Pixel-based analysis of FRAP data with a general initial bleaching profile

Glucose stockpile in the intestinal apical brush border in C. elegans

Revealing the mechanisms of glucose transport is crucial for studying pathological diseases caused by glucose toxicities. Numerous studies have revealed molecular functions involved in glucose transport in the nematode Caenorhabditis elegans, a commonly used model organism. However, the behavior of glucose in the intestinal lumen-to-cell remains elusive. To address that, we evaluated the diffusion coefficient of glucose in the intestinal apical brush border of C. elegans by using fluorescent glucose and fluorescence recovery after photobleaching. Fluorescent glucose taken in the intestine of worms accumulates in the apical brush border, and its diffusion coefficient of ∼10-8 cm2/s is two orders of magnitude slower than that in bulk. This result indicates that the intestinal brush border is a viscous layer. ERM-1 point mutations at the phosphorylation site, which shorten the microvilli length, did not significantly affect the diffusion coefficient of fluorescent glucose in the brush border. Our findings imply that glucose enrichment is dominantly maintained by the viscous layer composed of the glycocalyx and molecular complexes on the apical surface.

Evidence of residual micellar structures in a lipid nanocapsule dispersion. A multi-technique approach

Nanoemulsions are metastable emulsions in the nanometric range which can be obtained using low-energy processes. A decade ago, it was demonstrated that a non-negligible amount of residual surfactant micelles may coexist with the oil nanodroplets in a model oil/surfactant system. Those micelles were called "wasted" micelles as they did not participate in the formation of the nanodroplets. Little attention has been focused on the potential presence or effect of such secondary structures in nanoemulsions used as drug delivery systems. Here, we present an extensive characterization of lipid nanocapsules, a nanoemulsion obtained from a medium-chain triglyceride mixed with a pegylated surfactant by a process comprising a temperature-dependent phase inversion followed by a cold-water quench. Lipid nanocapsules demonstrate a very good shelf stability. First, for clarity and academic purposes, we briefly present the pros and the cons of the various diffusion-based characterization techniques used i.e., multi-angle and single-angle dynamic light scattering, nanoparticle tracking analysis, fluorescence recovery after photobleaching, and diffusometry nuclear magnetic resonance. Then, combining all these techniques, we show that up to 40 wt% of the surfactant is not involved in the lipid nanocapsule construction but forms residual micellar structures. Those micelles also contain a small quantity of medium-chain triglyceride (2 wt% of the initial amount) and encapsulate around 40 wt% of a fluorescent dye originally dispersed in the oily phase.

Fluorescence Recovery after Photobleaching in Colloidal Science: Introduction and Application

FRAP (fluorescence recovery after photo bleaching) is a method for determining diffusion in material science. In industrial applications such as medications, foods, Medtech, hygiene, and textiles, the diffusion process has a substantial influence on the overall qualities of goods. All these complex and heterogeneous systems have diffusion-based processes at the local level. FRAP is a fluorescence-based approach for detecting diffusion; in this method, a high-intensity laser is made for a brief period and then applied to the samples, bleaching the fluorescent chemical inside the region, which is subsequently filled up by natural diffusion. This brief Review will focus on the existing research on employing FRAP to measure colloidal system heterogeneity and explore diffusion into complicated structures. This description of FRAP will be followed by a discussion of how FRAP is intended to be used in colloidal science. When constructing the current Review, the most recent publications were reviewed for this assessment. Because of the large number of FRAP articles in colloidal research, there is currently a dearth of knowledge regarding the growth of FRAP's significance to colloidal science. Colloids make up only 2% of FRAP papers, according to ISI Web of Knowledge.

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.

Characterization of Cell Boundary and Confocal Effects Improves Quantitative FRAP Analysis

Fluorescence recovery after photobleaching (FRAP) is an important tool used by cell biologists to study the diffusion and binding kinetics of vesicles, proteins, and other molecules in the cytoplasm, nucleus, or cell membrane. Although many FRAP models have been developed over the past decades, the influence of the complex boundaries of 3D cellular geometries on the recovery curves, in conjunction with regions of interest and optical effects (imaging, photobleaching, photoswitching, and scanning), has not been well studied. Here, we developed a 3D computational model of the FRAP process that incorporates particle diffusion, cell boundary effects, and the optical properties of the scanning confocal microscope, and validated this model using the tip-growing cells of Physcomitrella patens. We then show how these cell boundary and optical effects confound the interpretation of FRAP recovery curves, including the number of dynamic states of a given fluorophore, in a wide range of cellular geometries-both in two and three dimensions-namely nuclei, filopodia, and lamellipodia of mammalian cells, and in cell types such as the budding yeast, Saccharomyces pombe, and tip-growing plant cells. We explored the performance of existing analytical and algorithmic FRAP models in these various cellular geometries, and determined that the VCell VirtualFRAP tool provides the best accuracy to measure diffusion coefficients. Our computational model is not limited only to these cells types, but can easily be extended to other cellular geometries via the graphical Java-based application we also provide. This particle-based simulation-called the Digital Confocal Microscopy Suite or DCMS-can also perform fluorescence dynamics assays, such as number and brightness, fluorescence correlation spectroscopy, and raster image correlation spectroscopy, and could help shape the way these techniques are interpreted.

