IMSC: DESI AND/OR LA-REIMS? ADJACENT AUTOMATED AMBIENT TECHNIQUES FOR THE PRECISE IDENTIFICATION OF CANCER TISSUE

Significance of the Topic

Ambient ionization imaging techniques enable rapid molecular profiling of tissue samples without extensive preparation. Precise differentiation of healthy and cancerous regions supports surgical decision making and the development of comprehensive tissue databases for real time guidance.

Aims and Study Overview

The study compares Automated DESI and Laser assisted REIMS imaging against surgical CO2 REIMS sampling. Objectives include assessment of data comparability, sampling resolution, tissue handling effects, spectral quality and practical applications in oncological tissue analysis.

Methodology and Instrumentation

Samples comprised veterinary tumorous and healthy tissues, fresh frozen mouse brain, and deparaffinized FFPE human tumors. Imaging was performed using AutoDESI with an angled charged solvent spray and LA-REIMS with laser ablation. All ionized material was analyzed on a Xevo G2-XS ToF mass spectrometer. Multivariate statistical models such as PCA and LDA enabled pixel by pixel classification and comparative analysis.

• DESI autoloader and REIMS source
• High performance solvent sprayer
• Optical parametric oscillator with custom optical path
• Motorized x y z stage and laser safe enclosure
• Heated transfer line to mass spectrometer

Main Results and Discussion

Principal component analysis revealed clear separation of healthy and tumorous spectra across AutoDESI, LA-REIMS imaging, and CO2 surgical REIMS. LA-REIMS imaging data aligned 100% with CO2 REIMS classifications, and CO2 data achieved 83% correct assignment on the imaging database. AutoDESI delivered higher overall signal intensities and allowed tissue resampling, while LA-REIMS provided finer spot detail and successful sampling of deparaffinized FFPE sections. Spectral comparisons highlighted enrichment of phosphatidylethanolamines in LA-REIMS and phosphatidylinositols in AutoDESI profiles.

Benefits and Practical Applications

Both methods support rapid molecular mapping of tumor margins. AutoDESI is suited for high sensitivity applications requiring repeated analysis, whereas LA-REIMS excels in high resolution imaging and compatibility with FFPE samples. Integration with surgical CO2 REIMS databases can enhance intraoperative diagnostics.

Future Trends and Potential Applications

Further optimization of laser parameters and solvent chemistry will improve comparability and spatial resolution. Advanced machine learning algorithms could refine tissue classification. Expanding clinical validation will pave the way for routine surgical guidance and personalized oncology research.

Conclusion

This comparative evaluation demonstrates complementary strengths of AutoDESI and LA-REIMS imaging. Reliable data alignment with surgical CO2 REIMS and distinct lipidomic signatures underscore the potential for multimodal ambient ionization in cancer tissue analysis.