson Johansen, Krissy Brouner, Andrew L. Ko, Allan R. Jones, Eliza Barkan, Hongkui Zeng, Richard G. Ellenbogen, C. Dirk Keene, Kanan Lathia, Lucas T. Graybuck, and Charles Cobbs
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0301 basic medicine ,Adult ,Male ,Cell type ,Adolescent ,General Science & Technology ,Middle temporal gyrus ,1.1 Normal biological development and functioning ,Biology ,03 medical and health sciences ,Mice ,Young Adult ,0302 clinical medicine ,Single-cell analysis ,Species Specificity ,Underpinning research ,Cortex (anatomy) ,medicine ,Genetics ,Animals ,Humans ,2.1 Biological and endogenous factors ,RNA-Seq ,Aetiology ,Aged ,Cerebral Cortex ,Neurons ,Principal Component Analysis ,Multidisciplinary ,Cellular architecture ,Neurosciences ,Neural Inhibition ,Human brain ,Middle Aged ,Biological Evolution ,030104 developmental biology ,medicine.anatomical_structure ,Cerebral cortex ,Astrocytes ,Neurological ,Excitatory postsynaptic potential ,Female ,Single-Cell Analysis ,Transcriptome ,Neuroscience ,030217 neurology & neurosurgery - 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.
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- 2019
18. Multimodal cell type correspondence by intersectional mFISH in intact tissues
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Brian Long, Thuc Nghi Nguyen, Bosiljka Tasic, Boaz P. Levi, Ed S. Lein, Jeremy A. Miller, Jennie L. Close, Christopher A. Baker, Hongkui Zeng, Philip R. Nicovich, Alice Bosma-Moody, M. J. Taormina, Elliot R. Thomsen, Melissa Gorham, Emma Garren, Gabe J. Murphy, and Travis A. Hage
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Cell type ,medicine.anatomical_structure ,Mouse cortex ,Two-photon excitation microscopy ,medicine.diagnostic_test ,Cortex (anatomy) ,medicine ,Brain tissue ,Optogenetics ,Biology ,Correspondence problem ,Neuroscience ,Fluorescence in situ hybridization - Abstract
Defining a complete set of cell types within the cortex requires reconciling disparate results achieved through diverging methodologies. To address this correspondence problem, multiple methodologies must be applied to the same cells across multiple single-cell experiments. Here we present a new approach applying spatial transcriptomics using multiplexed fluorescencein situhybridization, (mFISH) to brain tissue previously interrogated through two photon optogenetic mapping of synaptic connectivity. This approach can resolve the anatomical, transcriptomic, connectomic, electrophysiological, and morphological characteristics of single cells within the mouse cortex.
- Published
- 2019
19. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types
- Author
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Jeremy A. Miller, Soraya I. Shehata, Thuc Nghi Nguyen, Matthew Kroll, Nick Dee, Sheana Parry, Rebecca D. Hodge, Kimberly A. Smith, Brian D. Aevermann, Ed Lein, Christine Rimorin, Darren Bertagnolli, Amy Bernard, Michael Tieu, Bosiljka Tasic, Eliza Barkan, Jeff Goldy, Emma Garren, Tamara Casper, Richard H. Scheuermann, Hongkui Zeng, Nicholas J. Schork, Trygve E. Bakken, Roger S. Lasken, Kanan Lathia, John W. Phillips, Lucas T. Graybuck, and Zizhen Yao
- Subjects
0301 basic medicine ,Cell ,genetic processes ,Gene Expression ,Biochemistry ,Transcriptome ,Database and Informatics Methods ,Mice ,Single-cell analysis ,Animal Cells ,Gene expression ,Visual Cortex ,Neurons ,Multidisciplinary ,Mammalian Genomics ,Messenger RNA ,Genomics ,Cell biology ,Nucleic acids ,medicine.anatomical_structure ,Medicine ,Cellular Types ,Single-Cell Analysis ,Transcriptome Analysis ,Sequence Analysis ,Research Article ,Cell type ,Sequence analysis ,Bioinformatics ,Science ,Biology ,Research and Analysis Methods ,Genome Complexity ,03 medical and health sciences ,medicine ,Genetics ,Animals ,natural sciences ,Cell Lineage ,Molecular Biology Techniques ,Molecular Biology ,Cell Nucleus ,Sequence Analysis, RNA ,Gene Expression Profiling ,Intron ,Biology and Life Sciences ,Computational Biology ,Marker Genes ,Cell Biology ,Genome Analysis ,Introns ,030104 developmental biology ,Animal Genomics ,Cellular Neuroscience ,RNA ,Nucleus ,Sequence Alignment ,Neuroscience - Abstract
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.
