Metabolic Phenotyping of Colorectal Tissue Samples by DESI-MSI for Clinical Research

Importance of the Topic

Mass spectrometry imaging under ambient conditions enables mapping of hundreds of biomolecules directly on tissue surfaces. DESI-MSI in particular offers a minimally invasive way to visualize lipid distributions without extensive sample preparation. In colorectal cancer research, understanding metabolic alterations at the tissue level supports improved diagnosis, prognosis, and therapeutic strategies.

Study Objectives and Overview

The primary goal was to compare spatial lipid profiles in healthy versus cancerous colorectal tissues. By using DESI-MSI in negative ion mode, researchers aimed to reveal metabolic signatures associated with tumor progression and invasion.

Methodology

Fresh frozen colorectal biopsies were cryo-sectioned (10 µm) and mounted on glass slides. Samples were stored at –80°C until analysis. A DESI sprayer rastered the defined region line by line at 100 µm pixel resolution and 4 scans per second. Mass spectra were acquired in negative ion mode over m/z 50–1 000. After imaging, tissue sections were stained with H&E and overlaid with DESI-MSI data for histological correlation.

Instrumentation

• Waters SYNAPT G2-Si and Xevo G2-XS mass spectrometers
• Waters High Definition Imaging (HDI) software v1.4 for data acquisition and visualization
• SIMCA (Umetrics) for PLS-DA and class modelling
• In-house MATLAB toolbox for unsupervised PCA and supervised MMC analyses

Main Results and Discussion

DESI-MSI produced intense signals for fatty acids and phospholipids, enabling clear distinction of mucosa, submucosa, and muscle layers in healthy colon tissue. Tumor sections exhibited more homogeneous lipid distributions. Multivariate analysis (PLS-DA, SIMCA) yielded robust separation of tissue classes. Unsupervised PCA separated the three regions using one component, while supervised MMC with cross validation achieved classification accuracies of 98% for mucosa and muscle and 94% for submucosa.

Benefits and Practical Applications

• Minimal sample preparation and no matrix application
• Ambient analysis conditions, preserving tissue integrity
• Non-destructive workflow allowing subsequent histology
• High sensitivity for a broad range of lipid species
• Potential to guide surgical margin assessment and biomarker discovery

Future Trends and Opportunities

Integration of DESI-MSI with routine clinical workflows may enable real-time metabolic phenotyping during surgery. Advances in data processing, machine learning, and multi-omics integration will enhance tissue classification and biomarker identification. Development of portable DESI platforms could extend applications to intraoperative guidance and point-of-care diagnostics.

Conclusion

DESI-MSI provides a powerful platform for spatially resolved lipid profiling in colorectal tissues. Its minimal sample preparation, ambient operation, and compatibility with histological methods make it an attractive tool for clinical research and future diagnostic applications.

References

• Mroz A, Doria L, Claude E, Takats Z. Metabolic Phenotyping of Colorectal Tissue Samples by DESI-MSI for Clinical Research. Waters Technology Brief; July 2018.