Quantitative diffusion measurements using the open-source software PyFRAP

Bläßle, Alexander; Soh, Gary H; Braun, Theresa; Mörsdorf, David; Preiß, Hannes; Jordan, Ben M.; Müller, Patrick

doi:10.1038/s41467-018-03975-6

Cited by 34 publications

(43 citation statements)

References 66 publications

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“…SR-Tesseler and InferenceMap [48][49][50] . Compared to existing software such as PyFRAP, SuReSim, FERNET, or MCell [13][14][15] , FluoSim integrates many fluorescence modalities into a single program and achieves real-time display (Supplementary Table 1). In its present version, FluoSim is limited to Brownian motion and first order molecular reactions, but sub-or super-diffusive behaviors as well as more complex multi-state molecular reactions might be implemented on a case-by-case basis, depending on user needs (Table 1).…”

Section: Discussionmentioning

confidence: 99%

FluoSim: simulator of single molecule dynamics for fluorescence live-cell and super-resolution imaging of membrane proteins

Lagardère

Chamma

Bouilhol

et al. 2020

Sci Rep

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Fluorescence live-cell and super-resolution microscopy methods have considerably advanced our understanding of the dynamics and mesoscale organization of macro-molecular complexes that drive cellular functions. However, different imaging techniques can provide quite disparate information about protein motion and organization, owing to their respective experimental ranges and limitations. To address these issues, we present here a robust computer program, called FluoSim, which is an interactive simulator of membrane protein dynamics for live-cell imaging methods including SPT, FRAP, PAF, and FCS, and super-resolution imaging techniques such as PALM, dSTORM, and uPAINT. FluoSim integrates diffusion coefficients, binding rates, and fluorophore photo-physics to calculate in real time the localization and intensity of thousands of independent molecules in 2D cellular geometries, providing simulated data directly comparable to actual experiments. FluoSim was thoroughly validated against experimental data obtained on the canonical neurexin-neuroligin adhesion complex at cell–cell contacts. This unified software allows one to model and predict membrane protein dynamics and localization at the ensemble and single molecule level, so as to reconcile imaging paradigms and quantitatively characterize protein behavior in complex cellular environments.

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Section: Discussionmentioning

confidence: 99%

FluoSim: simulator of single molecule dynamics for fluorescence live-cell and super-resolution imaging of membrane proteins

Lagardère

Chamma

Bouilhol

et al. 2020

Sci Rep

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show abstract

“…SR-Tesseler and InferenceMap 41,42 . Compared to existing software such as PyFRAP, SuReSim, FERNET, or MCell [13][14][15] , FluoSim integrates many fluorescence modalities into a single program and achieves real-time display (Supplemental Table 2). In its present version, FluoSim is limited to Brownian motion and first order molecular reactions, but sub-or super-diffusive behaviors as well as more complex multi-state molecular reactions might be implemented on a case-by-case basis, depending on user needs.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, there is a pressing need for computer simulators that could unify those different imaging modes in a unique framework, estimate their respective biases, and serve as a predictive tool for experimenters, with the aim to quantitatively decipher protein organization and dynamics in living cells. Several particle-based packages relying on Monte Carlo simulations already exist to predict random motion and multi-state reactions of biological molecules, but either they do not integrate fluorescence properties or are limited to a specific type of imaging mode, and are usually not performing real-time visualization [11][12][13][14][15][16][17][18] .…”

Section: Introductionmentioning

confidence: 99%

FluoSim: simulator of single molecule dynamics for fluorescence live-cell and super-resolution imaging of membrane proteins

Lagardère

Chamma

Bouilhol

et al. 2020

Preprint

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Fluorescence live-cell and super-resolution microscopy methods have considerably advanced our understanding of the dynamics and mesoscale organization of macro-molecular complexes that drive cellular functions. However, different imaging techniques can provide quite disparate information about protein motion and organization, owing to their respective experimental ranges and limitations. To address these limitations, we present here a unified computer program that allows one to model and predict membrane protein dynamics at the ensemble and single molecule level, so as to reconcile imaging paradigms and quantitatively characterize protein behavior in complex cellular environments. FluoSim is an interactive realtime simulator of protein dynamics for live-cell imaging methods including SPT, FRAP, PAF, and FCS, and super-resolution imaging techniques such as PALM, dSTORM, and uPAINT. The software, thoroughly validated against experimental data on the canonical neurexinneuroligin adhesion complex, integrates diffusion coefficients, binding rates, and fluorophore photo-physics to calculate in real time the distribution of thousands of independent molecules in 2D cellular geometries, providing simulated data of protein dynamics and localization directly comparable to actual experiments.

