人类ips细胞神经变性的表型分析。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Annual Review of Biomedical Data Science Pub Date : 2021-07-20 Epub Date: 2021-04-23 DOI:10.1146/annurev-biodatasci-092820-025214
Jonathan Li, Ernest Fraenkel
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引用次数: 3

摘要

诱导多能干细胞(iPSC)技术有望模拟神经退行性疾病。使用动物和细胞模型进行疾病建模的传统方法需要了解疾病突变。然而,许多患有神经退行性疾病的患者并没有已知的遗传原因。iPSCs提供了一种在体外环境中生成患者特异性模型和研究功能障碍途径的方法,以便了解神经退行性变的原因和亚型。此外,基于ipsc的模型可以通过高通量筛选来搜索候选治疗方法。在这里,我们回顾了基于ipsc的模型目前如何被用于进一步我们对神经退行性疾病的理解,并讨论了它们的挑战和未来的方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Phenotyping Neurodegeneration in Human iPSCs.

Induced pluripotent stem cell (iPSC) technology holds promise for modeling neurodegenerative diseases. Traditional approaches for disease modeling using animal and cellular models require knowledge of disease mutations. However, many patients with neurodegenerative diseases do not have a known genetic cause. iPSCs offer a way to generate patient-specific models and study pathways of dysfunction in an in vitro setting in order to understand the causes and subtypes of neurodegeneration. Furthermore, iPSC-based models can be used to search for candidate therapeutics using high-throughput screening. Here we review how iPSC-based models are currently being used to further our understanding of neurodegenerative diseases, as well as discuss their challenges and future directions.

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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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