17 results on '"Alex M. Henry"'
Search Results
2. Human neocortical expansion involves glutamatergic neuron diversification
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Tim S. Heistek, Thomas Braun, Natalia A. Goriounova, Michael Tieu, Lindsay Ng, Michael Hawrylycz, Kris Bickley, Anton Arkhipov, Colin Farrell, Trangthanh Pham, Alexandra Glandon, Daniel Park, Gábor Molnár, Herman Tung, Allan R. Jones, Lisa Keene, Gáspár Oláh, Thomas Chartrand, Amy Torkelson, Jae Geun Yoon, Rachel A. Dalley, Aaron Szafer, Nick Dee, Brian E. Kalmbach, Eliza Barkan, Allison Beller, Krissy Brouner, Andrew L. Ko, Alex M. Henry, Viktor Szemenyei, Julie Nyhus, Staci A. Sorensen, Samuel Dingman Lee, Norbert Mihut, Amy Bernard, Lisa Kim, Anatoly Buchin, Melissa Gorham, Lucas T. Graybuck, Lydia Potekhina, Katelyn Ward, Caitlin S. Latimer, Aaron Oldre, Gabe J. Murphy, Boaz P. Levi, Trygve E. Bakken, René Wilbers, Jonathan T. Ting, Kimberly A. Smith, Amanda Gary, Songlin Ding, Alice Mukora, Matthew Kroll, Anoop P. Patel, Wayne Wakeman, Hongkui Zeng, Nadezhda Dotson, Rusty Mann, Victoria Omstead, Leona Mezei, Desiree A. Marshall, Shea Ransford, Lydia Ng, Sara Kebede, Gábor Tamás, Jeffrey G. Ojemann, Stephanie Mok, Nathan Hansen, Christina A. Pom, Brian Lee, Jim Berg, Ramkumar Rajanbabu, John W. Phillips, Philip R. Nicovich, Matthew Mallory, Richard G. Ellenbogen, Rachel Enstrom, Luke Esposito, Tim Jarsky, Di Jon Hill, Idan Segev, Darren Bertagnolli, Agata Budzillo, Sander Idema, Daniel L. Silbergeld, Costas A. Anastassiou, Chris Hill, Michelle Maxwell, Mean Hwan Kim, Charles Cobbs, Delissa McMillen, Bosiljka Tasic, Olivia Fong, Medea McGraw, Hong Gu, Kirsten Crichton, David Reid, Kristen Hadley, Lauren Alfiler, Manuel Ferreira, Elliot R. Thomsen, Kiet Ngo, Josef Sulc, Augustin Ruiz, Katherine Baker, Zizhen Yao, Erica J. Melief, Femke Waleboer, Hanchuan Peng, Grace Williams, Rebecca D. Hodge, Kyla Berry, Katherine E. Link, David Sandman, Tsega Desta, Christine Rimorin, Jeff Goldy, Ryder P. Gwinn, Djai B. Heyer, Changkyu Lee, Jeremy A. Miller, Nathan W. Gouwens, Pál Barzó, Attila Ozsvár, Huibert D. Mansvelder, Sergey L. Gratiy, Rafael Yuste, David Feng, Jessica Trinh, Clare Gamlin, Tamara Casper, C. Dirk Keene, Susan M. Sunkin, Tom Egdorf, Philip C. De Witt Hamer, Rebecca de Frates, Peter Chong, Szabina Furdan, Patrick R. Hof, Jasmine Bomben, Christiaan P. J. de Kock, Eline J. Mertens, Ed S. Lein, Anna A. Galakhova, Florence D’Orazi, Christof Koch, Madie Hupp, Neurosurgery, Amsterdam Neuroscience - Systems & Network Neuroscience, Integrative Neurophysiology, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, and Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention
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Cell type ,Multidisciplinary ,Neocortex ,Neurofilament ,Molecular neuroscience ,Biology ,Article ,Cellular neuroscience ,chemistry.chemical_compound ,Glutamatergic ,medicine.anatomical_structure ,chemistry ,Biocytin ,medicine ,Neuron ,Neuroscience - Abstract
The neocortex is disproportionately expanded in human compared with mouse1,2, both in its total volume relative to subcortical structures and in the proportion occupied by supragranular layers composed of neurons that selectively make connections within the neocortex and with other telencephalic structures. Single-cell transcriptomic analyses of human and mouse neocortex show an increased diversity of glutamatergic neuron types in supragranular layers in human neocortex and pronounced gradients as a function of cortical depth3. Here, to probe the functional and anatomical correlates of this transcriptomic diversity, we developed a robust platform combining patch clamp recording, biocytin staining and single-cell RNA-sequencing (Patch-seq) to examine neurosurgically resected human tissues. We demonstrate a strong correspondence between morphological, physiological and transcriptomic phenotypes of five human glutamatergic supragranular neuron types. These were enriched in but not restricted to layers, with one type varying continuously in all phenotypes across layers 2 and 3. The deep portion of layer 3 contained highly distinctive cell types, two of which express a neurofilament protein that labels long-range projection neurons in primates that are selectively depleted in Alzheimer’s disease4,5. Together, these results demonstrate the explanatory power of transcriptomic cell-type classification, provide a structural underpinning for increased complexity of cortical function in humans, and implicate discrete transcriptomic neuron types as selectively vulnerable in disease., Combined patch clamp recording, biocytin staining and single-cell RNA-sequencing of human neurocortical neurons shows an expansion of glutamatergic neuron types relative to mouse that characterizes the greater complexity of the human neocortex.
