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      High-resolution transcriptional landscape of xeno-free human induced pluripotent stem cell-derived cerebellar organoids

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          Abstract

          Current protocols for producing cerebellar neurons from human pluripotent stem cells (hPSCs) often rely on animal co-culture and mostly exist as monolayers, limiting their capability to recapitulate the complex processes in the developing cerebellum. Here, we employed a robust method, without the need for mouse co-culture to generate three-dimensional cerebellar organoids from hPSCs that display hallmarks of in vivo cerebellar development. Single-cell profiling followed by comparison to human and mouse cerebellar atlases revealed the presence and maturity of transcriptionally distinct populations encompassing major cerebellar cell types. Encapsulation with Matrigel aimed to provide more physiologically-relevant conditions through recapitulation of basement-membrane signalling, influenced both growth dynamics and cellular composition of the organoids, altering developmentally relevant gene expression programmes. We identified enrichment of cerebellar disease genes in distinct cell populations in the hPSC-derived cerebellar organoids. These findings ascertain xeno-free human cerebellar organoids as a unique model to gain insight into cerebellar development and its associated disorders.

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          Most cited references94

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          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.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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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
                Samuel.Nayler@dpag.ox.ac.uk
                Esther.Becker@ndcn.ox.ac.uk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                21 June 2021
                21 June 2021
                2021
                : 11
                : 12959
                Affiliations
                [1 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Department of Physiology, Anatomy and Genetics, , University of Oxford, ; Oxford, OX1 3PT United Kingdom
                [2 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Weatherall Institute for Molecular Medicine, , University of Oxford, ; Oxford, OX3 7BN United Kingdom
                [3 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Wellcome Centre for Human Genetics, , University of Oxford, ; Oxford, OX3 7BN United Kingdom
                [4 ]GRID grid.1042.7, Present Address: Walter and Eliza Hall Institute of Medical Research, ; Parkville, Victoria 3052 Australia
                [5 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Present Address: Nuffield Department of Clinical Neurosciences, , University of Oxford, ; Oxford, OX3 9DU United Kingdom
                Article
                91846
                10.1038/s41598-021-91846-4
                8217544
                33414495
                a1be346a-d0a4-4282-9500-6e88b4edd4fb
                © 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 November 2020
                : 26 May 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100008180, Action for A-T;
                Funded by: FundRef http://dx.doi.org/10.13039/501100000833, Rosetrees Trust;
                Funded by: BrAshA-T
                Funded by: FundRef http://dx.doi.org/10.13039/100010269, Wellcome Trust;
                Award ID: 203141/Z/16/Z
                Award ID: 203141/Z/16/Z
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                induced pluripotent stem cells,neuronal development,cell type diversity,transcriptomics

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