Quantitative Analysis of Structure and Dynamics in AFM Images of Lipid Membranes

Advances in understanding and in multi-disciplinary methodology used to assess lipid regulation of signalling cascades from the cancer cell plasma membrane

The lipid bilayer is a functional component of cells, forming a stable platform for the initiation of key biological processes, including cell signalling. There are distinct changes in the lipid composition of cell membranes during oncogenic transformation resulting in aberrant activation and inactivation of signalling transduction pathways. Studying the role of the cell membrane in cell signalling is challenging, since techniques are often limited to by timescale, resolution, sensitivity, and averaging. To overcome these limitations, combining 'computational', 'wet-lab' and 'semi-dry' approaches offers the best opportunity to resolving complex biological processes involved in membrane organisation. In this review, we highlight analytical tools that have been applied for the study of cell signalling initiation from the cancer cell membranes through computational microscopy, biological assays, and membrane biophysics. The cancer therapeutic potential of extracellular membrane-modulating agents, such as cholesterol-reducing agents is also discussed, as is the need for future collaborative inter-disciplinary research for studying the role of the cell membrane and its components in cancer therapy.

Grain analysis of atomic force microscopy images via persistent homology

Atomic force microscopy (AFM) is an established technique in nanoscale grain analysis due to its accuracy in producing 3-dimensional images. Even though height threshold and watershed algorithms are commonly used to determine the grain size and number of grains, they mostly require image processing that result in the change of topographical features of the surface that generates misleading conclusions. In this study, we use persistent homology, a method of representing topological features, to obtain more accurate information about the granular surfaces from unprocessed AFM images than the conventional methods. The method is also useful as a robust alternative to common parameters describing the topography of the AFM images. Most of these parameters such as arithmetic roughness and root-mean-squared roughness are represented by a single number which results in uncertainty in characterization of different surfaces. Persistent homology provides more precise summary about surface properties than a single parameter.