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      Human neocortical expansion involves glutamatergic neuron diversification

      research-article
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      Nature
      Nature Publishing Group UK
      Cellular neuroscience, Molecular neuroscience

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          Abstract

          The neocortex is disproportionately expanded in human compared with mouse 1, 2 , both in its total volume relative to subcortical structures and in the proportion occupied by supragranular layers composed of neurons that selectively make connections within the neocortex and with other telencephalic structures. Single-cell transcriptomic analyses of human and mouse neocortex show an increased diversity of glutamatergic neuron types in supragranular layers in human neocortex and pronounced gradients as a function of cortical depth 3 . Here, to probe the functional and anatomical correlates of this transcriptomic diversity, we developed a robust platform combining patch clamp recording, biocytin staining and single-cell RNA-sequencing (Patch-seq) to examine neurosurgically resected human tissues. We demonstrate a strong correspondence between morphological, physiological and transcriptomic phenotypes of five human glutamatergic supragranular neuron types. These were enriched in but not restricted to layers, with one type varying continuously in all phenotypes across layers 2 and 3. The deep portion of layer 3 contained highly distinctive cell types, two of which express a neurofilament protein that labels long-range projection neurons in primates that are selectively depleted in Alzheimer’s disease 4, 5 . Together, these results demonstrate the explanatory power of transcriptomic cell-type classification, provide a structural underpinning for increased complexity of cortical function in humans, and implicate discrete transcriptomic neuron types as selectively vulnerable in disease.

          Abstract

          Combined patch clamp recording, biocytin staining and single-cell RNA-sequencing of human neurocortical neurons shows an expansion of glutamatergic neuron types relative to mouse that characterizes the greater complexity of the human neocortex.

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          Most cited references72

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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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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              Integrating single-cell transcriptomic data across different conditions, technologies, and species

              Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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                Author and article information

                Contributors
                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
                : 151-158
                Affiliations
                [1 ]GRID grid.417881.3, Allen Institute for Brain Science, ; Seattle, WA USA
                [2 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Physiology and Biophysics, , University of Washington, ; Seattle, WA USA
                [3 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Pathology, , University of Washington, ; Seattle, WA USA
                [4 ]byte physics, Berlin, Germany
                [5 ]GRID grid.9008.1, ISNI 0000 0001 1016 9625, MTA-SZTE Research Group for Cortical Microcircuits, Department of Physiology, Anatomy, and Neuroscience, , University of Szeged, ; Szeged, Hungary
                [6 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), , Vrije Universiteit, ; Amsterdam, The Netherlands
                [7 ]GRID grid.281044.b, ISNI 0000 0004 0463 5388, Swedish Neuroscience Institute, ; Seattle, WA USA
                [8 ]GRID grid.9008.1, ISNI 0000 0001 1016 9625, Department of Neurosurgery, , University of Szeged, ; Szeged, Hungary
                [9 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, Cancer Center Amsterdam, Brain Tumor Center, Department of Neurosurgery, Amsterdam UMC, , Vrije Universiteit, ; Amsterdam, The Netherlands
                [10 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Neurological Surgery, , University of Washington, ; Seattle, WA USA
                [11 ]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
                [12 ]GRID grid.21729.3f, ISNI 0000000419368729, NeuroTechnology Center, , Columbia University, ; New York, NY USA
                [13 ]GRID grid.9619.7, ISNI 0000 0004 1937 0538, Edmond and Lily Safra Center for Brain Sciences and Department of Neurobiology, , The Hebrew University Jerusalem, ; Jerusalem, Israel
                Author information
                http://orcid.org/0000-0002-3300-5399
                http://orcid.org/0000-0003-4549-588X
                http://orcid.org/0000-0003-3373-7386
                http://orcid.org/0000-0002-2723-3272
                http://orcid.org/0000-0001-8429-4090
                http://orcid.org/0000-0002-5784-9668
                http://orcid.org/0000-0003-3136-8097
                http://orcid.org/0000-0002-0632-6322
                http://orcid.org/0000-0002-7820-093X
                http://orcid.org/0000-0002-1539-3332
                http://orcid.org/0000-0002-6770-9474
                http://orcid.org/0000-0002-5917-983X
                http://orcid.org/0000-0002-8814-6818
                http://orcid.org/0000-0002-4504-8724
                http://orcid.org/0000-0002-8118-1450
                http://orcid.org/0000-0002-7444-5286
                http://orcid.org/0000-0002-4399-539X
                http://orcid.org/0000-0002-3775-1583
                http://orcid.org/0000-0002-6432-3656
                http://orcid.org/0000-0001-7959-139X
                http://orcid.org/0000-0002-2006-8235
                http://orcid.org/0000-0002-4969-8759
                http://orcid.org/0000-0002-4738-5062
                http://orcid.org/0000-0002-8361-5152
                http://orcid.org/0000-0001-9092-8678
                http://orcid.org/0000-0002-0812-3318
                http://orcid.org/0000-0002-6743-2188
                http://orcid.org/0000-0002-9361-5607
                http://orcid.org/0000-0002-6793-0611
                http://orcid.org/0000-0003-2540-1153
                http://orcid.org/0000-0003-2988-8544
                http://orcid.org/0000-0002-5741-8024
                http://orcid.org/0000-0002-5291-1469
                http://orcid.org/0000-0002-3142-1970
                http://orcid.org/0000-0002-6861-4506
                http://orcid.org/0000-0002-6697-0179
                http://orcid.org/0000-0002-7905-6001
                http://orcid.org/0000-0002-0326-5878
                http://orcid.org/0000-0001-6482-8067
                http://orcid.org/0000-0001-9012-6552
                Article
                3813
                10.1038/s41586-021-03813-8
                8494638
                34616067
                29cdae1a-fd0a-43b2-b947-f0d49dcd5a59
                © 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
                : 1 April 2020
                : 7 July 2021
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                © The Author(s), under exclusive licence to Springer Nature Limited 2021

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                cellular neuroscience,molecular neuroscience
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                cellular neuroscience, molecular neuroscience

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