Single- and two-photon fluorescence recovery after photobleaching.
Ontology highlight
ABSTRACT:
SUBMITTER: Sullivan KD
PROVIDER: S-EPMC4399237 | biostudies-other | 2015 Jan
REPOSITORIES: biostudies-other
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Project description:Fluorescence recovery after photobleaching (FRAP) is a well-established experimental technique to study binding and diffusion of molecules in cells. Although a large number of analytical and numerical models have been developed to extract binding and diffusion rates from FRAP recovery curves, active transport of molecules is typically not included in the existing models that are used to estimate these rates. Here we present a validated numerical method for estimating diffusion, binding/unbinding rates, and active transport velocities using FRAP data that captures intracellular dynamics through partial differential equation models. We apply these methods to transport and localization of mRNA molecules in Xenopus laevis oocytes, where active transport processes are essential to generate developmental polarity. By providing estimates of the effective velocities and diffusion, as well as expected run times and lengths, this approach can help quantify dynamical properties of localizing and nonlocalizing RNA. Our results confirm the distinct transport dynamics in different regions of the cytoplasm, and suggest that RNA movement in both the animal and vegetal directions may influence the timescale of RNA localization in Xenopus oocytes. We also show that model initial conditions extracted from FRAP postbleach intensities prevent underestimation of diffusion, which can arise from the instantaneous bleaching assumption. The numerical and modeling approach presented here to estimate parameters using FRAP recovery data is a broadly applicable tool for systems where intracellular transport is a key molecular mechanism.
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Project description:Fluorescence recovery after photobleaching (FRAP) is now widely used to investigate binding interactions in live cells. Although various idealized solutions have been identified for the reaction-diffusion equations that govern FRAP, there has been no comprehensive analysis or systematic approach to serve as a guide for extracting binding information from an arbitrary FRAP curve. Here we present a complete solution to the FRAP reaction-diffusion equations for either single or multiple independent binding interactions, and then relate our solution to the various idealized cases. This yields a coherent approach to extract binding information from FRAP data which we have applied to the question of transcription factor mobility in the nucleus. We show that within the nucleus, the glucocorticoid receptor is transiently bound to a single state, with each molecule binding on average 65 sites per second. This rapid sampling is likely to be important in finding a specific promoter target sequence. Further we show that this predominant binding state is not the nuclear matrix, as some studies have suggested. We illustrate how our analysis provides several self-consistency checks on a FRAP fit. We also define constraints on what can be estimated from FRAP data, show that diffusion should play a key role in many FRAP recoveries, and provide tools to test its contribution. Overall our approach establishes a more general framework to assess the role of diffusion, the number of binding states, and the binding constants underlying a FRAP recovery.
| S-EPMC1304253 | biostudies-literature
Project description:The determination of diffusion coefficients from fluorescence recovery data is often complicated by geometric constraints imposed by the complex shapes of intracellular compartments. To address this issue, diffusion of proteins in the lumen of the endoplasmic reticulum (ER) is studied using cell biological and computational methods. Fluorescence recovery after photobleaching (FRAP) experiments are performed in tissue culture cells expressing GFP-KDEL, a soluble, fluorescent protein, in the ER lumen. The three-dimensional (3D) shape of the ER is determined by confocal microscopy and computationally reconstructed. Within these ER geometries diffusion of solutes is simulated using the method of particle strength exchange. The simulations are compared to experimental FRAP curves of GFP-KDEL in the same ER region. Comparisons of simulations in the 3D ER shapes to simulations in open 3D space show that the constraints imposed by the spatial confinement result in two- to fourfold underestimation of the molecular diffusion constant in the ER if the geometry is not taken into account. Using the same molecular diffusion constant in different simulations, the observed speed of fluorescence recovery varies by a factor of 2.5, depending on the particular ER geometry and the location of the bleached area. Organelle shape considerably influences diffusive transport and must be taken into account when relating experimental photobleaching data to molecular diffusion coefficients. This novel methodology combines experimental FRAP curves with high accuracy computer simulations of diffusion in the same ER geometry to determine the molecular diffusion constant of the solute in the particular ER lumen.
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Project description:Fluorescence recovery after photobleaching (FRAP) using confocal laser scanning microscopes (confocal FRAP) has become a valuable technique for studying the diffusion of biomolecules in cells. However, two-dimensional confocal FRAP sometimes yields results that vary with experimental setups, such as different bleaching protocols and bleaching spot sizes. In addition, when confocal FRAP is used to measure diffusion coefficients (D) for fast diffusing molecules, it often yields D-values that are one or two orders-of-magnitude smaller than that predicted theoretically or measured by alternative methods such as fluorescence correlation spectroscopy. Recently, it was demonstrated that this underestimation of D can be corrected by taking diffusion during photobleaching into consideration. However, there is currently no consensus on confocal FRAP theory, and no efforts have been made to unify theories on conventional and confocal FRAP. To this end, we generalized conventional FRAP theory to incorporate diffusion during photobleaching so that analysis by conventional FRAP theory for a circular region of interest is easily applicable to confocal FRAP. Finally, we demonstrate the accuracy of these new (to our knowledge) formulae by measuring D for soluble enhanced green fluorescent protein in aqueous glycerol solution and in the cytoplasm and nucleus of COS7 cells.
