810 results on '"Zeng, Hongkui"'
Search Results
2. The subcommissural organ regulates brain development via secreted peptides
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Zhang, Tingting, Ai, Daosheng, Wei, Pingli, Xu, Ying, Bi, Zhanying, Ma, Fengfei, Li, Fengzhi, Chen, Xing-jun, Zhang, Zhaohuan, Zou, Xiaoxiao, Guo, Zongpei, Zhao, Yue, Li, Jun-Liszt, Ye, Meng, Feng, Ziyan, Zhang, Xinshuang, Zheng, Lijun, Yu, Jie, Li, Chunli, Tu, Tianqi, Zeng, Hongkui, Lei, Jianfeng, Zhang, Hongqi, Hong, Tao, Zhang, Li, Luo, Benyan, Li, Zhen, Xing, Chao, Jia, Chenxi, Li, Lingjun, Sun, Wenzhi, and Ge, Woo-ping
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- 2024
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3. Single-cell analysis of chromatin accessibility in the adult mouse brain
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Zu, Songpeng, Li, Yang Eric, Wang, Kangli, Armand, Ethan J, Mamde, Sainath, Amaral, Maria Luisa, Wang, Yuelai, Chu, Andre, Xie, Yang, Miller, Michael, Xu, Jie, Wang, Zhaoning, Zhang, Kai, Jia, Bojing, Hou, Xiaomeng, Lin, Lin, Yang, Qian, Lee, Seoyeon, Li, Bin, Kuan, Samantha, Liu, Hanqing, Zhou, Jingtian, Pinto-Duarte, Antonio, Lucero, Jacinta, Osteen, Julia, Nunn, Michael, Smith, Kimberly A, Tasic, Bosiljka, Yao, Zizhen, Zeng, Hongkui, Wang, Zihan, Shang, Jingbo, Behrens, M Margarita, Ecker, Joseph R, Wang, Allen, Preissl, Sebastian, and Ren, Bing
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Biochemistry and Cell Biology ,Bioinformatics and Computational Biology ,Genetics ,Biological Sciences ,Stem Cell Research ,Neurosciences ,Biotechnology ,Underpinning research ,1.1 Normal biological development and functioning ,Neurological ,Animals ,Humans ,Mice ,Brain ,Cerebral Cortex ,Chromatin ,Deep Learning ,DNA Transposable Elements ,Gene Regulatory Networks ,Neurons ,Single-Cell Analysis ,General Science & Technology - Abstract
Recent advances in single-cell technologies have led to the discovery of thousands of brain cell types; however, our understanding of the gene regulatory programs in these cell types is far from complete1-4. Here we report a comprehensive atlas of candidate cis-regulatory DNA elements (cCREs) in the adult mouse brain, generated by analysing chromatin accessibility in 2.3 million individual brain cells from 117 anatomical dissections. The atlas includes approximately 1 million cCREs and their chromatin accessibility across 1,482 distinct brain cell populations, adding over 446,000 cCREs to the most recent such annotation in the mouse genome. The mouse brain cCREs are moderately conserved in the human brain. The mouse-specific cCREs-specifically, those identified from a subset of cortical excitatory neurons-are strongly enriched for transposable elements, suggesting a potential role for transposable elements in the emergence of new regulatory programs and neuronal diversity. Finally, we infer the gene regulatory networks in over 260 subclasses of mouse brain cells and develop deep-learning models to predict the activities of gene regulatory elements in different brain cell types from the DNA sequence alone. Our results provide a resource for the analysis of cell-type-specific gene regulation programs in both mouse and human brains.
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- 2023
4. Single-cell DNA methylome and 3D multi-omic atlas of the adult mouse brain.
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Liu, Hanqing, Zeng, Qiurui, Zhou, Jingtian, Bartlett, Anna, Wang, Bang-An, Tian, Wei, Kenworthy, Mia, Altshul, Jordan, Nery, Joseph, Chen, Huaming, Castanon, Rosa, Zu, Songpeng, Li, Yang, Lucero, Jacinta, Osteen, Julia, Pinto-Duarte, Antonio, Lee, Jasper, Rink, Jon, Cho, Silvia, Emerson, Nora, Nunn, Michael, OConnor, Carolyn, Wu, Zhanghao, Stoica, Ion, Yao, Zizhen, Smith, Kimberly, Tasic, Bosiljka, Luo, Chongyuan, Dixon, Jesse, Zeng, Hongkui, Ren, Bing, Behrens, M, Ecker, Joseph, and Berube, Peter
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Animals ,Mice ,Brain ,Chromatin ,Cytosine ,Datasets as Topic ,DNA Methylation ,Epigenome ,Multiomics ,Single-Cell Analysis ,Transcription Factors ,Transcription ,Genetic - Abstract
Cytosine DNA methylation is essential in brain development and is implicated in various neurological disorders. Understanding DNA methylation diversity across the entire brain in a spatial context is fundamental for a complete molecular atlas of brain cell types and their gene regulatory landscapes. Here we used single-nucleus methylome sequencing (snmC-seq3) and multi-omic sequencing (snm3C-seq)1 technologies to generate 301,626 methylomes and 176,003 chromatin conformation-methylome joint profiles from 117 dissected regions throughout the adult mouse brain. Using iterative clustering and integrating with companion whole-brain transcriptome and chromatin accessibility datasets, we constructed a methylation-based cell taxonomy with 4,673 cell groups and 274 cross-modality-annotated subclasses. We identified 2.6 million differentially methylated regions across the genome that represent potential gene regulation elements. Notably, we observed spatial cytosine methylation patterns on both genes and regulatory elements in cell types within and across brain regions. Brain-wide spatial transcriptomics data validated the association of spatial epigenetic diversity with transcription and improved the anatomical mapping of our epigenetic datasets. Furthermore, chromatin conformation diversities occurred in important neuronal genes and were highly associated with DNA methylation and transcription changes. Brain-wide cell-type comparisons enabled the construction of regulatory networks that incorporate transcription factors, regulatory elements and their potential downstream gene targets. Finally, intragenic DNA methylation and chromatin conformation patterns predicted alternative gene isoform expression observed in a whole-brain SMART-seq2 dataset. Our study establishes a brain-wide, single-cell DNA methylome and 3D multi-omic atlas and provides a valuable resource for comprehending the cellular-spatial and regulatory genome diversity of the mouse brain.
