• photobleaching;
  • photobleaching fluorescence resonance energy transfer;
  • LabVIEW;
  • computer program


  1. Top of page
  2. Abstract


The photobleaching fluorescence resonance energy transfer (pbFRET) technique is a spectroscopic method to measure proximity relations between fluorescently labeled macromolecules using digital imaging microscopy. To calculate the energy transfer values one has to determine the bleaching time constants in pixel-by-pixel fashion from the image series recorded on the donor-only and donor and acceptor double-labeled samples. Because of the large number of pixels and the time-consuming calculations, this procedure should be assisted by powerful image data processing software. There is no commercially available software that is able to fulfill these requirements.


New evaluation software was developed to analyze pbFRET data for Windows platform in National Instrument LabVIEW 6.1. This development environment contains a mathematical virtual instrument package, in which the Levenberg-Marquardt routine is also included. As a reference experiment, FRET efficiency between the two chains (β2-microglobulin and heavy chain) of major histocompatibility complex (MHC) class I glycoproteins and FRET between MHC I and MHC II molecules were determined in the plasma membrane of JY, human B lymphoma cells.


The bleaching time constants calculated on pixel-by-pixel basis can be displayed as a color-coded map or as a histogram from raw image format.


In this report we introduce a new version of pbFRET analysis and data processing software that is able to generate a full analysis pattern of donor photobleaching image series under various conditions. © 2005 International Society for Analytical Cytology

Investigation of protein-protein interactions is important in understanding the structure-function relations in living cells. Fluorescence resonance energy transfer (FRET) techniques are excellent tools for determining association patterns of transmembrane proteins at the cell surface. With the help of FRET, molecular dimensions and molecular proximity can be measured and determined in functioning, live cells and provide information that would be hindered by the implicit aggregating nature of other classic approaches.

The theory of FRET was first developed by Theodor Förster (1). The FRET process is a dipole-dipole interaction in which an excited donor fluorophore transfers its energy to an acceptor molecule in close vicinity (1–10 nm) in a nonradiative way. The main application of FRET as a spectroscopic ruler (2) is based on the fact that the rate of energy transfer depends on the inverse sixth power of the distance between the two interacting molecules. The common way to measure FRET processes is by the decrease in donor fluorescence in the presence of an acceptor, which can be accompanied by an increase of fluorescence of the acceptor (if the acceptor is a fluorescent molecule). In recent years numerous FRET-based techniques have been developed for flow cytometry (3–5) and microscopy (6–10). Several biological structures have been successfully investigated by these methods: receptors involved in immune response (11–13), growth factor receptors on tumor cells (14–16), determination of lipid microdomain structures (17, 18), and protein conformation (19, 20).

Fluorescence-activated cell sorting and analysis or flow cytometry is one solution for high-speed quantitative analysis of cell. With this technique cell populations can be studied and evaluated relatively fast and the energy transfer efficiency values can be determined on a cell-by-cell basis. Because of the large number of cells, it can provide relatively good statistics (21), but the energy transfer values are averaged for each cell, so no information is available about the diversity of these values on the cell surface of single cells.

Different fluorescent microscopic techniques have become very popular during the last 15 years for studying various components of the cell. Parallel to the evolution of these techniques has been the realization of the importance of the lateral distribution and topology of the different cell surface components. There are several spectroscopic methods adapted and applied together with microscopic techniques. For the imaging techniques the application of FRET for inspection of receptor associations provides an extra possibility for surpassing the actual diffraction limited resolution of the light microscope.

Upon continuous excitation of the donor molecules, their fluorescence intensity decreases due to photobleaching, an irreversible, oxygen-dependent photochemical degradation process. For a double-labeled sample, i.e., with donor and acceptor molecules present, FRET between the fluorophores opens an additional relaxation pathway for the excited donor molecules. Thus the decline in the number of donor excited states by photobleaching takes longer. This method was designated “donor photobleaching FRET (pbFRET)” at the time of its introduction (22–24).

Because of the uneven distribution of cell surface components, there are several regions at the labeled surface that demonstrate different fluorescence intensities. These spatial heterogeneities can also be very important to answer relevant biological questions (25–28).

