Author: "Hawrylycz, Michael" - Searchworks@Jio Institute Digital Library Search Results

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1. SEA-AD is a multimodal cellular atlas and resource for Alzheimer’s disease

Author: Hawrylycz, Michael, Kaplan, Eitan S., Travaglini, Kyle J., Gabitto, Mariano I., Miller, Jeremy A., Ng, Lydia, Close, Jennie L., Hodge, Rebecca D., Long, Brian, Mollenkopf, Tyler, Mufti, Shoaib, Gatto, Nicole M., Larson, Eric B., Crane, Paul K., Grabowski, Thomas J., Keene, C. Dirk, and Lein, Ed S.
Published: 2024
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2. Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

Author: Manubens-Gil, Linus, Zhou, Zhi, Chen, Hanbo, Ramanathan, Arvind, Liu, Xiaoxiao, Liu, Yufeng, Bria, Alessandro, Gillette, Todd, Ruan, Zongcai, Yang, Jian, Radojević, Miroslav, Zhao, Ting, Cheng, Li, Qu, Lei, Liu, Siqi, Bouchard, Kristofer E., Gu, Lin, Cai, Weidong, Ji, Shuiwang, Roysam, Badrinath, Wang, Ching-Wei, Yu, Hongchuan, Sironi, Amos, Iascone, Daniel Maxim, Zhou, Jie, Bas, Erhan, Conde-Sousa, Eduardo, Aguiar, Paulo, Li, Xiang, Li, Yujie, Nanda, Sumit, Wang, Yuan, Muresan, Leila, Fua, Pascal, Ye, Bing, He, Hai-yan, Staiger, Jochen F., Peter, Manuel, Cox, Daniel N., Simonneau, Michel, Oberlaender, Marcel, Jefferis, Gregory, Ito, Kei, Gonzalez-Bellido, Paloma, Kim, Jinhyun, Rubel, Edwin, Cline, Hollis T., Zeng, Hongkui, Nern, Aljoscha, Chiang, Ann-Shyn, Yao, Jianhua, Roskams, Jane, Livesey, Rick, Stevens, Janine, Liu, Tianming, Dang, Chinh, Guo, Yike, Zhong, Ning, Tourassi, Georgia, Hill, Sean, Hawrylycz, Michael, Koch, Christof, Meijering, Erik, Ascoli, Giorgio A., and Peng, Hanchuan
Published: 2024
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3. BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

Author: Manubens-Gil, Linus, Zhou, Zhi, Chen, Hanbo, Ramanathan, Arvind, Liu, Xiaoxiao, Liu, Yufeng, Bria, Alessandro, Gillette, Todd, Ruan, Zongcai, Yang, Jian, Radojević, Miroslav, Zhao, Ting, Cheng, Li, Qu, Lei, Liu, Siqi, Bouchard, Kristofer E, Gu, Lin, Cai, Weidong, Ji, Shuiwang, Roysam, Badrinath, Wang, Ching-Wei, Yu, Hongchuan, Sironi, Amos, Iascone, Daniel Maxim, Zhou, Jie, Bas, Erhan, Conde-Sousa, Eduardo, Aguiar, Paulo, Li, Xiang, Li, Yujie, Nanda, Sumit, Wang, Yuan, Muresan, Leila, Fua, Pascal, Ye, Bing, He, Hai-yan, Staiger, Jochen F, Peter, Manuel, Cox, Daniel N, Simonneau, Michel, Oberlaender, Marcel, Jefferis, Gregory, Ito, Kei, Gonzalez-Bellido, Paloma, Kim, Jinhyun, Rubel, Edwin, Cline, Hollis T, Zeng, Hongkui, Nern, Aljoscha, Chiang, Ann-Shyn, Yao, Jianhua, Roskams, Jane, Livesey, Rick, Stevens, Janine, Liu, Tianming, Dang, Chinh, Guo, Yike, Zhong, Ning, Tourassi, Georgia, Hill, Sean, Hawrylycz, Michael, Koch, Christof, Meijering, Erik, Ascoli, Giorgio A, and Peng, Hanchuan
Subjects: Biological Sciences, Bioengineering, Networking and Information Technology R&D (NITRD), Neurosciences, Benchmarking, Microscopy, Imaging, Three-Dimensional, Neurons, Algorithms, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
Abstract: BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.
Published: 2023

4. Author Correction: Brain Data Standards - A method for building data-driven cell-type ontologies

Author: Tan, Shawn Zheng Kai, Kir, Huseyin, Aevermann, Brian D, Gillespie, Tom, Harris, Nomi, Hawrylycz, Michael J, Jorstad, Nikolas L, Lein, Ed S, Matentzoglu, Nicolas, Miller, Jeremy A, Mollenkopf, Tyler S, Mungall, Christopher J, Ray, Patrick L, Sanchez, Raymond EA, Staats, Brian, Vermillion, Jim, Yadav, Ambika, Zhang, Yun, Scheuermann, Richard H, and Osumi-Sutherland, David
Abstract: Correction to: Scientific Data, published online 24 January 2023 In this article the funding from ‘NIMH/NIH:1U24MH114827-01 - “A Community Resource for Single Cell Data in the Brain”. ’ was omitted. The original article has been corrected.
Published: 2023

5. A guide to the BRAIN Initiative Cell Census Network data ecosystem

Author: Hawrylycz, Michael, Martone, Maryann E, Ascoli, Giorgio A, Bjaalie, Jan G, Dong, Hong-Wei, Ghosh, Satrajit S, Gillis, Jesse, Hertzano, Ronna, Haynor, David R, Hof, Patrick R, Kim, Yongsoo, Lein, Ed, Liu, Yufeng, Miller, Jeremy A, Mitra, Partha P, Mukamel, Eran, Ng, Lydia, Osumi-Sutherland, David, Peng, Hanchuan, Ray, Patrick L, Sanchez, Raymond, Regev, Aviv, Ropelewski, Alex, Scheuermann, Richard H, Tan, Shawn Zheng Kai, Thompson, Carol L, Tickle, Timothy, Tilgner, Hagen, Varghese, Merina, Wester, Brock, White, Owen, Zeng, Hongkui, Aevermann, Brian, Allemang, David, Ament, Seth, Athey, Thomas L, Baker, Cody, Baker, Katherine S, Baker, Pamela M, Bandrowski, Anita, Banerjee, Samik, Bishwakarma, Prajal, Carr, Ambrose, Chen, Min, Choudhury, Roni, Cool, Jonah, Creasy, Heather, D’Orazi, Florence, Degatano, Kylee, Dichter, Benjamin, Ding, Song-Lin, Dolbeare, Tim, Ecker, Joseph R, Fang, Rongxin, Fillion-Robin, Jean-Christophe, Fliss, Timothy P, Gee, James, Gillespie, Tom, Gouwens, Nathan, Zhang, Guo-Qiang, Halchenko, Yaroslav O, Harris, Nomi L, Herb, Brian R, Hintiryan, Houri, Hood, Gregory, Horvath, Sam, Huo, Bingxing, Jarecka, Dorota, Jiang, Shengdian, Khajouei, Farzaneh, Kiernan, Elizabeth A, Kir, Huseyin, Kruse, Lauren, Lee, Changkyu, Lelieveldt, Boudewijn, Li, Yang, Liu, Hanqing, Liu, Lijuan, Markuhar, Anup, Mathews, James, Mathews, Kaylee L, Mezias, Chris, Miller, Michael I, Mollenkopf, Tyler, Mufti, Shoaib, Mungall, Christopher J, Orvis, Joshua, Puchades, Maja A, Qu, Lei, Receveur, Joseph P, Ren, Bing, Sjoquist, Nathan, Staats, Brian, Tward, Daniel, van Velthoven, Cindy TJ, Wang, Quanxin, Xie, Fangming, Xu, Hua, Yao, Zizhen, and Yun, Zhixi
Subjects: Biological Sciences, Genetics, Neurosciences, Data Science, Mental Health, 1.1 Normal biological development and functioning, Neurological, Animals, Humans, Mice, Brain, Ecosystem, Neurons, Agricultural and Veterinary Sciences, Medical and Health Sciences, Developmental Biology, Agricultural, veterinary and food sciences, Biological sciences, Biomedical and clinical sciences
Abstract: Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.
Published: 2023

6. Brain Data Standards - A method for building data-driven cell-type ontologies

Author: Tan, Shawn Zheng Kai, Kir, Huseyin, Aevermann, Brian D, Gillespie, Tom, Harris, Nomi, Hawrylycz, Michael J, Jorstad, Nikolas L, Lein, Ed S, Matentzoglu, Nicolas, Miller, Jeremy A, Mollenkopf, Tyler S, Mungall, Christopher J, Ray, Patrick L, Sanchez, Raymond EA, Staats, Brian, Vermillion, Jim, Yadav, Ambika, Zhang, Yun, Scheuermann, Richard H, and Osumi-Sutherland, David
Subjects: Networking and Information Technology R&D (NITRD), Neurosciences, Neurological, Animals, Humans, Mice, Biological Ontologies, Brain, Callithrix, Data Collection
Abstract: Large-scale single-cell 'omics profiling is being used to define a complete catalogue of brain cell types, something that traditional methods struggle with due to the diversity and complexity of the brain. But this poses a problem: How do we organise such a catalogue - providing a standard way to refer to the cell types discovered, linking their classification and properties to supporting data? Cell ontologies provide a partial solution to these problems, but no existing ontology schemas support the definition of cell types by direct reference to supporting data, classification of cell types using classifications derived directly from data, or links from cell types to marker sets along with confidence scores. Here we describe a generally applicable schema that solves these problems and its application in a semi-automated pipeline to build a data-linked extension to the Cell Ontology representing cell types in the Primary Motor Cortex of humans, mice and marmosets. The methods and resulting ontology are designed to be scalable and applicable to similar whole-brain atlases currently in preparation.
Published: 2023

7. A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain

Author: Yao, Zizhen, van Velthoven, Cindy T. J., Kunst, Michael, Zhang, Meng, McMillen, Delissa, Lee, Changkyu, Jung, Won, Goldy, Jeff, Abdelhak, Aliya, Aitken, Matthew, Baker, Katherine, Baker, Pamela, Barkan, Eliza, Bertagnolli, Darren, Bhandiwad, Ashwin, Bielstein, Cameron, Bishwakarma, Prajal, Campos, Jazmin, Carey, Daniel, Casper, Tamara, Chakka, Anish Bhaswanth, Chakrabarty, Rushil, Chavan, Sakshi, Chen, Min, Clark, Michael, Close, Jennie, Crichton, Kirsten, Daniel, Scott, DiValentin, Peter, Dolbeare, Tim, Ellingwood, Lauren, Fiabane, Elysha, Fliss, Timothy, Gee, James, Gerstenberger, James, Glandon, Alexandra, Gloe, Jessica, Gould, Joshua, Gray, James, Guilford, Nathan, Guzman, Junitta, Hirschstein, Daniel, Ho, Windy, Hooper, Marcus, Huang, Mike, Hupp, Madie, Jin, Kelly, Kroll, Matthew, Lathia, Kanan, Leon, Arielle, Li, Su, Long, Brian, Madigan, Zach, Malloy, Jessica, Malone, Jocelin, Maltzer, Zoe, Martin, Naomi, McCue, Rachel, McGinty, Ryan, Mei, Nicholas, Melchor, Jose, Meyerdierks, Emma, Mollenkopf, Tyler, Moonsman, Skyler, Nguyen, Thuc Nghi, Otto, Sven, Pham, Trangthanh, Rimorin, Christine, Ruiz, Augustin, Sanchez, Raymond, Sawyer, Lane, Shapovalova, Nadiya, Shepard, Noah, Slaughterbeck, Cliff, Sulc, Josef, Tieu, Michael, Torkelson, Amy, Tung, Herman, Valera Cuevas, Nasmil, Vance, Shane, Wadhwani, Katherine, Ward, Katelyn, Levi, Boaz, Farrell, Colin, Young, Rob, Staats, Brian, Wang, Ming-Qiang Michael, Thompson, Carol L., Mufti, Shoaib, Pagan, Chelsea M., Kruse, Lauren, Dee, Nick, Sunkin, Susan M., Esposito, Luke, Hawrylycz, Michael J., Waters, Jack, Ng, Lydia, Smith, Kimberly, Tasic, Bosiljka, Zhuang, Xiaowei, and Zeng, Hongkui
Published: 2023
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8. Single cell enhancer activity distinguishes GABAergic and cholinergic lineages in embryonic mouse basal ganglia

Author: Su-Feher, Linda, Rubin, Anna N, Silberberg, Shanni N, Catta-Preta, Rinaldo, Lim, Kenneth J, Ypsilanti, Athena R, Zdilar, Iva, McGinnis, Christopher S, McKinsey, Gabriel L, Rubino, Thomas E, Hawrylycz, Michael J, Thompson, Carol, Gartner, Zev J, Puelles, Luis, Zeng, Hongkui, Rubenstein, John LR, and Nord, Alex S
Subjects: Biological Sciences, Biomedical and Clinical Sciences, Genetics, Human Genome, Mental Health, Neurosciences, Stem Cell Research, 1.1 Normal biological development and functioning, Underpinning research, Neurological, Animals, Basal Ganglia, Cell Lineage, Cholinergic Neurons, Enhancer Elements, Genetic, GABAergic Neurons, Mice, Neurogenesis, RNA-Seq, Single-Cell Analysis, Transcription Factors, genetics, neuroscience, development, enhancer, neurogenesis
Abstract: Enhancers integrate transcription factor signaling pathways that drive cell fate specification in the developing brain. We paired enhancer labeling and single-cell RNA-sequencing (scRNA-seq) to delineate and distinguish specification of neuronal lineages in mouse medial, lateral, and caudal ganglionic eminences (MGE, LGE, and CGE) at embryonic day (E)11.5. We show that scRNA-seq clustering using transcription factors improves resolution of regional and developmental populations, and that enhancer activities identify specific and overlapping GE-derived neuronal populations. First, we mapped the activities of seven evolutionarily conserved brain enhancers at single-cell resolution in vivo, finding that the selected enhancers had diverse activities in specific progenitor and neuronal populations across the GEs. We then applied enhancer-based labeling, scRNA-seq, and analysis of in situ hybridization data to distinguish transcriptionally distinct and spatially defined subtypes of MGE-derived GABAergic and cholinergic projection neurons and interneurons. Our results map developmental origins and specification paths underlying neurogenesis in the embryonic basal ganglia and showcase the power of scRNA-seq combined with enhancer-based labeling to resolve the complex paths of neuronal specification underlying mouse brain development.
Published: 2022

9. Is Neuroscience FAIR? A Call for Collaborative Standardisation of Neuroscience Data.

Author: Poline, Jean-Baptiste, Kennedy, David N, Sommer, Friedrich T, Ascoli, Giorgio A, Van Essen, David C, Ferguson, Adam R, Grethe, Jeffrey S, Hawrylycz, Michael J, Thompson, Paul M, Poldrack, Russell A, Ghosh, Satrajit S, Keator, David B, Athey, Thomas L, Vogelstein, Joshua T, Mayberg, Helen S, and Martone, Maryann E
Subjects: Data Collection, Neurosciences, International Neuroinformatics Coordinating Facility, Interoperability, Neuroscience, Standards, Networking and Information Technology R&D (NITRD), Standards, International Neuroinformatics Coordinating Facility, Biochemistry and Cell Biology, Neurology & Neurosurgery
Abstract: In this perspective article, we consider the critical issue of data and other research object standardisation and, specifically, how international collaboration, and organizations such as the International Neuroinformatics Coordinating Facility (INCF) can encourage that emerging neuroscience data be Findable, Accessible, Interoperable, and Reusable (FAIR). As neuroscientists engaged in the sharing and integration of multi-modal and multiscale data, we see the current insufficiency of standards as a major impediment in the Interoperability and Reusability of research results. We call for increased international collaborative standardisation of neuroscience data to foster integration and efficient reuse of research objects.
Published: 2022

10. Transcriptional network orchestrating regional patterning of cortical progenitors

Author: Ypsilanti, Athéna R, Pattabiraman, Kartik, Catta-Preta, Rinaldo, Golonzhka, Olga, Lindtner, Susan, Tang, Ke, Jones, Ian R, Abnousi, Armen, Juric, Ivan, Hu, Ming, Shen, Yin, Dickel, Diane E, Visel, Axel, Pennachio, Len A, Hawrylycz, Michael, Thompson, Carol L, Zeng, Hongkui, Barozzi, Iros, Nord, Alex S, and Rubenstein, John L
Subjects: Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Human Genome, Stem Cell Research - Nonembryonic - Non-Human, Genetics, Stem Cell Research, 1.1 Normal biological development and functioning, Neurological, Animals, COUP Transcription Factor I, Cerebral Cortex, Epigenome, Gene Regulatory Networks, Homeodomain Proteins, LIM-Homeodomain Proteins, Mice, PAX6 Transcription Factor, Pre-B-Cell Leukemia Transcription Factor 1, Regulatory Elements, Transcriptional, Transcription Factors, cortical patterning, epigenetics, transcription factors, progenitor cells
Abstract: We uncovered a transcription factor (TF) network that regulates cortical regional patterning in radial glial stem cells. Screening the expression of hundreds of TFs in the developing mouse cortex identified 38 TFs that are expressed in gradients in the ventricular zone (VZ). We tested whether their cortical expression was altered in mutant mice with known patterning defects (Emx2, Nr2f1, and Pax6), which enabled us to define a cortical regionalization TF network (CRTFN). To identify genomic programming underlying this network, we performed TF ChIP-seq and chromatin-looping conformation to identify enhancer-gene interactions. To map enhancers involved in regional patterning of cortical progenitors, we performed assays for epigenomic marks and DNA accessibility in VZ cells purified from wild-type and patterning mutant mice. This integrated approach has identified a CRTFN and VZ enhancers involved in cortical regional patterning in the mouse.
Published: 2021

11. Comparative cellular analysis of motor cortex in human, marmoset and mouse

Author: Bakken, Trygve E, Jorstad, Nikolas L, Hu, Qiwen, Lake, Blue B, Tian, Wei, Kalmbach, Brian E, Crow, Megan, Hodge, Rebecca D, Krienen, Fenna M, Sorensen, Staci A, Eggermont, Jeroen, Yao, Zizhen, Aevermann, Brian D, Aldridge, Andrew I, Bartlett, Anna, Bertagnolli, Darren, Casper, Tamara, Castanon, Rosa G, Crichton, Kirsten, Daigle, Tanya L, Dalley, Rachel, Dee, Nick, Dembrow, Nikolai, Diep, Dinh, Ding, Song-Lin, Dong, Weixiu, Fang, Rongxin, Fischer, Stephan, Goldman, Melissa, Goldy, Jeff, Graybuck, Lucas T, Herb, Brian R, Hou, Xiaomeng, Kancherla, Jayaram, Kroll, Matthew, Lathia, Kanan, van Lew, Baldur, Li, Yang Eric, Liu, Christine S, Liu, Hanqing, Lucero, Jacinta D, Mahurkar, Anup, McMillen, Delissa, Miller, Jeremy A, Moussa, Marmar, Nery, Joseph R, Nicovich, Philip R, Niu, Sheng-Yong, Orvis, Joshua, Osteen, Julia K, Owen, Scott, Palmer, Carter R, Pham, Thanh, Plongthongkum, Nongluk, Poirion, Olivier, Reed, Nora M, Rimorin, Christine, Rivkin, Angeline, Romanow, William J, Sedeño-Cortés, Adriana E, Siletti, Kimberly, Somasundaram, Saroja, Sulc, Josef, Tieu, Michael, Torkelson, Amy, Tung, Herman, Wang, Xinxin, Xie, Fangming, Yanny, Anna Marie, Zhang, Renee, Ament, Seth A, Behrens, M Margarita, Bravo, Hector Corrada, Chun, Jerold, Dobin, Alexander, Gillis, Jesse, Hertzano, Ronna, Hof, Patrick R, Höllt, Thomas, Horwitz, Gregory D, Keene, C Dirk, Kharchenko, Peter V, Ko, Andrew L, Lelieveldt, Boudewijn P, Luo, Chongyuan, Mukamel, Eran A, Pinto-Duarte, António, Preissl, Sebastian, Regev, Aviv, Ren, Bing, Scheuermann, Richard H, Smith, Kimberly, Spain, William J, White, Owen R, Koch, Christof, Hawrylycz, Michael, Tasic, Bosiljka, Macosko, Evan Z, McCarroll, Steven A, and Ting, Jonathan T
Subjects: Human Genome, Neurosciences, Genetics, Biotechnology, Underpinning research, 1.1 Normal biological development and functioning, Neurological, Animals, Atlases as Topic, Callithrix, Epigenesis, Genetic, Epigenomics, Female, GABAergic Neurons, Gene Expression Profiling, Glutamates, Humans, In Situ Hybridization, Fluorescence, Male, Mice, Middle Aged, Motor Cortex, Neurons, Organ Specificity, Phylogeny, Single-Cell Analysis, Species Specificity, Transcriptome, General Science & Technology
Abstract: The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.
Published: 2021

