Quantitative fluorescence loss in photobleaching for analysis of protein transport and aggregation
© Wustner et al.; licensee BioMed Central Ltd. 2012
Received: 26 May 2012
Accepted: 31 October 2012
Published: 13 November 2012
Fluorescence loss in photobleaching (FLIP) is a widely used imaging technique, which provides information about protein dynamics in various cellular regions. In FLIP, a small cellular region is repeatedly illuminated by an intense laser pulse, while images are taken with reduced laser power with a time lag between the bleaches. Despite its popularity, tools are lacking for quantitative analysis of FLIP experiments. Typically, the user defines regions of interest (ROIs) for further analysis which is subjective and does not allow for comparing different cells and experimental settings.
We present two complementary methods to detect and quantify protein transport and aggregation in living cells from FLIP image series. In the first approach, a stretched exponential (StrExp) function is fitted to fluorescence loss (FL) inside and outside the bleached region. We show by reaction–diffusion simulations, that the StrExp function can describe both, binding/barrier–limited and diffusion-limited FL kinetics. By pixel-wise regression of that function to FL kinetics of enhanced green fluorescent protein (eGFP), we determined in a user-unbiased manner from which cellular regions eGFP can be replenished in the bleached area. Spatial variation in the parameters calculated from the StrExp function allow for detecting diffusion barriers for eGFP in the nucleus and cytoplasm of living cells. Polyglutamine (polyQ) disease proteins like mutant huntingtin (mtHtt) can form large aggregates called inclusion bodies (IB’s). The second method combines single particle tracking with multi-compartment modelling of FL kinetics in moving IB’s to determine exchange rates of eGFP-tagged mtHtt protein (eGFP-mtHtt) between aggregates and the cytoplasm. This method is self-calibrating since it relates the FL inside and outside the bleached regions. It makes it therefore possible to compare release kinetics of eGFP-mtHtt between different cells and experiments.
We present two complementary methods for quantitative analysis of FLIP experiments in living cells. They provide spatial maps of exchange dynamics and absolute binding parameters of fluorescent molecules to moving intracellular entities, respectively. Our methods should be of great value for quantitative studies of intracellular transport.
KeywordsMathematical model Crowding Protein aggregation Fractal kinetics Rate coefficient Multi-compartment Neurodegeneration
Quantitative fluorescence microscopy witnesses an increasing demand for computational methods allowing for interpretation of the complex data generated by this imaging technique. To determine intracellular transport dynamics of proteins and lipids tagged with suitable fluorophores, one often relies on perturbing the steady state distribution of the probe and following the dynamics of re-establishing a steady state. One example for this approach are pulse-chase experiments, where a fluorescent ligand, like a dye-tagged transferrin (Tf) or fluorescent lipoprotein binds to its receptor at the cell surface at the start of the experiment (t = 0), and the transport of the fluorescent-ligand-receptor complex to the target organelle during the course of endocytosis is followed over time by quantitative fluorescence microscopy [1–3]. Pre-labeling of the target organelle with a suitable marker with different spectral characteristics allows, together with appropriate image analysis tools, to measure transport kinetics of the molecule of interest by multicolour fluorescence microscopy. Compartment modelling based on ordinary differential equations can be used to determine inter-organelle transport kinetics. For example, trafficking kinetics of fluorescent Tf and lipoproteins to sorting endosomes and the endocytic recycling compartment (ERC) have been measured in this way in various cell types [1–8]. Similarly, using a temperature-sensitive folding mutant, export of a viral protein tagged with eGFP has been analyzed by this approach .
An alternative way to perturb the steady state does not rely on pulse-labeling molecules in a particular compartment and is often used to elucidate transport dynamics along the secretory pathway or between nucleus and cytoplasm [10–16]. Selective photodestruction of fluorescence of the molecule of interest in one organelle and measuring fluorescence recovery after photobleaching (FRAP) of unbleached molecules into the area reveals transport kinetics and provides information about a possible immobile fraction of the labeled molecules in the target organelle . A variant of FRAP is continuous photobleaching (CP) introduced by Peters et al. in 1981 . In this approach, a small area of the cell is continuously illuminated, and the fluorescence decay in this region due to photobleaching and transport is monitored. Similar as in FRAP, diffusion constants, and with extensive modelling efforts, binding parameters or flow components can be estimated from CP measurements [18–20]. Both techniques, however, infer the transport parameters solely from fitting a time-dependent function to experimental data. Thus, any locally varying transport properties in the cell cannot be directly monitored, since FRAP and CP curves do not depend explicitly on spatial coordinates. Consequently, one often finds model redundancy; i.e., that various mathematical models can describe a measured FRAP curve equally well . Another complication in both, FRAP and CP is that the user has only very limited impact on the time hierarchy of the experiment. While varying the size of the bleach area allows for controlling the diffusion time, if binding and diffusion take place on different time scales, the user cannot isolate one of the processes by adjusting the time resolution of the measurement.
A method related to FRAP is fluorescence loss in photobleaching (FLIP). In FLIP, a region is repeatedly illuminated by an intense laser pulse, while images are taken with reduced laser power between the bleaches. A pause between the laser pulses allows for some recovery in the bleached region. The duration of the pause can be set by the user, who thereby can control the time resolution of the experiment. Repeating this protocol several times creates a sink for the fluorescent molecules in the local environment being in continuous exchange with the bleached region . A decrease of fluorescence of dye-tagged molecules outside the bleached area allows for assessing continuity between intracellular compartments, and in principal, for measuring the kinetics of recruitment to the bleached region from various cellular areas. Accordingly, FLIP has the potential to include spatial information in the computational analysis. However, only very few FLIP analysis efforts published so far try to include the whole image information into analysis of FLIP image sets: In one study, van Gemert et al. (2009) used independent component analysis to decompose FLIP image series into static and dynamic components . Very recently, van de Giessen et al. (2012) used a monoexponential decay model on a pixel basis combined with image registration and denoising to measure FL in FLIP image series . The widely used standard approach for analysis of FLIP data is to define rectangular regions of interest (ROI) at different sites in the cell and to quantify the mean fluorescence in these ROI as function of time . The user chooses the number, location and size of the ROI’s based on visual inspection of the image sets. This generates a very subjective element in the data analysis.
