FRET-FLIM applications in plant systems

SPECIAL ISSUE: NEW/EMERGING TECHNIQUES IN BIOLOGICAL MICROSCOPY

FRET-FLIM applications in plant systems

Christoph A. Bücherl & Arjen Bader &

Adrie H. Westphal & Sergey P. Laptenok &

Jan Willem Borst

Received: 4 December 2013 /Accepted: 5 December 2013 /Published online: 4 January 2014# Springer-Verlag Wien 2014

Abstract A hallmark of cellular processes is the spatio-temporally regulated interplay of biochemical components.Assessing spatial information of molecular interactions withinliving cells is difficult using traditional biochemical methods.Developments in green fluorescent protein technology incombination with advances in fluorescence microscopy haverevolutionised this field of research by providing the genetictools to investigate the spatio-temporal dynamics of biomole-cules in live cells. In particular, fluorescence lifetime imagingmicroscopy (FLIM) has become an inevitable technique forspatially resolving cellular processes and physical interactionsof cellular components in real time based on the detection ofFörster resonance energy transfer (FRET). In this review, weprovide a theoretical background of FLIM as well as FRET-FLIM analysis. Furthermore, we show two cases in whichadvanced microscopy applications revealed many new in-sights of cellular processes in living plant cells as well as inwhole plants.

Keywords Fluorescence lifetime imagingmicroscopy(FLIM) . Förster resonance energy transfer (FRET) . Visiblefluorescent protein (VFP) . Global analysis . Phasor plotanalysis

AbbreviationsACR4 Arabidopsis crinkly 4BiFC Bimolecular fluorescence complementationBRI1 Brassinosteroid insensitive 1CLV1 Clavata1CPK21 Calcium-dependent protein kinase 21FCS Fluorescence correlation spectroscopyFCCS Fluorescence cross-correlation spectroscopyFLIM Fluorescence lifetime imaging microscopyFRET Förster resonance energy transferFLS2 Flagellin sensing 2GFP Green fluorescent proteinLRR-RLK Leucine-rich repeat receptor-like kinasesMFIS Multiparameter fluorescence image

spectroscopyPM Plasma membraneSERK Somatic embryogenesis receptor-like kinaseSTED Stimulated emission depletionTCSPC Time-correlated single photon countingTIRF Total internal reflection fluorescenceVFP Visible fluorescent protein

Introduction

Cellular processes involve the assembly and disassembly ofmolecular complexes, which is often accompanied by physi-cal repositioning of individual proteins within the cellularmatrix (Day and Schaufele 2005). To gain access to thefundamental principles of processes like DNA replication,

Handling Editor: David Robinson

C. A. BücherlThe Sainsbury Laboratory, Norwich Research Park,Norwich NR4 7UH, UK

A. BaderLaboratory of Biophysics and Microspectroscopy Centre,Wageningen University, Wageningen, The Netherlands

S. P. LaptenokSchool of Chemistry, University of East Anglia,Norwich NR4 7TJ, UK

A. H. Westphal : J. W. Borst (*)Laboratory of Biochemistry and Microspectroscopy Centre,Wageningen University, Wageningen, The Netherlandse-mail: [email protected]

Protoplasma (2014) 251:383–394DOI 10.1007/s00709-013-0595-7

protein folding or signal transduction, the application of im-aging methodologies offers numerous possibilities (Dehmeltand Bastiaens 2010). In particular, fluorescence microscopyhas become an essential tool in biology for studying biochem-ical dynamics in living cells. Depending on the microscopicsetup used, various read-outs can be recorded to characterisefluorescently labeled biomolecules, of which the fluorescenceintensity is the most common parameter used for investigatingbiological events (Yasuda 2006).

Fluorescence imaging allows elucidating spatio-temporaldistributions of fluorescently tagged proteins in living plantcells and organisms. Expressing single fluorescently labeledproteins in (living) cells provides information about subcellu-lar protein localisation. Moreover, dual-colour imaging ofdifferently labeled proteins enables investigating subcellularco-localisation patterns. Co-localisation studies can be per-formed by using either a wide-field or confocal microscope,of which the latter has the advantage of providing increasedoptical resolution and higher contrast images.

Examples of fluorescent microscopic studies of plant sig-nalling components are the leucine-rich repeat receptor-likekinases (LRR-RLK) flagellin sensing 2 (FLS2) (Beck et al.2012), Clavata1 (CLV1) (Stahl et al. 2013), and calcium-dependent protein kinase 21 (CPK21) (Demir et al. 2013).Beck et al. (2012) used confocal imaging to investigateendosomal trafficking of the pattern recognition receptorFLS2 in Arabidopsis leaf discs (Beck et al. 2012). Quantitativeco-localisation analysis was used to dissect the different or-ganelles FLS2 constitutively transits to before and after fla-gellin (flg22) stimulation. High-throughput imaging based onspinning disk microscopy (Opera, PerkinElmer) has providedquantitative datasets of FLS2-expressing cells and has re-vealed that after signal perception FLS2, undergoes traditionalendosomal trafficking via early and late endosomes culminat-ing in vacuolar degradation. Fluorescence microscopy wasalso applied for the in planta characterisation of CLV1. Usingconfocal microscopy, Stahl et al. (2013) visualised the co-localisation of CLV1 with Arabidopsis crinkly 4 (ACR4) inspecific cell files and subcellular compartments (Stahl et al.2013).

Although co-localisation studies in the confocal mode al-low studying subcellular structures down to 200–300 nm inlateral and 1,000 nm in axial dimensions, the spatial resolutiondoes not extend to the molecular dimension of proteins. Tech-nologies have been developed to overcome this limited spatialresolution of confocal microscopy. For example, total internalreflection fluorescence (TIRF) microscopy that is based on theevanescent field occurring at glass substrate–specimen inter-faces improves the axial imaging resolution to 50–300 nm(Millis 2012). Therefore, TIRF microscopy is an ideal methodfor studying the molecular mobility within or close to theplasma membrane (PM) (Millis 2012). Due to the technicalchallenges of setting up this method, it has so far only been

rarely used in plant research. Improved spatial resolution hasbeen established by development of the so-calledsuperresolution imaging methods like structured illuminationmicroscopy, stimulated emission depletion (STED) microsco-py or photo-activated localisation microscopy. STED micros-copy was successfully used to assess the size of plant mem-brane domains (nanodomains) labeled by remorin proteins(Demir et al. 2013). Superresolution methods enable a lateralresolution down to 20 nm in biological samples; however, toachieve high resolution images, they still requires samplefixation (Leung and Chou 2011). Even though these imagingtechniques can resolve supramolecular structures and can beperformed in a dual-colour mode, they still do not allow forinvestigating physical protein–protein interactions.