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- 2021
3. Hierarchical organization of cortical and thalamic connectivity
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Jennifer D. Whitesell, Stephen McConoughey, Yaoyao Li, Yun Wang, Xiuli Kuang, Alex M. Henry, Ali Williford, Quanxin Wang, Amy Bernard, Karla E. Hirokawa, Stefan Mihalas, Robert Howard, Anh Ho, Wayne Wakeman, Maitham Naeemi, David Feng, Peter A. Groblewski, Seung Wook Oh, Leonard Kuan, Nile Graddis, Joseph E. Knox, Benjamin Ouellette, Andrew Cho, Jérôme Lecoq, Hongkui Zeng, Christof Koch, Jennifer Luviano, Hannah Choi, Marty Mortrud, Charles R. Gerfen, Julie A. Harris, Allan R. Jones, Phillip Bohn, Phil Lesnar, Linzy Casal, Shiella Caldejon, Staci A. Sorensen, Lydia Ng, John W. Phillips, Aaron Feiner, Nathalie Gaudreault, and Elise Shen
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Male ,0301 basic medicine ,Computer science ,Thalamus ,Cre recombinase ,Projection neuron ,Article ,Mice ,03 medical and health sciences ,0302 clinical medicine ,Neural Pathways ,Biological neural network ,medicine ,Animals ,Hierarchical organization ,Axon ,Cerebral Cortex ,Multidisciplinary ,Computational neuroscience ,Integrases ,Axons ,Mice, Inbred C57BL ,030104 developmental biology ,medicine.anatomical_structure ,Cortical network ,Female ,Neuroscience ,030217 neurology & neurosurgery - Abstract
The mammalian cortex is a laminar structure containing many areas and cell types that are densely interconnected in complex ways, and for which generalizable principles of organization remain mostly unknown. Here we describe a major expansion of the Allen Mouse Brain Connectivity Atlas resource1, involving around a thousand new tracer experiments in the cortex and its main satellite structure, the thalamus. We used Cre driver lines (mice expressing Cre recombinase) to comprehensively and selectively label brain-wide connections by layer and class of projection neuron. Through observations of axon termination patterns, we have derived a set of generalized anatomical rules to describe corticocortical, thalamocortical and corticothalamic projections. We have built a model to assign connection patterns between areas as either feedforward or feedback, and generated testable predictions of hierarchical positions for individual cortical and thalamic areas and for cortical network modules. Our results show that cell-class-specific connections are organized in a shallow hierarchy within the mouse corticothalamic network. Using mouse lines in which subsets of neurons are genetically labelled, the authors provide generalized anatomical rules for connections within and between the cortex and thalamus.
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- 2019
4. Local Connectivity and Synaptic Dynamics in Mouse and Human Neocortex
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Michael Tieu, Amy Bernard, Lisa Kim, Samuel Dingman Lee, Tim Jarsky, Corinne Teeter, Martin Schroedter, Alex Hoggarth, Kimberly A. Smith, Amanda Gary, Charles Cobb, John W. Phillips, Christina A. Pom, Herman Tung, Hongkui Zeng, Daniel Carey, Phillip Bohn, Colin Farrell, Bosiljka Tasic, Rusty Nicovich, Medea McGraw, Krissy Brouner, Andrew L. Ko, Katherine Wadhwani, Lauren Ellingwood, Tom Egdorf, Anton Arkhipov, Aaron Szafer, Michael Clark, Kirsten Crichton, Kyla Berry, Josef Sulc, Nick Dee, Gabe J. Murphy, Luke Esposito, Trangthanh Pham, Thomas Chartrand, Alex M. Henry, Rachel A. Dalley, Rachel Enstrom, Thomas Braun, Luke Campagnola, Cristina Radaelli, C. Dirk Keene, Tanya L. Daigle, Cliff Slaughterbeck, Sara Kebede, Rachael Larsen, Jeffrey G. Ojemann, Juia Andrade, Michelle Maxwell, Staci A. Sorensen, Jeff Goldy, Jessica Gloe, David Sandman, Shinya Ito, Susan M. Sunkin, Wayne Wakeman, Travis A. Hage, Melissa Gorham, Ryder P. Gwinn, Pasha A. Davoudian, Augustin Ruiz, Grace Williams, Clare Gamlin, Christof Koch, La'Akea Siverts, Stephanie C. Seeman, Jasmine Bomben, Florence D’Orazi, Madie Hupp, Ed S. Lein, Nadia Dotson, Shea Ransford, Nika Hejazinia, Mean Hwan Kim, Delissa McMillen, David Feng, Jessica Trinh, Lydia Potekhina, Alice Mukora, Lauren Alfiler, Tamara Casper, Shu Shi, Matthew Kroll, Kiet Ngo, Richard G. Ellenbogen, Daniel L. Silbergeld, and Miranda Walker
