Multivariate modeling was performed in SIMCA software (v.17.0; Sartorius Stedim Data Analytics AB -Umetrics, Umeå, Sweden). MALDI imaging raw data files were converted into the imzML format using SCiLS Lab 2021b (Bruker Daltonics) and imported into the MATLAB programming environment using the imzMLConverter by Race et al . Data were normalized to total ion count and, in addition, for multivariate modelling purposes, log-transformation was applied to address heteroscedasticity and skewness. Data were centered but not scaled for multivariate modeling. Spatial segmentation of MSI datasets was performed on scores images from principal components analysis (PCA), whereby images with most pronounced plaques or plaque core features, respectively, were used. Segmentation of plaque cores was achieved by thresholding pixel values followed by removal of off-plaque localizing pixels. Segmentation of plaque structures was achieved using Otsu's global threshold followed by a series refinement including image erosion, image dilation, active contours algorithm, and removal of off-plaque localizing pixels. Image segmentation was further guided by hyperspectral fluorescence microscopy data.