12. A multimodal cell census and atlas of the mammalian primary motor cortex

Author: Callaway, Edward M, Dong, Hong-Wei, Ecker, Joseph R, Hawrylycz, Michael J, Huang, Z Josh, Lein, Ed S, Ngai, John, Osten, Pavel, Ren, Bing, Tolias, Andreas Savas, White, Owen, Zeng, Hongkui, Zhuang, Xiaowei, Ascoli, Giorgio A, Behrens, M Margarita, Chun, Jerold, Feng, Guoping, Gee, James C, Ghosh, Satrajit S, Halchenko, Yaroslav O, Hertzano, Ronna, Lim, Byung Kook, Martone, Maryann E, Ng, Lydia, Pachter, Lior, Ropelewski, Alexander J, Tickle, Timothy L, Yang, X William, Zhang, Kun, Bakken, Trygve E, Berens, Philipp, Daigle, Tanya L, Harris, Julie A, Jorstad, Nikolas L, Kalmbach, Brian E, Kobak, Dmitry, Li, Yang Eric, Liu, Hanqing, Matho, Katherine S, Mukamel, Eran A, Naeemi, Maitham, Scala, Federico, Tan, Pengcheng, Ting, Jonathan T, Xie, Fangming, Zhang, Meng, Zhang, Zhuzhu, Zhou, Jingtian, Zingg, Brian, Armand, Ethan, Yao, Zizhen, Bertagnolli, Darren, Casper, Tamara, Crichton, Kirsten, Dee, Nick, Diep, Dinh, Ding, Song-Lin, Dong, Weixiu, Dougherty, Elizabeth L, Fong, Olivia, Goldman, Melissa, Goldy, Jeff, Hodge, Rebecca D, Hu, Lijuan, Keene, C Dirk, Krienen, Fenna M, Kroll, Matthew, Lake, Blue B, Lathia, Kanan, Linnarsson, Sten, Liu, Christine S, Macosko, Evan Z, McCarroll, Steven A, McMillen, Delissa, Nadaf, Naeem M, Nguyen, Thuc Nghi, Palmer, Carter R, Pham, Thanh, Plongthongkum, Nongluk, Reed, Nora M, Regev, Aviv, Rimorin, Christine, Romanow, William J, Savoia, Steven, Siletti, Kimberly, Smith, Kimberly, Sulc, Josef, Tasic, Bosiljka, Tieu, Michael, Torkelson, Amy, Tung, Herman, van Velthoven, Cindy TJ, Vanderburg, Charles R, Yanny, Anna Marie, Fang, Rongxin, Hou, Xiaomeng, Lucero, Jacinta D, Osteen, Julia K, Pinto-Duarte, Antonio, and Poirion, Olivier
Subjects: Genetics, Neurosciences, Human Genome, Biotechnology, Underpinning research, 1.1 Normal biological development and functioning, Neurological, Animals, Atlases as Topic, Callithrix, Epigenomics, Female, Gene Expression Profiling, Glutamates, Humans, In Situ Hybridization, Fluorescence, Male, Mice, Motor Cortex, Neurons, Organ Specificity, Phylogeny, Single-Cell Analysis, Species Specificity, Transcriptome, BRAIN Initiative Cell Census Network, General Science & Technology
Abstract: Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1-5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.
Published: 2021

13. Common Cell type Nomenclature for the mammalian brain: A systematic, extensible convention

Author: Miller, Jeremy A., Gouwens, Nathan W., Tasic, Bosiljka, Collman, Forrest, van Velthoven, Cindy T. J., Bakken, Trygve E., Hawrylycz, Michael J., Zeng, Hongkui, Lein, Ed S., and Bernard, Amy
Subjects: Quantitative Biology - Neurons and Cognition
Abstract: The advancement of single cell RNA-sequencing technologies has led to an explosion of cell type definitions across multiple organs and organisms. While standards for data and metadata intake are arising, organization of cell types has largely been left to individual investigators, resulting in widely varying nomenclature and limited alignment between taxonomies. To facilitate cross-dataset comparison, the Allen Institute created the Common Cell type Nomenclature (CCN) for matching and tracking cell types across studies that is qualitatively similar to gene transcript management across different genome builds. The CCN can be readily applied to new or established taxonomies and was applied herein to diverse cell type datasets derived from multiple quantifiable modalities. The CCN facilitates assigning accurate yet flexible cell type names in the mammalian cortex as a step towards community-wide efforts to organize multi-source, data-driven information related to cell type taxonomies from any organism., Comment: 29 pages, 5 figures, 4 tables, 1 supplementary table
Published: 2020

14. New Light on Cortical Neuropeptides and Synaptic Network Plasticity

Author: Smith, Stephen J., Hawrylycz, Michael, Rossier, Jean, and Sümbül, Uygar
Subjects: Quantitative Biology - Neurons and Cognition
Abstract: Neuropeptides, members of a large and evolutionarily ancient family of proteinaceous cell-cell signaling molecules, are widely recognized as extremely potent regulators of brain function and behavior. At the cellular level, neuropeptides are known to act mainly via modulation of ion channel and synapse function, but functional impacts emerging at the level of complex cortical synaptic networks have resisted mechanistic analysis. New findings from single-cell RNA-seq transcriptomics now illuminate intricate patterns of cortical neuropeptide signaling gene expression and new tools now offer powerful molecular access to cortical neuropeptide signaling. Here we highlight some of these new findings and tools, focusing especially on prospects for experimental and theoretical exploration of peptidergic and synaptic networks interactions underlying cortical function and plasticity., Comment: 22 pages, 4 figures, 1 table, to be published in Current Opinion in Neurobiology
Published: 2020

15. Integration of evidence across human and model organism studies: A meeting report

Author: Palmer, Rohan HC, Johnson, Emma C, Won, Hyejung, Polimanti, Renato, Kapoor, Manav, Chitre, Apurva, Bogue, Molly A, Benca‐Bachman, Chelsie E, Parker, Clarissa C, Verma, Anurag, Reynolds, Timothy, Ernst, Jason, Bray, Michael, Bin Kwon, Soo, Lai, Dongbing, Quach, Bryan C, Gaddis, Nathan C, Saba, Laura, Chen, Hao, Hawrylycz, Michael, Zhang, Shan, Zhou, Yuan, Mahaffey, Spencer, Fischer, Christian, Sanchez‐Roige, Sandra, Bandrowski, Anita, Lu, Qing, Shen, Li, Philip, Vivek, Gelernter, Joel, Bierut, Laura J, Hancock, Dana B, Edenberg, Howard J, Johnson, Eric O, Nestler, Eric J, Barr, Peter B, Prins, Pjotr, Smith, Desmond J, Akbarian, Schahram, Thorgeirsson, Thorgeir, Walton, Dave, Baker, Erich, Jacobson, Daniel, Palmer, Abraham A, Miles, Michael, Chesler, Elissa J, Emerson, Jake, Agrawal, Arpana, Martone, Maryann, and Williams, Robert W
Subjects: Biological Sciences, Biomedical and Clinical Sciences, Genetics, Human Genome, Substance Misuse, Networking and Information Technology R&D (NITRD), Brain Disorders, Drug Abuse (NIDA only), Generic health relevance, Good Health and Well Being, cross-species, data integration, drug abuse, genomics, GWAS, model organisms, multi-omic, substance use disorders, working group, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Neurosciences
Abstract: The National Institute on Drug Abuse and Joint Institute for Biological Sciences at the Oak Ridge National Laboratory hosted a meeting attended by a diverse group of scientists with expertise in substance use disorders (SUDs), computational biology, and FAIR (Findability, Accessibility, Interoperability, and Reusability) data sharing. The meeting's objective was to discuss and evaluate better strategies to integrate genetic, epigenetic, and 'omics data across human and model organisms to achieve deeper mechanistic insight into SUDs. Specific topics were to (a) evaluate the current state of substance use genetics and genomics research and fundamental gaps, (b) identify opportunities and challenges of integration and sharing across species and data types, (c) identify current tools and resources for integration of genetic, epigenetic, and phenotypic data, (d) discuss steps and impediment related to data integration, and (e) outline future steps to support more effective collaboration-particularly between animal model research communities and human genetics and clinical research teams. This review summarizes key facets of this catalytic discussion with a focus on new opportunities and gaps in resources and knowledge on SUDs.
Published: 2021

16. A community-based transcriptomics classification and nomenclature of neocortical cell types

Author: Yuste, Rafael, Hawrylycz, Michael, Aalling, Nadia, Arendt, Detlev, Armananzas, Ruben, Ascoli, Giorgio, Bielza, Concha, Bokharaie, Vahid, Bergmann, Tobias, Bystron, Irina, Capogna, Marco, Chang, Yoonjeung, Clemens, Ann, de Kock, Christiaan, DeFelipe, Javier, Santos, Sandra Dos, Dunville, Keagan, Feldmeyer, Dirk, Fiath, Richard, Fishell, Gordon, Foggetti, Angelica, Gao, Xuefan, Ghaderi, Parviz, Gunturkun, Onur, Hall, Vanessa Jane, Helmstaedter, Moritz, Herculano-Houzel, Suzana, Hilscher, Markus, Hirase, Hajime, Hjerling-Leffler, Jens, Hodge, Rebecca, Huang, Z. Josh, Huda, Rafiq, Juan, Yuan, Khodosevich, Konstantin, Kiehn, Ole, Koch, Henner, Kuebler, Eric, Kuhnemund, Malte, Larranaga, Pedro, Lelieveldt, Boudewijn, Louth, Emma Louise, Lui, Jan, Mansvelder, Huibert, Marin, Oscar, Martínez-Trujillo, Julio, Moradi, Homeira, Goriounova, Natalia, Mohapatra, Alok, Nedergaard, Maiken, Němec, Pavel, Ofer, Netanel, Pfisterer, Ulrich, Pontes, Samuel, Redmond, William, Rossier, Jean, Sanes, Joshua, Scheuermann, Richard, Saiz, Esther Serrano, Somogyi, Peter, Tamás, Gábor, Tolias, Andreas, Tosches, Maria, Garcia, Miguel Turrero, Aguilar-Valles, Argel, Munguba, Hermany, Wozny, Christian, Wuttke, Thomas, Yong, Liu, Zeng, Hongkui, and Lein, Ed S.
Subjects: Quantitative Biology - Genomics, Quantitative Biology - Neurons and Cognition
Abstract: To understand the function of cortical circuits it is necessary to classify their underlying cellular diversity. Traditional attempts based on comparing anatomical or physiological features of neurons and glia, while productive, have not resulted in a unified taxonomy of neural cell types. The recent development of single-cell transcriptomics has enabled, for the first time, systematic high-throughput profiling of large numbers of cortical cells and the generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data have revealed the existence of clear clusters, many of which correspond to cell types defined by traditional criteria, and which are conserved across cortical areas and species. To capitalize on these innovations and advance the field, we, the Copenhagen Convention Group, propose the community adopts a transcriptome-based taxonomy of the cell types in the adult mammalian neocortex. This core classification should be ontological, hierarchical and use a standardized nomenclature. It should be configured to flexibly incorporate new data from multiple approaches, developmental stages and a growing number of species, enabling improvement and revision of the classification. This community-based strategy could serve as a common foundation for future detailed analysis and reverse engineering of cortical circuits and serve as an example for cell type classification in other parts of the nervous system and other organs., Comment: 21 pages, 3 figures
Published: 2019

