FRAP analysis: accounting for bleaching during image capture

Probing Microscale Structuring-Induced Phase Separation with Fluorescence Recovery Diffusion Dynamics in Poly(ethylene glycol) Solutions

Apart from biocompatibility, poly(ethylene glycol) (PEG)-based biomedical constructs require mechanical tunability and optimization of microscale transport for regulation of the release kinetics of biomolecules. This study illustrates the role of inhomogeneities due to aggregates and structuring in the PEG matrix in the microscale diffusion of a fluorescent probe. Comparative analysis of fluorescence recovery after photobleaching (FRAP) profiles with the help of diffusion half-time is used to assess the diffusion coefficient (D). The observations support a nontrivial dependence of diffusion dynamics on polymer concentration (volume fraction, φ) and that of fillers carboxymethyl cellulose (CMC) and nanoclay bentonite (B). D values follow the Rouse scaling D ∼ φ-0.54 in PEG solutions. The diffusion time of the fluorescent probe in the PEG+bentonite matrix reveals the onset of depletion interaction-induced phase separation with an increase in bentonite concentration in the PEG matrix beyond 0.1 wt %. Beyond this concentration, structure factors obtained from prebleach FRAP images show a rapid increase at low Q. The two-phase system (PEG-rich and bentonite-rich) was characterized by the hierarchical structural topology of bentonite aggregates, and aggregate sizes were obtained at different length scales with phase contrast imaging, small-angle neutron scattering, and small-angle X-ray scattering. The microscale transport detection presented captures sensitively the commencement of phase separation in the PEG + bentonite matrix, as opposed to the PEG or PEG + CMC matrix, which are observed to be one-phase systems. This method of diffusion half-time and prebleach image analysis can be used for the fast, high-throughput experimental investigation of microscale mechanical response and its correlation with structuring in the polymer matrix.

Direct determination of oligomeric organization of integral membrane proteins and lipids from intact customizable bilayer

Hierarchical organization of integral membrane proteins (IMP) and lipids at the membrane is essential for regulating myriad downstream signaling. A quantitative understanding of these processes requires both detections of oligomeric organization of IMPs and lipids directly from intact membranes and determination of key membrane components and properties that regulate them. Addressing this, we have developed a platform that enables native mass spectrometry (nMS) analysis of IMP-lipid complexes directly from intact and customizable lipid membranes. Both the lipid composition and membrane properties (such as curvature, tension, and fluidity) of these bilayers can be precisely customized to a target membrane. Subsequent direct nMS analysis of these intact proteolipid vesicles can yield the oligomeric states of the embedded IMPs, identify bound lipids, and determine the membrane properties that can regulate the observed IMP-lipid organization. Applying this method, we show how lipid binding regulates neurotransmitter release and how membrane composition regulates the functional oligomeric state of a transporter.

A zona incerta-basomedial amygdala circuit modulates aversive expectation in emotional stress-induced aversive learning deficits

A previously published study showed that stress may interfere with associative aversive learning and facilitate mood-related disorders. However, whether emotional stress alone affects aversive learning is unknown. Using three chamber-vicarious social defeat stress (3C-VSDS) model mice, we investigated the effect of emotional stress on aversive learning. An important origin of dopamine (DA) neurons, the zona incerta (ZI), is expected to be a novel target for the modulation of aversive learning. However, less is known about the circuit mechanism of ZI^DA neurons in aversive learning. Here, we subjected mice to a fear-conditioning system (FCS) and observed an increased calcium activity of ZI TH⁺ neurons in aversive expectation during the conditioning phase, especially during the late stage of the conditional stimulus (CS) when CS and unconditional stimulus (US) pairings were used. Optogenetic inhibition of ZI TH⁺ neurons at the late stage of CS disrupted conditioned fear learning in mice. We further identified a TH⁺ projection from the ZI to the basomedial amygdala (BMA) and found that optogenetic inhibition of the ZI-BMA circuit could also block aversive learning. Finally, we used 3C-VSDS mice as a model of emotional stress. We found that the 3C-VSDS model mice demonstrated reduced aversive expectation associated with ZI TH⁺ neurons in the late stage of CS and impaired aversive learning in FCS. Optogenetic activation of ZI-BMA TH⁺ projections in the late stage of CS significantly reversed the aversive FCS learning disability of 3C-VSDS model mice. These data suggest that a TH⁺ circuit from the ZI to the BMA is required for aversive expectation, both at baseline and in 3C-VSDS-induced aversive learning deficits and that this circuit is a potential target for the modulation of aversive learning. Low activity of ZI-BMA TH⁺ projections is one reason for 3C-VSDS-induced aversive learning deficits.

Fluorescence Recovery After Photobleaching to Study the Dynamics of Membrane-Bound Proteins In Vivo Using the Drosophila Embryo

The epithelial-to-mesenchymal transition is a highly dynamic cell process and tools such as fluorescence recovery after photobleaching (FRAP), which allow the study of rapid protein dynamics, enable the following of this process in vivo. This technique uses a short intense pulse of photons to disrupt the fluorescence of a tagged protein in a region of a sample. The fluorescent signal intensity after this bleaching is then recorded and the signal recovery used to provide an indicator of the dynamics of the protein of interest. This technique can be applied to any fluorescently tagged protein, but membrane-bound proteins present an interesting challenge as they are spatially confined and subject to specialized cellular trafficking. Several methods of analysis can be applied which can disentangle these various processes and enable the extraction of information from the recovery curves. Here we describe this technique when applied to the quantification of the plasma membrane-bound E-cadherin protein in vivo using the epidermis of the late embryo of Drosophila melanogaster (Drosophila) as an example of this technique.

DeepFRAP: Fast fluorescence recovery after photobleaching data analysis using deep neural networks

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.

A Highly Accurate Pixel-Based FRAP Model Based on Spectral-Domain Numerical Methods

We introduce a new, to our knowledge, numerical model based on spectral methods for analysis of fluorescence recovery after photobleaching data. The model covers pure diffusion and diffusion and binding (reaction-diffusion) with immobile binding sites, as well as arbitrary bleach region shapes. Fitting of the model is supported using both conventional recovery-curve-based estimation and pixel-based estimation, in which all individual pixels in the data are utilized. The model explicitly accounts for multiple bleach frames, diffusion (and binding) during bleaching, and bleaching during imaging. To our knowledge, no other fluorescence recovery after photobleaching framework incorporates all these model features and estimation methods. We thoroughly validate the model by comparison to stochastic simulations of particle dynamics and find it to be highly accurate. We perform simulation studies to compare recovery-curve-based estimation and pixel-based estimation in realistic settings and show that pixel-based estimation is the better method for parameter estimation as well as for distinguishing pure diffusion from diffusion and binding. We show that accounting for multiple bleach frames is important and that the effect of neglecting this is qualitatively different for the two estimation methods. We perform a simple experimental validation showing that pixel-based estimation provides better agreement with literature values than recovery-curve-based estimation and that accounting for multiple bleach frames improves the result. Further, the software developed in this work is freely available online.

Effective scheme of photolysis of GFP in live cell as revealed with confocal fluorescence microscopy

We proposed an effective kinetics scheme of photolysis of green fluorescent protein (GFP) observed in live cells with a commercial confocal fluorescence microscope. We investigated the photolysis of GFP-tagged heterochromatin protein, HP1β-GFP, in live nucleus with the pulse position modulation approach, which has several advantages over the classical pump-and-probe method. At the basis of the proposed scheme lies a process of photoswitching from the native fluorescence state to the intermediate fluorescence state, which has a lower fluorescence yield and recovers back to native state in the dark. This kinetics scheme includes four effective parameters (photoswitching, reverse switching, photodegradation rate constants, and relative brightness of the intermediate state) and covers the time scale from dozens of milliseconds to minutes of the experimental fluorescence kinetics. Additionally, the applicability of the scheme was demonstrated in the cases of continuous irradiation and the classical pump-and-probe approach using numerical calculations and analytical solutions. An interesting finding of experimental data analysis was that the overall photodegradation of GFP proceeds dominantly from the intermediate state, and demonstrated approximately the second-order reaction versus irradiation power. As a practical example, the proposed scheme elucidates the artifacts of fluorescence recovery after the photobleaching method, and allows us to propose some suggestions on how to diminish them.

FRAP to Characterize Molecular Diffusion and Interaction in Various Membrane Environments

Fluorescence recovery after photobleaching (FRAP) is a standard method used to study the dynamics of lipids and proteins in artificial and cellular membrane systems. The advent of confocal microscopy two decades ago has made quantitative FRAP easily available to most laboratories. Usually, a single bleaching pattern/area is used and the corresponding recovery time is assumed to directly provide a diffusion coefficient, although this is only true in the case of unrestricted Brownian motion. Here, we propose some general guidelines to perform FRAP experiments under a confocal microscope with different bleaching patterns and area, allowing the experimentalist to establish whether the molecules undergo Brownian motion (free diffusion) or whether they have restricted or directed movements. Using in silico simulations of FRAP measurements, we further indicate the data acquisition criteria that have to be verified in order to obtain accurate values for the diffusion coefficient and to be able to distinguish between different diffusive species. Using this approach, we compare the behavior of lipids in three different membrane platforms (supported lipid bilayers, giant liposomes and sponge phases), and we demonstrate that FRAP measurements are consistent with results obtained using other techniques such as Fluorescence Correlation Spectroscopy (FCS) or Single Particle Tracking (SPT). Finally, we apply this method to show that the presence of the synaptic protein Munc18-1 inhibits the interaction between the synaptic vesicle SNARE protein, VAMP2, and its partner from the plasma membrane, Syn1A.

