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使用脑类器官模型来研究神经紊乱疾病。

Modeling neurological disorders using brain organoids.

机构信息

Biochemistry and Molecular Biophysics Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.

Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Institute for Regenerative Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; The Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.

出版信息

Semin Cell Dev Biol. 2021 Mar;111:4-14. doi: 10.1016/j.semcdb.2020.05.026. Epub 2020 Jun 17.

Abstract

Neurological disorders are challenging to study given the complexity and species-specific features of the organ system. Brain organoids are three dimensional structured aggregates of neural tissue that are generated by self-organization and differentiation from pluripotent stem cells under optimized culture conditions. These brain organoids exhibit similar features of structural organization and cell type diversity as the developing human brain, creating opportunities to recapitulate disease phenotypes that are not otherwise accessible. Here we review the initial attempt in the field to apply brain organoid models for the study of many different types of human neurological disorders across a wide range of etiologies and pathophysiologies. Forthcoming advancements in both brain organoid technology as well as analytical methods have significant potentials to advance the understanding of neurological disorders and to uncover opportunities for meaningful therapeutic intervention.

摘要

神经紊乱由于器官系统的复杂性和物种特异性而难以研究。脑类器官是通过多能干细胞在优化的培养条件下自我组织和分化产生的三维结构神经组织聚集体。这些脑类器官表现出与发育中的人类大脑相似的结构组织和细胞类型多样性特征,为再现其他方法无法获得的疾病表型创造了机会。在这里,我们回顾了该领域最初尝试应用脑类器官模型来研究多种不同类型的人类神经紊乱,这些紊乱涉及广泛的病因和病理生理学。脑类器官技术以及分析方法的即将到来的进展具有显著的潜力,可以促进对神经紊乱的理解,并为有意义的治疗干预提供机会。

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