In order to study protein interactions or the formation ofprotein oligomers in vivo, fluorescence correlative approacheslike fluorescence correlation spectroscopy (FCS) or the dual-colour version fluorescence cross-correlation spectroscopy(FCCS) can be applied. This technique is based on correlatingfluorescence intensity fluctuations caused by fluorescent mol-ecules passing through a confocal spot. From the generatedauto-correlation function the diffusion coefficients, confocalspot dimensions as well as the number of molecules present inthe observation volume can be assessed (Dehmelt andBastiaens 2010; Hink et al. 2008; Kwaaitaal et al. 2011). Sincethe diffusion constant depends on the size of the molecule,reduced diffusion is an indication of the formation of proteincomplexes. FCS and fluorescence recovery afterphotobleaching (FRAP) have been applied in live ovules ofArabidopsis, in which the dynamics of the LRR-RLK somaticembryogenesis receptor-like kinase 1 (SERK1) were investi-gated (Kwaaitaal et al. 2011). In this study, the receptormobility in plant protoplasts and in ovules was comparedand revealed a more restricted diffusion of SERK1 in nativeplant tissue, which is probably due to the presence of cellwalls. FCS has also been successfully applied to study PMreceptor dynamics and oligomerisation in plant protoplasts.The dual-colour FCCS approach allows one to followwhethertwo proteins diffuse as one complex and can therefore provideinformation about specific interactions between proteins. Ap-plication of FCCS has enabled assessing the mobility ofSERK1, SERK3 and brassinosteroid insensitive 1 (BRI1)receptors in cowpea protoplasts and in determining the mo-lecular composition of the respective protein complexes (Hinket al. 2008). Hink et al. (2008) estimated that approximately15 % of SERK1 and around 20 % of BRI1 are in a homodi-meric configuration in the PM of the investigated plant cells(Hink et al. 2008).

Next to FCCS, there are also other techniques for monitor-ing protein interactions such as bimolecular fluorescence com-plementation assay (BiFC). BiFC is based on the associationbetween two non-fluorescent fragments of a fluorescent pro-tein when in very close proximity. The interaction of these two

384 C.A. Bücherl et al.

fragments results in the formation of a functional FP and isindicative of complex formation of the tagged molecules ofinterest. The advantage of this technique is that no sophisti-cated imaging system is needed, but the drawback is lack ofgood negative controls and that the formation of the functionalFP is irreversible. Demir et al. (2013) applied BiFC to revealthe localisation and interaction within nanodomains of the twoabsisic acid signalling components CPK21 and SLAH3(SLAC1 homologue) expressed in leaves of (transientlytransfected) Nicotiana benthamiana plants (Demir et al.2013).

Still, the preferred method to investigate interactions be-tween proteins in complexes on a molecular scale is to useFörster resonance energy transfer (FRET). FRET gives aspecific spectroscopic signal that is dependent on the distancebetween two fluorophores. FRET is limited to roughly 1–10 nm, so it matches the size and scale of protein interactions.FRET-based fluorescence imaging techniques include ratio-imaging or acceptor photo-bleaching. These methods arebased on detecting FRET-dependent changes of fluorescenceintensities of two fluorophore labels, which are used to mon-itor protein–protein interactions. Though applicable to livecell imaging, these intensity-based methods require severalcorrection steps for reliable data analysis and depend on therelative abundance of the two fluorescently tagged proteins,which is often tedious to control. An elegant way to avoidthese drawbacks of intensity-based FRET approaches isachieved by measuring fluorescence lifetimes. The fluores-cence lifetime is a concentration-independent intrinsic prop-erty of a fluorophore, but is sensitive to the immediate envi-ronment of the dye molecule, which is exploited in fluores-cence lifetime-based FRET measurements.

In this review, we will focus solely on fluorescencelifetime-based methods. We will describe which informationcan be obtained from fluorescence lifetime measurements,how these measurements are performed and analyzed andhow fluorescence lifetime imaging microscopy (FLIM) en-ables the spatially resolved detection of protein–protein inter-actions in living plant tissue.

Visualising cellular signalling using visible fluorescent protein(VFP) tags

Independent of the approach applied and the processes stud-ied, a prerequisite for utilising fluorescence microscopy is thepresence of fluorescent probes in the investigated biologicalsystem. Because most proteins are not per se fluorescent in thevisible spectrum, usually a labeling of the proteins of interestis required. A breakthrough for in vivo fluorescence micros-copy was therefore the introduction of green fluorescent pro-tein (GFP) technology (Gerdes and Kaether 1996; Ehrhardt2003; Chudakov et al. 2010). The discovery of these nativefluorescent proteins and their subsequent modifications has

led to a vast portfolio of VFPs, for which the fluorescenceemission ranges from blue to near infrared (Shaner et al.2005), enabling multi-colour live cell imaging and visualisa-tion of dynamic protein behaviour in organisms on the singlemolecule level. However, due to the size of VFPs and theposition of attachment, mobility, localisation and functionalityof the tagged protein can be compromised (Ntoukakis et al.2011). To avoid these adverse effects of VFP tagging,immuno-labeling using native or secondary fluorophore-conjugated antibodies can be applied. The main advantageof this histochemical approach is the possibility to investigatewild-type protein behaviour. However, considering the size ofantibodies, especially if secondary labeling is required, theresulting images will suffer from some loss of spatial resolu-tion. This accounts in particular for the recently developedsuperresolution imaging methods. For these purposes, singledomain antibodies, also called nanobodies, offer a favorablealternative (Ries et al. 2012). Besides the uncertainties withrespect to spatial resolution as well as background labeling,another drawback of applying this methodology is the fixationof the samples and consequently the loss of molecular mobil-ity. Alternatively, biomolecules can be genetically tagged withsmall epitopes or labeled with endogenously expressed anti-bodies, which in turn bind synthetic membrane-permeablefluorophores (Farinas and Verkman 1999; Crivat andTaraska 2012; Wombacher and Cornish 2011). These ap-proaches, which can make use of fluorescent dyes such asFlasH and Reash, have shown the possibility to image pro-teins of interest in mammalian cells, but may suffer fromtoxicity and unspecific binding of the respective fluorescentagents (Crivat and Taraska 2012; Wombacher and Cornish2011).

To date, most fluorescence microscopic studies are basedon genetic VFP tagging, both because of its simplicity ofsample preparation and applicability to live cell imaging.Labeling proteins with fluorescent tags can either be used toreport on physiological changes caused by signalling activi-ties, in which the tagged proteins function as biosensors, or itcan used to directly study the features of the molecularcomponents.

FLIM

As discussed in the ‘Introduction’, fluorescence imaging suchas confocal microscopy, superresolution microscopy orutilising functional imaging techniques allows for acquiringa subcellular map based on steady-state fluorescence intensi-ties of fluorescently labeled biomolecules in living cells. Thedisadvantage of fluorescence intensity-based methods is theirdependence on fluorophore concentration, which often ex-hibits local variations and on attenuation by reabsorptionand/or scattering of the fluorescence in specific tissues(Siegel et al. 2003). A prevailing alternative approach is based

FRET-FLIM applications in plant systems 385

on displaying the fluorescence lifetime of the fluorophore(Peter et al. 2005). FLIM is a technique that produces imagesbased on fluorescence lifetime differences of the fluorescentmolecules in the sample. There are several advantages withFLIM such as: (1) FLIM can be used to discriminate differentfluorophores of the same colour, while identifying these dyesbased on differences in fluorescence lifetime, (2) singlefluorophore FLIM can be used to image pH patterning in livecells and (3) a combination of FLIM with FRET allows forprotein interaction studies in live cells (Kremers et al. 2008;Nakabayashi et al. 2012; Breusegem et al. 2006).

