基于超高效液相色谱-串联质谱的股骨头坏死组织外泌体脂质代谢组学分析

Osteonecrosis of the femoral head (ONFH) can lead to its collapse which requires total hip arthroplasty. Exosomes, which are important for intercellular communication are involved in a series of physiological and pathological processes, and therefore play a unique role in disease diagnosis and treatment. In this study, untargeted metabolomics was used to investigate the metabolic characteristics of lipids in exosomes of femoral head tissue with osteonecrosis and to explain the metabolic changes that occur in the body during this disease. Ultracentrifugation was used to separate and enrich exosomes from femoral head tissue with osteonecrosis. Exosomes were identified using dynamic light scattering (DLS), Western blotting, and transmission electron microscopy (TEM). Gradient elution was performed with ultrapure water and acetonitrile as mobile phases using a Kinetex XB-C18 column (100 mm×2.1 mm, 2.6 μm). The column oven temperature, flow rate of the mobile phase, and duration were 30 ℃, 300 μL/min, and 15 min, respectively. A triple TOF 4600 high resolution mass spectrometry system was used, and the mass scan range of m/z was set at 100 -1000. Other conditions were as follows: sheath gas, 380 kPa; auxiliary gas, 380 kPa; curtain gas, 170 kPa; and atomization temperature, 600 ℃. Ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) combined with multivariate statistical analysis was used to identify the lipid metabolic profile of ONFH-derived exosomes. The exosome metabolites were characterized in detail, which enables their identification and provided a reliable method for quality evaluation. After transforming the obtained original data using MarkView software, peak identification, peak alignment, subtraction of solvent peak, impurity peak, noise filtering, and other treatments, a three-dimensional matrix was obtained from the exported data table. Principal component analysis (PCA) and orthogonal partial least squares discrimination analysis (OPLS-DA) in the SIMCA-P14.1 software were used for multivariate statistical analysis of differentially expressed exosome lipid metabolites. This strategy was validated using lipid metabolites from patients with ONFH and healthy controls. The correlation distribution was shown according to the point dispersion of the PCA score plot, and lipid metabolites from the same disease showed ideal clustering. This result indicates a small difference between the groups. A good clustering effect is also obtained using OPLS-DA, and the statistical model has high reliability. A total of 18 significantly altered lipid metabolites were detected in the exosomes, including acrylolipids, fatty acid esters, glycerides, and their derivatives. The pathway analysis was conducted with MetaboAnalyst (https://www.metaboanalyst.ca/) via database source including the HMDB (http://www.hmdb.ca/) and MMCD (http://mmcd.nmrfam.wisc.edu/) for confirming the impacted metabolic pathways and visualization. Metabolic pathway analysis showed that glycerophospholipid and sphingolipid metabolism were the most significantly altered in exosomes. An imbalance between sphingolipids and glycerophospholipids leads to lipotoxic damage, which is implicated in the pathophysiology of common metabolic diseases. Furthermore, glycerophospholipids are correlated with cell proliferation, differentiation, and apoptosis, and the change in glycerophospholipid ratio can reflect the disturbance in lipid metabolism. The metabolic changes in exosomes may reflect the metabolic changes in ONFH. In this study, lipid metabolomics analysis based on UPLC-MS/MS was used to determine metabolic differences between exosomes extracted from ONFN and femoral neck fracture (FNF). Metabolomic analysis of necrotic femoral head tissue-derived exosomes can help explore the most relevant pathways for assessing the changes in exosome metabolism that affect exosome metabolism in necrotic bone tissue.