Machine learning-powered activatable NIR-II fluorescent nanosensor for in vivo monitoring of plant stress responses

Hong Hu, Hao Yuan, Shengchun Sun, Jianxing Feng, Ning Shi, Zexiang Wang, Yan Liang, Yibin Ying and Yixian Wang ()
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Hong Hu: Zhejiang University
Hao Yuan: Zhejiang University
Shengchun Sun: Zhejiang University
Jianxing Feng: Zhejiang University
Ning Shi: Zhejiang University
Zexiang Wang: Zhejiang University
Yan Liang: Zhejiang University
Yibin Ying: Zhejiang University
Yixian Wang: Zhejiang University

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Real-time monitoring of plant stress signaling molecules is crucial for early disease diagnosis and prevention. However, existing methods are often invasive and lack sensitivity, rendering them inadequate for continuous monitoring of subtle plant stress responses. In this study, we develop a non-destructive near-infrared-II (NIR-II) fluorescent nanosensor for real-time detection of stress-related H2O2 signaling in living plants. This nanosensor effectively avoids interference from plant autofluorescence and specifically responds to trace amounts of endogenous H2O2, thereby providing a reliable means to real-time report stress information. We validate that it is a species-independent nanosensor by effectively monitoring the stress responses of different plant species. Additionally, with the aid of a machine learning model, we demonstrate that the nanosensor can accurately differentiate between four types of stress with an accuracy of more than 96.67%. Our study enhances the understanding of plant stress signaling mechanisms and offers reliable optical tools for precision agriculture.

Date: 2025
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DOI: 10.1038/s41467-025-60182-w

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