One particular photo-physical property of fluorophores isthe fluorescence lifetime (τ), which is defined as the averagetime a fluorophore resides in the excited state before returningto the ground state. The fluorescence lifetime is stronglyaffected by the properties of the local environment, for exam-ple the refractive index (Borst et al. 2005), or by interactionwith another molecule through quenching or FRET, but isindependent of fluorophore concentration (Lakowicz 1999).Therefore, fluorescence lifetime measurements can be used toobtain information on processes in the close proximity ofdifferent fluorophores. The relaxation of the excited statepopulation is described by an exponential function. Experi-mentally, the excited state population is measured as fluores-cence intensity in time and is described by:

I tð Þ ¼ I0e−t=τ

where I(t) is the fluorescence intensity at time t, I0 is the initialfluorescence intensity after excitation and τ is the fluorescelifetime.

In a microscopic configuration, the fluorescence lifetimecan be determined using two different approaches: time do-main or frequency domain FLIM. The physical principles thatunderlie the two methods are essentially identical, as they arefinite Fourier transforms of each other (Sun et al. 2011). Forfrequency domain measurements, the phase and amplitudemodulations of the fluorescence emission with respect to themodulated excitation light are used to deduce the fluorescencelifetime, which has extensively been reviewed in van MunsterandGadella (2005), Bastiaens and Squire (1999),Wouters andBastiaens (2001) and Jares-Erijman and Jovin (2006)). Fre-quency domain FLIM is mostly performed using a wide-fieldsetup; the phase delay and modulation depth of the emissionare monitored for all pixels simultaneously with a CCD-baseddetector (Gadella et al. 1993).

In time domain FLIM, a picosecond pulsed laser excitationis used and the temporal profile of the fluorescence decay isrecorded using high-speed detectors. In laser scanningmicros-copy (confocal or multiphoton), FLIM is detected by time-correlated single photon counting (TCSPC) (Becker et al.2001) or time-gated detection (Buurman et al. 1992).

Alternatively, time-gated methods have been developed forwide-field imaging (Dowling et al. 1997). In a FLIM image,each pixel data point contains positional information, but inaddition also fluorescence decay data. Subsequently, the fluo-rescence decay profiles of all pixels are fitted with exponentialmodel functions to estimate their associated fluorescence life-times (Sun et al. 2011). Repetitive sample excitation by thepulsed light source generates photon histograms that representthe fluorescence decay profile of the fluorophore (Morton andParsons 2011). The most general and reliable method foranalyzing time-resolved FLIM data is the nonlinear leastsquare method (Lakowicz 1999). The goal of this method isto minimise goodness of fit (χ2) by varying the values ofmodel parameters.

FLIM in plant systems

An example of a FLIM image is presented in Fig. 1. Here, aprotoplast was transfected with the fluorescent proteinmTurquoise and imaged using time-domain TCSPC-basedFLIM. The fluorescence lifetime values of all individualpixels are displayed as a false colour-coded fluorescencelifetime image. Typically, a histogram of the fluorescencelifetime distribution in the image (including the colour table)is also provided.

There are many applications described, in which FLIM hasbeen used to study biological processes. The work of Elgasset al. (2010) for example successfully applied ‘one chromo-phore‘ FLIM in roots of Arabidopsis. In these experiments,FLIM was combined with wavelength-specific fluorescencemicroscopy, allowing dynamic and quantitative analysis ofcellular processes in vivo at high resolution. They detected achange of the fluorescence lifetime of GFP-tagged BRI1molecules in response to application of a physiological ligand(Elgass et al. 2010, 2011). Application of the plant hormonebrassinolide resulted in depolarisation of the plasma mem-brane potential and consequently altered the local environ-ment of the GFP fluorophore. This investigation nicely illus-trates that the fluorescence lifetime can provide moleculardetails within a biological system.

In addition, the same authors have developed an imagingalgorithm that allowed a more precise localisation of fluores-cence signals in live roots. The technique presented is calledfluorescence intensity decay shape analysis microscopy. Thismethod enhances the dynamic contrast of a fluorescenceimage by at least one order of magnitude (Schleifenbaumet al. 2010). The method is based on the fact that the fluores-cence decay curve of GFP strongly differs from the decaycurves of autofluorescent components present in the sample.These autofluorescent signals add to the mono-exponentialfluorescence decay signal of GFP, resulting in a bad fit,causing a high χ2 value. Next, the fluorescence intensitiesare multiplied by the 1/χ2 value, followed by refitting. The


authors demonstrated the applicability of this procedure byshowing BRI1-GFP localisation in single isolated membranesat high spatial resolution.

Visualising protein interactions using FRET-FLIMin living plant cells

We just described a FLIM application, where only a onechromophore type is present in the biological system. How-ever, most fluorescence microscopic techniques that areemployed to determine protein oligomerisation exploithetero-FRET (Förster 1948; Day and Davidson 2012), thusinvolving the interaction of two different fluorophores. Thisphoto-physical process describes the non-radiative transfer ofexcited-state energy from a donor fluorophore to an acceptorchromophore. Since the energy transfer is accomplished bydipole–dipole coupling of the transition dipole moments ofdonor and acceptor, this process is limited to distances smallerthan 10 nm, comparable to the dimensions of protein sizes(Dehmelt and Bastiaens 2010). Therefore, FRET is a suitableread-out for investigating protein–protein interactions as illus-trated in a schematic representation (Fig. 2), in which it isshown that confocal microscopy resolves subcellular struc-tures with a spatial resolution of 250 nm, whereas FRETdetected by FLIM enables detection protein interactions onthe molecular scale, i.e. between 1 and 10 nm.

FRET is a distance-dependent phenomenon and is there-fore also referred to as a microspectroscopic ruler. There areseveral methods applicable to detect FRET in living cells.Most of them rely on fluorescence intensity measurementslike ratio-imaging, acceptor photo-bleaching or acceptor-sensitised emission (Gordon et al. 1998; Xia and Liu 2001;Karpova et al. 2003; Thaler et al. 2005). A drawback ofintensity-based approaches is the dependency on fluorophore

concentrations, which are hard to assess in living systems(Yasuda 2006). Accordingly, various corrections have to beimplemented in the data analysis. To avoid these pitfalls ofintensity-based methods, FLIM can be applied for detectingFRET (Yasuda 2006).

