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1. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types.

Author

Trygve E Bakken, Rebecca D Hodge, Jeremy A Miller, Zizhen Yao, Thuc Nghi Nguyen, Brian Aevermann, Eliza Barkan, Darren Bertagnolli, Tamara Casper, Nick Dee, Emma Garren, Jeff Goldy, Lucas T Graybuck, Matthew Kroll, Roger S Lasken, Kanan Lathia, Sheana Parry, Christine Rimorin, Richard H Scheuermann, Nicholas J Schork, Soraya I Shehata, Michael Tieu, John W Phillips, Amy Bernard, Kimberly A Smith, Hongkui Zeng, Ed S Lein, and Bosiljka Tasic

Subjects
Medicine, Science
Abstract

Transcriptomic profiling of complex tissues by single-nucleus RNA-sequencing (snRNA-seq) affords some advantages over single-cell RNA-sequencing (scRNA-seq). snRNA-seq provides less biased cellular coverage, does not appear to suffer cell isolation-based transcriptional artifacts, and can be applied to archived frozen specimens. We used well-matched snRNA-seq and scRNA-seq datasets from mouse visual cortex to compare cell type detection. Although more transcripts are detected in individual whole cells (~11,000 genes) than nuclei (~7,000 genes), we demonstrate that closely related neuronal cell types can be similarly discriminated with both methods if intronic sequences are included in snRNA-seq analysis. We estimate that the nuclear proportion of total cellular mRNA varies from 20% to over 50% for large and small pyramidal neurons, respectively. Together, these results illustrate the high information content of nuclear RNA for characterization of cellular diversity in brain tissues.

Published
2018
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2. Neuropathological and transcriptomic characteristics of the aged brain

Author

Jeremy A Miller, Angela Guillozet-Bongaarts, Laura E Gibbons, Nadia Postupna, Anne Renz, Allison E Beller, Susan M Sunkin, Lydia Ng, Shannon E Rose, Kimberly A Smith, Aaron Szafer, Chris Barber, Darren Bertagnolli, Kristopher Bickley, Krissy Brouner, Shiella Caldejon, Mike Chapin, Mindy L Chua, Natalie M Coleman, Eiron Cudaback, Christine Cuhaciyan, Rachel A Dalley, Nick Dee, Tsega Desta, Tim A Dolbeare, Nadezhda I Dotson, Michael Fisher, Nathalie Gaudreault, Garrett Gee, Terri L Gilbert, Jeff Goldy, Fiona Griffin, Caroline Habel, Zeb Haradon, Nika Hejazinia, Leanne L Hellstern, Steve Horvath, Kim Howard, Robert Howard, Justin Johal, Nikolas L Jorstad, Samuel R Josephsen, Chihchau L Kuan, Florence Lai, Eric Lee, Felix Lee, Tracy Lemon, Xianwu Li, Desiree A Marshall, Jose Melchor, Shubhabrata Mukherjee, Julie Nyhus, Julie Pendergraft, Lydia Potekhina, Elizabeth Y Rha, Samantha Rice, David Rosen, Abharika Sapru, Aimee Schantz, Elaine Shen, Emily Sherfield, Shu Shi, Andy J Sodt, Nivretta Thatra, Michael Tieu, Angela M Wilson, Thomas J Montine, Eric B Larson, Amy Bernard, Paul K Crane, Richard G Ellenbogen, C Dirk Keene, and Ed Lein

Subjects
Alzheimer's disease, aging, dementia, gene expression, Medicine, Science, Biology (General), QH301-705.5
Abstract

As more people live longer, age-related neurodegenerative diseases are an increasingly important societal health issue. Treatments targeting specific pathologies such as amyloid beta in Alzheimer’s disease (AD) have not led to effective treatments, and there is increasing evidence of a disconnect between traditional pathology and cognitive abilities with advancing age, indicative of individual variation in resilience to pathology. Here, we generated a comprehensive neuropathological, molecular, and transcriptomic characterization of hippocampus and two regions cortex in 107 aged donors (median = 90) from the Adult Changes in Thought (ACT) study as a freely-available resource (http://aging.brain-map.org/). We confirm established associations between AD pathology and dementia, albeit with increased, presumably aging-related variability, and identify sets of co-expressed genes correlated with pathological tau and inflammation markers. Finally, we demonstrate a relationship between dementia and RNA quality, and find common gene signatures, highlighting the importance of properly controlling for RNA quality when studying dementia.