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- 2023
5. Brain-wide correspondence of neuronal epigenomics and distant projections.
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Zhou, Jingtian, Zhang, Zhuzhu, Wu, May, Liu, Hanqing, Pang, Yan, Bartlett, Anna, Peng, Zihao, Ding, Wubin, Rivkin, Angeline, Lagos, Will, Williams, Elora, Lee, Cheng-Ta, Miyazaki, Paula, Aldridge, Andrew, Zeng, Qiurui, Salinda, J, Claffey, Naomi, Liem, Michelle, Fitzpatrick, Conor, Boggeman, Lara, Yao, Zizhen, Smith, Kimberly, Tasic, Bosiljka, Altshul, Jordan, Kenworthy, Mia, Valadon, Cynthia, Nery, Joseph, Castanon, Rosa, Patne, Neelakshi, Vu, Minh, Rashid, Mohammad, Jacobs, Matthew, Ito, Tony, Osteen, Julia, Emerson, Nora, Lee, Jasper, Cho, Silvia, Rink, Jon, Huang, Hsiang-Hsuan, Pinto-Duartec, António, Dominguez, Bertha, Smith, Jared, OConnor, Carolyn, Zeng, Hongkui, Chen, Shengbo, Lee, Kuo-Fen, Jin, Xin, Margarita Behrens, M, Ecker, Joseph, Callaway, Edward, and Mukamel, Eran
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Animals ,Mice ,Amygdala ,Brain ,Consensus Sequence ,Datasets as Topic ,Epigenomics ,Gene Expression Profiling ,Hypothalamus ,Mesencephalon ,Neural Pathways ,Neurons ,Neurotransmitter Agents ,Regulatory Sequences ,Nucleic Acid ,Rhombencephalon ,Single-Cell Analysis ,Thalamus ,Transcription Factors - Abstract
Single-cell analyses parse the brains billions of neurons into thousands of cell-type clusters residing in different brain structures1. Many cell types mediate their functions through targeted long-distance projections allowing interactions between specific cell types. Here we used epi-retro-seq2 to link single-cell epigenomes and cell types to long-distance projections for 33,034 neurons dissected from 32 different regions projecting to 24 different targets (225 source-to-target combinations) across the whole mouse brain. We highlight uses of these data for interrogating principles relating projection types to transcriptomics and epigenomics, and for addressing hypotheses about cell types and connections related to genetics. We provide an overall synthesis with 926 statistical comparisons of discriminability of neurons projecting to each target for every source. We integrate this dataset into the larger BRAIN Initiative Cell Census Network atlas, composed of millions of neurons, to link projection cell types to consensus clusters. Integration with spatial transcriptomics further assigns projection-enriched clusters to smaller source regions than the original dissections. We exemplify this by presenting in-depth analyses of projection neurons from the hypothalamus, thalamus, hindbrain, amygdala and midbrain to provide insights into properties of those cell types, including differentially expressed genes, their associated cis-regulatory elements and transcription-factor-binding motifs, and neurotransmitter use.
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- 2023
6. Neuroscience needs Network Science
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Barabási, Dániel L, Bianconi, Ginestra, Bullmore, Ed, Burgess, Mark, Chung, SueYeon, Eliassi-Rad, Tina, George, Dileep, Kovács, István A., Makse, Hernán, Papadimitriou, Christos, Nichols, Thomas E., Sporns, Olaf, Stachenfeld, Kim, Toroczkai, Zoltán, Towlson, Emma K., Zador, Anthony M, Zeng, Hongkui, Barabási, Albert-László, Bernard, Amy, and Buzsáki, György
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Quantitative Biology - Neurons and Cognition - Abstract
The brain is a complex system comprising a myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such intricate systems, offering a framework for integrating multiscale data and complexity. Here, we discuss the application of network science in the study of the brain, addressing topics such as network models and metrics, the connectome, and the role of dynamics in neural networks. We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities. We underscore the importance of fostering interdisciplinary opportunities through funding initiatives, workshops, and conferences, as well as supporting students and postdoctoral fellows with interests in both disciplines. By uniting the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way towards a deeper understanding of the brain and its functions., Comment: 19 pages, 1 figure, 1 box
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- 2023
7. All-optical recreation of naturalistic neural activity with a multifunctional transgenic reporter mouse.
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Sadahiro, Masato, Hendricks, William, Gopakumar, Karthika, Quintana, Daniel, Tasic, Bosiljka, Daigle, Tanya, Zeng, Hongkui, Oldenburg, Ian, Adesnik, Hillel, Bounds, Hayley, and Gajowa, Marta
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2-photon imaging ,2-photon optogenetics ,CP: Neuroscience ,GCaMP ,TIGRE2.0 ,all-optical ,holographic optogenetics ,neural circuits ,neural manifold ,population vectors ,transgenic mouse ,visual cortex ,Mice ,Animals ,Mice ,Transgenic ,Calcium ,Optogenetics ,Neurons ,Recreation - Abstract
Determining which features of the neural code drive behavior requires the ability to simultaneously read out and write in neural activity patterns with high precision across many neurons. All-optical systems that combine two-photon calcium imaging and targeted photostimulation enable the activation of specific, functionally defined groups of neurons. However, these techniques are unable to test how patterns of activity across a population contribute to computation because of an inability to both read and write cell-specific firing rates. To overcome this challenge, we make two advances: first, we introduce a genetic line of mice for Cre-dependent co-expression of a calcium indicator and a potent soma-targeted microbial opsin. Second, using this line, we develop a method for read-out and write-in of precise population vectors of neural activity by calibrating the photostimulation to each cell. These advances offer a powerful and convenient platform for investigating the neural codes of computation and behavior.
