Dual-Mechanism-Driven Strategy for High-Coverage Detection of Serum Lipids on a Novel SALDI-MS Target

Serum lipid metabolites have been emerging as ideal biomarkers for disease diagnosis and prediction. In the current stage, nontargeted or targeted lipidomic research mainly relies on a liquid chromatography-mass spectrometry (LC-MS) platform, but future clinical applications need more robust and high-speed platforms. Surface-assisted laser desorption ionization mass spectrometry (SALDI-MS) has shown excellent advantages in the high-speed analysis of lipid metabolites. However, the platform in the positive ion mode is more inclined to target a certain class of lipids, leading to the low coverage of lipid detection and limiting its practical translation to clinical applications. Herein, we proposed a dual-mechanism-driven strategy for high-coverage detection of serum lipids on a novel SALDI-MS target, which is a composite nanostructure comprising vertical silicon nanowires (VSiNVVs) decorated with AuNPs and polydopamine (VSiNW-Au-PDA). The performance of laser desorption and ionization on the target can be enhanced by charge-driven desorption coupled with thermal-driven desorption. Simultaneous detection of 236 serum lipids (S/N >= 5) including neutral and polar lipids can be achieved in the positive ion mode. Among these, 107 lipid peaks were successfully identified. When combined with VSiNVV-Au-PDA and VSiNW chips, 479 lipid peaks can be detected in serum samples in positive and negative ion modes, respectively. Based on the platform, serum samples from 57 hepatocellular carcinoma (HCC) patients and 76 healthy controls were analyzed. After data mining, 14 lipids containing different lipid types (TAG, CE, PC) were selected as potential lipidomic biomarkers. With the assistance of an artificial neural network, a diagnostic model with a sensitivity of 92.7% and a specificity of 96% was constructed for HCC diagnosis.