纳米尺度基底粗糙度阻碍支撑脂质双层中结构域的形成
Nanoscale Substrate Roughness Hinders Domain Formation in Supported Lipid Bilayers
摘要:
支撑脂质双层(SLB)是在固体基质表面形成的模型膜。这种结构使得膜实验上可用于研究其性质的表面敏感技术,包括原子力显微镜(AFM),光学荧光显微镜,石英晶体微量天平(QCM)和X射线/中子反射计,并允许与潜在的生物技术应用,如药物筛选设备的技术集成。实验技术通常决定底物的选择或处理,并且据传闻,某些底物适合于特定的实验,但底物的确切影响尚未得到全面研究。在这里,我们研究了一个简单的模型双层的行为,相分离的各种常用的基板,包括玻璃,云母,硅和石英,有显着不同的结果。在云母上观察到的明显的微米级结构域,与在自由浮动的巨单层囊泡(GUV)中观察到的那些相同,在玻璃和石英上减少到纳米级结构域。逮捕域形成的机制进行了研究,和最有可能的候选人是纳米级的表面粗糙度,作为一个小域的流体动力学运动相分离过程中的阻力。有证据表明,表面的物理化学性质具有介导作用,最有可能是由于表面和双层之间的润滑间隙水层的变化。
英文摘要:
Supported Lipid Bilayers (SLBs) are model membranes formed at solid substrate surfaces. This architecture renders the membrane experimentally accessible to surface sensitive techniques used to study their properties, including Atomic Force Microscopy (AFM), optical fluorescence microscopy, Quartz Crystal Microbalance (QCM) and X-Ray/Neutron Reflectometry, and allows integration with technology for potential biotechnological applications such as drug screening devices. The experimental technique often dictates substrate choice or treatment, and it is anecdotally recognised that certain substrates are suitable for the particular experiment, but the exact influence of the substrate has not been comprehensively investigated. Here, we study the behavior of a simple model bilayer, phase separating on a variety of commonly used substrates, including glass, mica, silicon and quartz, with drastically different results. The distinct micron scale domains observed on mica, identical to those seen in free-floating Giant Unilamellar Vesicles (GUVs), are reduced to nanometer scale domains on glass and quartz. The mechanism for the arrest of domain formation is investigated, and the most likely candidate is nanoscale surface roughness, acting as a drag on the hydrodynamic motion of small domains during phase separation. Evidence was found that the physico-chemical properties of the surface have a mediating effect, most likely due to changes in the lubricating interstitial water layer between surface and bilayer.
机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。