We present two new approaches to quantify FLIP experiments reliably; either on an image basis to detect areas of different probe mobility, or by physical modelling of FL from moving entities. In the first section, we demonstrate that a stretched exponential (StrExp) function can be fitted to the decaying intensity at each pixel position in the bleached and non-bleached region of the cell. This is used to detect diffusion-limited depletion zones around the bleached area in cells expressing enhanced green fluorescent protein (eGFP). Pixel-wise fitting of the StrExp function to FLIP image sets also allows us to determine local heterogeneity in nucleocytoplasmic transport of eGFP. In the second section, we measure FL kinetics in moving inclusion bodies (IB’s) by combining FLIP with single-particle tracking. From that data, we infer exchange parameters of mutant Huntingtin tagged with eGFP (eGFP-mtHtt) by multi-compartment (MC) modelling of binding/release and fluorescence attenuation due to photobleaching. The presented methods should find wide application in quantitative cell biology and especially in analysis of protein aggregation in neurodegenerative diseases.
The stretched exponential function as empirical decay law for transport studies
The StrExp function is an empirical decay function with broad applications in modelling of physical, photochemical and biophysical data [24–28]. It can be considered as a generalization of the exponential function, since the stretching parameter h gives a time-dependent rate constant, called a rate coefficient (see Equation 1 in Methods and Additional file 1: Figure S1B and Equation S4) . Rudolf Kohlrausch first introduced this function in 1854 to describe the discharge of a capacitator, which is why it is sometimes also called the Kohlrausch function . It has been demonstrated that many physical relaxation processes can be described by a StrExp function including frequency-dependent dielectric constants of polymeric systems and glasses [30, 31], luminescence decays of macromolecules in heterogeneous environments [27, 28], luminescence quenching and resonance energy transfer in disordered media [24, 27], spin-relaxation  and mechanical stress relaxation in crystals . All of these processes have in common that a physical system is perturbed for a short time followed by relaxation towards a new equilibrium or steady state value, respectively. In some cases, the StrExp function can be used to discern a physical mechanism underlying the observed relaxation process . More often, it is used as empirical fitting function to describe an experiment in quantitative terms [26–28, 32]. Here, we use the StrExp function as empirical function to describe FLIP image sets on a pixel basis. In a classical FLIP experiment, the perturbation is caused by a series of intense bleach pulses in the selected ROI. We will consider two limiting cases of a FLIP experiment and demonstrate that the StrExp function concurs with simulated time courses for both situations.
Analysis of diffusion-limited FLIP experiments using the StrExp function
Relationships between parameter combinations of the StrExp function fitted to experimental FLIP data and the time hierarchy of photobleaching and possible intracellular transport processes
Parameters recovered from the fit of the StrExp function to experimental fluorescence loss in FLIP image sets
Time constant (τ)
Rate coefficient (k(t))
Diffusion of molecules to the bleached ROI is slower than the bleaching process. This results in a ’depletion zone’ around the bleached area. Binding and release, if any, are faster than diffusion.
1 < h < 2
Dependent on bleach rate constant and diffusion constant*.
Decreasing over time.
Fluorophores get depleted by the bleaching acting as diffusion-limited reaction.
~0.5 < h < 1
Dependent on diffusion constant and bleach rate constant*.
Increasing over time.
Compressed decay, because diffusion to the bleach ROI causes delayed response.
Diffusion of molecules to the bleached ROI as well as eventual binding and release are faster than the bleaching process. The only process causing FL is the repeated bleaching inside the ROI.
Dependent on bleach rate constant only.
Constant over time and equal to 1/τ.
Approx. mono-exponential decay determined by the bleach rate.
Dependent on bleach rate constant only.
Constant over time and equal to 1/τ.
Approx. mono-exponential decay determined by the bleach rate.
Binding # -limited FL
Diffusion is fast but molecules are hindered by binding to cellular organelles or by obstacles. Any spatial fluorescence gradient is rapidly equilibrated and binders/barriers become visible.
Dependent on bleach rate constant only.
Constant over time and equal to 1/τ.
Approx. mono-exponential decay determined by the bleach rate.
~0.8 < h < 1
Dependent on release rate constant and bleach rate constant*.
Increasing over time.
Compressed decay, because slow release causes delayed response.
Diffusion-limited FLIP of eGFP in the cytoplasm of McA cells
Nucleo-cytoplasmic transport of eGFP in McA cells as example of binding/barrier-limited FLIP experiments
In many experimental situations, diffusion is much faster than the photobleaching process in the bleach ROI. Still, transport to the bleached area in FLIP experiments can be hindered by transient binding events or barriers to diffusion, even though the diffusion of a molecule in the aqueous phase of the cytoplasm, i.e., the cytosol, is fast. For example, exchange of proteins between nucleus and cytoplasm has been shown to be limited by transport through the nuclear pore complex but not by diffusion [14–16, 38]. Binding-/barrier dominated transport can be modelled by a set of ordinary differential equations, as given in Eqs. 3–5 in Methods. For the given example, the constants k1 and k−1 reflect nuclear export and import rate constants, respectively, but the same equations would also describe binding/dissociation-limited FLIP experiments. We confirmed in additional simulations, that the StrExp function is able to describe binding/barrier-dominated FLIP experiments (see Additional file 1: Figure S5, S6, S7 and S8).