As mentioned earlier, FRET has become a routine methodfor the detection of protein interactions in live cells and inwhole organisms such as plants (Russinova et al. 2004;Bücherl et al. 2010, 2013; Stahl et al. 2013; Harter et al.2012). The presence of an acceptor molecule in the closeproximity of an excited donor fluorophore creates an

Fig. 1 Example of FLIM analysis. Fluorescence intensity and lifetimeimage of a protoplast expressingmTurquoise fluorescent protein recordedwith a Leica SP5 CLSM equipped with a Becker & Hickl (B&H) FLIMsystem. FLIM analysis is performed with the B&H SPCImage software.For each pixel in the image, there is a fluorescence decay (shown for the

pixel at the cross-section of the white lines). The decay is fitted with amono-exponential decay, which yields an average fluorescence lifetime(τ). The fluorescence lifetime is converted to a RGB colour, as indicatedby the colour bar. This colour displays the fluorescence lifetime at thepixel's location in the colour-coded fluorescence lifetime image

Fig. 2 Spatial resolution of different fluorescence microscopic tech-niques. Fluorescence microscopy can be used to investigate the spatialcorrelation between fluorescently labeled proteins. Depending on theapplied technique, various degrees of distances (d) are resolvable. Here,confocal microscopy and fluorescence lifetime imaging microscopy(FLIM) are compared. Fluorescence intensity images acquired in confocalmode allow resolving distances close to the diffraction limit of light. Forstudying the oligomerisation of proteins, donor FLIM is a suitablemethodto detect FRET between fluorescently tagged fusion proteins. SinceFRET can only occur in the range of protein dimensions, the physicalinteraction of the two appropriately labeled proteins can be deduced


additional relaxation pathway via FRET (see FRET sensorreview in this issue) resulting in a decreased donor fluores-cence lifetime. Since fluorophore concentrations are hard tocontrol and to quantify in live cells and because only donorfluorescence lifetimes need to be recorded, FLIM is consid-ered the most robust and efficient method to detect FRET(Nair et al. 2006).

Most FRET-FLIM applications in plants make use of over-expression of proteins in isolated plant cells (protoplasts). Forexample, Russinova et al. (2004) used cowpea protoplasts todetermine the interaction between the main ligand perceivingreceptor BRI1 and the coreceptor SERK3 (Russinova et al.2004). The major result of this research was the observation ofreceptor clusters in the PM and in emerging endosomal com-partments. In another study, maize protoplasts were transientlytransfected with different members of the aquaporin family.Here, a homogeneous reduction of fluorescence lifetime wasobserved confirming the establishment of water channelscomposed of several subunits (Zelazny et al. 2007). Stahlet al. (2013) demonstrated the interaction of CLV1 andACR4 in N. benthamiana leaves (Stahl et al. 2013). FLIMstudies have also been performed in protoplasts derived fromthe current model plant Arabidopsis thaliana. De Rybel et al.(2013) determined the interaction pattern of different tran-scription factors, involved in embryogenesis, and showed ina graph the difference in fluorescence lifetimes between donorsamples and the combination of transcription factors is shown(De Rybel et al. 2013). Recently, the interaction pattern ofBRI1 and SERK3, under control of their native promoter, hasbeen demonstrated in 5-day-old seedlings of Arabidopsis(Bücherl et al. 2013). Detection of endogenously expressedprotein is nowadays possible due to the development of a newtype of detectors, the so-called HyD detectors (Becker et al.2011), that have an approximately five times improved sensi-tivity over standard FLIM detectors.

Advances in FLIM analysis

In most FLIM analysis software, the fluorescence decay isfitted using an exponential function resulting in a fluorescencelifetime value for that particular pixel. However, to obtainreliable and statistically relevant fluorescence lifetimes of themicroscopic object, a sufficient photon count and signal-to-noise ratio are required. The final number of photons per pixeldepends on the pixel size, the number of time bins and theacquisition time used for obtaining a time-resolved fluores-cence intensity image. The choice of pixel size depends on theimaging system used, the magnification in combination withscan zoom and the desired image resolution. To record areliable fluorescence decay signal, a compromise betweenspatial resolution and photon counts per pixel is often re-quired. Similarly, the accuracy of fluorescence lifetime esti-mation improves with increasing number of time bins used.

However, high temporal resolution requires more photoncounts. Depending on the fit procedure, varying numbers ofexponential components are implemented in the model func-tion and various photon count statistics are required, respec-tively (Gerritsen et al. 2002; Becker et al. 2004). Unfortunate-ly, FLIM measurements in living cells always suffer fromautofluorescence and thus require compromised imagingsettings.

In order to detect FRET via FLIM, a comparison of thefluorescence lifetimes of donor-only samples with donor life-times in the presence of acceptor molecules has to be conduct-ed. In a first step, the analysis of the donor-only samples usinga mono-exponential decay fit is performed, which results in adistribution of donor fluorescence lifetimes in the biologicalsystem. Next, a time-resolved fluorescence intensity image ofa donor in the presence of an acceptor is analysed. A reductionin donor fluorescence lifetime can now be attributed to FRET.In case a fraction of donor molecules is not interacting withacceptors, a bi-exponential decay model can be used. Inde-pendent of the analysis model used, it should result in areliable fit with minimal χ2 and residual values close to zero.

To improve the analysis of FLIM data, a number ofmethods have been developed in recent years. Here, we willdiscuss the most promising ones: (1) global analysis and (2)phasor approach for FLIM.

In case of global analysis, fluorescence decay profiles of all(detected) pixels of a time-resolved fluorescence intensityimage are analyzed simultaneously (Barber et al. 2005;Grecco et al. 2009). If the fitting parameters are the same forall pixels (i.e. they are global parameters), the number ofparameters that should be fitted on a per pixel basis is reduced.Therefore, correct parameters can be estimated also fromimages with low photon counts per pixel. Common examplesof global parameters are the fluorescence lifetimes in two-component exponential decays (τ1, τ2) where τ1 and τ2 forexample are kept constant and only their amplitudes(contributions) are varying. This procedure may not be validfor all experimental systems (Sun et al. 2011).

In Fig. 3, spatially resolved fluorescence lifetime imagesare shown, which were obtained by global analysis using theGlotaran software (Laptenok et al. 2010, 2014). The globalanalysis yields the number of fluorescence lifetimes obtainedfrom the entire image and pixel-by-pixel amplitudes for thoselifetimes. Subsequently, an average fluorescence lifetime perpixel can be calculated using these parameter values.Figure 3a displays a fluorescence lifetime image of a proto-plast expressing mTurquiose. Due to the fact that mTurquioseshows a mono-exponential relaxation, all pixels have the samefluorescence lifetime of 3.6 ns. The distribution of the donorlifetime in that situation is represented by a single bar in thehistogram (Fig. 3c, red bar). In Fig. 3b, the mTurquiose–Venus FRET pair is expressed and a bi-exponential analysismodel is needed to describe the experimental dataset. The


analysis results in two fluorescence lifetimes of 1.2 and 3.6 ns,respectively, which results in a decreased average donor fluo-rescence lifetime of 2.5–2.6 ns, distributed over the entireimage. The average distance of this FRET couple in plantprotoplasts can be calculated by taking the ratio betweendonor fluorescence lifetime in the presence of acceptor (τDA)versus donor-only lifetime (τD). In this particular case, wedetermined a FRET efficiency of 67 %. If true mono-exponential decay of a donor is observed, as in the presentexample, then for the donor with the acceptor situation, theshort component of 1.2 ns as in our case represents thefluorescence lifetime of the FRETing population donor mol-ecules. This kind of experiments has to be assessed carefully,because using only average fluorescence lifetime values forFRET efficiency calculations will always result inunderestimated FRET values, due to the presence of thenon-FRETing population. Only in case all donor moleculesare either not interacting or all donor molecules are complexedwith acceptor molecules, the analysis results in one populationof molecules and will show a single fluorescence lifetimecomponent. The estimated distributions of the amplitudes ofdifferent lifetime components allow visualising fractions ofinteracting and non-interacting species.

