Analysis of Protein Kinetics Using Fluorescence Recovery After Photobleaching (FRAP)

  • Nickolaos Nikiforos Giakoumakis
  • Maria Anna Rapsomaniki
  • Zoi LygerouEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1563)


Fluorescence recovery after photobleaching (FRAP) is a cutting-edge live-cell functional imaging technique that enables the exploration of protein dynamics in individual cells and thus permits the elucidation of protein mobility, function, and interactions at a single-cell level. During a typical FRAP experiment, fluorescent molecules in a defined region of interest within the cell are bleached by a short and powerful laser pulse, while the recovery of the fluorescence in the region is monitored over time by time-lapse microscopy. FRAP experimental setup and image acquisition involve a number of steps that need to be carefully executed to avoid technical artifacts. Equally important is the subsequent computational analysis of FRAP raw data, to derive quantitative information on protein diffusion and binding parameters. Here we present an integrated in vivo and in silico protocol for the analysis of protein kinetics using FRAP. We focus on the most commonly encountered challenges and technical or computational pitfalls and their troubleshooting so that valid and robust insight into protein dynamics within living cells is gained.

Key words

Live-cell imaging Fluorescence recovery after photobleaching Protein kinetics Parameter inference Stochastic hybrid models Artificial neural networks easyFRAP 



We thank the Advanced Light Microscopy Facility of the University of Patras for assistance with live-cell imaging experiments and all members of the Cell Cycle and Stem Cell labs, Medical School, University of Patras for helpful discussions.

Work in our lab is supported by a grant from the European Research Council (DYNACOM, 281851).


Let y(t)ROI1, y(t)ROI2, and y(t)ROI3 represent the fluorescence intensity in the bleaching region, the total area of fluorescence, and a random background area. Background correction is performed by simply subtracting the background measurements from the rest:
$$ \begin{array}{c} y{(t)}_{ROI1*}= y(t){}_{ROI1}- y(t){}_{ROI3}\\ {} y{(t)}_{ROI2*}= y(t){}_{ROI2}- y(t){}_{ROI3}\end{array} $$

Let \( {y}_{ROI1}^{pre} \) denote the average intensity in the bleaching region during the pre-bleach interval and tbleach + 1 denote the first time point after the bleach. Bleaching depth (bd) is computed as follows:

\( bd=\frac{y_{ROI1}^{pre}- y{\left({t}_{\mathrm{bleach}+1}\right)}_{ROI1*}}{y_{ROI1}^{pre}} \) (1)

Similarly, let \( {y}_{ROI2}^{pre},{y}_{ROI2}^{\mathrm{post}} \) denote the average intensities in the total area of fluorescence during the pre- and post-bleach interval respectively. Gap ratio (gr) is computed as follows:

\( gr=\frac{y_{ROI2}^{\mathrm{post}}}{y_{ROI2}^{pre}} \) (2)

Double normalization is computed as follows:

\( y{(t)}_{\mathrm{double}}=\frac{y{(t)}_{ROI1*}}{y_{ROI1}^{pre}}\times \frac{y_{ROI2}^{pre}}{\mathrm{y}{(t)}_{ROI2*}} \) (3)

Similarly, full-scale normalization is computed as follows:

\( y{(t)}_{\mathrm{fullscale}}=\frac{y{(t)}_{\mathrm{double}}- y{\left({t}_{\mathrm{bleach}+1}\right)}_{\mathrm{double}}}{1- y{\left({t}_{\mathrm{bleach}+1}\right)}_{\mathrm{double}}} \) (4)

To compute quantitative parameters such as t-half (t1/2) and mobile fraction (F mob ), only the post-bleach part of the curve is necessary. Dropping normalization index for simplicity, let y(tend) denote the normalized intensity (double or full-scale) when the curve has reached its plateau and y(tbleach + 1) denote the first post-bleach measurement. To remove the pre-bleach part of the curve from the measurements, we simply subtract tbleach + 1 from the rest of the time points, leading to y(tbleach + 1) = y(t = 0). It is:

\( {F}_{mob}=\frac{y\left({t}_{end}\right)- y\left( t=0\right)}{1- y\left( t=0\right)} \) (5)

Immobile fraction (Fimm) is defined as the fraction of bleached molecules that were bound and do not diffuse away from the bleaching area by the end of the experiment. It is:

\( {F}_{imm}=\frac{1- y\left({t}_{end}\right)}{1- y\left( t=0\right)} \) (6)

Naturally, Fmob + Fimm = 1. It is clear that for curves that exhibit full recovery, y(tend) = 1, leading to Fimm = 0 and Fimm = 1.

The value of t1/2 is computed as follows:

\( y\left({t}_{1/2}\right)=\frac{y\left({t}_{end}\right)+ y\left( t=0\right)}{2} \) (7)

To estimate the values of these parameters, the experimental data are fitted to one of the following exponential equations:

y(t)single = y0αeβt(8)

y(t)double = y0αeβtγeδt(9)

If full-scale normalization was used, then it is: y(t = 0) = 0 and from Eq. (5) it is Fmob = y(tend) = y0, since for both Eqs. (8) and (9) as t → ∞,  y(tend) = y0. For double normalization, we have:
  1. 1.

    Using single exponential fitting (Eq. (8)) it is y(t = 0)single = y0a,and from Eq. (5) it is:

$$ {F}_{mob}=\frac{y_0-{y}_0+ a}{1-{y}_0+ a}=\frac{a}{1-{y}_0+ a} $$
  1. 2.

    Using double exponential fitting (Eq. (9)) it is y(t = 0)double = y0aγ, and again from Eq. (5) it is:

$$ {F}_{mob}=\frac{y_0-{y}_0+ a+\gamma}{1-{y}_0+ a+\gamma}=\frac{a+\gamma}{1-{y}_0+ a+\gamma} $$
The value of t1/2 is estimated from Eq. (7) as follows:
  1. 1.

    Using single exponential fitting and since similarly as above y(t = 0)single = y0a

$$ \begin{array}{c}\frac{y_0+{y}_0- a}{2}={y}_0-\alpha {e}^{-\beta {t}_{1/2}}\\ {}{e}^{-\beta {t}_{1/2}}=\frac{1}{2}\\ {}{t}_{1/2}=\frac{ \ln 2}{\beta}\end{array} $$
  1. 2.

    Using a double exponential fitting equation, the value of t1/2 is estimated numerically, since there is no closed form solution.



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Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Nickolaos Nikiforos Giakoumakis
    • 1
  • Maria Anna Rapsomaniki
    • 1
    • 2
  • Zoi Lygerou
    • 1
    Email author
  1. 1.Laboratory of Biology, School of MedicineUniversity of PatrasPatrasGreece
  2. 2.IBM Research ZurichRüschlikonSwitzerland

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