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  • Title: High Throughput Complementary Analysis and Quantitation of Metabolites by MALDI- and Silicon Nanopost Array-Laser Desorption/Ionization-Mass Spectrometry.
    Author: Korte AR, Morris NJ, Vertes A.
    Journal: Anal Chem; 2019 Mar 19; 91(6):3951-3958. PubMed ID: 30786207.
    Abstract:
    Silicon nanopost array (NAPA) structures have been shown to be effective substrates for laser desorption/ionization-mass spectrometry (LDI-MS) and have been used to analyze a variety of samples including peptides, metabolites, drugs, explosives, and intact cells, as well as to image lipids and metabolites in tissue sections. However, no direct comparison has yet been conducted between NAPA-MS and the most commonly used LDI-MS technique, matrix-assisted laser desorption/ionization (MALDI)-MS. In this work, we compare the utility of NAPA-MS to that of MALDI-MS using two common matrices for the analysis of metabolites in cellular extracts and human urine. Considerable complementarity of molecular coverage was observed between the two techniques. Of 178 total metabolites assigned from cellular extracts, 68 were uniquely detected by NAPA-MS and 62 were uniquely detected by MALDI-MS. NAPA-MS was found to provide enhanced coverage of low-molecular weight compounds such as amino acids, whereas MALDI afforded better detection of larger, labile compounds including nucleotides. In the case of urine, a sample largely devoid of higher-mass labile compounds, 88 compounds were uniquely detected by NAPA-MS and 13 by MALDI-MS. NAPA-MS also favored more extensive alkali metal cation adduction relative to MALDI-MS, with the [M + 2Na/K - H]+ species accounting for as much as 97% of the total metabolite ion signal in positive mode. The capability of NAPA-MS for targeted quantitation of endogenous metabolites in urine via addition of isotopically labeled standards was also examined. Both NAPA-MS and MALDI-MS provided quantitative results in good agreement with one another and the concentrations reported in the literature, as well as good sample-to-sample reproducibility (RSD < 10%).
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