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

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 spatiotemporally regulated interplay of biochemical components. Assessing spatial information of molecular interactions within living cells is difficult using traditional biochemical methods. Developments in green fluorescent protein technology in combination with advances in fluorescence microscopy have revolutionised this field of research by providing the genetic tools to investigate the spatio-temporal dynamics of biomolecules in live cells. In particular, fluorescence lifetime imaging microscopy (FLIM) has become an inevitable technique for spatially resolving cellular processes and physical interactions of cellular components in real time based on the detection of Förster resonance energy transfer (FRET). In this review, we provide a theoretical background of FLIM as well as FRETFLIM analysis. Furthermore, we show two cases in which advanced microscopy applications revealed many new insights of cellular processes in living plant cells as well as in whole plants.

Handling Editor: David Robinson C. A. Bücherl The Sainsbury Laboratory, Norwich Research Park, Norwich NR4 7UH, UK

Keywords Fluorescence lifetime imaging microscopy (FLIM) . Förster resonance energy transfer (FRET) . Visible fluorescent protein (VFP) . Global analysis . Phasor plot analysis Abbreviations ACR4 Arabidopsis crinkly 4 BiFC Bimolecular fluorescence complementation BRI1 Brassinosteroid insensitive 1 CLV1 Clavata1 CPK21 Calcium-dependent protein kinase 21 FCS Fluorescence correlation spectroscopy FCCS Fluorescence cross-correlation spectroscopy FLIM Fluorescence lifetime imaging microscopy FRET Förster resonance energy transfer FLS2 Flagellin sensing 2 GFP Green fluorescent protein LRR-RLK Leucine-rich repeat receptor-like kinases MFIS Multiparameter fluorescence image spectroscopy PM Plasma membrane SERK Somatic embryogenesis receptor-like kinase STED Stimulated emission depletion TCSPC Time-correlated single photon counting TIRF Total internal reflection fluorescence VFP Visible fluorescent protein

A. Bader Laboratory of Biophysics and Microspectroscopy Centre, Wageningen University, Wageningen, The Netherlands S. P. Laptenok School 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 Netherlands e-mail: [email protected]

Introduction Cellular processes involve the assembly and disassembly of molecular complexes, which is often accompanied by physical repositioning of individual proteins within the cellular matrix (Day and Schaufele 2005). To gain access to the fundamental principles of processes like DNA replication,

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protein folding or signal transduction, the application of imaging methodologies offers numerous possibilities (Dehmelt and Bastiaens 2010). In particular, fluorescence microscopy has become an essential tool in biology for studying biochemical dynamics in living cells. Depending on the microscopic setup used, various read-outs can be recorded to characterise fluorescently labeled biomolecules, of which the fluorescence intensity is the most common parameter used for investigating biological events (Yasuda 2006). Fluorescence imaging allows elucidating spatio-temporal distributions of fluorescently tagged proteins in living plant cells and organisms. Expressing single fluorescently labeled proteins in (living) cells provides information about subcellular protein localisation. Moreover, dual-colour imaging of differently labeled proteins enables investigating subcellular co-localisation patterns. Co-localisation studies can be performed by using either a wide-field or confocal microscope, of which the latter has the advantage of providing increased optical resolution and higher contrast images. Examples of fluorescent microscopic studies of plant signalling components are the leucine-rich repeat receptor-like kinases (LRR-RLK) flagellin sensing 2 (FLS2) (Beck et al. 2012), Clavata1 (CLV1) (Stahl et al. 2013), and calciumdependent protein kinase 21 (CPK21) (Demir et al. 2013). Beck et al. (2012) used confocal imaging to investigate endosomal trafficking of the pattern recognition receptor FLS2 in Arabidopsis leaf discs (Beck et al. 2012). Quantitative co-localisation analysis was used to dissect the different organelles FLS2 constitutively transits to before and after flagellin (flg22) stimulation. High-throughput imaging based on spinning disk microscopy (Opera, PerkinElmer) has provided quantitative datasets of FLS2-expressing cells and has revealed that after signal perception FLS2, undergoes traditional endosomal trafficking via early and late endosomes culminating in vacuolar degradation. Fluorescence microscopy was also applied for the in planta characterisation of CLV1. Using confocal microscopy, Stahl et al. (2013) visualised the colocalisation of CLV1 with Arabidopsis crinkly 4 (ACR4) in specific cell files and subcellular compartments (Stahl et al. 2013). Although co-localisation studies in the confocal mode allow studying subcellular structures down to 200–300 nm in lateral and 1,000 nm in axial dimensions, the spatial resolution does not extend to the molecular dimension of proteins. Technologies have been developed to overcome this limited spatial resolution of confocal microscopy. For example, total internal reflection fluorescence (TIRF) microscopy that is based on the evanescent field occurring at glass substrate–specimen interfaces improves the axial imaging resolution to 50–300 nm (Millis 2012). Therefore, TIRF microscopy is an ideal method for studying the molecular mobility within or close to the plasma membrane (PM) (Millis 2012). Due to the technical challenges of setting up this method, it has so far only been

