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Title: | A nanobar-supported lipid bilayer system for the study of membrane curvature sensing proteins in vitro | Authors: | Miao, Xinwen Wu, Jiawei Zhao, Wenting |
Keywords: | Science::Chemistry | Issue Date: | 2022 | Source: | Miao, X., Wu, J. & Zhao, W. (2022). A nanobar-supported lipid bilayer system for the study of membrane curvature sensing proteins in vitro. Journal of Visualized Experiments (JoVE), November 2022(189), e64340-. https://dx.doi.org/10.3791/64340 | Project: | RG112/20 RG95/21 MOE-T2EP30220-0009 NTU-SUG |
Journal: | Journal of Visualized Experiments (JoVE) | Abstract: | Membrane curvature plays important roles in various essential processes of cells, such as cell migration, cell division, and vesicle trafficking. It is not only passively generated by cellular activities, but also actively regulates protein interactions and is involved in many intracellular signaling. Thus, it is of great value to examine the role of membrane curvature in regulating the distribution and dynamics of proteins and lipids. Recently, many techniques have been developed to study the relationship between the curved membrane and protein in vitro. Compared to traditional techniques, the newly developed nanobar-supported lipid bilayer (SLB) offers both high-throughput and better accuracy in membrane curvature generation by forming a continuous lipid bilayer on patterned arrays of nanobars with a pre-defined membrane curvature and local flat control. Both the lipid fluidity and protein sensitivity to curved membranes can be quantitatively characterized using fluorescence microscopy imaging. Here, a detailed procedure on how to form a SLB on fabricated glass surfaces containing nanobar arrays and the characterization of curvature-sensitive proteins on such SLB are introduced. In addition, protocols for nanochip reusing and image processing are covered. Beyond the nanobar-SLB, this protocol is readily applicable to all types of nanostructured glass chips for curvature sensing studies. | URI: | https://hdl.handle.net/10356/168768 | ISSN: | 1940-087X | DOI: | 10.3791/64340 | Schools: | School of Chemistry, Chemical Engineering and Biotechnology | Research Centres: | Institute for Digital Molecular Analytics and Science | Rights: | © 2022 JoVE Journal of Visualized Experiments. All rights reserved. This paper was published in Journal of Visualized Experiments (JoVE) and is made available with permission of JoVE Journal of Visualized Experiments. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | CCEB Journal Articles |
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