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

      research-article
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      Nature
      Nature Publishing Group UK
      Molecular evolution, Cellular neuroscience, Genetics of the nervous system, Molecular neuroscience

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          Abstract

          The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals 1 . Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch–seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

          Abstract

          An examination of motor cortex in humans, marmosets and mice reveals a generally conserved cellular makeup that is likely to extend to many mammalian species, but also differences in gene expression, DNA methylation and chromatin state that lead to species-dependent specializations.

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          Most cited references94

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          edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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            Comprehensive Integration of Single-Cell Data

            Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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                Author and article information

                Contributors
                trygveb@alleninstitute.org
                edl@alleninstitute.org
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                6 October 2021
                6 October 2021
                2021
                : 598
                : 7879
                : 111-119
                Affiliations
                [1 ]GRID grid.417881.3, ISNI 0000 0001 2298 2461, Allen Institute for Brain Science, ; Seattle, WA USA
                [2 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Biomedical Informatics, , Harvard Medical School, ; Boston, MA USA
                [3 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Department of Bioengineering, , University of California, San Diego, ; La Jolla, CA USA
                [4 ]GRID grid.250671.7, ISNI 0000 0001 0662 7144, The Salk Institute for Biological Studies, ; La Jolla, CA USA
                [5 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Physiology and Biophysics, , University of Washington, ; Seattle, WA USA
                [6 ]GRID grid.225279.9, ISNI 0000 0004 0387 3667, Stanley Institute for Cognitive Genomics, , Cold Spring Harbor Laboratory, ; Cold Spring Harbor, NY USA
                [7 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Genetics, , Harvard Medical School, ; Boston, MA USA
                [8 ]GRID grid.10419.3d, ISNI 0000000089452978, LKEB, Department of Radiology, , Leiden University Medical Center, ; Leiden, The Netherlands
                [9 ]GRID grid.469946.0, J. Craig Venter Institute, ; La Jolla, CA USA
                [10 ]GRID grid.250671.7, ISNI 0000 0001 0662 7144, Genomic Analysis Laboratory, , The Salk Institute for Biological Studies, ; La Jolla, CA USA
                [11 ]GRID grid.413919.7, ISNI 0000 0004 0420 6540, Epilepsy Center of Excellence, Department of Veterans Affairs Medical Center, ; Seattle, WA USA
                [12 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Bioinformatics and Systems Biology Graduate Program, , University of California, San Diego, ; La Jolla, CA USA
                [13 ]GRID grid.411024.2, ISNI 0000 0001 2175 4264, Institute for Genomes Sciences, , University of Maryland School of Medicine, ; Baltimore, MD USA
                [14 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Center for Epigenomics, Department of Cellular and Molecular Medicine, , University of California, San Diego, ; La Jolla, CA USA
                [15 ]GRID grid.164295.d, ISNI 0000 0001 0941 7177, Department of Computer Science, , University of Maryland College Park, ; College Park, MD USA
                [16 ]GRID grid.1052.6, ISNI 0000000097371625, Ludwig Institute for Cancer Research, ; La Jolla, CA USA
                [17 ]GRID grid.479509.6, ISNI 0000 0001 0163 8573, Sanford Burnham Prebys Medical Discovery Institute, ; La Jolla, CA USA
                [18 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Biomedical Sciences Program, School of Medicine, , University of California, San Diego, ; La Jolla, CA USA
                [19 ]GRID grid.63054.34, ISNI 0000 0001 0860 4915, University of Connecticut, ; Storrs, CT USA
                [20 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Computer Science and Engineering Program, , University of California, San Diego, ; La Jolla, CA USA
                [21 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Department of Medical Biochemistry and Biophysics, , Karolinska Institutet, ; Stockholm, Sweden
                [22 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, McDonnell Genome Institute, , Washington University School of Medicine, ; St Louis, MO USA
                [23 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Department of Physics, , University of California, San Diego, ; La Jolla, CA USA
                [24 ]GRID grid.225279.9, ISNI 0000 0004 0387 3667, Cold Spring Harbor Laboratory, ; Cold Spring Harbor, NY USA
                [25 ]GRID grid.411024.2, ISNI 0000 0001 2175 4264, Departments of Otorhinolaryngology, Anatomy and Neurobiology, , University of Maryland School of Medicine, ; Baltimore, MD USA
