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1. Integrated multimodal cell atlas of Alzheimer’s disease

Author

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

Abstract

Alzheimer’s disease (AD) is the most common cause of dementia in older adults. Neuropathological and imaging studies have demonstrated a progressive 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 the BRAIN Initiative Cell Census Network experimental practices, combining quantitative neuropathology with single cell genomics and spatial transcriptomics, to understand the impact of disease progression on middle temporal gyrus cell types. We used quantitative neuropathology to place 84 cases spanning the spectrum of AD pathology along a continuous disease pseudoprogression score. We used multiomic technologies to profile single nuclei from each donor, mapping their identity 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 at SEA-AD.org.

Published
2023
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2. 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
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3. A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain

Author

Zizhen Yao, Cindy T. J. van Velthoven, Michael Kunst, Meng Zhang, Delissa McMillen, Changkyu Lee, Won Jung, Jeff Goldy, Aliya Abdelhak, Pamela Baker, Eliza Barkan, Darren Bertagnolli, Jazmin Campos, Daniel Carey, Tamara Casper, Anish Bhaswanth Chakka, Rushil Chakrabarty, Sakshi Chavan, Min Chen, Michael Clark, Jennie Close, Kirsten Crichton, Scott Daniel, Tim Dolbeare, Lauren Ellingwood, James Gee, Alexandra Glandon, Jessica Gloe, Joshua Gould, James Gray, Nathan Guilford, Junitta Guzman, Daniel Hirschstein, Windy Ho, Kelly Jin, Matthew Kroll, Kanan Lathia, Arielle Leon, Brian Long, Zoe Maltzer, Naomi Martin, Rachel McCue, Emma Meyerdierks, Thuc Nghi Nguyen, Trangthanh Pham, Christine Rimorin, Augustin Ruiz, Nadiya Shapovalova, Cliff Slaughterbeck, Josef Sulc, Michael Tieu, Amy Torkelson, Herman Tung, Nasmil Valera Cuevas, Katherine Wadhwani, Katelyn Ward, Boaz Levi, Colin Farrell, Carol L. Thompson, Shoaib Mufti, Chelsea M. Pagan, Lauren Kruse, Nick Dee, Susan M. Sunkin, Luke Esposito, Michael J. Hawrylycz, Jack Waters, Lydia Ng, Kimberly A. Smith, Bosiljka Tasic, Xiaowei Zhuang, and Hongkui Zeng

Subjects
Article
Abstract

The mammalian brain is composed of millions to billions of cells that are organized into numerous cell types with specific spatial distribution patterns and structural and functional properties. An essential step towards understanding brain function is to obtain a parts list, i.e., a catalog of cell types, of the brain. Here, we report a comprehensive and high-resolution transcriptomic and spatial cell type atlas for the whole adult mouse brain. The cell type atlas was created based on the combination of two single-cell-level, whole-brain-scale datasets: a single- cell RNA-sequencing (scRNA-seq) dataset of ∼7 million cells profiled, and a spatially resolved transcriptomic dataset of ∼4.3 million cells using MERFISH. The atlas is hierarchically organized into five nested levels of classification: 7 divisions, 32 classes, 306 subclasses, 1,045 supertypes and 5,200 clusters. We systematically analyzed the neuronal, non-neuronal, and immature neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell type organization in different brain regions, in particular, a dichotomy between the dorsal and ventral parts of the brain: the dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. We also systematically characterized cell-type specific expression of neurotransmitters, neuropeptides, and transcription factors. The study uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types across the brain, suggesting they mediate a myriad of modes of intercellular communications. Finally, we found that transcription factors are major determinants of cell type classification in the adult mouse brain and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole-mouse-brain transcriptomic and spatial cell type atlas establishes a benchmark reference atlas and a foundational resource for deep and integrative investigations of cell type and circuit function, development, and evolution of the mammalian brain.

Published
2023

4. 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
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5. Transcriptomic cytoarchitecture reveals principles of human neocortex organization

Author

Nikolas L. Jorstad, Jennie Close, Nelson Johansen, Anna Marie Yanny, Eliza R. Barkan, Kyle J. Travaglini, Darren Bertagnolli, Jazmin Campos, Tamara Casper, Kirsten Crichton, Nick Dee, Song-Lin Ding, Emily Gelfand, Jeff Goldy, Daniel Hirschstein, Matthew Kroll, Michael Kunst, Kanan Lathia, Brian Long, Naomi Martin, Delissa McMillen, Trangthanh Pham, Christine Rimorin, Augustin Ruiz, Nadiya Shapovalova, Soraya Shehata, Kimberly Siletti, Saroja Somasundaram, Josef Sulc, Michael Tieu, Amy Torkelson, Herman Tung, Katelyn Ward, Edward M. Callaway, Patrick R. Hof, C. Dirk Keene, Boaz P. Levi, Sten Linnarsson, Partha P. Mitra, Kimberly Smith, Rebecca D. Hodge, Trygve E. Bakken, and Ed S. Lein

