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      Molecular blueprints for spinal circuit modules controlling locomotor speed in zebrafish

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          Abstract

          The flexibility of motor actions is ingrained in the diversity of neurons and how they are organized into functional circuit modules, yet our knowledge of the molecular underpinning of motor circuit modularity remains limited. Here we use adult zebrafish to link the molecular diversity of motoneurons (MNs) and the rhythm-generating V2a interneurons (INs) with the modular circuit organization that is responsible for changes in locomotor speed. We show that the molecular diversity of MNs and V2a INs reflects their functional segregation into slow, intermediate or fast subtypes. Furthermore, we reveal shared molecular signatures between V2a INs and MNs of the three speed circuit modules. Overall, by characterizing how the molecular diversity of MNs and V2a INs relates to their function, connectivity and behavior, our study provides important insights not only into the molecular mechanisms for neuronal and circuit diversity for locomotor flexibility but also for charting circuits for motor actions in general.

          Abstract

          The study by Pallucchi et al. links the molecular identity of motoneuron and V2a interneuron subtypes to their function and uncovers orthogonal transcriptomic rules for their assembly into separate circuit modules controlling locomotor speed.

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          Most cited references58

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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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              Fast, sensitive, and accurate integration of single cell data with Harmony

              The emerging diversity of single cell RNAseq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies. Here, real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms. We show that Harmony requires dramatically fewer computational resources. It is the only currently available algorithm that makes the integration of ~106 cells feasible on a personal computer. We apply Harmony to PBMCs from datasets with large experimental differences, 5 studies of pancreatic islet cells, mouse embryogenesis datasets, and cross-modality spatial integration.
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                Author and article information

                Contributors
                abdel.elmanira@ki.se
                Journal
                Nat Neurosci
                Nat Neurosci
                Nature Neuroscience
                Nature Publishing Group US (New York )
                1097-6256
                1546-1726
                2 November 2023
                2 November 2023
                2024
                : 27
                : 1
                : 78-89
                Affiliations
                [1 ]Department of Neuroscience, Karolinska Institutet, ( https://ror.org/056d84691) Stockholm, Sweden
                [2 ]Division of Behavioral Neurobiology, National Institute for Basic Biology, ( https://ror.org/05q8wtt20) Okazaki, Japan
                [3 ]Neuronal Networks Research Group, Exploratory Research Center on Life and Living Systems (ExCELLS), ( https://ror.org/005t7z309) Okazaki, Japan
                Author information
                http://orcid.org/0000-0001-6937-7306
                http://orcid.org/0000-0002-6017-2547
                http://orcid.org/0000-0001-6350-4992
                http://orcid.org/0000-0001-5920-9384
                Article
                1479
                10.1038/s41593-023-01479-1
                10774144
                37919423
                5ffcf629-f3d1-4df9-9acc-7f1c2c80ce99
                © The Author(s) 2023

                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
                : 28 February 2023
                : 2 October 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100004359, Vetenskapsrådet (Swedish Research Council;
                Award ID: 2017-02905
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100004063, Knut och Alice Wallenbergs Stiftelse (Knut and Alice Wallenberg Foundation);
                Award ID: KAW 2018.0010
                Award ID: KAW 2022.0130
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100003792, Hjärnfonden (Swedish Brain Foundation);
                Award ID: FO2021-0317
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature America, Inc. 2024

                Neurosciences
                central pattern generators,motor neuron
                Neurosciences
                central pattern generators, motor neuron

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