To determine the transfer efficiency (E), two series of images should be recorded, one from the sample labeled solely by donor and one from a sample double-labeled by donor and acceptor. A convenient means for generating the “acceptor-alone” reference state is to photobleach the acceptor in an appropriate region of the sample and convenient combined protocols for donor and acceptor photobleaching have been implemented (29).

The photobleaching decay curves can be obtained from the image series by fitting exponential function to each pixel series (22–24). At the time when the initial fluorescence intensity decreases to 1/e, part of its initial value is defined as the photobleaching time constant. The energy transfer efficiency can be calculated from the bleaching time constants of the donor-only labeled and donor-acceptor double-labeled samples.

Several commercially available programs have options to record and display bleaching image series and to generate bleaching curves from the recorded images, but they do not support curve fitting or only single curve fitting is possible (30). Because of the large number of pixels (in general 512 × 512 pixel image size) and the time-consuming calculations, this procedure should be assisted by a powerful image processing software.

We have developed a program to calculate FRET efficiency from raw image file series in Windows 9x and Windows XP operating systems. The program automatically generates the bleaching decay curves from the image series and fits them with optional single, double, or triple exponential functions. The bleaching time constant values can be displayed and saved in color-coded images (“tau map”) and histograms. The program also calculates statistical values of histograms to determine the mean bleaching time constant value and its standard deviation. We provide a short description of the theoretical background of pbFRET and the functionality of the software in addition to a representative experiment to demonstrate how the program works.


  1. Top of page
  2. Abstract

Fluorescence Resonance Energy Transfer

In a fluorescence process, light of intensity I0 excites the donor fluorophore from the ground state D0 at a rate kex to one of the electronic excited levels D*. The excited donor molecule has several ways for relaxation, including the radiative fluorescence (kfl) and the nonradiative internal conversion (kic) and intersystem crossing (kisc), with overall decay rate constant krel. Introducing a suitable acceptor, a new decay path is opened by the nonradiative resonance energy transfer with the rate ket. Förster's theory shows that the rate of energy transfer (ket) for a single donor-acceptor pair is given by the following formula:

  • equation image(1)

where R0 is the Förster distance that corresponds to an energy transfer efficiency probability of 0.5 for a given donor-acceptor pair. It depends on the index of refraction of the conveying medium, the fluorescence quantum yield of the donor in the absence of energy transfer, the mutual orientation of the transition dipole moment of the donor and acceptor molecules, and the overlap integral of the donor emission and acceptor excitation spectra. The energy transfer efficiency E is defined by the following equation:

  • equation image(2)

where τda = 1/(krel + ket) and τd = 1/krel are the fluorescence lifetimes of the excited state D*, with and without energy transfer, i.e., in the presence or absence of an acceptor, respectively.

Determination of Energy Transfer by Photobleaching

It is possible to measure the τda and τa in the nanosecond time domain and to determine the E value; however, considerable simplification can be achieved if the fluorescence lifetime dependence of the fluorophore on the photobleaching time constant τ1 is used.

Each molecule in the excited state has a given probability (kb) to be decomposed by photochemical processes. To simplify the equations, the overall rate of the relaxation processes is marked with krel, excluding energy transfer and photobleaching (Fig. 1). Two coupled differential equations can be formed (22–24, 31–33), one for the excitation (rate of populating the excited state) and one for the relaxation (rate of depletion of the excited state) process of the donor molecules. By solving the equations, the following two estimated photobleaching time constants can be obtained:

  • equation image(3)
  • equation image(4)

in the absence and presence of acceptor molecules, respectively. The photobleaching time constant (τmath image) of the fluorophore is increased by energy transfer because of the additional pathway for relaxation (Fig. 2). By measuring the lifetime τ1 and τmath image by photobleaching experiments, the energy transfer efficiency can be determined by the following formula:

  • equation image(5)
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Figure 1. Photophysical scheme of the combined process of fluorescence resonance energy transfer and donor photobleaching. The possible relaxation ways of an excited donor molecule are displayed in the figure, where D*, D#, D, and A* represent the excited, bleached and ground states of the donor molecule, and the excited state of the acceptor molecule, respectively. The overall rate of relaxation is marked with krel, excluding energy transfer and photobleaching, which are denoted by ket and kb, respectively.

