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Fluorogenic Crystallization via Enzyme-Instructed Excited-State Intramolecular Proton Transfer for Dynamic
Yanglin Jiang1,2, Qizheng Zhang2,3, Xunwu Hu2
1Key Laboratory of Advanced Light Conversion Materials and Biophotonics, Department of Chemistry and Life Resources, Renmin University of China, Beijing 100872, China.
ACS Sensors
|March 7, 2025
Summary
This study introduces a new method using enzyme-instructed excited-state intramolecular proton transfer (ESIPT) fluorophores for rapid tumor microenvironment profiling. This technique allows for real-time detection of enzymatic activity and pH variations in cells.
Area of Science:
- Biochemistry
- Cell Biology
- Chemical Biology
Background:
- Cellular microenvironments are critical for biological processes and disease development.
- Targeted therapies require precise understanding of tumor microenvironment dynamics.
- Current methods for microenvironment profiling can be complex and time-consuming.
Purpose of the Study:
- To develop a rapid and straightforward strategy for profiling tumor microenvironments.
- To enable real-time visualization of microenvironmental variations using fluorescence.
- To detect key tumor microenvironmental features such as enzymatic activity and pH heterogeneity.
Main Methods:
- Engineering of enzyme-instructed excited-state intramolecular proton transfer (ESIPT)-based fluorophores.
- Utilizing fluorogenic crystallization for signal amplification.
- Employing dual-emission ratiometric fluorescence for quantitative analysis.
- Applying the method across different cellular models.
Main Results:
- Achieved selective crystallization of ESIPT fluorophores.
- Generated strong dual-emission ratiometric fluorescence signals for visualization.
- Demonstrated efficient detection of enzymatic activity and pH heterogeneity in tumor microenvironments.
- Validated the method's effectiveness in various cellular models.
Conclusions:
- The developed ESIPT-based fluorogenic crystallization strategy offers a simple, efficient, and versatile tool for studying microenvironment dynamics.
- This approach provides a real-time readout of tumor microenvironmental variations.
- Has broad potential applications in disease diagnosis, drug discovery, and personalized medicine.