Fluorescence recovery after photobleaching in material and life sciences: putting theory into practice

Fluorescence recovery after photobleaching (FRAP) is a versatile tool for determining diffusion and interaction/binding properties in biological and material sciences. An understanding of the mechanisms controlling the diffusion requires a deep understanding of structure-interaction-diffusion relationships. In cell biology, for instance, this applies to the movement of proteins and lipids in the plasma membrane, cytoplasm and nucleus. In industrial applications related to pharmaceutics, foods, textiles, hygiene products and cosmetics, the diffusion of solutes and solvent molecules contributes strongly to the properties and functionality of the final product. All these systems are heterogeneous, and accurate quantification of the mass transport processes at the local level is therefore essential to the understanding of the properties of soft (bio)materials. FRAP is a commonly used fluorescence microscopy-based technique to determine local molecular transport at the micrometer scale. A brief high-intensity laser pulse is locally applied to the sample, causing substantial photobleaching of the fluorescent molecules within the illuminated area. This causes a local concentration gradient of fluorescent molecules, leading to diffusional influx of intact fluorophores from the local surroundings into the bleached area. Quantitative information on the molecular transport can be extracted from the time evolution of the fluorescence recovery in the bleached area using a suitable model. A multitude of FRAP models has been developed over the years, each based on specific assumptions. This makes it challenging for the non-specialist to decide which model is best suited for a particular application. Furthermore, there are many subtleties in performing accurate FRAP experiments. For these reasons, this review aims to provide an extensive tutorial covering the essential theoretical and practical aspects so as to enable accurate quantitative FRAP experiments for molecular transport measurements in soft (bio)materials.

Oil diffusivity through fat crystal networks

Oil migration in chocolate and chocolate-based confections leads to undesirable visual and textural changes. Establishing ways to slow this unavoidable process would increase shelf life and reduce consumer rejection. Diffusion is most often credited as the main pathway by which oil migration occurs. Here, we use fluorescence recovery after photobleaching (FRAP) to explore the diffusion coefficients of vegetable and mineral oil through fat crystal networks at different solid fat contents (SFC). Differences in compatibility between the fat and oil lead to unique primary crystal clusters, yet those variations do not affect diffusion at low SFCs. Trends deviate at higher SFCs, which we ascribe to the influence of the differing crystal cluster structures. We relate our results to the strong and weak-link rheological regimes of fat crystal networks. Finally, we connect the results to relationships developed for polymer gel systems.

Validation of Normalizations, Scaling, and Photofading Corrections for FRAP Data Analysis

Fluorescence Recovery After Photobleaching (FRAP) has been a versatile tool to study transport and reaction kinetics in live cells. Since the fluorescence data generated by fluorescence microscopy are in a relative scale, a wide variety of scalings and normalizations are used in quantitative FRAP analysis. Scaling and normalization are often required to account for inherent properties of diffusing biomolecules of interest or photochemical properties of the fluorescent tag such as mobile fraction or photofading during image acquisition. In some cases, scaling and normalization are also used for computational simplicity. However, to our best knowledge, the validity of those various forms of scaling and normalization has not been studied in a rigorous manner. In this study, we investigate the validity of various scalings and normalizations that have appeared in the literature to calculate mobile fractions and correct for photofading and assess their consistency with FRAP equations. As a test case, we consider linear or affine scaling of normal or anomalous diffusion FRAP equations in combination with scaling for immobile fractions. We also consider exponential scaling of either FRAP equations or FRAP data to correct for photofading. Using a combination of theoretical and experimental approaches, we show that compatible scaling schemes should be applied in the correct sequential order; otherwise, erroneous results may be obtained. We propose a hierarchical workflow to carry out FRAP data analysis and discuss the broader implications of our findings for FRAP data analysis using a variety of kinetic models.

Fluorescence recovery after photobleaching (FRAP): acquisition, analysis, and applications

A significant number of biological processes occur at, or involve cellular membranes, including; cell adhesion, migration, endocytosis, signal transduction, and many biochemical reactions involving membrane anchored scaffolds. Each process involves a complex arrangement of interacting molecules whose location in space and time influence the outcome of the event. In this protocol we discuss the application of fluorescence recovery after photobleaching (FRAP) to study the dynamics of membrane associated molecules. We discuss the principles, acquisition and the analysis of FRAP data and address issues surrounding its interpretation.

