Depth prediction of nanotags in tissue using surface enhanced spatially offset Raman scattering (SESORS)

Area of Science:

• Biomedical optics
• Spectroscopy
• Nanotechnology

Background:

• Surface-enhanced Raman scattering (SERS) is a powerful technique for molecular detection.
• Accurate depth profiling of SERS-active materials in biological tissues remains a challenge.
• Non-invasive imaging of SERS nanotags deep within tissue is crucial for various biomedical applications.

Purpose of the Study:

• To develop and demonstrate a predictive model for SERS nanotag depth in porcine tissue.
• To establish a new capability for detecting SERS signals at unprecedented depths.
• To validate the use of a backscattering spatially offset Raman spectroscopy (SORS) geometry for deep tissue imaging.

Main Methods:

• Development of a ratiometric analysis model for depth prediction.
• Utilizing a handheld spatially offset Raman (SORS) instrument.
• Employing a backscattering SORS geometry for signal detection.

Main Results:

• Demonstrated a model capable of predicting the depth of two SERS nanotag types in porcine tissue.
• Achieved the first-ever detection of SERS signals from nanotags at depths down to 48 mm.
• Validated the effectiveness of the backscattering SORS geometry for deep tissue SERS imaging.

Conclusions:

• The developed ratiometric model enables accurate depth prediction of SERS nanotags in tissue.
• The backscattering SORS approach significantly advances the depth penetration capabilities for SERS-based tissue analysis.
• This work paves the way for deeper in vivo SERS imaging and diagnostics.