2. Academic Validation
  3. Sensight enables quantitative multivariate engineering of high-performance chemical imaging tools

Sensight enables quantitative multivariate engineering of high-performance chemical imaging tools

  • Nat Commun. 2026 Jan 27;17(1):2061. doi: 10.1038/s41467-026-68663-2.
Chenglong Wen 1 2,  Ying Jiang 1,  Tianruo Shen 3,  Xuefeng Jiang 1,  Shiqi Fan 1,  Taorui Yang 1,  Xiaogang Liu 3 4,  Qiong Luo 5,  Xin Li 6 7
Affiliations

Affiliations

  • 1 College of Pharmaceutical Sciences, Women's Hospital School of Medicine, Zhejiang University, Hangzhou, China.
  • 2 State Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China.
  • 3 Fluorescence Research Group, Singapore University of Technology and Design, Singapore, Singapore.
  • 4 School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, Singapore.
  • 5 College of Pharmaceutical Sciences, Women's Hospital School of Medicine, Zhejiang University, Hangzhou, China. [email protected].
  • 6 College of Pharmaceutical Sciences, Women's Hospital School of Medicine, Zhejiang University, Hangzhou, China. [email protected].
  • 7 State Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, China. [email protected].
PMID: 41593056 DOI: 10.1038/s41467-026-68663-2
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.

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