18
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Synthetic Analyses of Single-Cell Transcriptomes from Multiple Brain Organoids and Fetal Brain

      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.

          SUMMARY

          Human brain organoid systems offer unprecedented opportunities to investigate both neurodevelopmental and neurological disease. Single-cell-based transcriptomics or epigenomics have dissected the cellular and molecular heterogeneity in the brain organoids, revealing a complex organization. Similar but distinct protocols from different labs have been applied to generate brain organoids, providing a large resource to perform a comparative analysis of brain developmental processes. Here, we take a systematic approach to compare the single-cell transcriptomes of various human cortical brain organoids together with fetal brain to define the identity of specific cell types and differentiation routes in each method. Importantly, we identify unique developmental programs in each protocol compared to fetal brain, which will be a critical benchmark for the utility of human brain organoids in the future.

          In Brief

          Tanaka et al. report integrative analyses of single-cell RNA-seq for human brain organoids derived from different protocols. They find a unique preference of cell differentiation routes across protocols and provide a benchmark for the use and the improvement of human brain organoids.

          Graphical Abstract

          Related collections

          Most cited references20

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

          DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

          The information about the genetic basis of human diseases lies at the heart of precision medicine and drug discovery. However, to realize its full potential to support these goals, several problems, such as fragmentation, heterogeneity, availability and different conceptualization of the data must be overcome. To provide the community with a resource free of these hurdles, we have developed DisGeNET (http://www.disgenet.org), one of the largest available collections of genes and variants involved in human diseases. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. DisGeNET data are homogeneously annotated with controlled vocabularies and community-driven ontologies. Additionally, several original metrics are provided to assist the prioritization of genotype–phenotype relationships. The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages and an R package. DisGeNET is a versatile platform that can be used for different research purposes including the investigation of the molecular underpinnings of specific human diseases and their comorbidities, the analysis of the properties of disease genes, the generation of hypothesis on drug therapeutic action and drug adverse effects, the validation of computationally predicted disease genes and the evaluation of text-mining methods performance.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

            Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              In vitro differentiation of transplantable neural precursors from human embryonic stem cells.

              The remarkable developmental potential and replicative capacity of human embryonic stem (ES) cells promise an almost unlimited supply of specific cell types for transplantation therapies. Here we describe the in vitro differentiation, enrichment, and transplantation of neural precursor cells from human ES cells. Upon aggregation to embryoid bodies, differentiating ES cells formed large numbers of neural tube-like structures in the presence of fibroblast growth factor 2 (FGF-2). Neural precursors within these formations were isolated by selective enzymatic digestion and further purified on the basis of differential adhesion. Following withdrawal of FGF-2, they differentiated into neurons, astrocytes, and oligodendrocytes. After transplantation into the neonatal mouse brain, human ES cell-derived neural precursors were incorporated into a variety of brain regions, where they differentiated into both neurons and astrocytes. No teratoma formation was observed in the transplant recipients. These results depict human ES cells as a source of transplantable neural precursors for possible nervous system repair.
                Bookmark

                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                17 February 2020
                11 February 2020
                26 February 2020
                : 30
                : 6
                : 1682-1689.e3
                Affiliations
                [1 ]Department of Genetics, Yale Stem Cell Center, Yale School of Medicine, New Haven, CT 06520, USA
                [2 ]Hybrid Technology Hub—Centre of Excellence, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
                [3 ]Department of Pediatric Research, Oslo University Hospital and University of Oslo, PO Box 1112 Blindern, 0317 Oslo, Norway
                [4 ]Lead Contact
                Author notes

                AUTHOR CONTRIBUTIONS

                Y.T. and I.-H. P. conceived the study. Y.T. performed bioinformatics analyses. B.C. and Y.X. prepared a part of the single-cell RNA-seq datasets. G.J.S. and I.-H.P. supervised the analyses. Y.X., B.C., and G.J.S. edited the manuscript. Y.T. and I.-H.P. wrote the manuscript.

                [* ]Correspondence: inhyun.park@ 123456yale.edu
                Article
                NIHMS1560133
                10.1016/j.celrep.2020.01.038
                7043376
                32049002
                c93c8125-41c2-4d12-90f3-28f8d6e83820

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Categories
                Article

                Cell biology
                Cell biology

                Comments

                Comment on this article