| S-EPMC2749741 | biostudies-literature
Project description:Cells contain many subcellular structures in which specialized proteins locally cluster. Binding interactions within such clusters may be analyzed in live cells using models for fluorescence recovery after photobleaching (FRAP). Here we analyze a three-dimensional FRAP model that accounts for a single spatially localized cluster of binding sites in the presence of both diffusion and impermeable boundaries. We demonstrate that models completely ignoring the spatial localization of binding yield poor estimates for the binding parameters within the binding site cluster. In contrast, we find that ignoring only the restricted axial height of the binding-site cluster is far less detrimental, thereby enabling the use of computationally less expensive models. We also identify simplified solutions to the FRAP model for limiting behaviors where either diffusion or binding dominate. We show how ignoring a role for diffusion can sometimes produce serious errors in binding parameter estimation. We illustrate application of the method by analyzing binding of a transcription factor, the glucocorticoid receptor, to a tandem array of mouse mammary tumor virus promoter sites in live cells, obtaining an estimate for an in vivo binding constant (10(-7) M), and a first approximation of an upper bound on the transcription-factor residence time at the promoter (approximately 170 ms). These FRAP analysis tools will be important for measuring key cellular binding parameters necessary for a complete and accurate description of the networks that regulate cellular behavior.
| S-EPMC1518639 | biostudies-literature
Project description:BackgroundAcetylation of lysine residues in histone tails plays an important role in the regulation of gene transcription. Bromdomains are the readers of acetylated histone marks, and, consequently, bromodomain-containing proteins have a variety of chromatin-related functions. Moreover, they are increasingly being recognised as important mediators of a wide range of diseases. The first potent and selective bromodomain inhibitors are beginning to be described, but the diverse or unknown functions of bromodomain-containing proteins present challenges to systematically demonstrating cellular efficacy and selectivity for these inhibitors. Here we assess the viability of fluorescence recovery after photobleaching (FRAP) assays as a target agnostic method for the direct visualisation of an on-target effect of bromodomain inhibitors in living cells.ResultsMutation of a conserved asparagine crucial for binding to acetylated lysines in the bromodomains of BRD3, BRD4 and TRIM24 all resulted in reduction of FRAP recovery times, indicating loss of or significantly reduced binding to acetylated chromatin, as did the addition of known inhibitors. Significant differences between wild type and bromodomain mutants for ATAD2, BAZ2A, BRD1, BRD7, GCN5L2, SMARCA2 and ZMYND11 required the addition of the histone deacetylase inhibitor suberoylanilide hydroxamic acid (SAHA) to amplify the binding contribution of the bromodomain. Under these conditions, known inhibitors decreased FRAP recovery times back to mutant control levels. Mutation of the bromodomain did not alter FRAP recovery times for full-length CREBBP, even in the presence of SAHA, indicating that other domains are primarily responsible for anchoring CREBBP to chromatin. However, FRAP assays with multimerised CREBBP bromodomains resulted in a good assay to assess the efficacy of bromodomain inhibitors to this target. The bromodomain and extraterminal protein inhibitor PFI-1 was inactive against other bromodomain targets, demonstrating the specificity of the method.ConclusionsViable FRAP assays were established for 11 representative bromodomain-containing proteins that broadly cover the bromodomain phylogenetic tree. Addition of SAHA can overcome weak binding to chromatin, and the use of tandem bromodomain constructs can eliminate masking effects of other chromatin binding domains. Together, these results demonstrate that FRAP assays offer a potentially pan-bromodomain method for generating cell-based assays, allowing the testing of compounds with respect to cell permeability, on-target efficacy and selectivity.
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Project description:Cells contain numerous membraneless organelles that assemble by intracellular liquid-liquid phase separation. The viscous properties and associated biomolecular mobility within these condensed phase droplets, or condensates, are increasingly recognized as important for cellular function and also dysfunction, for example, in protein aggregation pathologies. Fluorescence recovery after photobleaching (FRAP) is widely used to assess condensate fluidity and to estimate protein diffusion coefficients. However, the models and assumptions utilized in FRAP analysis of protein condensates are often not carefully considered. Here, we combine FRAP experiments on both in vitro reconstituted droplets and intracellular condensates with systematic examination of different models that can be used to fit the data and evaluate the impact of model choice on measured values. A key finding is that model boundary conditions can give rise to widely divergent measured values. This has important implications, for example, in experiments that bleach subregions versus the entire condensate, two commonly employed experimental approaches. We suggest guidelines for determining the appropriate modeling framework and highlight emerging questions about the molecular dynamics at the droplet interface. The ability to accurately determine biomolecular mobility both in the condensate interior and at the interface is important for obtaining quantitative insights into condensate function, a key area for future research.
