The Devil Is in the Details: What Do We Really Track in Single-Particle Tracking Experiments of Diffusion in Biological Membranes?
Tutkimustuotos › › vertaisarvioitu
|Julkaisu||Journal of Physical Chemistry Letters|
|DOI - pysyväislinkit|
|Tila||Julkaistu - 7 maaliskuuta 2019|
Single-particle tracking (SPT) is an experimental technique that allows one to follow the dynamics of individual molecules in biological membranes with unprecedented precision. Given the importance of lipid and membrane protein diffusion in the formation of nanoscale functional complexes, it is critical to understand what exactly is measured in SPT experiments. To clarify this issue, we employed nanoscale computer simulations designed to match SPT experiments that exploit streptavidin-functionalized Au nanoparticles (AuNPs). The results show that lipid labeling interferes critically with the diffusion process; thus, the diffusion measured in SPT is a far more complex process than what has been assumed. It turns out that the influence of AuNP-based labels on the dynamics of probe lipids includes not only the AuNP-induced viscous drag that is the more significant the larger the NP but, more importantly, also the effects related to the interactions of the streptavidin linker with membrane lipids. Due to these effects, the probe lipid moves in a concerted manner as a complex with the linker protein and numerous unlabeled lipids, which can slow down the motion of the probe by almost an order of magnitude. Furthermore, our simulations show that nonlinker streptavidin tetramers on the AuNP surface are able to interact with the membrane lipids, which could potentially lead to multivalent labeling of the NPs by the probe lipids. Our results further demonstrate that in the submicrosecond time domain the motion of the probe lipid is uncorrelated with the motion of the AuNP, showing that there is a 1 μs limit for the temporal resolution of the SPT technique. However, this limit for the temporal resolution depends on the nanoparticle size and increases rapidly with growing AuNPs. Overall, the results provide a molecular-scale framework to accurately interpret SPT data and to design protocols that minimize label-induced artifacts.