Published
2017
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3. 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

4. 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

5. 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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6. A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex

Author

Cindy T. J. van Velthoven, Eran A. Mukamel, Kanan Lathia, Jeff Goldy, Elizabeth Purdom, Hanqing Liu, Zizhen Yao, Xinxin Wang, Victor Felix, Olivia Fong, Brian R. Herb, Jacinta Lucero, Elizabeth L. Dougherty, Carlo Colantuoni, Thanh Pham, Bing Ren, Valentine Svensson, Sheng-Yong Niu, Rongxin Fang, Davide Risso, Seth A. Ament, Michael Tieu, Christine Rimorin, John Ngai, Ricky S. Adkins, Sandrine Dudoit, Naeem Nadaf, Stephan Fischer, Michael Hawrylycz, Evan Z. Macosko, Anup Mahurkar, Joshua Orvis, Lior Pachter, Thuc Nghi Nguyen, Peter V. Kharchenko, Vasilis Ntranos, Bosiljka Tasic, Joshua D. Welch, Darren Bertagnolli, Charles R. Vanderburg, Yang Eric Li, Eeshit Dhaval Vaishnav, Xiaomeng Hou, Joseph R. Ecker, Delissa McMillen, Kirsten Crichton, Heather Huot Creasy, Antonio Pinto-Duarte, Josef Sulc, A. Sina Booeshaghi, Megan Crow, Chongyuan Luo, Owen White, Kimberly A. Smith, Jayaram Kancherla, Jonathan Crabtree, Herman Tung, Wayne I. Doyle, Angeline Rivkin, M. Margarita Behrens, Hongkui Zeng, Kelly Street, Amy Torkelson, Tommaso Biancalani, Julia K. Osteen, Héctor Corrada Bravo, Aviv Regev, Anna Bartlett, Olivier Poirion, Nick Dee, Qiwen Hu, Michelle G. Giglio, Z. Josh Huang, Andrew Aldridge, Ronna Hertzano, Sebastian Preissl, Matthew Kroll, Koen Van den Berge, Fangming Xie, Jesse Gillis, Joseph R. Nery, Tamara Casper, and Hector Roux de Bézieux

Subjects
Epigenomics, Male, Cell type, General Science & Technology, 1.1 Normal biological development and functioning, Population, Datasets as Topic, Bioengineering, Molecular neuroscience, Computational biology, Biology, CLASSIFICATION, Article, Epigenesis, Genetic, Mice, Atlases as Topic, Single-cell analysis, Genetic, Underpinning research, MAPS, medicine, Genetics, Animals, education, Neurons, education.field_of_study, ARCHITECTURE, Multidisciplinary, Gene Expression Profiling, Human Genome, Neurosciences, Motor Cortex, Biology and Life Sciences, Reproducibility of Results, Cellular neuroscience, Gene expression profiling, medicine.anatomical_structure, Mathematics and Statistics, Organ Specificity, Neurological, Female, Primary motor cortex, Single-Cell Analysis, Transcriptome, Motor cortex, Epigenesis, Biotechnology
Abstract

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1–3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas—containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities—is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis., The authors describe an integrated atlas of the diverse cell types in the mouse primary motor cortex.

