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      ZnT8 loss-of-function accelerates functional maturation of hESC-derived β cells and resists metabolic stress in diabetes

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          Abstract

          Human embryonic stem cell-derived β cells (SC-β cells) hold great promise for treatment of diabetes, yet how to achieve functional maturation and protect them against metabolic stresses such as glucotoxicity and lipotoxicity remains elusive. Our single-cell RNA-seq analysis reveals that ZnT8 loss of function (LOF) accelerates the functional maturation of SC-β cells. As a result, ZnT8 LOF improves glucose-stimulated insulin secretion (GSIS) by releasing the negative feedback of zinc inhibition on insulin secretion. Furthermore, we demonstrate that ZnT8 LOF mutations endow SC-β cells with resistance to lipotoxicity/glucotoxicity-triggered cell death by alleviating endoplasmic reticulum (ER) stress through modulation of zinc levels. Importantly, transplantation of SC-β cells with ZnT8 LOF into mice with preexisting diabetes significantly improves glycemia restoration and glucose tolerance. These findings highlight the beneficial effect of ZnT8 LOF on the functional maturation and survival of SC-β cells that are useful as a potential source for cell replacement therapies.

          Abstract

          Immature function and fragility hinder application of hESC-derived β cells (SC-β cell) for diabetes cell therapy. Here, the authors identify ZnT8 as a gene editing target to enhance the insulin secretion and cell survival under metabolic stress by abolishing zinc transport in SC-β cells.

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          Most cited references106

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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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            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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              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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                Author and article information

                Contributors
                qiaolin.deng@ki.se
                xcheng@sibcb.ac.cn
                liweida@tongji.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                16 July 2022
                16 July 2022
                2022
                : 13
                : 4142
                Affiliations
                [1 ]GRID grid.24516.34, ISNI 0000000123704535, Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, , Tongji University, ; Shanghai, 200092 China
                [2 ]GRID grid.24516.34, ISNI 0000000123704535, Tsingtao Advanced Research Institute, , Tongji University, ; Qingdao, 266073 China
                [3 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, , University of Chinese Academy of Sciences, ; 320 Yueyang Road, 200031 Shanghai, China
                [4 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Department of Physiology and Pharmacology, , Karolinska Institute, ; 17177 Solna, Sweden
                [5 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Department of Neurosurgery, Huashan Hospital, Institute for Translational Brain Research, State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, , Fudan University, ; Shanghai, 200032 China
                [6 ]GRID grid.440637.2, ISNI 0000 0004 4657 8879, School of Life Science and Technology, , ShanghaiTech University, ; 230 Haike Road, 201210 Shanghai, China
                [7 ]GRID grid.4422.0, ISNI 0000 0001 2152 3263, Ministry of Education Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, , Ocean University of China, ; Tsingtao, 266003 China
                Author information
                http://orcid.org/0000-0002-5931-7948
                http://orcid.org/0000-0002-2006-279X
                http://orcid.org/0000-0002-7622-5711
                http://orcid.org/0000-0002-4977-1872
                http://orcid.org/0000-0002-1660-5384
                http://orcid.org/0000-0002-8869-3109
                http://orcid.org/0000-0002-1748-5273
                http://orcid.org/0000-0001-5934-7816
                http://orcid.org/0000-0002-6649-7959
                http://orcid.org/0000-0001-8970-0633
                Article
                31829
                10.1038/s41467-022-31829-9
                9288460
                35842441
                35d8bfcd-115d-41e9-95fd-3f2149a6da8d
                © The Author(s) 2022

                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
                : 24 August 2020
                : 4 July 2022
                Funding
                Funded by: This work was supported by the National Key R&D Program of China (Grant Nos. 2016YFA0102200, 2017YFA0106500, 2018YFA0107102 and 2020YFA0112500). WL is also supported by the Key Project of the Science and Technology Commission of Shanghai Municipality (Grant No. 19JC1415300) and the National Natural Science Foundation of China (Grant No. 31970751, 32170740).
                Categories
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                Custom metadata
                © The Author(s) 2022

                Uncategorized
                cell death,embryonic stem cells,diabetes
                Uncategorized
                cell death, embryonic stem cells, diabetes

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