Photobleaching of fluorescein as a probe for oxidative stress in single cells

BACKGROUND

ROS are involved in the regulation of many physiological and pathological processes. Apoptosis and necrosis are processes that are induced by changes in concentrations of Reactive Oxygen Species (ROS). This study aims to detect and quantify the cellular response to changing ROS concentrations in the scope of apoptosis and necrosis.

METHODS

Photobleaching of the fluorescent substrate fluorescein is used as a probe to detect the response of individual Jurkat-T-lymphocytes and Prostate-Cancer-3(PC-3) cells to oxidative stress, induced by hydrogen peroxide (H₂O₂). A kinetic model is proposed to describe changes in intracellular dye quantities due to photobleaching, dye hydrolysis, influx and leakage, yielding a single time-dependent decaying exponent+constant.

RESULTS

Fluorescein photobleaching is controlled and used to detect intracellular ROS. An increase in the decay time of fluorescence of intracellular fluorescein (slow photobleaching) was measured from cells incubated with H₂O₂ at 50 μM. At higher H₂O₂ concentrations a decrease in the decay time was measured (fast photobleaching), in contrast to in vitro results with fluorescein and H₂O₂ in phosphate buffer saline (PBS), where the addition of H₂O₂ decreases the decay time, regardless of the irradiation dose used.

CONCLUSIONS

The anomalous, ROS-concentration dependent reduction of the photobleaching rate in cells, as opposed to solutions, might indicate on the regulation of the activity of intracellular oxidative-stress protective mechanisms, as seen earlier with other methods.

SIGNIFICANCE

Assessing photobleaching via the time decay of the fluorescence intensity of an ROS-sensitive fluorophore may be adapted to monitor oxidative stress or ROS-related processes in cells.

Dynamics of mechanosensing in the bacterial flagellar motor

Mechanosensing by flagella is thought to trigger bacterial swarmer-cell differentiation, an important step in pathogenesis. How flagellar motors sense mechanical stimuli is not known. To study this problem, we suddenly increased the viscous drag on motors by a large factor, from very low loads experienced by motors driving hooks or hooks with short filament stubs, to high loads, experienced by motors driving tethered cells or 1-μm latex beads. From the initial speed (after the load change), we inferred that motors running at very low loads are driven by one or at most two force-generating units. Following the load change, motors gradually adapted by increasing their speeds in a stepwise manner (over a period of a few minutes). Motors initially spun exclusively counterclockwise, but then increased the fraction of time that they spun clockwise over a time span similar to that observed for adaptation in speed. Single-motor total internal reflection fluorescence imaging of YFP-MotB (part of a stator force-generating unit) confirmed that the response to sudden increments in load occurred by the addition of new force-generating units. We estimate that 6-11 force-generating units drive motors at high loads. Wild-type motors and motors locked in the clockwise or counterclockwise state behaved in a similar manner, as did motors in cells deleted for the motor protein gene fliL or for genes in the chemotaxis signaling pathway. Thus, it appears that stators themselves act as dynamic mechanosensors. They change their structure in response to changes in external load. How such changes might impact cellular functions other than motility remains an interesting question.

Monitoring and quantifying dynamic physiological processes in live neurons using fluorescence recovery after photobleaching

The direct visualization of subcellular dynamic processes is often hampered by limitations in the resolving power achievable with conventional microscopy techniques. Fluorescence recovery after photobleaching has emerged as a highly informative approach to address this challenge, permitting the quantitative measurement of the movement of small organelles and proteins in living functioning cells, and offering detailed insights into fundamental cellular phenomena of physiological importance. In recent years, its implementation has benefited from the increasing availability of confocal microscopy systems and of powerful labeling techniques based on genetically encoded fluorescent proteins or other chemical markers. In this review, we present fluorescence recovery after photobleaching and related techniques in the context of contemporary neurobiological research and discuss quantitative and semi-quantitative approaches to their interpretation.

Mechanism for adaptive remodeling of the bacterial flagellar switch

The bacterial flagellar motor has been shown in previous work to adapt to changes in the steady-state concentration of the chemotaxis signaling molecule, CheY-P, by changing the FliM content. We show here that the number of FliM molecules in the motor and the fraction of FliM molecules that exchange depend on the direction of flagellar rotation, not on CheY-P binding per se. Our results are consistent with a model in which the structural differences associated with the direction of rotation modulate the strength of FliM binding. When the motor spins counterclockwise, FliM binding strengthens, the fraction of FliM molecules that exchanges decreases, and the ring content increases. The larger number of CheY-P binding sites enhances the motor's sensitivity, i.e., the motor adapts. An interesting unresolved question is how additional copies of FliM might be accommodated.