To distinguish two fluorescence lifetimes, which are ex-pected in a FRET experiment due to FRETing and non-

FRETing donor populations, at least 1,000 photons per pixelare needed (Gratton et al. 2003). This can be problematic, inparticular for live cell imaging, because expression levels offluorophores may be low and mild excitation intensity isapplied to avoid bleaching of fluorophores and to preventphoto-toxicity. Therefore, pixel binning is often applied toincrease the number of photons per summated pixel. Unfor-tunately, this leads to a decreased spatial resolution. To avoidloss of spatial resolution, new analytical methods that signif-icantly improve the analysis of FLIM data containing lowsignal-to-noise ratios have been developed in recent years(Grecco et al. 2009; Laptenok et al. 2010; Visser et al. 2010).

Another approach to analyse low-photon-count FLIM de-cays is the so-called phasor approach. Initially, it was used infrequency domain FLIM, where the phase and modulationinformation was displayed in a two-dimensional polar plot(Clayton et al. 2004; Redford and Clegg 2005). Later, it wasshown that the same representation could be used in time-domain FLIM (Digman et al. 2008). The phasor plot analysisdistinguishes itself from other analytical approaches by theabsence of fitting algorithms. Purely by mathematical opera-tions, information about the fluorescence decay characteristicsis obtained. For time-domain FLIM, these steps include (1)normalisation of the decay curve so that fluorescence intensityinformation is discarded, (2) a Fourier transformation and (3)

Fig. 3 Example of global analysis of FLIM images. aFLIM image of theplant protoplast transfected with mTurquoise only (case A), using mono-exponential model for global analysis; therefore, all pixels have samelifetimes of 3.6 ns. b FLIM image of the protoplast transfected withmTurquoise–Venus fusion protein (case B). Different colours representdifferent fluorescence lifetimes. Similar colour code was used for both

analyses allowing the comparison of lifetime images based on differencesin colours. cDistribution histograms of the average fluorescence lifetimesfor cases A and B. Due to the mono-exponential model used for analysisof the case A, only one value represents caseA (red bar). dRepresentativecurves of a single pixel from case A (red) and case B (blue) with fittedcurves obtained from the analysis


plotting of the first harmonic real versus imaginary number ina phasor plot (see Fig. 4). These steps are performed for allindividual decays, and the resulting phasors are displayed in atwo-dimensional histogram (phasor plot).

In the phasor plot, mono-exponential decays will be locat-ed on a semicircle that starts at (Re=1, Im=0) for τ=0 andends at (Re=0, Im=0) for τ→∞. Deviations from mono-exponentiality will shift the phasor away from the semicircle.Phasors are vectors, so the phasor of a bi-exponential decaycurve will be located on a line connecting the two mono-exponential phasors it is composed of. From the relativedistances to its reference phasor, the contributions can becalculated. This means that phasors of multi-exponential de-cays move inside the semicircle. Shifts of the phasors can alsobe systematic. For example, they can be induced by irregular-ities of the instrumental response function (IRF) (in mostcases, the effect of IRF is neglected and the decay starts atthe time point that corresponds to the maximum of the

exCitation pulse). Also, the decay curves can be under-sampled, as is typically the case for time-gated FLIM. Amodified phasor approach to circumvent these issues has beendeveloped (Fereidouni et al. 2011).

Figure 4 shows images of protoplasts expressing donoronly (CFP) and donor in the presence of acceptor (CFP +YFP) evaluated by phasor analysis on FRET-FLIM data. Theintensity images show fluorescence mainly in the nucleus, butalso at the periphery of the cell where some signal is visible. Inthe phasor plot of the donor-only image, a cloud of pointsalong a line is observed. This indicates that two lifetimecomponents are present with a varying contribution ratiobetween them. A line (blue) is fitted through this cloud, sothe two reference lifetimes can be extracted at its crossingswith the reference semicircle. The long lifetime can be attrib-uted to CFP, whereas the short lifetime is an endogenoussignal from the chloroplasts. The approximate average life-time is calculated from the values at the horizontal axis of the

Fig. 4 Concept of phasor analysis (top) and example of phasor analysisapplied to FRET/FLIM data. Intermolecular FRET occurs between CFPand YFP tagged proteins. Images were recorded with a Leica SP5 CLSMequipped with a Picoquant FLIM system. Phasor analysis is performedwith an ImageJ/Fiji plugin developed by Farzad Fereidouni (Fereidouni

et al. 2011). This plugin provides a phasor plot and a false colourfluorescence lifetime image. The ‘global two components’ option givesaverage lifetime values. The unmixed images and regions of interest canbe projected back with the ‘phasor to image’ option


phasor plot and is mapped in the lifetime image. Also, theunmixed images of τ1 (green) and τ2 (purple) are displayed.Comparison with the FLIM image of the donor + acceptor(where two cells were imaged) shows that the τ1 decreasesfrom 3.33 to 2.86 ns, while τ2 stays constant at 0.18 ns. Thisreduced fluorescence lifetime of CFP can be attributed toFRET. For the donor with acceptor image, the phasor cloudis split in two, near the CFP reference. The phasors can betraced back to their original location in the image, so separateimages of the two-segmented clouds can be made. This re-veals that the long lifetime cloud is from the nucleus of the cellat the top of the image (light blue image and region of interest(RoI)) and the short lifetime cloud is from the one at thebottom (orange image and RoI). The two cells have a signif-icantly different FRET signal.

The example displayed in Fig. 4 shows the potential ofphasor analysis for FRET-FLIM. The main advantage is thatthe procedure is based on simple mathematics and analysis istherefore fast. Moreover, it is not dependent on input param-eters and there has minimal constraints, which makes it areliable analysis method. On the downside, it can be difficultto quantify the fluorescence lifetimes and their contributionsinmulti-exponential decays, but in many FRET-FLIM studies,this is of limited importance.

Advanced FRET-FLIM studies in planta

In this section, we will discuss two recent examples of FRET-FLIM in plant sciences. The work of Bücherl et al. (2013)reports on the formation of (preformed) complexes of BRI1and SERK3 in the PM of epidermal cells of 5-day-old seed-lings of Arabidopsis. BRI1 and SERK3 were under control oftheir native promoters and tagged with GFP and mCherry,respectively (Bücherl et al. 2013). First, the fluorescencelifetime of BRI1-GFP along the PM of epidermal cells wasdetermined to be approximately 2.4 ns. In the presence of theco-receptor SERK3-mCherry, the GFP fluorescence lifetimeshowed a strong reduction (below 2 ns) in small areas alongthe PM (see Fig. 5). A subsequent fluorescence lifetime anal-ysis was performed on samples treated with brassinolide (BL),an agonist for BRI1. A colour-coded FLIM image is presentedin Fig. 5 showing no change of the fluorescence lifetime inBRI1-GFP plants upon ligand application. However, in BRI1-GFP/SERK3-mCherry-expressing plants, the GFP fluores-cence lifetime was also strongly reduced, resulting in averagevalues of about 2.2 ns. These data showed that the averageFRET efficiencies hardly changed after activating the BRsignalling system.