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Figure 2. Bleaching curves of single- and double-labeled samples with bleaching time constants ττ1 and τmath image. The bleaching time constant (τ1′) of the transfer sample (labeled with donor and acceptor) is larger than that of the donor-only labeled sample (τ1) because of the additional way for relaxation. Thus the resulting bleaching curve is flatter and the initial fluorescence intensity is lower.

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  1. Top of page
  2. Abstract


The Epstein-Barr virus–transformed human B lymphoblastoid cell line, JY, was grown in RPMI 1640 medium containing 10% heat-inactivated fetal calf serum, 2 mM I-glutamine, and 50 μg/ml gentamicin in humidified air containing 5% CO2 at 37°C.

Monoclonal Antibodies

W6/32 (immunoglobulin G2aκ) specific for the heavy chain of major histocompatibility complex (MHC) class I (HLA-A, -B, -C), L368 (immunoglobulin G1κ) binding β2-microglobulin (the light chain of MHC class I), and L243 (immunoglobulin G2a) specific for MHC class II (HLA-DR) were prepared from hybridoma supernatants (kindly provided by F. Brodsky, University of California at San Francisco, USA) by protein A-affinity chromatography. Aliquots of purified monoclonal antibodies (mAbs) were conjugated with 6-(fluorescein-5-carboxamido) hexanoic acid succinimidylester (SFX) or 6-(tetramethyl-rhodamine-5-(and-6)-carboxamido) hexanoic acid succinimidyl ester (TAMRAX; Molecular Probes, Eugene, OR, USA) as previously described (34). Labeling ratios were determined by a spectrophotometer. The fluorescently conjugated mAbs retained their affinity according to competition with identical, unlabeled antibodies.

Labeling of Cells With Fluorescent Antibodies

Cells were washed twice and suspended in phosphate buffered saline (PBS; pH 7.4) at a concentration of 0.5 to 1 × 106 cells/50 μl and were incubated with saturating amounts of SFX- and/or TAMRAX-conjugated mAbs for 45 min on ice. Thereafter cells were washed twice in PBS and fixed with 1% formaldehyde in PBS on ice for 30 min. During labeling special care was taken to keep the cells at ice-cold temperature to avoid induced aggregation of cell surface molecules.

Donor Photobleaching Procedure and Image Acquisition

Photobleaching data collection was developed for an inverted microscope (Zeiss Axiovert 100). The fluorescence microscope is configured with exchangeable XBO 75-W and HBO 100-W lamps. The optical path is shown in Figure 3. The instrument incorporates an electronic shutter that allows time-gated excitation. During one period of excitation (1 to 3 s) the intensified video camera collects several frames (full frame is 512 × 480 pixels, but 1/4, 1/16, 1/64 parts can also be obtained), which are digitized by a Matrox frame grabber card at 8-bit resolution. The measurement also can be performed on a confocal laser scanning microscope.

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Figure 3. Schematic drawing of a conventional inverted epifluorescence microscope with mercury arc-lamp excitation used for pbFRET measurements. Dotted lines represent the optical pathways. The electric PC-driven shutter is installed after the lamp to generate the excitation (ex.) light pulses. The excitation and emission (em.) wavelengths are selected by a filter cube containing three different filter configurations that can be adjusted manually. The emitted fluorescent light can be recorded with an intensified video camera (CCD).

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The irradiance level under which the emission process is measured has to be considered (35) because the photobleaching process can be influenced by different levels of excitation. At low levels of excitation (low irradiance) photobleaching is not significant because there is no appreciable depletion of the ground state of the donor molecule. If the applied level of excitation is too high, the various excited states of donor molecules are saturated, and the long-lived intermediate states (i.e., triplet state) are in a steady-state distribution. Under this condition the fluorescence no longer is linearly dependent on the excitation intensity and the photobleaching rate constant does not provide a direct measure of FRET efficiency. Thus a moderate light level is necessary to perform photobleaching measurement because in this case the rate of photobleaching and the fluorescence intensity of donor molecules linearly depend on the intensity (photon flux) of excitation light.

