1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Modelling post-implantation human development to yolk sac blood emergence

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Implantation of the human embryo begins a critical developmental stage that comprises profound events including axis formation, gastrulation and the emergence of haematopoietic system 1, 2 . Our mechanistic knowledge of this window of human life remains limited due to restricted access to in vivo samples for both technical and ethical reasons 35 . Stem cell models of human embryo have emerged to help unlock the mysteries of this stage 616 . Here we present a genetically inducible stem cell-derived embryoid model of early post-implantation human embryogenesis that captures the reciprocal codevelopment of embryonic tissue and the extra-embryonic endoderm and mesoderm niche with early haematopoiesis. This model is produced from induced pluripotent stem cells and shows unanticipated self-organizing cellular programmes similar to those that occur in embryogenesis, including the formation of amniotic cavity and bilaminar disc morphologies as well as the generation of an anterior hypoblast pole and posterior domain. The extra-embryonic layer in these embryoids lacks trophoblast and shows advanced multilineage yolk sac tissue-like morphogenesis that harbours a process similar to distinct waves of haematopoiesis, including the emergence of erythroid-, megakaryocyte-, myeloid- and lymphoid-like cells. This model presents an easy-to-use, high-throughput, reproducible and scalable platform to probe multifaceted aspects of human development and blood formation at the early post-implantation stage. It will provide a tractable human-based model for drug testing and disease modelling.

          Abstract

          A genetically inducible stem cell-derived embryoid model of early post-implantation human embryogenesis captures the codevelopment of embryonic tissue and extra-embryonic endoderm and mesoderm niche with early haematopoiesis, with potential for drug testing and disease modelling.

          Related collections

          Most cited references65

          • Record: found
          • Abstract: found
          • Article: not found

          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

              Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
                Bookmark

                Author and article information

                Contributors
                mo.ebr@pitt.edu
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                13 December 2023
                13 December 2023
                2024
                : 626
                : 7998
                : 367-376
                Affiliations
                [1 ]Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, ( https://ror.org/01an3r305) Pittsburgh, PA USA
                [2 ]GRID grid.21925.3d, ISNI 0000 0004 1936 9000, Department of Pathology, Division of Experimental Pathology, School of Medicine, , University of Pittsburgh, ; Pittsburgh, PA USA
                [3 ]Pittsburgh Liver Research Center, University of Pittsburgh, ( https://ror.org/01an3r305) Pittsburgh, PA USA
                [4 ]Computational Biology Department, School of Computer Science, Carnegie Mellon University, ( https://ror.org/05x2bcf33) Pittsburgh, PA USA
                [5 ]Machine Learning Department, School of Computer Science, Carnegie Mellon University, ( https://ror.org/05x2bcf33) Pittsburgh, PA USA
                [6 ]Department of Anatomy and Embryology, Leiden University Medical Center, Einthovenweg, ( https://ror.org/05xvt9f17) Leiden, The Netherlands
                [7 ]Center for Biologic Imaging, University of Pittsburgh, ( https://ror.org/01an3r305) Pittsburgh, PA USA
                [8 ]Department of Cell Biology and Molecular Physiology, University of Pittsburgh, ( https://ror.org/01an3r305) Pittsburgh, PA USA
                [9 ]GRID grid.21925.3d, ISNI 0000 0004 1936 9000, McGowan Institute for Regenerative Medicine, , University of Pittsburgh, ; Pittsburgh, PA USA
                [10 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Genetics, Yale School of Medicine, , Yale University, ; New Haven, CT USA
                Author information
                http://orcid.org/0000-0001-6156-9717
                http://orcid.org/0000-0001-5420-4031
                http://orcid.org/0000-0003-3866-2803
                http://orcid.org/0000-0003-4092-1552
                http://orcid.org/0000-0001-5834-5819
                http://orcid.org/0000-0001-5753-6779
                Article
                6914
                10.1038/s41586-023-06914-8
                10849971
                38092041
                580873e2-2448-48de-894f-12b8cf056cd0
                © 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 January 2022
                : 29 November 2023
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                haematopoiesis,embryogenesis,tissue engineering,embryonic stem cells,stem-cell biotechnology

                Comments

                Comment on this article