基于图像的斑马鱼血管系统分析方法的发展

Q4 Neuroscience Vascular Cell Pub Date : 2017-12-15 DOI:10.24238/13221-9-1-172
Victoria A Simms, R. Bicknell, Victoria L. Heath
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引用次数: 12

摘要

带有荧光标记血管的斑马鱼为研究血管生成提供了一个很好的模型。最常见的是研究节段间血管的生长,以应对化合物或基因表达的操纵,并使用人工方法进行分析,通常对这些血管与背纵向吻合血管的连通性进行评分。这种方法很费力,最适合在这些船只的连通性建立后的时间点。相比之下,报道的基于图像处理的方法很难实现,往往依赖于专业软件。本研究旨在利用免费的ImageJ软件开发和评估一种计算方法,以量化节段间血管形成的发展。这种方法的发展使血管发育的快速分析成为可能。总血管长度和连接数的输出最好地记录了不同严重程度的缺陷血管发育,并给出了与经常使用的人工方法计算节段间血管连通性百分比相当的结果。这种基于imagj的方法可以对斑马鱼的血管网络形成进行客观定量,从而可以自由、直接和快速地确定新化合物或基因操作对血管生成过程的影响。
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Development of an ImageJ-based method for analysing the developing zebrafish vasculature
Zebrafish with fluorescently labelled blood vessels provide an excellent model for studying angiogenesis. Most commonly the growth of the intersegmental blood vessels is investigated in response to compounds or manipulation of gene expression and analysed using manual methods, typically scoring the connectivity of these blood vessels to the dorsal longitudinal anastomotic vessel.  Such methods are laborious and best suited to time points after the connectivity of these vessels have been established.  By contrast, reported image processing-based methods are difficult to implement and often depend on specialist software. This study aimed to develop and evaluate a computational method using the freely available ImageJ software to quantify the development of intersegmental blood vessel formation. This methodology developed allowed rapid analysis of vascular development.  The outputs of total vessel length and number of junctions best documented defective vascular development at differing levels of severity and gave comparable results to the frequently used manual approach of calculating percentage connectivity of intersegmental vessels. This ImageJ-based method allowed objective quantitation of vascular network formation in zebrafish enabling a free, straightforward and rapid approach to determine the effect of novel compounds or genetic manipulation of the angiogenic process.
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来源期刊
Vascular Cell
Vascular Cell Neuroscience-Neurology
CiteScore
0.70
自引率
0.00%
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