Setsu Selena, Morimoto Satoru, Nakamura Shiho, Ozawa Fumiko, Utami Kagistia Hana, Nishiyama Ayumi, Suzuki Naoki, Aoki Masashi, Takeshita Yukio, Tomari Yukihide, Okano Hideyuki
Keio University Regenerative Medicine Research Center, Kanagawa 210-0821, Japan; Laboratory of RNA Function, Institute for Quantitative Biosciences, The University of Tokyo, Bunkyo-ku, Tokyo 113-0032, Japan; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Bunkyo-ku, Tokyo 113-0032, Japan.
Keio University Regenerative Medicine Research Center, Kanagawa 210-0821, Japan; Division of Neurodegenerative Disease Research, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo 173-0015, Japan.
Stem Cell Reports. 2025 Jan 14;20(1):102377. doi: 10.1016/j.stemcr.2024.11.007. Epub 2024 Dec 19.
This study introduces a novel method for rapidly and efficiently inducing human spinal lower motor neurons (LMNs) from induced pluripotent stem cells (iPSCs) to eventually elucidate the pathomechanisms of amyotrophic lateral sclerosis (ALS) and facilitate drug screening. Previous methods were limited by low induction efficiency, poor LMN purity, or labor-intensive induction and evaluation processes. Our protocol overcomes these challenges, achieving around 80% induction efficiency within just two weeks by combining a small molecule-based approach with transcription factor transduction. Moreover, to exclude non-LMN cells from the analysis, we utilized time-lapse microscopy and machine learning to analyze the morphology and viability of iPSC-derived LMNs on a single-cell basis, establishing an effective pathophysiological evaluation system. This rapid, efficient, and streamlined protocol, along with our single-cell-based evaluation method, enables large-scale analysis and drug screening using iPSC-derived motor neurons.
本研究介绍了一种从诱导多能干细胞(iPSC)快速高效诱导人脊髓下运动神经元(LMN)的新方法,以最终阐明肌萎缩侧索硬化症(ALS)的发病机制并促进药物筛选。先前的方法受到诱导效率低、LMN纯度差或诱导和评估过程 labor-intensive 的限制。我们的方案克服了这些挑战,通过将基于小分子的方法与转录因子转导相结合,在短短两周内实现了约80%的诱导效率。此外,为了在分析中排除非LMN细胞,我们利用延时显微镜和机器学习在单细胞基础上分析iPSC衍生的LMN的形态和活力,建立了一个有效的病理生理评估系统。这种快速、高效且简化的方案,连同我们基于单细胞的评估方法,能够使用iPSC衍生的运动神经元进行大规模分析和药物筛选。