Neuronal differentiation strategies: insights from single-cell sequencing and machine learning
Neuronal differentiation strategies: insights from single-cell sequencing and machine learning
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DOI：
10.1242/dev.193631
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发表时间：
2020-12-01
期刊：
影响因子：
4.6
通讯作者：
Desplan, Claude
中科院分区：
文献类型：
--
作者：
Konstantinides, Nikolaos;Desplan, Claude
神经元替代疗法依赖于从胚胎干细胞或诱导多能干细胞在体外分化出特定细胞类型，或者通过转录因子或信号分子的表达对分化的成体细胞进行直接重编程。用于诱导分化或重编程的因子通常是根据差异基因表达或这些因子在发育过程中的已知作用进行有根据的推测来确定的。此外，分化方案通常会产生部分分化的细胞或多种细胞类型的混合物。在这篇假说文章中，我们提出，为了克服这些低效问题并改进神经元分化方案，我们需要考虑所需细胞类型的发育史。具体而言，我们提出一种策略，即利用单细胞测序技术结合机器学习作为一种有原则的方法，来选择一系列不仅在成年神经元中而且在分化过程中都很重要的编程因子。
Neuronal replacement therapies rely on the in vitro differentiation of specific cell types from embryonic or induced pluripotent stem cells, or on the direct reprogramming of differentiated adult cells via the expression of transcription factors or signaling molecules. The factors used to induce differentiation or reprogramming are often identified by informed guesses based on differential gene expression or known roles for these factors during development. Moreover, differentiation protocols usually result in partly differentiated cells or the production of a mix of cell types. In this Hypothesis article, we suggest that, to overcome these inefficiencies and improve neuronal differentiation protocols, we need to take into account the developmental history of the desired cell types. Specifically, we present a strategy that uses single-cell sequencing techniques combined with machine learning as a principled method to select a sequence of programming factors that are important not only in adult neurons but also during differentiation.