Characterization of Cell Boundary and Confocal Effects Improves Quantitative FRAP Analysis

Abstract

Fluorescence recovery after photobleaching (FRAP) is an important tool used by cell biologists to study the diffusion and binding kinetics of vesicles, proteins, and other molecules in the cytoplasm, nucleus, or cell membrane. Although many FRAP models have been developed over the past decades, the influence of the complex boundaries of 3D cellular geometries on the recovery curves, in conjunction with regions of interest and optical effects (imaging, photobleaching, photoswitching, and scanning), has not been well studied. Here, we developed a 3D computational model of the FRAP process that incorporates particle diffusion, cell boundary effects, and the optical properties of the scanning confocal microscope, and validated this model using the tip-growing cells of Physcomitrella patens. We then show how these cell boundary and optical effects confound the interpretation of FRAP recovery curves, including the number of dynamic states of a given fluorophore, in a wide range of cellular geometries-both in two and three dimensions-namely nuclei, filopodia, and lamellipodia of mammalian cells, and in cell types such as the budding yeast, Saccharomyces pombe, and tip-growing plant cells. We explored the performance of existing analytical and algorithmic FRAP models in these various cellular geometries, and determined that the VCell VirtualFRAP tool provides the best accuracy to measure diffusion coefficients. Our computational model is not limited only to these cells types, but can easily be extended to other cellular geometries via the graphical Java-based application we also provide. This particle-based simulation-called the Digital Confocal Microscopy Suite or DCMS-can also perform fluorescence dynamics assays, such as number and brightness, fluorescence correlation spectroscopy, and raster image correlation spectroscopy, and could help shape the way these techniques are interpreted.

In vivo interactions between myosin XI, vesicles and filamentous actin are fast and transient in Physcomitrella patens

“…Without F-actin, myosin XIa recovered from the cell shank towards the cell tip (Fig. 2B,C and Movie 3), in a pattern that is consistent with simple diffusion (Bibeau et al, 2018;Kingsley et al, 2018). This indicates that myosin XIa exhibits actin-dependent apical recovery in the absence of latrunculin B treatment.…”

“…Similar recovery curves were found by applying these best-fit parameters to the two-dimensional infinite-boundary reaction-diffusion model from Kang et al (Fig. 2F) (Kang et al, 2010) and by using the Digital Confocal Microscopy Suite (DCMS) with reaction-diffusion (Kingsley et al, 2018) (Fig. 2F).…”

“…1C). This spatial recovery profile is not consistent with diffusionwhich we have previously characterized in detailspecifically when considering the effect of boundaries and cell shape (Bibeau et al, 2018;Kingsley et al, 2018). The pattern of recovery is inconsistent with simple diffusion, which should be from the shank towards the cell tip.…”

“…Since myosin V is found in the open or closed configuration, and the type of configuration modify the Stokes radius of the protein and, consequently, its diffusion coefficient, it is possible that myosin XI exhibits two unique diffusion coefficients. Given the known changes for myosin V hydrodynamic radius measured by analytical centrifugation (Krementsov et al, 2004;Li et al, 2004;Wang et al, 2004) and the known viscosity of the moss cytoplasm (Kingsley et al, 2018), we estimate the myosin XI diffusion coefficient to be between 2.3 and 3.4 μm 2 /s, for the open and closed configuration, respectively. By using the diffusion coefficients for myosin XI and vesicle (Bibeau et al, 2018), we modeled the interaction as a reversible bimolecular reaction, i.e.…”

“…On the basis of these assumptions, we can now model the process as a reaction-diffusion system between two-species (Kang and Kenworthy, 2008;Kang et al, 2010) (Kang and Kenworthy, 2008;Kang et al, 2010;Sprague and McNally, 2005), can be solved analytically for simple boundary conditions, no analytical solution exists for our cell shape. We have previously shown, through extensive experimentation and simulations of FRAP, that cell shape is important to model FRAP (Bibeau et al, 2018;Kingsley et al, 2018). Here, we used the finite element modeling software Comsol (Comsol Inc, Stockholm, Sweden) to simulate FRAP in the presence of myosin vesicle binding ( Fig.…”