Ouonkap Sorel V Yimga, Oseguera Yahir, Okihiro Bryce, Johnson Mark A
Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, RI, USA.
Plant Reprod. 2025 Jun 16;38(3):16. doi: 10.1007/s00497-025-00526-0.
TubeTracker provides a method to partially automate analysis of pollen tube growth using live imaging. Pollen function is critical for successful plant reproduction and crop productivity and it is important to develop accessible methods to quantitatively analyze pollen performance to enhance reproductive resilience. Here we introduce TubeTracker as a method to quantify key parameters of pollen performance such as, time to pollen grain germination, pollen tube tip velocity and maintenance of pollen tube integrity. TubeTracker integrates manual and automatic image processing routines and the graphical interface allows the user to interact with the software to make manual corrections of automated steps. TubeTracker does not depend on training data sets required to implement machine learning approaches and thus can be immediately implemented using readily available imaging systems. Furthermore, TubeTracker is an excellent tool to produce the pollen performance data sets necessary to take advantage of emerging AI-based methods to fully automate analysis. We tested TubeTracker and found it to be accurate in measuring pollen tube germination and pollen tube tip elongation across multiple cultivars of tomato.
TubeTracker提供了一种利用实时成像对花粉管生长分析进行部分自动化的方法。花粉功能对于植物繁殖成功和作物生产力至关重要，开发可用于定量分析花粉性能的方法以增强繁殖恢复力很重要。在此，我们介绍TubeTracker作为一种量化花粉性能关键参数的方法，例如花粉粒萌发时间、花粉管顶端速度和花粉管完整性的维持。TubeTracker整合了手动和自动图像处理程序，其图形界面允许用户与软件交互，对自动步骤进行人工校正。TubeTracker不依赖于实施机器学习方法所需的训练数据集，因此可以立即使用现成的成像系统来实施。此外，TubeTracker是生成利用新兴的基于人工智能的方法实现完全自动化分析所需的花粉性能数据集的优秀工具。我们测试了TubeTracker，发现它在测量多个番茄品种的花粉管萌发和花粉管顶端伸长方面是准确的。