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rarely used in plant research. Improved spatial resolution has been established by development of the so-called superresolution imaging methods like structured illumination microscopy, stimulated emission depletion (STED) microscopy or photo-activated localisation microscopy. STED microscopy was successfully used to assess the size of plant membrane domains (nanodomains) labeled by remorin proteins (Demir et al. 2013). Superresolution methods enable a lateral resolution down to 20 nm in biological samples; however, to achieve high resolution images, they still requires sample fixation (Leung and Chou 2011). Even though these imaging techniques can resolve supramolecular structures and can be performed in a dual-colour mode, they still do not allow for investigating physical protein–protein interactions. In order to study protein interactions or the formation of protein oligomers in vivo, fluorescence correlative approaches like fluorescence correlation spectroscopy (FCS) or the dualcolour version fluorescence cross-correlation spectroscopy (FCCS) can be applied. This technique is based on correlating fluorescence intensity fluctuations caused by fluorescent molecules passing through a confocal spot. From the generated auto-correlation function the diffusion coefficients, confocal spot dimensions as well as the number of molecules present in the observation volume can be assessed (Dehmelt and Bastiaens 2010; Hink et al. 2008; Kwaaitaal et al. 2011). Since the diffusion constant depends on the size of the molecule, reduced diffusion is an indication of the formation of protein complexes. FCS and fluorescence recovery after photobleaching (FRAP) have been applied in live ovules of Arabidopsis, in which the dynamics of the LRR-RLK somatic embryogenesis receptor-like kinase 1 (SERK1) were investigated (Kwaaitaal et al. 2011). In this study, the receptor mobility in plant protoplasts and in ovules was compared and revealed a more restricted diffusion of SERK1 in native plant tissue, which is probably due to the presence of cell walls. FCS has also been successfully applied to study PM receptor dynamics and oligomerisation in plant protoplasts. The dual-colour FCCS approach allows one to follow whether two proteins diffuse as one complex and can therefore provide information about specific interactions between proteins. Application of FCCS has enabled assessing the mobility of SERK1, SERK3 and brassinosteroid insensitive 1 (BRI1) receptors in cowpea protoplasts and in determining the molecular composition of the respective protein complexes (Hink et al. 2008). Hink et al. (2008) estimated that approximately 15 % of SERK1 and around 20 % of BRI1 are in a homodimeric configuration in the PM of the investigated plant cells (Hink et al. 2008). Next to FCCS, there are also other techniques for monitoring protein interactions such as bimolecular fluorescence complementation assay (BiFC). BiFC is based on the association between two non-fluorescent fragments of a fluorescent protein when in very close proximity. The interaction of these two

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fragments results in the formation of a functional FP and is indicative of complex formation of the tagged molecules of interest. The advantage of this technique is that no sophisticated imaging system is needed, but the drawback is lack of good negative controls and that the formation of the functional FP is irreversible. Demir et al. (2013) applied BiFC to reveal the localisation and interaction within nanodomains of the two absisic acid signalling components CPK21 and SLAH3 (SLAC1 homologue) expressed in leaves of (transiently transfected) Nicotiana benthamiana plants (Demir et al. 2013). Still, the preferred method to investigate interactions between proteins in complexes on a molecular scale is to use Förster resonance energy transfer (FRET). FRET gives a specific spectroscopic signal that is dependent on the distance between 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 ratioimaging or acceptor photo-bleaching. These methods are based on detecting FRET-dependent changes of fluorescence intensities of two fluorophore labels, which are used to monitor protein–protein interactions. Though applicable to live cell imaging, these intensity-based methods require several correction steps for reliable data analysis and depend on the relative abundance of the two fluorescently tagged proteins, which is often tedious to control. An elegant way to avoid these drawbacks of intensity-based FRET approaches is achieved by measuring fluorescence lifetimes. The fluorescence lifetime is a concentration-independent intrinsic property of a fluorophore, but is sensitive to the immediate environment of the dye molecule, which is exploited in fluorescence lifetime-based FRET measurements. In this review, we will focus solely on fluorescence lifetime-based methods. We will describe which information can be obtained from fluorescence lifetime measurements, how these measurements are performed and analyzed and how fluorescence lifetime imaging microscopy (FLIM) enables the spatially resolved detection of protein–protein interactions in living plant tissue.

