Quantitative diffusion measurements using the open-source software PyFRAP

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

doi:10.1038/s41467-018-03975-6

Abstract

Fluorescence Recovery After Photobleaching (FRAP) and inverse FRAP (iFRAP) assays can be used to assess the mobility of fluorescent molecules. These assays measure diffusion by monitoring the return of fluorescence in bleached regions (FRAP), or the dissipation of fluorescence from photoconverted regions (iFRAP). However, current FRAP/iFRAP analysis methods suffer from simplified assumptions about sample geometry, bleaching/photoconversion inhomogeneities, and the underlying reaction-diffusion kinetics. To address these shortcomings, we developed the software PyFRAP, which fits numerical simulations of three-dimensional models to FRAP/iFRAP data and accounts for bleaching/photoconversion inhomogeneities. Using PyFRAP we determined the diffusivities of fluorescent molecules spanning two orders of magnitude in molecular weight. We measured the tortuous effects that cell-like obstacles exert on effective diffusivity and show that reaction kinetics can be accounted for by model selection. These applications demonstrate the utility of PyFRAP, which can be widely adapted as a new extensible standard for FRAP analysis.

Highlights

Fluorescence Recovery After Photobleaching (FRAP) and inverse FRAP assays can be used to assess the mobility of fluorescent molecules
Current FRAP analysis methods often make simplified assumptions about FRAP experimental conditions to aid in the derivation of analytical solutions[11,12,13,14,15,16,18], and to facilitate numerical simulations[20,21]
PyFRAP numerically simulates FRAP experiments in realistic three-dimensional geometries using an interpolation of the first post-bleach image as initial condition

Summary

Introduction

Fluorescence Recovery After Photobleaching (FRAP) and inverse FRAP (iFRAP) assays can be used to assess the mobility of fluorescent molecules. Diffusion coefficients are commonly extracted from FRAP experiments by fitting analytical solutions computed from theoretical models to the measured recovery curves[11,12,13,14,15,16,17,18], and a few simulation-based analysis methods have been developed[19,20,21] This allows for a rapid assessment of qualitative mobility differences in identical experimental settings, current approaches rely on several assumptions that can affect the accuracy of the analysis. In vivo FRAP experiments can be strongly influenced by reaction kinetics such as production or degradation of fluorescent molecules, which can contribute to the observed recovery curve (Fig. 1c) This is mostly neglected in classical FRAP analysis models and can lead to erroneous diffusion estimates (Fig. 1f)[11,12,13,14,15,16,17]

Methods

Results

Conclusion