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A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex.
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.
Afiliación
  • Yao Z; Allen Institute for Brain Science, Seattle, WA, USA.
  • Liu H; Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.
  • Xie F; Department of Physics, University of California, San Diego, La Jolla, CA, USA.
  • Fischer S; Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
  • Adkins RS; Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Aldridge AI; Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.
  • Ament SA; Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Bartlett A; Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.
  • Behrens MM; Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.
  • Van den Berge K; Department of Statistics, University of California, Berkeley, Berkeley, CA, USA.
  • Bertagnolli D; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, Belgium.
  • de Bézieux HR; Allen Institute for Brain Science, Seattle, WA, USA.
  • Biancalani T; Division of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA, USA.
  • Booeshaghi AS; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Bravo HC; California Institute of Technology, Pasadena, CA, USA.
  • Casper T; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA.
  • Colantuoni C; Allen Institute for Brain Science, Seattle, WA, USA.
  • Crabtree J; Johns Hopkins School of Medicine, Department of Neurology, Baltimore, MD, USA.
  • Creasy H; Johns Hopkins School of Medicine, Department of Neuroscience, Baltimore, MD, USA.
  • Crichton K; University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, USA.
  • Crow M; Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Dee N; Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Dougherty EL; Allen Institute for Brain Science, Seattle, WA, USA.
  • Doyle WI; Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
  • Dudoit S; Allen Institute for Brain Science, Seattle, WA, USA.
  • Fang R; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Felix V; Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA.
  • Fong O; Department of Statistics, University of California, Berkeley, Berkeley, CA, USA.
  • Giglio M; Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, San Diego, CA, USA.
  • Goldy J; Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Hawrylycz M; Allen Institute for Brain Science, Seattle, WA, USA.
  • Herb BR; Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Hertzano R; Allen Institute for Brain Science, Seattle, WA, USA.
  • Hou X; Allen Institute for Brain Science, Seattle, WA, USA.
  • Hu Q; Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Kancherla J; Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Kroll M; Department of Otorhinolaryngology, Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Lathia K; Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA.
  • Li YE; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Lucero JD; Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA.
  • Luo C; Allen Institute for Brain Science, Seattle, WA, USA.
  • Mahurkar A; Allen Institute for Brain Science, Seattle, WA, USA.
  • McMillen D; Ludwig Institute for Cancer Research, La Jolla, CA, USA.
  • Nadaf NM; Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.
  • Nery JR; Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.
  • Nguyen TN; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
  • Niu SY; Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA.
  • Ntranos V; Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Orvis J; Allen Institute for Brain Science, Seattle, WA, USA.
  • Osteen JK; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Nature ; 598(7879): 103-110, 2021 10.
Article en En | MEDLINE | ID: mdl-34616066
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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Perfilación de la Expresión Génica / Análisis de la Célula Individual / Epigenómica / Transcriptoma / Corteza Motora / Neuronas Límite: Animals Idioma: En Revista: Nature Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Perfilación de la Expresión Génica / Análisis de la Célula Individual / Epigenómica / Transcriptoma / Corteza Motora / Neuronas Límite: Animals Idioma: En Revista: Nature Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos