Magnetic Liposomes Infused with GPCR-Expressing Cell Membrane for Targeted Extraction Using Minimum Organic Solvent:
Zhihang Zhu1,2, Lancheng Wang1,3, Yan Jia1,3
1China National Narcotics Control Commission-China Pharmaceutical University Joint Laboratory on Key Technologies of Narcotics Control, No. 24 Tongjiaxiang Road, 210009 Nanjing, PR China.
Abstract:
Trace analysis of lipophilic substances in complex environmental, food, or biological matrices has proven to be a challenge, on account of their high susceptibility to adsorption by particulate matter and liquid-solid interfaces. For this purpose, liquid-liquid extraction (LLE) is often employed as the separation method, which uses water-immiscible organic solvents. As an alternative, magnetic solid-phase extraction (MSPE) allows for adsorption, separation, and recovery of analytes from large volumes of aqueous samples with minimum usage of organic solvents. However, the poor selectivity hampers its performance in various scenarios, especially in sewage samples where complicated and unpredictable interference exists, resulting in block of the active adsorption sites of the sorbent. To this end, we propose receptor-affinity MSPE employing magnetic liposomes decorated with cell membranes expressing G-protein-coupled receptor as the sorbents. Application of the novel sorbent CM@Lip@Fe infused with CB1 cannabinoid receptors was demonstrated for the targeted extraction and enrichment of tetrahydrocannabinol from sewage matrix. Thanks to the high affinity and molecular selectivity of the ligand-receptor interactions, a limit of quantitation of 5.17 ng/L was achieved coupled with HPLC-MS/MS in unfiltered raw sewage, featuring minimum usage of organic solvents, fivefold enhanced sensitivity, low sorbent dosage (75 mg/L of sewage), and high efficiency as major advantages over conventional LLE. This work establishes a framework for efficient separation of specific molecules from complex media, thus promising to extend and refine standard LLE as the clean-up procedure for trace analysis.
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