Heterogeneous transport of mutant huntingtin detected by quantitative FLIP analysis
Multi-compartment (MC) modeling of eGFP-mtHtt exchange between cytoplasm and IB’s
Although we were able to quantify FL of eGFP-mtHtt in moving IB’s using the strategy outlined above, this method does not provide cell-independent measures of eGFP-mtHtt dynamics. This is, since the FL depends not only on the protein exchange dynamics but also on the total cellular pool size of eGFP-mtHtt. In other words, for a larger cell the same FLIP imaging settings would give slower FL from a given IB than for a smaller cell, simply because it would take longer to deplete the whole protein pool in the larger cell. To directly compare exchange dynamics of eGFP-mtHtt between cytoplasm and IB’s from various cells, we developed an analytical MC model providing association and dissociation rate constants (see Figure 7 and Appendix 2). The basic idea of that modeling strategy is that FL kinetics in the IB’s becomes weighted by that in the cytoplasm for each cell thereby creating a cell-independent measure of protein exchange dynamics in the aggregates. For that MC model, we assumed that eGFP-mtHtt is distributed in two pools; several small IB’s as the first pool and the large cytoplasm as the second pool. Exchange between the IB’s (compartment(s) 1, C1) and the cytoplasm (compartment 2, C2) takes place with rate constants k1 and k−1 (Figure 7A’). The number of the IB’s can be large as long as they together contain a much smaller amount of eGFP-mtHtt than the cytoplasmic compartment. Furthermore, the experimental conditions should be chosen such that binding-limited FLIP conditions are established (i.e., no depletion zone like in diffusion-limited FLIP experiments; this can be verified by pixel-wise fitting the StrExp function to FLIP image sets). Finally, it is presumed that the cytoplasmic pool of eGFP-mtHtt changes only because of the bleach but not due to release of eGFP-mtHtt from IB’s. These conditions are justified by the large size of the cytoplasmic compared to the IB pool (see above), and they allow for decoupling the differential equation system (see Appendix 2 for further details). We first fitted the FL in the cytoplasm caused by the repeated bleaching with a mono-exponential decay function (Figure 7B; green symbols, data; dark green line, fit). Next, we used the estimated rate constant as input for fitting FL in the IB outlined by the blue box in Figure 7A (Figure 7B shows FL data as blue symbols and the fit as dark blue line).The model fitted the experimental data very well (R2 > 0.98) and gave a half-time for FL in the cytoplasm of t1/2= 51.7 sec. Note that the FL in the cytoplasm is solely a consequence of the bleach protocol. In contrast, the FL in the IB is a result of both, the dissociation of eGFP-Q73 from that protein aggregate and the subsequent photodestruction of the released eGFP-Q73 in the bleach ROI. Using the FL kinetics in the cytoplasm (see above) and the MC model, eGFP-mtHtt dissociation could be calculated to occur here with a half time of t1/2= ln2/k1 =73.8 sec. Another example of a cell with 2 IB’s of varying mobility is shown in Figure 7C-G. The larger IB is more mobile but its associated eGFP-Q73 monomers are in slightly slower exchange with the cytoplasm than for the smaller IB (i.e., half-time of dissociation was t1/2= 200.6 sec and t1/2= 142.3 sec for the large and small IB, respectively). We tracked additional IB’s in the cytoplasm of several cells and found mean off- and on-rate constants for eGFP-Q73 of k1 = 0.0127 ± 0.004 sec-1 and k−1 = 0.016 ± 0.006 sec-1 (n=6; mean ± SE). Thus, the average half-time of eGFP-Q73 dissociation from IB’s is 101.2 ± 30 sec in our experiments. The observed heterogeneity in exchange dynamics of eGFP-mtHtt between cytoplasm and IB’s is in line with earlier qualitative FLIP and FRAP experiments [43, 44]. Together, combined fitting of FL kinetics to the MC model and SPT of moving IB’s enabled us to determine in parallel aggregate mobility and exchange dynamics of mtHtt between moving aggregates and the cytoplasm.
Fluorescence loss in photobleaching (FLIP) is a dynamic imaging technique with the potential to include spatial information into analysis of protein dynamics, but this potential has not been explored. By treating images as data arrays rather than pictures, we present two FLIP analysis methods for assessing intracellular transport dynamics of fluorescent proteins. Our first approach comprises fitting a StrExp function to FL kinetics on a pixel-by-pixel basis. The rationale behind this idea is that the time-dependent rate coefficient of the StrExp function is suitable to describe transport under conditions, where the “well-stirred compartment” assumption fails. Diffusion gradients or topological constraints cause deviation from the concept of compartment homogeneity, and that can be modelled with differential equations having time-dependent rate coefficients . The StrExp function is based on such an equation, and our method of pixel-wise fitting this function to FLIP image sets provides kinetic maps of locally heterogeneous transport and allows for exact data reconstruction using the derived model parameters (for example, see Figure 4A). FLIP as an imaging method, has the advantage that the user can determine the time resolution of the experiment by setting pauses of arbitrary length between individual bleaches. For short pauses, i.e., fast repeated bleaching; FLIP-experiments might become diffusion-limited (see Figures 1, 2 and 3 and Additional file 1: Figure S1, S2 and S3). We demonstrate on simulated and experimental FLIP image sets that a spatial gradient of the fitting parameters (i.e., stretching, h, and time or rate constant, respectively) of the StrExp function is characteristic for a diffusion-limited FLIP experiment.
For larger pauses between the bleaches, eventual hindrance to diffusion due to transient binding or barriers can be detected on the sub-cellular level by pixel-wise FLIP analysis without pre-selection of ROIs (Figures 4 and 5). This result is not in line with isotropic normal diffusion of eGFP in cells, though eGFP is known to show minimal interaction with its surroundings . An often (but not always, see ) overlooked aspect in the literature of biophysical diffusion studies (for example using FRAP) is that not only homogenous diffusion but also locally heterogeneous diffusion (i.e., with a space-dependent diffusion constant) with continuity between the regions of varying D will cause an equilibration of any concentration gradient. This is illustrated in the simulation shown in Figure 2 (see above). Thus, already the initial distribution of eGFP in the prebleach image tells us that crowding effects exclude eGFP from some regions in the nucleus and the cytoplasm. We are aware of one recent study, where the structured fluorescence intensity of an inert fluorescent protein (i.e., yellow fluorescent protein, YFP) in a single image of the cell has been ascribed to the cytoplasm and nucleoplasm being an ‘effective porous medium’ . In porous media, one distinguishes diffusion in the liquid phase (here the cytosol or nucleosol) from that in the medium with constrained motion. We speculate that the large variation in rate coefficients, we find for FLIP data of eGFP in the cytoplasm is a consequence of this porosity. Since the optical resolution of the confocal microscope (i.e., about 250 nm) is several times larger than the pore size of the cytoplasm (i.e., 30–100 nm), one cannot segment this cytoplasmic ultrastructure, though it obstructs protein motion . Furthermore, even for ‘inert’ biomolecules lacking specific interactions, like eGFP, not only obstruction is found but also enhancement of diffusion could be triggered by active transport of other components causing enforced motor activity and dynamic remodelling of the cytoskeleton network . Thus, the cytosol might provide rapid decay channels for eGFP, while the high protein concentration, organelles and cytoskeleton constrain eGFP mobility otherwise. Rapid decay channels can be detected with the StrExp fit to FLIP data as individual pixels with high rate coefficients, even far from the bleached ROI (see Figures 4 and 5 and Additional file 4: Movie S3). Additional simulations of binding-barrier-limited FLIP experiments using the compartment model of Equations. 3–5 but with rate constants varying on a pixel-by-pixel basis were performed to account for spatial heterogeneity (Additional file 1: Figures S7 and S8). By fitting the StrExp function to these simulated time courses, we found local variation of the recovered parameters (i.e. h-map and time constant map), but in a much narrower range than observed for the FLIP data for eGFP in cells (compare Figure 4 and 5 with Additional file 1: Figure S8). Thus, transport of eGFP as measured by FLIP follows a complex dynamic relaxation pattern due to local hindrance to diffusion on one hand and local enhancement of diffusive transport on the other. Such complex behaviour cannot be understood from a simple binding-barrier or diffusion model of intracellular transport but it can be revealed using pixel-wise fitting of the StrExp function to FLIP image sets.