Published
2020

7. An integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types

Author

Z. Josh Huang, Valentine Svensson, Christine Rimorin, Sebastian Preissl, Qiwen Hu, Yang Eric Li, Carlo Colantuoni, Olivier Poirion, Darren Bertagnolli, Vasilis Ntranos, Antonio Pinto-Duarte, Megan Crow, Delissa McMillen, Evan Z. Macosko, Nick Dee, Zizhen Yao, Hongkui Zeng, Hector Roux de Bézieux, Bing Ren, Sheng-Yong Niu, Brian R. Herb, Jacinta Lucero, Ricky S. Adkins, Rongxin Fang, Eeshit Dhaval Vaishnav, Peter V. Kharchenko, Charles R. Vanderburg, Xiaomeng Hou, Joshua D. Welch, Angeline Rivkin, Sandrine Dudoit, Michael Tieu, Michael Hawrylycz, Jayaram Kancherla, Anup Mahurkar, Victor Felix, Lior Pachter, Jonathan Crabtree, Ronna Hertzano, Héctor Corrada Bravo, Aviv Regev, Wayne I. Doyle, Fangming Xie, Owen White, A. Sina Booeshaghi, Chongyuan Luo, Jeff Goldy, Andrew I. Aldrige, Joseph R. Ecker, Naeem Nadaf, Elizabeth Purdom, Hanqing Liu, Eran A. Mukamel, Kanan Lathia, Kelly Street, Michelle G. Giglio, Xinxin Wang, Julia K. Osteen, Olivia Fong, Bosiljka Tasic, Matthew Kroll, Tommaso Biancalani, Thanh Pham, John Ngai, Amy Torkelson, Thuc Nghi Nguyen, Ann Bartlett, Kimberly A. Smith, Kirsten Crichton, Herman Tung, Heather Huot Creasy, Josef Sulc, M. Margarita Behrens, Cindy T. J. van Velthoven, Koen Van den Berge, Jesse Gillis, Joseph R. Nery, Tamara Casper, Elizabeth L. Dougherty, Davide Risso, Seth A. Ament, Stephan Fischer, and Joshua Orvis

Subjects
0303 health sciences, Cell type, Cell, Computational biology, Epigenome, Biology, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, medicine.anatomical_structure, medicine, Primary motor cortex, Nucleus, Gene, 030217 neurology & neurosurgery, 030304 developmental biology, Epigenomics
Abstract

Single cell transcriptomics has transformed the characterization of brain cell identity by providing quantitative molecular signatures for large, unbiased samples of brain cell populations. With the proliferation of taxonomies based on individual datasets, a major challenge is to integrate and validate results toward defining biologically meaningful cell types. We used a battery of single-cell transcriptome and epigenome measurements generated by the BRAIN Initiative Cell Census Network (BICCN) to comprehensively assess the molecular signatures of cell types in the mouse primary motor cortex (MOp). We further developed computational and statistical methods to integrate these multimodal data and quantitatively validate the reproducibility of the cell types. The reference atlas, based on more than 600,000 high quality single-cell or -nucleus samples assayed by six molecular modalities, is a comprehensive molecular account of the diverse neuronal and non-neuronal cell types in MOp. Collectively, our study indicates that the mouse primary motor cortex contains over 55 neuronal cell types that are highly replicable across analysis methods, sequencing technologies, and modalities. We find many concordant multimodal markers for each cell type, as well as thousands of genes and gene regulatory elements with discrepant transcriptomic and epigenomic signatures. These data highlight the complex molecular regulation of brain cell types and will directly enable design of reagents to target specific MOp cell types for functional analysis.

Published
2020

8. 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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9. 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

10. A Taxonomy of Transcriptomic Cell Types Across the Isocortex and Hippocampal Formation

Author

James Gray, Adriana E. Sedeno-Cortes, Michael Tieu, Songlin Ding, Michael Hawrylycz, Herman Tung, Olivia Fong, Matthew Kroll, Stephanie Mok, Zizhen Yao, Darren Bertagnolli, Fahimeh Baftizadeh, Thanh Pham, Delissa McMillen, Thuc Nghi Nguyen, Hongkui Zeng, Tamara Casper, Katelyn Ward, Emma Garren, Kimberly A. Smith, Qingzhong Ren, Christine Rimorin, Jeff Goldy, Alexandra Glandon, Kanan Lathia, Lucas T. Graybuck, Amy Torkelson, Nick Dee, Nadiya V. Shapovalova, Susan M. Sunkin, Daniel Hirschstein, Bosiljka Tasic, Kirsten Crichton, Josef Sulc, Boaz P. Levi, and Cindy T. J. van Velthoven

Subjects
Transcriptome, Cell type, Glutamatergic, Neocortex, medicine.anatomical_structure, Taxonomy (general), medicine, Hippocampus, Hippocampal formation, Biology, Neuroscience, Function (biology)
Abstract

The isocortex and hippocampal formation are two major structures in the mammalian brain that play critical roles in perception, cognition, emotion and learning. Using single-cell RNA-sequencing approaches, we profiled ~1.2 million cells covering all regions in the adult mouse isocortex and hippocampal formation. The cell types are organized hierarchically and exhibit varying degrees of discrete or continuous variations. Such molecular relationships correlate strongly with the spatial distribution patterns of the cell types, which can be region-specific, shared across multiple regions, or part of one or more gradients. Glutamatergic neuron types display much greater diversity than GABAergic neuron types, both molecularly and spatially, and define regional identities as well as inter-region relationships. Our study establishes a molecular architecture of the mammalian isocortex and hippocampal formation for the first time, and begins to shed light on its underlying relationship with the development, evolution, connectivity and function of these two brain structures.