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- 2023
8. Cell Type Organization Across the Mouse Brain
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Zeng, Hongkui
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brain cell types ,mouse ,circuit function ,abstracts ,conference proceedings ,Center for Neural Circuit Mapping ,University of California Irvine - Abstract
To understand the function of the brain and how its dysfunction leads to brain diseases, it is essential to uncover the cell type composition of the brain, how the cell types are connected with each other and what their roles are in circuit function. At the Allen Institute, we have built multiple technology platforms, including single-cell transcriptomics, spatial transcriptomics, single and multi-patching electrophysiology, 3D reconstruction of neuronal morphology, and brain-wide connectivity mapping, to characterize the molecular, anatomical, physiological, and connectional properties of brain cell types in a systematic manner, towards the creation of multi-modal cell atlases for the mouse and human brains. We have now generated a comprehensive and high-resolution transcriptomic and spatial cell type atlas for the whole adult mouse brain, based on the combination of two single-cell-level, whole-brain-scale datasets by scRNA- seq and MERFISH. The atlas is hierarchically organized into five nested levels of classification: 7 divisions, 32 classes, ~300 subclasses, ~1,000 supertypes and ~5,200 clusters. We systematically analyzed the neuronal, non-neuronal, and immature neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The study uncovered tremendous heterogeneity in neurotransmitter and neuropeptide expression and co- expression patterns in different cell types, suggesting they mediate myriad modes of intercellular communications. We also found that transcription factors are major determinants of cell type classification in the adult mouse brain and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. This study reveals extraordinary cellular diversity and underlying rules of brain organization. It establishes a benchmark reference atlas and a foundational resource for deep and integrative investigations of cell type and circuit function, development, and evolution of the mammalian brain.
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- 2023
9. Cross-modality mapping using image varifolds to align tissue-scale atlases to molecular-scale measures with application to 2D brain sections
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Stouffer, Kaitlin M., Trouvé, Alain, Younes, Laurent, Kunst, Michael, Ng, Lydia, Zeng, Hongkui, Anant, Manjari, Fan, Jean, Kim, Yongsoo, Chen, Xiaoyin, Rue, Mara, and Miller, Michael I.
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- 2024
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10. Long-term labeling and imaging of synaptically connected neuronal networks in vivo using double-deletion-mutant rabies viruses
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Jin, Lei, Sullivan, Heather A., Zhu, Mulangma, Lavin, Thomas K., Matsuyama, Makoto, Fu, Xin, Lea, Nicholas E., Xu, Ran, Hou, YuanYuan, Rutigliani, Luca, Pruner, Maxwell, Babcock, Kelsey R., Ip, Jacque Pak Kan, Hu, Ming, Daigle, Tanya L., Zeng, Hongkui, Sur, Mriganka, Feng, Guoping, and Wickersham, Ian R.
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- 2024
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11. BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets
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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
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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.
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- 2023
12. Robust enhancer-gene regulation identified by single-cell transcriptomes and epigenomes
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Xie, Fangming, Armand, Ethan J, Yao, Zizhen, Liu, Hanqing, Bartlett, Anna, Behrens, M Margarita, Li, Yang Eric, Lucero, Jacinta D, Luo, Chongyuan, Nery, Joseph R, Pinto-Duarte, Antonio, Poirion, Olivier B, Preissl, Sebastian, Rivkin, Angeline C, Tasic, Bosiljka, Zeng, Hongkui, Ren, Bing, Ecker, Joseph R, and Mukamel, Eran A
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Biological Sciences ,Bioinformatics and Computational Biology ,Genetics ,Epidemiology ,Health Sciences ,Biotechnology ,Human Genome ,Generic health relevance ,DNA methylation ,brain ,chromatin accessibility ,enhancer ,epigenome - Abstract
Single-cell sequencing could help to solve the fundamental challenge of linking millions of cell-type-specific enhancers with their target genes. However, this task is confounded by patterns of gene co-expression in much the same way that genetic correlation due to linkage disequilibrium confounds fine-mapping in genome-wide association studies (GWAS). We developed a non-parametric permutation-based procedure to establish stringent statistical criteria to control the risk of false-positive associations in enhancer-gene association studies (EGAS). We applied our procedure to large-scale transcriptome and epigenome data from multiple tissues and species, including the mouse and human brain, to predict enhancer-gene associations genome wide. We tested the functional validity of our predictions by comparing them with chromatin conformation data and causal enhancer perturbation experiments. Our study shows how controlling for gene co-expression enables robust enhancer-gene linkage using single-cell sequencing data.