- Published
- 2018
20. Conserved cell types with divergent features between human and mouse cortex
- Author
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Jeff Goldy, Sheana Parry, Jeremy A. Miller, Brian Long, Susan M. Sunkin, Saroja Somasundaram, Rebecca D. Hodge, Hongkui Zeng, Aaron Oldre, Kimberly A. Smith, Zoe Maltzer, Brian D. Aevermann, Mohamed Keshk, Jeroen Eggermont, Ed Lein, Daniel Hirschstein, Darren Bertagnolli, Jennie L. Close, Osnat Penn, John W. Phillips, Rachel A. Dalley, Allan R. Jones, Ahmed Mahfouz, Olivia Fong, Allison Beller, Soraya I. Shehata, Thuc Nghi Nguyen, Jeffrey G. Ojemann, Shannon Reynolds, Eliza Barkan, Michael Tieu, Christof Koch, Michael Hawrylycz, Songlin Ding, Richard H. Scheuermann, Ryder P. Gwinn, Elliot R. Thomsen, Medea McGraw, Emma Garren, Christine Rimorin, Lydia Ng, Boudewijn P. F. Lelieveldt, C. Dirk Keene, Amy Bernard, Richard G. Ellenbogen, Rafael Yuste, David Feng, Boaz P. Levi, Trygve E. Bakken, Tamara Casper, Bosiljka Tasic, Aaron Szafer, Nick Dee, Nadiya V. Shapovalova, Kanan Lathia, Lucas T. Graybuck, Charles Cobbs, Julie Nyhus, Thomas Höllt, Zizhen Yao, Krissy Brouner, and Andrew L. Ko
- Subjects
0303 health sciences ,Cell type ,Neocortex ,Cellular architecture ,Middle temporal gyrus ,Cell ,Human brain ,Biology ,03 medical and health sciences ,0302 clinical medicine ,medicine.anatomical_structure ,Cerebral cortex ,medicine ,Neuroscience ,Nucleus ,030217 neurology & neurosurgery ,030304 developmental biology - Abstract
Elucidating the cellular architecture of the human neocortex is central to understanding our cognitive abilities and susceptibility to disease. Here we applied single nucleus RNA-sequencing to perform a comprehensive analysis of cell types in the middle temporal gyrus of human cerebral cortex. We identify a highly diverse set of excitatory and inhibitory neuronal types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to a similar mouse cortex single cell RNA-sequencing dataset revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of human cell type properties. Despite this general conservation, we also find extensive differences between homologous human and mouse cell types, including dramatic alterations in proportions, laminar distributions, gene expression, and morphology. These species-specific features emphasize the importance of directly studying human brain.
