Fluorescent nanoprobe array based on carbon nanodots for qualitative and quantitative determination of biogenic polyamine.

A nanoprobe array based on fluorescent nitrogen-rich carbon dots (N-CDs) and Ag+ was constructed for simultaneous qualitative and quantitative determination of seven kinds of biogenic polyamines (BAs), including tryptamine (Try), histamine (His), putrescine (Put), cadaverine (Cad), spermine (Spm), spermidine (Spd), and agmatine (Agm). Ag+ can specifically bind to the N-CDs and quench the fluorescence of the N-CDs through a static mechanism. BAs further statically quench the fluorescence of the N-CD@Ag+ composite by bridging two Ag+ centers of the N-CD@Ag+. The nanoprobe array was constructed based on the differential fluorescence response arising from the differential binding affinity of various BAs. BAs can be differentiated and analyzed by the nanoprobe array within the concentration range 0.5-500 μM. The preliminary diluted and artificially spiked commercial human serum was utilized to simulate the serum environment for assessing the performance of the nanoprobe array in real samples. The N-CD@Ag+ system can recognize BAs with 100% accuracy in simulated human serum samples. The quantitative determination of BAs - no matter in a one-component system or a three-component system - was also realized by using the N-CD@Ag+ system even in the simulated serum environment. The recovery rates from spiked serum samples were higher 99%, and the relative standard deviation (RSD) was less than 3%. Based on the excellent multi-BA determination performance, a BA-related disease model about cerebral ischemia was constructed. Healthy cases as well as mild, moderate, and severe cerebral ischemia cases can be well identified from the disease model based on the N-CD@Ag+ nanoprobe array. Schematic representation of fluorescent nanoprobe array constructed by carbon nanodots (N-CDs) and Ag+ for qualitative and quantitative analyses of biogenic polyamines (BAs) and diagnosis of cerebral ischemia (CI) through linear discriminant analysis (LDA) and support vector machine (SVM).