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led to a vast portfolio of VFPs, for which the fluorescence emission ranges from blue to near infrared (Shaner et al. 2005), enabling multi-colour live cell imaging and visualisation of dynamic protein behaviour in organisms on the single molecule level. However, due to the size of VFPs and the position of attachment, mobility, localisation and functionality of the tagged protein can be compromised (Ntoukakis et al. 2011). To avoid these adverse effects of VFP tagging, immuno-labeling using native or secondary fluorophoreconjugated antibodies can be applied. The main advantage of this histochemical approach is the possibility to investigate wild-type protein behaviour. However, considering the size of antibodies, especially if secondary labeling is required, the resulting images will suffer from some loss of spatial resolution. This accounts in particular for the recently developed superresolution imaging methods. For these purposes, single domain antibodies, also called nanobodies, offer a favorable alternative (Ries et al. 2012). Besides the uncertainties with respect to spatial resolution as well as background labeling, another drawback of applying this methodology is the fixation of the samples and consequently the loss of molecular mobility. Alternatively, biomolecules can be genetically tagged with small epitopes or labeled with endogenously expressed antibodies, which in turn bind synthetic membrane-permeable fluorophores (Farinas and Verkman 1999; Crivat and Taraska 2012; Wombacher and Cornish 2011). These approaches, which can make use of fluorescent dyes such as FlasH and Reash, have shown the possibility to image proteins of interest in mammalian cells, but may suffer from toxicity and unspecific binding of the respective fluorescent agents (Crivat and Taraska 2012; Wombacher and Cornish 2011). To date, most fluorescence microscopic studies are based on genetic VFP tagging, both because of its simplicity of sample preparation and applicability to live cell imaging. Labeling proteins with fluorescent tags can either be used to report on physiological changes caused by signalling activities, in which the tagged proteins function as biosensors, or it can used to directly study the features of the molecular components.

Visualising cellular signalling using visible fluorescent protein (VFP) tags

FLIM

Independent of the approach applied and the processes studied, a prerequisite for utilising fluorescence microscopy is the presence of fluorescent probes in the investigated biological system. Because most proteins are not per se fluorescent in the visible spectrum, usually a labeling of the proteins of interest is required. A breakthrough for in vivo fluorescence microscopy was therefore the introduction of green fluorescent protein (GFP) technology (Gerdes and Kaether 1996; Ehrhardt 2003; Chudakov et al. 2010). The discovery of these native fluorescent proteins and their subsequent modifications has

As discussed in the ‘Introduction’, fluorescence imaging such as confocal microscopy, superresolution microscopy or utilising functional imaging techniques allows for acquiring a subcellular map based on steady-state fluorescence intensities of fluorescently labeled biomolecules in living cells. The disadvantage of fluorescence intensity-based methods is their dependence on fluorophore concentration, which often exhibits local variations and on attenuation by reabsorption and/or scattering of the fluorescence in specific tissues (Siegel et al. 2003). A prevailing alternative approach is based

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on displaying the fluorescence lifetime of the fluorophore (Peter et al. 2005). FLIM is a technique that produces images based on fluorescence lifetime differences of the fluorescent molecules in the sample. There are several advantages with FLIM such as: (1) FLIM can be used to discriminate different fluorophores of the same colour, while identifying these dyes based on differences in fluorescence lifetime, (2) single fluorophore FLIM can be used to image pH patterning in live cells and (3) a combination of FLIM with FRET allows for protein interaction studies in live cells (Kremers et al. 2008; Nakabayashi et al. 2012; Breusegem et al. 2006). One particular photo-physical property of fluorophores is the fluorescence lifetime (τ), which is defined as the average time a fluorophore resides in the excited state before returning to the ground state. The fluorescence lifetime is strongly affected by the properties of the local environment, for example the refractive index (Borst et al. 2005), or by interaction with another molecule through quenching or FRET, but is independent of fluorophore concentration (Lakowicz 1999). Therefore, fluorescence lifetime measurements can be used to obtain information on processes in the close proximity of different fluorophores. The relaxation of the excited state population is described by an exponential function. Experimentally, the excited state population is measured as fluorescence intensity in time and is described by: I ðt Þ ¼ I 0 e−t=τ