- Published
- 2018
21. Classification of electrophysiological and morphological types in mouse visual cortex
- Author
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Tom Egdorf, Rebecca de Frates, Emma Garren, Sara Kebede, Peter Chong, John W. Phillips, Nivretta Thatra, Samuel R Josephsen, Philip R. Nicovich, Tim Jarsky, Xiaoxiao Liu, Susan M. Sunkin, Brian Lee, Keith B. Godfrey, Matthew Kroll, Nicole Blesie, Bosiljka Tasic, Amy Bernard, Lisa Kim, Costas A. Anastassiou, Kristen Hadley, Staci A. Sorensen, Thuc Nghi Nguyen, Martin Schroedter, Corinne Teeter, Kirsten Crichton, Josef Sulc, Rachel A. Dalley, David Feng, Tracy Lemon, Michael Hawrylycz, Miranda Robertson, Christine Cuhaciyan, Eliza Barkan, Shiella Caldejon, Tsega Desta, Kris Bickley, Dan Castelli, Wayne Wakeman, Herman Tung, Hongkui Zeng, Grace Williams, Nadezhda Dotson, Rusty Mann, Tamara Casper, Anton Arkhipov, Daniel Park, Sheana Parry, Jed Perkins, Alyse Doperalski, Brian Long, Thomas Braun, Christof Koch, Gabe J. Murphy, Aaron Oldre, Changkyu Lee, Colin Farrell, Medea McGraw, Amanda Gary, Kiet Ngo, Melissa Gorham, Naz Taskin, Jim Berg, Samuel Dingman, Tanya L. Daigle, Agata Budzillo, Marissa Garwood, Gilberto J. Soler-Llavina, Aaron Szafer, Nick Dee, Jonathan T. Ting, Lydia Ng, Alex M. Henry, James Harrington, Julie A. Harris, Michael S. Fisher, Lindsay Ng, Caroline Habel, Nathalie Gaudreault, Krissy Brouner, David Reid, Lydia Potekhina, Rob Young, DiJon Hill, Cliff Slaughterbeck, Ed Lein, Alice Mukora, David Sandman, Stefan Mihalas, Nathan W. Gouwens, Zhi Zhou, Hanchuan Peng, and Hong Gu
- Subjects
Genetically modified mouse ,Cell type ,Cell ,Laboratory mouse ,Biology ,Electrophysiology ,chemistry.chemical_compound ,Visual cortex ,medicine.anatomical_structure ,chemistry ,Biocytin ,medicine ,Patch clamp ,Neuroscience - Abstract
Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To profile morpho-electric properties of mammalian neurons systematically, we established a single cell characterization pipeline using standardized patch clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly-accessible online database, the Allen Cell Types Database, to display these data sets. Intrinsic physiological and morphological properties were measured from over 1,800 neurons from the adult laboratory mouse visual cortex. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We establish a taxonomy of morphologically- and electrophysiologically-defined cell types for this region of cortex with 17 e-types and 35 m-types, as well as an initial correspondence with previously-defined transcriptomic cell types using the same transgenic mouse lines.
- Published
- 2018
- Full Text
- View/download PDF
22. Equivalent high-resolution identification of neuronal cell types with single-nucleus and single-cell RNA-sequencing
- Author
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Trygve E. Bakken, Rebecca D. Hodge, Jeremy M. Miller, Zizhen Yao, Thuc N. Nguyen, Brian Aevermann, Eliza Barkan, Darren Bertagnolli, Tamara Casper, Nick Dee, Emma Garren, Jeff Goldy, Lucas T. Gray, Matthew Kroll, Roger S. Lasken, Kanan Lathia, Sheana Parry, Christine Rimorin, Richard H. Scheuermann, Nicholas J. Schork, Soraya I. Shehata, Michael Tieu, John W. Phillips, Amy Bernard, Kimberly A. Smith, Hongkui Zeng, Ed S. Lein, and Bosiljka Tasic
- Subjects
Messenger RNA ,Cell type ,genetic processes ,Cell ,RNA ,High resolution ,Biology ,Cell biology ,medicine.anatomical_structure ,Visual cortex ,medicine ,natural sciences ,Gene ,Nucleus - Abstract
Transcriptional profiling of complex tissues by RNA-sequencing of single nuclei presents some advantages over whole cell analysis. It enables unbiased cellular coverage, lack of cell isolation-based transcriptional effects, and application to archived frozen specimens. Using a well-matched pair of single-nucleus RNA-seq (snRNA-seq) and single-cell RNA-seq (scRNA-seq) SMART-Seq v4 datasets from mouse visual cortex, we demonstrate that similarly high-resolution clustering of closely related neuronal types can be achieved with both methods if intronic sequences are included in nuclear RNA-seq analysis. More transcripts are detected in individual whole cells (∼11,000 genes) than nuclei (∼7,000 genes), but the majority of genes have similar detection across cells and nuclei. 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.
