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Observation 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.
Pubmed ID: 29853718
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Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.
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