where I(t) is the fluorescence intensity at time t, I0 is the initial fluorescence intensity after excitation and τ is the fluoresce lifetime. In a microscopic configuration, the fluorescence lifetime can be determined using two different approaches: time domain or frequency domain FLIM. The physical principles that underlie the two methods are essentially identical, as they are finite Fourier transforms of each other (Sun et al. 2011). For frequency domain measurements, the phase and amplitude modulations of the fluorescence emission with respect to the modulated excitation light are used to deduce the fluorescence lifetime, which has extensively been reviewed in van Munster and Gadella (2005), Bastiaens and Squire (1999), Wouters and Bastiaens (2001) and Jares-Erijman and Jovin (2006)). Frequency domain FLIM is mostly performed using a wide-field setup; the phase delay and modulation depth of the emission are monitored for all pixels simultaneously with a CCD-based detector (Gadella et al. 1993). In time domain FLIM, a picosecond pulsed laser excitation is used and the temporal profile of the fluorescence decay is recorded using high-speed detectors. In laser scanning microscopy (confocal or multiphoton), FLIM is detected by timecorrelated single photon counting (TCSPC) (Becker et al. 2001) or time-gated detection (Buurman et al. 1992).

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Alternatively, time-gated methods have been developed for wide-field imaging (Dowling et al. 1997). In a FLIM image, each pixel data point contains positional information, but in addition also fluorescence decay data. Subsequently, the fluorescence decay profiles of all pixels are fitted with exponential model functions to estimate their associated fluorescence lifetimes (Sun et al. 2011). Repetitive sample excitation by the pulsed light source generates photon histograms that represent the fluorescence decay profile of the fluorophore (Morton and Parsons 2011). The most general and reliable method for analyzing time-resolved FLIM data is the nonlinear least square method (Lakowicz 1999). The goal of this method is to minimise goodness of fit (χ2) by varying the values of model parameters. FLIM in plant systems An example of a FLIM image is presented in Fig. 1. Here, a protoplast was transfected with the fluorescent protein mTurquoise and imaged using time-domain TCSPC-based FLIM. The fluorescence lifetime values of all individual pixels are displayed as a false colour-coded fluorescence lifetime image. Typically, a histogram of the fluorescence lifetime distribution in the image (including the colour table) is also provided. There are many applications described, in which FLIM has been used to study biological processes. The work of Elgass et al. (2010) for example successfully applied ‘one chromophore‘ FLIM in roots of Arabidopsis. In these experiments, FLIM was combined with wavelength-specific fluorescence microscopy, allowing dynamic and quantitative analysis of cellular processes in vivo at high resolution. They detected a change of the fluorescence lifetime of GFP-tagged BRI1 molecules in response to application of a physiological ligand (Elgass et al. 2010, 2011). Application of the plant hormone brassinolide resulted in depolarisation of the plasma membrane potential and consequently altered the local environment of the GFP fluorophore. This investigation nicely illustrates that the fluorescence lifetime can provide molecular details within a biological system. In addition, the same authors have developed an imaging algorithm that allowed a more precise localisation of fluorescence signals in live roots. The technique presented is called fluorescence intensity decay shape analysis microscopy. This method enhances the dynamic contrast of a fluorescence image by at least one order of magnitude (Schleifenbaum et al. 2010). The method is based on the fact that the fluorescence decay curve of GFP strongly differs from the decay curves of autofluorescent components present in the sample. These autofluorescent signals add to the mono-exponential fluorescence decay signal of GFP, resulting in a bad fit, causing a high χ2 value. Next, the fluorescence intensities are multiplied by the 1/χ2 value, followed by refitting. The

