Non-Invasive In-Vivo Glucose-Based Stress Monitoring in Plants
体内
压力(语言学)
医学
生物
生物技术
语言学
哲学
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:Social Science Research Network [Social Science Electronic Publishing] 日期:2023-01-01
标识
DOI:10.2139/ssrn.4365663
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 tattoo-like technology integrates a reverse iontophoresis glucose extraction capability with an enzymatic glucose biosensor that offers a sensitivity of 1.6 nA/µM, 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.