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Synapse ,Cell type ,Neocortex ,medicine.anatomical_structure ,Excitatory synapse ,Cortex (anatomy) ,medicine ,Excitatory postsynaptic potential ,Biology ,Inhibitory postsynaptic potential ,Neuroscience ,Subclass - Abstract
To elucidate cortical microcircuit structure and synaptic properties we present a unique, extensive, and public synaptic physiology dataset and analysis platform. Through its application, we reveal principles that relate cell type to synapse properties and intralaminar circuit organization in the mouse and human cortex. The dynamics of excitatory synapses align with the postsynaptic cell subclass, whereas inhibitory synapse dynamics partly align with presynaptic cell subclass but with considerable overlap. Despite these associations, synaptic properties are heterogeneous in most subclass to subclass connections. The two main axes of heterogeneity are strength and variability. Cell subclasses divide along the variability axis, while the strength axis accounts for significant heterogeneity within the subclass. In human cortex, excitatory to excitatory synapse dynamics are distinct from those in mouse and short-term plasticity varies with depth across layers 2 and 3. With a novel connectivity analysis that enables fair comparisons between circuit elements, we find that intralaminar connection probability among cell subclasses exhibits a strong layer dependence.These and other findings combined with the analysis platform create new opportunities for the neuroscience community to advance our understanding of cortical microcircuits.
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- 2021
5. 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
6. 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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7. Human cortical expansion involves diversification and specialization of supragranular intratelencephalic-projecting neurons
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Sergey L. Gratiy, Sara Kebede, Chris Hill, Clare Gamlin, Jeffrey G. Ojemann, Tom Egdorf, Ed S. Lein, Lydia Potekhina, Alice Mukora, Shea Ransford, Matthew Mallory, Tim S. Heistek, Jonathan T. Ting, Gábor Tamás, Philip C. De Witt Hamer, Rebecca de Frates, Medea McGraw, Gábor Molnár, Jim Berg, Szabina Furdan, Patrick R. Hof, Natalia A. Goriounova, David Feng, David Reid, Elliot R. Thomsen, Michael Tieu, Katelyn Ward, C. Dirk Keene, Florence D’Orazi, Mean Hwan Kim, Daniel Park, Amy Torkelson, Agata Budzillo, Katherine Baker, Michael Hawrylycz, Krissy Brouner, Andrew L. Ko, DiJon Hill, Kyla Berry, Peter Chong, Jessica Trinh, Desiree A. Marshall, Katherine E. Link, Brian Lee, Jasmine Bomben, Aaron Szafer, Gabe J. Murphy, Viktor Szemenyei, Madie Hupp, Lauren Alfiler, Nick Dee, Zizhen Yao, Luke Esposito, Tamara Casper, Erica J. Melief, Susan M. Sunkin, Lindsay Ng, Hongkui Zeng, Pál Barzó, Allison Beller, Lydia Ng, Charles Cobbs, Darren Bertagnolli, Kiet Ngo, Bosiljka Tasic, John W. Phillips, Christine Rimorin, Alex M. Henry, Aaron Oldre, Michelle Maxwell, Wayne Wakeman, Delissa McMillen, Amanda Gary, Tsega Desta, Nathan Hansen, Hong Gu, Julie Nyhus, Staci A. Sorensen, Gáspár Oláh, Thomas Chartrand, Kirsten Crichton, Matthew Kroll, Josef Sulc, Jeremy A. Miller, Amy Bernard, Lisa Kim, Herman Tung, Idan Segev, Kristen Hadley, David Sandman, Anoop P. Patel, Colin Farrell, Allan R. Jones, Lisa Keene, Sander Idema, Changkyu Lee, Stephanie Mok, Augustin Ruiz, Caitlin S. Latimer, Tim Jarsky, Kris Bickley, Anton Arkhipov, Ramkumar Rajanbabu, Thomas Braun, Costas A. Anastassiou, Anatoly Buchin, Nathan W. Gouwens, Philip R. Nicovich, Richard G. Ellenbogen, Olivia Fong, Grace Williams, Rachel Enstrom, Rachel A. Dalley, Daniel L. Silbergeld, Attila Ozsvár, Kimberly A. Smith, Ryder P. Gwinn, Songlin Ding, Rafael Yuste, Manuel Ferreira, Victoria Omstead, Samuel Dingman Lee, Norbert Mihut, Hanchuan Peng, Brian E. Kalmbach, Eliza Barkan, Melissa Gorham, Boaz P. Levi, Trygve E. Bakken, Jeff Goldy, Djai B. Heyer, Nadezhda Dotson, Rusty Mann, Rebecca D. Hodge, Christof Koch, René Wilbers, Leona Mezei, Eline J. Mertens, Jae-Geun Yoon, Anna A. Galakhova, Christina A. Pom, Trangthanh Pham, Alexandra Glandon, Christiaan P. J. de Kock, Lucas T. Graybuck, and Huibert D. Mansvelder