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- 2023
13. PROJECT MINDSCOPE
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Koch, Christof, primary, Reid, Clay, additional, Zeng, Hongkui, additional, Mihalas, Stefan, additional, Hawrylycz, Mike, additional, Philips, John, additional, Dang, Chinh, additional, and Jones, Allan, additional
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- 2024
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14. BUILDING ATLASES OF THE BRAIN
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Hawrylycz, Mike, primary, Dang, Chinh, additional, Koch, Christof, additional, and Zeng, Hongkui, additional
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- 2024
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15. A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain
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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
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- 2023
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16. A transcriptomic taxonomy of mouse brain-wide spinal projecting neurons
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Winter, Carla C., Jacobi, Anne, Su, Junfeng, Chung, Leeyup, van Velthoven, Cindy T. J., Yao, Zizhen, Lee, Changkyu, Zhang, Zicong, Yu, Shuguang, Gao, Kun, Duque Salazar, Geraldine, Kegeles, Evgenii, Zhang, Yu, Tomihiro, Makenzie C., Zhang, Yiming, Yang, Zhiyun, Zhu, Junjie, Tang, Jing, Song, Xuan, Donahue, Ryan J., Wang, Qing, McMillen, Delissa, Kunst, Michael, Wang, Ning, Smith, Kimberly A., Romero, Gabriel E., Frank, Michelle M., Krol, Alexandra, Kawaguchi, Riki, Geschwind, Daniel H., Feng, Guoping, Goodrich, Lisa V., Liu, Yuanyuan, Tasic, Bosiljka, Zeng, Hongkui, and He, Zhigang
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- 2023
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17. Molecularly defined and spatially resolved cell atlas of the whole mouse brain
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Zhang, Meng, Pan, Xingjie, Jung, Won, Halpern, Aaron R., Eichhorn, Stephen W., Lei, Zhiyun, Cohen, Limor, Smith, Kimberly A., Tasic, Bosiljka, Yao, Zizhen, Zeng, Hongkui, and Zhuang, Xiaowei
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- 2023
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18. A guide to the BRAIN Initiative Cell Census Network data ecosystem
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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
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Biological Sciences ,Genetics ,Data Science ,Neurosciences ,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.
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- 2023
19. Single cell enhancer activity distinguishes GABAergic and cholinergic lineages in embryonic mouse basal ganglia
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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
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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.
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- 2022
20. Publisher Correction: Long-term labeling and imaging of synaptically connected neuronal networks in vivo using double-deletion-mutant rabies viruses
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Jin, Lei, Sullivan, Heather A., Zhu, Mulangma, Lavin, Thomas K., Matsuyama, Makoto, Fu, Xin, Lea, Nicholas E., Xu, Ran, Hou, YuanYuan, Rutigliani, Luca, Pruner, Maxwell, Babcock, Kelsey R., Ip, Jacque Pak Kan, Hu, Ming, Daigle, Tanya L., Zeng, Hongkui, Sur, Mriganka, Feng, Guoping, and Wickersham, Ian R.
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- 2024
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21. Transcriptional network orchestrating regional patterning of cortical progenitors
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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
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Biochemistry and Cell Biology ,Biomedical and Clinical Sciences ,Biological Sciences ,Stem Cell Research - Nonembryonic - Non-Human ,Genetics ,Human Genome ,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.
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- 2021
22. Phenotypic variation of transcriptomic cell types in mouse motor cortex
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Scala, Federico, Kobak, Dmitry, Bernabucci, Matteo, Bernaerts, Yves, Cadwell, Cathryn René, Castro, Jesus Ramon, Hartmanis, Leonard, Jiang, Xiaolong, Laturnus, Sophie, Miranda, Elanine, Mulherkar, Shalaka, Tan, Zheng Huan, Yao, Zizhen, Zeng, Hongkui, Sandberg, Rickard, Berens, Philipp, and Tolias, Andreas S
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Brain Disorders ,Genetics ,Neurosciences ,Neurological ,Animals ,Atlases as Topic ,Female ,GABAergic Neurons ,Gene Expression Profiling ,Glutamates ,Lysine ,Male ,Mice ,Motor Cortex ,Neurons ,Organ Specificity ,Patch-Clamp Techniques ,Phenotype ,Sequence Analysis ,RNA ,Single-Cell Analysis ,Staining and Labeling ,Transcriptome ,General Science & Technology - Abstract
Cortical neurons exhibit extreme diversity in gene expression as well as in morphological and electrophysiological properties1,2. Most existing neural taxonomies are based on either transcriptomic3,4 or morpho-electric5,6 criteria, as it has been technically challenging to study both aspects of neuronal diversity in the same set of cells7. Here we used Patch-seq8 to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of more than 1,300 neurons in adult mouse primary motor cortex, providing a morpho-electric annotation of almost all transcriptomically defined neural cell types. We found that, although broad families of transcriptomic types (those expressing Vip, Pvalb, Sst and so on) had distinct and essentially non-overlapping morpho-electric phenotypes, individual transcriptomic types within the same family were not well separated in the morpho-electric space. Instead, there was a continuum of variability in morphology and electrophysiology, with neighbouring transcriptomic cell types showing similar morpho-electric features, often without clear boundaries between them. Our results suggest that neuronal types in the neocortex do not always form discrete entities. Instead, neurons form a hierarchy that consists of distinct non-overlapping branches at the level of families, but can form continuous and correlated transcriptomic and morpho-electrical landscapes within families.
