Genetically Encoded Fluorescent Biosensors Enable Noninvasive Real-Time Visualization of Nitrate Dynamics in Intact Living Plants.

Abstract

Nitrate (NO 3 - ) serves as a pivotal molecule with dual functions in nutrient supply and signaling during plant growth and development. Precise monitoring of its spatiotemporal dynamics in planta is therefore essential for dissecting the regulatory mechanisms underlying plant nitrogen metabolism. However, conventional nitrate detection methods suffer from inherent limitations, including destructive sampling, insufficient spatiotemporal resolution, and an inability to achieve real-time whole-plant monitoring. Here, we report a genetically encoded nitrate biosensor, designated NitNRCL1, constructed using a split firefly luciferase complementation system. Functional validation in both prokaryotic and eukaryotic systems demonstrates that NitNRCL1 responds to changes in nitrate availability and generates stable chemiluminescent signals in bacteria and diverse plant species. Importantly, NitNRCL1 enables non-invasive, real-time, and whole-plant monitoring of nitrate levels in living plants. Using NitNRCL1, we successfully imaged the spatiotemporal dynamics of nitrate signaling in Arabidopsis thaliana . Collectively, our findings establish NitNRCL1 as a robust and novel tool for investigating nitrate transport, signaling, and metabolic pathways in plants. This biosensor advances our mechanistic understanding of plant nitrate biology and provides a technical foundation for breeding nitrogen-use-efficient crops and developing precision fertilization strategies.

Plant phenotyping relevance

植物体内の硝酸動態を非破壊・リアルタイム・全身的に可視化する遺伝子コード型バイオセンサーを開発し、複数の生物・植物種で機能検証しているため、植物生理状態の取得手法が中心です。

Code and data availability

The paper reports a nitrate biosensor with chemiluminescent imaging in plants. The only public URLs are the generic Fiji software site and the MDPI supplementary PDF, which contains supplementary figures/tables (sequences, PCR identifications, primer list) but no public phenotype/trait datasets, raw images, or author's

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