Using our quantitative FLIP analysis, we could also visualize local diffusion barriers for eGFP in the nucleus (see Figures 4, 5 and Additional file 1: Figure S9). These kinetic domains in the nucleus are likely a consequence of the fractal chromatin organization . Recent results published by Gratton and co-workers detect also barriers for eGFP diffusion in the nucleus of CHO cells using pair correlation functions (pCF) . An advantage of their method is the ability to extract local diffusion coefficients directly from the cross-correlation of intensity fluctuations of the same fluorophore at two different but adjacent points in the sample . Rare intensity bursts report on eGFP molecules travelling across regions with strongly varying DNA density . The pCF method has also been applied to study transport of eGFP between nucleus and cytoplasm, where the nuclear membrane including the NPC appeared as obstacle to eGFP inter-compartment diffusion . This diffusion barrier is nicely detected in the time constant map of our quantitative pixel-based FLIP analysis (see Figure 4 G, J), suggesting that fluorescence fluctuation techniques like pCF and our approach provide complementary information.
To the best of our knowledge, there is only one full publication published this year, which also applies a pixel-wise regression of a decay function to FL data of FLIP image sets . Lelieveldt and colleagues assumed a simple exponential decay of the FL allowing for linear regression in the logarithmic space. They also performed a noise reduction as pre-processing step and were able to correct for sudden motion of the specimen or the image field during acquisition of the FLIP data . Our approach implemented in ‘PixBleach’ uses a Gaussian filtering in the space and time domain as pre-processing step which corrects in our hands sufficiently for image noise and for small movements (in a range of one or two pixels) . Since we apply a motion correction as pre-processing step in case of larger displacement, no further corrections were necessary for the image data used here (see Methods section and ). Using the StrExp function is clearly superior to simple mono- or bi-exponential decay models, since it can model FL inside and outside the bleached region for both, binding/barrier- and diffusion-limited FLIP experiments. The delayed decay described by a compressed exponential function as a consequence of binding or barriers (in binding/barrier -limited FLIP experiments) or due to long distance to the bleached area (in diffusion-limited FLIP experiments) cannot be adequately described with simple exponential decay functions (see Table 1).
Quantitative FLIP microscopy should also be a method to compare results from different cells. This, however, is not possible with the pixel-based FLIP analysis, because the kinetic parameters recovered from the StrExp fit to data are a function of the total amount of fluorophore in a given cell, the diameter of the bleach spot and the length of the pauses between bleaches. The empirical StrExp fitting function only provides information about the nature of the observed transport process (e.g. diffusion- or binding-limited, see Table 1) and about local variation in transport dynamics for a given experiment and cell. Experiment-independent binding and dissociation parameters of a fluorescent protein can therefore not be measured by this approach. To overcome this limitation, we developed a complementary method based on compartment modelling of FLIP data and applied it to determine local heterogeneity in intracellular protein aggregation. Such aggregation is observed in cellular models of various neurodegenerative diseases [45, 58, 59]. Since protein aggregates tend to move during FLIP experiments, we combine tracking of individual IB’s containing mtHtt with extended polyQ repeat (eGFP-Q73) with non-linear regression of an analytical MC model to the FLIP-induced intensity decay in these structures (Figures 6, 7). This enables us to measure mobility parameters, like diffusion constants from the calculated MSD and to determine in parallel association and dissociation rate constants for eGFP-mtHtt from the measured FL kinetics in IB’s. Thus, we demonstrate that mtHtt in IB’s can exchange with mtHtt in the cytoplasm in agreement with earlier observations for other polyQ diseases . Using our MC modeling strategy, we can derive for the first time in-vivo binding parameters of eGFP-mtHtt showing that the exchange takes place with a half-time of 2–4 min. Since MC modeling of FLIP data provides physical parameters, this method will be of high value for quantitative comparison of protein aggregates in Huntington disease with that in other polyQ diseases, like various forms of ataxia. In fact, compressed exponential FL, similar as we found for eGFP-mtHtt has been reported in FLIP-experiments of ataxin-3 aggregates, in the nucleoplasm of COS7 cells . We are aware of the fact, that the presented two modelling approaches for FLIP data analysis are based on somehow contradictive assumptions: pixel-wise fitting reveals local heterogeneity of protein transport in various cellular pools, while MC modelling of FLIP experiments requires the assumption of well-mixed compartments. Solving this contradiction satisfactorily would require modelling the whole spatiotemporal protein dynamics including cell geometry, space-dependent diffusion constants, diffusion barriers, moving entities etc., but this is too ambitious at the moment and outside the scope of this article. We found that deviation of FL kinetics from a mono-exponential decay becomes negligible when larger cytoplasmic regions (above ~5 pixels) are included in the analysis (not shown). We also implemented a StrExp decay model for the FL in the bleached compartment (C2) in our MC model, but that recovered a mono-exponential decay with h = 1 (not shown, but see Figure 7C, green symbols and line). Together, with the observed time hierarchy, this makes the assumption of a well-mixed cytoplasmic compartment compared to measured FL of eGFP-Q73 in IB’s reasonable.
We present two new approaches for quantitative analysis of FLIP experiments in living cells. Pixel-wise fitting of a StrExp function to FLIP image sets allows for detecting areas of different probe mobility, while physical modelling of FLIP data in the second method provides for the first time dissociation parameters of fluorescent proteins from moving entities. Our methods are easy to apply to other transport problems, where a fluorescent biomolecule is soluble in the nucleus or cytoplasm and binds to or partitions into static structures (for pixel-based FLIP quantification) or dynamic structures (for combined SPT and MC modeling). Typical other applications could be transient binding of ras-protein to the Golgi apparatus and plasma membrane , of lipases and other proteins to lipid droplets , cohesin mobility in the nucleus of yeast cells , recruitment of rab proteins to endosomes  or the dynamic partitioning of fluorescent drugs and lipids into subcellular membranes .
