Time-Resolved Chemical Phenotyping of Whole Plant Roots with Printed Electrochemical Sensors and Machine Learning
Time-Resolved Chemical Phenotyping of Whole Plant Roots with Printed Electrochemical Sensors and Machine Learning
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DOI：
10.1101/2023.03.09.531921
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发表时间：
2023-03
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影响因子：
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通讯作者：
Philip Coatsworth;Y. Cotur;Atharv Naik;Tarek Asfour;A. Collins;S. Olenik;L. Gonzalez-Macia;T. Bozkurt;Dai-Yin Chao;Firat Güder
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作者：
Philip Coatsworth;Y. Cotur;Atharv Naik;Tarek Asfour;A. Collins;S. Olenik;L. Gonzalez-Macia;T. Bozkurt;Dai-Yin Chao;Firat Güder
植物是由依赖时间的生物过程组成的非平衡系统。然而，化学反应的表型鉴定通常是使用植物组织进行的，在一次性测量中，植物组织的行为与整个植物不同。单点测量不能捕捉植物中与营养吸收、免疫或生长相关的化学信号的丰富的时间分辨变化。在这项工作中，我们报告了一个高通量、模块化、实时的化学表型平台，用于连续监测经常被忽视的整个植物的根环境中的化学信号：Tetris(用于植物根原位化学传感的时间分辨电化学技术)。俄罗斯方块由丝网印刷电化学传感器组成，用于监测整个植物根环境中的盐、pH和过氧化氢浓度。俄罗斯方块可以探测对时间敏感的化学信号，并通过多路复用并行操作，以阐明活植物的整体化学行为。利用俄罗斯方块，我们测定了甘蓝对一系列离子(包括营养和重金属)的吸收速率。我们还使用离子通道阻滞剂LaCl3来调节离子摄取，我们可以使用俄罗斯方块进行监测。我们开发了一个机器学习模型来预测盐的吸收速度，无论是有害的还是有益的，证明了俄罗斯方块可以用于快速绘制新植物品种的离子吸收图。俄罗斯方块有潜力在高通量筛选中克服紧迫的“瓶颈”，培育出抗逆性更强的高产植物品种。
Plants are non-equilibrium systems consisting of time-dependent biological processes. Phenotyping of chemical responses, however, is typically performed using plant tissues, which behave differently to whole plants, in one-off measurements. Single point measurements cannot capture the information rich time-resolved changes in chemical signals in plants associated with nutrient uptake, immunity or growth. In this work, we report a high-throughput, modular, real-time chemical phenotyping platform for continuous monitoring of chemical signals in the often-neglected root environment of whole plants: TETRIS (Time-resolved Electrochemical Technology for plant Root In-situ chemical Sensing). TETRIS consists of screen-printed electrochemical sensors for monitoring concentrations of salt, pH and H2O2 in the root environment of whole plants. TETRIS can detect time-sensitive chemical signals and be operated in parallel through multiplexing to elucidate the overall chemical behavior of living plants. Using TETRIS, we determined the rates of uptake of a range of ions (including nutrients and heavy metals) in Brassica oleracea acephala. We also modulated ion uptake using the ion channel blocker LaCl3, which we could monitor using TETRIS. We developed a machine learning model to predict the rates of uptake of salts, both harmful and beneficial, demonstrating that TETRIS can be used for rapid mapping of ion uptake for new plant varieties. TETRIS has the potential to overcome the urgent “bottleneck” in high-throughput screening in producing high yielding plant varieties with improved resistance against stress.