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0303 health sciences ,Cell type ,Neocortex ,Neurofilament ,Biology ,Transcriptome ,03 medical and health sciences ,Glutamatergic ,0302 clinical medicine ,medicine.anatomical_structure ,Cortex (anatomy) ,Specialization (functional) ,medicine ,Neuroscience ,030217 neurology & neurosurgery ,Function (biology) ,030304 developmental biology - Abstract
The neocortex is disproportionately expanded in human compared to mouse, both in its total volume relative to subcortical structures and in the proportion occupied by supragranular layers that selectively make connections within the cortex and other telencephalic structures. Single-cell transcriptomic analyses of human and mouse cortex show an increased diversity of glutamatergic neuron types in supragranular cortex in human and pronounced gradients as a function of cortical depth. To probe the functional and anatomical correlates of this transcriptomic diversity, we describe a robust Patch-seq platform using neurosurgically-resected human tissues. We characterize the morphological and physiological properties of five transcriptomically defined human glutamatergic supragranular neuron types. Three of these types have properties that are specialized compared to the more homogeneous properties of transcriptomically defined homologous mouse neuron types. The two remaining supragranular neuron types, located exclusively in deep layer 3, do not have clear mouse homologues in supragranular cortex but are transcriptionally most similar to deep layer mouse intratelencephalic-projecting neuron types. Furthermore, we reveal the transcriptomic types in deep layer 3 that express high levels of non-phosphorylated heavy chain neurofilament protein that label long-range neurons known to be selectively depleted in Alzheimer’s disease. Together, these results demonstrate the power of transcriptomic cell type classification, provide a mechanistic underpinning for increased complexity of cortical function in human cortical evolution, and implicate discrete transcriptomic cell types as selectively vulnerable in disease.
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- 2020
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8. Toward an integrated classification of neuronal cell types: morphoelectric and transcriptomic characterization of individual GABAergic cortical neurons
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David Feng, Jessica Trinh, Tamara Casper, Lisa Kim, Rohan Gala, Clare Gamlin, Matthew Kroll, Uygar Sümbül, Lauren Alfiler, Thomas Braun, Jasmine Bomben, Bosiljka Tasic, Colin Farrell, Hongkui Zeng, Lydia Potekhina, Tsega Desta, Kiet Ngo, Lydia Ng, Alice Mukora, Fahimeh Baftizadeh, Aaron Szafer, Rachel A. Dalley, Shea Ransford, Changkyu Lee, Nick Dee, Brian Lee, Kirsten Crichton, Luke Esposito, Miranda Robertson, Josef Sulc, Alex M. Henry, Darren Bertagnolli, Tom Egdorf, Nadezhda Dotson, Zhi Zhou, Jim Berg, Philip R. Nicovich, Rusty Mann, Madie Hupp, Daniel Park, Delissa McMillen, Samuel Dingman Lee, Agata Budzillo, Eliza Barkan, Olivia Fong, Thanh Pham, Jeff Goldy, Ed S. Lein, Rebecca de Frates, Kimberly A. Smith, Amy Torkelson, Tim Jarsky, Michelle Maxwell, Michael Tieu, Susan M. Sunkin, Michael Hawrylycz, Lucas T. Graybuck, Herman Tung, David Reid, DiJon Hill, Alexandra Glandon, Kara Ronellenfitch, Aaron Oldre, Amanda Gary, Nathan W. Gouwens, Christof Koch, Alice Pom, Wayne Wakeman, Sara Kebede, Matthew Mallory, Tae Kyung Kim, Tanya L. Daigle, Kris Bickley, Anton Arkhipov, Osnat Penn, Staci A. Sorensen, Rachel Enstrom, Hanchuan Peng, Ramkumar Rajanbabu, Jonathan T. Ting, Zizhen Yao, Lauren Ellingwood, Medea McGraw, Gabe J. Murphy, Katherine Baker, Krissy Brouner, Hong Gu, David Sandman, Katelyn Ward, Kyla Berry, Katherine E. Link, Lindsay Ng, Christine Rimorin, Kristen Hadley, Augustin Ruiz, Grace Williams, and Melissa Gorham
