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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.
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页数:28
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