Multi-Fractional Analysis of Molecular Diffusion in Polymer Multilayers by FRAP: A New Simulation-Based Approach

Comprehensive analysis of the multifractional molecular diffusion provides a deeper understanding of the diffusion phenomenon in the fields of material science, molecular and cell biology, advanced biomaterials, etc. Fluorescence recovery after photobleaching (FRAP) is commonly employed to probe the molecular diffusion. Despite FRAP being a very popular method, it is not easy to assess multifractional molecular diffusion due to limited possibilities of approaches for analysis. Here we present a novel simulation-optimization-based approach (S-approach) that significantly broadens possibilities of the analysis. In the S-approach, possible fluorescence recovery scenarios are primarily simulated and afterward compared with a real measurement while optimizing parameters of a model until a sufficient match is achieved. This makes it possible to reveal multifractional molecular diffusion. Fluorescent latex particles of different size and fluorescein isothiocyanate in an aqueous medium were utilized as test systems. Finally, the S-approach has been used to evaluate diffusion of cytochrome c loaded into multilayers made of hyaluronan and polylysine. Software for evaluation of multifractional molecular diffusion by S-approach has been developed aiming to offer maximal versatility and user-friendly way for analysis.

Complex Coacervate Core Micelles with Spectroscopic Labels for Diffusometric Probing of Biopolymer Networks

We present the design, preparation, and characterization of two types of complex coacervate core micelles (C3Ms) with cross-linked cores and spectroscopic labels and demonstrate their use as diffusional probes to investigate the microstructure of percolating biopolymer networks. The first type consists of poly(allylamine hydrochloride) (PAH) and poly(ethylene oxide)-poly(methacrylic acid) (PEO-b-PMAA), labeled with ATTO 488 fluorescent dyes. We show that the size of these probes can be tuned by choosing the length of the PEO-PMAA chains. ATTO 488-labeled PEO113-PMAA15 micelles are very bright with 18 dye molecules incorporated into their cores. The second type is a (19)F-labeled micelle, for which we used PAH and a (19)F-labeled diblock copolymer tailor-made from poly(ethylene oxide)-poly(acrylic acid) (mPEO79-b-PAA14). These micelles contain approximately 4 wt % of (19)F and can be detected by (19)F NMR. The (19)F labels are placed at the end of a small spacer to allow for the necessary rotational mobility. We used these ATTO- and (19)F-labeled micelles to probe the microstructures of a transient gel (xanthan gum) and a cross-linked, heterogeneous gel (κ-carrageenan). For the transient gel, sensitive optical diffusometry methods, including fluorescence correlation spectroscopy, fluorescence recovery after photobleaching, and super-resolution single nanoparticle tracking, allowed us to measure the diffusion coefficient in networks with increasing density. From these measurements, we determined the diameters of the constituent xanthan fibers. In the heterogeneous κ-carrageenan gels, bimodal nanoparticle diffusion was observed, which is a signpost of microstructural heterogeneity of the network.

Interactions and diffusion in fine-stranded β-lactoglobulin gels determined via FRAP and binding

The effects of electrostatic interactions and obstruction by the microstructure on probe diffusion were determined in positively charged hydrogels. Probe diffusion in fine-stranded gels and solutions of β-lactoglobulin at pH 3.5 was determined using fluorescence recovery after photobleaching (FRAP) and binding, which is widely used in biophysics. The microstructures of the β-lactoglobulin gels were characterized using transmission electron microscopy. The effects of probe size and charge (negatively charged Na2-fluorescein (376Da) and weakly anionic 70kDa FITC-dextran), probe concentration (50 to 200 ppm), and β-lactoglobulin concentration (9% to 12% w/w) on the diffusion properties and the electrostatic interaction between the negatively charged probes and the positively charged gels or solutions were evaluated. The results show that the diffusion of negatively charged Na2-fluorescein is strongly influenced by electrostatic interactions in the positively charged β-lactoglobulin systems. A linear relationship between the pseudo-on binding rate constant and the β-lactoglobulin concentration for three different probe concentrations was found. This validates an important assumption of existing biophysical FRAP and binding models, namely that the pseudo-on binding rate constant equals the product of the molecular binding rate constant and the concentration of the free binding sites. Indicators were established to clarify whether FRAP data should be analyzed using a binding-diffusion model or an obstruction-diffusion model.