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      Human ALS/FTD brain organoid slice cultures display distinct early astrocyte and targetable neuronal pathology

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          Abstract

          Amyotrophic lateral sclerosis overlapping with frontotemporal dementia (ALS/FTD) is a fatal and currently untreatable disease characterized by rapid cognitive decline and paralysis. Elucidating initial cellular pathologies is central to therapeutic target development, but obtaining samples from presymptomatic patients is not feasible. Here, we report the development of a cerebral organoid slice model derived from human induced pluripotent stem cells (iPSCs) that recapitulates mature cortical architecture and displays early molecular pathology of C9ORF72 ALS/FTD. Using a combination of single-cell RNA sequencing and biological assays, we reveal distinct transcriptional, proteostasis and DNA repair disturbances in astroglia and neurons. We show that astroglia display increased levels of the autophagy signaling protein P62 and that deep layer neurons accumulate dipeptide repeat protein poly(GA), DNA damage and undergo nuclear pyknosis that could be pharmacologically rescued by GSK2606414. Thus, patient-specific iPSC-derived cortical organoid slice cultures are a reproducible translational platform to investigate preclinical ALS/FTD mechanisms as well as novel therapeutic approaches.

          Abstract

          By developing a long-term ALS/FTD patient-specific iPSC-derived organoid model that recapitulates mature cortical cell types, the authors pinpoint early selective molecular pathologies at single-cell resolution and a druggable neuronal vulnerability.

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          WGCNA: an R package for weighted correlation network analysis

          Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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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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              SCENIC: Single-cell regulatory network inference and clustering

              Although single-cell RNA-seq is revolutionizing biology, data interpretation remains a challenge. We present SCENIC for the simultaneous reconstruction of gene regulatory networks and identification of cell states. We apply SCENIC to a compendium of single-cell data from tumors and brain, and demonstrate that the genomic regulatory code can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity.
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                Author and article information

                Contributors
                gb318@cam.ac.uk
                AL291@cam.ac.uk
                Journal
                Nat Neurosci
                Nat Neurosci
                Nature Neuroscience
                Nature Publishing Group US (New York )
                1097-6256
                1546-1726
                21 October 2021
                21 October 2021
                2021
                : 24
                : 11
                : 1542-1554
                Affiliations
                [1 ]GRID grid.5335.0, ISNI 0000000121885934, John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, , University of Cambridge, Cambridge Biomedical Campus, ; Cambridge, UK
                [2 ]GRID grid.511435.7, UK Dementia Research Institute, , Cambridge Biomedical Campus, ; Cambridge, UK
                [3 ]GRID grid.5335.0, ISNI 0000000121885934, Department of Clinical Neurosciences, , University of Cambridge, Cambridge Biomedical Campus, ; Cambridge, UK
                [4 ]GRID grid.5335.0, ISNI 0000000121885934, Department of Physiology, Development and Neuroscience, , University of Cambridge, ; Cambridge, UK
                [5 ]Wellcome Trust–MRC Cambridge Stem Cell Institute, Cambridge Biomedical Campus, Cambridge, UK
                Author information
                http://orcid.org/0000-0002-5356-3048
                http://orcid.org/0000-0003-1504-499X
                http://orcid.org/0000-0002-2258-5455
                http://orcid.org/0000-0002-1220-6423
                http://orcid.org/0000-0003-2872-4468
                http://orcid.org/0000-0002-1301-2292
                Article
                923
                10.1038/s41593-021-00923-4
                8553627
                34675437
                796e6c4d-a7c1-4ba2-95d3-1368b166ee27
                © 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
                : 2 April 2021
                : 16 August 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100005339, American Academy of Neurology (AAN);
                Award ID: RG97060
                Award Recipient :
                Funded by: UK Dementia Research Institute
                Funded by: FundRef https://doi.org/10.13039/501100000265, RCUK | Medical Research Council (MRC);
                Award ID: MR/P008658/1
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100004282, Evelyn Trust;
                Award ID: G100774
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100007922, Spinal Research;
                Award ID: G100346
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s), under exclusive licence to Springer Nature America, Inc. 2021

                Neurosciences
                amyotrophic lateral sclerosis,neurological models,induced pluripotent stem cells

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