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- 2021
23. A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex
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Yao, Zizhen, Liu, Hanqing, Xie, Fangming, Fischer, Stephan, Adkins, Ricky S, Aldridge, Andrew I, Ament, Seth A, Bartlett, Anna, Behrens, M Margarita, Van den Berge, Koen, Bertagnolli, Darren, de Bézieux, Hector Roux, Biancalani, Tommaso, Booeshaghi, A Sina, Bravo, Héctor Corrada, Casper, Tamara, Colantuoni, Carlo, Crabtree, Jonathan, Creasy, Heather, Crichton, Kirsten, Crow, Megan, Dee, Nick, Dougherty, Elizabeth L, Doyle, Wayne I, Dudoit, Sandrine, Fang, Rongxin, Felix, Victor, Fong, Olivia, Giglio, Michelle, Goldy, Jeff, Hawrylycz, Mike, Herb, Brian R, Hertzano, Ronna, Hou, Xiaomeng, Hu, Qiwen, Kancherla, Jayaram, Kroll, Matthew, Lathia, Kanan, Li, Yang Eric, Lucero, Jacinta D, Luo, Chongyuan, Mahurkar, Anup, McMillen, Delissa, Nadaf, Naeem M, Nery, Joseph R, Nguyen, Thuc Nghi, Niu, Sheng-Yong, Ntranos, Vasilis, Orvis, Joshua, Osteen, Julia K, Pham, Thanh, Pinto-Duarte, Antonio, Poirion, Olivier, Preissl, Sebastian, Purdom, Elizabeth, Rimorin, Christine, Risso, Davide, Rivkin, Angeline C, Smith, Kimberly, Street, Kelly, Sulc, Josef, Svensson, Valentine, Tieu, Michael, Torkelson, Amy, Tung, Herman, Vaishnav, Eeshit Dhaval, Vanderburg, Charles R, van Velthoven, Cindy, Wang, Xinxin, White, Owen R, Huang, Z Josh, Kharchenko, Peter V, Pachter, Lior, Ngai, John, Regev, Aviv, Tasic, Bosiljka, Welch, Joshua D, Gillis, Jesse, Macosko, Evan Z, Ren, Bing, Ecker, Joseph R, Zeng, Hongkui, and Mukamel, Eran A
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Human Genome ,Neurosciences ,Genetics ,Bioengineering ,Biotechnology ,1.1 Normal biological development and functioning ,Underpinning research ,Neurological ,Animals ,Atlases as Topic ,Datasets as Topic ,Epigenesis ,Genetic ,Epigenomics ,Female ,Gene Expression Profiling ,Male ,Mice ,Motor Cortex ,Neurons ,Organ Specificity ,Reproducibility of Results ,Single-Cell Analysis ,Transcriptome ,General Science & Technology - 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.
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- 2021
24. A multimodal cell census and atlas of the mammalian primary motor cortex
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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
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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.
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- 2021
25. A whole-brain monosynaptic input connectome to neuron classes in mouse visual cortex
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Yao, Shenqin, Wang, Quanxin, Hirokawa, Karla E., Ouellette, Benjamin, Ahmed, Ruweida, Bomben, Jasmin, Brouner, Krissy, Casal, Linzy, Caldejon, Shiella, Cho, Andy, Dotson, Nadezhda I., Daigle, Tanya L., Egdorf, Tom, Enstrom, Rachel, Gary, Amanda, Gelfand, Emily, Gorham, Melissa, Griffin, Fiona, Gu, Hong, Hancock, Nicole, Howard, Robert, Kuan, Leonard, Lambert, Sophie, Lee, Eric Kenji, Luviano, Jennifer, Mace, Kyla, Maxwell, Michelle, Mortrud, Marty T., Naeemi, Maitham, Nayan, Chelsea, Ngo, Nhan-Kiet, Nguyen, Thuyanh, North, Kat, Ransford, Shea, Ruiz, Augustin, Seid, Sam, Swapp, Jackie, Taormina, Michael J., Wakeman, Wayne, Zhou, Thomas, Nicovich, Philip R., Williford, Ali, Potekhina, Lydia, McGraw, Medea, Ng, Lydia, Groblewski, Peter A., Tasic, Bosiljka, Mihalas, Stefan, Harris, Julie A., Cetin, Ali, and Zeng, Hongkui
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- 2023
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26. Common Cell type Nomenclature for the mammalian brain: A systematic, extensible convention
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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
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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
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- 2020
27. A coupled autoencoder approach for multi-modal analysis of cell types
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Gala, Rohan, Gouwens, Nathan, Yao, Zizhen, Budzillo, Agata, Penn, Osnat, Tasic, Bosiljka, Murphy, Gabe, Zeng, Hongkui, and Sümbül, Uygar
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Quantitative Biology - Neurons and Cognition ,Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
Recent developments in high throughput profiling of individual neurons have spurred data driven exploration of the idea that there exist natural groupings of neurons referred to as cell types. The promise of this idea is that the immense complexity of brain circuits can be reduced, and effectively studied by means of interactions between cell types. While clustering of neuron populations based on a particular data modality can be used to define cell types, such definitions are often inconsistent across different characterization modalities. We pose this issue of cross-modal alignment as an optimization problem and develop an approach based on coupled training of autoencoders as a framework for such analyses. We apply this framework to a Patch-seq dataset consisting of transcriptomic and electrophysiological profiles for the same set of neurons to study consistency of representations across modalities, and evaluate cross-modal data prediction ability. We explore the problem where only a subset of neurons is characterized with more than one modality, and demonstrate that representations learned by coupled autoencoders can be used to identify types sampled only by a single modality., Comment: Main text : 10 pages, 5 figures. Supp text : 6 pages, 3 figures
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- 2019
28. All-optical recreation of naturalistic neural activity with a multifunctional transgenic reporter mouse
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Bounds, Hayley A., Sadahiro, Masato, Hendricks, William D., Gajowa, Marta, Gopakumar, Karthika, Quintana, Daniel, Tasic, Bosiljka, Daigle, Tanya L., Zeng, Hongkui, Oldenburg, Ian Antón, and Adesnik, Hillel
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- 2023
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29. A community-based transcriptomics classification and nomenclature of neocortical cell types
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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
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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.
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- 2020
30. A community-based transcriptomics classification and nomenclature of neocortical cell types
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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.
