Journal article
Fusion of Regionally Specified hPSC-Derived Organoids Models Human Brain Development and Interneuron Migration
Y Xiang, Y Tanaka, B Patterson, YJ Kang, G Govindaiah, N Roselaar, B Cakir, KY Kim, AP Lombroso, SM Hwang, M Zhong, EG Stanley, AG Elefanty, JR Naegele, SH Lee, SM Weissman, IH Park
Cell Stem Cell | Published : 2017
Abstract
Organoid techniques provide unique platforms to model brain development and neurological disorders. Whereas several methods for recapitulating corticogenesis have been described, a system modeling human medial ganglionic eminence (MGE) development, a critical ventral brain domain producing cortical interneurons and related lineages, has been lacking until recently. Here, we describe the generation of MGE and cortex-specific organoids from human pluripotent stem cells that recapitulate the development of MGE and cortex domains, respectively. Population and single-cell RNA sequencing (RNA-seq) profiling combined with bulk assay for transposase-accessible chromatin with high-throughput sequenci..
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Awarded by National Institutes of Health
Funding Acknowledgements
We thank Dr. Stewart A. Anderson for sharing MGE derivation protocol and Guilin Wang for Chromium service. I.-H.P. was partly supported by NIH (GM0099130-01 and GM111667-01), CSCRF (12-SCB-YALE-11 and 13-SCB-YALE-06), KRIBB/KRCF research initiative program (NAP-09-3), and CTSA grant UL1 RR025750 from the National Center for Advancing Translational Science (NCATS), a component of the NIH, and NIH roadmap for Medical Research. This paper's contents are solely the responsibility of the authors and do not necessarily represent the official view of NIH. This work was also supported by the College of Medicine, University of Arkansas for Medical Sciences (startup funding to S.-H.L.) and Core Facilities of the Center for Translational Neuroscience, award P30GM110702, from the IDeA program at NIGMS. A.E. and E.G.S. are supported by research fellowships from the NHMRC (GNT1117596 and GNT1079004). Computation time was provided by Yale University Biomedical High Performance Computing Center.