Interpretable Machine Learning to Decipher Myelodysplastic Syndrome-Associated Alterations of the Extracellular...

Machine learning (ML) accelerates progress in many areas, including biomedical and clinical research. ML algorithms provide powerful options for efficiently analyzing multivariate data sets. We developed and validated an ML pipeline to detect myelodysplastic syndrome (MDS)-associated pathological alterations of extracellular matrices (ECMs) by time-of-flight secondary ion mass spectrometry (ToF-SIMS). A Bayesian-optimized neural network (NN) was trained and applied to classify ToF-SIMS spectra of ECM secreted by mesenchymal stromal cells (MSCs) derived from MDS patients and healthy reference donors. Validated by principal component analysis, the explainer tool SHapley Additive exPlanations (known as SHAP) was integrated into the analysis pipeline to unravel characteristic compositional and structural differences of the ECM variants. Our results demonstrate the potential of ToF-SIMS-ML for the label-free investigation of pathogenic alterations of the ECM. Integrated into the multiscale ECM analysis of cell and organoid-based disease models, the introduced methodology may facilitate advances in the development of novel diagnostic and therapeutic strategies.