Reagents and cell culture
Fetal calf serum and DMEM were from GIBCO BRL (Life Technologies, Paisley, Scotland). 3,3,3’,3’-tetramethylindocarbocyanine perchlorate (DiIC12) was purchased from Molecular Probes (Eugene, Oregon, USA). All other chemicals were from Sigma Chemical (St. Louis, MO). Medium 1 contained 150 mM NaCl, 5 mM KCl, 1 mM CaCl2, 1 mM MgCl2, 5 mM glucose and 20 mM HEPES (pH 7.4). McArdle RH7777 (McA) cells expressing enhanced green fluorescent protein (EGFP) have been reported previously . These cells were grown in DMEM with 4.5 g/l glucose, supplemented with 10% heat-inactivated FCS and antibiotics. Chinese hamster ovarian (CHO) cells were purchased from ATCC (http://www.atcc.org; LGC Standards Office Europe, AB, Boras, Sweden) and grown in bicarbonate buffered Ham’s F-12 medium supplemented with 5% FCS and antibiotics. Cells were routinely passaged in plastic tissue culture dishes. Two to three days prior to experiments, cells were seeded on microscope slide dishes coated with poly-D-lysine.
Confocal laser scanning fluorescence microscopy
Confocal microscopy was performed using a laser scanning inverted fluorescence microscope (Zeiss LSM 510 META, Zeiss, Jena, Germany) equipped with a 63x 1.4 NA plan Apochromat water immersion objective and a 37°C temperature control (Zeiss, Jena, Germany). Fluorescein and eGFP fluorescence was collected with a 505–530 bandpass filter after excitation with a 25-milliwatt argon laser emitting at 488 nm. FLIP experiments were performed by first defining regions of interest (ROI), which were repeatedly bleached, while an image was acquired with reduced laser power (0.5% output) at the start of the experiment and after each bleach. Either one iteration (for eGFP-Huntingtin constructs in CHO cells) or five iterations (for eGFP in McA cells) with 100% laser power were used for the bleaching pulses. An eventual pause between the bleaches ensured some recovery in the ROI. Images were acquired using the time-lapse function of the Zeiss LSM510 Meta confocal system. The microscope was located at a nitrogen-floated table to prevent vibrations and focus drift and contained a temperature-controlled stage maintained at 35 ± 1°C. For spatial registration of image stacks, a plugin to ImageJ named “StackReg” developed by Dr. Thevenaz at the Biomedical Imaging Group, EPFL, Lausanne, Switzerland was used . One pixel typically corresponded to 0.055 × 0.055 μm (for example in Figures 4 and 5).
Non-linear regression of a stretched exponential function to experimental and synthetic FLIP data
using “PixBleach” our recently developed image fitting program implemented in ImageJ software (download at: http://bigwww.epfl.ch/algorithms/pixbleach/) . Here, I0 is the pre-bleach intensity of the dynamic fluorescence pool, τ is the decay time constant and I∞ is the remaining intensity at infinite time, resembling either an immobile fraction or autofluorescence of the cells (most often, I∞ approximates the background noise level). The decay time constant τ describes the rate of decrease in fluorescence intensity of the probe, either directly in the bleached region due to the intense laser beam, or outside the bleached region due to transport towards the repeatedly bleached ROI. Thus, the bleached regions act like a sink for fluorescent molecules being located outside the ROI. The stretched exponential differs from a normal mono-exponential function by an additional parameter, h, describing the stretching or compression of the decay compared to a mono-exponential function. This parameter is for h >1 a direct measure of the width of the decay constant distribution (see Results section). The stretched exponential model can therefore be considered as a linear superposition of simple mono-exponential decays . All parameter maps generated by ‘PixBleach’ are given in 32-bit format and were used in that format for further calculations.
Compartment modeling of fluorophore transport in FLIP experiments
Here, f(θ) is a geometric factor, g(λ) is the wavelength-dependent quantum efficiency of the detector, Φ F is the quantum yield of the fluorophore, L is the excitation intensity, ε is the molar extinction coefficient (in M-1 cm-1) and b is the optical path length. Thus, we can infer relative values for the concentration or density of probe molecules in certain cellular regions from the pixel-wise detected fluorescence in the images.
Here, the initial values N10 and N20 describe the initial amounts outside and inside the bleached region, respectively. We have eliminated k−1 by detailed balance, k1 · N10 = k− 1 · N20. This kinetic compartment model was simulated with varying parameter combinations as function of time using SigmaPlot 9.0 (SPSS Inc, Chicago, IL, USA) or as function of time and pixel coordinates using self-programmed Macros in ImageJ. An extension of that model to an arbitrary number of non-bleached compartments 1 is given in Appendix 2.
Analytical model of FLIP experiments with spatially invariant diffusion coefficient
with the boundary conditions that are continuous at r = r1 and at r = r2. The solution of this model is given in Appendix 1.
Numerical simulation of FLIP experiments with spatially heterogeneous diffusion
is easily implemented in FEniCS (http://www.fenicsproject.org). The FEniCS project DOLFIN compiles the pseudo-code and assembles the discrete finite element system. The resulting linear system is solved using the PETSc (http://www.mcs.anl.gov/petsc) toolkit for scientific computing. We simulated a cylindrical bleaching experiment on a disk with r2 = 1μm. A circular bleached area of radius with r1 = 0.5 μm was placed in the disk center. The bleaching rate is set to k = 10 sec-1 and the diffusion coefficient is dL = 0.2 μm2/sec and dR = 0.8 μm2/sec on the left and right half circle, respectively.
The diffusion problem in Equation 6 reads in Laplace space (): for where n0 is the initial density and
where with X = K, IA numerical inverse Laplace transform has been used to generate solutions in real space with Mathematica Version 7.0 (Wolfram research Inc.).
where i numbers individual compartments of type 1 embedded in a large compartment labeled 2. This gives the following system of differential equations:
with the initial amount in the ith compartment of type 1 denoted as N1i,0, we obtain the following solution
DW acknowledges funding by grants of the Lundbeck Foundation, the Novo Nordisk Foundation and the Danish Research Foundations FNU and FSS. We are grateful to Tanja Christensen for expert technical assistance. Dr. E. Snapp (Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, New York, USA) is acknowledged for kindly providing plasmids for mtHtt-constructs used in this study.
