Cavity Filling-Based Enzyme Activatable Luminophores Leveraged In Vivo Bioimaging.

Area of Science:

• Biochemistry
• Chemical Biology
• Molecular Imaging

Background:

• Enzymes are critical biocatalysts, and their dysregulation is implicated in diseases.
• Fluorescent sensors offer high sensitivity for enzyme detection but face challenges in balancing sensitivity, specificity, and catalytic efficiency.

Purpose of the Study:

• To introduce a novel cavity filling-based design strategy (CFRD) for optimizing enzyme-activated fluorescent sensors.
• To computationally design and experimentally validate new sensors for improved enzyme detection and imaging.

Main Methods:

• Computational design of fluorescent sensors using the CFRD strategy.
• Experimental validation of designed sensors, including catalytic efficiency measurements (kcat/Km).
• In vivo fluorescence imaging in HepG2 cells and zebrafish, and plant stress monitoring.

Main Results:

• Six fluorescent sensors were designed and validated, with HC-SF showing a catalytic efficiency of 435.54 μM⁻¹·min⁻¹.
• HC-SF demonstrated effective nitroreductase (NTR) detection in HepG2 cells and zebrafish.
• The sensor enabled real-time, noninvasive monitoring of environmental stresses in plants.

Conclusions:

• The CFRD strategy significantly enhances the catalytic performance of enzyme-activated fluorescent sensors.
• The developed HC-SF sensor is suitable for bioimaging in living organisms and monitoring plant stress.
• This approach provides a promising pathway for designing advanced fluorescent sensors for diverse bioimaging applications.