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.

The integration of spatial omics technologies can provide important insights into the biology of tissues. Here we combined mass spectrometry imaging-based metabolomics and imaging mass cytometry-based immunophenotyping on a single tissue section to reveal metabolic heterogeneity at single-cell resolution within tissues and its association with specific cell populations such as cancer cells or immune cells. This approach has the potential to greatly increase our understanding of tissue-level interplay between metabolic processes and their cellular components.

Analytical techniques with high sensitivity and selectivity are essential to the quantitative analysis of clinical samples. Liquid chromatography coupled to tandem mass spectrometry is the gold standard in clinical chemistry. However, tandem mass spectrometers come at high capital expenditure and maintenance costs. We recently showed that it is possible to generate very similar results using a much simpler single mass spectrometry detector by performing enhanced in-source fragmentation/annotation (EISA) combined with correlated ion monitoring. Here we provide a step-by-step protocol for optimizing the analytical conditions for EISA, so anyone properly trained in liquid chromatography–mass spectrometry can follow and apply this technique for any given analyte. We exemplify the approach by using 2-hydroxyglutarate (2-HG) which is a clinically relevant metabolite whose d-enantiomer is considered an ‘oncometabolite’, characteristic of cancers associated with mutated isocitrate dehydrogenases 1 or 2 (IDH1/2). We include procedures for determining quantitative robustness, and show results of these relating to the analysis of dl-2-hydroxyglutarate in cells, as well as in serum samples from patients with acute myeloid leukemia that contain the IDH1/2 mutation. This EISA–mass spectrometry protocol is a broadly applicable and low-cost approach for the quantification of small molecules that has been developed to work well for both single-quadrupole and time-of-flight mass analyzers.

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.

Accumulating evidence demonstrates important roles for metabolism in cell fate determination. However, it is a challenge to assess metabolism at a spatial resolution that acknowledges both heterogeneity and cellular dynamics in its tissue microenvironment. Using a multi-omics platform to study cell-type-specific dynamics in metabolism in complex tissues, we describe the metabolic trajectories during nephrogenesis in the developing human kidney. Exploiting in situ analysis of isotopic labeling, a shift from glycolysis toward fatty acid β-oxidation was observed during the differentiation from the renal vesicle toward the S-shaped body and the proximal tubules. In addition, we show that hiPSC-derived kidney organoids are characterized by a metabolic immature phenotype that fails to use mitochondrial long-chain fatty acids for energy metabolism. Furthermore, supplementation of butyrate enhances tubular epithelial differentiation and maturation in cultured kidney organoids. Our findings highlight the relevance of understanding metabolic trajectories to efficiently guide stem cell differentiation.

A common drawback of metabolic analyses of complex biological samples is the inability to consider cell-to-cell heterogeneity in the context of an organ or tissue. To overcome this limitation, we present an advanced high-spatial-resolution metabolomics approach using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) combined with isotope tracing. This method allows mapping of cell-type-specific dynamic changes in central carbon metabolism in the context of a complex heterogeneous tissue architecture, such as the kidney. Combined with multiplexed immunofluorescence staining, this method can detect metabolic changes and nutrient partitioning in targeted cell types, as demonstrated in a bilateral renal ischemia–reperfusion injury (bIRI) experimental model. Our approach enables us to identify region-specific metabolic perturbations associated with the lesion and throughout recovery, including unexpected metabolic anomalies in cells with an apparently normal phenotype in the recovery phase. These findings may be relevant to an understanding of the homeostatic capacity of the kidney microenvironment. In sum, this method allows us to achieve resolution at the single-cell level in situ and hence to interpret cell-type-specific metabolic dynamics in the context of structure and metabolism of neighboring cells.