In flowering plants, successful reproduction and generation of seed depends on the delivery of immotile sperm to female gametes via the pollen tube. As reproduction in flowering plants is the cornerstone of our agricultural industry, there is a need to uncover the genes, small molecules, and environmental conditions that affect pollen tube growth dynamics. However, methods for measuring pollen tube phenotypes are labor intensive, and suffer from a tradeoff between workload and resolution. To approach these problems, we use an image analysis technique called Automated Stack Iterative Subtraction (ASIST). Our tool converts growing pollen tube tips into closed particles, making the automated simultaneous extraction of multiple pollen tube phenotypes from hundreds of individual cells tractable via existing particle identification technology. Here we use our tool to analyze growth dynamics of pollen tubes in vitro, and semi in vivo. We show that ASIST provides a framework for robust, high throughput analysis of pollen tube growth behaviors in populations of cells, thus facilitating pollen tube phenomics.

中文翻译：

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迭代减法有助于自动，定量分析多个花粉管的生长特征。

在开花植物中，种子的成功繁殖和产生取决于通过花粉管将不育精子传递给雌配子。由于开花植物的繁殖是我们农业产业的基石，因此有必要揭示影响花粉管生长动态的基因，小分子和环境条件。但是，测量花粉管表型的方法需要大量劳动，并且需要在工作量和分辨率之间进行权衡。为了解决这些问题，我们使用了一种称为自动堆栈迭代减法（ASIST）的图像分析技术。我们的工具可以将生长中的花粉管尖端转换为封闭的颗粒，从而可以通过现有的颗粒识别技术从数百个单个细胞中自动同时提取多种花粉管表型。在这里，我们使用我们的工具来分析花粉管在体外和半体内的生长动态。我们表明，ASIST为细胞群体中的花粉管生长行为提供了强大，高通量的分析框架，从而促进了花粉管的表型学研究。