α-Synuclein senses lipid packing defects and induces lateral expansion of lipids leading to membrane remodeling.

There is increasing evidence for the involvement of lipid membranes in both the functional and pathological properties of α-synuclein (α-Syn). Despite many investigations to characterize the binding of α-Syn to membranes, there is still a lack of understanding of the binding mode linking the properties of lipid membranes to α-Syn insertion into these dynamic structures. Using a combination of an optical biosensing technique and in situ atomic force microscopy, we show that the binding strength of α-Syn is related to the specificity of the lipid environment (the lipid chemistry and steric properties within a bilayer structure) and to the ability of the membranes to accommodate and remodel upon the interaction of α-Syn with lipid membranes. We show that this interaction results in the insertion of α-Syn into the region of the headgroups, inducing a lateral expansion of lipid molecules that can progress to further bilayer remodeling, such as membrane thinning and expansion of lipids out of the membrane plane. We provide new insights into the affinity of α-Syn for lipid packing defects found in vesicles of high curvature and in planar membranes with cone-shaped lipids and suggest a comprehensive model of the interaction between α-Syn and lipid bilayers. The ability of α-Syn to sense lipid packing defects and to remodel membrane structure supports its proposed role in vesicle trafficking.

A Bayesian approach to tracking in single molecule fluorescence microscopy

Using fluorescence microscopy with single molecule sensitivity it is now possible to follow the movement of individual fluorophore tagged molecules such as proteins and lipids in the cell membrane with nanometer precision. These experiments are important as they allow many key biological processes on the cell membrane and in the cell, such as transcription, translation and DNA replication, to be studied at new levels of detail. Computerized microscopes generate sequences of images (in the order of tens to hundreds) of the molecules diffusing and one of the challenges is to track these molecules to obtain reliable statistics such as speed distributions, diffusion patterns, intracellular positioning, etc. The data set is challenging because the molecules are tagged with a single or small number of fluorophores, which makes it difficult to distinguish them from the background, the fluorophore bleaches irreversibly over time, the number of tagged molecules are unknown and there is occasional loss of signal from the tagged molecules. All these factors make accurate tracking over long trajectories difficult. Also the experiments are technically difficulty to conduct and thus there is a pressing need to develop better algorithms to extract the maximum information from the data. For this purpose we propose a Bayesian approach and apply our technique to synthetic and a real experimental data set.