CAN THE EXTRACTED CHEMICAL INFORMATION FROM FFPE SAMPLES USING LA-REIMS IMAGING SUPPORT PATHOLOGICAL DIAGNOSIS? (ASMS)

Significance of the Topic

A vast global archive of formalin-fixed paraffin-embedded (FFPE) tissue blocks represents an invaluable resource for retrospective molecular analysis and pathological research.
Conventional sample processing often leads to loss of chemical information, limiting the utility of these samples for mass spectrometry imaging.
Laser assisted rapid evaporative ionization mass spectrometry (LA-REIMS) offers ambient, preparation-free lipid profiling with potential for direct analysis of FFPE specimens, supporting diagnostic workflows and retrospective studies.

Study Objectives and Overview

The study aimed to assess whether LA-REIMS imaging can recover phospholipid signatures (600–900 m/z) from human renal carcinoma FFPE sections to support pathological diagnosis.
An initial workflow using pork liver tissue was established to track spectral changes across FFPE processing steps.
A comprehensive evaluation involved imaging ten annotated clear cell renal cell carcinoma FFPE samples, applying multivariate statistics and supervised classification to differentiate tumour and healthy regions.

Methodology and Instrumentation

A laser-safe, enclosed LA-REIMS imaging setup equipped with a 2940 nm optical parametric oscillator and motorized XYZ stage interfaced to a Xevo G2 QToF mass spectrometer (negative mode) was used.
Sample set: ten 10 μm FFPE human kidney tumour sections.
Experimental workflow:

• Stepwise FFPE preparation of pork liver to monitor formalin fixation, paraffin embedding, and deparaffinization effects.
• Acquisition of point-by-point chemical images with HDI 1.4 software.
• Unsupervised analysis: principal component analysis (PCA) and k-nearest neighbour (kNN) clustering for quality control and spectral visualization.
• Supervised model building: linear support vector classifier (LSVC) via homebuilt Abstract Model Builder (AMX) using regions of interest defined by pathological annotation.
• Cross-validation of each sample within the full-group model and supervised image classification.

Main Results and Discussion

FFPE processing led to a substantial drop in signal intensity and lipid complexity, especially at m/z 766.5 (PE(38:4)), 742.5 (PE(36:2)), and 885.5 (PI(38:4)).
PCA separated processing steps but showed overlap in deparaffinized samples due to reduced spectral contrast.
Unsupervised kNN clustering generated spatial distributions consistent with histological structures and served as an effective quality-control metric.
Supervised LSVC models using four key phospholipid peaks achieved classification rates ranging from 48% to 100% across samples, with tumour versus healthy regions correctly visualized in all eight samples with sufficient signal quality.
Two samples were excluded due to poor classification performance.
Comparative analysis indicated that native frozen tissue produces higher signal-to-noise profiles than FFPE, but LA-REIMS remains applicable to archival blocks with variable success.

Practical Benefits and Applications

LA-REIMS imaging of FFPE sections offers a rapid, preparation-free approach to map lipid distributions, complementing traditional histopathology.
It enables the reuse of existing tissue archives for molecular diagnostics, quality control, and retrospective biomarker discovery.
Unsupervised clustering provides a fast check of sample integrity prior to in-depth analysis.

Future Trends and Potential Applications

Developments in signal amplification and alternative fixation protocols may improve spectral quality from archival material.
Integration with advanced AI models and multi-omics data could enhance diagnostic accuracy and biomarker identification.
Application to diverse tissue types and disease models may broaden clinical utility.

Conclusion

LA-REIMS imaging can extract diagnostically relevant phospholipid information from FFPE renal carcinoma sections despite signal degradation.
While fresh frozen specimens yield superior spectra, archival FFPE blocks remain valuable for rapid chemical mapping and supervised classification.
Further optimization of sample preparation and instrument sensitivity is needed to maximize the potential of this technique for routine pathological support.

Reference

1. Waters Technologies Corporation. Fully Automated Chemical Imaging with LA-REIMS.
2. Waters Technologies Corporation. DESI and/or LA-REIMS? Adjacent Automated Ambient Techniques for the Precise Identification of Cancer Tissue.