                [26 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Nash Family Department of Neuroscience and Friedman Brain Institute, , Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [27 ]GRID grid.5292.c, ISNI 0000 0001 2097 4740, Computer Graphics and Visualization Group, Delt University of Technology, ; Delft, The Netherlands
                [28 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Physiology and Biophysics, Washington National Primate Research Center, , University of Washington, ; Seattle, WA USA
                [29 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Laboratory Medicine and Pathology, , University of Washington, ; Seattle, WA USA
                [30 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Neurological Surgery, , University of Washington School of Medicine, ; Seattle, WA USA
                [31 ]GRID grid.412618.8, ISNI 0000 0004 0433 5561, Regional Epilepsy Center, , Harborview Medical Center, ; Seattle, WA USA
                [32 ]GRID grid.5292.c, ISNI 0000 0001 2097 4740, Pattern Recognition and Bioinformatics group, , Delft University of Technology, ; Delft, The Netherlands
                [33 ]GRID grid.19006.3e, ISNI 0000 0000 9632 6718, Department of Human Genetics, , University of California, Los Angeles, ; Los Angeles, CA USA
                [34 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Department of Cognitive Science, , University of California, San Diego, ; La Jolla, CA USA
                [35 ]GRID grid.66859.34, Broad Institute of MIT and Harvard, ; Cambridge, MA USA
                [36 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Department of Pathology, , University of California, ; San Diego, CA USA
                [37 ]GRID grid.185006.a, ISNI 0000 0004 0461 3162, Division of Vaccine Discovery, , La Jolla Institute for Immunology, ; La Jolla, CA USA
                [38 ]GRID grid.511294.a, McGovern Institute for Brain Research, MIT, ; Cambridge, MA USA
                [39 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Department of Brain and Cognitive Sciences, , MIT, ; Cambridge, MA USA
                [40 ]GRID grid.66859.34, Stanley Center for Psychiatric Research, , Broad Institute of MIT and Harvard, ; Cambridge, MA USA
                [41 ]GRID grid.250671.7, ISNI 0000 0001 0662 7144, Howard Hughes Medical Institute, , The Salk Institute for Biological Studies, ; La Jolla, CA USA
                Author information
                http://orcid.org/0000-0003-3373-7386
                http://orcid.org/0000-0003-3136-8097
                http://orcid.org/0000-0002-1172-5897
                http://orcid.org/0000-0002-5784-9668
                http://orcid.org/0000-0002-1400-6820
                http://orcid.org/0000-0002-9361-5607
                http://orcid.org/0000-0003-1962-8802
                http://orcid.org/0000-0002-7699-6591
                http://orcid.org/0000-0002-8814-6818
                http://orcid.org/0000-0003-0628-1264
                http://orcid.org/0000-0001-6997-6018
                http://orcid.org/0000-0002-4999-2296
                http://orcid.org/0000-0003-4549-588X
                http://orcid.org/0000-0003-0153-5659
                http://orcid.org/0000-0001-7058-3297
                http://orcid.org/0000-0002-4738-5062
                http://orcid.org/0000-0002-0812-3318
                http://orcid.org/0000-0001-6393-2276
                http://orcid.org/0000-0001-6443-7509
                http://orcid.org/0000-0002-7168-8186
                http://orcid.org/0000-0003-3964-0921
                http://orcid.org/0000-0001-8125-1650
                http://orcid.org/0000-0001-5130-5259
                http://orcid.org/0000-0002-5291-1469
                http://orcid.org/0000-0002-6036-5875
                http://orcid.org/0000-0001-8269-7603
                http://orcid.org/0000-0003-3203-9535
                http://orcid.org/0000-0002-2215-7653
                http://orcid.org/0000-0001-8971-5616
                http://orcid.org/0000-0003-3293-3158
                http://orcid.org/0000-0002-5435-1127
                http://orcid.org/0000-0003-1355-892X
                http://orcid.org/0000-0002-3142-1970
                http://orcid.org/0000-0001-6482-8067
                http://orcid.org/0000-0002-5741-8024
                http://orcid.org/0000-0002-6861-4506
                http://orcid.org/0000-0002-2794-5165
                http://orcid.org/0000-0002-6954-8184
                http://orcid.org/0000-0002-0326-5878
                http://orcid.org/0000-0002-7596-5224
                http://orcid.org/0000-0002-8021-277X
                http://orcid.org/0000-0001-5799-5895
                http://orcid.org/0000-0002-3491-3444
                http://orcid.org/0000-0001-9012-6552
                Article
                3465
                10.1038/s41586-021-03465-8
                8494640
                34616062
                c0701652-0ed2-4344-bb27-596376d0fc01
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 31 March 2020
                : 17 March 2021
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                © The Author(s), under exclusive licence to Springer Nature Limited 2021

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                molecular evolution,cellular neuroscience,genetics of the nervous system,molecular neuroscience

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