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      Cross-site reproducibility of human cortical organoids reveals consistent cell type composition and architecture

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          Abstract

          Background:

          Reproducibility of human cortical organoid (hCO) phenotypes remains a concern for modeling neurodevelopmental disorders. While guided hCO protocols reproducibly generate cortical cell types in multiple cell lines at one site, variability across sites using a harmonized protocol has not yet been evaluated. We present an hCO cross-site reproducibility study examining multiple phenotypes.

          Methods:

          Three independent research groups generated hCOs from one induced pluripotent stem cell (iPSC) line using a harmonized miniaturized spinning bioreactor protocol. scRNA-seq, 3D fluorescent imaging, phase contrast imaging, qPCR, and flow cytometry were used to characterize the 3 month differentiations across sites.

          Results:

          In all sites, hCOs were mostly cortical progenitor and neuronal cell types in reproducible proportions with moderate to high fidelity to the in vivo brain that were consistently organized in cortical wall-like buds. Cross-site differences were detected in hCO size and morphology. Differential gene expression showed differences in metabolism and cellular stress across sites. Although iPSC culture conditions were consistent and iPSCs remained undifferentiated, primed stem cell marker expression prior to differentiation correlated with cell type proportions in hCOs.

          Conclusions:

          We identified hCO phenotypes that are reproducible across sites using a harmonized differentiation protocol. Previously described limitations of hCO models were also reproduced including off-target differentiations, necrotic cores, and cellular stress. Improving our understanding of how stem cell states influence early hCO cell types may increase reliability of hCO differentiations. Cross-site reproducibility of hCO cell type proportions and organization lays the foundation for future collaborative prospective meta-analytic studies modeling neurodevelopmental disorders in hCOs.

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          Most cited references92

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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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            Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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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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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                29 July 2023
                : 2023.07.28.550873
                Affiliations
                [1. ] UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
                [2. ] Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
                [3. ] Center for Cellular and Molecular Therapeutics, The Children’s Hospital of Philadelphia, Philadelphia, PA
                [4. ] Center for Neuroscience Research, Children’s National Hospital, Washington, DC
                [5. ] Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
                [6. ] Departments of Pediatrics, and Pharmacology & Physiology, School of Medicine and Health Sciences, The George Washington University, Washington, DC
                [7. ] Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA
                Author notes
                [*]

                these authors contributed equally

                [#]

                these authors contributed equally

                Author contributions:

                J.L.S., D.F., K.H.T, S.Y., E.W. and M.G. conceived the experiments. M.G., E.W., and S.Y. generated all replicate organoid differentiations in this study. A.B. assisted in feeding and imaging the organoids. N.P. generated day 56 organoids. M.G. performed scRNA-seq library preparation. M.G. and M.L. analyzed scRNAseq data with N.M.’s assistance. E.W. performed flow cytometry experiments and analysis. S.Y. performed qPCR experiments and analysis. S.A., M.S., L.D., M.Y., and K.S. visually classified the organoids. T.F. supervised organoid ranking and area measurements, and annotated 3D organoid images. M.I.L. provided advice on statistical analysis. K.H.T, D.F., and J.L.S. supervised the work. M.G., E.W., S.Y., K.H.T, D.F., and J.L.S. wrote the manuscript. All authors read and approved the final manuscript.

                Article
                10.1101/2023.07.28.550873
                10402155
                37546772
                a1f26c81-3eca-4010-ad7d-9960793c51e1

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

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