Eu3+ Complex-Protein Co-Crystals as Smart Sensors of Biologically Relevant Cations in Blood
Miao Qiu1, Min Zhang1, Runnian Han1,2
1Institute of Hybrid Materials, National Center of International Research for Hybrid Materials Technology, National Base of International Science & Technology Cooperation, College of Materials Science and Engineering, Qingdao University, Qingdao 266071, China.
Abstract:
This study aimed to develop a novel fluorescent sensor based on Eu3+ complex-doped protein crystal (EC-PC) for the efficient detection of metal ions in blood. By meticulously controlling the crystallization and annealing conditions in the co-crystallization strategy, the crystal growth processes were optimized to obtain doped Eu3+ complex-co-protein crystalline (EC-PC) structures. Thus, through co-crystallization of hen egg white lysozyme (HEWL) as a model protein and Eu3+ complex as fluorescent center, we successfully prepared Eu3+ complex-doped-HEWL co-crystals (EC-HC) with excellent fluorescent properties. Further treatment with 4% glutaraldehyde cross-linking enhanced the structural stability of the co-crystals. Moreover, the characteristic of sensitive, selective quenching of EC-PC fluorescence by biologically relevant cations, such as Cu2+, Zn2+, Mg2+, Ca2+ and Fe3+ ions, set up a smart sensing system in blood. For example, the fluorescence intensity of the crystals at 610 nm, as measured by a UV-visible spectrophotometer, decreases dose-dependently with the concentration of copper ions, thereby validating the sensor's high sensitivity to copper ion detection. Significantly, we also found that this hybrid protein-based sensor did not induce hemolysis, at various volume concentrations, confirming good anticoagulation in blood. This research not only provides a new perspective on the application of Eu3+ complex-doped protein crystals in the field of biosensing but also offers a new strategy for the detection of biologically relevant cations in blood. Future work will focus on further optimizing the sensor's performance and exploring its potential applications in clinical sample analysis.