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Transcriptome ,Electrophysiology ,Cell type ,medicine.anatomical_structure ,Visual cortex ,Interneuron ,nervous system ,genetic structures ,medicine ,GABAergic ,Cortical neurons ,Biology ,Neuroscience - Abstract
Neurons are frequently classified into distinct groups or cell types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 3,700 GABAergic mouse visual cortical neurons and reconstructed the local morphologies of 350 of those neurons. We found that most transcriptomic types (t-types) occupy specific laminar positions within mouse visual cortex, and many of those t-types exhibit consistent electrophysiological and morphological features. We observed that these properties could vary continuously between t-types, which limited the ability to predict specific t-types from other data modalities. Despite that, the data support the presence of at least 20 interneuron met-types that have congruent morphological, electrophysiological, and transcriptomic properties.HighlightsPatch-seq data obtained from >3,700 GABAergic cortical interneuronsComprehensive characterization of morpho-electric features of transcriptomic types20 interneuron met-types that have congruent properties across data modalitiesDifferent Sst met-types preferentially innervate different cortical layers
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- 2020
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9. 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
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10. Toward an Integrated Classification of Cell Types: Morphoelectric and Transcriptomic Characterization of Individual GABAergic Cortical Neurons
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Kimberly A. Smith, Matthew Kroll, Sara Kebede, Susan M. Sunkin, David Reid, Nadezhda Dotson, Rusty Mann, DiJon Hill, Kara Ronellenfitch, Shea Ransford, Hongkui Zeng, David Feng, Jasmine Bomben, Bosiljka Tasic, Rachel Enstrom, Jessica Trinh, Matthew Mallory, Aaron Szafer, Rachel A. Dalley, Aaron Oldre, Amanda Gary, Eliza Barkan, Nick Dee, Lydia Ng, Tae Kyung Kim, Ed S. Lein, Colin Farrell, Tamara Casper, Tom Egdorf, Kirsten Crichton, Josef Sulc, Fahimeh Baftizadeh, Katelyn Ward, Kirsten Hadley, Alex M. Henry, Alice Pom, Brian Lee, Uygar Sümbül, Lisa Kim, Tim Jarsky, Madie Happ, Wayne Wakeman, Lauren Ellingwood, Luke Esposito, Daniel Park, Tanya L. Daigle, Darren Bertagnolli, Lucas T. Graybuck, Olivia Fong, Philip R. Nicovich, Gabe J. Murphy, Michelle Maxwell, Lindsay Ng, Rebeeca de Frates, Rohan Gala, Alice Mukora, Delissa McMillen, Miranda Robertson, Thanh Pham, Samuel Dingman Lee, Kris Bickley, Anton Arkhipov, Osnat Penn, Staci A. Sorensen, Alexandra Glandon, Zizhen Yao, Amy Torkelson, Jonathan T. Ting, Lauren Alfiler, Ramkumar Rajanbabu, Kiet Ngo, Kirssy Brouner, David Sandman, Michael Tieu, Michael Hawrylycz, Nathan W. Gouwens, Hanchuan Peng, Zhi Zhou, Jeff Goldy, Hong Gu, Herman Tung, Medea McGraw, Lyida Potekhina, Katherine Baker, Tsega Desta, Christof Koch, Changkyu Lee, Melissa Gorham, Clare Gamlin, Augustin Ruiz, Grace Williams, Jim Berg, Kyla Berry, Katherine E. Link, Agata Budzillo, Christine Rimorin, and Thomas Braun
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Cell type ,genetic structures ,Interneuron ,Cortical neurons ,Biology ,Transcriptome ,Electrophysiology ,medicine.anatomical_structure ,Visual cortex ,nervous system ,medicine ,biology.protein ,GABAergic ,Neuroscience ,Parvalbumin - Abstract
Neurons are frequently classified into distinct groups or cell types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 3,700 GABAergic mouse visual cortical neurons and reconstructed the local morphologies of 350 of those neurons. We found that most transcriptomic types (t-types) occupy specific laminar positions within mouse visual cortex, and many of those t-types exhibit consistent electrophysiological and morphological features. We observed that these properties could vary continuously between t- types, which limited the ability to predict specific t-types from other data modalities. Despite that, the data support the presence of at least 20 interneuron met-types that have congruent morphological, electrophysiological, and transcriptomic properties.