- Published
- 2017
23. Shared and distinct transcriptomic cell types across neocortical areas
- Author
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Daniel Hirschstein, Michael N. Economo, Allan R. Jones, Christine Rimorin, Eliza Barkan, Linda Madisen, Seana Parry, Susan M. Sunkin, Rachael Larsen, Hongkui Zeng, Tae Kyung Kim, Emma Garren, Kimberly A. Smith, Jeremy A. Miller, Osnat Penn, Olivia Fong, Sarada Viswanathan, Julie A. Harris, Bosiljka Tasic, Thuc Nghi Nguyen, Vilas Menon, Karel Svoboda, Matthew Kroll, Ed S. Lein, Peter A. Groblewski, Karla E. Hirokawa, Ali Cetin, Julie Pendergraft, Ian R. Wickersham, Tanya L. Daigle, Darren Bertagnolli, Jeff Goldy, Zizhen Yao, John W. Phillips, Michael Tieu, Loren L. Looger, Michael Hawrylycz, Aaron Szafer, Boaz P. Levi, Trygve E. Bakken, Nick Dee, Nadiya V. Shapovalova, Amy Bernard, Tamara Casper, Christof Koch, Kanan Lathia, and Lucas T. Graybuck
- Subjects
Transcriptome ,Cell type ,medicine.anatomical_structure ,Neocortex ,Visual cortex ,Cell ,medicine ,Excitatory postsynaptic potential ,Biology ,Inhibitory postsynaptic potential ,Neuroscience ,Motor cortex - Abstract
Neocortex contains a multitude of cell types segregated into layers and functionally distinct regions. To investigate the diversity of cell types across the mouse neocortex, we analyzed 12,714 cells from the primary visual cortex (VISp), and 9,035 cells from the anterior lateral motor cortex (ALM) by deep single-cell RNA-sequencing (scRNA-seq), identifying 116 transcriptomic cell types. These two regions represent distant poles of the neocortex and perform distinct functions. We define 50 inhibitory transcriptomic cell types, all of which are shared across both cortical regions. In contrast, 49 of 52 excitatory transcriptomic types were found in either VISp or ALM, with only three present in both. By combining single cell RNA-seq and retrograde labeling, we demonstrate correspondence between excitatory transcriptomic types and their region-specific long-range target specificity. This study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct regions of the mouse cortex.
- Published
- 2017
- Full Text
- View/download PDF
24. Shared and distinct transcriptomic cell types across neocortical areas
- Author
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Zizhen Yao, Christof Koch, Daniel Hirschstein, Aaron Szafer, Nick Dee, Sheana Parry, Michael Tieu, Michael Hawrylycz, Jeff Goldy, Susan M. Sunkin, Nadiya V. Shapovalova, Amy Bernard, Kanan Lathia, Lucas T. Graybuck, Boaz P. Levi, Trygve E. Bakken, Matthew Kroll, Sarada Viswanathan, Kimberly A. Smith, Thuc Nghi Nguyen, Olivia Fong, Tae Kyung Kim, Tanya L. Daigle, Jeremy A. Miller, Christine Rimorin, Linda Madisen, Karla E. Hirokawa, Tamara Casper, Julie A. Harris, Ali Cetin, Heather A. Sullivan, Bosiljka Tasic, Karel Svoboda, Julie Pendergraft, Osnat Penn, John W. Phillips, Ian R. Wickersham, Ed S. Lein, Loren L. Looger, Peter A. Groblewski, Hongkui Zeng, Allan R. Jones, Rachael Larsen, Emma Garren, Darren Bertagnolli, Michael N. Economo, Eliza Barkan, and Vilas Menon
- Subjects
0301 basic medicine ,Male ,Cell type ,Glutamic Acid ,Neocortex ,Biology ,Article ,Transcriptome ,03 medical and health sciences ,Glutamatergic ,Mice ,Single-cell analysis ,medicine ,Animals ,GABAergic Neurons ,Visual Cortex ,Multidisciplinary ,Sequence Analysis, RNA ,Gene Expression Profiling ,Motor Cortex ,Gene expression profiling ,030104 developmental biology ,medicine.anatomical_structure ,Visual cortex ,nervous system ,Organ Specificity ,Female ,Single-Cell Analysis ,Neuroscience ,Biomarkers ,Motor cortex - 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.
