Scientific Center for Optical and Electron Microscopy (ScopeM), ETH Zürich, Wolfgang-Pauli-Str. 14, 8093, Zurich, Switzerland.
Institute of Pharmaceutical Sciences, ETH Zürich, Vladimir-Prelog-Weg 1-5/10, 8093, Zurich, Switzerland.
Angiogenesis. 2019 May;22(2):223-236. doi: 10.1007/s10456-018-9652-3. Epub 2018 Oct 28.
Due to their involvement in many physiologic and pathologic processes, there is a great interest in identifying new molecular pathways that mediate the formation and function of blood and lymphatic vessels. Vascular research increasingly involves the image-based analysis and quantification of vessel networks in tissue whole-mounts or of tube-like structures formed by cultured endothelial cells in vitro. While both types of experiments deliver important mechanistic insights into (lymph)angiogenic processes, the manual analysis and quantification of such experiments are typically labour-intensive and affected by inter-experimenter variability. To bypass these problems, we developed AutoTube, a new software that quantifies parameters like the area covered by vessels, vessel width, skeleton length and branching or crossing points of vascular networks in tissues and in in vitro assays. AutoTube is freely downloadable, comprises an intuitive graphical user interface and helps to perform otherwise highly time-consuming image analyses in a rapid, automated and reproducible manner. By analysing lymphatic and blood vascular networks in whole-mounts prepared from different tissues or from gene-targeted mice with known vascular abnormalities, we demonstrate the ability of AutoTube to determine vascular parameters in close agreement to the manual analyses and to identify statistically significant differences in vascular morphology in tissues and in vascular networks formed in in vitro assays.
由于它们参与了许多生理和病理过程,因此人们非常感兴趣的是确定新的分子途径,这些途径介导血液和淋巴管的形成和功能。血管研究越来越多地涉及基于图像的组织全切片中血管网络或体外培养的内皮细胞形成的管状结构的分析和定量。虽然这两种类型的实验都提供了对(淋)血管生成过程的重要机制见解,但这些实验的手动分析和定量通常非常耗时,并且受到实验者之间变异性的影响。为了克服这些问题,我们开发了 AutoTube,这是一种新的软件,可以定量分析组织和体外实验中血管网络的覆盖面积、血管宽度、骨架长度以及分支或交叉点等参数。AutoTube 可免费下载,具有直观的图形用户界面,有助于快速、自动和可重复地进行原本非常耗时的图像分析。通过分析来自不同组织的全切片或具有已知血管异常的基因靶向小鼠的淋巴管和血管网络,我们证明了 AutoTube 能够以与手动分析非常吻合的方式确定血管参数,并识别组织中的血管形态和体外实验中形成的血管网络的统计学上显著差异。