*Simulation Assisted Analysis of FRAP Experiments*
- Flourescence Photobleaching
Modern confocal microscopes provide the ability to perform a variety of fluorescence photobleaching and photoactivation experiments that were previously limited to biophysicists with highly specialized equipment. These experiments allow researchers to explore numerous questions about the localization and dynamic behavior of cellular constituents including
- Diffusive behavior and mobile fraction
- On rates and off rates for binding interactions
Although confocal microscopes have made the experimental method accessible, interpreting and analyzing the data obtained from these experiments remains a difficult undertaking due to the complexity of the cellular milieu and the nature of diffusion itself. Because the exact geometry of the cell and the bleaching experiment dramatically affects the expected time constant obtained from the experiment, there are rarely analytical expressions that can be easily utilized for fitting experimental data from photobleaching experiments. Thus, for each experimental protocol new equations must be derived to appropriately fit the experimental data. Furthermore, in many cases, analytical expressions are only poor approximations of the expected redistributions, and thus are inadequate to obtain accurate quantitative data.
Spatial simulations provide the means to analyze the redistribution of species within any given geometry, and have been used to analyze photobleaching and photoactivation experiments. The VirtualFRAP
tool was designed to take advantage of the power of the Virtual Cell modeling environment to use spatial modeling and simulation within realistic experimental geometries to extract diffusion coefficients and fractional recoveries from photobleaching experiments.
In a photobleaching experiment, the basic experimental design is to perturb the original distribution of a molecular species, and then to assess three basic parameters:
- the extent to which molecules return to the original distribution (mobile fraction, %R)
- The time constant(s) for the return to the original distribution, from which the diffusion coefficient (D) can be obtained.
- The extent to which flow, or directional movement, is involved in the redistribution of the molecules.
Photobleaching is generally currently accomplished using a laser scanning confocal microscope, or custom widefield fluorescence systems. A basic assumption (but not necessarily an accurate assumption) is that the fluorescent version of the protein faithfully mirrors the behavior of the unlabeled protein. The basic experimental protocol is as follows:
- Images of the cells are collected of the initial distribution of the molecule with a highly attenuated laser beam. This defines the initial distribution of the molecule. (The Virtual FRAP tool requires you to have at least one prebleach image.)
- A region of the cell is selected for bleaching, generally by selecting a particular region of interest (ROI) using the software. This region is subjected to a high intensity laser pulse, which photobleaches a significant fraction of the fluorescent molecules within the region of interest. True photobleaching is an irreversible process, so once bleached the molecules do not recover the ability to fluoresce. (Note that some fluorophores can undergo other forms of light induced changes that are reversible; for example light induced transitions of GFP between fluorescent and non-fluorescent states. Proper controls need to be included in the experiment to determine if this occurs.)
- After photobleaching, a time series is collected to determine the mechanism, time constant, and extent of redistribution of the molecules to the original state. When collecting the data, it is important to collect immediately after the bleach pulse, collect with a high enough time resolution (short enough time step) to ensure that the initial regions of the redistribution curve are sufficiently sampled, and to collect long enough that the new steady-state distribution has been reached. Because bleaching during monitoring can become a problem, in many cases it may be necessary to change to a longer timestep in the middle of the time series.
Virtual FRAP is designed to analyze FRAP experiments that collect all of the fluorescence associated with the cell, and where the bleach region does not vary through the Z dimension. In order to achieve this, certain experimental conditions must be met.
- The fluorescence of the entire depth (z-dimension) of the cell must be collected in the image. This is accomplished by working at a low enough numerical aperture and, if you are using a confocal microscope, opening the pinhole aperture in the confocal system such that the full width at half maximal intensity (FWHM) of the collection system is larger than the depth of the cell. If the cell is 10 Ám thick at its highest point, then the FWHM must be at least 10 Ám.
- The geometry of the bleach in Z needs to approximate a column throughout the depth of the specimen. This is controlled solely by the numerical aperture of the objective lens; it is independent of the confocal aperture.
- In order to correct for bleaching during monitoring, you should collect the fluorescence from the entire cell during monitoring. Changes in total cell fluorescence can be used to determine the bleaching rate.
- Virtual FRAP requires you to provide at least 1 prebleach image.
Currently, Virtual FRAP can be used to fit D and %R for either one or two diffusing components of cytosolic (soluble) proteins. It does not analyze lateral diffusion within the plasma membrane.
- Intuitive wizard based work flow to load and manipulate FRAP data
- Able to load file from multiple file resources including more 50 image file formats such as lsm, jpeg, tiff, and vcell .log file.
- Able to load multiple files in a batch run.
- Able to adjust images by zooming in/out, increasing/decreasing contrast, Customized/Auto croping.
- Provides basic drawing tool such as paint, earse and fill.
- Provides advanced functions to assist in creating image masks or to load image masks from other files.
- Initial estimation of diffusion rate
- Uses two simple analytic models (options are Gaussian Spot and Half Cell) for fitting data from the bleached region only
- Results in a single diffusion rate, mobile fraction and bleach while monitoring rate.
- Refined parameter estimation
- Uses full 2D spatiotemporal data.
- Applies full spatial simulation.
- Applies time scaling and data reduction to greatly improve the performance.
- Results in a more precise single diffusion rate, mobile fraction and bleach while monitoring rate or two diffusion rates (one fast + one slow) with related mobile fractions and a bleach while monitoring rate.
Please follow the link to check out the newest VFrap screen shots
To properly run Virtual Frap, your computer has to meet the following basic requirements,
- Microsoft Windows 2000/XP/Vista
- 1GHz Intel/AMD CPU
- 1GB RAM
- 10GB Free Disk Space
- Monitor resolution 1024 by 768 pixels
- Mac OS X 10.x
- 1GHz Intel CPU
- 1GB RAM
- 10GB Free Disk Space
- Monitor resolution 1024 by 768 pixels
Virtual FRAP requires Java Runtime Environment 5.0 or later (If you don't have it, please install it from here
To download Virtual Frap WINDOWS standalone installer, please click here
To download Virtual Frap MAC standalone installer, please click here
NOTE: The Virtual FRAP standalone is based on the development as of November 2011. The Virtual FRAP has been integrated into the Virtual Cell 5.1 and onwards. The future development on the Virtual FRAP will only be available through the Virtual Cell
(main menu "tools -> launch Virtual FRAP").
A lightweight User Guide is available here.
Tips for Running VFRAP
- Apply "Auto Crop" to any raw data to remove unnecessary information in order to speed up the performance.
- Use "Import ROI" If you have mask file (same size as raw data) ready to avoid drawing ROI. The mask file can be an image file or a ".vfrap" file.
- Use "ROI Assistant" to help drawing ROI.
- Choose diffusion with two diffusing components model if your model is not a single diffusion model.
- Evaluate "Confidence Intervals" to check whether parameters leading to a good fit are really identifiable or not.
- Explicit Reactions
- 3D geometry / projections / segmentation
- Membrane diffusion
Center for Cell Analysis and Modeling
University of Connecticut Health Center
400 Farmington Avenue
Farmington, Connecticut 06032-6406
For more information email CCAM