Taks Nanne W, Batstra Mathijs D, Kortekaas Ronald F, Stevens Floris D, Pfeilmeier Sebastian, van den Burg Harrold A
Molecular Plant Pathology, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, Netherlands.
Technology Center FNWI, Faculty of Science, University of Amsterdam, Amsterdam, Netherlands.
Mol Plant Pathol. 2025 Feb;26(2):e70055. doi: 10.1111/mpp.70055.
Plant-pathogenic bacteria colonise their hosts using various strategies, exploiting both natural openings and wounds in leaves and roots. The vascular pathogen Xanthomonas campestris pv. campestris (Xcc) enters its host through hydathodes, organs at the leaf margin involved in guttation. Subsequently, Xcc breaches the hydathode-xylem barrier and progresses into the xylem vessels causing systemic disease. To elucidate the mechanisms that underpin the different stages of an Xcc infection, a need exists to image bacterial progression in planta in a non-invasive manner. Here, we describe a phenotyping setup and Python image analysis pipeline for capturing 16 independent Xcc infections in Arabidopsis thaliana plants in parallel over time. The setup combines an RGB camera for imaging disease symptoms and an ultrasensitive CCD camera for monitoring bacterial progression inside leaves using bioluminescence. The method reliably quantified bacterial growth in planta for two bacterial species, that is, vascular Xcc and the mesophyll pathogen Pseudomonas syringae pv. tomato (Pst). The camera resolution allowed Xcc imaging already in the hydathodes, yielding reproducible data for the first stages prior to the systemic infection. Data obtained through the image analysis pipeline was robust and validated findings from other bioluminescence imaging methods, while requiring fewer samples. Moreover, bioluminescence was reliably detected within 5 min, offering a significant time advantage over our previously reported method with light-sensitive films. Thus, this method is suitable to quantify the resistance level of a large number of Arabidopsis thaliana accessions and mutant lines to different bacterial strains in a non-invasive manner for phenotypic screenings.
植物致病细菌通过多种策略定殖于宿主，利用叶片和根部的自然开口及伤口。维管束病原体野油菜黄单胞菌野油菜致病变种（Xcc）通过水孔进入宿主，水孔是叶缘参与吐水的器官。随后，Xcc突破水孔 - 木质部屏障并进入木质部导管，引发系统性疾病。为阐明支撑Xcc感染不同阶段的机制，需要以非侵入性方式对植物体内细菌的进展进行成像。在此，我们描述了一种表型分析装置和Python图像分析流程，用于随时间并行捕获拟南芥植物中16个独立的Xcc感染。该装置结合了用于对病害症状成像的RGB相机和用于利用生物发光监测叶片内部细菌进展的超灵敏CCD相机。该方法可靠地定量了两种细菌在植物体内的生长，即维管束Xcc和叶肉病原体番茄丁香假单胞菌（Pst）。相机分辨率使得在水孔中就能对Xcc进行成像，为系统性感染之前的初始阶段提供了可重复的数据。通过图像分析流程获得的数据稳健，验证了其他生物发光成像方法的结果，同时所需样本更少。此外，在5分钟内就能可靠地检测到生物发光，与我们之前报道的使用感光胶片的方法相比，具有显著的时间优势。因此，该方法适用于以非侵入性方式对大量拟南芥种质和突变系对不同细菌菌株的抗性水平进行定量，用于表型筛选。
