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9 results on '"Krissy Brouner"'

1. Integrated multimodal cell atlas of Alzheimer’s disease

Author

Mariano I. Gabitto, Kyle J. Travaglini, Victoria M. Rachleff, Eitan S. Kaplan, Brian Long, Jeanelle Ariza, Yi Ding, Joseph T. Mahoney, Nick Dee, Jeff Goldy, Erica J. Melief, Krissy Brouner, Jazmin Campos, John Campos, Ambrose J. Carr, Tamara Casper, Rushil Chakrabarty, Michael Clark, Jonah Cool, Nasmil J. Valera Cuevas, Rachel Dalley, Martin Darvas, Song-Lin Ding, Tim Dolbeare, Christine L. Mac Donald, Tom Egdorf, Luke Esposito, Rebecca Ferrer, Rohan Gala, Amanda Gary, Jessica Gloe, Nathan Guilford, Junitta Guzman, Daniel Hirschstein, Windy Ho, Tim Jarksy, Nelson Johansen, Brian E. Kalmbach, Lisa M. Keene, Sarah Khawand, Mitch Kilgore, Amanda Kirkland, Michael Kunst, Brian R. Lee, Jocelin Malone, Zoe Maltzer, Naomi Martin, Rachel McCue, Delissa McMillen, Emma Meyerdierks, Kelly P. Meyers, Tyler Mollenkopf, Mark Montine, Amber L. Nolan, Julie Nyhus, Paul A. Olsen, Maiya Pacleb, Nicholas Peña, Thanh Pham, Christina Alice Pom, Nadia Postupna, Augustin Ruiz, Aimee M. Schantz, Nadiya V. Shapovalova, Staci A. Sorensen, Brian Staats, Matt Sullivan, Susan M. Sunkin, Carol Thompson, Michael Tieu, Jonathan Ting, Amy Torkelson, Tracy Tran, Ming-Qiang Wang, Jack Waters, Angela M. Wilson, David Haynor, Nicole Gatto, Suman Jayadev, Shoaib Mufti, Lydia Ng, Shubhabrata Mukherjee, Paul K. Crane, Caitlin S. Latimer, Boaz P. Levi, Kimberly Smith, Jennie L. Close, Jeremy A. Miller, Rebecca D. Hodge, Eric B. Larson, Thomas J. Grabowski, Michael Hawrylycz, C. Dirk Keene, and Ed S. Lein

Abstract

Alzheimer’s disease (AD) is the most common cause of dementia in older adults. Neuropathological and imaging studies have demonstrated a progressive and stereotyped accumulation of protein aggregates, but the underlying molecular and cellular mechanisms driving AD progression and vulnerable cell populations affected by disease remain coarsely understood. The current study harnesses single cell and spatial genomics tools and knowledge from the BRAIN Initiative Cell Census Network to understand the impact of disease progression on middle temporal gyrus cell types. We used image-based quantitative neuropathology to place 84 donors spanning the spectrum of AD pathology along a continuous disease pseudoprogression score and multiomic technologies to profile single nuclei from each donor, mapping their transcriptomes, epigenomes, and spatial coordinates to a common cell type reference with unprecedented resolution. Temporal analysis of cell-type proportions indicated an early reduction of Somatostatin-expressing neuronal subtypes and a late decrease of supragranular intratelencephalic-projecting excitatory and Parvalbumin-expressing neurons, with increases in disease-associated microglial and astrocytic states. We found complex gene expression differences, ranging from global to cell type-specific effects. These effects showed different temporal patterns indicating diverse cellular perturbations as a function of disease progression. A subset of donors showed a particularly severe cellular and molecular phenotype, which correlated with steeper cognitive decline. We have created a freely available public resource to explore these data and to accelerate progress in AD research atSEA-AD.org.