- Published
- 2017
25. A robot for high yield electrophysiology and morphology of single neurons in vivo
- Author
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Lu Li, Christof Koch, William A. Stoy, Benjamin Ouellette, Craig R. Forest, Emma Garren, Tanya L. Daigle, and Hongkui Zeng
- Subjects
Male ,0301 basic medicine ,Science ,General Physics and Astronomy ,Hippocampal formation ,Biology ,Article ,General Biochemistry, Genetics and Molecular Biology ,Mice ,03 medical and health sciences ,In vivo ,Animals ,Neurons ,Multidisciplinary ,Electroporation ,Brain ,Equipment Design ,Robotics ,General Chemistry ,Anatomy ,Electrophysiological Phenomena ,Mice, Inbred C57BL ,Electrophysiology ,030104 developmental biology ,nervous system ,Models, Animal ,Robot ,Female ,Single-Cell Analysis ,Microelectrodes ,Neuroscience ,Software - Abstract
Single-cell characterization and perturbation of neurons provides knowledge critical to addressing fundamental neuroscience questions including the structure–function relationship and neuronal cell-type classification. Here we report a robot for efficiently performing in vivo single-cell experiments in deep brain tissues optically difficult to access. This robot automates blind (non-visually guided) single-cell electroporation (SCE) and extracellular electrophysiology, and can be used to characterize neuronal morphological and physiological properties of, and/or manipulate genetic/chemical contents via delivering extraneous materials (for example, genes) into single neurons in vivo. Tested in the mouse brain, our robot successfully reveals the full morphology of single-infragranular neurons recorded in multiple neocortical regions, as well as deep brain structures such as hippocampal CA3, with high efficiency. Our robot thus can greatly facilitate the study of in vivo full morphology and electrophysiology of single neurons in the brain., Single-cell characterization and perturbation of neurons is critical for revealing the structure-function relationship of brain cells. Here the authors develop a robot that performs single-cell electroporation and extracellular electrophysiology and can be used for performing in vivo single-cell experiments in deep brain tissues optically difficult to access.
- Published
- 2017
26. A Suite of Transgenic Driver and Reporter Mouse Lines with Enhanced Brain-Cell-Type Targeting and Functionality
- Author
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Travis A. Hage, Christopher A. Baker, Linda Madisen, Alice Bosma-Moody, Rylan S. Larsen, Matthew T. Valley, Jonathan T. Ting, Karla E. Hirokawa, Kimberly A. Smith, Olivia Fong, Jérôme Lecoq, Garreck H. Lenz, Julie Pendergraft, Susan M. Sunkin, Julie A. Harris, La'Akea Siverts, Maya Mills, Zizhen Yao, Michael Z. Lin, Thuc Nghi Nguyen, Mariya Chavarha, Philip R. Nicovich, Nuno Maçarico da Costa, Lawrence Huang, Lu Li, Miranda Walker, Marc Takeno, Gabe J. Murphy, Lucas T. Graybuck, Jack Waters, Emma Garren, Edward S. Boyden, Medea McGraw, Rachael Larsen, James Harrington, Douglas R. Ollerenshaw, Ulf Knoblich, Tanya L. Daigle, Hongkui Zeng, Bosiljka Tasic, and Hong Gu
- Subjects
0301 basic medicine ,Genetically modified mouse ,Cell type ,RNA, Untranslated ,Light ,Transgene ,Cell ,Mice, Transgenic ,Computational biology ,Optogenetics ,Biology ,Brain Cell ,General Biochemistry, Genetics and Molecular Biology ,Cell Line ,Gene Knockout Techniques ,Mice ,03 medical and health sciences ,Genes, Reporter ,health services administration ,medicine ,Animals ,natural sciences ,Transgenes ,In Situ Hybridization, Fluorescence ,Neurons ,Brain ,Transgenesis ,030104 developmental biology ,medicine.anatomical_structure ,Microscopy, Fluorescence ,Calcium ,human activities ,Function (biology) - Abstract
Modern genetic approaches are powerful in providing access to diverse cell types in the brain and facilitating the study of their function. Here, we report a large set of driver and reporter transgenic mouse lines, including 23 new driver lines targeting a variety of cortical and subcortical cell populations and 26 new reporter lines expressing an array of molecular tools. In particular, we describe the TIGRE2.0 transgenic platform and introduce Cre-dependent reporter lines that enable optical physiology, optogenetics, and sparse labeling of genetically defined cell populations. TIGRE2.0 reporters broke the barrier in transgene expression level of single-copy targeted-insertion transgenesis in a wide range of neuronal types, along with additional advantage of a simplified breeding strategy compared to our first-generation TIGRE lines. These novel transgenic lines greatly expand the repertoire of high-precision genetic tools available to effectively identify, monitor, and manipulate distinct cell types in the mouse brain.
- Published
- 2018
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