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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
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- 2019
31. Brainwide Genetic Sparse Cell Labeling to Illuminate the Morphology of Neurons and Glia with Cre-Dependent MORF Mice
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Veldman, Matthew B, Park, Chang Sin, Eyermann, Charles M, Zhang, Jason Y, Zuniga-Sanchez, Elizabeth, Hirano, Arlene A, Daigle, Tanya L, Foster, Nicholas N, Zhu, Muye, Langfelder, Peter, Lopez, Ivan A, Brecha, Nicholas C, Zipursky, S Lawrence, Zeng, Hongkui, Dong, Hong-Wei, and Yang, X William
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Biomedical and Clinical Sciences ,Neurosciences ,Genetics ,Brain Disorders ,1.1 Normal biological development and functioning ,Underpinning research ,Neurological ,Animals ,Astrocytes ,Brain ,Frameshift Mutation ,Green Fluorescent Proteins ,Integrases ,Mice ,Mice ,Transgenic ,Microglia ,Microsatellite Repeats ,Neurons ,Retinal Horizontal Cells ,Cre ,MORF ,astrocyte ,imaging ,microglia ,morphology ,neuron ,reconstruction ,spaghetti monster ,sparse labeling ,Psychology ,Cognitive Sciences ,Neurology & Neurosurgery ,Biological psychology - Abstract
Cajal recognized that the elaborate shape of neurons is fundamental to their function in the brain. However, there are no simple and generalizable genetic methods to study neuronal or glial cell morphology in the mammalian brain. Here, we describe four mouse lines conferring Cre-dependent sparse cell labeling based on mononucleotide repeat frameshift (MORF) as a stochastic translational switch. Notably, the optimized MORF3 mice, with a membrane-bound multivalent immunoreporter, confer Cre-dependent sparse and bright labeling of thousands of neurons, astrocytes, or microglia in each brain, revealing their intricate morphologies. MORF3 mice are compatible with imaging in tissue-cleared thick brain sections and with immuno-EM. An analysis of 151 MORF3-labeled developing retinal horizontal cells reveals novel morphological cell clusters and axonal maturation patterns. Our study demonstrates a conceptually novel, simple, generalizable, and scalable mouse genetic solution to sparsely label and illuminate the morphology of genetically defined neurons and glia in the mammalian brain.
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- 2020
32. Transcriptional Network Orchestrating Regional Patterning of Cortical Progenitors
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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
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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.
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- 2020
33. Genetic Identification of Vagal Sensory Neurons That Control Feeding
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Bai, Ling, Mesgarzadeh, Sheyda, Ramesh, Karthik S, Huey, Erica L, Liu, Yin, Gray, Lindsay A, Aitken, Tara J, Chen, Yiming, Beutler, Lisa R, Ahn, Jamie S, Madisen, Linda, Zeng, Hongkui, Krasnow, Mark A, and Knight, Zachary A
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Biological Sciences ,Biomedical and Clinical Sciences ,Neurosciences ,Nutrition ,Obesity ,Digestive Diseases ,Underpinning research ,1.1 Normal biological development and functioning ,Oral and gastrointestinal ,Agouti-Related Protein ,Animals ,Brain ,Feeding Behavior ,Gastrointestinal Tract ,Genetic Markers ,Genetic Phenomena ,Mechanoreceptors ,Mice ,Sensory Receptor Cells ,Vagus Nerve ,Viscera ,AgRP Neurons ,RNA sequencing ,chemogenetics ,fiber photometry ,hypothalamus ,optogenetics ,satiation ,stretch ,vagal afferents ,vagus nerve ,Medical and Health Sciences ,Developmental Biology ,Biological sciences ,Biomedical and clinical sciences - Abstract
Energy homeostasis requires precise measurement of the quantity and quality of ingested food. The vagus nerve innervates the gut and can detect diverse interoceptive cues, but the identity of the key sensory neurons and corresponding signals that regulate food intake remains unknown. Here, we use an approach for target-specific, single-cell RNA sequencing to generate a map of the vagal cell types that innervate the gastrointestinal tract. We show that unique molecular markers identify vagal neurons with distinct innervation patterns, sensory endings, and function. Surprisingly, we find that food intake is most sensitive to stimulation of mechanoreceptors in the intestine, whereas nutrient-activated mucosal afferents have no effect. Peripheral manipulations combined with central recordings reveal that intestinal mechanoreceptors, but not other cell types, potently and durably inhibit hunger-promoting AgRP neurons in the hypothalamus. These findings identify a key role for intestinal mechanoreceptors in the regulation of feeding.
