Integrated approach for the analysis of fluorescence recovery after photobleaching (FRAP) intensities in live cells Aliaksandr Halavatyi1, Ziad Al Tanoury1, Mikalai Yatskou2, Evelyne Friederich1 1Cytoskeleton and Cell Plasticity Laboratory, Life Sciences Research Unit, Faculty of Sciences, Technology and Communication, University of Luxembourg, Luxembourg 2Department of Systems Analysis, Belarusian State University, Minsk, Republic of Belarus Introduction FRAPAnalyser software Utilizes the developed integrated analysis approach Merits: User-friendly interface Free, available for scientific community (actinsim.uni.lu) Extendable for the new models and normalisation algorithms due to object-oriented architecture Fluorescence recovery after photobleaching (FRAP) is a microscopy-based technique for the quantification of molecular kinetics in vivo. The rate of the fluorescence intensity recovery in time depends on the underlying cellular processes and their numerical characteristics. It allows estimation of mobile and immobile fractions of proteins, turnover rates, spatial ordering and localisation characteristics of biocomplexes1. Estimation of relevant parameters from the time series of FRAP intensities is still an arduous task since analysis protocols and computational tools required for particular experimental configurations are not always available. Available options: Data import/export Graphical support Normalisation (four protocols) Fitting models: binding, diffusion, binding&diffusion, actin polymerisation4 Error analysis Program interface In plans: Global/associated analysis for series of experiments We aim creating integrated systems methodology for the FRAP-based quantitative investigation of protein kinetic properties in live cells and their effects on complex cellular phenomena. Data processing Analysis of the protein binding kinetics The developed analysis approach was first applied to study the binding kinetics of GFP-fusion variants of the actin-regulatory protein Tes in HeLa cells. As was expected [ref], one of the Tes variants exhibited free diffusion resulting in fast recovery. The diffusion coefficient of the protein cofilin in cytoplasm was estimated using the pure diffusion model3. For the second variant of Tes, situated in area of focal adhesions, its dynamics was limited by the binding/unbinding reactions - thus, the full model (binding & diffusion)3 has been used for estimation of the binding/unbinding rate constants. Experimental design We used actin polymerisation process regulated by actin-binding proteins2 as an example to install and validate our analysis approach. Actin filament structure and reactions NIH 3T3 β-actin-GFP cell. Confocal image Diffusing Tes variant Binding Tes variant The size and position of the region of interest correspond to the area of specific actin structures (e.g. focal adhesions, stress fibers, lamellipodia). Both dynamics of fluorescently-labelled actin and actin-binding proteins are analysed to provide more information about filament regulation. FRAP in NIH 3T3 β-actin-GFP cell t=0 s t=tbleach t=90 s Current progress: FRAP analysis of the dynamics of actin filaments using the specifically developed model4. Question: Addressing the identifiability and interdependence of model parameters Integrated analysis approach The integrated approach has been developed for analysing FRAP recoveries in a standardized manner. Measurements undergo normalisation step to eliminate artefacts in the data. The estimation of parameters values and their confidence intervals is performed by fitting of the experimental data with the appropriate mathematical model. The scheme allows selection of normalisation or modelling procedures in accordance with the properties of analysed data3. Parameters estimation can be performed when either 1) the average recovery curve, obtained from several cells, is fitted by model equation or 2) each individual cell recovery is fitted independently, providing the distribution of the model parameters. Conclusions The described approach has shown itself as an robust and efficient systems analysis tool for mining the complex FRAP datasets and recovering the meaningful biochemical parameters. Application of the presented software FRAPAnalyser is not limited to investigating the actin cytoskeleton data systems but can be extended easily for studding other cellular processes like …. References 1. Mueller, F., et al., FRAP and kinetic modelling in the analysis of nuclear protein dynamics: what do we really know? CurrOpinCellBiol, 2010. 22: p. 403-11. 2. Huber, F., et al., Growing actin networks form lamellipodium and lamellum by self-assembly. Biophys J, 2008. 95(12): p. 5508-23. 3. McNally, J.G., Quantitative FRAP in analysis of molecular binding dynamics in vivo. Methods Cell Biol, 2008. 85: p. 329-51. 4. Halavatyi, A.A., et al., A mathematical model of actin filament turnover for fitting FRAP data. Eur Biophys J, 2010. 39(4): p. 669-77. Acknowledgements We thank members of Advanced Light Microscopy Facility (EMBL, Heidelberg) for the providing access to the experimental platforms, technical support and useful discussions concerning planning of experiments and aspects of data analysis. ± Grants and fellowships ± ± European Science Foundation (Grant № 1500) Human Frontier Science Program Organisation National Research Fund (FNR), Luxembourg P-CUBE transnational access ±