Kirschen Gregory W, Ge Shaoyu, Park Il Memming
Medical Scientist Training Program (MSTP), Stony Brook School of Medicine, United States; Department of Neurobiology & Behavior, Stony Brook University, Stony Brook, NY, 11794, United States.
Department of Neurobiology & Behavior, Stony Brook University, Stony Brook, NY, 11794, United States.
Neurosci Lett. 2018 Aug 10;681:17-18. doi: 10.1016/j.neulet.2018.05.016. Epub 2018 May 16.
In the neuroscience field over the past several decades, viral vectors have become powerful gene delivery systems to study neural populations of interest. For neural stem cell (NSC) biology, such viruses are often used to birth-date and track NSCs over developmental time in lineage tracing experiments. Yet, the probability of successful infection of a given stem cell in vivo remains unknown. This information would be helpful to inform investigators interested in titrating their viruses to selectively target sparsely-populated clusters of cells in the nervous system. Here, we describe a novel approach to calculate the probability of successful viral infection of NSCs using experimentally-derived cell cluster data from our newly-developed method to sparsely label adult NSCs, and a simple statistical derivation. Others interested in precisely defining their viral infection efficiency can use this method for a variety of basic and translational studies.
在过去几十年的神经科学领域，病毒载体已成为研究感兴趣的神经群体的强大基因递送系统。对于神经干细胞（NSC）生物学而言，此类病毒常用于谱系追踪实验中在发育过程中标记神经干细胞并对其进行追踪。然而，给定干细胞在体内成功感染的概率仍然未知。这些信息将有助于为那些想要滴定病毒以选择性靶向神经系统中稀疏分布的细胞簇的研究人员提供参考。在这里，我们描述了一种新方法，该方法利用我们新开发的稀疏标记成年神经干细胞方法所获得的实验性细胞簇数据以及一个简单的统计推导来计算神经干细胞成功病毒感染的概率。其他想要精确确定其病毒感染效率的研究人员可以将此方法用于各种基础研究和转化研究。