Although reduced fluorescence lifetimes of BRI1-GFPwere observed in distinct areas at the PM and after inductionof the brassinosteroid signalling pathway by application ofexogenously applied ligand, only a minor change of theaverage fluorescence lifetimes of the total PM was observed.

However, the number of pixels showing reduced fluorescencelifetimes appeared to be changed. In order to quantify thisobservation, we performed a novel FRET-FLIM analysis pro-cedure. This analysis was especially designed to reveal smallpopulations of interacting molecules as observed for the bio-logical example used here, the BRI1-SERK3 hetero-oligomerisation in the PM of live Arabidopsis root cells.

The so-called interacting pixel analysis was performedusing analysed time-resolved fluorescence intensity images,which were selected for photon counts, fluorescence lifetimesand goodness of fit. Based on these parameters, pixels wereselected to ensure reliable and statistically relevant results.Subsequently, an interaction threshold was applied as a furtherselection criterion for donor- and acceptor-containing samplesand allowed for the determination of the number of interactionpixels (IPS), i.e. pixels showing strongly reduced donor fluo-rescence lifetimes, from the already pre-selected pixel ensem-ble. The percentage of IPS per FLIM image was calculatedusing the ratio between IPS and the total number of pre-selected pixels. Using this analysis procedure, the percentageof interacting pixels of BRI1-GFP/SERK3-mCherry sampleschanged from approximately 7 to 12 % after application ofexogenous BL. By applying this refined analysis procedure,the dynamic range of the FRET-FLIM measurements im-proved (Bücherl et al. 2013).

In another example, Stahl et al. (2013) have visualisedhomo- and heteromeric complex formation by multiparameter

Fig. 5 Fluorescence lifetime-based FRET-FLIM analysis reveals ligand-independent and ligand-dependent BRI1-SERK3 hetero-oligomers. a, bColour-coded fluorescence lifetime images of BRI1-GFP (a) and BRI1-GFP in the presence of SERK3-mCherry (b) at very low hormoneconcentration. c, d Colour-coded fluorescence lifetime images of BRI1-GFP (c) and BRI1-GFP in the presence of SERK3-mCherry (d) upon BLtreatment (1 μM, 1 h). The percentage of IPS per FLIM image wascalculated by using the ratio between IPS and the total number ofpreselected pixels. This revealed a change of IPS of BRI1-SERK3 com-plexes from approximately 7 to 12 % after application of exogenous BL


fluorescence image spectroscopy (MFIS) analysis. The uniquefeature of this method is the simultaneous recording of thefluorescence intensity, lifetime and anisotropy (Stahl et al.2013). In this study, donor fluorescence lifetimes were mea-sured for elucidating hetero-oligomer protein complexes. Inaddition, fluorescence anisotropy data was also acquired,which allowed for the detection of homo-FRET betweenidentically labeled molecules as shown by Bader et al.(2009) in mammalian cells (Bader et al. 2009) and in plantplasmodesmata by Stahl et al. (2013). MFIS analysis of (timeresolved) fluorescence anisotropy microscopy data provides astraightforward way of studying multiple combinations ofmolecular interactions in living tissue. Here, the location-dependent formation of homo- and heterodimers of CLV1and ACR4 was determined in N. benthamiana leaves, respec-tively. This study revealed restricted CLV1 homodimerisationat plasmodesmata, whereas hetero-oligomerisation withACR4 was observed along the PM (Stahl et al. 2013).

Future perspectives

In the last decade, many fluorescence imaging tools have beendeveloped for visualising biological processes in living cells.Confocal microscopy is still the method of choice for gaininginformation of subcellular structures, but lacks spatial resolu-tion to obtain molecular information. Various superresolutionimaging techniques allow resolving molecular structures withhigh precision, as recently has been shown for membranedomains (nanodomains) labeled by remorin proteins usingSTED microscopy. However, high-resolution imaging doesnot allow visualisation of proteins at the molecular scale or tovisualise protein interactions in their natural habitat. Asdiscussed in this review, FRET is a unique methodology witha high potential to detect in vivo protein interactions.

Although a large number of FRET techniques have alreadybeen developed and implemented, there is still the need toimprove and extend current experimental setups. In mostcases, FRET is mainly used for detecting two interactingmolecules. Next to MFIS for revealing higher order com-plexes, it is also possible to combine BiFC with FRET anal-ysis. This approach allows visualising simultaneous interac-tions between three fluorophore-tagged proteins as has beendemonstrated by Kwaaitaal et al. (2010). In this study, theternary SNARE complex formation was determined in barleyleaf epidermal cells by expressing one SNARE protein as atranslational fusion to CrFP (cerulean) and the other two as asplit-YFP couple (translationally fused to NYFP and CYFP,respectively) (Kwaaitaal et al. 2010). FRET-FLIM analysis ofCrFP resulted in a reduction of the fluorescence lifetimeyielding a FRET efficiency of 13 % (Kwaaitaal et al. 2010)demonstrating that ternary SNARE complexes are formed.

Another interesting possibility providing new insights inprotein complex formation in plant cells is the combination of

TCSPC and FCS, the so-called fluorescence lifetime correla-tion spectroscopy (FLCS) (Kapusta et al. 2007). FLCS is apowerful tool to improve the quality of FCS data using fluo-rescence lifetime components, assuming that the various com-ponents have distinct and non-changing lifetime signatures(Kapusta et al. 2007).

Moreover, there are alternative ways to detect FRET byFLIM. We have elaborated on donor fluorescence lifetimeimaging for the detection of FRET. However, an intriguingapproach is described by Laptenok et al. (2010), where time-dependent formation of acceptor fluorescence is monitored(Laptenok et al. 2010). In this study, plant protoplasts weretransfected with fusions of VFPs and it was demonstrated thatthe distances estimated with this method are substantiallysmaller than those estimated from average donor fluorescencelifetime measurements. Because this method reports onlythose molecules that undergo FRET, the fraction of non-transferring donor molecules is excluded in this analysis.These experiments were performed in a sequential manner,but future integration of multiple FLIM detectors will allowsimultaneous monitoring of donor quenching and of acceptorfluorescence increase, yielding quantitative interaction results.

Some novel applications of FRET that minimise the com-plexity of the instrumentation and methodology have beendeveloped as well. For example, intramolecular FRET-basedbiosensor technology is used to report on specific biologicalprocesses. Expression of these sensors results in a 1:1 ratio ofdonor–acceptor moieties and crosstalk factors hardly affectFRET signal output. Therefore, simple ratiometric measure-ments of FRET signals are sufficient and can be easily detect-ed with most high-content bio-imaging devices.

Conflict of interest The authors declare that they have no conflict ofinterest.

