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      A village in a dish model system for population-scale hiPSC studies

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          Abstract

          The mechanisms by which DNA alleles contribute to disease risk, drug response, and other human phenotypes are highly context-specific, varying across cell types and different conditions. Human induced pluripotent stem cells are uniquely suited to study these context-dependent effects but cell lines from hundreds or thousands of individuals are required. Village cultures, where multiple induced pluripotent stem lines are cultured and differentiated in a single dish, provide an elegant solution for scaling induced pluripotent stem experiments to the necessary sample sizes required for population-scale studies. Here, we show the utility of village models, demonstrating how cells can be assigned to an induced pluripotent stem line using single-cell sequencing and illustrating that the genetic, epigenetic or induced pluripotent stem line-specific effects explain a large percentage of gene expression variation for many genes. We demonstrate that village methods can effectively detect induced pluripotent stem line-specific effects, including sensitive dynamics of cell states.

          Abstract

          Village cultures, where multiple stem cell lines are cultured in a single dish, provide an elegant solution for population-scale studies. Here, authors show the utility of village models – showing that expression heterogeneity is largely a result of line-specific effects and not village cultures.

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          Most cited references31

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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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            Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors.

            Differentiated cells can be reprogrammed to an embryonic-like state by transfer of nuclear contents into oocytes or by fusion with embryonic stem (ES) cells. Little is known about factors that induce this reprogramming. Here, we demonstrate induction of pluripotent stem cells from mouse embryonic or adult fibroblasts by introducing four factors, Oct3/4, Sox2, c-Myc, and Klf4, under ES cell culture conditions. Unexpectedly, Nanog was dispensable. These cells, which we designated iPS (induced pluripotent stem) cells, exhibit the morphology and growth properties of ES cells and express ES cell marker genes. Subcutaneous transplantation of iPS cells into nude mice resulted in tumors containing a variety of tissues from all three germ layers. Following injection into blastocysts, iPS cells contributed to mouse embryonic development. These data demonstrate that pluripotent stem cells can be directly generated from fibroblast cultures by the addition of only a few defined factors.
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              Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

              Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments. We propose that the Pearson residuals from “regularized negative binomial regression,” where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity. Importantly, we show that an unconstrained negative binomial model may overfit scRNA-seq data, and overcome this by pooling information across genes with similar abundances to obtain stable parameter estimates. Our procedure omits the need for heuristic steps including pseudocount addition or log-transformation and improves common downstream analytical tasks such as variable gene selection, dimensional reduction, and differential expression. Our approach can be applied to any UMI-based scRNA-seq dataset and is freely available as part of the R package sctransform, with a direct interface to our single-cell toolkit Seurat.
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                Author and article information

                Contributors
                j.powell@garvan.org.au
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                9 June 2023
                9 June 2023
                2023
                : 14
                : 3240
                Affiliations
                [1 ]GRID grid.415306.5, ISNI 0000 0000 9983 6924, Garvan-Weizmann Centre for Cellular Genomics, , Garvan Institute of Medical Research, ; Darlinghurst, 2010 Sydney Australia
                [2 ]GRID grid.1005.4, ISNI 0000 0004 4902 0432, Graduate School of Biomedical Engineering, , University of New South Wales, ; Kensington, 2033 Sydney Australia
                [3 ]GRID grid.1003.2, ISNI 0000 0000 9320 7537, Institute for Molecular Bioscience, , University of Queensland, ; Brisbane, Australia
                [4 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Department of Anatomy and Physiology, , the University of Melbourne, ; Melbourne, Australia
                [5 ]GRID grid.1057.3, ISNI 0000 0000 9472 3971, Victor Chang Cardiac Research Institute, ; Darlinghurst, NSW Australia
                [6 ]GRID grid.1005.4, ISNI 0000 0004 4902 0432, UNSW Medicine & Health, UNSW Sydney, ; Kensington, NSW Australia
                [7 ]GRID grid.437825.f, ISNI 0000 0000 9119 2677, St Vincent’s Hospital, ; Darlinghurst, 2010 NSW Australia
                [8 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, , University of Melbourne, ; Melbourne, Australia
                [9 ]GRID grid.1009.8, ISNI 0000 0004 1936 826X, School of Medicine, Menzies Institute for Medical Research, , University of Tasmania, ; Hobart, Australia
                [10 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Department of Surgery, Royal Melbourne Hospital, Anatomy and Neuroscience, , the University of Melbourne, ; Melbourne, Australia
                [11 ]GRID grid.1005.4, ISNI 0000 0004 4902 0432, UNSW Cellular Genomics Futures Institute, School of Medical Sciences, University of New South Wales, ; 2052 Sydney, Australia
                Author information
                http://orcid.org/0000-0002-1783-6491
                http://orcid.org/0000-0001-8461-236X
                http://orcid.org/0000-0003-3468-4941
                http://orcid.org/0000-0003-2079-9262
                http://orcid.org/0000-0003-2468-0250
                http://orcid.org/0000-0002-1042-2587
                http://orcid.org/0000-0002-5123-5999
                http://orcid.org/0000-0002-7408-9453
                http://orcid.org/0000-0002-9334-8107
                http://orcid.org/0000-0002-5070-4124
                Article
                38704
                10.1038/s41467-023-38704-1
                10256711
                37296104
                c50d23a8-33e5-4e6e-b478-1c46b56749bf
                © 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
                : 31 August 2021
                : 26 April 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000925, Department of Health | National Health and Medical Research Council (NHMRC);
                Award ID: 1143163
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                rna sequencing,gene expression,stem-cell biotechnology,induced pluripotent stem cells

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