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

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

Abstract

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 

Notes

Acknowledgments

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).

Appendix

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.

     

References

  1. 1.
    Lippincott-Schwartz J, Patterson GH (2003) Development and use of fluorescent protein markers in living cells. Science 300(5616):87–91CrossRefPubMedGoogle Scholar
  2. 2.
    Reits EAJ, Neefjes JJ (2001) From fixed to FRAP: measuring protein mobility and activity in living cells. Nat Cell Biol 3(6):145–145CrossRefGoogle Scholar
  3. 3.
    White J, Stelzer E (1999) Photobleaching GFP reveals protein dynamics inside live cells. Trends Cell Biol 9(2):61–65CrossRefPubMedGoogle Scholar
  4. 4.
    Phair RD, Misteli T (2001) Kinetic modelling approaches to in vivo imaging. Nat Rev Mol Cell Biol 2(12):898–907CrossRefPubMedGoogle Scholar
  5. 5.
    Bancaud A, Huet S, Rabut G et al (2010) Fluorescence perturbation techniques to study mobility and molecular dynamics of proteins in live cells: FRAP, photoactivation, photoconversion, and FLIP. Cold Spring Harb Protoc 2010(12):pdb.top90CrossRefPubMedGoogle Scholar
  6. 6.
    Beaudouin J, Mommer MS, Bock HG et al (2013) Experiment setups and parameter estimation in fluorescence recovery after photobleaching experiments: a review of current practice. In: Bock H G, Carraro T, Jäger W et al (eds) Model based parameter estimation. Springer, Berlin HeidelbergGoogle Scholar
  7. 7.
    Rapsomaniki MA, Kotsantis P, Symeonidou IE et al (2012) easyFRAP: an interactive, easy-to-use tool for qualitative and quantitative analysis of FRAP data. Bioinformatics 28(13):1800–1801CrossRefPubMedGoogle Scholar
  8. 8.
    Phair RD, Gorski SA, Misteli T (2003) Measurement of dynamic protein binding to chromatin in vivo, using photobleaching microscopy. Methods Enzymol 37:393–414CrossRefGoogle Scholar
  9. 9.
    Ellenberg J (1997) Nuclear membrane dynamics and reassembly in living cells: targeting of an inner nuclear membrane protein in interphase and mitosis. J Cell Biol 138(6):1193–1206CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Mueller F, Mazza D, Stasevich TJ et al (2010) FRAP and kinetic modeling in the analysis of nuclear protein dynamics: what do we really know? Curr Opin Cell Biol 22(3):403–411CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Sprague BL, McNally JG (2005) FRAP analysis of binding: proper and fitting. Trends Cell Biol 15(2):84–91CrossRefPubMedGoogle Scholar
  12. 12.
    Carrero G, McDonald D, Crawford E et al (2003) Using FRAP and mathematical modeling to determine the in vivo kinetics of nuclear proteins. Methods 29(1):14–28CrossRefPubMedGoogle Scholar
  13. 13.
    Beaudouin J, Mora-Bermúdez F, Klee T et al (2006) Dissecting the contribution of diffusion and interactions to the mobility of nuclear proteins. Biophys J 90(6):1878–1894CrossRefPubMedGoogle Scholar
  14. 14.
    Sprague BL, Pego RL, Stavreva DA et al (2004) Analysis of binding reactions by fluorescence recovery after photobleaching. Biophys J 86(6):3473–3495CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Royen ME, Farla P, Mattern KA et al (2012) Fluorescence recovery after photobleaching (FRAP) to study nuclear protein dynamics in living cells. In: Hancock R (ed) The nucleus: chromatin, transcription, envelope, proteins, dynamics, and imaging, vol 2. Humana Press, New York, pp 2363–2385Google Scholar
  16. 16.
    Schaff JC, Cowan AE, Loew LM, Moraru II (2009) Virtual FRAP - an experiment-oriented simulation tool. Biophys J 96(3 Supplement 1):30CrossRefGoogle Scholar
  17. 17.
    Cinquemani E, Roukos V, Lygerou Z, and Lygeros J (2008) Numerical analysis of FRAP experiments for DNA replication and repair. Proceedings of the 47th IEEE conference on decision and control. Cancun, Mexico pp. 155–160Google Scholar
