Metabolic heat maps of tea and coffee variants

High-throughput metabolic phenotyping is a challenge, but it provides an alternative and comprehensive access to the rapid and accurate characterization of plants. In addition to the technical issues of obtaining quantitative data of plenty of metabolic traits from numerous samples, a suitable data processing and statistical evaluation strategy must be developed. We present a simple, robust and highly scalable strategy for the comparison of multiple chemical profiles from coffee and tea leaf extracts, based on direct-injection electrospray mass spectrometry (DIESI-MS) and hierarchical cluster analysis (HCA). More than 3500 individual Coffea canephora and Coffea arabica trees from experimental fields in Mexico were sampled and processed using this method. Our strategy permits the classification of trees according to their metabolic fingerprints and the screening for families with desired characteristics, such as extraordinarily high or low caffeine content in their leaves.

高通量代谢表型分析（high-throughput metabolic phenotyping）虽极具挑战性，却为植物的快速精准表征提供了全面且可行的替代方案。除了需从海量样本中获取大量代谢性状的定量数据这一技术难题外，还需开发适配的数据分析与统计评估策略。本研究提出了一种基于直接注射电喷雾质谱（direct-injection electrospray mass spectrometry，简称DIESI-MS）与系统聚类分析（hierarchical cluster analysis，简称HCA）的简单、稳健且可扩展性极强的分析策略，用于比对咖啡与茶叶叶片提取物的多组化学特征谱。共计3500余株来自墨西哥试验田的刚果咖啡（Coffea canephora）与阿拉比卡咖啡（Coffea arabica）单株，均通过该方法完成采样与处理。本策略可依据植株的代谢指纹图谱对其进行分类，并筛选出具备目标性状的家系，例如叶片中咖啡因含量极高或极低的株系。