Published
2023

2. Signature morpho-electric properties of diverse GABAergic interneurons in the human neocortex

Author

Brian Lee, Rachel Dalley, Jeremy A Miller, Thomas Chartrand, Jennie Close, Rusty Mann, Alice Mukora, Lindsay Ng, Lauren Alfiler, Katherine Baker, Darren Bertagnolli, Krissy Brouner, Tamara Casper, Eva Csajbok, Nick Dee, Nicholas Donadio, Stan L.W. Driessens, Tom Egdorf, Rachel Enstrom, Anna A Galakhova, Amanda Gary, Emily Gelfand, Jeff Goldy, Kristen Hadley, Tim S. Heistek, Dijon Hill, Nelson Johansen, Nik Jorstad, Lisa Kim, Agnes Katalin Kocsis, Lauren Kruse, Michael Kunst, Gabriela Leon, Brian Long, Matthew Mallory, Michelle Maxwell, Medea McGraw, Delissa McMillen, Erica J Melief, Gabor Molnar, Marty T Mortrud, Dakota Newman, Julie Nyhus, Ximena Opitz-Araya, Trangthanh Pham, Alice Pom, Lydia Potekhina, Ram Rajanbabu, Augustin Ruiz, Susan M Sunkin, Ildiko Szots, Naz Taskin, Bargavi Thyagarajan, Michael Tieu, Jessica Trinh, Sara Vargas, David Vumbaco, Femke Waleboer, Natalie Weed, Grace Williams, Julia Wilson, Shenqin Yao, Thomas Zhou, Pal Barzo, Trygve Bakken, Charles Cobbs, Richard G. Ellenbogen, Luke Esposito, Manuel Ferreira, Nathan W Gouwens, Benjamin Grannan, Ryder P. Gwinn, Jason S. Hauptman, Rebecca Hodge, Tim Jarsky, C.Dirk Keene, Andrew L. Ko, Boaz Levi, Jeffrey G. Ojemann, Anoop Patel, Jacob Ruzevick, Daniel L. Silbergeld, Kim Smith, Jack Waters, Hongkui Zeng, Jim Berg, Natalia A. Goriounova, Brian Kalmbach, Christiaan P.J. de Kock, Huib D Mansvelder, Staci A Sorensen, Gabor Tamas, Ed S. Lein, and Jonathan T Ting

Abstract

Human cortical interneurons have been challenging to study due to high diversity and lack of mature brain tissue platforms and genetic targeting tools. We employed rapid GABAergic neuron viral labeling plus unbiased Patch-seq sampling in brain slices to define the signature morpho-electric properties of GABAergic neurons in the human neocortex. Viral targeting greatly facilitated sampling of the SST subclass, including primate specialized double bouquet cells which mapped to two SST transcriptomic types. Multimodal analysis uncovered an SST neuron type with properties inconsistent with original subclass assignment; we instead propose reclassification into PVALB subclass. Our findings provide novel insights about functional properties of human cortical GABAergic neuron subclasses and types and highlight the essential role of multimodal annotation for refinement of emerging transcriptomic cell type taxonomies.One Sentence SummaryViral genetic labeling of GABAergic neurons in humanex vivobrain slices paired with Patch-seq recording yields an in-depth functional annotation of human cortical interneuron subclasses and types and highlights the essential role of multimodal functional annotation for refinement of emerging transcriptomic cell type taxonomies.

Published
2022

3. Morpho-electric and transcriptomic divergence of the layer 1 interneuron repertoire in human versus mouse neocortex

Author

Thomas Chartrand, Rachel Dalley, Jennie Close, Natalia A. Goriounova, Brian R. Lee, Rusty Mann, Jeremy A. Miller, Gabor Molnar, Alice Mukora, Lauren Alfiler, Katherine Baker, Trygve E. Bakken, Jim Berg, Darren Bertagnolli, Thomas Braun, Krissy Brouner, Tamara Casper, Eva Adrienn Csajbok, Nick Dee, Tom Egdorf, Rachel Enstrom, Anna A. Galakhova, Amanda Gary, Emily Gelfand, Jeff Goldy, Kristen Hadley, Tim S. Heistek, DiJon Hill, Nik Jorstad, Lisa Kim, Agnes Katalin Kocsis, Lauren Kruse, Michael Kunst, Gabriela Leon, Brian Long, Matthew Mallory, Medea McGraw, Delissa McMillen, Erica J. Melief, Norbert Mihut, Lindsay Ng, Julie Nyhus, Victoria Omstead, Zoltan Peterfi, Alice Pom, Lydia Potekhina, Ramkumar Rajanbabu, Marton Rozsa, Augustin Ruiz, Joanna Sandle, Susan M. Sunkin, Ildiko Szots, Michael Tieu, Martin Toth, Jessica Trinh, Sara Vargas, David Vumbaco, Grace Williams, Julia Wilson, Zizhen Yao, Pal Barzo, Charles Cobbs, Richard G. Ellenbogen, Luke Esposito, Manuel Ferreira, Nathan W. Gouwens, Benjamin Grannan, Ryder P. Gwinn, Jason S. Hauptman, Tim Jarsky, C.Dirk Keene, Andrew L. Ko, Christof Koch, Jeffrey G. Ojemann, Anoop Patel, Jacob Ruzevick, Daniel L. Silberberg, Kimberly Smith, Staci A. Sorensen, Bosiljka Tasic, Jonathan T. Ting, Jack Waters, Christiaan P.J. de Kock, Huib D. Mansvelder, Gabor Tamas, Hongkui Zeng, Brian Kalmbach, and Ed S. Lein

