Anne Kenworthy
Last active: 2/12/2016

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

Kang M, Andreani M, Kenworthy AK
PLoS One. 2015 10 (5): e0127966

PMID: 26017223 · PMCID: PMC4446327 · DOI:10.1371/journal.pone.0127966

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.

MeSH Terms (8)

Diffusion Fluorescence Fluorescence Recovery After Photobleaching Green Fluorescent Proteins Kinetics Microscopy, Fluorescence Models, Theoretical Statistics as Topic

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