Kirschnick Nils, Drees Dominik, Redder Esther, Erapaneedi Raghu, Pereira da Graca Abel, Schäfers Michael, Jiang Xiaoyi, Kiefer Friedemann
European Institute of Molecular Imaging, University of Münster, Waldeyerstraße 15, 48149 Münster, Germany.
Institute of Computer Science, University of Münster, Einsteinstraße 62, 48149 Münster, Germany.
iScience. 2021 May 26;24(6):102650. doi: 10.1016/j.isci.2021.102650. eCollection 2021 Jun 25.
Light sheet fluorescence microscopy (LSFM) of large tissue samples does not require mechanical sectioning and allows efficient visualization of spatially complex or rare structures. Therefore, LSFM has become invaluable in developmental and biomedical research. Because sample size may limit whole-mount staining, LSFM benefits from transgenic reporter organisms expressing fluorescent proteins (FPs) and, however, requires optical clearing and computational data visualization and analysis. The former often interferes with FPs, while the latter requires massive computing resources. Here, we describe 3D-polymerized cell dispersions, a rapid and straightforward method, based on recombinant FP expression in freely selectable tester cells, to evaluate and compare fluorescence retention in different tissue-clearing protocols. For the analysis of large LSFM data, which usually requires huge computing resources, we introduce a refined, interactive, hierarchical random walker approach that is capable of efficient segmentation of the vasculature in data sets even on a consumer grade PC.
大型组织样本的光片荧光显微镜(LSFM)无需机械切片,能够有效地可视化空间复杂或罕见的结构。因此,LSFM在发育生物学和生物医学研究中变得至关重要。由于样本大小可能会限制整体染色,LSFM受益于表达荧光蛋白(FP)的转基因报告生物体,然而,它需要光学清除以及计算数据的可视化和分析。前者常常会干扰荧光蛋白,而后者需要大量的计算资源。在此,我们描述了3D聚合细胞分散体,这是一种基于在自由选择的测试细胞中重组表达荧光蛋白的快速且直接的方法,用于评估和比较不同组织清除方案中的荧光保留情况。对于通常需要巨大计算资源的大型LSFM数据分析,我们引入了一种经过改进的、交互式的、分层随机游走方法,即使在消费级个人电脑上,该方法也能够有效地分割数据集中的脉管系统。