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Project description:The multiphoton fluorescence recovery after photobleaching (MP-FRAP) technique has been developed to measure the three-dimensional (3D) solute diffusion within biological systems. However, current 3D MP-FRAP models are based on isotropic diffusion and spatial domain analysis. The 3D anisotropic diffusion and frequency domain analysis for MP-FRAP measurements are rarely studied. In this study, a new technique is demonstrated for the quantitative and non-destructive determination of 3D anisotropic solute diffusion tensors within biological fibrosis tissues by multiphoton photobleaching and spatial Fourier analysis (SFA). Compared to the spatial domain analysis based MP-FRAP techniques, this SFA-based method has the capability for determining the 3D anisotropic diffusion tensors as well as the flexibility for satisfying initial and boundary conditions. First, a close-form solution of the 3D anisotropic diffusion equation is derived by solely using SFA. Next, this new method is validated by computer-simulated MP-FRAP experiments with pre-defined 3D anisotropic diffusion tensors as well as experimental diffusion measurements of FITC-Dextran (FD) molecules in aqueous glycerol solutions. Finally, this MP-FRAP technique is applied to the measurement of 3D anisotropic diffusion tensors of FD molecules within porcine tendon tissues. This study provides a new tool for complete determination of 3D anisotropic solute diffusion tensor in biological tissues.
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Project description:Conventional analysis of fluorescence recovery after photobleaching (FRAP) data for diffusion coefficient estimation typically involves fitting an analytical or numerical FRAP model to the recovery curve data using non-linear least squares. Depending on the model, this can be time consuming, especially for batch analysis of large numbers of data sets and if multiple initial guesses for the parameter vector are used to ensure convergence. In this work, we develop a completely new approach, DeepFRAP, utilizing machine learning for parameter estimation in FRAP. From a numerical FRAP model developed in previous work, we generate a very large set of simulated recovery curve data with realistic noise levels. The data are used for training different deep neural network regression models for prediction of several parameters, most importantly the diffusion coefficient. The neural networks are extremely fast and can estimate the parameters orders of magnitude faster than least squares. The performance of the neural network estimation framework is compared to conventional least squares estimation on simulated data, and found to be strikingly similar. Also, a simple experimental validation is performed, demonstrating excellent agreement between the two methods. We make the data and code used publicly available to facilitate further development of machine learning-based estimation in FRAP. LAY DESCRIPTION: Fluorescence recovery after photobleaching (FRAP) is one of the most frequently used methods for microscopy-based diffusion measurements and broadly used in materials science, pharmaceutics, food science and cell biology. In a FRAP experiment, a laser is used to photobleach fluorescent particles in a region. By analysing the recovery of the fluorescence intensity due to the diffusion of still fluorescent particles, the diffusion coefficient and other parameters can be estimated. Typically, a confocal laser scanning microscope (CLSM) is used to image the time evolution of the recovery, and a model is fit using least squares to obtain parameter estimates. In this work, we introduce a new, fast and accurate method for analysis of data from FRAP. The new method is based on using artificial neural networks to predict parameter values, such as the diffusion coefficient, effectively circumventing classical least squares fitting. This leads to a dramatic speed-up, especially noticeable when analysing large numbers of FRAP data sets, while still producing results in excellent agreement with least squares. Further, the neural network estimates can be used as very good initial guesses for least squares estimation in order to make the least squares optimization convergence much faster than it otherwise would. This provides for obtaining, for example, diffusion coefficients as soon as possible, spending minimal time on data analysis. In this fashion, the proposed method facilitates efficient use of the experimentalist's time which is the main motivation to our approach. The concept is demonstrated on pure diffusion. However, the concept can easily be extended to the diffusion and binding case. The concept is likely to be useful in all application areas of FRAP, including diffusion in cells, gels and solutions.
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Project description:A fully-mechanistic understanding of protein transport and sorption in chromatographic materials has remained elusive despite the application of modern continuum and molecular observation techniques. While measuring overall uptake rates in proteins in chromatographic media is relatively straightforward, quantifying mechanistic contributions is much more challenging. Further, at equilibrium in fully-loaded particles, measuring rates of kinetic exchange and diffusion can be very challenging. As models of multicomponent separations rely on accurate depictions of protein displacement and elution, a straightforward method is desired to measure the mobility of bound protein in chromatographic media. We have adapted fluorescence recovery after photobleaching (FRAP) methods to study transport and exchange of protein at equilibrium in a single particle. Further, we have developed a mathematical model to capture diffusion and desorption rates governing fluorescence recovery and investigate how these rates vary as a function of protein size, binding strength and media type. An emphasis is placed on explaining differences between polymer-modified and traditional media, which in the former case is characterized by rapid uptake, slow displacement and large elution pools, differences that have been postulated to result from steric and kinetic limitations. Finally, good qualitative agreement is achieved predicting flow confocal displacement profiles in polymer-modified materials, based solely on estimates of kinetic and diffusion parameters from FRAP observations.
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