Advances in nanomaterials based laser desorption/ionization mass spectrometry for metabolic analysis

被引:1
|
作者
Yang, Chenjie [1 ]
Ji, Shuangshuang [5 ]
Shen, Shun [3 ]
Yu, Hailong [2 ]
Deng, Chunhui [1 ,4 ]
机构
[1] Fudan Univ, Inst Biomed Sci, Ctr Med Res & Innovat, Shanghai Pudong Hosp,Pudong Med Ctr,Dept Chem, Shanghai 201399, Peoples R China
[2] Second Mil Med Univ, Naval Med Univ, Shanghai Changhai Hosp, Dept Urol, Shanghai 200433, Peoples R China
[3] Fudan Univ, Shanghai Pudong Hosp, Ctr Med Res & Innovat, Pudong Med Ctr, Shanghai 201399, Peoples R China
[4] Shanghai Ocean Univ, Coll Food Sci & Technol, Shanghai 201306, Peoples R China
[5] Jiangsu Maiyuan Biol Technol Co Ltd, Yangzhou 225000, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Nanomaterials; Laser desorption/ionization mass spectrometry; Metabolites; Diagnosis; MSI; COVALENT-ORGANIC FRAMEWORK; ION DESORPTION EFFICIENCY; INTERNAL ENERGY-TRANSFER; GOLD NANOPARTICLES; SMALL MOLECULES; IONIZATION; MATRIX; MALDI; SERUM; MS;
D O I
10.1016/j.trac.2025.118190
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Laser desorption/ionization mass spectrometry (LDI-MS) has emerged as a promising analytical tool for biomolecules analysis due to its high throughput, fast analysis speed, and simple sample preparation. The emerging of nanomaterials substrates make it a pivotal technique for metabolic analysis which overcome the limitations of traditional organic matrices by providing a non-interference background, enhanced desorption/ionization efficiency, and superior thermal and chemical stability, and ease of functionalization. In recent years, a variety of nanomaterials including noble metals, silicon/carbon-based nanomaterials, metal oxides, metal/covalentorganic frameworks, and hybrids nanomaterials have been explored. This review, with a focus on an overview of nanomaterial substrates, highlights the mechanism of LDI-MS, its applications in biofluids/exosomes metabolic analysis for disease diagnosis, and MALDI-MS imaging. Furthermore, the integration of advanced machine learning algorithms and metabolic fingerprints has significantly improved the diagnostic performance in specificity, sensitivity, accuracy, etc. Finally, we discussed the prospects of nanomaterial-based LDI-MS for metabolic analysis, emphasizing both its potential and challenges in clinical applications.
引用
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页数:28
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