The stability of the metabolic turnover of arachidonic acid in human unruptured intracranial aneurysmal walls is sustained

Intracranial Aneurysms and Lipid Metabolism Disorders: From Molecular Mechanisms to Clinical Implications

Many vascular diseases are linked to lipid metabolism disorders, which cause lipid accumulation and peroxidation in the vascular wall. These processes lead to degenerative changes in the vessel, such as phenotypic transformation of smooth muscle cells and dysfunction and apoptosis of endothelial cells. In intracranial aneurysms, the coexistence of lipid plaques is often observed, indicating localized lipid metabolism disorders. These disorders may impair the function of the vascular wall or result from it. We summarize the literature on the relationship between lipid metabolism disorders and intracranial aneurysms below.

Lipid Polyunsaturated Fatty Acid Chains in Mouse Kidneys Were Increased within 5 min of a Single High Dose Whole Body Irradiation

To understand the ultra-early reaction of normal organ lipids during irradiation, we investigated the response of lipids, including polyunsaturated fatty acid (PUFA) chains, which are particularly susceptible to damage by ROS, in mice's kidneys, lungs, brains, and livers within 5 min of single high-dose irradiation. In this study, we set up three groups of C56BL/6 male mice and conducted whole-body irradiation with 0 Gy, 10 Gy, and 20 Gy single doses. Kidney, lung, brain, and liver tissues were collected within 5 min of irradiation. PUFA-targeted and whole lipidomic analyses were conducted using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The results showed that PUFA chains of kidney phosphatidylcholine (PC), phosphatidylethanolamine (PE), and triacylglycerol (TG) significantly increased within 5 min of 10 Gy and 20 Gy irradiation. The main components of increased PUFA chains in PC and PE were C18:2, C20:4, and C22:6, and in TG the main component was C18:2. The kidney lipidomes also showed significant changes from the perspective of lipid species, mainly dominated by an increase in PC, PE, TG, and signal lipids, while lipidomes of the lung, brain, and liver were slightly changed. Our results revealed that acute PUFA chains increase and other lipidomic changes in the kidney upon whole-body irradiation within 5 min of irradiation. The significantly increased lipids also showed a consistent preference for possessing PUFA chains. The lipidomic changes varied from organ to organ, which indicates that the response upon irradiation within a short time is tissue-specific.

Changes of Mass Spectra Patterns on a Brain Tissue Section Revealed by Deep Learning with Imaging Mass Spectrometry Data

The characteristic patterns of mass spectra in imaging mass spectrometry (IMS) strongly reflect the tissue environment. However, the boundaries formed where different tissue environments collide have not been visually assessed. In this study, IMS and convolutional neural network (CNN), one of the deep learning methods, were applied to the extraction of characteristic mass spectra patterns from training brain regions on rodents' brain sections. CNN produced classification models with high accuracy and low loss rate in any test data sets of mouse coronal sections measured by desorption electrospray ionization (DESI)-IMS and of mouse and rat sagittal sections by matrix-assisted laser desorption (MALDI)-IMS. On the basis of the extracted mass spectra pattern features, the histologically plausible segmentation and classification score imaging of the brain sections were obtained. The boundary imaging generated from classification scores showed the extreme changes of mass spectra patterns between the tissue environments, with no significant buffer zones for the intermediate state. The CNN-based analysis of IMS data is a useful tool for visually assessing the changes of mass spectra patterns on a tissue section, and it will contribute to a comprehensive view of the tissue environment.