Published
2020

11. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation

Author

Zizhen Yao, Olivia Fong, Thanh Pham, Katelyn Ward, James Gray, Susan M. Sunkin, Stephanie Mok, Hongkui Zeng, Songlin Ding, Boaz P. Levi, Qingzhong Ren, Daniel Hirschstein, Emma Garren, Nick Dee, Megan Chiang, Fahimeh Baftizadeh, Christine Rimorin, Kanan Lathia, Herman Tung, Cindy T. J. van Velthoven, Darren Bertagnolli, Nadiya V. Shapovalova, Lucas T. Graybuck, Jeff Goldy, Michael Tieu, Delissa McMillen, Kimberly A. Smith, Michael Hawrylycz, Bosiljka Tasic, Amy Torkelson, Kirsten Crichton, Josef Sulc, Alexandra Glandon, Nathan W. Gouwens, Thuc Nghi Nguyen, Tamara Casper, Matthew Kroll, Adriana E. Sedeno-Cortes, and Changkyu Lee

Subjects
Cell type, Interneuron, Glutamic Acid, Hippocampus, Mice, Transgenic, Neocortex, Hippocampal formation, Biology, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Glutamatergic, 0302 clinical medicine, Cortex (anatomy), medicine, Animals, GABAergic Neurons, 030304 developmental biology, 0303 health sciences, Subiculum, Mice, Inbred C57BL, medicine.anatomical_structure, GABAergic, Transcriptome, Neuroscience, 030217 neurology & neurosurgery
Abstract

The isocortex and hippocampal formation (HPF) in the mammalian brain play critical roles in perception, cognition, emotion, and learning. We profiled ∼1.3 million cells covering the entire adult mouse isocortex and HPF and derived a transcriptomic cell-type taxonomy revealing a comprehensive repertoire of glutamatergic and GABAergic neuron types. Contrary to the traditional view of HPF as having a simpler cellular organization, we discover a complete set of glutamatergic types in HPF homologous to all major subclasses found in the six-layered isocortex, suggesting that HPF and the isocortex share a common circuit organization. We also identify large-scale continuous and graded variations of cell types along isocortical depth, across the isocortical sheet, and in multiple dimensions in hippocampus and subiculum. Overall, our study establishes a molecular architecture of the mammalian isocortex and hippocampal formation and begins to shed light on its underlying relationship with the development, evolution, connectivity, and function of these two brain structures.

Published
2021

12. Conserved cell types with divergent features in human versus mouse cortex

Author

Elliot R. Thomsen, Ahmed Mahfouz, Saroja Somasundaram, Aaron Oldre, Bosiljka Tasic, Songlin Ding, Richard H. Scheuermann, Daniel Hirschstein, Thomas Höllt, Christine Rimorin, Thuc Nghi Nguyen, Jennie L. Close, John W. Phillips, Lydia Ng, Jeff Goldy, Darren Bertagnolli, Amy Bernard, Zizhen Yao, Boaz P. Levi, Trygve E. Bakken, Soraya I. Shehata, Susan M. Sunkin, Osnat Penn, Michael Tieu, Allison Beller, Boudewijn P. F. Lelieveldt, Jeffrey G. Ojemann, Shannon Reynolds, Michael Hawrylycz, Jeroen Eggermont, Medea McGraw, Ryder P. Gwinn, Sheana Parry, Kimberly A. Smith, Brian Long, Olivia Fong, Zoe Maltzer, Rafael Yuste, David Feng, Julie Nyhus, Rebecca D. Hodge, Ed Lein, Jeremy A. Miller, Brian D. Aevermann, Gerald Quon, Emma Garren, Christof Koch, Aaron Szafer, Nick Dee, Nadiya V. Shapovalova, Rachel A. Dalley, Tamara Casper, Mohamed Keshk, Nelson Johansen, Krissy Brouner, Andrew L. Ko, Allan R. Jones, Eliza Barkan, Hongkui Zeng, Richard G. Ellenbogen, C. Dirk Keene, Kanan Lathia, Lucas T. Graybuck, and Charles Cobbs

