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Derivation of Multipotent Neural Progenitors from Human Embryonic Stem Cells for Cell Therapy and Biomedical Applications

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Embryonic Stem Cell Protocols

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2520))

Abstract

Long-term neuroepithelial-like stem cells (lt-NES) derived from human embryonic stem cells are a stable self-renewing progenitor population with high neurogenic potential and phenotypic plasticity. Lt-NES are amenable to regional patterning toward neurons and glia subtypes and thus represent a valuable source of cells for many biomedical applications. For use in regenerative medicine and cell therapy, lt-NES and their progeny require derivation with high-quality culture conditions suitable for clinical use. In this chapter, we describe a robust method to derive multipotent and expandable lt-NES based on good manufacturing practice and cell therapy-grade reagents. We further describe fully defined protocols to terminally differentiate lt-NES toward GABA-ergic, dopaminergic, and motor neurons.

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Acknowledgments

This work was supported with a UK Regenerative Medicine Platform grant funded by the Medical Research Council, the Biotechnology and Biological Sciences Research Council, the Engineering and Physical Sciences Research Council, the European Research Council Grant New-Chol (L.V.), the Cambridge Hospitals National Institute for Health Research Biomedical Research Center (L.V.), and a core support grant from the Wellcome Trust and Medical Research Council to the Wellcome Trust—Medical Research Council Cambridge Stem Cell Institute.

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Correspondence to Loriana Vitillo .

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Vitillo, L., Vallier, L. (2021). Derivation of Multipotent Neural Progenitors from Human Embryonic Stem Cells for Cell Therapy and Biomedical Applications. In: Turksen, K. (eds) Embryonic Stem Cell Protocols . Methods in Molecular Biology, vol 2520. Humana, New York, NY. https://doi.org/10.1007/7651_2021_401

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  • DOI: https://doi.org/10.1007/7651_2021_401

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2436-4

  • Online ISBN: 978-1-0716-2437-1

  • eBook Packages: Springer Protocols

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