Abstract

Neocortical layer 1 (L1) is a site of convergence between pyramidal neuron dendrites and feedback axons where local inhibitory signaling can profoundly shape cortical processing. Evolutionary expansion of human neocortex is marked by distinctive pyramidal neuron types with extensive branching in L1, but whether L1 interneurons are similarly diverse is underexplored. Using patch-seq recordings from human neurosurgically resected tissues, we identified four transcriptomically defined subclasses, unique subtypes within those subclasses and additional types with no mouse L1 homologue. Compared with mouse, human subclasses were more strongly distinct from each other across all modalities. Accompanied by higher neuron density and more variable cell sizes compared with mouse, these findings suggest L1 is an evolutionary hotspot, reflecting the increasing demands of regulating the expanding human neocortical circuit.One Sentence SummaryUsing transcriptomics and morpho-electric analyses, we describe innovations in human neocortical layer 1 interneurons.

Published
2022

4. A whole-brain monosynaptic input connectome to neuron classes in mouse visual cortex

Author

Kat North, Jasmin Bomben, Shenqin Yao, Bosiljka Tasic, Nicole Hancock, Shea Ransford, Thuyanh V. Nguyen, Ali Williford, Karla E. Hirokawa, Wayne Wakeman, Stefan Mihalas, Lydia Ng, Fiona Griffin, Robert Howard, Julie A. Harris, Rachel Enstrom, Tom Egdorf, Nadezhda Dotson, Eric Lee, Kyla Mace, Amanda Gary, Peter A. Groblewski, Michelle Maxwell, Lydia Potekhina, Tanya L. Daigle, Nhan-Kiet Ngo, Medea McGraw, Krissy Brouner, Philip R. Nicovich, Benjamin Ouellette, Jennifer Luviano, Emily Gelfand, Sam Seid, Chelsea Nayan, Maitham Naeemi, Marty Mortrud, Jackie Swapp, Cho A, Ali Cetin, Hong Gu, Hongkui Zeng, M. J. Taormina, Sophie Lambert, Leonard Kuan, Ruweida Ahmed, Thomas Zhou, Melissa Gorham, Augustin Ruiz, Linzy Casal, Shiella Caldejon, and Quanxin Wang

Subjects
General Neuroscience, Rabies virus, Tracing, Biology, medicine.disease_cause, Entire visual cortex, Visual cortex, medicine.anatomical_structure, medicine, Connectome, Excitatory postsynaptic potential, Neuron, Neuroscience, Brain function
Abstract

Identification of the structural connections between neurons is a prerequisite to understanding brain function. We developed a pipeline to systematically map brain-wide monosynaptic inputs to specific neuronal populations using Cre-driver mouse lines and the recombinant rabies tracing system. We first improved the rabies virus tracing strategy to accurately identify starter cells and to efficiently quantify presynaptic inputs. We then mapped brain-wide presynaptic inputs to different excitatory and inhibitory neuron subclasses in the primary visual cortex and seven higher visual areas. Our results reveal quantitative target-, layer- and cell-class-specific differences in the retrograde connectomes, despite similar global input patterns to different neuronal populations in the same anatomical area. The retrograde connectivity we define is consistent with the presence of the ventral and dorsal visual information processing streams and reveals further subnetworks within the dorsal stream. The hierarchical organization of the entire visual cortex can be derived from intracortical feedforward and feedback pathways mediated by upper- and lower-layer input neurons, respectively. This study expands our knowledge of the brain-wide inputs regulating visual areas and demonstrates that our improved rabies virus tracing strategy can be used to scale up the effort in dissecting connectivity of genetically defined cell populations in the whole mouse brain.

Published
2021

5. Local Connectivity and Synaptic Dynamics in Mouse and Human Neocortex

Author

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

Subjects
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.

Published
2021

6. Human cortical expansion involves diversification and specialization of supragranular intratelencephalic-projecting neurons

Author

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

Subjects
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.

Published
2020
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7. Toward an integrated classification of neuronal cell types: morphoelectric and transcriptomic characterization of individual GABAergic cortical neurons

Author

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

Subjects
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

Published
2020
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8. Conserved cell types with divergent features between human and mouse cortex

Author

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

9. Classification of electrophysiological and morphological types in mouse visual cortex

Author

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
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