6
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Cell type specializations of the vocal-motor cortex in songbirds

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          SUMMARY

          Identifying molecular specializations in cortical circuitry supporting complex behaviors, like learned vocalizations, requires understanding of the neuroanatomical context from which these circuits arise. In songbirds, the robust arcopallial nucleus (RA) provides descending cortical projections for fine vocal-motor control. Using single-nuclei transcriptomics and spatial gene expression mapping in zebra finches, we have defined cell types and molecular specializations that distinguish RA from adjacent regions involved in non-vocal motor and sensory processing. We describe an RA-specific projection neuron, differential inhibitory subtypes, and glia specializations and have probed predicted GABAergic interneuron subtypes electrophysiologically within RA. Several cell-specific markers arise developmentally in a sex-dependent manner. Our interactive apps integrate cellular data with developmental and spatial distribution data from the gene expression brain atlas ZEBrA. Users can explore molecular specializations of vocal-motor neurons and support cells that likely reflect adaptations key to the physiology and evolution of vocal control circuits and refined motor skills.

          Graphical abstract

          In brief

          Nevue et al. define cell types in the vocal robust arcopallial nucleus (RA) and adjacent areas of zebra finches based on snRNA-seq. They demonstrate neuronal and astrocyte types unique to RA and how cell-defining markers arise during vocal development. Cell type and developmental transcriptomics datasets can be easily mined with interactive applications.

          Related collections

          Most cited references126

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Trimmomatic: a flexible trimmer for Illumina sequence data

            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              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/.
                Bookmark

                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                20 December 2023
                28 November 2023
                30 October 2023
                28 December 2023
                : 42
                : 11
                : 113344
                Affiliations
                [1 ]Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, USA
                [2 ]Vollum Institute, Oregon Health & Science University, Portland, OR 97239, USA
                [3 ]Lead contact
                Author notes

                AUTHOR CONTRIBUTIONS

                A.A.N. performed all molecular experiments with input from C.V.M. B.M.Z. performed all electrophysiology experiments with input from A.A.N. and H.v.G. A.A.N. and B.M.Z. analyzed data. A.A.N. and S.R.F. developed the interactive applications. A.A.N. and C.V.M. wrote the manuscript with input on molecular aspects from S.R.F. and input on electrophysiology aspects written by B.M.Z. and H.v.G.

                [* ]Correspondence: vongersd@ 123456ohsu.edu (H.v.G.), melloc@ 123456ohsu.edu (C.V.M.)
                Article
                NIHMS1948187
                10.1016/j.celrep.2023.113344
                10752865
                37910500
                8c93647b-ce22-4702-a67b-a05f13e90678

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Categories
                Article

                Cell biology
                Cell biology

                Comments

                Comment on this article