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Callaway EM, Dong HW, Ecker JR, Hawrylycz MJ, Huang ZJ, Lein ES, Ngai J, Osten P, Ren B, Tolias AS, White O, Zeng H, Zhuang X, Ascoli GA, Behrens MM, Chun J, Feng G, Gee JC, Ghosh SS, Halchenko YO, Hertzano R, Lim BK, Martone ME, Ng L, Pachter L, Ropelewski AJ, Tickle TL, Yang XW, Zhang K, Bakken TE, Berens P, Daigle TL, Harris JA, Jorstad NL, Kalmbach BE, Kobak D, Li YE, Liu H, Matho KS, Mukamel EA, Naeemi M, Scala F, Tan P, Ting JT, Xie F, Zhang M, Zhang Z, Zhou J, Zingg B, Armand E, Yao Z, Bertagnolli D, Casper T, Crichton K, Dee N, Diep D, Ding SL, Dong W, Dougherty EL, Fong O, Goldman M, Goldy J, Hodge RD, Hu L, Keene CD, Krienen FM, Kroll M, Lake BB, Lathia K, Linnarsson S, Liu CS, Macosko EZ, McCarroll SA, McMillen D, Nadaf NM, Nguyen TN, Palmer CR, Pham T, Plongthongkum N, Reed NM, Regev A, Rimorin C, Romanow WJ, Savoia S, Siletti K, Smith K, Sulc J, Tasic B, Tieu M, Torkelson A, Tung H, van Velthoven CTJ, Vanderburg CR, Yanny AM, Fang R, Hou X, Lucero JD, Osteen JK, Pinto-Duarte A, Poirion O, Preissl S, Wang X, Aldridge AI, Bartlett A, Boggeman L, O’Connor C, Castanon RG, Chen H, Fitzpatrick C, Luo C, Nery JR, Nunn M, Rivkin AC, Tian W, Dominguez B, Ito-Cole T, Jacobs M, Jin X, Lee CT, Lee KF, Miyazaki PA, Pang Y, Rashid M, Smith JB, Vu M, Williams E, Biancalani T, Booeshaghi AS, Crow M, Dudoit S, Fischer S, Gillis J, Hu Q, Kharchenko PV, Niu SY, Ntranos V, Purdom E, Risso D, de Bézieux HR, Somasundaram S, Street K, Svensson V, Vaishnav ED, Van den Berge K, Welch JD, An X, Bateup HS, Bowman I, Chance RK, Foster NN, Galbavy W, Gong H, Gou L, Hatfield JT, Hintiryan H, Hirokawa KE, Kim G, Kramer DJ, Li A, Li X, Luo Q, Muñoz-Castañeda R, Stafford DA, Feng Z, Jia X, Jiang S, Jiang T, Kuang X, Larsen R, Lesnar P, Li Y, Li Y, Liu L, Peng H, Qu L, Ren M, Ruan Z, Shen E, Song Y, Wakeman W, Wang P, Wang Y, Wang Y, Yin L, Yuan J, Zhao S, Zhao X, Narasimhan A, Palaniswamy R, Banerjee S, Ding L, Huilgol D, Huo B, Kuo HC, Laturnus S, Li X, Mitra PP, Mizrachi J, Wang Q, Xie P, Xiong F, Yu Y, Eichhorn SW, Berg J, Bernabucci M, Bernaerts Y, Cadwell CR, Castro JR, Dalley R, Hartmanis L, Horwitz GD, Jiang X, Ko AL, Miranda E, Mulherkar S, Nicovich PR, Owen SF, Sandberg R, Sorensen SA, Tan ZH, Allen S, Hockemeyer D, Lee AY, Veldman MB, Adkins RS, Ament SA, Bravo HC, Carter R, Chatterjee A, Colantuoni C, Crabtree J, Creasy H, Felix V, Giglio M, Herb BR, Kancherla J, Mahurkar A, McCracken C, Nickel L, Olley D, Orvis J, Schor M, Hood G, Dichter B, Grauer M, Helba B, Bandrowski A, Barkas N, Carlin B, D’Orazi FD, Degatano K, Gillespie TH, Khajouei F, Konwar K, Thompson C, Kelly K, Mok S, Sunkin S. A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 2021; 598:86-102. [PMID: 34616075 PMCID: PMC8494634 DOI: 10.1038/s41586-021-03950-0] [Citation(s) in RCA: 205] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Accepted: 08/25/2021] [Indexed: 12/14/2022]
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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Pool AH, Wang T, Stafford DA, Chance RK, Lee S, Ngai J, Oka Y. The cellular basis of distinct thirst modalities. Nature 2020; 588:112-117. [PMID: 33057193 PMCID: PMC7718410 DOI: 10.1038/s41586-020-2821-8] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 07/16/2020] [Indexed: 12/16/2022]
Abstract
Fluid intake is an essential innate behavior mainly caused by two distinct types of thirst1–3. Increased blood osmolality induces osmotic thirst that drives animals to consume pure water. Conversely, the loss of body fluid induces hypovolemic thirst in which animals seek both water and minerals (salts) to recover blood volume. Circumventricular organs (CVOs) in the lamina terminalis (LT) are critical sites for sensing both types of thirst-inducing stimuli4–6. However, how different thirst modalities are encoded in the brain remains unknown. Here, we employed stimulus to cell-type mapping using single-cell RNA-seq (scRNA-seq) to determine the cellular substrate underlying distinct types of thirst. These studies revealed diverse excitatory and inhibitory neuron types in each CVO structure. Among them, we show that unique combinations of neuron types are activated under osmotic and hypovolemic stresses. These results elucidate the cellular logic underlying distinct thirst modalities. Furthermore, optogenetic gain-of-function in thirst-modality-specific cell types recapitulated water-specific and non-specific fluid appetite caused by the two distinct dipsogenic stimuli. Taken together, this study demonstrates that thirst is a multimodal physiological state, and that different thirst states are mediated by specific neuron types in the mammalian brain.
