Knopf Marie Christin, Bauer Petra
Institute of Botany, Heinrich-Heine-University, Düsseldorf, Germany.
Cluster of Excellence on Plant Science (CEPLAS), Heinrich-Heine-University, Düsseldorf, Germany.
Physiol Plant. 2025 Jul-Aug;177(4):e70427. doi: 10.1111/ppl.70427.
Arabidopsis thaliana is a model species for uncovering genetic adaptation to alkaline calcareous soils (ACS). This species thrives in ACS, often occurring in dry marginal and urban environments. Existing research largely focused on vegetatively grown seedlings, with a notable lack of studies examining phenotypic variations across the life cycle. A valuable tool for understanding stress resilience is machine-aided phenotyping, as it is non-invasive, rapid, and accurate, but often unavailable to small plant labs. Here, we established and validated an affordable multispectral machine-aided phenotyping approach implementable by individual labs. We collected and correlated quantitative growth data across the entire plant life cycle in response to ACS. We used an A. thaliana wildtype and the coumarin-deficient mutant f6'h1-1, exhibiting chlorosis under alkaline conditions, to assess weekly morphological and leaf color data, both manually and using a multispectral 3D phenotyping scanner. Through correlation analysis, we selected machine parameters to differentiate size and leaf chlorosis phenotypes. The correlation analysis indicated a close connection between rosette size and multiple spectral parameters, highlighting the importance of rosette size for growth of A. thaliana in ACS. The most reliable phenotyping was at the beginning of the bolting stage. This methodology is further validated to detect novel leaf chlorosis phenotypes of known iron deficiency mutants across growth stages. Hence, our affordable machine-aided phenotyping procedure is suitable for high-throughput, accurate screening of small-grown rosette plants, including A. thaliana, and enables the discovery of novel genetic and phenotypic variations during the plant's life cycle for understanding plant resilience in challenging soil environments.
拟南芥是揭示植物对碱性石灰性土壤（ACS）遗传适应性的模式物种。该物种在ACS中生长良好，常见于干旱边缘和城市环境。现有研究主要集中在营养生长的幼苗上，显著缺乏对整个生命周期表型变异的研究。理解植物抗逆性的一个有价值工具是机器辅助表型分析，因为它具有非侵入性、快速且准确的特点，但小型植物实验室往往无法使用。在此，我们建立并验证了一种单个实验室可实施的经济实惠的多光谱机器辅助表型分析方法。我们收集了拟南芥在整个生命周期中响应ACS的定量生长数据，并进行了相关性分析。我们使用拟南芥野生型和香豆素缺陷型突变体f6'h1-1（在碱性条件下表现出黄化），通过手动和使用多光谱3D表型扫描仪来评估每周的形态和叶片颜色数据。通过相关性分析，我们选择了区分大小和叶片黄化表型的机器参数。相关性分析表明莲座叶大小与多个光谱参数之间存在密切联系，突出了莲座叶大小对拟南芥在ACS中生长的重要性。最可靠的表型分析是在抽薹期开始时。该方法进一步验证可检测已知缺铁突变体在不同生长阶段的新叶片黄化表型。因此，我们经济实惠的机器辅助表型分析程序适用于对包括拟南芥在内的小型莲座叶植物进行高通量、准确的筛选，并能够在植物生命周期中发现新的遗传和表型变异，以了解植物在具有挑战性的土壤环境中的抗逆性。