The number of images to be collected depends on the bleaching time constant of the fluorophore and the energy transfer efficiency. The higher the bleaching time constant, the longer illumination time required for bleaching the same amount of donor molecules. The occurrence of energy transfer also slows down the bleaching process because it opens an additional way for relaxation of excited state donor molecules, and thus the mean bleaching time constant of donor fluorophore increases.

Determination of Photobleaching Time Constant Values

The pixel-by-pixel distribution of photobleaching time constants is best determined by fitting the photobleaching decay curve in each pixel with an exponential function. In accordance with previous studies (36–38), the following double exponential equation gave the best fit with the smallest least-squares errors in our experiments:

  • equation image(6)

where I(t) is the fluorescence intensity at time t, I is the background fluorescence, A1 and A2 are the amplitudes of the two components, and τ1 and τ2 are the bleaching time constants. Nonetheless, the program described herein is designed to be versatile and can optionally fit one or three exponentials in addition to the above formula.

From the fitted parameters the weighted mean bleaching time constant (〈τ〉) can be calculated as follows:

  • equation image(7)

To perform the exponential fitting for each pixel, the intensity values of every pixel has to be collected to a linear array from all images of bleaching series (Fig. 4). The photobleaching decay curves are generated by plotting these intensity values as a function of time (in arbitrary units).

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Figure 4. Bleaching series. A representative series of images obtained in a photobleaching experiment. The bleaching decay curve of a single pixel (+) and its double exponential fitted curve (solid line) are also plotted.

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Flatfield Correction

As discussed under Donor Photobleaching Procedure and Image Acquisition, the bleaching time constant of a fluorophore is a function of the intensity of the excitation light. At sufficiently low intensities that are below saturation, the equilibrium percentage of excited state fluorophores is a linear function of the illumination flux. Further, if reactants around the fluorophore are in great excess, photobleaching becomes a quasi-monomolecular reaction, with a rate constant inversely proportional to the excited state population and, hence, excitation intensity. To correct for the inhomogeneity of excitation intensity, we have used a uranyl acetate glass fluorescence standard of even fluorophore distribution. By measuring the fluorescence intensity of the standard, the calculated mean bleaching time constant values can be corrected according to the following formula:

  • equation image(8)

where 〈τ〉i,j and 〈τ〉i,j,corr are the uncorrected and corrected mean bleaching time constant values of pixel [i, j], and cfi,j is the correction factor of pixel [i, j] calculated by equation:

  • equation image(9)

where Ii,j is the fluorescence intensity of pixel [i, j] in the standard image of the uranyl acetate glass, Bgi,j is the dark current value from the same pixel, and 〈IBg〉 is the average fluorescence intensity from the same background-corrected image.

Fitting Algorithm

Our software uses the Levenberg-Marquardt nonlinear fitting algorithm to fit bleaching curves, with three different formulas, single, double, or triple exponential functions. The fitting procedure is based on the minimization of the χ2 merit function to find the best fit parameters, with nonlinear dependences. The minimization must proceed iteratively by giving trial values for the parameters and the algorithm improves the trial solution until the χ2 stops (or effectively stops) decreasing (39). LabVIEW contains a mathematical virtual instrument (VI) package in which the Levenberg-Marquart subVI is also included. The subVI icon of the Levenberg-Marquardt (Lev-Mar subroutine) is shown in Figure 5.

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Figure 5. The scheme of subVI Levenberg-Marquardt of the developing program LabVIEW. The objects, structures, and subroutines are “hidden” inside subVIs, represented by single icons on the developer interface. Each one of these can have input and output parameters indicated with different color pins depending on the type of parameters. The subVI Levenberg-Marquardt has six input (left side of the icon) and five output (right side) parameters, described in detail under Materials and Methods.