17. A community-based transcriptomics classification and nomenclature of neocortical cell types

Author: Yuste, Rafael, Hawrylycz, Michael, Aalling, Nadia, Aguilar-Valles, Argel, Arendt, Detlev, Armañanzas, Ruben, Ascoli, Giorgio A, Bielza, Concha, Bokharaie, Vahid, Bergmann, Tobias Borgtoft, Bystron, Irina, Capogna, Marco, Chang, YoonJeung, Clemens, Ann, de Kock, Christiaan PJ, DeFelipe, Javier, Dos Santos, Sandra Esmeralda, Dunville, Keagan, Feldmeyer, Dirk, Fiáth, Richárd, Fishell, Gordon James, Foggetti, Angelica, Gao, Xuefan, Ghaderi, Parviz, Goriounova, Natalia A, Güntürkün, Onur, Hagihara, Kenta, Hall, Vanessa Jane, Helmstaedter, Moritz, Herculano-Houzel, Suzana, Hilscher, Markus M, Hirase, Hajime, Hjerling-Leffler, Jens, Hodge, Rebecca, Huang, Josh, Huda, Rafiq, Khodosevich, Konstantin, Kiehn, Ole, Koch, Henner, Kuebler, Eric S, Kühnemund, Malte, Larrañaga, Pedro, Lelieveldt, Boudewijn, Louth, Emma Louise, Lui, Jan H, Mansvelder, Huibert D, Marin, Oscar, Martinez-Trujillo, Julio, Chameh, Homeira Moradi, Mohapatra, Alok Nath, Munguba, Hermany, Nedergaard, Maiken, Němec, Pavel, Ofer, Netanel, Pfisterer, Ulrich Gottfried, Pontes, Samuel, Redmond, William, Rossier, Jean, Sanes, Joshua R, Scheuermann, Richard H, Serrano-Saiz, Esther, Staiger, Jochen F, Somogyi, Peter, Tamás, Gábor, Tolias, Andreas Savas, Tosches, Maria Antonietta, García, Miguel Turrero, Wozny, Christian, Wuttke, Thomas V, Liu, Yong, Yuan, Juan, Zeng, Hongkui, and Lein, Ed
Subjects: Biological Psychology, Biomedical and Clinical Sciences, Neurosciences, Psychology, Biotechnology, Genetics, Underpinning research, 1.1 Normal biological development and functioning, Generic health relevance, Animals, Cells, Computational Biology, Humans, Neocortex, Neuroglia, Neurons, Single-Cell Analysis, Terminology as Topic, Transcriptome, Cognitive Sciences, Neurology & Neurosurgery, Biological psychology
Abstract: To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.
Published: 2020

18. Transcriptional Network Orchestrating Regional Patterning of Cortical Progenitors

Author: Ypsilanti, Athéna R, Pattabiraman, Kartik, Catta-Preta, Rinaldo, Golonzhka, Olga, Lindtner, Susan, Tang, Ke, Jones, Ian, Abnousi, Armen, Juric, Ivan, Hu, Ming, Shen, Yin, Dickel, Diane E, Visel, Axel, Pennachio, Len A, Hawrylycz, Michael, Thompson, Carol, Zeng, Hongkui, Barozzi, Iros, Nord, Alex S, and Rubenstein, John
Subjects: Biological Sciences, Biomedical and Clinical Sciences, Genetics, Human Genome, Stem Cell Research, Stem Cell Research - Nonembryonic - Non-Human, 1.1 Normal biological development and functioning
Abstract: SUMMARY We uncovered a transcription factor (TF) network that regulates cortical regional patterning. Screening the expression of hundreds of TFs in the developing mouse cortex identified 38 TFs that are expressed in gradients in the ventricular zone (VZ). We tested whether their cortical expression was altered in mutant mice with known patterning defects ( Emx2, Nr2f1 and Pax6) , which enabled us to define a cortical regionalization TF network (CRTFN). To identify genomic programming underlying this network, we performed TF ChIP-seq and chromatin-looping conformation to identify enhancer-gene interactions. To map enhancers involved in regional patterning of cortical progenitors, we performed assays for epigenomic marks and DNA accessibility in VZ cells purified from wild-type and patterning mutant mice. This integrated approach has identified a CRTFN and VZ enhancers involved in cortical regional patterning.
Published: 2020

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

Author: Hodge, Rebecca D, Bakken, Trygve E, Miller, Jeremy A, Smith, Kimberly A, Barkan, Eliza R, Graybuck, Lucas T, Close, Jennie L, Long, Brian, Johansen, Nelson, Penn, Osnat, Yao, Zizhen, Eggermont, Jeroen, Höllt, Thomas, Levi, Boaz P, Shehata, Soraya I, Aevermann, Brian, Beller, Allison, Bertagnolli, Darren, Brouner, Krissy, Casper, Tamara, Cobbs, Charles, Dalley, Rachel, Dee, Nick, Ding, Song-Lin, Ellenbogen, Richard G, Fong, Olivia, Garren, Emma, Goldy, Jeff, Gwinn, Ryder P, Hirschstein, Daniel, Keene, C Dirk, Keshk, Mohamed, Ko, Andrew L, Lathia, Kanan, Mahfouz, Ahmed, Maltzer, Zoe, McGraw, Medea, Nguyen, Thuc Nghi, Nyhus, Julie, Ojemann, Jeffrey G, Oldre, Aaron, Parry, Sheana, Reynolds, Shannon, Rimorin, Christine, Shapovalova, Nadiya V, Somasundaram, Saroja, Szafer, Aaron, Thomsen, Elliot R, Tieu, Michael, Quon, Gerald, Scheuermann, Richard H, Yuste, Rafael, Sunkin, Susan M, Lelieveldt, Boudewijn, Feng, David, Ng, Lydia, Bernard, Amy, Hawrylycz, Michael, Phillips, John W, Tasic, Bosiljka, Zeng, Hongkui, Jones, Allan R, Koch, Christof, and Lein, Ed S
Subjects: Cerebral Cortex, Astrocytes, Neurons, Animals, Humans, Mice, Species Specificity, Neural Inhibition, Principal Component Analysis, Adolescent, Adult, Aged, Middle Aged, Female, Male, Young Adult, Biological Evolution, Single-Cell Analysis, Transcriptome, RNA-Seq, Genetics, Neurosciences, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Aetiology, Underpinning research, Neurological, General Science & Technology
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

20. Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains

Author: Jiang, Shengdian, Wang, Yimin, Liu, Lijuan, Ding, Liya, Ruan, Zongcai, Dong, Hong-Wei, Ascoli, Giorgio A., Hawrylycz, Michael, Zeng, Hongkui, and Peng, Hanchuan
Published: 2022
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21. Cross-modal coherent registration of whole mouse brains

Author: Qu, Lei, Li, Yuanyuan, Xie, Peng, Liu, Lijuan, Wang, Yimin, Wu, Jun, Liu, Yu, Wang, Tao, Li, Longfei, Guo, Kaixuan, Wan, Wan, Ouyang, Lei, Xiong, Feng, Kolstad, Anna C., Wu, Zhuhao, Xu, Fang, Zheng, Yefeng, Gong, Hui, Luo, Qingming, Bi, Guoqiang, Dong, Hongwei, Hawrylycz, Michael, Zeng, Hongkui, and Peng, Hanchuan
Published: 2022
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22. Integrative functional genomic analysis of human brain development and neuropsychiatric risks

Author: Li, Mingfeng, Santpere, Gabriel, Imamura Kawasawa, Yuka, Evgrafov, Oleg V, Gulden, Forrest O, Pochareddy, Sirisha, Sunkin, Susan M, Li, Zhen, Shin, Yurae, Zhu, Ying, Sousa, André MM, Werling, Donna M, Kitchen, Robert R, Kang, Hyo Jung, Pletikos, Mihovil, Choi, Jinmyung, Muchnik, Sydney, Xu, Xuming, Wang, Daifeng, Lorente-Galdos, Belen, Liu, Shuang, Giusti-Rodríguez, Paola, Won, Hyejung, de Leeuw, Christiaan A, Pardiñas, Antonio F, Hu, Ming, Jin, Fulai, Li, Yun, Owen, Michael J, O’Donovan, Michael C, Walters, James TR, Posthuma, Danielle, Reimers, Mark A, Levitt, Pat, Weinberger, Daniel R, Hyde, Thomas M, Kleinman, Joel E, Geschwind, Daniel H, Hawrylycz, Michael J, State, Matthew W, Sanders, Stephan J, Sullivan, Patrick F, Gerstein, Mark B, Lein, Ed S, Knowles, James A, Sestan, Nenad, Willsey, A Jeremy, Oldre, Aaron, Szafer, Aaron, Camarena, Adrian, Cherskov, Adriana, Charney, Alexander W, Abyzov, Alexej, Kozlenkov, Alexey, Safi, Alexias, Jones, Allan R, Ashley-Koch, Allison E, Ebbert, Amanda, Price, Amanda J, Sekijima, Amanda, Kefi, Amira, Bernard, Amy, Amiri, Anahita, Sboner, Andrea, Clark, Andrew, Jaffe, Andrew E, Tebbenkamp, Andrew TN, Sodt, Andy J, Guillozet-Bongaarts, Angie L, Nairn, Angus C, Carey, Anita, Huttner, Anita, Chervenak, Ann, Szekely, Anna, Shieh, Annie W, Harmanci, Arif, Lipska, Barbara K, Carlyle, Becky C, Gregor, Ben W, Kassim, Bibi S, Sheppard, Brooke, Bichsel, Candace, Hahn, Chang-Gyu, Lee, Chang-Kyu, Chen, Chao, Kuan, Chihchau L, Dang, Chinh, Lau, Chris, Cuhaciyan, Christine, Armoskus, Christoper, Mason, Christopher E, Liu, Chunyu, Slaughterbeck, Cliff R, Bennet, Crissa, Pinto, Dalila, Polioudakis, Damon, Franjic, Daniel, Miller, Daniel J, Bertagnolli, Darren, and Lewis, David A
Subjects: Human Genome, Genetics, Neurosciences, Biotechnology, Pediatric, Mental Health, Brain, Epigenesis, Genetic, Epigenomics, Gene Expression Regulation, Developmental, Gene Regulatory Networks, Humans, Mental Disorders, Nervous System Diseases, Neurogenesis, Single-Cell Analysis, Transcriptome, BrainSpan Consortium, PsychENCODE Consortium, PsychENCODE Developmental Subgroup, General Science & Technology
Abstract: To broaden our understanding of human neurodevelopment, we profiled transcriptomic and epigenomic landscapes across brain regions and/or cell types for the entire span of prenatal and postnatal development. Integrative analysis revealed temporal, regional, sex, and cell type-specific dynamics. We observed a global transcriptomic cup-shaped pattern, characterized by a late fetal transition associated with sharply decreased regional differences and changes in cellular composition and maturation, followed by a reversal in childhood-adolescence, and accompanied by epigenomic reorganizations. Analysis of gene coexpression modules revealed relationships with epigenomic regulation and neurodevelopmental processes. Genes with genetic associations to brain-based traits and neuropsychiatric disorders (including MEF2C, SATB2, SOX5, TCF4, and TSHZ3) converged in a small number of modules and distinct cell types, revealing insights into neurodevelopment and the genomic basis of neuropsychiatric risks.
Published: 2018

