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- ItemComplementary studies of lipid membrane dynamics using iSCAT and super-resolved fluorescence correlation spectroscopy(Bristol : IOP Publ., 2018) Reina, Francesco; Galiani, Silvia; Shrestha, Dilip; Sezgin, Erdinc; de Wit, Gabrielle; Cole, Daniel; Christoffer Lagerholm, B.; Kukura, Philipp; Eggeling, ChristianObservation techniques with high spatial and temporal resolution, such as single-particle tracking based on interferometric scattering (iSCAT) microscopy, and fluorescence correlation spectroscopy applied on a super-resolution STED microscope (STED-FCS), have revealed new insights of the molecular organization of membranes. While delivering complementary information, there are still distinct differences between these techniques, most prominently the use of fluorescent dye tagged probes for STED-FCS and a need for larger scattering gold nanoparticle tags for iSCAT. In this work, we have used lipid analogues tagged with a hybrid fluorescent tag–gold nanoparticle construct, to directly compare the results from STED-FCS and iSCAT measurements of phospholipid diffusion on a homogeneous supported lipid bilayer (SLB). These comparative measurements showed that while the mode of diffusion remained free, at least at the spatial (>40 nm) and temporal (50 ⩽ t ⩽ 100 ms) scales probed, the diffussion coefficient was reduced by 20- to 60-fold when tagging with 20 and 40 nm large gold particles as compared to when using dye tagged lipid analogues. These FCS measurements of hybrid fluorescent tag–gold nanoparticle labeled lipids also revealed that commercially supplied streptavidin-coated gold nanoparticles contain large quantities of free streptavidin. Finally, the values of apparent diffusion coefficients obtained by STED-FCS and iSCAT differed by a factor of 2–3 across the techniques, while relative differences in mobility between different species of lipid analogues considered were identical in both approaches. In conclusion, our experiments reveal that large and potentially cross-linking scattering tags introduce a significant slow-down in diffusion on SLBs but no additional bias, and our labeling approach creates a new way of exploiting complementary information from STED-FCS and iSCAT measurements.
- ItemTRAIT2D: a Software for Quantitative Analysis of Single Particle Diffusion Data(London : F1000 Research Ltd, 2021) Reina, Francesco; Wigg, John M.A.; Dmitrieva, Mariia; Lefebvre, Joël; Rittscher, Jens; Eggeling, ChristianSingle particle tracking (SPT) is one of the most widely used tools in optical microscopy to evaluate particle mobility in a variety of situations, including cellular and model membrane dynamics. Recent technological developments, such as Interferometric Scattering microscopy, have allowed recording of long, uninterrupted single particle trajectories at kilohertz framerates. The resulting data, where particles are continuously detected and do not displace much between observations, thereby do not require complex linking algorithms. Moreover, while these measurements offer more details into the short-term diffusion behaviour of the tracked particles, they are also subject to the influence of localisation uncertainties, which are often underestimated by conventional analysis pipelines. we thus developed a Python library, under the name of TRAIT2D (Tracking Analysis Toolbox – 2D version), in order to track particle diffusion at high sampling rates, and analyse the resulting trajectories with an innovative approach. The data analysis pipeline introduced is more localisation-uncertainty aware, and also selects the most appropriate diffusion model for the data provided on a statistical basis. A trajectory simulation platform also allows the user to handily generate trajectories and even synthetic time-lapses to test alternative tracking algorithms and data analysis approaches. A high degree of customisation for the analysis pipeline, for example with the introduction of different diffusion modes, is possible from the source code. Finally, the presence of graphical user interfaces lowers the access barrier for users with little to no programming experience.