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- 2020
11. Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells
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Kris Bickley, Anton Arkhipov, Osnat Penn, Hanchuan Peng, Shea Ransford, Sara Kebede, Kara Ronellenfitch, Matthew Mallory, Krissy Brouner, Madie Hupp, Lydia Ng, Daniel Park, Staci A. Sorensen, Alice Pom, Susan M. Sunkin, Tanya L. Daigle, Fahimeh Baftizadeh, Wayne Wakeman, Aaron Oldre, Amanda Gary, Herman Tung, Brian Lee, Ed S. Lein, Medea McGraw, Rachel A. Dalley, Bosiljka Tasic, Hong Gu, Miranda Robertson, Katherine Baker, Lindsay Ng, David Sandman, Jasmine Bomben, Uygar Sümbül, Tae Kyung Kim, David Reid, Eliza Barkan, Luke Esposito, Kirsten Crichton, DiJon Hill, Zoran Popović, Josef Sulc, Nathan W. Gouwens, Ramkumar Rajanbabu, Lydia Potekhina, Thomas Braun, Alexandra Glandon, Tim Jarsky, Darren Bertagnolli, Tom Egdorf, Olivia Fong, Alice Mukora, Rebecca de Frates, Lauren Ellingwood, Jonathan T. Ting, Gabe J. Murphy, Katelyn Ward, Delissa McMillen, Samuel Dingman Lee, Melissa Gorham, Michelle Maxwell, Clare Gamlin, Zhi Zhou, Jeff Goldy, Rachel Enstrom, Kyla Berry, Colin Farrell, Katherine E. Link, Christine Rimorin, Zizhen Yao, Hongkui Zeng, Kristen Hadley, Augustin Ruiz, Grace Williams, Amy Torkelson, Kimberly A. Smith, Lisa Kim, Aaron Szafer, Nick Dee, Alex M. Henry, Rohan Gala, David Feng, Jessica Trinh, Tamara Casper, Matthew Kroll, Christof Koch, Michael Tieu, Michael Hawrylycz, Lauren Alfiler, Kiet Ngo, Philip R. Nicovich, Thanh Pham, Nadezhda Dotson, Rusty Mann, Tsega Desta, Lucas T. Graybuck, Changkyu Lee, Jim Berg, and Agata Budzillo
- Subjects
0303 health sciences ,Cell type ,biology ,Interneuron ,General Biochemistry, Genetics and Molecular Biology ,Transcriptome ,03 medical and health sciences ,Electrophysiology ,0302 clinical medicine ,medicine.anatomical_structure ,Visual cortex ,medicine ,biology.protein ,GABAergic ,Axon ,Neuroscience ,030217 neurology & neurosurgery ,Parvalbumin ,030304 developmental biology - Abstract
Neurons are frequently classified into distinct types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 4,200 mouse visual cortical GABAergic interneurons and reconstructed the local morphologies of 517 of those neurons. We find that most transcriptomic types (t-types) occupy specific laminar positions within visual cortex, and, for most types, the cells mapping to a t-type exhibit consistent electrophysiological and morphological properties. These properties display both discrete and continuous variation among t-types. Through multimodal integrated analysis, we define 28 met-types that have congruent morphological, electrophysiological, and transcriptomic properties and robust mutual predictability. We identify layer-specific axon innervation pattern as a defining feature distinguishing different met-types. These met-types represent a unified definition of cortical GABAergic interneuron types, providing a systematic framework to capture existing knowledge and bridge future analyses across different modalities.
- Published
- 2020
12. Classification of electrophysiological and morphological types in mouse visual cortex
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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
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13. Altered gene expression in the dorsolateral prefrontal cortex of individuals with schizophrenia
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Patrick R. Hof, Thomas M. Hyde, Elise Shen, Chihchau L. Kuan, Joel E. Kleinman, Michael Hawrylycz, Rachel A. Dalley, Angela L. Guillozet-Bongaarts, Beryl Swanson, Hongkui Zeng, Joshua J. Royall, John G. Hohmann, Alex M. Henry, and Allan R. Jones
- Subjects
Adult ,Male ,Cell type ,Cell Count ,Nerve Tissue Proteins ,Neuroimaging ,In situ hybridization ,Biology ,Cellular and Molecular Neuroscience ,Young Adult ,medicine ,Humans ,Brodmann area 9 ,Prefrontal cortex ,Molecular Biology ,Regulation of gene expression ,Neurons ,prefrontal cortex ,Middle Aged ,medicine.disease ,Dorsolateral prefrontal cortex ,schizophrenia ,Psychiatry and Mental health ,medicine.anatomical_structure ,Gene Expression Regulation ,Schizophrenia ,gene expression ,GABAergic ,Original Article ,Female ,in situ hybridization ,Neuroscience ,Neuroglia - Abstract
The underlying pathology of schizophrenia (SZ) is likely as heterogeneous as its symptomatology. A variety of cortical and subcortical regions, including the prefrontal cortex, have been implicated in its pathology, and a number of genes have been identified as risk factors for disease development. We used in situ hybridization (ISH) to examine the expression of 58 genes in the dorsolateral prefrontal cortex (DLPFC, comprised of Brodmann areas 9 and 46) from 19 individuals with a premorbid diagnosis of SZ and 33 control individuals. Genes were selected based on: (1) previous identification as risk factors for SZ; (2) cell type markers or (3) laminar markers. Cell density and staining intensity were compared in the DLPFC, as well as separately in Brodmann areas 9 and 46. The expression patterns of a variety of genes, many of which are associated with the GABAergic system, were altered in SZ when compared with controls. Additional genes, including C8orf79 and NR4A2, showed alterations in cell density or staining intensity between the groups, highlighting the need for additional studies. Alterations were, with only a few exceptions, limited to Brodmann area 9, suggesting regional specificity of pathology in the DLPFC. Our results agree with previous studies on the GABAergic involvement in SZ, and suggest that areas 9 and 46 may be differentially affected in the disease. This study also highlights additional genes that may be altered in SZ, and indicates that these potentially interesting genes can be identified by ISH and high-throughput image analysis techniques.