Abstract

Variation in cortical cytoarchitecture is the basis for histology-based definition of cortical areas, such as Brodmann areas. Single cell transcriptomics enables higher-resolution characterization of cell types in human cortex, which we used to revisit the idea of the canonical cortical microcircuit and to understand functional areal specialization. Deeply sampled single nucleus RNA-sequencing of eight cortical areas spanning cortical structural variation showed highly consistent cellular makeup for 24 coarse cell subclasses. However, proportions of excitatory neuron subclasses varied strikingly, reflecting differences in intra- and extracortical connectivity across primary sensorimotor and association cortices. Astrocytes and oligodendrocytes also showed differences in laminar organization across areas. Primary visual cortex showed dramatically different organization, including major differences in the ratios of excitatory to inhibitory neurons, expansion of layer 4 excitatory neuron types and specialized inhibitory neurons. Finally, gene expression variation in conserved neuron subclasses predicts differences in synaptic function across areas. Together these results provide a refined cellular and molecular characterization of human cortical cytoarchitecture that reflects functional connectivity and predicts areal specialization.

Published
2022
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6. Human neocortical expansion involves glutamatergic neuron diversification

Author

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

Subjects
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.

Published
2021
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7. 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
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8. Local Connectivity and Synaptic Dynamics in Mouse and Human Neocortex

Author

Luke Campagnola, Stephanie C. Seeman, Thomas Chartrand, Lisa Kim, Alex Hoggarth, Clare Gamlin, Shinya Ito, Jessica Trinh, Pasha Davoudian, Cristina Radaelli, Mean-Hwan Kim, Travis Hage, Thomas Braun, Lauren Alfiler, Julia Andrade, Phillip Bohn, Rachel Dalley, Alex Henry, Sara Kebede, Mukora Alice, David Sandman, Grace Williams, Rachael Larsen, Corinne Teeter, Tanya L. Daigle, Kyla Berry, Nadia Dotson, Rachel Enstrom, Melissa Gorham, Madie Hupp, Samuel Dingman Lee, Kiet Ngo, Philip R. Nicovich, Lydia Potekhina, Shea Ransford, Amanda Gary, Jeff Goldy, Delissa McMillen, Trangthanh Pham, Michael Tieu, La’Akea Siverts, Miranda Walker, Colin Farrell, Martin Schroedter, Cliff Slaughterbeck, Charles Cobb, Richard Ellenbogen, Ryder P. Gwinn, C. Dirk Keene, Andrew L. Ko, Jeffrey G. Ojemann, Daniel L. Silbergeld, Daniel Carey, Tamara Casper, Kirsten Crichton, Michael Clark, Nick Dee, Lauren Ellingwood, Jessica Gloe, Matthew Kroll, Josef Sulc, Herman Tung, Katherine Wadhwani, Krissy Brouner, Tom Egdorf, Michelle Maxwell, Medea McGraw, Christina Alice Pom, Augustin Ruiz, Jasmine Bomben, David Feng, Nika Hejazinia, Shu Shi, Aaron Szafer, Wayne Wakeman, John Phillips, Amy Bernard, Luke Esposito, Florence D. D’Orazi, Susan Sunkin, Kimberly Smith, Bosiljka Tasic, Anton Arkhipov, Staci Sorensen, Ed Lein, Christof Koch, Gabe Murphy, Hongkui Zeng, and Tim Jarsky

Subjects
Adult, Male, Neurons, Multidisciplinary, Models, Neurological, Datasets as Topic, Excitatory Postsynaptic Potentials, Mice, Transgenic, Neocortex, Synaptic Transmission, Article, Temporal Lobe, Mice, Inhibitory Postsynaptic Potentials, Neural Pathways, Synapses, Animals, Humans, Female, Visual Cortex
Abstract

We present a unique, extensive, and open synaptic physiology analysis platform and dataset. Through its application, we reveal principles that relate cell type to synaptic 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. 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, whereas the strength axis accounts for substantial heterogeneity within the subclass. In the human cortex, excitatory-to-excitatory synaptic dynamics are distinct from those in the mouse cortex and vary with depth across layers 2 and 3.

Published
2022

9. 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
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10. 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
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11. 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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12. 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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13. Toward an Integrated Classification of Cell Types: Morphoelectric and Transcriptomic Characterization of Individual GABAergic Cortical Neurons

Author

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

Subjects
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.

Published
2020
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14. Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells

Author

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
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