Cerebral amyloid angiopathy (CAA) commonly co-occurs with Alzheimer’s disease (AD), yet the molecular changes that accompany vascular \(\upbeta \)-amyloid deposition in human tissue remain incompletely defined. Herein, we use a novel imaging approach that combines matrix-assisted laser desorption/ionization imaging mass spectrometry (IMS) with immunofluorescence microscopy on the same sections of postmortem human frontal cortex to map the lipid microenvironment of leptomeningeal vasculature in cases with and without CAA. Autofluorescence-guided regions-of-interest were imaged by IMS in both negative and positive ion modes and registered to post-IMS-acquired microscopy images. Immunofluorescence microscopy using markers for collagen IV, \(\upalpha \)-smooth muscle actin (\(\upalpha \)SMA), and thiazine red enabled automated segmentation of total, amyloid-positive, and amyloid-negative vasculature regions. A CAA index, the ratio of amyloid-positive area to total vasculature area in a region imaged by IMS, was used to define vasculature and classify each case into having CAA, or CAA-present, and not having CAA, or CAA-absent. An interpretable machine learning approach (XGBoost models with Shapley additive explanations for interpretation) was trained on pixel-level spectra and identified lipid signatures of vascular identity shared across groups as well as class-specific marker candidates that distinguished CAA-present from CAA-absent vasculature. CAA-absent vessels were characterized by higher contributions from phosphatidylserines (e.g., long-chain polyunsaturated PS species). Univariate differences were inconsistent between the two groups, but multivariate models in negative mode yielded stable discriminatory features. These results define spatial lipid correlates of vascular amyloid pathology in the human brain and establish a multimodal framework for mechanistically linking lipid metabolism, vascular integrity, and CAA in AD.
