Spatial lipidomics of coronary atherosclerotic plaque development in a familial hypercholesterolemia swine model.

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 = 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 = 6), mild (n = 6), and advanced disease (n = 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 ceramide-phosphoethanolamine(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.

Application of spatial-omics to the classification of kidney biopsy samples in transplantation.

Improvement of long-term outcomes through targeted treatment is a primary concern in kidney transplant medicine. Currently, the validation of a rejection diagnosis and subsequent treatment depends on the histological assessment of allograft biopsy samples, according to the Banff classification system. However, the lack of (early) disease-specific tissue markers hinders accurate diagnosis and thus timely intervention. This challenge mainly results from an incomplete understanding of the pathophysiological processes underlying late allograft failure. Integration of large-scale multimodal approaches for investigating allograft biopsy samples might offer new insights into this pathophysiology, which are necessary for the identification of novel therapeutic targets and the development of tailored immunotherapeutic interventions. Several omics technologies - including transcriptomic, proteomic, lipidomic and metabolomic tools (and multimodal data analysis strategies) - can be applied to allograft biopsy investigation. However, despite their successful application in research settings and their potential clinical value, several barriers limit the broad implementation of many of these tools into clinical practice. Among spatial-omics technologies, mass spectrometry imaging, which is under-represented in the transplant field, has the potential to enable multi-omics investigations that might expand the insights gained with current clinical analysis technologies.

A cell-free nutrient-supplemented perfusate allows four-day ex vivo metabolic preservation of human kidneys.

The growing disparity between the demand for transplants and the available donor supply, coupled with an aging donor population and increasing prevalence of chronic diseases, highlights the urgent need for the development of platforms enabling reconditioning, repair, and regeneration of deceased donor organs. This necessitates the ability to preserve metabolically active kidneys ex vivo for days. However, current kidney normothermic machine perfusion (NMP) approaches allow metabolic preservation only for hours. Here we show that human kidneys discarded for transplantation can be preserved in a metabolically active state up to 4 days when perfused with a cell-free perfusate supplemented with TCA cycle intermediates at subnormothermia (25 °C). Using spatially resolved isotope tracing we demonstrate preserved metabolic fluxes in the kidney microenvironment up to Day 4 of perfusion. Beyond Day 4, significant changes were observed in renal cell populations through spatial lipidomics, and increases in injury markers such as LDH, NGAL and oxidized lipids. Finally, we demonstrate that perfused kidneys maintain functional parameters up to Day 4. Collectively, these findings provide evidence that this approach enables metabolic and functional preservation of human kidneys over multiple days, establishing a solid foundation for future clinical investigations.

Recognition of pseudoinvasion in colorectal adenoma using spatial glycomics.

Pseudoinvasion (PI) is a benign lesion in which cancer is mimicked in the colon by misplacement of dysplastic glands in the submucosa. Although there are morphological clues, the discrimination of PI from true invasion can be a challenge during pathological evaluation of colon adenomas. Both overdiagnosis and underdiagnosis can result in inadequate clinical decisions. This calls for novel tools to aid in cases where conventional methods do not suffice. We performed mass spectrometry imaging (MSI)-based spatial glycomics analysis on a cohort of formalin-fixed paraffin-embedded tissue (FFPE) material from 16 patients who underwent polypectomy. We used this spatial glycomic data to reconstruct the molecular histology of the tissue section using spatial segmentation based on uniform manifold approximation and projection for dimension reduction (UMAP). We first showed that the spatial glycomic phenotypes of the different morphological entities separated as distinct clusters in colon tissues, we separated true invasion from the other morphological entities. Then, we found that the glycomic phenotype in areas with suspected PI in the submucosa was strongly correlating with the corresponding glycomic phenotype of the adenomatous colon epithelium from the same tissue section (Pearson correlation distance average = 0.18). These findings suggest that using spatial glycomics, we can distinguish PI as having a molecular phenotype similar to the corresponding surface epithelium and true invasion as having a different phenotype even when compared to high-grade dysplasia. Therefore, when a novel molecular phenotype is found in the deepest submucosal region, this may be used as an argument in favor of true invasion.