Coupling Isotope-Coded Derivatization with Fragment-Imprinted Selective Recognition for Comprehensive Analysis of Temporal Dynamics in the Serum Neurotransmitters' Metabolic Network by UHPLC-MS/MS.

Serum neurotransmitters (NTs) metabolic network plays a crucial role in bone metabolism. However, due to low concentration, poor chromatographic retention, and limited sensitivity of conventional detection methods, the metabolic characteristics and regulatory mechanisms of the NT metabolic network remain unclear. In this study, a highly specific, sensitive, and accurate strategy integrating d0/d6 DnsCl-based isotope-coded derivatization (ICD) with fragment-imprinted magnetic molecularly imprinted polymers (MMIPs) was established, which enables reliable quantitative determination of 22 NTs (such as HVA, 3-MT, Trpm, Tyr, NAS) in serum. The ICD strategy enhanced chromatographic separation, ionization efficiency, and enabled accurate quantification. The detection sensitivity was enhanced 5.50-346-fold compared with nonderivatized methods. In addition, 4-VP was selected as the optimal functional monomer via molecular docking, and DFT calculations further indicated that the DnsA/4-VP complex is stabilized through the synergistic effects of hydrogen bonding, π-π stacking, and van der Waals forces. The synthesized MMIPs efficiently enriched labeled NTs, with good selectivity (imprinting factors 1.46-2.28), remarkable adsorption capacity (85.0 mg·g-1), and excellent regeneration ability (8 times). This method was further applied to serum samples of ovariectomized postmenopausal osteoporosis rats, and 2 dysregulated metabolic pathways (the Trp metabolic disorder and the Phe/Tyr metabolic disorder) were identified. Longitudinal analysis further identified 7 potential time-dependent biomarkers (such as NAS, Tyr, GABA) associated with bone metabolism. In conclusion, this study developed an integrated approach combining the d0/d6 DnsCl ICD strategy with fragment-imprinted MMIPs to profile the serum NT metabolic network, demonstrating promising application prospects for submetabolome analysis in complex biological matrices.