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- 2019
34. A suite of enhancer AAVs and transgenic mouse lines for genetic access to cortical cell types
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Ben-Simon, Yoav, primary, Hooper, Marcus, additional, Narayan, Sujatha, additional, Daigle, Tanya, additional, Dwivedi, Deepanjali, additional, Way, Sharon W, additional, Oster, Aaron, additional, Stafford, David A, additional, Mich, John K, additional, Taormina, Michael J, additional, Martinez, Refugio A, additional, Opitz-Araya, Ximena, additional, Roth, Jada R, additional, Allen, Shona, additional, Ayala, Angela, additional, Bakken, Trygve E, additional, Barcelli, Tyler, additional, Barta, Stuard, additional, Bendrick, Jacqueline, additional, Bertagnolli, Darren, additional, Bowlus, Jessica, additional, Boyer, Gabriella, additional, Brouner, Krissy, additional, Casian, Brittny, additional, Casper, Tamara, additional, Chakka, Anish B, additional, Chakrabarty, Rushil, additional, Chance, Rebecca K, additional, Chavan, Sakshi, additional, Departee, Maxwell, additional, Donadio, Nicholas, additional, Dotson, Nadezhda, additional, Egdorf, Tom, additional, Gabitto, Mariano, additional, Gary, Amanda, additional, Gasperini, Molly, additional, Goldy, Jeff, additional, Gore, Bryan B, additional, Graybuck, Lucas, additional, Greisman, Noah, additional, Haeseleer, Francoise, additional, Halterman, Carliana, additional, Helback, Olivia, additional, Hockmeyer, Dirk, additional, Huang, Cindy, additional, Huff, Sydney, additional, Hunker, Avery, additional, Johansen, Nelson, additional, Juneau, Zoe, additional, Kalmbach, Brian, additional, Khem, Shannon, additional, Kutsal, Rana, additional, Larsen, Rachael, additional, Lee, Changkyu, additional, Lee, Angus Y, additional, Leibly, Madison, additional, Lenz, Garreck H, additional, Liang, Elizabeth, additional, Lusk, Nicholas, additional, Malone, Jocelin, additional, Mollenkopf, Tyler, additional, Morin, Elyse, additional, Newman, Dakota, additional, Ng, Lydia, additional, Ngo, Kiet, additional, Omstead, Victoria, additional, Oyama, Alana, additional, Pham, Trangthanh, additional, Pom, Christina A, additional, Potekhina, Lydia, additional, Ransford, Shea, additional, Rette, Dean, additional, Rimorin, Christine, additional, Rocha, Dana, additional, Ruiz, Augustin, additional, Sanchez, Raymond E.A., additional, Sedeno-Cortes, Adriana, additional, Sevigny, Joshua P, additional, Shapovalova, Nadiya, additional, Shulga, Lyudmila, additional, Sigler, Ana R, additional, Siverts, La Akea, additional, Somasundaram, Saroja, additional, Stewart, Kaiya, additional, Tieu, Michael, additional, Trader, Cameron, additional, van Velthoven, Cindy T.J., additional, Walker, Miranda, additional, Weed, Natalie, additional, Wirthlin, Morgan, additional, Wood, Toren, additional, Wynalda, Brooke, additional, Yao, Zizhen, additional, Zhou, Thomas, additional, Ariza, Jeanelle, additional, Dee, Nick, additional, Reding, Melissa, additional, Ronellenfitch, Kara, additional, Mufti, Shoaib, additional, Sunkin, Susan M, additional, Smith, Kimberly A, additional, Esposito, Luke, additional, Waters, Jack, additional, Thyagarajan, Bargavi, additional, Yao, Shenqin, additional, Lein, Ed, additional, Zeng, Hongkui, additional, Levi, Boaz P, additional, Ngai, John, additional, Ting, Jonathan T, additional, and Tasic, Bosiljka, additional
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- 2024
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35. Neurogliaform Cells Exhibit Laminar-specific Responses in the Visual Cortex and Modulate Behavioral State-dependent Cortical Activity
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Fishell, Gord, primary, Huang, shuhan, additional, Rizo, Daniella, additional, Wu, Sherry Jingjing, additional, Xu, Qing, additional, Zaine, Leena, additional, Alghamdi, Norah, additional, Stafford, David A., additional, Daigle, Tanya, additional, Tasic, Bosilijka, additional, Zeng, Hongkui, additional, and Ibrahim, Leena, additional
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- 2024
- Full Text
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36. Data-driven fine-grained region discovery in the mouse brain with transformers
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Lee, Alex Jihun, primary, Yao, Shenqin, additional, Lusk, Nicholas, additional, Zeng, Hongkui, additional, Tasic, Bosiljka, additional, and Abbasi-Asl, Reza, additional
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- 2024
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37. Conserved cell types with divergent features in human versus mouse cortex.
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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
38. A gut-to-brain signal of fluid osmolarity controls thirst satiation
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Zimmerman, Christopher A, Huey, Erica L, Ahn, Jamie S, Beutler, Lisa R, Tan, Chan Lek, Kosar, Seher, Bai, Ling, Chen, Yiming, Corpuz, Timothy V, Madisen, Linda, Zeng, Hongkui, and Knight, Zachary A
- Subjects
Biological Psychology ,Biomedical and Clinical Sciences ,Psychology ,Neurosciences ,Digestive Diseases ,Underpinning research ,1.1 Normal biological development and functioning ,Neurological ,Animals ,Brain ,Drinking ,Female ,GABAergic Neurons ,Gastrointestinal Tract ,Glutamates ,Male ,Mice ,Neurons ,Oropharynx ,Osmolar Concentration ,Prosencephalon ,Satiation ,Thirst ,Vagus Nerve ,Vasopressins ,General Science & Technology - Abstract
Satiation is the process by which eating and drinking reduce appetite. For thirst, oropharyngeal cues have a critical role in driving satiation by reporting to the brain the volume of fluid that has been ingested1-12. By contrast, the mechanisms that relay the osmolarity of ingested fluids remain poorly understood. Here we show that the water and salt content of the gastrointestinal tract are precisely measured and then rapidly communicated to the brain to control drinking behaviour in mice. We demonstrate that this osmosensory signal is necessary and sufficient for satiation during normal drinking, involves the vagus nerve and is transmitted to key forebrain neurons that control thirst and vasopressin secretion. Using microendoscopic imaging, we show that individual neurons compute homeostatic need by integrating this gastrointestinal osmosensory information with oropharyngeal and blood-borne signals. These findings reveal how the fluid homeostasis system monitors the osmolarity of ingested fluids to dynamically control drinking behaviour.
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- 2019
39. Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains
- Author
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Jiang, Shengdian, Wang, Yimin, Liu, Lijuan, Ding, Liya, Ruan, Zongcai, Dong, Hong-Wei, Ascoli, Giorgio A., Hawrylycz, Michael, Zeng, Hongkui, and Peng, Hanchuan
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- 2022
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40. Voltage imaging in the olfactory bulb using transgenic mouse lines expressing the genetically encoded voltage indicator ArcLight
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Platisa, Jelena, Zeng, Hongkui, Madisen, Linda, Cohen, Lawrence B., Pieribone, Vincent A., and Storace, Douglas A.
