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      Charting cellular identity during human in vitro β-cell differentiation

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          Abstract

          In vitro differentiation of human stem cells can produce pancreatic β-cells; the loss of this insulin-secreting cell type underlies type 1 diabetes. Here, as a step towards understanding this differentiation process, we report the transcriptional profiling of more than 100,000 human cells undergoing in vitro β-cell differentiation, and describe the cells that emerged. We resolve populations that correspond to β-cells, α-like poly-hormonal cells, non-endocrine cells that resemble pancreatic exocrine cells and a previously unreported population that resembles enterochromaffin cells. We show that endocrine cells maintain their identity in culture in the absence of exogenous growth factors, and that changes in gene expression associated with in vivo β-cell maturation are recapitulated in vitro. We implement a scalable re-aggregation technique to deplete non-endocrine cells and identify CD49a (also known as ITGA1) as a surface marker of the β-cell population, which allows magnetic sorting to a purity of 80%. Finally, we use a high-resolution sequencing time course to characterize gene-expression dynamics during the induction of human pancreatic endocrine cells, from which we develop a lineage model of in vitro β-cell differentiation. This study provides a perspective on human stem-cell differentiation, and will guide future endeavours that focus on the differentiation of pancreatic islet cells, and their applications in regenerative medicine.

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          Most cited references33

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          Is Open Access

          From Louvain to Leiden: guaranteeing well-connected communities

          Community detection is often used to understand the structure of large and complex networks. One of the most popular algorithms for uncovering community structure is the so-called Louvain algorithm. We show that this algorithm has a major defect that largely went unnoticed until now: the Louvain algorithm may yield arbitrarily badly connected communities. In the worst case, communities may even be disconnected, especially when running the algorithm iteratively. In our experimental analysis, we observe that up to 25% of the communities are badly connected and up to 16% are disconnected. To address this problem, we introduce the Leiden algorithm. We prove that the Leiden algorithm yields communities that are guaranteed to be connected. In addition, we prove that, when the Leiden algorithm is applied iteratively, it converges to a partition in which all subsets of all communities are locally optimally assigned. Furthermore, by relying on a fast local move approach, the Leiden algorithm runs faster than the Louvain algorithm. We demonstrate the performance of the Leiden algorithm for several benchmark and real-world networks. We find that the Leiden algorithm is faster than the Louvain algorithm and uncovers better partitions, in addition to providing explicit guarantees.
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            Generation of functional human pancreatic β cells in vitro.

            The generation of insulin-producing pancreatic β cells from stem cells in vitro would provide an unprecedented cell source for drug discovery and cell transplantation therapy in diabetes. However, insulin-producing cells previously generated from human pluripotent stem cells (hPSC) lack many functional characteristics of bona fide β cells. Here, we report a scalable differentiation protocol that can generate hundreds of millions of glucose-responsive β cells from hPSC in vitro. These stem-cell-derived β cells (SC-β) express markers found in mature β cells, flux Ca(2+) in response to glucose, package insulin into secretory granules, and secrete quantities of insulin comparable to adult β cells in response to multiple sequential glucose challenges in vitro. Furthermore, these cells secrete human insulin into the serum of mice shortly after transplantation in a glucose-regulated manner, and transplantation of these cells ameliorates hyperglycemia in diabetic mice. Copyright © 2014 Elsevier Inc. All rights reserved.
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              A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure.

              Although the function of the mammalian pancreas hinges on complex interactions of distinct cell types, gene expression profiles have primarily been described with bulk mixtures. Here we implemented a droplet-based, single-cell RNA-seq method to determine the transcriptomes of over 12,000 individual pancreatic cells from four human donors and two mouse strains. Cells could be divided into 15 clusters that matched previously characterized cell types: all endocrine cell types, including rare epsilon-cells; exocrine cell types; vascular cells; Schwann cells; quiescent and activated stellate cells; and four types of immune cells. We detected subpopulations of ductal cells with distinct expression profiles and validated their existence with immuno-histochemistry stains. Moreover, among human beta- cells, we detected heterogeneity in the regulation of genes relating to functional maturation and levels of ER stress. Finally, we deconvolved bulk gene expression samples using the single-cell data to detect disease-associated differential expression. Our dataset provides a resource for the discovery of novel cell type-specific transcription factors, signaling receptors, and medically relevant genes.
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                Author and article information

                Journal
                Nature
                Nature
                Springer Science and Business Media LLC
                0028-0836
                1476-4687
                May 8 2019
                Article
                10.1038/s41586-019-1168-5
                6903417
                31068696
                6c34217f-30f3-4442-adff-a5ba399fe7b1
                © 2019

                http://www.springer.com/tdm

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