Automated Tissue Classification and Candidate Biomarker Feature Extraction in Mass Spectrometry Imaging Based on...

Mass spectrometry imaging (MSI) generates high-dimensional spatial-spectral data that requires efficient computational methods for tissue classification and candidate biomarker feature extraction. Deep learning offers a promising approach, yet the interpretability of model predictions and identification of biologically relevant spectral features remain challenging.

A comprehensive computational pipeline was developed for automatic tissue layer classification of a public mouse urinary bladder MSI dataset. Building upon prior work that compared manual tissue layer labels with those automatically generated via spectral preprocessing, t-SNE, and hierarchical clustering, in this study we separately use each type of class label to train convolutional neural networks (CNNs) for supervised classification. Gradient-weighted Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP) were employed to compute layer-specific summed importance scores to evaluate each mass spectral feature and extract class-discriminative features.

On the mouse urinary bladder MSI dataset, both manual labels and cluster-derived labels (t-SNE + hierarchical clustering) enabled the CNN model to achieve training accuracies exceeding 0.9 for classifying three tissue layers. Interpretability methods successfully identified discriminative m/z features, including known lipids such as SM(34:1) (m/z 741.54) and PC(34:1) (m/z 798.54), consistent with previously reported biological markers. Compared to the intensity values, the importance scores of the top class-discriminative features generated by both interpretability methods exhibited a sharper contrast and superior ability to delineate tissue-layer-specific distributions in their ion images. Furthermore, evaluation on an independent colorectal cancer dataset yielded a test accuracy of 0.758, suggesting that cross-patient generalizability varied across different data sources.

This study presented an effective deep learning framework for accurate and interpretable tissue classification in MSI data. The CNN modeling and deep learning interpretability provided a robust approach for both automated segmentation and biological discovery, facilitating the identification of spatially resolved metabolic features in tissue sections.