23. Hierarchical Bayesian inference to model continuous phenotypical progression in Alzheimer's Disease

Author: Agrawal, Anamika, primary, Rachleff, Victoria Mallett, additional, Travaglini, Kyle J, additional, Mukherjee, Shubhabrata, additional, Crane, Paul, additional, Hawrylycz, Michael, additional, Keene, C. Dirk, additional, Lein, Ed, additional, Mena, Gonzalo E., additional, and Gabitto, Mariano I., additional
Published: 2024
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24. Morphological diversity of single neurons in molecularly defined cell types

Author: Peng, Hanchuan, Xie, Peng, Liu, Lijuan, Kuang, Xiuli, Wang, Yimin, Qu, Lei, Gong, Hui, Jiang, Shengdian, Li, Anan, Ruan, Zongcai, Ding, Liya, Yao, Zizhen, Chen, Chao, Chen, Mengya, Daigle, Tanya L., Dalley, Rachel, Ding, Zhangcan, Duan, Yanjun, Feiner, Aaron, He, Ping, Hill, Chris, Hirokawa, Karla E., Hong, Guodong, Huang, Lei, Kebede, Sara, Kuo, Hsien-Chi, Larsen, Rachael, Lesnar, Phil, Li, Longfei, Li, Qi, Li, Xiangning, Li, Yaoyao, Li, Yuanyuan, Liu, An, Lu, Donghuan, Mok, Stephanie, Ng, Lydia, Nguyen, Thuc Nghi, Ouyang, Qiang, Pan, Jintao, Shen, Elise, Song, Yuanyuan, Sunkin, Susan M., Tasic, Bosiljka, Veldman, Matthew B., Wakeman, Wayne, Wan, Wan, Wang, Peng, Wang, Quanxin, Wang, Tao, Wang, Yaping, Xiong, Feng, Xiong, Wei, Xu, Wenjie, Ye, Min, Yin, Lulu, Yu, Yang, Yuan, Jia, Yuan, Jing, Yun, Zhixi, Zeng, Shaoqun, Zhang, Shichen, Zhao, Sujun, Zhao, Zijun, Zhou, Zhi, Huang, Z. Josh, Esposito, Luke, Hawrylycz, Michael J., Sorensen, Staci A., Yang, X. William, Zheng, Yefeng, Gu, Zhongze, Xie, Wei, Koch, Christof, Luo, Qingming, Harris, Julie A., Wang, Yun, and Zeng, Hongkui
Published: 2021
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25. Cellular anatomy of the mouse primary motor cortex

Author: Muñoz-Castañeda, Rodrigo, Zingg, Brian, Matho, Katherine S., Chen, Xiaoyin, Wang, Quanxin, Foster, Nicholas N., Li, Anan, Narasimhan, Arun, Hirokawa, Karla E., Huo, Bingxing, Bannerjee, Samik, Korobkova, Laura, Park, Chris Sin, Park, Young-Gyun, Bienkowski, Michael S., Chon, Uree, Wheeler, Diek W., Li, Xiangning, Wang, Yun, Naeemi, Maitham, Xie, Peng, Liu, Lijuan, Kelly, Kathleen, An, Xu, Attili, Sarojini M., Bowman, Ian, Bludova, Anastasiia, Cetin, Ali, Ding, Liya, Drewes, Rhonda, D’Orazi, Florence, Elowsky, Corey, Fischer, Stephan, Galbavy, William, Gao, Lei, Gillis, Jesse, Groblewski, Peter A., Gou, Lin, Hahn, Joel D., Hatfield, Joshua T., Hintiryan, Houri, Huang, Junxiang Jason, Kondo, Hideki, Kuang, Xiuli, Lesnar, Philip, Li, Xu, Li, Yaoyao, Lin, Mengkuan, Lo, Darrick, Mizrachi, Judith, Mok, Stephanie, Nicovich, Philip R., Palaniswamy, Ramesh, Palmer, Jason, Qi, Xiaoli, Shen, Elise, Sun, Yu-Chi, Tao, Huizhong W., Wakemen, Wayne, Wang, Yimin, Yao, Shenqin, Yuan, Jing, Zhan, Huiqing, Zhu, Muye, Ng, Lydia, Zhang, Li I., Lim, Byung Kook, Hawrylycz, Michael, Gong, Hui, Gee, James C., Kim, Yongsoo, Chung, Kwanghun, Yang, X. William, Peng, Hanchuan, Luo, Qingming, Mitra, Partha P., Zador, Anthony M., Zeng, Hongkui, Ascoli, Giorgio A., Josh Huang, Z., Osten, Pavel, Harris, Julie A., and Dong, Hong-Wei
Published: 2021
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26. Human neocortical expansion involves glutamatergic neuron diversification

Author: Berg, Jim, Sorensen, Staci A., Ting, Jonathan T., Miller, Jeremy A., Chartrand, Thomas, Buchin, Anatoly, Bakken, Trygve E., Budzillo, Agata, Dee, Nick, Ding, Song-Lin, Gouwens, Nathan W., Hodge, Rebecca D., Kalmbach, Brian, Lee, Changkyu, Lee, Brian R., Alfiler, Lauren, Baker, Katherine, Barkan, Eliza, Beller, Allison, Berry, Kyla, Bertagnolli, Darren, Bickley, Kris, Bomben, Jasmine, Braun, Thomas, Brouner, Krissy, Casper, Tamara, Chong, Peter, Crichton, Kirsten, Dalley, Rachel, de Frates, Rebecca, Desta, Tsega, Lee, Samuel Dingman, D’Orazi, Florence, Dotson, Nadezhda, Egdorf, Tom, Enstrom, Rachel, Farrell, Colin, Feng, David, Fong, Olivia, Furdan, Szabina, Galakhova, Anna A., Gamlin, Clare, Gary, Amanda, Glandon, Alexandra, Goldy, Jeff, Gorham, Melissa, Goriounova, Natalia A., Gratiy, Sergey, Graybuck, Lucas, Gu, Hong, Hadley, Kristen, Hansen, Nathan, Heistek, Tim S., Henry, Alex M., Heyer, Djai B., Hill, DiJon, Hill, Chris, Hupp, Madie, Jarsky, Tim, Kebede, Sara, Keene, Lisa, Kim, Lisa, Kim, Mean-Hwan, Kroll, Matthew, Latimer, Caitlin, Levi, Boaz P., Link, Katherine E., Mallory, Matthew, Mann, Rusty, Marshall, Desiree, Maxwell, Michelle, McGraw, Medea, McMillen, Delissa, Melief, Erica, Mertens, Eline J., Mezei, Leona, Mihut, Norbert, Mok, Stephanie, Molnar, Gabor, Mukora, Alice, Ng, Lindsay, Ngo, Kiet, Nicovich, Philip R., Nyhus, Julie, Olah, Gaspar, Oldre, Aaron, Omstead, Victoria, Ozsvar, Attila, Park, Daniel, Peng, Hanchuan, Pham, Trangthanh, Pom, Christina A., Potekhina, Lydia, Rajanbabu, Ramkumar, Ransford, Shea, Reid, David, Rimorin, Christine, Ruiz, Augustin, Sandman, David, Sulc, Josef, Sunkin, Susan M., Szafer, Aaron, Szemenyei, Viktor, Thomsen, Elliot R., Tieu, Michael, Torkelson, Amy, Trinh, Jessica, Tung, Herman, Wakeman, Wayne, Waleboer, Femke, Ward, Katelyn, Wilbers, René, Williams, Grace, Yao, Zizhen, Yoon, Jae-Geun, Anastassiou, Costas, Arkhipov, Anton, Barzo, Pal, Bernard, Amy, Cobbs, Charles, de Witt Hamer, Philip C., Ellenbogen, Richard G., Esposito, Luke, Ferreira, Manuel, Gwinn, Ryder P., Hawrylycz, Michael J., Hof, Patrick R., Idema, Sander, Jones, Allan R., Keene, C. Dirk, Ko, Andrew L., Murphy, Gabe J., Ng, Lydia, Ojemann, Jeffrey G., Patel, Anoop P., Phillips, John W., Silbergeld, Daniel L., Smith, Kimberly, Tasic, Bosiljka, Yuste, Rafael, Segev, Idan, de Kock, Christiaan P. J., Mansvelder, Huibert D., Tamas, Gabor, Zeng, Hongkui, Koch, Christof, and Lein, Ed S.
Published: 2021
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27. Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model

Author: Huang, Furong, Anandkumar, Animashree, Borgs, Christian, Chayes, Jennifer, Fraenkel, Ernest, Hawrylycz, Michael, Lein, Ed, Ingrosso, Alessandro, and Turaga, Srinivas
Subjects: Quantitative Biology - Neurons and Cognition, Statistics - Machine Learning
Abstract: Cataloging the neuronal cell types that comprise circuitry of individual brain regions is a major goal of modern neuroscience and the BRAIN initiative. Single-cell RNA sequencing can now be used to measure the gene expression profiles of individual neurons and to categorize neurons based on their gene expression profiles. While the single-cell techniques are extremely powerful and hold great promise, they are currently still labor intensive, have a high cost per cell, and, most importantly, do not provide information on spatial distribution of cell types in specific regions of the brain. We propose a complementary approach that uses computational methods to infer the cell types and their gene expression profiles through analysis of brain-wide single-cell resolution in situ hybridization (ISH) imagery contained in the Allen Brain Atlas (ABA). We measure the spatial distribution of neurons labeled in the ISH image for each gene and model it as a spatial point process mixture, whose mixture weights are given by the cell types which express that gene. By fitting a point process mixture model jointly to the ISH images, we infer both the spatial point process distribution for each cell type and their gene expression profile. We validate our predictions of cell type-specific gene expression profiles using single cell RNA sequencing data, recently published for the mouse somatosensory cortex. Jointly with the gene expression profiles, cell features such as cell size, orientation, intensity and local density level are inferred per cell type.
Published: 2016

28. Knowledge graphs can help make sense of the flood of cell-type data

Author: Hawrylycz, Michael, primary
Published: 2024
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29. Consistent cross-modal identification of cortical neurons with coupled autoencoders

Author: Gala, Rohan, Budzillo, Agata, Baftizadeh, Fahimeh, Miller, Jeremy, Gouwens, Nathan, Arkhipov, Anton, Murphy, Gabe, Tasic, Bosiljka, Zeng, Hongkui, Hawrylycz, Michael, and Sümbül, Uygar
Published: 2021
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30. A comprehensive transcriptional map of primate brain development.

