journal article·2023-12

Controlling molecular self-assembly of inverse-phosphocholine lipids at oxide interfaces with divalent cations

Tun Naw Sut, Soohyun Park, Joshua A. Jackman, Nam-Joon Cho

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Abstract

Sub-100 nm vesicles composed of inverse-phosphocholine (CP) lipids are versatile building blocks for interfacing with inorganic material surfaces and demonstrate unique chemical functionalities due to outward-facing phosphate groups. Compared to typical phospholipids, the surface presentation of CP lipids has enabled new covalent possibilities like click chemistry for drug delivery and imaging applications while utilizing this phosphate chemistry in noncovalent self-assembly contexts remains unexplored. Herein, we report that the presence of phosphate-binding divalent cations such as Ca2+ and Mg2+ can dramatically shift the self-assembly behavior of CP lipid vesicles on TiO2 and SiO2 surfaces, inhibiting spontaneous supported lipid bilayer (SLB) formation on TiO2 in cases where it typically occurs and promoting SLB formation on SiO2 in cases where lipid adsorption is otherwise negligible. Quartz crystal microbalance-dissipation (QCM-D) measurements were performed to track the corresponding adsorption kinetics and self-assembly outcomes. Importantly, an optimal combination of CP lipid fraction in vesicles and specific divalent cation type was also identified, which enabled SLB formation on both TiO2 and SiO2 surfaces under equivalent conditions. SLB fluidity on SiO2 surfaces was additionally confirmed by fluorescence recovery after photobleaching (FRAP) experiments. We rationalize these molecular-level phenomena in terms of vesicle stability and lipid-surface interactions, demonstrating a versatile approach to control CP lipid self-assembly with divalent cations.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Lipid Membrane Structure and Behavior · Force Microscopy Techniques and Applications · Analytical Chemistry and Sensors

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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