- Dunn KW, McGraw TE, Maxfield FR: Iterative fractionation of recycling receptors from lysosomally destined ligands in an early sorting endosome. J Cell Biol. 1989, 109: 3303-3314. 10.1083/jcb.109.6.3303.View ArticlePubMedGoogle Scholar
- Ghosh RN, Maxfield FR: Evidence for nonvectorial, retrograde transferrin trafficking in the early endosomes of HEp2 cells. J Cell Biol. 1995, 128: 549-561. 10.1083/jcb.128.4.549.View ArticlePubMedGoogle Scholar
- Ghosh RN, Gelman DL, Maxfield FR: Quantification of low density lipoprotein and transferrin endocytic sorting in HEp2 cells using confocal microscopy. J Cell Sci. 1994, 107: 2177-2189.PubMedGoogle Scholar
- Wüstner D: Quantification of polarized trafficking of transferrin and comparison with bulk membrane transport in hepatic cells. Biochem J. 2006, 400: 267-280.PubMed CentralView ArticlePubMedGoogle Scholar
- Wüstner D: Mathematical analysis of hepatic high density lipoprotein transport based on quantitative imaging data. J Biol Chem. 2005, 280: 6766-6779. 10.1074/jbc.M413238200.View ArticlePubMedGoogle Scholar
- Wüstner D: Steady state analysis and experimental validation of a model for hepatic high density lipoprotein transport. Traffic. 2006, 7: 699-715. 10.1111/j.1398-9219.2006.00421.x.View ArticlePubMedGoogle Scholar
- Dunn KW, Maxfield FR: Delivery of ligands from sorting endosomes to late endosomes occurs by maturation of sorting endosomes. J Cell Biol. 1992, 117: 301-310. 10.1083/jcb.117.2.301.View ArticlePubMedGoogle Scholar
- Sheff DR, Daro EA, Hull M, Mellman I: The receptor recycling pathway contains two distinct populations of early endosomes with different sorting functions. J Cell Biol. 1999, 145: 123-139. 10.1083/jcb.145.1.123.PubMed CentralView ArticlePubMedGoogle Scholar
- Hirschberg K, Miller CM, Ellenberg J, Presley JF, Siggia ED, Phair RD, Lippincott-Schwartz J: Kinetic analysis of secretory protein traffic and characterization of golgi to plasma membrane transport intermediates in living cells. J Cell Biol. 1998, 143: 1485-1503. 10.1083/jcb.143.6.1485.PubMed CentralView ArticlePubMedGoogle Scholar
- Dahm T, White J, Grill S, Füllekrug J, Stelzer EHK: Quantitative ER left-right-arrow golgi transport kinetics and protein separation upon golgi exit revealed by vesicular integral membrane protein 36 dynamics in live cells. Mol Biol Cell. 2001, 12: 1481-1498.PubMed CentralView ArticlePubMedGoogle Scholar
- White J, Keller P, Stelzer EH: Spatial partitioning of secretory cargo from Golgi resident proteins in live cells. BMC Cell Biol. 2001, 2: 19-10.1186/1471-2121-2-19.PubMed CentralView ArticlePubMedGoogle Scholar
- Rhee SW, Starr T, Forsten-Williams K, Storrie B: The steady-state distribution of glycosyltransferases between the Golgi apparatus and the endoplasmic reticulum is approximately 90:10. Traffic. 2005, 6: 1-13.View ArticleGoogle Scholar
- Patterson GH, Hirschberg K, Polishchuk RS, Gerlich D, Phair RD, Lippincott-Schwartz J: Transport through the Golgi apparatus by rapid partitioning within a two-phase membrane system. Cell. 2008, 133: 1055-1067. 10.1016/j.cell.2008.04.044.PubMed CentralView ArticlePubMedGoogle Scholar
- Peters R: Nuclear envelope permeability measured by fluorescence microphotolysis of single liver cell nuclei. J Biol Chem. 1983, 258: 11427-11429.PubMedGoogle Scholar
- Peters R: Nucleo-cytoplasmic flux and intracellular mobility in single hepatocytes measured by fluorescence microphotolysis. EMBO J. 1985, 3: 1831-1836.Google Scholar
- Wei X, Henke VG, Strübing C, Brown EB, Clapham DE: Real-time imaging of nuclear permeation by EGFP in single intact cells. Biophys J. 2003, 84: 1317-1327. 10.1016/S0006-3495(03)74947-9.PubMed CentralView ArticlePubMedGoogle Scholar
- Lippincott-Schwartz J, Altan-Bonnet N, Patterson GH: Photobleaching and photoactivation: following protein dynamics in living cells. Nat Cell Biol. 2003, Suppl 7: S7-14.Google Scholar
- Peters R, Brünger A, Schulten K: Continuous fluorescence microphotolysis: a sensitive method for studying diffusion processes in single cells. Proc Natl Acad Sci U S A. 1981, 78: 962-966. 10.1073/pnas.78.2.962.PubMed CentralView ArticlePubMedGoogle Scholar
- Kubitscheck U, Wedekind P, Peters R: Three-dimensional diffusion measurements by scanning microphotolysis. J Microsc. 1998, 192: 126-138. 10.1046/j.1365-2818.1998.00406.x.View ArticleGoogle Scholar
- Wachsmuth M, Weidemann T, Müller G, Hoffmann-Rohrer UW, Knoch TA, Waldeck W, Langowski J: Analyzing intracellular binding and diffusion with continuous fluorescence photobleaching. Biophys J. 2003, 84: 3353-3363. 10.1016/S0006-3495(03)70059-9.PubMed CentralView ArticlePubMedGoogle Scholar
- Müller F, Mazza D, Stasevich TJ, McNally JG: FRAP and kinetic modeling in the analysis of nuclear protein dynamics: what do we really know?. Curr Opin Cell Biol. 2010, 22: 403-411. 10.1016/j.ceb.2010.03.002.View ArticleGoogle Scholar
- van Gemert AMC, Jost CR, van der Laan A, Dirks R, Reiber JH, Tanke HJ, Lelieveldt BP: Identification of cellular dynamic patterns resulting from repetitive photobleaching using independent component analysis. I S Biomed Imaging. 2009, 1382-1385.Google Scholar