Author: Bakken, Trygve E, Miller, Jeremy A, Ding, Song-Lin, Sunkin, Susan M, Smith, Kimberly A, Ng, Lydia, Szafer, Aaron, Dalley, Rachel A, Royall, Joshua J, Lemon, Tracy, Shapouri, Sheila, Aiona, Kaylynn, Arnold, James, Bennett, Jeffrey L, Bertagnolli, Darren, Bickley, Kristopher, Boe, Andrew, Brouner, Krissy, Butler, Stephanie, Byrnes, Emi, Caldejon, Shiella, Carey, Anita, Cate, Shelby, Chapin, Mike, Chen, Jefferey, Dee, Nick, Desta, Tsega, Dolbeare, Tim A, Dotson, Nadia, Ebbert, Amanda, Fulfs, Erich, Gee, Garrett, Gilbert, Terri L, Goldy, Jeff, Gourley, Lindsey, Gregor, Ben, Gu, Guangyu, Hall, Jon, Haradon, Zeb, Haynor, David R, Hejazinia, Nika, Hoerder-Suabedissen, Anna, Howard, Robert, Jochim, Jay, Kinnunen, Marty, Kriedberg, Ali, Kuan, Chihchau L, Lau, Christopher, Lee, Chang-Kyu, Lee, Felix, Luong, Lon, Mastan, Naveed, May, Ryan, Melchor, Jose, Mosqueda, Nerick, Mott, Erika, Ngo, Kiet, Nyhus, Julie, Oldre, Aaron, Olson, Eric, Parente, Jody, Parker, Patrick D, Parry, Sheana, Pendergraft, Julie, Potekhina, Lydia, Reding, Melissa, Riley, Zackery L, Roberts, Tyson, Rogers, Brandon, Roll, Kate, Rosen, David, Sandman, David, Sarreal, Melaine, Shapovalova, Nadiya, Shi, Shu, Sjoquist, Nathan, Sodt, Andy J, Townsend, Robbie, Velasquez, Lissette, Wagley, Udi, Wakeman, Wayne B, White, Cassandra, Bennett, Crissa, Wu, Jennifer, Young, Rob, Youngstrom, Brian L, Wohnoutka, Paul, Gibbs, Richard A, Rogers, Jeffrey, Hohmann, John G, Hawrylycz, Michael J, Hevner, Robert F, Molnár, Zoltán, Phillips, John W, Dang, Chinh, Jones, Allan R, Amaral, David G, Bernard, Amy, and Lein, Ed S
Subjects: Brain, Neocortex, Animals, Macaca mulatta, Humans, Microcephaly, Risk Factors, Schizophrenia, Cell Adhesion, Species Specificity, Transcription, Genetic, Conserved Sequence, Aging, Female, Male, Neurogenesis, Intellectual Disability, Transcriptome, Spatio-Temporal Analysis, Autism Spectrum Disorder, Neurodevelopmental Disorders, Transcription, Genetic, General Science & Technology
Abstract: The transcriptional underpinnings of brain development remain poorly understood, particularly in humans and closely related non-human primates. We describe a high-resolution transcriptional atlas of rhesus monkey (Macaca mulatta) brain development that combines dense temporal sampling of prenatal and postnatal periods with fine anatomical division of cortical and subcortical regions associated with human neuropsychiatric disease. Gene expression changes more rapidly before birth, both in progenitor cells and maturing neurons. Cortical layers and areas acquire adult-like molecular profiles surprisingly late in postnatal development. Disparate cell populations exhibit distinct developmental timing of gene expression, but also unexpected synchrony of processes underlying neural circuit construction including cell projection and adhesion. Candidate risk genes for neurodevelopmental disorders including primary microcephaly, autism spectrum disorder, intellectual disability, and schizophrenia show disease-specific spatiotemporal enrichment within developing neocortex. Human developmental expression trajectories are more similar to monkey than rodent, although approximately 9% of genes show human-specific regulation with evidence for prolonged maturation or neoteny compared to monkey.
Published: 2016

31. Cell-type-specific neuroanatomy of brain-wide expression of autism-related genes

Author: Grange, Pascal, Menashe, Idan, and Hawrylycz, Michael
Subjects: Quantitative Biology - Neurons and Cognition
Abstract: Two cliques of genes identified computationally for their high co-expression in the mouse brain according to the Allen Brain Atlas, and for their enrichment in genes related to autism spectrum disorder, have recently been shown to be highly co-expressed in the cerebellar cortex, compared to what could be expected by chance. Moreover, the expression of these cliques of genes is not homogeneous across the cerebellum, and it has been noted that their gene expression pattern seems to highlight the granular layer. However, this observation was only made by eye, and recent advances in computational neuroanatomy allow to rank cell types in the mouse brain (characterized by their transcriptome profiles) according to the similarity between their density profiles and the expression profiles of the cliques. We establish by Monte Carlo simulation that with probability at least 99 percent, the expression profiles of the two cliques are more similar to the density profile of granule cells than 99 percent of the expression of cliques containing the same number of genes (Purkinje cells also score above 99 percent in one of the cliques). Thresholding the expression profiles shows that the signal is more intense in the granular layer., Comment: 13 pages, 3 figures
Published: 2014

32. Cell-type-specific transcriptomes and the Allen Atlas (II): discussion of the linear model of brain-wide densities of cell types

Author: Grange, Pascal, Bohland, Jason W., Okaty, Benjamin, Sugino, Ken, Bokil, Hemant, Nelson, Sacha, Ng, Lydia, Hawrylycz, Michael, and Mitra, Partha P.
Subjects: Quantitative Biology - Neurons and Cognition, Quantitative Biology - Quantitative Methods
Abstract: The voxelized Allen Atlas of the adult mouse brain (at a resolution of 200 microns) has been used in [arXiv:1303.0013] to estimate the region-specificity of 64 cell types whose transcriptional profile in the mouse brain has been measured in microarray experiments. In particular, the model yields estimates for the brain-wide density of each of these cell types. We conduct numerical experiments to estimate the errors in the estimated density profiles. First of all, we check that a simulated thalamic profile based on 200 well-chosen genes can transfer signal from cerebellar Purkinje cells to the thalamus. This inspires us to sub-sample the atlas of genes by repeatedly drawing random sets of 200 genes and refitting the model. This results in a random distribution of density profiles, that can be compared to the predictions of the model. This results in a ranking of cell types by the overlap between the original and sub-sampled density profiles. Cell types with high rank include medium spiny neurons, several samples of cortical pyramidal neurons, hippocampal pyramidal neurons, granule cells and cholinergic neurons from the brain stem. In some cases with lower rank, the average sub-sample can have better contrast properties than the original model (this is the case for amygdalar neurons and dopaminergic neurons from the ventral midbrain). Finally, we add some noise to the cell-type-specific transcriptomes by mixing them using a scalar parameter weighing a random matrix. After refitting the model, we observe than a mixing parameter of $5\%$ leads to modifications of density profiles that span the same interval as the ones resulting from sub-sampling., Comment: 178 pages, 207 figures, 7 tables; v2: typos corrected; v3: more typos corrected, missing pseudo-code in section 3.5 written, image attachment changed in Figure 6, misuse of notation $r^{signal}$ corrected, conclusions unchanged
Published: 2014

33. Cell-type-specific microarray data and the Allen atlas: quantitative analysis of brain-wide patterns of correlation and density

Author: Grange, Pascal, Hawrylycz, Michael, and Mitra, Partha P.
Subjects: Quantitative Biology - Neurons and Cognition, Quantitative Biology - Quantitative Methods
Abstract: The Allen Atlas of the adult mouse brain is used to estimate the region-specificity of 64 cell types whose transcriptional profile in the mouse brain has been measured in microarray experiments. We systematically analyze the preliminary results presented in [arXiv:1111.6217], using the techniques implemented in the Brain Gene Expression Analysis toolbox. In particular, for each cell-type-specific sample in the study, we compute a brain-wide correlation profile to the Allen Atlas, and estimate a brain-wide density profile by solving a quadratic optimization problem at each voxel in the mouse brain. We characterize the neuroanatomical properties of the correlation and density profiles by ranking the regions of the left hemisphere delineated in the Allen Reference Atlas. We compare these rankings to prior biological knowledge of the brain region from which the cell-type-specific sample was extracted., Comment: V1: 88 pages, 37 figures, 43 tables; V2: 137 pages, new section discussing different fitting panels
Published: 2013

34. Computational neuroanatomy and co-expression of genes in the adult mouse brain, analysis tools for the Allen Brain Atlas

Author: Grange, Pascal, Hawrylycz, Michael, and Mitra, Partha P.
Subjects: Quantitative Biology - Quantitative Methods, Quantitative Biology - Neurons and Cognition
Abstract: We review quantitative methods and software developed to analyze genome-scale, brain-wide spatially-mapped gene-expression data. We expose new methods based on the underlying high-dimensional geometry of voxel space and gene space, and on simulations of the distribution of co-expression networks of a given size. We apply them to the Allen Atlas of the adult mouse brain, and to the co-expression network of a set of genes related to nicotine addiction retrieved from the NicSNP database. The computational methods are implemented in {\ttfamily{BrainGeneExpressionAnalysis}}, a Matlab toolbox available for download., Comment: 25 pages, 8 figures, accepted in Quantitative Biology (2012) 0002
Published: 2013

35. Brain Gene Expression Analysis: a MATLAB toolbox for the analysis of brain-wide gene-expression data

Author: Grange, Pascal, Bohland, Jason W., Hawrylycz, Michael, and Mitra, Partha P.
Subjects: Quantitative Biology - Neurons and Cognition, Quantitative Biology - Genomics, Quantitative Biology - Quantitative Methods
Abstract: The Allen Brain Atlas project (ABA) generated a genome-scale collection of gene-expression profiles using in-situ hybridization. These profiles were co-registered to the three-dimensional Allen Reference Atlas (ARA) of the adult mouse brain. A set of more than 4,000 such volumetric data are available for the full brain, at a resolution of 200 microns. These data are presented in a voxel-by-gene matrix. The ARA comes with several systems of annotation, hierarchical (40 cortical regions, 209 sub-cortical regions in the whole brain), or non-hierarchical (12 regions in the left hemisphere, with refinement into 94 regions, and cortical layers). The high-dimensional nature of this dataset and the possible connection between anatomy and gene expression pose challenges to data analysis. We developed the Brain Gene Expression Analysis Toolbox, whose functionalities include: determination of marker genes for brain regions, statistical analysis of brain-wide co-expression patterns, and the computation of brain-wide correlation maps with cell-type specific microarray data., Comment: 59 pages; v2: bug fixed on page 5, preface added; v3: more bugs fixed, cell-type-specific data released (chapter 5); v4: references added, chapter 2 expanded; v5: download link fixed, more typos corrected; v6: download link fixed
Published: 2012

36. A cell-type based model explaining co-expression patterns of genes in the brain

Author: Grange, Pascal, Bohland, Jason, Bokil, Hemant, Nelson, Sacha, Okaty, Benjamin, Sugino, Ken, Ng, Lydia, Hawrylycz, Michael, and Mitra, Partha P.
Subjects: Quantitative Biology - Quantitative Methods, Quantitative Biology - Neurons and Cognition
Abstract: Much of the genome is expressed in the vertebrate brain, with individual genes exhibiting different spatially-varying patterns of expression. These variations are not independent, with pairs of genes exhibiting complex patterns of co-expression, such that two genes may be similarly expressed in one region, but differentially expressed in other regions. These correlations have been previously studied quantitatively, particularly for the gene expression atlas of the mouse brain, but the biological meaning of the co-expression patterns remains obscure. We propose a simple model of the co-expression patterns in terms of spatial distributions of underlying cell types. We establish the plausibility of the model in terms of a test set of cell types for which both the gene expression profiles and the spatial distributions are known., Comment: 41 pages; v2: typos corrected
Published: 2011