Subjects
0301 basic medicine, Adult, Male, Cell type, Adolescent, General Science & Technology, Middle temporal gyrus, 1.1 Normal biological development and functioning, Biology, 03 medical and health sciences, Mice, Young Adult, 0302 clinical medicine, Single-cell analysis, Species Specificity, Underpinning research, Cortex (anatomy), medicine, Genetics, Animals, Humans, 2.1 Biological and endogenous factors, RNA-Seq, Aetiology, Aged, Cerebral Cortex, Neurons, Principal Component Analysis, Multidisciplinary, Cellular architecture, Neurosciences, Neural Inhibition, Human brain, Middle Aged, Biological Evolution, 030104 developmental biology, medicine.anatomical_structure, Cerebral cortex, Astrocytes, Neurological, Excitatory postsynaptic potential, Female, Single-Cell Analysis, Transcriptome, Neuroscience, 030217 neurology & neurosurgery
Abstract

Elucidating the cellular architecture of the human cerebral cortex is central to understanding our cognitive abilities and susceptibility to disease. Here we used single-nucleus RNA-sequencing analysis to perform a comprehensive study of cell types in the middle temporal gyrus of human cortex. We identified a highly diverse set of excitatory and inhibitory neuron types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to similar mouse cortex single-cell RNA-sequencing datasets revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of properties of human cell types. Despite this general conservation, we also found extensive differences between homologous human and mouse cell types, including marked alterations in proportions, laminar distributions, gene expression and morphology. These species-specific features emphasize the importance of directly studying human brain.

Published
2019

13. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types

Author

Jeremy A. Miller, Soraya I. Shehata, Thuc Nghi Nguyen, Matthew Kroll, Nick Dee, Sheana Parry, Rebecca D. Hodge, Kimberly A. Smith, Brian D. Aevermann, Ed Lein, Christine Rimorin, Darren Bertagnolli, Amy Bernard, Michael Tieu, Bosiljka Tasic, Eliza Barkan, Jeff Goldy, Emma Garren, Tamara Casper, Richard H. Scheuermann, Hongkui Zeng, Nicholas J. Schork, Trygve E. Bakken, Roger S. Lasken, Kanan Lathia, John W. Phillips, Lucas T. Graybuck, and Zizhen Yao

Subjects
0301 basic medicine, Cell, genetic processes, Gene Expression, Biochemistry, Transcriptome, Database and Informatics Methods, Mice, Single-cell analysis, Animal Cells, Gene expression, Visual Cortex, Neurons, Multidisciplinary, Mammalian Genomics, Messenger RNA, Genomics, Cell biology, Nucleic acids, medicine.anatomical_structure, Medicine, Cellular Types, Single-Cell Analysis, Transcriptome Analysis, Sequence Analysis, Research Article, Cell type, Sequence analysis, Bioinformatics, Science, Biology, Research and Analysis Methods, Genome Complexity, 03 medical and health sciences, medicine, Genetics, Animals, natural sciences, Cell Lineage, Molecular Biology Techniques, Molecular Biology, Cell Nucleus, Sequence Analysis, RNA, Gene Expression Profiling, Intron, Biology and Life Sciences, Computational Biology, Marker Genes, Cell Biology, Genome Analysis, Introns, 030104 developmental biology, Animal Genomics, Cellular Neuroscience, RNA, Nucleus, Sequence Alignment, Neuroscience
Abstract

Transcriptomic profiling of complex tissues by single-nucleus RNA-sequencing (snRNA-seq) affords some advantages over single-cell RNA-sequencing (scRNA-seq). snRNA-seq provides less biased cellular coverage, does not appear to suffer cell isolation-based transcriptional artifacts, and can be applied to archived frozen specimens. We used well-matched snRNA-seq and scRNA-seq datasets from mouse visual cortex to compare cell type detection. Although more transcripts are detected in individual whole cells (~11,000 genes) than nuclei (~7,000 genes), we demonstrate that closely related neuronal cell types can be similarly discriminated with both methods if intronic sequences are included in snRNA-seq analysis. We estimate that the nuclear proportion of total cellular mRNA varies from 20% to over 50% for large and small pyramidal neurons, respectively. Together, these results illustrate the high information content of nuclear RNA for characterization of cellular diversity in brain tissues.