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Affiliation(s)
- Allan-Hermann Pool
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Tongtong Wang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.,College of Life Sciences, Nankai University, Tianjin, China
| | - David A Stafford
- Department of Molecular & Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Rebecca K Chance
- Department of Molecular & Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Sangjun Lee
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - John Ngai
- Department of Molecular & Cell Biology, University of California, Berkeley, Berkeley, CA, USA.,National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Yuki Oka
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
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Naka A, Veit J, Shababo B, Chance RK, Risso D, Stafford D, Snyder B, Egladyous A, Chu D, Sridharan S, Mossing DP, Paninski L, Ngai J, Adesnik H. Complementary networks of cortical somatostatin interneurons enforce layer specific control. eLife 2019; 8:43696. [PMID: 30883329 PMCID: PMC6422636 DOI: 10.7554/elife.43696] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Accepted: 02/08/2019] [Indexed: 12/03/2022] Open
Abstract
The neocortex is functionally organized into layers. Layer four receives the densest bottom up sensory inputs, while layers 2/3 and 5 receive top down inputs that may convey predictive information. A subset of cortical somatostatin (SST) neurons, the Martinotti cells, gate top down input by inhibiting the apical dendrites of pyramidal cells in layers 2/3 and 5, but it is unknown whether an analogous inhibitory mechanism controls activity in layer 4. Using high precision circuit mapping, in vivo optogenetic perturbations, and single cell transcriptional profiling, we reveal complementary circuits in the mouse barrel cortex involving genetically distinct SST subtypes that specifically and reciprocally interconnect with excitatory cells in different layers: Martinotti cells connect with layers 2/3 and 5, whereas non-Martinotti cells connect with layer 4. By enforcing layer-specific inhibition, these parallel SST subnetworks could independently regulate the balance between bottom up and top down input.
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Affiliation(s)
- Alexander Naka
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States
| | - Julia Veit
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States
| | - Ben Shababo
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States
| | - Rebecca K Chance
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
| | - Davide Risso
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States.,Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,Department of Statistical Sciences, University of Padova, Padova, Italy.,Division of Biostatistics and Epidemiology, Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, United States
| | - David Stafford
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
| | - Benjamin Snyder
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
| | - Andrew Egladyous
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
| | - Desiree Chu
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
| | - Savitha Sridharan
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
| | - Daniel P Mossing
- Department of Biophysics, University of California, Berkeley, Berkeley, United States
| | - Liam Paninski
- Neurobiology and Behavior Program, Columbia University, New York, United States.,Center for Theoretical Neuroscience, Columbia University, New York, United States.,Departments of Statistics and Neuroscience, Columbia University, New York, United States.,Grossman Center for the Statistics of Mind, Columbia University, New York, United States
| | - John Ngai
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States.,Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States.,QB3 Functional Genomics Laboratory, University of California, Berkeley, Berkeley, United States
| | - Hillel Adesnik
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States.,Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
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