Atomic force microscopy for quantitative understanding of peptide-induced lipid bilayer remodeling

• Published in: Methods (San Diego, Calif.) Vol. 197; pp. 20 - 29
• Main Authors: Schaefer, K.G., Pittman, A.E., Barrera, F.N., King, G.M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.01.2022
• Subjects: AFM; Bayes Theorem; Lipid Bilayers - chemistry; Microscopy, Atomic Force - methods; Peptides - chemistry; Pore forming; Scanning probe; Single Molecule Imaging; SLB; Supported lipid bilayer; AFM; SLB; Pore forming; Supported lipid bilayer; Scanning probe
• ISSN: 1046-2023; 1095-9130
• DOI: 10.1016/j.ymeth.2020.10.014

•Membrane-permeabilizing peptides exhibit distinct remodeling modes.•AFM directly visualizes lipid bilayer remodeling in fluid.•Methods to achieve high precision AFM data & robust statistical analysis presented.•Localized pore-like voids & dispersed membrane-thinned regions detected.•Dynamics and colocalization of distinct remodeling modes can be studied. A number of peptides are known to bind lipid bilayer membranes and cause these natural barriers to leak in an uncontrolled manner. Though membrane permeabilizing peptides play critical roles in cellular activity and may have promising future applications in the therapeutic arena, significant questions remain about their mechanisms of action. The atomic force microscope (AFM) is a single molecule imaging tool capable of addressing lipid bilayers in near-native fluid conditions. The apparatus complements traditional assays by providing local topographic maps of bilayer remodeling induced by membrane permeabilizing peptides. The information garnered from the AFM includes direct visualization and statistical analyses of distinct bilayer remodeling modes such as highly localized pore-like voids in the bilayer and dispersed thinned membrane regions. Colocalization of distinct remodeling modes can be studied. Here we examine recent work in the field and outline methods used to achieve precise AFM image data. Experimental challenges and common pitfalls are discussed as well as techniques for unbiased analysis including the Hessian blob detection algorithm, bootstrapping, and the Bayesian information criterion. When coupled with robust statistical analyses, high precision AFM data is poised to advance understanding of an important family of peptides that cause poration of membrane bilayers.