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Updated: Sep 26, 2026

Sample Preparation for Metabolic Profiling using MALDI Mass Spectrometry Imaging
Published on: December 22, 2020
Chemical Derivatization Strategies for Expanding Small Biomolecule Analysis by MALDI-MS
Xintong Hu1, Qinxue Wang1, Yingying Lin1
1School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200241, China.
Abstract:
Small biomolecules provide important information on cellular metabolism, signaling and disease-associated molecular changes. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) enables rapid, high-throughput and spatially resolved molecular analysis, but its application to small biomolecules is often limited by poor ionization, low-mass background interference, ion suppression and insufficient structural information. To address these analytical challenges, this review summarizes chemical derivatization strategies that improve MALDI-MS analysis of small biomolecules. The representative strategies are discussed according to reaction mode and target functional group, including solution-phase, on-target, on-tissue, reactive-matrix-assisted and photo-/in-source approaches, with emphasis on amine-, carbonyl-, carboxyl- and double-bond-containing biomolecules. Chemical derivatization improves MALDI-MS performance by selectively modifying target functional groups, introducing charged or ionizable tags, enhancing molecular discrimination and providing additional structural information. These strategies have expanded the detection and annotation of neuroactive amines, carbonyl compounds, glycans, carboxylic acid metabolites and lipid double-bond isomers, while also extending MALDI-MS analysis to other specific functional groups, natural products and drug-related molecules. Overall, chemical derivatization is an important approach for improving the sensitivity, selectivity, structural annotation and analytical coverage of MALDI-MS-based small biomolecule analysis. Future developments should further address reaction selectivity, spatial fidelity, quantitative reliability and confident identification of derivatization products.
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