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The input parameters are the following:

  • 1
    Standard deviation: the array of standard deviations, σ[i], for data point (x[i],y[i]). The default value is 1.0 if the standard deviations are all equal or unknown.
  • 2
    X and Y: the array of data points representing independent variables.
  • 3
    Initial guess coefficients: initial array of trial values.
  • 4
    Max iteration: maximum number of iterations.
  • 5
    Derivative: the formula for the derivative of a fitted function can be specified in the “Formula Node” (graphical mathematical expression evaluator) on the block diagram.

The output parameters of the subVI are the following:

  • 1
    Covariance: matrix of covariances C.
  • 2
    Best-fit coefficients: set of coefficients that minimize χ2, defined in the following equation:
    • equation image(10)
  • 3
    Best fit: fitted data array. Values are computed by using the best-fit coefficients.
  • 4
    MSE: mean squared error.
  • 5
    Error: returns any error or warning condition from the subVI.

Detailed information about this subVI can be found in the Help of LabVIEW (LabVIEW Help, National Instruments Corporation, Austin, TX, USA).


  1. Top of page
  2. Abstract


The program was developed in National Instrument LabVIEW 6.1 (National Instruments Corporation). We chose this software because of its powerful graphical development environment, which provides several objects (graph controls, image display control) to aid designing the program, and it contains several precompiled mathematical VIs and routines.

The minimum system requirements are the following:

  • Windows 9x or later versions of the Windows operating system

  • IBM-compatible PC with Pentium 166 MHz

  • 128 MB RAM

  • Desktop area 1024 × 768 pixels

  • 32-Bit color palette

  • Mouse or compatible pointing device

Description of the Program

User interfaces

This software was developed to handle the image files generated by the acquisition software of our instrument (Attofluor, Zeis, Oberkochen Germany), i.e., 8-bit resolution raw image files with dimensions 60 × 64, 120 × 128, 240 × 256, and 480 × 512. This image type has no header or other additional information about the structure of the file. We chose this image format for our application because most of the image analysis software have the ability to convert image files to this format. Thus this program can be used to analyze bleaching image series measured by any other instrument (e.g., confocal laser scanning microscope).

The main window of the program contains only four buttons to run the subprograms, Analyze Pixel, Multifit, Multi Analyze, and Distribution Viewer, in sequence.

Analyze Pixel subprogram

The aim of this subprogram is to help the user decide which type of function provides the best fit to the photobleaching curves. The interface contains a frame that shows the first image of the bleaching series. The pixel of interest can be chosen by moving a small yellow cursor over the first image. Next to the image display frame, a plot is placed to show the photobleaching and the fitted curve together, which can be saved as a tab-delimited ASCII text file. A table is placed under the image window in which the initial and the result fitting parameters are displayed. The Movie subVI can be opened from this window to see the bleaching process as a movie and to check whether the studied cells shifted during acquisition.

Multifit subprogram

Fitting all the bleaching curves in all series would be a remarkably time-consuming procedure. The Multifit subprogram provides an interface where all bleaching series and their initial fitting parameters can be adjusted before analysis. The threshold intensity and the standard images (for flatfield correction) can be adjusted also. It is also possible to include and delete image series from the analysis procedure before starting the overall fitting course. After all relevant series are added to the series list and all initial parameters are defined, the program automatically fits the bleaching curves of all pixels above the threshold and writes the results into a tab-delimited ASCII text file. The structure of this temporary file (resu.lts) is shown in Figure 6.

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Figure 6. The structure of the result file generated by the subprogram Multifit. The output parameters of the fitting functions are stored pixel by pixel in a tab-delimited ASCII list mode file. The columns contain the X and Y positions of the pixel, the mean photobleaching time constant and the mean squared error values, and the separate exponential function parameters. The initial intensity of the pixel is also stored at the end of each row.

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Multi Analyze subprogram

After the fitting procedure of the Multifit, the results can be surveyed in this subprogram. The first image is displayed in a frame that includes two cursors with which one rectangular part of the image can be selected. Selecting one portion of the image enables choosing only a subset of the calculated bleaching times according to user-adjusted minimum and the maximum bleaching times, fluorescence intensities, and the maximum of the standard error of mean of the fitting. Sliders are placed on the interface to change acceptance ranges of these parameters. The selected pixels and their fitting parameters are appended to an ASCII text result file for further analysis. A plot is also included to display the bleaching and fitted curves together. The fitting parameters can be displayed in a frame as a color-coded map.