37. A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale

Author: Bohland, Jason W., Wu, Caizhi, Barbas, Helen, Bokil, Hemant, Bota, Mihail, Breiter, Hans C., Cline, Hollis T., Doyle, John C., Freed, Peter J., Greenspan, Ralph J., Haber, Suzanne N., Hawrylycz, Michael, Herrera, Daniel G., Hilgetag, Claus C., Huang, Z. Josh, Jones, Allan, Jones, Edward G., Karten, Harvey J., Kleinfeld, David, Kotter, Rolf, Lester, Henry A., Lin, John M., Mensh, Brett D., Mikula, Shawn, Panksepp, Jaak, Price, Joseph L., Safdieh, Joseph, Saper, Clifford B., Schiff, Nicholas D., Schmahmann, Jeremy D., Stillman, Bruce W., Svoboda, Karel, Swanson, Larry W., Toga, Arthur W., Van Essen, David C., Watson, James D., and Mitra, Partha P.
Subjects: Quantitative Biology - Neurons and Cognition
Abstract: In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is however critical both for basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brain-wide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brain-wide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open access data repository; compatibility with existing resources, and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget., Comment: 41 pages
Published: 2009
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38. A High-Resolution Spatiotemporal Atlas of Gene Expression of the Developing Mouse Brain

Author: Thompson, Carol L, Ng, Lydia, Menon, Vilas, Martinez, Salvador, Lee, Chang-Kyu, Glattfelder, Katie, Sunkin, Susan M, Henry, Alex, Lau, Christopher, Dang, Chinh, Garcia-Lopez, Raquel, Martinez-Ferre, Almudena, Pombero, Ana, Rubenstein, John LR, Wakeman, Wayne B, Hohmann, John, Dee, Nick, Sodt, Andrew J, Young, Rob, Smith, Kimberly, Nguyen, Thuc-Nghi, Kidney, Jolene, Kuan, Leonard, Jeromin, Andreas, Kaykas, Ajamete, Miller, Jeremy, Page, Damon, Orta, Geri, Bernard, Amy, Riley, Zackery, Smith, Simon, Wohnoutka, Paul, Hawrylycz, Michael J, Puelles, Luis, and Jones, Allan R
Subjects: Neurosciences, Biotechnology, Genetics, Pediatric, 1.1 Normal biological development and functioning, Underpinning research, Neurological, Animals, Brain, Brain Mapping, Gene Expression, Gene Expression Profiling, Gene Expression Regulation, Developmental, Mice, Psychology, Cognitive Sciences, Neurology & Neurosurgery
Abstract: To provide a temporal framework for the genoarchitecture of brain development, we generated in situ hybridization data for embryonic and postnatal mouse brain at seven developmental stages for ∼2,100 genes, which were processed with an automated informatics pipeline and manually annotated. This resource comprises 434,946 images, seven reference atlases, an ontogenetic ontology, and tools to explore coexpression of genes across neurodevelopment. Gene sets coinciding with developmental phenomena were identified. A temporal shift in the principles governing the molecular organization of the brain was detected, with transient neuromeric, plate-based organization of the brain present at E11.5 and E13.5. Finally, these data provided a transcription factor code that discriminates brain structures and identifies the developmental age of a tissue, providing a foundation for eventual genetic manipulation or tracking of specific brain structures over development. The resource is available as the Allen Developing Mouse Brain Atlas (http://developingmouse.brain-map.org).
Published: 2014

39. Transcriptional landscape of the prenatal human brain.

Author: Miller, Jeremy A, Ding, Song-Lin, Sunkin, Susan M, Smith, Kimberly A, Ng, Lydia, Szafer, Aaron, Ebbert, Amanda, Riley, Zackery L, Royall, Joshua J, Aiona, Kaylynn, Arnold, James M, Bennet, Crissa, Bertagnolli, Darren, Brouner, Krissy, Butler, Stephanie, Caldejon, Shiella, Carey, Anita, Cuhaciyan, Christine, Dalley, Rachel A, Dee, Nick, Dolbeare, Tim A, Facer, Benjamin AC, Feng, David, Fliss, Tim P, Gee, Garrett, Goldy, Jeff, Gourley, Lindsey, Gregor, Benjamin W, Gu, Guangyu, Howard, Robert E, Jochim, Jayson M, Kuan, Chihchau L, Lau, Christopher, Lee, Chang-Kyu, Lee, Felix, Lemon, Tracy A, Lesnar, Phil, McMurray, Bergen, Mastan, Naveed, Mosqueda, Nerick, Naluai-Cecchini, Theresa, Ngo, Nhan-Kiet, Nyhus, Julie, Oldre, Aaron, Olson, Eric, Parente, Jody, Parker, Patrick D, Parry, Sheana E, Stevens, Allison, Pletikos, Mihovil, Reding, Melissa, Roll, Kate, Sandman, David, Sarreal, Melaine, Shapouri, Sheila, Shapovalova, Nadiya V, Shen, Elaine H, Sjoquist, Nathan, Slaughterbeck, Clifford R, Smith, Michael, Sodt, Andy J, Williams, Derric, Zöllei, Lilla, Fischl, Bruce, Gerstein, Mark B, Geschwind, Daniel H, Glass, Ian A, Hawrylycz, Michael J, Hevner, Robert F, Huang, Hao, Jones, Allan R, Knowles, James A, Levitt, Pat, Phillips, John W, Sestan, Nenad, Wohnoutka, Paul, Dang, Chinh, Bernard, Amy, Hohmann, John G, and Lein, Ed S
Subjects: Brain, Neocortex, Fetus, Animals, Humans, Mice, Anatomy, Artistic, Species Specificity, Gene Expression Regulation, Developmental, Conserved Sequence, Gene Regulatory Networks, Atlases as Topic, Transcriptome, Anatomy, Artistic, Gene Expression Regulation, Developmental, General Science & Technology
Abstract: The anatomical and functional architecture of the human brain is mainly determined by prenatal transcriptional processes. We describe an anatomically comprehensive atlas of the mid-gestational human brain, including de novo reference atlases, in situ hybridization, ultra-high-resolution magnetic resonance imaging (MRI) and microarray analysis on highly discrete laser-microdissected brain regions. In developing cerebral cortex, transcriptional differences are found between different proliferative and post-mitotic layers, wherein laminar signatures reflect cellular composition and developmental processes. Cytoarchitectural differences between human and mouse have molecular correlates, including species differences in gene expression in subplate, although surprisingly we find minimal differences between the inner and outer subventricular zones even though the outer zone is expanded in humans. Both germinal and post-mitotic cortical layers exhibit fronto-temporal gradients, with particular enrichment in the frontal lobe. Finally, many neurodevelopmental disorder and human-evolution-related genes show patterned expression, potentially underlying unique features of human cortical formation. These data provide a rich, freely-accessible resource for understanding human brain development.
Published: 2014

40. Molecular hallmarks of Alzheimer’s disease with a multimodal single cell atlas of the cortex

Author: Travaglini, Kyle J, primary, Gabitto, Mariano I, additional, Ariza, Jeanelle, additional, Ding, Yi, additional, Mahoney, Joseph T, additional, Casper, Tamara, additional, Chakrabarty, Rushil, additional, Clark, Michael, additional, Crane, Paul K, additional, Dee, Nick, additional, Ferrer, Rebecca, additional, Gatto, Nicole M, additional, Gloe, Jessica, additional, Goldy, Jeff, additional, Grabowski, Thomas J, additional, Guilford, Nathan, additional, Guzman, Junitta, additional, Hawrylycz, Michael J, additional, Hodge, Rebecca D, additional, Jayadev, Suman, additional, Kaplan, Eitan S, additional, Keene, C Dirk, additional, Larson, Eric B, additional, Latimer, Caitlin S, additional, Levi, Boaz P, additional, Melief, Erica J, additional, Meyerdierks, Emma, additional, Mukherjee, Shubhabrata, additional, Pham, Thanh, additional, Rachleff, Victoria M, additional, Smith, Kimberly, additional, Torkelson, Amy, additional, Lein, Ed S, additional, and Miller, Jeremy A, additional
Published: 2023
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41. Multimodal and multiregional atlas of Alzheimer’s disease changes

Author: Gabitto, Mariano I, primary, Travaglini, Kyle J, additional, Ariza, Jeanelle, additional, Close, Jennie, additional, Ding, Yi, additional, Long, Brian, additional, Rachleff, Victoria M, additional, Chakrabarty, Rushil, additional, Crane, Paul K, additional, Ferrer, Rebecca, additional, Gatto, Nicole M, additional, Goldy, Jeff, additional, Grabowski, Thomas J, additional, Guilford, Nathan, additional, Guzman, Junitta, additional, Hawrylycz, Michael J, additional, Hodge, Rebecca D, additional, Jayadev, Suman, additional, Kaplan, Eitan S, additional, Keene, C Dirk, additional, Larson, Eric B, additional, Latimer, Caitlin S, additional, Levi, Boaz P, additional, Mahoney, Joseph T, additional, Melief, Erica J, additional, Miller, Jeremy A, additional, Mukherjee, Shubhabrata, additional, Pham, Thanh, additional, Smith, Kimberly, additional, Torkelson, Amy, additional, Chakka, Anish Bhaswanth, additional, and Lein, Ed S, additional
Published: 2023
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42. Linking Neurogenetics and Functional Connectivity in Autism

Author: Hawrylycz, Michael, primary and Nickl-Jockschat, Thomas, additional
Published: 2023
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43. Whole human-brain mapping of single cortical neurons for profiling morphological diversity and stereotypy

Author: Han, Xiaofeng, primary, Guo, Shuxia, additional, Ji, Nan, additional, Li, Tian, additional, Liu, Jian, additional, Ye, Xiangqiao, additional, Wang, Yi, additional, Yun, Zhixi, additional, Xiong, Feng, additional, Rong, Jing, additional, Liu, Di, additional, Ma, Hui, additional, Wang, Yujin, additional, Huang, Yue, additional, Zhang, Peng, additional, Wu, Wenhao, additional, Ding, Liya, additional, Hawrylycz, Michael, additional, Lein, Ed, additional, Ascoli, Giorgio A., additional, Xie, Wei, additional, Liu, Lijuan, additional, Zhang, Liwei, additional, and Peng, Hanchuan, additional
Published: 2023
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44. Author Correction: Comparative cellular analysis of motor cortex in human, marmoset and mouse

