Abstract
Chemical imaging probes enable the visualization of dynamic biology; however, engineering high sensitivity in live cells remains challenging. Here we present Sensight, a quantitative multivariate framework that integrates key photophysical and physicochemical descriptors to predict and optimize probe performance. Using a structurally diverse library, we identify five dominant parameters, define their optimal ranges, and unify them into a radar map representation with strong predictive power and intuitive visualization. This framework extends the structure–activity relationship analysis into imaging sensitivity, capturing complex structure–function relationships that shape probe behavior in live cells. Guided by Sensight, we design G3, a superoxide probe with exceptional se…
Abstract
Chemical imaging probes enable the visualization of dynamic biology; however, engineering high sensitivity in live cells remains challenging. Here we present Sensight, a quantitative multivariate framework that integrates key photophysical and physicochemical descriptors to predict and optimize probe performance. Using a structurally diverse library, we identify five dominant parameters, define their optimal ranges, and unify them into a radar map representation with strong predictive power and intuitive visualization. This framework extends the structure–activity relationship analysis into imaging sensitivity, capturing complex structure–function relationships that shape probe behavior in live cells. Guided by Sensight, we design G3, a superoxide probe with exceptional sensitivity for detecting early oxidative events. The framework’s generality is further demonstrated across distinct systems, including tetrazine–bicyclononyne bioorthogonal chemistry and formaldehyde sensing. Together, these findings establish Sensight as a predictive and generalizable strategy for high-performance probe design, with broad implications for sensing, imaging, and even theranostics.
Data availability
Source data are provided with this paper. All other datasets are included in the Supplementary Information and are available from the corresponding authors upon request. Source data are provided with this paper.
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Acknowledgements
X. Li was supported by the National Program for Support of Top-notch Young Professionals (grant 2021). This study was supported by grants from the National Natural Science Foundation of China (22377106, 82471713), the Fundamental Research Funds for the Central Universities (226-2025-00106), and the National Key Research and Development Program of China (grant numbers 2022YFC2704600, 2022YFC2704601). We thank Ms. S.S. Liu from the Core Facilities, Zhejiang University School of Medicine, for technical support during imaging experiments.
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Authors and Affiliations
College of Pharmaceutical Sciences, Women’s Hospital School of Medicine, Zhejiang University, Hangzhou, China
Chenglong Wen, Ying Jiang, Xuefeng Jiang, Shiqi Fan, Taorui Yang, Qiong Luo & Xin Li 1.
State Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China
Chenglong Wen & Xin Li 1.
Fluorescence Research Group, Singapore University of Technology and Design, Singapore, Singapore
Tianruo Shen & Xiaogang Liu 1.
School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, Singapore
Xiaogang Liu
Authors
- Chenglong Wen
- Ying Jiang
- Tianruo Shen
- Xuefeng Jiang
- Shiqi Fan
- Taorui Yang
- Xiaogang Liu
- Qiong Luo
- Xin Li
Contributions
X. Li designed the project. L.Q. and X. Liu advised the project. C.W., X.J., and S.F. performed organic synthesis and characterization. C.W. performed photophysical measurements and analyzed the data. C.W. and T.Y. conducted confocal cell imaging experiments and analyzed the data. J.Y. validated the imaging results. T.S. advised probe design. X. Li and C.W. drafted the manuscript. All authors have reviewed and approved the final version of the manuscript.
Corresponding authors
Correspondence to Qiong Luo or Xin Li.
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Cite this article
Wen, C., Jiang, Y., Shen, T. et al. Sensight enables quantitative multivariate engineering of high-performance chemical imaging tools. Nat Commun (2026). https://doi.org/10.1038/s41467-026-68663-2
Received: 01 February 2025
Accepted: 12 January 2026
Published: 27 January 2026
DOI: https://doi.org/10.1038/s41467-026-68663-2