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      Modelling Human Post-Implantation Development via Extra-Embryonic Niche Engineering

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          Abstract

          Implantation of the human embryo commences a critical developmental stage that comprises profound morphogenetic alteration of embryonic and extra-embryonic tissues, axis formation, and gastrulation events. 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. Additionally, human stem cell models of early post-implantation development with both embryonic and extra-embryonic tissue morphogenesis are lacking. Here, we present iDiscoid, produced from human induced pluripotent stem cells via an engineered a synthetic gene circuit. iDiscoids exhibit reciprocal co-development of human embryonic tissue and engineered extra-embryonic niche in a model of human post-implantation. They exhibit unanticipated self-organization and tissue boundary formation that recapitulates yolk sac-like tissue specification with extra-embryonic mesoderm and hematopoietic characteristics, the formation of bilaminar disc-like embryonic morphology, the development of an amniotic-like cavity, and acquisition of an anterior-like hypoblast pole and posterior-like axis. iDiscoids offer an easy-to-use, high-throughput, reproducible, and scalable platform to probe multifaceted aspects of human early post-implantation development. Thus, they have the potential to provide a tractable human model for drug testing, developmental toxicology, and disease modeling.

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

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            Integrating single-cell transcriptomic data across different conditions, technologies, and species

            Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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              Massively parallel digital transcriptional profiling of single cells

              Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.
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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                24 July 2023
                : 2023.06.15.545118
                Affiliations
                [1. ]Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
                [2. ]Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
                [3. ]Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA 15261, USA
                [4. ]Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
                [5. ]Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
                [6. ]Center for Biologic Imaging, University of Pittsburgh, Pittsburgh, PA, USA
                [7. ]Department of Cell Biology and Molecular Physiology, University of Pittsburgh, Pittsburgh, PA, USA
                [8. ]Department of Anatomy and Embryology, Leiden University Medical Center, Einthovenweg, 2333 ZC Leiden, the Netherlands
                [9. ]McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA 15219, USA
                [10. ]Department of Genetics, Yale School of Medicine, Yale University, New Haven, CT, 06510, USA
                Author notes

                Author Contributions

                J.H. and M.R.E. conceived the study; M.R.E., Z.B.J., B.S., and S.K. supervised the project; J.H., M.R.E., S.K., A.A., K.K.F., J.V., R.L., D.S., Z.B.J. conceived the methodology for experiments; J.H., R.S., K.K.F., J.V., R.L., M.N.T., and M.R. performed iDiscoid experiments; J.H., R.S., K.K.F., T.M., M.N.T., S.W. and D.S. performed imaging and analysis; J.H., A.A., and Q.S. performed the bioinformatic analysis of scRNA-seq datasets; R.L. and K.K.F. performed flow cytometry and analysis; J.H., A.A., K.K.F., J.V., R.L., D.S., M.R.E. performed visualization of the data; M.R.E., S.K., Z.B.J., and J.H. acquired funding for the project; J.H. and M.R.E. wrote the original draft of the manuscript; J.H., R.S., K.K.F., M.R.E., B.S., S.M.C.S.L., A.A., J.V., S.K., and Z.B.J. were involved in editing and input to the final draft of the manuscript.

                [* ]Correspondence: mo.ebr@ 123456pitt.edu
                Article
                10.1101/2023.06.15.545118
                10312773
                37398391
                abebb9c9-aca8-4d15-a245-fa807fcbe9d7

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

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