Carbon Dots and 9AA as a Binary Matrix for the Detection of Small Molecules by Matrix-Assisted Laser
Yongli Chen1, Dan Gao2,3, Hangrui Bai4,5
1Key Laboratory of Chemical Genomics, School of Chemical Biology and Biotechnology, Graduate School at Shenzhen, Peking University, Shenzhen, 518055, China.
Abstract:
Application of matrix-assisted laser-desorption/ionization mass spectrometry (MALDI MS) to analyze small molecules have some limitations, due to the inhomogeneous analyte/matrix co-crystallization and interference of matrix-related peaks in low m/z region. In this work, carbon dots (CDs) were for the first time applied as a binary matrix with 9-Aminoacridine (9AA) in MALDI MS for small molecules analysis. By 9AA/CDs assisted desorption/ionization (D/I) process, a wide range of small molecules, including nucleosides, amino acids, oligosaccharides, peptides, and anticancer drugs with a higher sensitivity were demonstrated in the positive ion mode. A detection limit down to 5 fmol was achieved for cytidine. 9AA/CDs matrix also exhibited excellent reproducibility compared with 9AA matrix. Moreover, by exploring the ionization mechanism of the matrix, the influence factors might be attributed to the four parts: (1) the strong UV absorption of 9AA/CDs due to their π-conjugated network; (2) the carboxyl groups modified on the CDs surface act as protonation sites for proton transfer in positive ion mode; (3) the thin layer crystal of 9AA/CDs could reach a high surface temperature more easily and lower transfer energy for LDI MS; (4) CDs could serve as a matrix additive to suppress 9AA ionization. Furthermore, this matrix was allowed for the analysis of glucose as well as nucleosides in human urine, and the level of cytidine was quantified with a linear range of 0.05-5 mM (R(2) > 0.99). Therefore, the 9AA/CDs matrix was proven to be an effective MALDI matrix for the analysis of small molecules with improved sensitivity and reproducibility. This work provides an alternative solution for small molecules detection that can be further used in complex samples analysis. Graphical Abstract ᅟ.
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