Distribution Viewer subprogram

With this subprogram it is possible to collect the results of the individual measurements as one array and display the bleaching time constants in a histogram. The histograms can be fitted with Gaussian functions and the resulting parameters are displayed in the histogram frame. Photobleaching time constants can be gated on the histograms with three different colored gates: red, green, and yellow. The color-coded photobleaching time constants are overlaid on the first image of the bleaching series. The color code is generated according to the maximum and minimum values of the photobleaching time constant subset. The lowest and highest values of the subset get the darkest and brightest tones of the gate color. The color-coded image can be saved as a 24-bit bitmap image.

Test sample

To demonstrate the functionality of the program, FRET efficiencies between the two chains (β2m, heavy chain) of MHC class I and between MHC class I (β2m) and MHC class II were determined at the surface of JY human B lymphoma cells. Proteins were targeted by SFX- or TAMRAX-conjugated mAbs serving as donor and acceptor, respectively.

FRET efficiencies were determined by using the following samples:

  • 1
    JY cells labeled with SFX-L368 (targeting β2m; donor)
  • 2
    JY cells simultaneously labeled with SFX-L368 and TAMRAX-W6/32 (targeting β2m and MHC class I heavy chain, respectively; donor + acceptor)
  • 3
    JY cells labeled with SFX-L368 and TAMRAX-L243 (targeting β2m and MHC class II, respectively; donor + acceptor)
  • 4
    JY cells labeled with TAMRAX-L243 (binding MHC class II; acceptor-only labeled)
  • 5
    Unlabeled JY cells

Because the photobleaching process is highly sensitive to oxygen concentration, experiments were performed on the mixture of donor-only and double-labeled samples to ensure the same level of dissolved oxygen. To distinguish donor-only and double-labeled cells, at the end of the experiment an additional image was recorded in the acceptor channel (λex = 543 nm, λem > 580 nm). The sample labeled with TAMRAX-L243 alone (acceptor-only labeled sample) was used to check whether the intensity of the acceptor was affected by donor excitation. If the acceptor is also bleached upon donor photobleaching, it loses its ability to accept energy from the excited donors and, as a result, the bleaching time constant decreases. The fifth sample was used as a background control.

To prove the applicability of our software for any acquisition system, the same samples were measured on a Zeiss LSM 510 microscope (lasers: Ar ion at 488 for donor excitation, HeNe at 543 nm for acceptor excitation), and image series were converted to raw format and analyzed with our software. Four image series of two to three cells were taken from each sample (∼10 cells/sample). We determined 600 to 1,500 photobleaching time constants (τ) per cell, i.e., the mean time constants were calculated from ∼7,000 data for each sample. The resulting distribution histograms of “tau” values along with representative image triplets (images taken in the donor and acceptor channels and the color-coded map of photobleaching time constants) for the double-labeled samples are shown in Figure 7.

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Figure 7. Representative pbFRET experiments between the two chains (β2m and heavy chain) of MHC class I (A–C, G) and between MHC class I (β2m) and MHC class II (D–F, H) glycoproteins on JY human B lymphoma cells. The two chains of MHC I were targeted by SFX-L368 and TAMRAX-W6/32 mAbs, respectively, whereas MHC II was labeled by TAMRAX-L243 mAb. SFX was used as donor and TAMRAX as acceptor. Photobleaching experiments were performed on the mixture of donor-only and double-labeled cells as detailed in the Sample Run. A, D: First images of the bleaching series recorded in the donor channel. B, E: Images recorded in the acceptor channel after finishing donor photobleaching. C, F: Color-coded map of photobleaching time constants (“tau” map). G, H: Distribution histograms of photobleaching time constants. The presence of FRET resulted in the shift of the distribution histograms of double-labeled cells (solid lines) toward the higher values in comparison with those of donor-only labeled cells (dashed-dotted lines). This is also clearly visible on the tau maps. In the case of G, the shift is more prominent, i.e., the two chains of MHC I molecules are in closer proximity than are β2m and MHC II; thus the probability of FRET is higher.