Published
2018

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

15. 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

16. Equivalent high-resolution identification of neuronal cell types with single-nucleus and single-cell RNA-sequencing

Author

Trygve E. Bakken, Rebecca D. Hodge, Jeremy M. Miller, Zizhen Yao, Thuc N. Nguyen, Brian Aevermann, Eliza Barkan, Darren Bertagnolli, Tamara Casper, Nick Dee, Emma Garren, Jeff Goldy, Lucas T. Gray, Matthew Kroll, Roger S. Lasken, Kanan Lathia, Sheana Parry, Christine Rimorin, Richard H. Scheuermann, Nicholas J. Schork, Soraya I. Shehata, Michael Tieu, John W. Phillips, Amy Bernard, Kimberly A. Smith, Hongkui Zeng, Ed S. Lein, and Bosiljka Tasic

Subjects
Messenger RNA, Cell type, genetic processes, Cell, RNA, High resolution, Biology, Cell biology, medicine.anatomical_structure, Visual cortex, medicine, natural sciences, Gene, Nucleus
Abstract

Transcriptional profiling of complex tissues by RNA-sequencing of single nuclei presents some advantages over whole cell analysis. It enables unbiased cellular coverage, lack of cell isolation-based transcriptional effects, and application to archived frozen specimens. Using a well-matched pair of single-nucleus RNA-seq (snRNA-seq) and single-cell RNA-seq (scRNA-seq) SMART-Seq v4 datasets from mouse visual cortex, we demonstrate that similarly high-resolution clustering of closely related neuronal types can be achieved with both methods if intronic sequences are included in nuclear RNA-seq analysis. More transcripts are detected in individual whole cells (∼11,000 genes) than nuclei (∼7,000 genes), but the majority of genes have similar detection across cells and nuclei. We estimate that the nuclear proportion of total cellular mRNA varies from 20% to over 50% for large and small pyramidal neurons, respectively. Together, these results illustrate the high information content of nuclear RNA for characterization of cellular diversity in brain tissues.

Published
2017

17. Shared and distinct transcriptomic cell types across neocortical areas

Author

Daniel Hirschstein, Michael N. Economo, Allan R. Jones, Christine Rimorin, Eliza Barkan, Linda Madisen, Seana Parry, Susan M. Sunkin, Rachael Larsen, Hongkui Zeng, Tae Kyung Kim, Emma Garren, Kimberly A. Smith, Jeremy A. Miller, Osnat Penn, Olivia Fong, Sarada Viswanathan, Julie A. Harris, Bosiljka Tasic, Thuc Nghi Nguyen, Vilas Menon, Karel Svoboda, Matthew Kroll, Ed S. Lein, Peter A. Groblewski, Karla E. Hirokawa, Ali Cetin, Julie Pendergraft, Ian R. Wickersham, Tanya L. Daigle, Darren Bertagnolli, Jeff Goldy, Zizhen Yao, John W. Phillips, Michael Tieu, Loren L. Looger, Michael Hawrylycz, Aaron Szafer, Boaz P. Levi, Trygve E. Bakken, Nick Dee, Nadiya V. Shapovalova, Amy Bernard, Tamara Casper, Christof Koch, Kanan Lathia, and Lucas T. Graybuck

Subjects
Transcriptome, Cell type, medicine.anatomical_structure, Neocortex, Visual cortex, Cell, medicine, Excitatory postsynaptic potential, Biology, Inhibitory postsynaptic potential, Neuroscience, Motor cortex
Abstract

Neocortex contains a multitude of cell types segregated into layers and functionally distinct regions. To investigate the diversity of cell types across the mouse neocortex, we analyzed 12,714 cells from the primary visual cortex (VISp), and 9,035 cells from the anterior lateral motor cortex (ALM) by deep single-cell RNA-sequencing (scRNA-seq), identifying 116 transcriptomic cell types. These two regions represent distant poles of the neocortex and perform distinct functions. We define 50 inhibitory transcriptomic cell types, all of which are shared across both cortical regions. In contrast, 49 of 52 excitatory transcriptomic types were found in either VISp or ALM, with only three present in both. By combining single cell RNA-seq and retrograde labeling, we demonstrate correspondence between excitatory transcriptomic types and their region-specific long-range target specificity. This study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct regions of the mouse cortex.

