Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis
Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis
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DOI：
10.1105/tpc.114.129601
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发表时间：
2014-12-01
期刊：
影响因子：
11.6
通讯作者：
Klukas, Christian
中科院分区：
文献类型：
--
作者：
Chen, Dijun;Neumann, Kerstin;Klukas, Christian
迫切需要大幅改良作物品种，以满足气候变化下快速增长的人口的需求。虽然基因组序列信息和优秀的基因组工具已经用于主要作物物种，但以高通量方式系统量化表型特征或其组成部分仍然是一个巨大的挑战。为了帮助弥合基因到表型的差距，我们开发了一个用于高通量植物表型数据分析的综合框架，该框架能够随着时间的推移从非破坏性的植物成像中提取大量的表型性状。作为概念的验证，我们研究了18个不同大麦品种营养生长期干旱反应的表型成分。基于54个具有代表性的表型性状，我们分析了性状表达随生长时间的动态特性。这些数据对了解植物发育和进一步量化生长和作物表现特征具有很高的价值。我们测试了不同的生长模型来预测植物的生物量积累，并确定了几个相关的参数，这些参数支持对植物生长和抗逆性的生物学解释。这些基于图像的性状和模型导出的参数有望用于后续的遗传作图，以揭示复杂农艺性状的遗传基础。综上所述，我们预计，这里提出的分析框架和分析结果将有助于推进我们对植物发育及其对环境提示的反应的表型性状成分的看法。
Significantly improved crop varieties are urgently needed to feed the rapidly growing human population under changing climates. While genome sequence information and excellent genomic tools are in place for major crop species, the systematic quantification of phenotypic traits or components thereof in a high-throughput fashion remains an enormous challenge. In order to help bridge the genotype to phenotype gap, we developed a comprehensive framework for highthroughput phenotype data analysis in plants, which enables the extraction of an extensive list of phenotypic traits from nondestructive plant imaging over time. As a proof of concept, we investigated the phenotypic components of the drought responses of 18 different barley (Hordeum vulgare) cultivars during vegetative growth. We analyzed dynamic properties of trait expression over growth time based on 54 representative phenotypic features. The data are highly valuable to understand plant development and to further quantify growth and crop performance features. We tested various growth models to predict plant biomass accumulation and identified several relevant parameters that support biological interpretation of plant growth and stress tolerance. These image-based traits and model-derived parameters are promising for subsequent genetic mapping to uncover the genetic basis of complex agronomic traits. Taken together, we anticipate that the analytical framework and analysis results presented here will be useful to advance our views of phenotypic trait components underlying plant development and their responses to environmental cues.