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用于斑马鱼胚胎不同形态表型的全自动移液分选系统

Fully Automated Pipetting Sorting System for Different Morphological Phenotypes of Zebrafish Embryos.

作者信息

Breitwieser Helmut, Dickmeis Thomas, Vogt Marcel, Ferg Marco, Pylatiuk Christian

机构信息

1 Institute for Applied Computer Science, Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany.

2 Institute of Toxicology and Genetics, Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany.

出版信息

SLAS Technol. 2018 Apr;23(2):128-133. doi: 10.1177/2472630317745780. Epub 2017 Dec 8.

Abstract

Systems biology methods, such as transcriptomics and metabolomics, require large numbers of small model organisms, such as zebrafish embryos. Manual separation of mutant embryos from wild-type embryos is a tedious and time-consuming task that is prone to errors, especially if there are variable phenotypes of a mutant. Here we describe a zebrafish embryo sorting system with two cameras and image processing based on template-matching algorithms. In order to evaluate the system, zebrafish rx3 mutants that lack eyes due to a patterning defect in brain development were separated from their wild-type siblings. These mutants show glucocorticoid deficiency due to pituitary defects and serve as a model for human secondary adrenal insufficiencies. We show that the variable phenotypes of the mutant embryos can be safely distinguished from phenotypic wild-type zebrafish embryos and sorted from one petri dish into another petri dish or into a 96-well microtiter plate. On average, classification of a zebrafish embryo takes approximately 1 s, with a sensitivity and specificity of 87% to 95%, respectively. Other morphological phenotypes may be classified and sorted using similar techniques.

摘要

系统生物学方法,如转录组学和代谢组学,需要大量的小型模式生物,如斑马鱼胚胎。手动从野生型胚胎中分离突变胚胎是一项繁琐且耗时的任务,容易出错,特别是当突变体存在可变表型时。在此,我们描述了一种具有两个摄像头并基于模板匹配算法进行图像处理的斑马鱼胚胎分选系统。为了评估该系统,我们将因脑发育模式缺陷而无眼的斑马鱼rx3突变体与其野生型同胞进行了分离。这些突变体由于垂体缺陷而表现出糖皮质激素缺乏,可作为人类继发性肾上腺功能不全的模型。我们表明,突变胚胎的可变表型能够安全地与表型野生型斑马鱼胚胎区分开来,并从一个培养皿分选到另一个培养皿或96孔微量滴定板中。平均而言,对一条斑马鱼胚胎进行分类大约需要1秒,灵敏度和特异性分别为87%至95%。其他形态表型也可使用类似技术进行分类和分选。

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