Department of Biology, New York University, New York, NY 10003, USA
Development. 2020 Dec 8;147(23):dev193631. doi: 10.1242/dev.193631.
Neuronal replacement therapies rely on the 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.
神经细胞替代疗法依赖于从胚胎或诱导多能干细胞中分化出特定的细胞类型，或者通过表达转录因子或信号分子直接重编程分化的成年细胞。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猜测来确定的。此外，分化方案通常导致部分分化的细胞或产生混合的细胞类型。在这篇假说文章中，我们认为，为了克服这些效率低下的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历史。具体来说，我们提出了一种策略，该策略使用单细胞测序技术和机器学习相结合，作为一种有原则的方法来选择一系列编程因子，这些因子不仅在成年神经元中很重要，而且在分化过程中也很重要。
