Multiphoton fluorescence recovery after photobleaching in bounded systems

Sullivan, Kelley D.; Brown, Edward B.

doi:10.1103/physreve.83.051916

Cited by 7 publications

(12 citation statements)

References 51 publications

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“…The effect of macromolecular crowding on diffusion has recently been simulated using molecular dynamics and atomistic cytoplasm models. 181 In another study, 182 to explore the influence of barriers of various geometry and placements relative to the two-photon focus volume, authors conducted Monte Carlo simulations of diffusion and multiphoton-FRAP. Furthermore, the authors provided barrier location ranges within which multiphoton-FRAP may be used to accurately assess diffusion coefficients.…”

Section: Simulation and Frapmentioning

confidence: 99%

Fluorescence Recovery after Photobleaching in Colloidal Science: Introduction and Application

Moud

2022

ACS Biomater. Sci. Eng.

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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.

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Section: Simulation and Frapmentioning

confidence: 99%

Fluorescence Recovery after Photobleaching in Colloidal Science: Introduction and Application

Moud

2022

ACS Biomater. Sci. Eng.

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“…(2008). A recent study has systematically evaluated the effect of boundaries on multi-photon FRAP experiments (Sullivan & Brown, 2011).…”

Section: Practical Considerations For Optimal Frap Experimentsmentioning

confidence: 99%

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

Lorén¹,

Hagman²,

Jonasson

et al. 2015

Quart. Rev. Biophys.

134

121

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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.

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“…The two most commonly cited FRAP models are by Axelrod et al (2) and Soumpasis (3), and they use an analytically calculated recovery profile of a 2D ROI inside a cell with an infinite boundary. Although these models are frequently used-over 650 combined citations to date-more recent studies have included and explored additional relevant FRAP parameters such as the finite confocal scan rate during photobleaching (4-9), arbitrary photobleaching profiles (10), confocal imaging (5), and cell shape (11)(12)(13)(14). Despite this wealth of analytical models, as well as algorithmic approaches (10,13,(15)(16)(17)(18)(19), it remains unclear whether these models can yield accurate estimates of the diffusion coefficients, for instance, when applied using realistic optical settings in actual cellular geometries.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Cell Boundary and Confocal Effects Improves Quantitative FRAP Analysis

Kingsley

Bibeau

Mousavi

et al. 2018

Biophysical Journal

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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.

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Multiphoton fluorescence recovery after photobleaching in bounded systems

Cited by 7 publications

References 51 publications

Fluorescence Recovery after Photobleaching in Colloidal Science: Introduction and Application

Fluorescence Recovery after Photobleaching in Colloidal Science: Introduction and Application

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

Characterization of Cell Boundary and Confocal Effects Improves Quantitative FRAP Analysis

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