Author: Bakken, Trygve E., Jorstad, Nikolas L., Hu, Qiwen, Lake, Blue B., Tian, Wei, Kalmbach, Brian E., Crow, Megan, Hodge, Rebecca D., Krienen, Fenna M., Sorensen, Staci A., Eggermont, Jeroen, Yao, Zizhen, Aevermann, Brian D., Aldridge, Andrew I., Bartlett, Anna, Bertagnolli, Darren, Casper, Tamara, Castanon, Rosa G., Crichton, Kirsten, Daigle, Tanya L., Dalley, Rachel, Dee, Nick, Dembrow, Nikolai, Diep, Dinh, Ding, Song-Lin, Dong, Weixiu, Fang, Rongxin, Fischer, Stephan, Goldman, Melissa, Goldy, Jeff, Graybuck, Lucas T., Herb, Brian R., Hou, Xiaomeng, Kancherla, Jayaram, Kroll, Matthew, Lathia, Kanan, van Lew, Baldur, Li, Yang Eric, Liu, Christine S., Liu, Hanqing, Lucero, Jacinta D., Mahurkar, Anup, McMillen, Delissa, Miller, Jeremy A., Moussa, Marmar, Nery, Joseph R., Nicovich, Philip R., Niu, Sheng-Yong, Orvis, Joshua, Osteen, Julia K., Owen, Scott, Palmer, Carter R., Pham, Thanh, Plongthongkum, Nongluk, Poirion, Olivier, Reed, Nora M., Rimorin, Christine, Rivkin, Angeline, Romanow, William J., Sedeño-Cortés, Adriana E., Siletti, Kimberly, Somasundaram, Saroja, Sulc, Josef, Tieu, Michael, Torkelson, Amy, Tung, Herman, Wang, Xinxin, Xie, Fangming, Yanny, Anna Marie, Zhang, Renee, Ament, Seth A., Behrens, M. Margarita, Bravo, Hector Corrada, Chun, Jerold, Dobin, Alexander, Gillis, Jesse, Hertzano, Ronna, Hof, Patrick R., Höllt, Thomas, Horwitz, Gregory D., Keene, C. Dirk, Kharchenko, Peter V., Ko, Andrew L., Lelieveldt, Boudewijn P., Luo, Chongyuan, Mukamel, Eran A., Pinto-Duarte, António, Preiss, Sebastian, Regev, Aviv, Ren, Bing, Scheuermann, Richard H., Smith, Kimberly, Spain, William J., White, Owen R., Koch, Christof, Hawrylycz, Michael, Tasic, Bosiljka, Macosko, Evan Z., McCarroll, Steven A., Ting, Jonathan T., Zeng, Hongkui, Zhang, Kun, Feng, Guoping, Ecker, Joseph R., Linnarsson, Sten, and Lein, Ed S.
Published: 2022
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45. Classification of electrophysiological and morphological neuron types in the mouse visual cortex

Author: Gouwens, Nathan W., Sorensen, Staci A., Berg, Jim, Lee, Changkyu, Jarsky, Tim, Ting, Jonathan, Sunkin, Susan M., Feng, David, Anastassiou, Costas A., Barkan, Eliza, Bickley, Kris, Blesie, Nicole, Braun, Thomas, Brouner, Krissy, Budzillo, Agata, Caldejon, Shiella, Casper, Tamara, Castelli, Dan, Chong, Peter, Crichton, Kirsten, Cuhaciyan, Christine, Daigle, Tanya L., Dalley, Rachel, Dee, Nick, Desta, Tsega, Ding, Song-Lin, Dingman, Samuel, Doperalski, Alyse, Dotson, Nadezhda, Egdorf, Tom, Fisher, Michael, de Frates, Rebecca A., Garren, Emma, Garwood, Marissa, Gary, Amanda, Gaudreault, Nathalie, Godfrey, Keith, Gorham, Melissa, Gu, Hong, Habel, Caroline, Hadley, Kristen, Harrington, James, Harris, Julie A., Henry, Alex, Hill, DiJon, Josephsen, Sam, Kebede, Sara, Kim, Lisa, Kroll, Matthew, Lee, Brian, Lemon, Tracy, Link, Katherine E., Liu, Xiaoxiao, Long, Brian, Mann, Rusty, McGraw, Medea, Mihalas, Stefan, Mukora, Alice, Murphy, Gabe J., Ng, Lindsay, Ngo, Kiet, Nguyen, Thuc Nghi, Nicovich, Philip R., Oldre, Aaron, Park, Daniel, Parry, Sheana, Perkins, Jed, Potekhina, Lydia, Reid, David, Robertson, Miranda, Sandman, David, Schroedter, Martin, Slaughterbeck, Cliff, Soler-Llavina, Gilberto, Sulc, Josef, Szafer, Aaron, Tasic, Bosiljka, Taskin, Naz, Teeter, Corinne, Thatra, Nivretta, Tung, Herman, Wakeman, Wayne, Williams, Grace, Young, Rob, Zhou, Zhi, Farrell, Colin, Peng, Hanchuan, Hawrylycz, Michael J., Lein, Ed, Ng, Lydia, Arkhipov, Anton, Bernard, Amy, Phillips, John W., Zeng, Hongkui, and Koch, Christof
Published: 2019
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46. Author Correction: Human neocortical expansion involves glutamatergic neuron diversification

Author: Berg, Jim, Sorensen, Staci A., Ting, Jonathan T., Miller, Jeremy A., Chartrand, Thomas, Buchin, Anatoly, Bakken, Trygve E., Budzillo, Agata, Dee, Nick, Ding, Song-Lin, Gouwens, Nathan W., Hodge, Rebecca D., Kalmbach, Brian, Lee, Changkyu, Lee, Brian R., Alfiler, Lauren, Baker, Katherine, Barkan, Eliza, Beller, Allison, Berry, Kyla, Bertagnolli, Darren, Bickley, Kris, Bomben, Jasmine, Braun, Thomas, Brouner, Krissy, Casper, Tamara, Chong, Peter, Crichton, Kirsten, Dalley, Rachel, de Frates, Rebecca, Desta, Tsega, Lee, Samuel Dingman, D’Orazi, Florence, Dotson, Nadezhda, Egdorf, Tom, Enstrom, Rachel, Farrell, Colin, Feng, David, Fong, Olivia, Furdan, Szabina, Galakhova, Anna A., Gamlin, Clare, Gary, Amanda, Glandon, Alexandra, Goldy, Jeff, Gorham, Melissa, Goriounova, Natalia A., Gratiy, Sergey, Graybuck, Lucas, Gu, Hong, Hadley, Kristen, Hansen, Nathan, Heistek, Tim S., Henry, Alex M., Heyer, Djai B., Hill, DiJon, Hill, Chris, Hupp, Madie, Jarsky, Tim, Kebede, Sara, Keene, Lisa, Kim, Lisa, Kim, Mean-Hwan, Kroll, Matthew, Latimer, Caitlin, Levi, Boaz P., Link, Katherine E., Mallory, Matthew, Mann, Rusty, Marshall, Desiree, Maxwell, Michelle, McGraw, Medea, McMillen, Delissa, Melief, Erica, Mertens, Eline J., Mezei, Leona, Mihut, Norbert, Mok, Stephanie, Molnar, Gabor, Mukora, Alice, Ng, Lindsay, Ngo, Kiet, Nicovich, Philip R., Nyhus, Julie, Olah, Gaspar, Oldre, Aaron, Omstead, Victoria, Ozsvar, Attila, Park, Daniel, Peng, Hanchuan, Pham, Trangthanh, Pom, Christina A., Potekhina, Lydia, Rajanbabu, Ramkumar, Ransford, Shea, Reid, David, Rimorin, Christine, Ruiz, Augustin, Sandman, David, Sulc, Josef, Sunkin, Susan M., Szafer, Aaron, Szemenyei, Viktor, Thomsen, Elliot R., Tieu, Michael, Torkelson, Amy, Trinh, Jessica, Tung, Herman, Wakeman, Wayne, Waleboer, Femke, Ward, Katelyn, Wilbers, René, Williams, Grace, Yao, Zizhen, Yoon, Jae-Geun, Anastassiou, Costas, Arkhipov, Anton, Barzo, Pal, Bernard, Amy, Cobbs, Charles, de Witt Hamer, Philip C., Ellenbogen, Richard G., Esposito, Luke, Ferreira, Manuel, Gwinn, Ryder P., Hawrylycz, Michael J., Hof, Patrick R., Idema, Sander, Jones, Allan R., Keene, C. Dirk, Ko, Andrew L., Murphy, Gabe J., Ng, Lydia, Ojemann, Jeffrey G., Patel, Anoop P., Phillips, John W., Silbergeld, Daniel L., Smith, Kimberly, Tasic, Bosiljka, Yuste, Rafael, Segev, Idan, de Kock, Christiaan P. J., Mansvelder, Huibert D., Tamas, Gabor, Zeng, Hongkui, Koch, Christof, and Lein, Ed S.
Published: 2022
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47. Discovery of Functional Noncoding Elements by Digital Analysis of Chromatin Structure

Author: Sabo, Peter J., Hawrylycz, Michael, Wallace, James C., Humbert, Richard, Yu, Man, Shafer, Anthony, Kawamoto, Janelle, Hall, Robert, Mack, Joshua, Dorschner, Michael O., McArthur, Michael, Stamatoyannopoulos, John A., and Gartler, Stanley M.
Published: 2004

48. Genome-Wide Identification of DNasel Hypersensitive Sites Using Active Chromatin Sequence Libraries

Author: Sabo, Peter J., Humbert, Richard, Hawrylycz, Michael, Wallace, James C., Dorschner, Michael O., McArthur, Michael, Stamatoyannopoulos, John A., and Davie, Earl W.
Published: 2004

49. Neuronal Connectivity as a Determinant of Cell Types and Subtypes

Author: Liu, Lijuan, primary, Yun, Zhixi, additional, Manubens-Gil, Linus, additional, Chen, Hanbo, additional, Xiong, Feng, additional, Dong, Hongwei, additional, Zeng, Hongkui, additional, Hawrylycz, Michael, additional, Ascoli, Giorgio A., additional, and Peng, Hanchuan, additional
Published: 2023
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50. Full-Spectrum Neuronal Diversity and Stereotypy through Whole Brain Morphometry

Author: Peng, Hanchuan, primary, Liu, Yufeng, additional, Jiang, Shengdian, additional, Li, Yingxin, additional, Zhao, Sujun, additional, Yun, Zhixi, additional, Zhao, Zuo-Han, additional, Zhang, Lingli, additional, Wang, Gaoyu, additional, Chen, Xin, additional, Manubens-Gil, Linus, additional, Hang, Yuning, additional, Garcia-forn, Marta, additional, Wang, Wei, additional, De Rubeis, Silvia, additional, Wu, Zhuhao, additional, Osten, Pavel, additional, Gong, Hui, additional, Hawrylycz, Michael, additional, Mitra, Partha, additional, Dong, Hong-Wei, additional, Luo, Qingming, additional, Ascoli, Giorgio, additional, Zeng, Hongkui, additional, and Liu, Lijuan, additional
Published: 2023
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