Ultrasensitive Acetylcholinesterase detection based on a surface-enhanced Raman scattering lever strategy for...

Area of Science:

• Biomedical Engineering
• Analytical Chemistry
• Neuroscience

Background:

• Accurate identification of motor and sensory nerve fibers is crucial for successful nerve transfer surgery.
• Current methods for nerve fiber identification lack the simplicity and sensitivity required for clinical application.

Purpose of the Study:

• To develop a highly sensitive and reliable method for detecting Acetylcholinesterase (AchE) to distinguish nerve fiber types.
• To create a clinically applicable technique for identifying motor and sensory fibers in nerve repair.

Main Methods:

• Designed a surface-enhanced Raman scattering (SERS) lever strategy for ultrasensitive AchE detection.
• Utilized SERS-active microneedles to minimize sample volume and an internal reference for signal repeatability.
• AchE detection achieved by adjusting Rhodamine B absorbance with thiocholine, leveraging their differing Raman activities.

Main Results:

• The developed SERS method detected AchE with high sensitivity, distinguishing samples with a fourfold difference in enzyme activity down to 1 × 10⁻⁴ U/mL within 10 minutes.
• Measured AchE levels in nerve roots: 0.00025–0.001 U in ventral roots and 0.01–0.02 U in dorsal roots.
• Demonstrated the method's ability to discriminate between samples with significant differences in enzyme concentration.

Conclusions:

• The ultrasensitive SERS-based AchE detection method is a reliable tool for identifying motor and sensory nerve fibers.
• This technique meets the clinical requirements for precise nerve fiber discrimination in surgical procedures.
• The developed method offers enhanced sensitivity and repeatability for nerve fiber analysis.