Published
2017
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18. Shared and distinct transcriptomic cell types across neocortical areas

Author

Zizhen Yao, Christof Koch, Daniel Hirschstein, Aaron Szafer, Nick Dee, Sheana Parry, Michael Tieu, Michael Hawrylycz, Jeff Goldy, Susan M. Sunkin, Nadiya V. Shapovalova, Amy Bernard, Kanan Lathia, Lucas T. Graybuck, Boaz P. Levi, Trygve E. Bakken, Matthew Kroll, Sarada Viswanathan, Kimberly A. Smith, Thuc Nghi Nguyen, Olivia Fong, Tae Kyung Kim, Tanya L. Daigle, Jeremy A. Miller, Christine Rimorin, Linda Madisen, Karla E. Hirokawa, Tamara Casper, Julie A. Harris, Ali Cetin, Heather A. Sullivan, Bosiljka Tasic, Karel Svoboda, Julie Pendergraft, Osnat Penn, John W. Phillips, Ian R. Wickersham, Ed S. Lein, Loren L. Looger, Peter A. Groblewski, Hongkui Zeng, Allan R. Jones, Rachael Larsen, Emma Garren, Darren Bertagnolli, Michael N. Economo, Eliza Barkan, and Vilas Menon

Subjects
0301 basic medicine, Male, Cell type, Glutamic Acid, Neocortex, Biology, Article, Transcriptome, 03 medical and health sciences, Glutamatergic, Mice, Single-cell analysis, medicine, Animals, GABAergic Neurons, Visual Cortex, Multidisciplinary, Sequence Analysis, RNA, Gene Expression Profiling, Motor Cortex, Gene expression profiling, 030104 developmental biology, medicine.anatomical_structure, Visual cortex, nervous system, Organ Specificity, Female, Single-Cell Analysis, Neuroscience, Biomarkers, Motor cortex
Abstract

The neocortex contains a multitude of cell types that are segregated into layers and functionally distinct areas. To investigate the diversity of cell types across the mouse neocortex, here we analysed 23,822 cells from two areas at distant poles of the mouse neocortex: the primary visual cortex and the anterior lateral motor cortex. We define 133 transcriptomic cell types by deep, single-cell RNA sequencing. Nearly all types of GABA (γ-aminobutyric acid)-containing neurons are shared across both areas, whereas most types of glutamatergic neurons were found in one of the two areas. By combining single-cell RNA sequencing and retrograde labelling, we match transcriptomic types of glutamatergic neurons to their long-range projection specificity. Our study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex.

Published
2017

19. Neuropathological and transcriptomic characteristics of the aged brain

Author

Terri L. Gilbert, Thomas J. Montine, Aaron Szafer, Eiron Cudaback, Nick Dee, Christine Cuhaciyan, Xianwu Li, Kimberly A. Smith, Samuel R Josephsen, Tim A. Dolbeare, Paul K. Crane, Amy Bernard, Nadezhda Dotson, Michael Tieu, Fiona Griffin, Shannon E. Rose, Ed S. Lein, Jeff Goldy, Laura E. Gibbons, Julie Pendergraft, Felix Lee, Nivretta Thatra, Elaine Shen, Elizabeth Rha, Susan M. Sunkin, Garrett Gee, Lydia Potekhina, Nadia Postupna, Darren Bertagnolli, Andy J. Sodt, Nikolas L. Jorstad, Angela L. Guillozet-Bongaarts, Samantha Rice, Jeremy A. Miller, Natalie M Coleman, Zeb Haradon, Tsega Desta, David Rosen, Angela M. Wilson, Justin Johal, Shubhabrata Mukherjee, Kristopher Bickley, Anne Renz, Abharika Sapru, Robert Howard, Desiree A. Marshall, Tracy Lemon, Nika Hejazinia, Rachel A. Dalley, Shu Shi, Steve Horvath, Mindy L Chua, Aimee Schantz, Eric B. Larson, Allison Beller, Michael S. Fisher, Julie Nyhus, C. Dirk Keene, Lydia Ng, Kim Howard, Caroline Habel, Nathalie Gaudreault, Krissy Brouner, Leanne L Hellstern, Shiella Caldejon, Jose Melchor, Emily Sherfield, Chihchau L. Kuan, Mike Chapin, Richard G. Ellenbogen, Chris Barber, Florence Lai, and Eric Lee