References

Bader AN, Hofman EG, Voortman J, Henegouwen PMPVE, GerritsenHC (2009) Homo-FRET imaging enables quantification of proteincluster sizes with subcellular resolution. Biophys J 97(9):2613–2622

Barber PR, Ameer-Beg SM, Gilbey J, Edens RJ, Ezike I, Vojnovic B(2005) Global and pixel kinetic data analysis for FRET detection bymulti-photon time-domain FLIM. MultiphotonMicrosc Biomed Sci5700:171–181

Bastiaens PI, Squire A (1999) Fluorescence lifetime imagingmicroscopy:spatial resolution of biochemical processes in the cell. Trends CellBiol 9(2):48–52

Beck M, Zhou J, Faulkner C, MacLean D, Robatzek S (2012) Spatio-temporal cellular dynamics of the Arabidopsis flagellin receptorreveal activation status-dependent endosomal sorting. Plant Cell24(10):4205–4219

Becker W, Bergmann A, Konig K, Tirlapur U (2001) Picosecond fluo-rescence lifetime microscopy by TCSPC imaging. MultiphotonMicrosc Biomed Sci 2(19):414–419


Becker W, Bergmann A, Hink MA, Konig K, Benndorf K, Biskup C(2004) Fluorescence lifetime imaging by time-correlated single-photon counting. Microsc Res Tech 63(1):58–66

Becker W, Su B, Holub O, Weisshart K (2011) FLIM and FCS detectionin laser-scanning microscopes: increased efficiency by GaAsP hy-brid detectors. Microsc Res Tech 74(9):804–811

Borst JW, Hink MA, van Hoek A, Visser AJ (2005) Effects of refractiveindex and viscosity on fluorescence and anisotropy decays of en-hanced cyan and yellow fluorescent proteins. J Fluoresc 15(2):153–160

Breusegem SY, Levi M, Barry NP (2006) Fluorescence correlation spec-troscopy and fluorescence lifetime imaging microscopy. NephronExp Nephrol 103(2):e41–e49

Bücherl C, Aker J, de Vries S, Borst JW (2010) Probing protein-proteininteractions with FRET-FLIM. Methods Mol Biol 655:389–399

Bücherl CA, van Esse GW, Kruis A, Luchtenberg J, Westphal AH, AkerJ, van Hoek A, Albrecht C, Borst JW, de Vries SC (2013)Visualization of BRI1 and BAK1(SERK3) membrane receptorheterooligomers during brassinosteroid signaling. Plant Physiol162(4):1911–1925

Buurman EP, Sanders R, Draaijer A, Gerritsen HC, Vanveen JJF, HouptPM, Levine YK (1992) Fluorescence lifetime imaging using aconfocal laser scanning microscope. Scanning 14(3):155–159

Chudakov DM, Matz MV, Lukyanov S, Lukyanov KA (2010)Fluorescent proteins and their applications in imaging living cellsand tissues. Physiol Rev 90(3):1103–1163

Clayton AHA, Hanley QS, Verveer PJ (2004) Graphical representationand multicomponent analysis of single-frequency fluorescence life-time imaging microscopy data. J Microsc Oxf 213:1–5

Crivat G, Taraska JW (2012) Imaging proteins inside cells with fluores-cent tags. Trends Biotechnol 30(1):8–16

Day RN, Davidson MW (2012) Fluorescent proteins for FRET micros-copy: monitoring protein interactions in living cells. Bioessays34(5):341–350

Day RN, Schaufele F (2005) Imaging molecular interactions in livingcells. Mol Endocrinol 19(7):1675–1686

De Rybel B, Moller B, Yoshida S, Grabowicz I, Barbier de Reuille P,Boeren S, Smith RS, Borst JW, Weijers D (2013) A bHLH complexcontrols embryonic vascular tissue establishment and indeterminategrowth in Arabidopsis. Dev Cell 24(4):426–437

Dehmelt L, Bastiaens PI (2010) Spatial organization of intracellularcommunication: insights from imaging. Nat Rev Mol Cell Biol11(6):440–452

Demir F, Horntrich C, Blachutzik JO, Scherzer S, Reinders Y,Kierszniowska S, Schulze WX, Harms GS, Hedrich R, Geiger D,Kreuzer I (2013) Arabidopsis nanodomain-delimited ABA signal-ing pathway regulates the anion channel SLAH3. Proc Natl AcadSci U S A 110(20):8296–8301

Digman MA, Caiolfa VR, Zamai M, Gratton E (2008) The phasorapproach to fluorescence lifetime imaging analysis. Biophys J94(2):L14–L16

Dowling K, Hyde SCW, Dainty JC, French PMW, Hares JD (1997) 2-Dfluorescence lifetime imaging using a time-gated image intensifier.Opt Commun 135(1–3):27–31

Ehrhardt D (2003) GFP technology for live cell imaging. Curr Opin PlantBiol 6(6):622–628

Elgass K, Caesar K, Schleifenbaum F, Meixner AJ, Harter K (2010) Thefluorescence lifetime of BRI1-GFP as probe for the noninvasivedetermination of the membrane potential in living cells. ImagingManip Anal Biomol Cells Tissues Viii 7568

Elgass K, Caesar K, Harter K, Meixner AJ, Schleifenbaum F (2011)Combining ocFLIM and FIDSAM reveals fast and dynamic phys-iological responses at subcellular resolution in living plant cells. JMicrosc 242(2):124–131

Farinas J, VerkmanAS (1999) Receptor-mediated targeting of fluorescentprobes in living cells. J Biol Chem 274(12):7603–7606

Fereidouni F, Esposito A, Blab GA, Gerritsen HC (2011) A modifiedphasor approach for analyzing time-gated fluorescence lifetimeimages. J Microsc 244(3):248–258

Förster T (1948) Zwischenmolekulare Energiewanderung undFluoreszenz. Ann Phys 437(1–2):55–75

Gadella TWJ, Jovin TM, Clegg RM (1993) Fluorescence lifetime imag-ing microscopy (FLIM)—spatial-resolution of microstructures onthe nanosecond time-scale. Biophys Chem 48(2):221–239

Gerdes HH, Kaether C (1996) Green fluorescent protein: applications incell biology. FEBS Lett 389(1):44–47

Gerritsen HC, Asselbergs MAH, Agronskaia AV, Van Sark WGJHM(2002) Fluorescence lifetime imaging in scanning microscopes:acquisition speed, photon economy and lifetime resolution. JMicrosc Oxf 206:218–224

Gordon GW, Berry G, Liang XH, Levine B, Herman B (1998)Quantitative fluorescence resonance energy transfer measurementsusing fluorescence microscopy. Biophys J 74(5):2702–2713

Gratton E, Breusegem S, Sutin J, Ruan Q, Barry N (2003) Fluorescencelifetime imaging for the two-photon microscope: time-domain andfrequency-domain methods. J Biomed Opt 8(3):381–390

Grecco HE, Roda-Navarro P, Verveer PJ (2009) Global analysis of timecorrelated single photon counting FRET-FLIM data. Opt Express17(8):6493–6508

Harter K, Meixner AJ, Schleifenbaum F (2012) Spectro-microscopy ofliving plant cells. Mol Plant 5(1):14–26

Hink MA, Shah K, Russinova E, de Vries SC, Visser AJ (2008)Fluorescence fluctuation analysis of Arabidopsis thaliana somaticembryogenesis receptor-like kinase and brassinosteroid insensitive 1receptor oligomerization. Biophys J 94(3):1052–1062

Jares-Erijman EA, Jovin TM (2006) Imaging molecular interactions inliving cells by FRET microscopy. Curr Opin Chem Biol 10(5):409–416