- Published
- 2013
14. High-resolution gene expression atlases for adult and developing mouse brain and spinal cord
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John G. Hohmann and Alex M. Henry
- Subjects
Central nervous system ,Brain atlas ,Brain ,Gene Expression ,Atlases as Topic ,Human brain ,Biology ,Bioinformatics ,Spinal cord ,Human genetics ,Mice ,medicine.anatomical_structure ,Spinal Cord ,Informatics ,Gene expression ,Genetics ,medicine ,Animals ,Neuroscience - Abstract
Knowledge of the structure, genetics, circuits, and physiological properties of the mammalian brain in both normal and pathological states is ever increasing as research labs worldwide probe the various aspects of brain function. Until recently, however, comprehensive cataloging of gene expression across the central nervous system has been lacking. The Allen Institute for Brain Science, as part of its mission to propel neuroscience research, has completed several large gene-mapping projects in mouse, nonhuman primate, and human brain, producing informative online public resources and tools. Here we present the Allen Mouse Brain Atlas, covering ~20,000 genes throughout the adult mouse brain; the Allen Developing Mouse Brain Atlas, detailing expression of approximately 2,000 important developmental genes across seven embryonic and postnatal stages of brain growth; and the Allen Spinal Cord Atlas, revealing expression for ~20,000 genes in the adult and neonatal mouse spinal cords. Integrated data-mining tools, including reference atlases, informatics analyses, and 3-D viewers, are described. For these massive-scale projects, high-throughput industrial techniques were developed to standardize and reliably repeat experimental goals. To verify consistency and accuracy, a detailed analysis of the 1,000 most viewed genes for the adult mouse brain (according to website page views) was performed by comparing our data with peer-reviewed literature and other databases. We show that our data are highly consistent with independent sources and provide a comprehensive compendium of information and tools used by thousands of researchers each month. All data and tools are freely available via the Allen Brain Atlas portal ( www.brain-map.org ).
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- 2012
15. A mesoscale connectome of the mouse brain
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Nicholas Cain, Allan R. Jones, Michael Hawrylycz, Karla E. Hirokawa, Phillip Bohn, Stefan Mihalas, Chris Lau, Eric Nicholas, Chinh Dang, Brent Winslow, Staci A. Sorensen, Seung Wook Oh, Julie A. Harris, Clifford R. Slaughterbeck, Quanxin Wang, Kevin M. Joines, Anh Ho, Wayne Wakeman, Hanchuan Peng, John G. Hohmann, Yang Li, Benjamin Ouellette, John W. Phillips, Hongkui Zeng, Lydia Ng, Paul Wohnoutka, Thuc Nghi Nguyen, Amy Bernard, Alex M. Henry, David Feng, Leonard Kuan, Christof Koch, Marty Mortrud, and Charles R. Gerfen
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Nervous system ,Male ,Multidisciplinary ,Mesoscale meteorology ,Neuroanatomical Tract-Tracing Techniques ,Brain ,Biology ,Bioinformatics ,3d topography ,Article ,Reference space ,medicine.anatomical_structure ,medicine ,Connectome ,Biological neural network ,Animals ,Neuroscience ,Tractography - Abstract
Comprehensive knowledge of the brain's wiring diagram is fundamental for understanding how the nervous system processes information at both local and global scales. However, with the singular exception of the C. elegans microscale connectome, there are no complete connectivity data sets in other species. Here we report a brain-wide, cellular-level, mesoscale connectome for the mouse. The Allen Mouse Brain Connectivity Atlas uses enhanced green fluorescent protein (EGFP)-expressing adeno-associated viral vectors to trace axonal projections from defined regions and cell types, and high-throughput serial two-photon tomography to image the EGFP-labelled axons throughout the brain. This systematic and standardized approach allows spatial registration of individual experiments into a common three dimensional (3D) reference space, resulting in a whole-brain connectivity matrix. A computational model yields insights into connectional strength distribution, symmetry and other network properties. Virtual tractography illustrates 3D topography among interconnected regions. Cortico-thalamic pathway analysis demonstrates segregation and integration of parallel pathways. The Allen Mouse Brain Connectivity Atlas is a freely available, foundational resource for structural and functional investigations into the neural circuits that support behavioural and cognitive processes in health and disease.
