Abstract
Cells rely on versatile diffusion dynamics in their plasma membrane. Quantification of this often heterogeneous diffusion is essential to the understanding of cell regulation and function. Yet such measurements remain a major challenge in cell biology, usually due to low sampling throughput, a necessity for dedicated equipment, sophisticated fluorescent label strategies, and limited sensitivity. Here, we introduce a robust, broadly applicable statistical analysis pipeline for large scanning fluorescence correlation spectroscopy data sets, which uncovers the nanoscale heterogeneity of the plasma membrane in living cells by differentiating free from hindered diffusion modes of fluorescent lipid and protein analogues.
Highlights
IntroductionQuantification of molecule diffusion dynamics in the plasma membrane of living cells is essential for the understanding of their function.[1−3] Most fluorescence-based methodologies for examining the heterogeneity of lipid and protein diffusion dynamics, such as single-particle tracking,[4,5] fluorescence recovery after photobleaching,[6,7] or fluorescence correlation spectroscopy (FCS)[1,2] and their advancements, especially super-resolution stimulated emission depletion (STED) FCS,[8−10] either necessitate specialized hardware, sophisticated fluorescence labeling, or constrain themselves to quantification with limited spatial and temporal sensitivity
Revisiting well-established experiments, we demonstrated how this statistical analysis pipeline of scanning fluorescence correlation spectroscopy (FCS) data systematically and robustly differentiates free from hindered diffusion
Whereas single-LogNorm quantifications allowed the determination of free Brownian diffusion, double-LogNorm or LogNorm and exponential analysis enabled the detection of hindered trapped and hop diffusion dynamics
Summary
Quantification of molecule diffusion dynamics in the plasma membrane of living cells is essential for the understanding of their function.[1−3] Most fluorescence-based methodologies for examining the heterogeneity of lipid and protein diffusion dynamics, such as single-particle tracking,[4,5] fluorescence recovery after photobleaching,[6,7] or fluorescence correlation spectroscopy (FCS)[1,2] and their advancements, especially super-resolution stimulated emission depletion (STED) FCS,[8−10] either necessitate specialized hardware, sophisticated fluorescence labeling, or constrain themselves to quantification with limited spatial and temporal sensitivity. The protocol allows a systematic mathematical characterization of the distribution of sFCS diffusion data, involving fitting analysis and the calculation of the biological-relevant parameters, and a quantitative evaluation of the results using weighted fitting residuals and maximum likelihood estimations Applying this framework to the free and non-Brownian diffusion dynamics of lipids and glycosylphosphatidylinositol (GPI)anchored proteins demonstrates the power of the method and allows a comprehensive characterization of well-known molecular particle diffusion in computer-simulated data sets, supported lipid bilayers (SLBs), and the plasma membrane of living cells. Our strategy comprised a third step, wherein we computed and analyzed the transit time histograms to extract further information on the diffusion modes (Figure 1a), exploiting the statistical power of large ensemble measurements independent of local spatial heterogeneities
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