- van de Giessen M, van der Laan A, Hendriks EA, Vidorreta M, Reiber JH, Jost CR, Tanke HJ, Lelieveldt BP: Fully automated attenuation measurement and motion correction in FLIP image sequences. IEEE Trans Med Imaging. 2012, 31: 461-473.View ArticlePubMedGoogle Scholar
- Klafter J, Shlesinger MF: On the relationship among three theories of relaxation in disordered systems. Proc Natl Acad Sci U S A. 1986, 83: 848-851. 10.1073/pnas.83.4.848.PubMed CentralView ArticlePubMedGoogle Scholar
- Koppel DE: Analysis of macromolecular polydispersity in intensity correlation spectroscopy: the method of cumulants. J Chem Phys. 1972, 57: 4814-4820. 10.1063/1.1678153.View ArticleGoogle Scholar
- Koppel DE, Carlson C, Smilowitz H: Analysis of heterogeneous fluorescence photobleaching by video kinetics imaging: the method of cumulants. J Microsc. 1989, 155: 199-206. 10.1111/j.1365-2818.1989.tb02882.x.View ArticlePubMedGoogle Scholar
- Berberan-Santos MN, Bodunov EN, Valeur B: Mathematical functions for the analysis of luminescence decays with underlying distributions 1. Kohlrausch decay function (stretched exponential). Chemical Physics. 2005, 315: 171-182. 10.1016/j.chemphys.2005.04.006.View ArticleGoogle Scholar
- Lee KC, Siegel J, Webb SE, Lévêque-Fort S, Cole MJ, Jones R, Dowling K, Lever MJ, French PM: Application of the stretched exponential function to fluorescence lifetime imaging. Biophys J. 2001, 81: 1265-1274. 10.1016/S0006-3495(01)75784-0.PubMed CentralView ArticlePubMedGoogle Scholar
- Kohlrausch R: Theorie des elektrischen Rückstandes in der Leidner Flasche. Pogg Ann Phys Chem. 1854, 91: 179-213.View ArticleGoogle Scholar
- Williams G, Watts DC: Non-symmetrical dielectric relaxation behaviour arising from a simple empirical decay function. Trans Farad Soc. 1970, 66: 80-85.View ArticleGoogle Scholar
- Shlesinger MF, Montroll EW: On the Williams-Watts function of dielectric relaxation. Proc Natl Acad Sci U S A. 1984, 81: 1280-1283. 10.1073/pnas.81.4.1280.PubMed CentralView ArticlePubMedGoogle Scholar
- Cumbrera FL, Sanchez-Bajo F, Guiberteau F, Solier JD, Muñoz A: The Williams-Watts dependence as a common phenomenological approach to relaxation processes in condensed matter. J Mat Sci. 1993, 28: 5387-5396. 10.1007/BF00570095.View ArticleGoogle Scholar
- Braga J, Desterro JM, Carmo-Fonseca M: Intracellular macromolecular mobility measured by fluorescence recovery after photobleaching with confocal laser scanning microscopes. Mol Biol Cell. 2004, 15: 4749-4760. 10.1091/mbc.E04-06-0496.PubMed CentralView ArticlePubMedGoogle Scholar
- Berberan-Santos MN: Mathematical basis of the integral formalism of chemical kinetics. Compact representation of the general solution of the first-order linear differential equation. J Math Chem. 2010, 47: 1184-1188. 10.1007/s10910-009-9631-4.View ArticleGoogle Scholar
- Modeling in Biopharmaceutics, Pharmacokinetics, and Pharmacodynamics. Edited by: Macheras P, Iliadis A. 2006, New Yrok: Springer
- Berberan-Santos MN, Martinho JMG: A linear response approach to kinetics with time-dependent rate coefficients. Chem Phys. 1992, 164: 259-269. 10.1016/0301-0104(92)87149-4.View ArticleGoogle Scholar
- Lund FW, Lomholt MA, Solanko LM, Wüstner D: Two-photon time-lapse microscopy of BODIPY-cholesterol reveals anomalous sterol diffusion in chinese hamster ovary cells. 2012, In pressGoogle Scholar
- Chen Y, Müller JD: Probing nucleocytoplasmic transport by two-photon activation of PA-GFP. Microsc Res Tech. 2006, 69: 220-226. 10.1002/jemt.20252.View ArticlePubMedGoogle Scholar
- Van Oostveldt P, Verhaegen F, Messens K: Heterogeneous photobleaching in confocal microscopy caused by differences in refractive index and excitation mode. Cytometry. 1998, 32: 137-146. 10.1002/(SICI)1097-0320(19980601)32:2<137::AID-CYTO9>3.0.CO;2-I.View ArticlePubMedGoogle Scholar
- Wüstner D, Landt Larsen A, Færgeman NJ, Brewer JR, Sage D: Selective visualization of fluorescent sterols in Caenorhabditis elegans by bleach-rate based image segmentation. Traffic. 2010, 11: 440-454. 10.1111/j.1600-0854.2010.01040.x.View ArticlePubMedGoogle Scholar
- Li SH, Li XJ: Huntingtin-protein interactions and the pathogenesis of Huntington's disease. Trends Genet. 2004, 20: 146-154. 10.1016/j.tig.2004.01.008.View ArticlePubMedGoogle Scholar
- Hatters DM: Protein misfolding inside cells: the case of Huntingtin and Huntington's disease. IUBMB Life. 2008, 60: 724-728. 10.1002/iub.111.View ArticlePubMedGoogle Scholar
- Kim S, Nollen EA, Kitagawa K, Bindokas VP, Morimoto RI: Polyglutamine protein aggregates are dynamic. Nat Cell Biol. 2002, 4: 826-831. 10.1038/ncb863.View ArticlePubMedGoogle Scholar
- Matsumoto G, Kim S, Morimoto RI: Huntingtin and mutant SOD1 form aggregate structures with distinct molecular properties in human cells. J Biol Chem. 2006, 281: 4477-4485. 10.1074/jbc.M509201200.View ArticlePubMedGoogle Scholar
- Lajoie P, Snapp EL: Formation and toxicity of soluble polyglutamine oligomers in living cells. PloS One. 2010, 5: e15245-10.1371/journal.pone.0015245.PubMed CentralView ArticlePubMedGoogle Scholar
