Mass spectrometry imaging (MSI) is a powerful tool for detecting lipids in tissue sections, with matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI) as its key ionization techniques. In this study, we examine how MALDI compares with state-of-the-art DESI ionization in identifying lipids in heterogeneous samples, specifically atherosclerotic plaques. Carotid plaques (n = 4) from patients undergoing endarterectomy were snap-frozen, stored at −80°C, and then sectioned for MSI analysis and H&E staining. Measurements were conducted using a SYNAPT XS mass spectrometer in positive ion mode, employing MALDI with a 2,5-dihydroxybenzoic acid (DHB) matrix and DESI with a methanol: water (98:2) (v/v) solvent. Our comparison covered spectral profiles, sensitivity, and image quality generated by these two techniques. We found that both MALDI and DESI are highly suitable techniques for detecting a wide range of lipids in atherosclerotic plaque sections. DESI-MSI exhibited higher ion counts for most lipid classes than MALDI-MSI and provided sharper images. MALDI detected larger amounts of ceramide and hexosylceramide species, possibly due to its efficient generation of dehydrated ions. In contrast, DESI showed greater peak intensities of cholesteryl ester and triacylglyceride species than MALDI, consistent with reduced fragmentation. These findings establish the relative merits of DESI and MALDI and demonstrate their complementarity as techniques for lipid research in MSI.

Coronary atherosclerosis is caused by plaque build-up, with lipids playing a pivotal role in its progression. However, lipid composition and distribution within coronary atherosclerosis remain unknown. This study aims to characterize lipids and investigate differences in lipid composition across disease stages to aid in the understanding of disease progression. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was used to visualize lipid distributions in coronary artery sections (n ¼ 17) from hypercholesterolemic swine. We performed histology on consecutive sections to classify the artery segments and to investigate colocalization between lipids and histological regions of interest in advanced plaque, including necrotic core and inflammatory cells. Segments were classified as healthy (n ¼ 6), mild (n ¼ 6), and advanced disease (n ¼ 5) artery segments. Multivariate data analysis was employed to find differences in lipid composition between the segment types, and the lipids' spatial distribution was investigated using non-negative matrix factorization (NMF). Through this process, MALDI-MSI detected 473 lipid-related features. NMF clustering described three components in positive ionization mode: triacylglycerides (TAG), phosphatidylcholines (PC), and cholesterol species. In negative ionization mode, two components were identified: one driven by phosphatidylinositol(PI)(38:4), and one driven by ceramidephosphoethanolamine(36:1). Multivariate data analysis showed the association between advanced disease and specific lipid signatures like PC(O-40:5) and cholesterylester(CE)(18:2). Ether-linked phospholipids and LysoPC species were found to colocalize with necrotic core, and mostly CE, ceramide, and PI species colocalized with inflammatory cells. This study, therefore, uncovers distinct lipid signatures correlated with plaque development and their colocalization with necrotic core and inflammatory cells, enhancing our understanding of coronary atherosclerosis progression.

Background and aims: Lipids play an important role in atherosclerotic plaque development and are interesting candidate predictive biomarkers. However, the link between circulating lipids, accumulating lipids in the vessel wall, and plaque destabilization processes in humans remains largely unknown. This study aims to provide new insights into the role of lipids in atherosclerosis using lipidomics and mass spectrometry imaging to investigate lipid signatures in advanced human carotid plaque and plasma samples. Methods: We used lipidomics and desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to investigate lipid signatures of advanced human carotid plaque and plasma obtained from patients who underwent carotid endarterectomy (n = 14 out of 17 whose plaque samples were analyzed by DESI-MSI). Multivariate data analysis and unsupervised clustering were applied to identify lipids that were the most discriminative species between different patterns in plaque and plasma. These patterns were interpreted by quantitative comparison with conventional histology. Results: Lipidomics detected more than 300 lipid species in plasma and plaque, with markedly different relative abundances. DESI-MSI visualized the spatial distribution of 611 lipid-related m/z features in plaques, of which 330 m/z features could be assigned based on exact mass, comparison to the lipidomic data, and high mass resolution MSI. Matching spatial lipid patterns to histological areas of interest revealed several molecular species that were colocalized with pertinent disease processes in plaque including specific sphingomyelin and ceramide species with calcification, phospholipids and free fatty acids with inflammation, and triacylglycerols and phosphatidylinositols with fibrin-rich areas. Conclusions: By comparing lipid species in plaque and plasma, we identified those circulating species that were also prominently present in plaque. Quantitative comparison of lipid spectral patterns with histology revealed the presence of specific lipid species in destabilized plaque areas, corroborating previous in vitro and animal studies.

Significance: Spectroscopic analysis of optical coherence tomography (OCT) data can yield added information about the sample's chemical composition, along with high-resolution images. Typical commercial OCT systems operate at wavelengths that may not be optimal for identifying lipid-containing samples based on absorption features. Aim: The main aim of this study was to develop a 1200 nm spectroscopic OCT (SOCT) for the classification of lipid-based and water-based samples by extracting the lipid absorption peak at 1210 nm from the OCT data. Approach: We developed a 1200 nm OCT system and implemented a signal processing algorithm that simultaneously retrieves spectroscopic and structural information from the sample. In this study, we validated the performance of our OCT system by imaging weakly scattering phantoms with and without lipid absorption features. An orthogonal projections to latent structures-discriminant analysis (OPLS-DA) model was developed and applied to classify weakly scattering samples based on their absorption features. Results: The OCT system achieved an axial resolution of 7.2 m and a sensitivity of 95 dB. The calibrated OPLS-DA model on weakly scattering samples with lipid and water-based absorption features correctly classified 19/20 validation samples. Conclusions: The 1200 nm SOCT system can discriminate the lipid-containing weakly scattering samples from water-based weakly scattering samples with good predictive ability.