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Fig. 1 Example of FLIM analysis. Fluorescence intensity and lifetime image of a protoplast expressing mTurquoise fluorescent protein recorded with a Leica SP5 CLSM equipped with a Becker & Hickl (B&H) FLIM system. 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 a mono-exponential decay, which yields an average fluorescence lifetime (τ). The fluorescence lifetime is converted to a RGB colour, as indicated by the colour bar. This colour displays the fluorescence lifetime at the pixel's location in the colour-coded fluorescence lifetime image

authors demonstrated the applicability of this procedure by showing BRI1-GFP localisation in single isolated membranes at high spatial resolution.

concentrations, which are hard to assess in living systems (Yasuda 2006). Accordingly, various corrections have to be implemented in the data analysis. To avoid these pitfalls of intensity-based methods, FLIM can be applied for detecting FRET (Yasuda 2006). As mentioned earlier, FRET has become a routine method for the detection of protein interactions in live cells and in whole 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 close proximity of an excited donor fluorophore creates an

Visualising protein interactions using FRET-FLIM in living plant cells We just described a FLIM application, where only a one chromophore type is present in the biological system. However, most fluorescence microscopic techniques that are employed to determine protein oligomerisation exploit hetero-FRET (Förster 1948; Day and Davidson 2012), thus involving the interaction of two different fluorophores. This photo-physical process describes the non-radiative transfer of excited-state energy from a donor fluorophore to an acceptor chromophore. Since the energy transfer is accomplished by dipole–dipole coupling of the transition dipole moments of donor and acceptor, this process is limited to distances smaller than 10 nm, comparable to the dimensions of protein sizes (Dehmelt and Bastiaens 2010). Therefore, FRET is a suitable read-out for investigating protein–protein interactions as illustrated in a schematic representation (Fig. 2), in which it is shown that confocal microscopy resolves subcellular structures with a spatial resolution of 250 nm, whereas FRET detected by FLIM enables detection protein interactions on the molecular scale, i.e. between 1 and 10 nm. FRET is a distance-dependent phenomenon and is therefore also referred to as a microspectroscopic ruler. There are several methods applicable to detect FRET in living cells. Most of them rely on fluorescence intensity measurements like ratio-imaging, acceptor photo-bleaching or acceptorsensitised emission (Gordon et al. 1998; Xia and Liu 2001; Karpova et al. 2003; Thaler et al. 2005). A drawback of intensity-based approaches is the dependency on fluorophore

Fig. 2 Spatial resolution of different fluorescence microscopic techniques. Fluorescence microscopy can be used to investigate the spatial correlation between fluorescently labeled proteins. Depending on the applied technique, various degrees of distances (d) are resolvable. Here, confocal microscopy and fluorescence lifetime imaging microscopy (FLIM) are compared. Fluorescence intensity images acquired in confocal mode allow resolving distances close to the diffraction limit of light. For studying the oligomerisation of proteins, donor FLIM is a suitable method to detect FRET between fluorescently tagged fusion proteins. Since FRET can only occur in the range of protein dimensions, the physical interaction of the two appropriately labeled proteins can be deduced

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additional relaxation pathway via FRET (see FRET sensor review in this issue) resulting in a decreased donor fluorescence lifetime. Since fluorophore concentrations are hard to control and to quantify in live cells and because only donor fluorescence lifetimes need to be recorded, FLIM is considered the most robust and efficient method to detect FRET (Nair et al. 2006). Most FRET-FLIM applications in plants make use of overexpression of proteins in isolated plant cells (protoplasts). For example, Russinova et al. (2004) used cowpea protoplasts to determine the interaction between the main ligand perceiving receptor BRI1 and the coreceptor SERK3 (Russinova et al. 2004). The major result of this research was the observation of receptor clusters in the PM and in emerging endosomal compartments. In another study, maize protoplasts were transiently transfected with different members of the aquaporin family. Here, a homogeneous reduction of fluorescence lifetime was observed confirming the establishment of water channels composed of several subunits (Zelazny et al. 2007). Stahl et al. (2013) demonstrated the interaction of CLV1 and ACR4 in N. benthamiana leaves (Stahl et al. 2013). FLIM studies have also been performed in protoplasts derived from the current model plant Arabidopsis thaliana. De Rybel et al. (2013) determined the interaction pattern of different transcription factors, involved in embryogenesis, and showed in a graph the difference in fluorescence lifetimes between donor samples and the combination of transcription factors is shown (De Rybel et al. 2013). Recently, the interaction pattern of BRI1 and SERK3, under control of their native promoter, has been demonstrated in 5-day-old seedlings of Arabidopsis (Bücherl et al. 2013). Detection of endogenously expressed protein is nowadays possible due to the development of a new type of detectors, the so-called HyD detectors (Becker et al. 2011), that have an approximately five times improved sensitivity over standard FLIM detectors. Advances in FLIM analysis In most FLIM analysis software, the fluorescence decay is fitted using an exponential function resulting in a fluorescence lifetime value for that particular pixel. However, to obtain reliable and statistically relevant fluorescence lifetimes of the microscopic object, a sufficient photon count and signal-tonoise ratio are required. The final number of photons per pixel depends on the pixel size, the number of time bins and the acquisition time used for obtaining a time-resolved fluorescence intensity image. The choice of pixel size depends on the imaging system used, the magnification in combination with scan zoom and the desired image resolution. To record a reliable fluorescence decay signal, a compromise between spatial resolution and photon counts per pixel is often required. Similarly, the accuracy of fluorescence lifetime estimation improves with increasing number of time bins used.