- Ossato G, Digman MA, Aiken C, Lukacsovich T, Marsh JL, Gratton E: A two-step path to inclusion formation of huntingtin peptides revealed by number and brightness analysis. Biophys J. 2010, 98: 3078-3085. 10.1016/j.bpj.2010.02.058.PubMed CentralView ArticlePubMedGoogle Scholar
- Sage D, Neumann FR, Hediger F, Gasser SM, Unser M: Automatic tracking of individual fluorescence particles: application to the study of chromosome dynamics. IEEE T Image Process. 2005, 14: 1372-1383.View ArticleGoogle Scholar
- Brandt S: Datenanalyse. 1999, Heidelberg: Spektrum VerlagGoogle Scholar
- Brown CM, Dalal RB, Hebert B, Digman MA, Horwitz AR, Gratton E: Raster image correlation spectroscopy (RICS) for measuring fast protein dynamics and concentrations with a commercial laser scanning confocal microscope. J Microsc. 2008, 229: 78-91. 10.1111/j.1365-2818.2007.01871.x.PubMed CentralView ArticlePubMedGoogle Scholar
- Kühn T, Ihalainen TO, Hyväluoma J, Dross N, Willman SF, Langowski J, Vihinen-Ranta M, Timonen J: Protein diffusion in mammalian cell cytoplasm. PLoS One. 2011, 6: e22962-10.1371/journal.pone.0022962.PubMed CentralView ArticlePubMedGoogle Scholar
- Kasza KE, Rowat AC, Liu J, Angelini TE, Brangwynne CP, Koenderink GH, Weitz DA: The cell as a material. Curr Opin Cell Biol. 2007, 19: 101-107. 10.1016/j.ceb.2006.12.002.View ArticlePubMedGoogle Scholar
- Brangwynne CP, Koenderink GH, MacKintosh FC, Weitz DA: Intracellular transport by active diffusion. Trends Cell Biol. 2009, 19: 423-427. 10.1016/j.tcb.2009.04.004.View ArticlePubMedGoogle Scholar
- Bancaud A, Huet S, Daigle N, Mozziconacci J, Beaudouin J, Ellenberg J: Molecular crowding affects diffusion and binding of nuclear proteins in heterochromatin and reveals the fractal organization of chromatin. EMBO J. 2009, 28: 3785-3798. 10.1038/emboj.2009.340.PubMed CentralView ArticlePubMedGoogle Scholar
- Hinde E, Cardarelli F, Digman MA, Gratton E: In vivo pair correlation analysis of EGFP intranuclear diffusion reveals DNA-dependent molecular flow. Proc Natl Acad Sci U S A. 2010, 107: 16560-16565. 10.1073/pnas.1006731107.PubMed CentralView ArticlePubMedGoogle Scholar
- Digman MA, Gratton E: Imaging barriers to diffusion by pair correlation functions. Biophys J. 2009, 97: 665-673. 10.1016/j.bpj.2009.04.048.PubMed CentralView ArticlePubMedGoogle Scholar
- Cardarelli F, Gratton E: In vivo imaging of single-molecule translocation through nuclear pore complexes by pair correlation functions. PLoS One. 2010, 5: e10475-10.1371/journal.pone.0010475.PubMed CentralView ArticlePubMedGoogle Scholar
- Thevenaz P, Ruttimann UE, Unser E: A pyramid approach to subpixel registration based on intensity. IEEE T Image Process. 1998, 7: 27-41. 10.1109/83.650848.View ArticleGoogle Scholar
- Chai Y, Shao J, Miller VM, Williams A, Paulson HL: Live-cell imaging reveals divergent intracellular dynamics of polyglutamine disease proteins and supports a sequestration model of pathogenesis. Proc Natl Acad Sci U S A. 2002, 99: 9310-9315. 10.1073/pnas.152101299.PubMed CentralView ArticlePubMedGoogle Scholar
- Nag S, Chen J, Irudayaraj J, Maiti S: Measurement of the attachment and assembly of small amyloid-beta oligomers on live cell membranes at physiological concentrations using single-molecule tools. Biophys J. 2010, 99: 1969-1975. 10.1016/j.bpj.2010.07.020.PubMed CentralView ArticlePubMedGoogle Scholar
- Goodwin JS, Drake KR, Remmert CL, Kenworthy AK: Ras diffusion is sensitive to plasma membrane viscosity. Biophys J. 2005, 89: 1398-1410. 10.1529/biophysj.104.055640.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang H, Hu L, Dalen K, Dorward H, Marcinkiewicz A, Russell D, Gong D, Londos C, Yamaguchi T, Holm C, et al: Activation of hormone-sensitive lipase requires two steps, protein phosphorylation and binding to the PAT-1 domain of lipid droplet coat proteins. J Biol Chem. 2009, 284: 32116-32125. 10.1074/jbc.M109.006726.PubMed CentralView ArticlePubMedGoogle Scholar
- McNairn AJ, Gerton JL: Intersection of ChIP and FLIP, genomic methods to study the dynamics of the cohesin proteins. Chromosome Res. 2009, 17: 155-163. 10.1007/s10577-008-9007-9.View ArticlePubMedGoogle Scholar
- Rink J, Ghigo E, Kalaidzidis Y, Zerial M: Rab conversion as a mechanism of progression from early to late endosomes. Cell. 2005, 122: 735-749. 10.1016/j.cell.2005.06.043.View ArticlePubMedGoogle Scholar
- Cunningham CW, Mukhopadhyay A, Lushington GH, Blagg BS, Prisinzano TE, Krise JP: Uptake, distribution and diffusivity of reactive fluorophores in cells: implications toward target identification. Mol Pharm. 2010, 7: 1301-1310. 10.1021/mp100089k.PubMed CentralView ArticlePubMedGoogle Scholar
- Yu L, Bharadwaj S, Brown JM, Ma Y, Du W, Davis MA, Michaely P, Liu P, Willingham MC, Rudel LL: Cholesterol-regulated translocation of NPC1L1 to the cell surface facilitates free cholesterol uptake. J Biol Chem. 2006, 281: 6616-6624. 10.1074/jbc.M511123200.View ArticlePubMedGoogle Scholar
- Bright GR, Fisher GW, Rogowska J, Taylor DL: Fluorescence ratio imaging microscopy. Methods in Cell Biology. 1989, 30: 157-192.View ArticlePubMedGoogle Scholar
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