Advancing Drug Discovery for Neurological Disorders Using iPSC-Derived Neural Organoids.
Advancing Drug Discovery for Neurological Disorders Using iPSC-Derived Neural Organoids.
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DOI：
10.3390/ijms22052659
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发表时间：
2021-03-06
影响因子：
5.6
通讯作者：
Corti S
中科院分区：
文献类型：
--
作者：
Costamagna G;Comi GP;Corti S
在过去的十年中，学术环境中的不同研究小组已经开发出基于诱导多能干细胞的方案，以生成三维，多细胞，神经类器官。它们用于模拟脑生物学，早期神经发育和人类疾病，为神经精神和神经系统疾病的病理生理学提供了新的见解，包括小头畸形，自闭症，帕金森病和阿尔茨海默病。然而，在工业中采用类器官技术进行大规模药物筛选受到了可重复性、可扩展性和对人类疾病的可翻译性的挑战的阻碍。扩大其在药物发现管道中的应用的潜在技术解决方案包括用于创建等基因模型的规则间隔短回文重复序列（CRISPR），用于在细胞水平上表征模型的单细胞RNA测序，以及用于分析复杂数据集的机器学习。此外，高含量成像、自动化液体处理和标准化分析代表了实现这一目标的其他有价值的工具。尽管一些悬而未决的问题仍然阻碍着类器官技术在学术界之外的全面实施，但这一领域的快速进展将有助于促进其向神经系统疾病的大规模药物筛选转化。
In the last decade, different research groups in the academic setting have developed induced pluripotent stem cell-based protocols to generate three-dimensional, multicellular, neural organoids. Their use to model brain biology, early neural development, and human diseases has provided new insights into the pathophysiology of neuropsychiatric and neurological disorders, including microcephaly, autism, Parkinson’s disease, and Alzheimer’s disease. However, the adoption of organoid technology for large-scale drug screening in the industry has been hampered by challenges with reproducibility, scalability, and translatability to human disease. Potential technical solutions to expand their use in drug discovery pipelines include Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) to create isogenic models, single-cell RNA sequencing to characterize the model at a cellular level, and machine learning to analyze complex data sets. In addition, high-content imaging, automated liquid handling, and standardized assays represent other valuable tools toward this goal. Though several open issues still hamper the full implementation of the organoid technology outside academia, rapid progress in this field will help to prompt its translation toward large-scale drug screening for neurological disorders.
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影响因子：
23.9
作者：
Bershteyn M;Nowakowski TJ;Pollen AA;Di Lullo E;Nene A;Wynshaw-Boris A;Kriegstein AR
通讯作者：
Kriegstein AR
影响因子：
48
作者：
Bian S;Repic M;Guo Z;Kavirayani A;Burkard T;Bagley JA;Krauditsch C;Knoblich JA
通讯作者：
Knoblich JA
影响因子：
23.9
作者：
Czerniecki SM;Cruz NM;Harder JL;Menon R;Annis J;Otto EA;Gulieva RE;Islas LV;Kim YK;Tran LM;Martins TJ;Pippin JW;Fu H;Kretzler M;Shankland SJ;Himmelfarb J;Moon RT;Paragas N;Freedman BS
通讯作者：
Freedman BS
影响因子：
48
作者：
Chung, Kwanghun;Deisseroth, Karl
通讯作者：
Deisseroth, Karl
影响因子：
5.8
作者：
Chadwick, Michelle;Yang, Chen;Sabaawy, Hatem E.
通讯作者：
Sabaawy, Hatem E.