  18. 18.
    Farla P, Hersmus R, Geverts B, Mari PO, Nigg AL, Dubbink HJ, Trapman J, Houtsmuller AB (2004) The androgen receptor ligand-binding domain stabilizes DNA binding in living cells. J Struct Biol 147(1):50–61CrossRefPubMedGoogle Scholar
  19. 19.
    Geverts B, van Royen ME, Houtsmuller AB (2015) Analysis of biomolecular dynamics by FRAP and Computer Simulation. In: PJ V (ed) Advanced Fluorescence Microscopy, vol 1251. Springer, New York, pp 109–133Google Scholar
  20. 20.
    Rapsomaniki MA, Cinquemani E, Giakoumakis NN et al (2015) Inference of protein kinetics by stochastic modeling and simulation of fluorescence recovery after photobleaching experiments. Bioinformatics 31(3):355–362CrossRefPubMedGoogle Scholar
  21. 21.
    Mazza D, Abernathy A, Golob N et al (2012) A benchmark for chromatin binding measurements in live cells. Nucleic Acids Res 40(15):e119 p. gks70CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Cole R (2014) Live-cell imaging: The cell’s perspective. Cell Adh Migr 8(5):452–459CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Hagen GM, Caarls W, Lidke KA et al (2009) FRAP and photoconversion in multiple arbitrary regions of interest using a programmable array microscope (PAM). Microsc Res Tech 72(6):431CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Xouri G, Squire A, Dimaki M et al (2007) Cdt1 associates dynamically with chromatin throughout G1 and recruits geminin onto chromatin. EMBO J 26(5):1303–1314CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Roukos V, Kinkhabwala A, Colombelli J et al (2011) Dynamic recruitment of licensing factor Cdt1 to sites of DNA damage. J Cell Sci 124(3):422–434CrossRefPubMedGoogle Scholar
  26. 26.
    Symeonidou IE, Kotsantis P, Roukos V et al (2013) Multi-step loading of human minichromosome maintenance proteins in live human cells. J Biol Chem 288(50):35852–35867CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Kourti M, Ikonomou G, Giakoumakis NN et al (2015) CK1δ restrains lipin-1 induction, lipid droplet formation and cell proliferation under hypoxia by reducing HIF-1α/ARNT complex formation. Cell Signal 27(6):129–1140CrossRefGoogle Scholar
  28. 28.
    Rapsomaniki MA (2014) Applications of stochastic hybrid models in biological systems. University of Patras, Doctoral dissertationGoogle Scholar
  29. 29.
    Halavatyi A (2008) Mathematical model and software FRAPAnalyser for analysis of actin-cytoskeleton dynamics with FRAP experiments, in Proceedings of FEBS/ECF workshop. Potsdam, GermanyGoogle Scholar
  30. 30.
    Kota M (2011) Analysis of FRAP curves, online available via EMBL: http://cmci.embl.de/documents/frapmanu. Accessed 05 May 2011
  31. 31.
    Vakaloglou KM, Chrysanthis G, Rapsomaniki MA et al (2016) IPP complex reinforces adhesion by relaying tension-dependent signals to inhibit integrin turnover. Cell Rep 14(11):2668–2682CrossRefPubMedGoogle Scholar
  32. 32.
    Soumpasis DM (1983) Theoretical analysis of fluorescence photobleaching recovery experiments. Biophys J 41(1):95–97CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Axelrod D, Koppel DE, Schlessinger J et al (1976) Mobility measurement by analysis of fluorescence photobleaching recovery kinetics. Biophys J 16(9):1055–1069CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Frigault MM, Lacoste J, Swift JL, Brown CM (2009) Live-cell microscopy–tips and tools. J Cell Sci 122(6):753–767CrossRefPubMedGoogle Scholar
  35. 35.
    Dickson RM, Cubitt AB et al (1997) On/off blinking and switching behaviour of single molecules of green fluorescent protein. Nature 388(6640):355–358CrossRefPubMedGoogle Scholar

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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