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However, high temporal resolution requires more photon counts. Depending on the fit procedure, varying numbers of exponential components are implemented in the model function and various photon count statistics are required, respectively (Gerritsen et al. 2002; Becker et al. 2004). Unfortunately, FLIM measurements in living cells always suffer from autofluorescence and thus require compromised imaging settings. In order to detect FRET via FLIM, a comparison of the fluorescence lifetimes of donor-only samples with donor lifetimes in the presence of acceptor molecules has to be conducted. In a first step, the analysis of the donor-only samples using a mono-exponential decay fit is performed, which results in a distribution of donor fluorescence lifetimes in the biological system. Next, a time-resolved fluorescence intensity image of a donor in the presence of an acceptor is analysed. A reduction in donor fluorescence lifetime can now be attributed to FRET. In case a fraction of donor molecules is not interacting with acceptors, a bi-exponential decay model can be used. Independent of the analysis model used, it should result in a reliable fit with minimal χ2 and residual values close to zero. To improve the analysis of FLIM data, a number of methods have been developed in recent years. Here, we will discuss 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 intensity image are analyzed simultaneously (Barber et al. 2005; Grecco et al. 2009). If the fitting parameters are the same for all pixels (i.e. they are global parameters), the number of parameters that should be fitted on a per pixel basis is reduced. Therefore, correct parameters can be estimated also from images with low photon counts per pixel. Common examples of global parameters are the fluorescence lifetimes in twocomponent exponential decays (τ1, τ2) where τ1 and τ2 for example are kept constant and only their amplitudes (contributions) are varying. This procedure may not be valid for all experimental systems (Sun et al. 2011). In Fig. 3, spatially resolved fluorescence lifetime images are shown, which were obtained by global analysis using the Glotaran software (Laptenok et al. 2010, 2014). The global analysis yields the number of fluorescence lifetimes obtained from the entire image and pixel-by-pixel amplitudes for those lifetimes. Subsequently, an average fluorescence lifetime per pixel can be calculated using these parameter values. Figure 3a displays a fluorescence lifetime image of a protoplast expressing mTurquiose. Due to the fact that mTurquiose shows a mono-exponential relaxation, all pixels have the same fluorescence lifetime of 3.6 ns. The distribution of the donor lifetime in that situation is represented by a single bar in the histogram (Fig. 3c, red bar). In Fig. 3b, the mTurquiose– Venus FRET pair is expressed and a bi-exponential analysis model is needed to describe the experimental dataset. The

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Fig. 3 Example of global analysis of FLIM images. a FLIM image of the plant protoplast transfected with mTurquoise only (case A), using monoexponential model for global analysis; therefore, all pixels have same lifetimes of 3.6 ns. b FLIM image of the protoplast transfected with mTurquoise–Venus fusion protein (case B). Different colours represent different fluorescence lifetimes. Similar colour code was used for both

analyses allowing the comparison of lifetime images based on differences in colours. c Distribution histograms of the average fluorescence lifetimes for cases A and B. Due to the mono-exponential model used for analysis of the case A, only one value represents case A (red bar). d Representative curves of a single pixel from case A (red) and case B (blue) with fitted curves obtained from the analysis