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“…The shape of the FRAP recovery curve, the so‐called mobile fraction, reflects all of the complexity of the reaction diffusion dynamics of the molecule of interest. Using a theoretical model or numerical simulations for the analysis of the molecular actions combined with knowledge of the recovery time(s) of the respective molecule, the reaction kinetics and diffusion dynamics can be calculated and interpreted . Analysis of the experiments reveals whether a molecule undergoes reaction kinetics or diffusion dynamics or a combination of both processes .…”

Section: Summary Of Frap Fitting Parameters In Living Hela Cells Sinmentioning

confidence: 99%

Simultaneous Quantification of the Interplay Between Molecular Turnover and Cell Mechanics by AFM–FRAP

Skamrahl

Colin‐York

Barbieri

et al. 2019

Small

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of their actin cytoskeleton. [5][6][7][8][9] Despite the significance for cell function, [10,11] our understanding of the interplay of cell mechanics and its underlying actin kinetics controlling the adaptive mechanical behavior of cells remains at best correlative from independent measurements.Fluorescence recovery after photobleaching (FRAP) is perhaps the most successful quantification methodology of molecule kinetics and dynamics, owing to its versatility to measure reaction and diffusion dynamics at the right spatiotemporal scales. [12][13][14] In a typical FRAP experiment, a small region of interest (ROI) is bleached by a short exposure to highpower laser light, and subsequently the recovery of fluorescently tagged molecules is monitored over time. [15][16][17][18] The shape of the FRAP recovery curve, the so-called mobile fraction, reflects all of the complexity of the reaction diffusion dynamics of the molecule of interest. Using a theoretical model or numerical simulations for the analysis of the molecular actions combined with knowledge of the recovery time(s) of the respective molecule, the reaction kinetics and diffusion dynamics can be calculated and interpreted. [19][20][21] Analysis of the experiments reveals whether a molecule undergoes reaction kinetics or diffusion dynamics or a combination of both processes. [13,22] The presence of a substantial immobile fraction may be the result of the loss of fluorescence due to imaging as experienced by the fluorescent molecules during image acquisition, or it may signify that recovery has been followed over a duration that is short in comparison with the molecule's actual recovery time. [13] To this end, FRAP has been employed to identify and quantify the different types of filamentous actin (F-actin), their turnover dynamics, and lengths in the actin cortex, lamellipodium, and stress fibers. [22][23][24][25][26] Atomic force microscopy (AFM) is the most broadly used quantification methodology of cell mechanics. AFM allows the precise quantification and application of mechanical forces on the apical cell surface with piconewton (pN) resolution. [27][28][29][30][31] For the application of mechanical force over a microscaled subregion of a cell, the cantilever tip is typically functionalized with a micron-sized fluorescent bead, and then exerted against the apical cell surface. Using AFM electronic feedback loops allows the recording of nanoscale force indentations of the cell surface as a function of the applied constant mechanical force. For example, this type of approach has been applied to investigate the biological behavior and function of living cells in response to external mechanical force. [32,33] Quantifying the adaptive mechanical behavior of living cells is essential for the understanding of their inner working and function. Yet, despite the establishment of quantitative methodologies correlating independent mea surements of cell mechanics and its underlying molecular kinetics, explicit evidence and knowledge of the sensitivity of the feedback me...

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Quantitative diffusion measurements using the open-source software PyFRAP

Cited by 34 publications

References 66 publications

FluoSim: simulator of single molecule dynamics for fluorescence live-cell and super-resolution imaging of membrane proteins

FluoSim: simulator of single molecule dynamics for fluorescence live-cell and super-resolution imaging of membrane proteins

FluoSim: simulator of single molecule dynamics for fluorescence live-cell and super-resolution imaging of membrane proteins

Simultaneous Quantification of the Interplay Between Molecular Turnover and Cell Mechanics by AFM–FRAP

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