Laboratory of Stem Cell Biology, Department of Cellular, Computational and Integrative Biology-CIBIO, University of Trento, 38123 Trento, Italy.
Institute of Biophysics (IBF), Trento Unit, National Research Council (CNR) & LabSSAH, Bruno Kessler Foundation (FBK), 38123 Trento, Italy.
Cells. 2021 May 7;10(5):1127. doi: 10.3390/cells10051127.
Methods for the conversion of human induced pluripotent stem cells (hiPSCs) into motor neurons (MNs) have opened to the generation of patient-derived in vitro systems that can be exploited for MN disease modelling. However, the lack of simplified and consistent protocols and the fact that hiPSC-derived MNs are often functionally immature yet limit the opportunity to fully take advantage of this technology, especially in research aimed at revealing the disease phenotypes that are manifested in functionally mature cells. In this study, we present a robust, optimized monolayer procedure to rapidly convert hiPSCs into enriched populations of motor neuron progenitor cells (MNPCs) that can be further amplified to produce a large number of cells to cover many experimental needs. These MNPCs can be efficiently differentiated towards mature MNs exhibiting functional electrical and pharmacological neuronal properties. Finally, we report that MN cultures can be long-term maintained, thus offering the opportunity to study degenerative phenomena associated with pathologies involving MNs and their functional, networked activity. These results indicate that our optimized procedure enables the efficient and robust generation of large quantities of MNPCs and functional MNs, providing a valid tool for MNs disease modelling and for drug discovery applications.
将人类诱导多能干细胞(hiPSCs)转化为运动神经元(MNs)的方法为生成可用于 MN 疾病建模的患者来源的体外系统开辟了道路。然而,缺乏简化和一致的方案,以及 hiPSC 衍生的 MNs 通常功能不成熟,这限制了充分利用这项技术的机会,特别是在旨在揭示表型在功能成熟细胞中表现的疾病的研究中。在这项研究中,我们提出了一种稳健、优化的单层程序,可快速将 hiPSCs 转化为富含运动神经元祖细胞(MNPCs)的富集群体,这些细胞可以进一步扩增,以产生大量细胞,满足许多实验需求。这些 MNPCs 可以有效地分化为具有功能性电和药理学神经元特性的成熟 MNs。最后,我们报告说 MN 培养物可以长期维持,从而有机会研究与涉及 MNs 及其功能、网络活动的病理学相关的退行性现象。这些结果表明,我们优化的程序可高效且稳健地生成大量的 MNPCs 和功能性 MNs,为 MNs 疾病建模和药物发现应用提供了有效的工具。