Evaluating the interaction of human serum albumin (HSA) and 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) liposomes in different aqueous environments using anisotropy resolved multi-dimensional emission spectroscopy (ARMES)

Abstract

Studying the interaction between plasma proteins and liposomes is critical, particularly for their use as drug delivery systems. Here, the efficacy of anisotropy resolved multidimensional emission spectroscopy (ARMES) for investigating the interaction of human serum albumin (HSA) with liposomes was explored and compared to conventional spectroscopic techniques. Dynamic Light Scattering (DLS) and absorbance spectroscopy (with Multivariate Curve Resolution (MCR) modeling) indicated that the highest degree of liposome rupturing, and aggregation occurred in water, with less in ammonium bicarbonate buffer (ABC) and phosphate buffered saline (PBS). Fluorescence emission spectra of HSA-liposome mixtures revealed significant hypsochromic shifts for water and ABC, but much less in PBS, where the data suggests a non-penetrating protein layer was formed. Average fluorescence lifetimes decreased upon liposome interaction in water (6.2→5.2 ns) and ABC buffer (6.3→5.6 ns) but increased slightly for PBS (5.6→5.8 ns). ARMES using polarized Total Synchronous Fluorescence Scan measurements with parallel factor (PARAFAC) analysis resolved intrinsic HSA fluorescence into two components for interactions in water and ABC buffer, but only one component for PBS. These components, in water and ABC buffer, corresponded to two different HSA populations, one blue-shifted and penetrating the liposomes (λ_ex/em = ~ 280/320 nm) and a second, similar to free HSA in solution (λ_ex/em = ~ 282/356 nm). PARAFAC scores for water and ABC buffer suggested that a large proportion of HSA interacted in an end on configuration. ARMES provides a new way for investigating protein-liposome interactions that exploits the full intrinsic emission space of the protein and thus avoids the use of extrinsic labels. The use of multivariate data analysis provided a comprehensive and structured framework to extract a variety of useful information (resolving different fluorescent species, quantifying their signal contribution, and extracting light scatter signals) all of which can be used to discriminate between interaction mechanisms.

Keywords: Anisotropy; Chemometrics; Fluorescence; Liposome; Modeling; Proteins.