Non-invasive in-vivo glucose-based stress monitoring in plants

代谢组 代谢组学 生物 计算生物学 生物技术 生化工程 生物信息学 工程类
作者
Sammy A. Perdomo,Ernesto De la Paz,Rafael Del Caño,Sumeyye Seker,Tamoghna Saha,Joseph Wang,Andrés Jaramillo-Botero
出处
期刊:Biosensors and Bioelectronics [Elsevier BV]
卷期号:231: 115300-115300 被引量:44
标识
DOI:10.1016/j.bios.2023.115300
摘要

Plant stress responses involve a suite of genetically encoded mechanisms triggered by real-time interactions with their surrounding environment. Although sophisticated regulatory networks maintain proper homeostasis to prevent damage, the tolerance thresholds to these stresses vary significantly among organisms. Current plant phenotyping techniques and observables must be better suited to characterize the real-time metabolic response to stresses. This impedes practical agronomic intervention to avoid irreversible damage and limits our ability to breed improved plant organisms. Here, we introduce a sensitive, wearable electrochemical glucose-selective sensing platform that addresses these problems. Glucose is a primary plant metabolite, a source of energy produced during photosynthesis, and a critical molecular modulator of various cellular processes ranging from germination to senescence. The wearable-like technology integrates a reverse iontophoresis glucose extraction capability with an enzymatic glucose biosensor that offers a sensitivity of 22.7 nA/(μM·cm2), a limit of detection (LOD) of 9.4 μM, and a limit of quantification (LOQ) of 28.5 μM. The system's performance was validated by subjecting three different plant models (sweet pepper, gerbera, and romaine lettuce) to low-light and low-high temperature stresses and demonstrating critical differential physiological responses associated with their glucose metabolism. This technology enables non-invasive, non-destructive, real-time, in-situ, and in-vivo identification of early stress response in plants and provides a unique tool for timely agronomic management of crops and improving breeding strategies based on the dynamics of genome-metabolome-phenome relationships.
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