analysis results in two fluorescence lifetimes of 1.2 and 3.6 ns, respectively, which results in a decreased average donor fluorescence lifetime of 2.5–2.6 ns, distributed over the entire image. The average distance of this FRET couple in plant protoplasts can be calculated by taking the ratio between donor fluorescence lifetime in the presence of acceptor (τDA) versus donor-only lifetime (τD). In this particular case, we determined a FRET efficiency of 67 %. If true monoexponential decay of a donor is observed, as in the present example, then for the donor with the acceptor situation, the short component of 1.2 ns as in our case represents the fluorescence lifetime of the FRETing population donor molecules. This kind of experiments has to be assessed carefully, because using only average fluorescence lifetime values for FRET efficiency calculations will always result in underestimated FRET values, due to the presence of the non-FRETing population. Only in case all donor molecules are either not interacting or all donor molecules are complexed with acceptor molecules, the analysis results in one population of molecules and will show a single fluorescence lifetime component. The estimated distributions of the amplitudes of different lifetime components allow visualising fractions of interacting and non-interacting species. To distinguish two fluorescence lifetimes, which are expected in a FRET experiment due to FRETing and non-

FRETing donor populations, at least 1,000 photons per pixel are needed (Gratton et al. 2003). This can be problematic, in particular for live cell imaging, because expression levels of fluorophores may be low and mild excitation intensity is applied to avoid bleaching of fluorophores and to prevent photo-toxicity. Therefore, pixel binning is often applied to increase the number of photons per summated pixel. Unfortunately, this leads to a decreased spatial resolution. To avoid loss of spatial resolution, new analytical methods that significantly improve the analysis of FLIM data containing low signal-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 decays is the so-called phasor approach. Initially, it was used in frequency domain FLIM, where the phase and modulation information was displayed in a two-dimensional polar plot (Clayton et al. 2004; Redford and Clegg 2005). Later, it was shown that the same representation could be used in timedomain FLIM (Digman et al. 2008). The phasor plot analysis distinguishes itself from other analytical approaches by the absence of fitting algorithms. Purely by mathematical operations, information about the fluorescence decay characteristics is obtained. For time-domain FLIM, these steps include (1) normalisation of the decay curve so that fluorescence intensity information is discarded, (2) a Fourier transformation and (3)

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plotting of the first harmonic real versus imaginary number in a phasor plot (see Fig. 4). These steps are performed for all individual decays, and the resulting phasors are displayed in a two-dimensional histogram (phasor plot). In the phasor plot, mono-exponential decays will be located on a semicircle that starts at (Re=1, Im=0) for τ=0 and ends at (Re=0, Im=0) for τ→∞. Deviations from monoexponentiality will shift the phasor away from the semicircle. Phasors are vectors, so the phasor of a bi-exponential decay curve will be located on a line connecting the two monoexponential phasors it is composed of. From the relative distances to its reference phasor, the contributions can be calculated. This means that phasors of multi-exponential decays move inside the semicircle. Shifts of the phasors can also be systematic. For example, they can be induced by irregularities of the instrumental response function (IRF) (in most cases, the effect of IRF is neglected and the decay starts at the time point that corresponds to the maximum of the

exCitation pulse). Also, the decay curves can be undersampled, as is typically the case for time-gated FLIM. A modified phasor approach to circumvent these issues has been developed (Fereidouni et al. 2011). Figure 4 shows images of protoplasts expressing donor only (CFP) and donor in the presence of acceptor (CFP + YFP) evaluated by phasor analysis on FRET-FLIM data. The intensity images show fluorescence mainly in the nucleus, but also at the periphery of the cell where some signal is visible. In the phasor plot of the donor-only image, a cloud of points along a line is observed. This indicates that two lifetime components are present with a varying contribution ratio between them. A line (blue) is fitted through this cloud, so the two reference lifetimes can be extracted at its crossings with the reference semicircle. The long lifetime can be attributed to CFP, whereas the short lifetime is an endogenous signal from the chloroplasts. The approximate average lifetime is calculated from the values at the horizontal axis of the

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

et al. 2011). This plugin provides a phasor plot and a false colour fluorescence lifetime image. The ‘global two components’ option gives average lifetime values. The unmixed images and regions of interest can be projected back with the ‘phasor to image’ option