- Published
- 2022
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41. Spatially resolved gene regulatory and disease-related vulnerability map of the adult Macaque cortex
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Lei, Ying, Cheng, Mengnan, Li, Zihao, Zhuang, Zhenkun, Wu, Liang, sun, Yunong, Han, Lei, Huang, Zhihao, Wang, Yuzhou, Wang, Zifei, Xu, Liqin, Yuan, Yue, Liu, Shang, Pan, Taotao, Xie, Jiarui, Liu, Chuanyu, Volpe, Giacomo, Ward, Carl, Lai, Yiwei, Xu, Jiangshan, Wang, Mingyue, Yu, Hao, Sun, Haixi, Yu, Qichao, Wu, Liang, Wang, Chunqing, Wong, Chi Wai, Liu, Wei, Xu, Liangzhi, Wei, Jingkuan, Chen, Dandan, Shang, Zhouchun, Li, Guibo, Ma, Kun, Cheng, Le, Ling, Fei, Tan, Tao, Chen, Kai, Tasic, Bosiljka, Dean, Michael, Ji, Weizhi, Yang, Huanming, Gu, Ying, Esteban, Miguel A., Li, Yuxiang, Chen, Ao, Niu, Yuyu, Zeng, Hongkui, Hou, Yong, Liu, Longqi, Liu, Shiping, and Xu, Xun
- Published
- 2022
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42. Cross-modal coherent registration of whole mouse brains
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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
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- 2022
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43. The BRAIN Initiative Cell Census Consortium: Lessons Learned toward Generating a Comprehensive Brain Cell Atlas.
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Ecker, Joseph R, Geschwind, Daniel H, Kriegstein, Arnold R, Ngai, John, Osten, Pavel, Polioudakis, Damon, Regev, Aviv, Sestan, Nenad, Wickersham, Ian R, and Zeng, Hongkui
- Subjects
Brain ,Nerve Net ,Animals ,Humans ,Brain Mapping ,Pilot Projects ,Atlases as Topic ,BRAIN initiative ,anatomy ,cell census ,connectivity ,electrophysiology ,human brain ,mouse brain ,single-cell RNA-seq ,single-cell epigenomics ,single-cell transcriptomics ,Neurosciences ,Pediatric Research Initiative ,Genetics ,Underpinning research ,1.1 Normal biological development and functioning ,Neurological ,Psychology ,Cognitive Sciences ,Neurology & Neurosurgery - Abstract
A comprehensive characterization of neuronal cell types, their distributions, and patterns of connectivity is critical for understanding the properties of neural circuits and how they generate behaviors. Here we review the experiences of the BRAIN Initiative Cell Census Consortium, ten pilot projects funded by the U.S. BRAIN Initiative, in developing, validating, and scaling up emerging genomic and anatomical mapping technologies for creating a complete inventory of neuronal cell types and their connections in multiple species and during development. These projects lay the foundation for a larger and longer-term effort to generate whole-brain cell atlases in species including mice and humans.
- Published
- 2017
44. A Comprehensive Framework of Mu Opioid Receptor Mechanisms of Function in Anterior Cingulate and Somatosensory Cortex Neuron Types
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Ochandarena, Nicole, primary, Niehaus, Jesse, additional, Picken, Riley, additional, Yao, Zizhen, additional, Kim, Dong-Wook, additional, Zeng, Hongkui, additional, and Scherrer, Gregory, additional
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- 2024
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45. The subcommissural organ regulates brain development via secreted peptides
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Zhang, Tingting, primary, Ai, Daosheng, additional, Wei, Pingli, additional, Xu, Ying, additional, Bi, Zhanying, additional, Ma, Fengfei, additional, Li, Fengzhi, additional, Chen, Xing-jun, additional, Zhang, Zhaohuan, additional, Zou, Xiaoxiao, additional, Guo, Zongpei, additional, Zhao, Yue, additional, Li, Jun-Liszt, additional, Ye, Meng, additional, Feng, Ziyan, additional, Zhang, Xingshuang, additional, Zheng, Lijun, additional, Yu, Jie, additional, Li, Chunli, additional, Tu, Tianqi, additional, Zeng, Hongkui, additional, Lei, Jianfeng, additional, Zhang, Hongqi, additional, Hong, Tao, additional, Zhang, Li, additional, Luo, Benyan, additional, Li, Zhen, additional, Xing, Chao, additional, Jia, Chenxi, additional, Li, Lingjun, additional, Sun, Wenzhi, additional, and Ge, Woo-ping, additional
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- 2024
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46. Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH
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Zhang, Meng, Eichhorn, Stephen W., Zingg, Brian, Yao, Zizhen, Cotter, Kaelan, Zeng, Hongkui, Dong, Hongwei, and Zhuang, Xiaowei
- Published
- 2021
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47. Morphological diversity of single neurons in molecularly defined cell types
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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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48. Isoform cell-type specificity in the mouse primary motor cortex
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Booeshaghi, A. Sina, Yao, Zizhen, van Velthoven, Cindy, Smith, Kimberly, Tasic, Bosiljka, Zeng, Hongkui, and Pachter, Lior
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- 2021
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49. Cellular anatomy of the mouse primary motor cortex
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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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50. Comparative cellular analysis of motor cortex in human, marmoset and mouse
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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., Ting, Jonathan T., Zeng, Hongkui, Zhang, Kun, Feng, Guoping, Ecker, Joseph R., Linnarsson, Sten, and Lein, Ed S.
- Published
- 2021
- Full Text
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