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

Area of Science:

• Chemical biology
• Biomedical imaging
• Probe design

Background:

• Visualizing dynamic biological processes in live cells using chemical imaging probes is crucial but technically challenging due to sensitivity limitations.
• Engineering probes with high sensitivity and specificity for live-cell imaging requires a deep understanding of complex structure-function relationships.

Purpose of the Study:

• To present Sensight, a quantitative multivariate framework for predicting and optimizing chemical imaging probe performance.
• To establish a generalized strategy for designing high-performance probes for live-cell applications.

Main Methods:

• Sensight integrates key photophysical and physicochemical descriptors to predict probe performance.
• A structurally diverse probe library was analyzed to identify dominant parameters and their optimal ranges.
• A radar map visualization was developed to represent probe performance and guide optimization.

Main Results:

• The Sensight framework demonstrated strong predictive power for probe performance in live cells.
• Five dominant parameters influencing probe sensitivity were identified and their optimal ranges defined.
• Guided by Sensight, a novel superoxide probe (G3) with exceptional sensitivity for early oxidative event detection was designed.

Conclusions:

• Sensight provides a predictive and generalizable strategy for designing high-performance chemical imaging probes.
• The framework successfully extended structure-activity relationship analysis to imaging sensitivity.
• Sensight has broad implications for advancing live-cell sensing, chemical imaging, and theranostics.