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      Single-nucleus multi-omics of human stem cell-derived islets identifies deficiencies in lineage specification

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          Abstract

          Insulin-producing β cells created from human pluripotent stem cells have potential as a therapy for insulin-dependent diabetes, but human pluripotent stem cell-derived islets (SC-islets) still differ from their in vivo counterparts. To better understand the state of cell types within SC-islets and identify lineage specification deficiencies, we used single-nucleus multi-omic sequencing to analyse chromatin accessibility and transcriptional profiles of SC-islets and primary human islets. Here we provide an analysis that enabled the derivation of gene lists and activity for identifying each SC-islet cell type compared with primary islets. Within SC-islets, we found that the difference between β cells and awry enterochromaffin-like cells is a gradient of cell states rather than a stark difference in identity. Furthermore, transplantation of SC-islets in vivo improved cellular identities overtime, while long-term in vitro culture did not. Collectively, our results highlight the importance of chromatin and transcriptional landscapes during islet cell specification and maturation.

          Abstract

          Augsornworawat et al. perform single-nucleus multi-omics and integrated transcriptional and chromatin analysis to identify differences between human stem cell-derived and primary islets.

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Integrated analysis of multimodal single-cell data

            Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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              The single cell transcriptional landscape of mammalian organogenesis

              Mammalian organogenesis is an astonishing process. Within a short window of time, the cells of the three germ layers transform into an embryo that includes most major internal and external organs. Here we set out to investigate the transcriptional dynamics of mouse organogenesis at single cell resolution. With sci-RNA-seq3, we profiled ~2 million cells, derived from 61 embryos staged between 9.5 and 13.5 days of gestation, in a single experiment. The resulting ‘mouse organogenesis cell atlas’ (MOCA) provides a global view of developmental processes during this critical window. We identify hundreds of cell types and 56 trajectories, many of which are detected only because of the depth of cellular coverage, and collectively define thousands of corresponding marker genes. With Monocle 3, we explore the dynamics of gene expression within cell types and trajectories over time, including focused analyses of the apical ectodermal ridge, limb mesenchyme and skeletal muscle.
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                Author and article information

                Contributors
                jmillman@wustl.edu
                Journal
                Nat Cell Biol
                Nat Cell Biol
                Nature Cell Biology
                Nature Publishing Group UK (London )
                1465-7392
                1476-4679
                15 May 2023
                15 May 2023
                2023
                : 25
                : 6
                : 904-916
                Affiliations
                [1 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Division of Endocrinology, Metabolism and Lipid Research, , Washington University School of Medicine, MSC 8127-057-08, ; St. Louis, MO USA
                [2 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Biomedical Engineering, , Washington University in St. Louis, ; St. Louis, MO USA
                Author information
                http://orcid.org/0000-0001-8602-5433
                http://orcid.org/0000-0002-6282-2056
                http://orcid.org/0000-0002-7494-6048
                http://orcid.org/0000-0002-1303-9911
                http://orcid.org/0000-0003-0426-3492
                Article
                1150
                10.1038/s41556-023-01150-8
                10264244
                37188763
                8b048930-b10d-4261-8249-5c4a63e16c24
                © 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
                : 20 September 2022
                : 17 April 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100008871, JDRF;
                Award ID: 3-APF-2020-930-A-N
                Award ID: 5-CDA-2017-391-A-N
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000062, U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases);
                Award ID: T32DK007120
                Award ID: T32GM139774
                Award ID: F31DK125068
                Award ID: R01DK114233
                Award ID: R01DK127497
                Award ID: P30DK020579
                Award Recipient :
                Funded by: Rita Levi-Montalcini Postdoctoral Fellowship in Regenerative Medicine
                Funded by: FundRef https://doi.org/10.13039/100000152, NSF | BIO | Division of Molecular and Cellular Biosciences (MCB);
                Award ID: DGE-2139839 and DGE-1745038
                Award Recipient :
                Funded by: Washington University BioSURF award
                Funded by: FundRef https://doi.org/10.13039/100011912, WUSTL | Washington University School of Medicine in St. Louis;
                Funded by: FundRef https://doi.org/10.13039/100009340, Children’s Discovery Institute (CDI);
                Award ID: CDI-CORE-2015-505
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100011541, U.S. Department of Health & Human Services | NIH | NCI | Division of Cancer Epidemiology and Genetics, National Cancer Institute (National Cancer Institute Division of Cancer Epidemiology and Genetics);
                Award ID: P30CA91842
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000097, U.S. Department of Health & Human Services | NIH | National Center for Research Resources (NCRR);
                Award ID: UL1TR002345
                Award Recipient :
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                © Springer Nature Limited 2023

                Cell biology
                embryonic germ cells,type 1 diabetes,stem-cell biotechnology
                Cell biology
                embryonic germ cells, type 1 diabetes, stem-cell biotechnology

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