Subjects
0301 basic medicine, Male, Amyloid beta, QH301-705.5, Science, Hippocampus, Disease, General Biochemistry, Genetics and Molecular Biology, Transcriptome, 03 medical and health sciences, 0302 clinical medicine, Alzheimer Disease, medicine, Dementia, Humans, Biology (General), Gene, Pathological, Aged, Aged, 80 and over, Cerebral Cortex, General Immunology and Microbiology, biology, business.industry, General Neuroscience, Gene Expression Profiling, aging, Cognition, General Medicine, Alzheimer's disease, medicine.disease, 3. Good health, Tools and Resources, 030104 developmental biology, biology.protein, gene expression, Medicine, Female, business, Neuroscience, 030217 neurology & neurosurgery, Human, dementia
Abstract

As more people live longer, age-related neurodegenerative diseases are an increasingly important societal health issue. Treatments targeting specific pathologies such as amyloid beta in Alzheimer’s disease (AD) have not led to effective treatments, and there is increasing evidence of a disconnect between traditional pathology and cognitive abilities with advancing age, indicative of individual variation in resilience to pathology. Here, we generated a comprehensive neuropathological, molecular, and transcriptomic characterization of hippocampus and two regions cortex in 107 aged donors (median = 90) from the Adult Changes in Thought (ACT) study as a freely-available resource (http://aging.brain-map.org/). We confirm established associations between AD pathology and dementia, albeit with increased, presumably aging-related variability, and identify sets of co-expressed genes correlated with pathological tau and inflammation markers. Finally, we demonstrate a relationship between dementia and RNA quality, and find common gene signatures, highlighting the importance of properly controlling for RNA quality when studying dementia.

Published
2017

20. Author response: Neuropathological and transcriptomic characteristics of the aged brain

Author

Lydia Ng, Shubhabrata Mukherjee, Amy Bernard, Mindy L Chua, Jeff Goldy, Fiona Griffin, Darren Bertagnolli, Shu Shi, Ed S. Lein, Susan M. Sunkin, Nika Hejazinia, Justin Johal, Krissy Brouner, Jeremy A. Miller, Steve Horvath, Zeb Haradon, Natalie M Coleman, Jose Melchor, Desiree A. Marshall, Samuel R Josephsen, Paul K. Crane, Kristopher Bickley, Tim A. Dolbeare, Kim Howard, Michael S. Fisher, C. Dirk Keene, Angela M. Wilson, Tracy Lemon, Aaron Szafer, Eiron Cudaback, Nick Dee, Eric B. Larson, Elizabeth Rha, Chris Barber, Julie Nyhus, Michael Tieu, Lydia Potekhina, Caroline Habel, Nathalie Gaudreault, Kimberly A. Smith, Shannon E. Rose, Nikolas L. Jorstad, Angela L. Guillozet-Bongaarts, Terri L. Gilbert, Felix Lee, Nivretta Thatra, Florence Lai, Julie Pendergraft, Elaine Shen, Eric Lee, Christine Cuhaciyan, Chihchau L. Kuan, Mike Chapin, Richard G. Ellenbogen, Andy J. Sodt, Leanne L Hellstern, Shiella Caldejon, Nadia Postupna, David Rosen, Emily Sherfield, Tsega Desta, Anne Renz, Xianwu Li, Abharika Sapru, Robert Howard, Rachel A. Dalley, Aimee Schantz, Allison Beller, Samantha Rice, Nadezhda Dotson, Laura E. Gibbons, Garrett Gee, and Thomas J. Montine

Subjects
0301 basic medicine, Transcriptome, 03 medical and health sciences, 030104 developmental biology, business.industry, Medicine, business, Neuroscience
Published
2017

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