- Published
- 2014
16. Systematic comparison of adeno-associated virus and biotinylated dextran amine reveals equivalent sensitivity between tracers and novel projection targets in the mouse brain
- Author
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Quanxin, Wang, Alex M, Henry, Julie A, Harris, Seung Wook, Oh, Kevin M, Joines, Julie, Nyhus, Karla E, Hirokawa, Nick, Dee, Marty, Mortrud, Sheana, Parry, Benjamin, Ouellette, Shiella, Caldejon, Amy, Bernard, Allan R, Jones, Hongkui, Zeng, and John G, Hohmann
- Subjects
Neurons ,Photomicrography ,Microscopy, Confocal ,Biotin ,Brain ,Cell Count ,Dextrans ,Dependovirus ,Immunohistochemistry ,Sensitivity and Specificity ,Mice, Inbred C57BL ,Neuroanatomical Tract-Tracing Techniques ,Microscopy, Fluorescence ,Neural Pathways ,Animals ,Neuronal Tract-Tracers ,Fluorescent Dyes - Abstract
As an anterograde neuronal tracer, recombinant adeno-associated virus (AAV) has distinct advantages over the widely used biotinylated dextran amine (BDA). However, the sensitivity and selectivity of AAV remain uncharacterized for many brain regions and species. To validate this tracing method further, AAV (serotype 1) was systematically compared with BDA as an anterograde tracer by injecting both tracers into three cortical and 15 subcortical regions in C57BL/6J mice. Identical parameters were used for our sequential iontophoretic injections, producing injections of AAV that were more robust in size and in density of neurons infected compared with those of BDA. However, these differences did not preclude further comparison between the tracers, because the pairs of injections were suitably colocalized and contained some percentage of double-labeled neurons. A qualitative analysis of projection patterns showed that the two tracers behave very similarly when injection sites are well matched. Additionally, a quantitative analysis of relative projection intensity for cases targeting primary motor cortex (MOp), primary somatosensory cortex (SSp), and caudoputamen (CP) showed strong agreement in the ranked order of projection intensities between the two tracers. A detailed analysis of the projections of two brain regions (SSp and MOp) revealed many targets that have not previously been described in the mouse or rat. Minor retrograde labeling of neurons was observed in all cases examined, for both AAV and BDA. Our results show that AAV has actions equivalent to those of BDA as an anterograde tracer and is suitable for analysis of neural circuitry throughout the mouse brain.
- Published
- 2013
17. Systematic comparison of adeno-associated virus and biotinylated dextran amine reveals equivalent sensitivity between tracers and novel projection targets in the mouse brain
- Author
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Karla E. Hirokawa, Allan R. Jones, Julie A. Harris, Sheana Parry, Quanxin Wang, Benjamin Ouellette, Kevin M. Joines, Nick Dee, Julie Nyhus, Shiella Caldejon, John G. Hohmann, Hongkui Zeng, Alex M. Henry, Amy Bernard, Marty Mortrud, and Seung Wook Oh
- Subjects
Biotinylated dextran amine ,Iontophoresis ,General Neuroscience ,Biology ,Somatosensory system ,medicine.disease_cause ,law.invention ,law ,Recombinant DNA ,medicine ,Biophysics ,Biological neural network ,Primary motor cortex ,Neuroscience ,Quantitative analysis (chemistry) ,Adeno-associated virus - Abstract
As an anterograde neuronal tracer, recombinant adeno-associated virus (AAV) has distinct advantages over the widely used biotinylated dextran amine (BDA). However, the sensitivity and selectivity of AAV remain uncharacterized for many brain regions and species. To validate this tracing method further, AAV (serotype 1) was systematically compared with BDA as an anterograde tracer by injecting both tracers into three cortical and 15 subcortical regions in C57BL/6J mice. Identical parameters were used for our sequential iontophoretic injections, producing injections of AAV that were more robust in size and in density of neurons infected compared with those of BDA. However, these differences did not preclude further comparison between the tracers, because the pairs of injections were suitably colocalized and contained some percentage of double-labeled neurons. A qualitative analysis of projection patterns showed that the two tracers behave very similarly when injection sites are well matched. Additionally, a quantitative analysis of relative projection intensity for cases targeting primary motor cortex (MOp), primary somatosensory cortex (SSp), and caudoputamen (CP) showed strong agreement in the ranked order of projection intensities between the two tracers. A detailed analysis of the projections of two brain regions (SSp and MOp) revealed many targets that have not previously been described in the mouse or rat. Minor retrograde labeling of neurons was observed in all cases examined, for both AAV and BDA. Our results show that AAV has actions equivalent to those of BDA as an anterograde tracer and is suitable for analysis of neural circuitry throughout the mouse brain. J. Comp. Neurol. 522:1989–2012, 2014. © 2014 Wiley Periodicals, Inc.
- Published
- 2014
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