Department of Biosciences, University of Milan, Milan, Italy.
Istituto Nazionale di Genetica Molecolare "Romeo ed Enrica Invernizzi", Milan, Italy.
Nat Rev Neurol. 2021 Jun;17(6):381-392. doi: 10.1038/s41582-021-00465-0. Epub 2021 Mar 3.
Human induced pluripotent stem cells (hiPSCs) were first generated in 2007, but the full translational potential of this valuable tool has yet to be realized. The potential applications of hiPSCs are especially relevant to neurology, as brain cells from patients are rarely available for research. hiPSCs from individuals with neuropsychiatric or neurodegenerative diseases have facilitated biological and multi-omics studies as well as large-scale screening of chemical libraries. However, researchers are struggling to improve the scalability, reproducibility and quality of this descriptive disease modelling. Addressing these limitations will be the first step towards a new era in hiPSC research - that of predictive disease modelling - involving the correlation and integration of in vitro experimental data with longitudinal clinical data. This approach is a key element of the emerging precision medicine paradigm, in which hiPSCs could become a powerful diagnostic and prognostic tool. Here, we consider the steps necessary to achieve predictive modelling of neurodegenerative disease with hiPSCs, using Huntington disease as an example.
人诱导多能干细胞（hiPSCs）于 2007 年首次被发现，但这种有价值的工具的全部转化潜力尚未实现。hiPSCs 的潜在应用与神经科特别相关，因为很少有患者的脑细胞可用于研究。来自患有神经精神或神经退行性疾病的个体的 hiPSCs 促进了生物学和多组学研究以及化学文库的大规模筛选。然而，研究人员正在努力提高这种描述性疾病建模的可扩展性、可重复性和质量。解决这些局限性将是 hiPSC 研究进入新时代的第一步——即预测疾病建模，涉及将体外实验数据与纵向临床数据进行关联和整合。这种方法是新兴精准医疗范例的关键要素，hiPSCs 可能成为一种强大的诊断和预后工具。在这里，我们以亨廷顿病为例，考虑使用 hiPSCs 实现神经退行性疾病预测建模所需的步骤。