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The mean values of photobleaching time constants for donor-only labeled cells (sample 1) were independent of whether they were determined in the mixture of samples 1 and 2 or that of samples 1 and 3: the calculated values were 1.69 ± 0.35 and 1.65 ± 0.48, respectively. In the case of the double-labeled cells, the mean constants for samples 2 and 3 were 2.62 ± 0.56 and 1.78 ± 0.56, respectively. FRET efficiencies for the SFX-L368/TAMRAX-W6/32 (β2m − MHC class I heavy chain) and for the SFX-L368/TAMRAX-L243 (β2m − MHC class II) pairs were 35.5% and 6.9%, respectively.


  1. Top of page
  2. Abstract

The photophysical consequences of the FRET phenomenon offer several ways to measure FRET efficiency. One of these possibilities is the acceptor-depletion FRET technique (13, 40–42), which is a useful pixel-by-pixel based fluorescence method. Using conventional fluorescence farfield image microscopic technique, a whole energy transfer map can be generated for cell surface receptors. With this method, some corrections have to be considered in the calculation because the results can be affected by background fluorescence and photobleaching of the donor molecules during acceptor bleaching. Despite these problems, the acceptor-depletion method has the obvious advantage that FRET can be determined from a single sample labeled with donor and acceptor.

Using the donor pbFRET method, the above-mentioned two technical drawbacks (background fluorescence and acceptor bleaching upon donor excitation) can be minimized. This technique is a powerful microscopic method to study the proximity and distribution of cell surface proteins on a molecular scale. To determine the energy transfer efficiency, two series of images should be recorded, one with a cell sample labeled solely with donor fluorophores and one with cells labeled with donor and acceptor. From the image series the distribution of the cell surface molecules can be studied. Moreover, from the image series the photobleaching time constants can be calculated by fitting the decay curves with an exponential function. To increase the accuracy of the mean of photobleaching time constant, values can be determined on a pixel-by-pixel basis. In general, the method allows for the selection of any desired region of interest in various regions of single cells, cell conjugates, or thin tissue slices.

Although the method is simple and does not need a complicated hardware configuration, there is a lack of analysis software because commercially available software can only record and display a bleaching image series and generate bleaching curves from the recorded images, but usually do not support curve fitting algorithms or only single exponential curve fitting is possible.

Here we present a LabVIEW application, with which the raw image series can be processed to determine the photobleaching time constant values. The resulting values can be plotted as a color-coded photobleaching time constant map to investigate the spatial distribution of the cell membrane components. Further, histograms can be generated from the fitting coefficients calculated on a pixel-by-pixel basis. The histograms can be gated by different color gates and fitted with Gaussian functions. The gated values are displayed on the first image of the captured image series by the gating color.

The program can be used to analyze bleaching image series measured by any instrument because it was developed to process raw image files. The program uses this image format because different equipments have several image formats to store the measured data, but most image analysis programs are able to convert image files to raw format.

The fitted data are saved to a simple ASCII text file, so they can be opened by any data processing software. The software package contains tools to convert the resultant data map to Tagged Image File Format (TIFF) or Image Cytometry Standard (ICS) image formats for further analysis. These tools for formatting results are also installed by the software. Details about their usage can be found in the software manual.

The application can be used without an existing LabVIEW developer environment because the LabVIEW runtime engine is automatically installed by the Install Shield wizard. The software is freely distributed and can be obtained free of charge from the authors. The software package includes a complete help and tutorial to help the first-time user.

In conclusion, we have introduced a new version of pbFRET analysis and data processing software that are able to generate a full analysis pattern of donor photobleaching decay image series at various conditions, independently of the complexity of the decay process and therefore of the nature of applied fluorophore. It allows for analysis of selected regions of interest on single cells or high throughput screening statistics on a large number of cells. The donor pbFRET approach together with the acceptor-depletion method used in conventional fluorescence or confocal laser scanning microscopes are of a continuously wide interest nowadays in studying molecular aspects of cellular recognition, communication, and signal processes in living cells, particularly in cell biology, immunobiology, and neurobiology.


  1. Top of page
  2. Abstract
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