Kapusta P, Wahl M, Benda A, Hof M, Enderlein J (2007) Fluorescencelifetime correlation spectroscopy. J Fluoresc 17(1):43–48

Karpova TS, Baumann CT, He L, Wu X, Grammer A, Lipsky P, HagerGL, McNally JG (2003) Fluorescence resonance energy transferfrom cyan to yellow fluorescent protein detected by acceptorphotobleaching using confocal microscopy and a single laser. JMicros Oxf 209:56–70

Kremers GJ, van Munster EB, Goedhart J, Gadella TWJ (2008)Quantitative lifetime unmixing of multiexponentially decayingfluorophores using single-frequency fluorescence lifetime imagingmicroscopy. Biophys J 95(1):378–389

Kwaaitaal M, Keinath NF, Pajonk S, Biskup C, Panstruga R (2010)Combined bimolecular fluorescence complementation and Forsterresonance energy transfer reveals ternary SNARE complex forma-tion in living plant cells. Plant Physiol 152(3):1135–1147

Kwaaitaal M, Schor M, Hink MA, Visser AJ, de Vries SC (2011)Fluorescence correlation spectroscopy and fluorescence recoveryafter photobleaching to study receptor kinase mobility in planta.Methods Mol Biol 779:225–242

Lakowicz JR (1999) Principles of fluorescence spectroscopy (secondedition). Springer, Heidelberg

Laptenok SP, Borst JW, Mullen KM, van Stokkum IH, Visser AJ, vanAmerongen H (2010) Global analysis of Forster resonance energytransfer in live cells measured by fluorescence lifetime imagingmicroscopy exploiting the rise time of acceptor fluorescence. PhysChem Chem Phys 12(27):7593–7602

Laptenok SP, Snellenburg JJ, Bucherl CA, Konrad KR, Borst JW (2014)Global analysis of FRET-FLIM data in live plant cells.MethodsMolBiol 1076:481–502

Leung BO, Chou KC (2011) Review of super-resolution fluorescencemicroscopy for biology. Appl Spectrosc 65(9):967–980

Millis BA (2012) Evanescent-wave field imaging: an introduction to totalinternal reflection fluorescence microscopy. Methods Mol Biol 823:295–309


Morton PE, Parsons M (2011) Measuring FRET using time-resolvedFLIM. Methods Mol Biol 769:403–413

Nair DK, Jose M, Kuner T, Zuschratter W, Hartig R (2006) FRET-FLIMat nanometer spectral resolution from living cells. Opt Express14(25):12217–12229

Nakabayashi T, Oshita S, Sumikawa R, Sun F, Kinjo M, Ohta N (2012)pH dependence of the fluorescence lifetime of enhanced yellowfluorescent protein in solution and cells. J Photochem PhotobiolChem 235:65–71

Ntoukakis V, Schwessinger B, Segonzac C, Zipfel C (2011) Cautionarynotes on the use of C-terminal BAK1 fusion proteins for functionalstudies. Plant Cell 23(11):3871–3878

Peter M, Ameer-Beg SM, Hughes MK, Keppler MD, Prag S, Marsh M,Vojnovic B, Ng T (2005) Multiphoton-FLIM quantification of theEGFP-mRFP1 FRET pair for localization of membrane receptor-kinase interactions. Biophys J 88(2):1224–1237

Redford GI, Clegg RM (2005) Polar plot representation for frequency-domain analysis of fluorescence lifetimes. J Fluoresc 15(5):805–815

Ries J, Kaplan C, Platonova E, Eghlidi H, Ewers H (2012) A simple,versatile method for GFP-based super-resolution microscopy viananobodies. Nat Methods 9(6):582–584

Russinova E, Borst JW, Kwaaitaal M, Cano-Delgado A, Yin Y, Chory J,de Vries SC (2004) Heterodimerization and endocytosis ofArabidopsis brassinosteroid receptors BRI1 and AtSERK3(BAK1). Plant Cell 16(12):3216–3229

Schleifenbaum F, Elgass K, Sackrow M, Caesar K, Berendzen K,Meixner AJ, Harter K (2010) Fluorescence intensity decay shapeanalysis microscopy (FIDSAM) for quantitative and sensitive live-cell imaging: a novel technique for fluorescence microscopy ofendogenously expressed fusion-proteins. Mol Plant 3(3):555–562

Shaner NC, Steinbach PA, Tsien RY (2005) A guide to choosing fluo-rescent proteins. Nat Methods 2(12):905–909

Siegel J, Elson DS, Webb SE, Lee KC, Vlandas A, Gambaruto GL,Leveque-Fort S, Lever MJ, Tadrous PJ, Stamp GW, Wallace AL,Sandison A, Watson TF, Alvarez F, French PM (2003) Studying

biological tissue with fluorescence lifetime imaging: microscopy,endoscopy, and complex decay profiles. Appl Opt 42(16):2995–3004

Stahl Y, Grabowski S, Bleckmann A, Kuhnemuth R, Weidtkamp-PetersS, Pinto KG, Kirschner GK, Schmid JB, Wink RH, Hulsewede A,Felekyan S, Seidel CA, Simon R (2013) Moderation of Arabidopsisroot stemness by CLAVATA1 and ARABIDOPSIS CRINKLY4receptor kinase complexes. Curr Biol 23(5):362–371

Sun Y, Day RN, Periasamy A (2011) Investigating protein-protein inter-actions in living cells using fluorescence lifetime imaging micros-copy. Nat Protoc 6(9):1324–1340

Thaler C, Koushik SV, Blank PS, Vogel SS (2005) Quantitative multi-photon spectral imaging and its use for measuring resonance energytransfer. Biophys J 89(4):2736–2749

van Munster EB, Gadella TW (2005) Fluorescence lifetime imagingmicroscopy (FLIM). Adv Biochem Eng Biotechnol 95:143–175

Visser AJ, Laptenok SP, Visser NV, van Hoek A, Birch DJ, BrochonJC, Borst JW (2010) Time-resolved FRET fluorescence spectros-copy of visible fluorescent protein pairs. Eur Biophys J 39(2):241–253

Wombacher R, Cornish VW (2011) Chemical tags: applications in livecell fluorescence imaging. J Biophotonics 4(6):391–402

Wouters FS, Bastiaens PI (2001) Imaging protein-protein interactions byfluorescence resonance energy transfer (FRET) microscopy. CurrProtoc Cell Biol Chapter 17:Unit 17.1

Xia ZP, Liu YH (2001) Reliable and global measurement of fluorescenceresonance energy transfer using fluorescence microscopes. BiophysJ 81(4):2395–2402

Yasuda R (2006) Imaging spatiotemporal dynamics of neuronal signalingusing fluorescence resonance energy transfer and fluorescence life-time imaging microscopy. Curr Opin Neurobiol 16(5):551–561

Zelazny E, Borst JW,Muylaert M, Batoko H, HemmingaMA, ChaumontF (2007) FRET imaging in living maize cells reveals that plasmamembrane aquaporins interact to regulate their subcellular localiza-tion. Proc Natl Acad Sci U S A 104(30):12359–12364


Documents

FRET-FLIM applications in plant systems