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      Spatially patterned excitatory neuron subtypes and projections of the claustrum

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          Abstract

          The claustrum is a functionally and structurally complex brain region, whose very spatial extent remains debated. Histochemical-based approaches typically treat the claustrum as a relatively narrow anatomical region that primarily projects to the neocortex, whereas circuit-based approaches can suggest a broader claustrum region containing projections to the neocortex and other regions. Here, in the mouse, we took a bottom-up and cell-type-specific approach to complement and possibly unite these seemingly disparate conclusions. Using single-cell RNA-sequencing, we found that the claustrum comprises two excitatory neuron subtypes that are differentiable from the surrounding cortex. Multicolor retrograde tracing in conjunction with 12-channel multiplexed in situ hybridization revealed a core-shell spatial arrangement of these subtypes, as well as differential downstream targets. Thus, the claustrum comprises excitatory neuron subtypes with distinct molecular and projection properties, whose spatial patterns reflect the narrower and broader claustral extents debated in previous research. This subtype-specific heterogeneity likely shapes the functional complexity of the claustrum.

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          Most cited references56

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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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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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                16 August 2021
                2021
                : 10
                : e68967
                Affiliations
                [1 ] Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia Vancouver Canada
                [2 ] Neuroscience and Mental Health Institute, University of Alberta Edmonton Canada
                [3 ] Janelia Research Campus, Howard Hughes Medical Institute Ashburn United States
                [4 ] Department of Physiology, University of Alberta Edmonton Canada
                [5 ] Djavad Mowafaghian Centre for Brain Health, University of British Columbia Vancouver Canada
                [6 ] School of Biomedical Engineering, University of British Columbia Vancouver Canada
                Oregon Health and Science University United States
                Oregon Health and Science University United States
                Oregon Health and Science University United States
                University of Southern California United States
                Author notes
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0001-8710-1848
                https://orcid.org/0000-0002-0411-916X
                https://orcid.org/0000-0001-8043-7111
                https://orcid.org/0000-0002-0624-3789
                https://orcid.org/0000-0001-8275-7362
                Article
                68967
                10.7554/eLife.68967
                8367382
                34397382
                a17b436b-622a-4800-bbbb-2e3275aa0afc
                © 2021, Erwin et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 31 March 2021
                : 28 July 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000038, Natural Sciences and Engineering Research Council of Canada;
                Award ID: RGPIN-2019-04507
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000024, Canadian Institutes of Health Research;
                Award ID: PJT-419798
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001805, Canada Foundation for Innovation;
                Award ID: John R. Evans Leaders Fund 38369
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000038, Natural Sciences and Engineering Research Council of Canada;
                Award ID: RGPIN-2018-05212
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000874, Brain and Behavior Research Foundation;
                Award Recipient :
                Funded by: Canadian Institute for Military and Veteran Health Research;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000038, Natural Sciences and Engineering Research Council of Canada;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000011, Howard Hughes Medical Institute;
                Award ID: Visiting Scientist Program
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100010629, Fulbright Association;
                Award ID: US Student Program
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Short Report
                Neuroscience
                Custom metadata
                The claustrum comprises spatially patterned excitatory neuron subtypes with distinct molecular and projection properties.

                Life sciences
                claustrum,transcriptome,cell type,mouse
                Life sciences
                claustrum, transcriptome, cell type, mouse

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