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phasor plot and is mapped in the lifetime image. Also, the unmixed 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 decreases from 3.33 to 2.86 ns, while τ2 stays constant at 0.18 ns. This reduced fluorescence lifetime of CFP can be attributed to FRET. For the donor with acceptor image, the phasor cloud is split in two, near the CFP reference. The phasors can be traced back to their original location in the image, so separate images of the two-segmented clouds can be made. This reveals that the long lifetime cloud is from the nucleus of the cell at the top of the image (light blue image and region of interest (RoI)) and the short lifetime cloud is from the one at the bottom (orange image and RoI). The two cells have a significantly different FRET signal. The example displayed in Fig. 4 shows the potential of phasor analysis for FRET-FLIM. The main advantage is that the procedure is based on simple mathematics and analysis is therefore fast. Moreover, it is not dependent on input parameters and there has minimal constraints, which makes it a reliable analysis method. On the downside, it can be difficult to quantify the fluorescence lifetimes and their contributions in multi-exponential decays, but in many FRET-FLIM studies, this is of limited importance.

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Fig. 5 Fluorescence lifetime-based FRET-FLIM analysis reveals ligandindependent and ligand-dependent BRI1-SERK3 hetero-oligomers. a, b Colour-coded fluorescence lifetime images of BRI1-GFP (a) and BRI1GFP in the presence of SERK3-mCherry (b) at very low hormone concentration. c, d Colour-coded fluorescence lifetime images of BRI1GFP (c) and BRI1-GFP in the presence of SERK3-mCherry (d) upon BL treatment (1 μM, 1 h). The percentage of IPS per FLIM image was calculated by using the ratio between IPS and the total number of preselected pixels. This revealed a change of IPS of BRI1-SERK3 complexes from approximately 7 to 12 % after application of exogenous BL

Advanced FRET-FLIM studies in planta In this section, we will discuss two recent examples of FRETFLIM in plant sciences. The work of Bücherl et al. (2013) reports on the formation of (preformed) complexes of BRI1 and SERK3 in the PM of epidermal cells of 5-day-old seedlings of Arabidopsis. BRI1 and SERK3 were under control of their native promoters and tagged with GFP and mCherry, respectively (Bücherl et al. 2013). First, the fluorescence lifetime of BRI1-GFP along the PM of epidermal cells was determined to be approximately 2.4 ns. In the presence of the co-receptor SERK3-mCherry, the GFP fluorescence lifetime showed a strong reduction (below 2 ns) in small areas along the PM (see Fig. 5). A subsequent fluorescence lifetime analysis was performed on samples treated with brassinolide (BL), an agonist for BRI1. A colour-coded FLIM image is presented in Fig. 5 showing no change of the fluorescence lifetime in BRI1-GFP plants upon ligand application. However, in BRI1GFP/SERK3-mCherry-expressing plants, the GFP fluorescence lifetime was also strongly reduced, resulting in average values of about 2.2 ns. These data showed that the average FRET efficiencies hardly changed after activating the BR signalling system. Although reduced fluorescence lifetimes of BRI1-GFP were observed in distinct areas at the PM and after induction of the brassinosteroid signalling pathway by application of exogenously applied ligand, only a minor change of the average fluorescence lifetimes of the total PM was observed.

However, the number of pixels showing reduced fluorescence lifetimes appeared to be changed. In order to quantify this observation, we performed a novel FRET-FLIM analysis procedure. This analysis was especially designed to reveal small populations of interacting molecules as observed for the biological example used here, the BRI1-SERK3 heterooligomerisation in the PM of live Arabidopsis root cells. The so-called interacting pixel analysis was performed using analysed time-resolved fluorescence intensity images, which were selected for photon counts, fluorescence lifetimes and goodness of fit. Based on these parameters, pixels were selected to ensure reliable and statistically relevant results. Subsequently, an interaction threshold was applied as a further selection criterion for donor- and acceptor-containing samples and allowed for the determination of the number of interaction pixels (IPS), i.e. pixels showing strongly reduced donor fluorescence lifetimes, from the already pre-selected pixel ensemble. The percentage of IPS per FLIM image was calculated using the ratio between IPS and the total number of preselected pixels. Using this analysis procedure, the percentage of interacting pixels of BRI1-GFP/SERK3-mCherry samples changed from approximately 7 to 12 % after application of exogenous BL. By applying this refined analysis procedure, the dynamic range of the FRET-FLIM measurements improved (Bücherl et al. 2013). In another example, Stahl et al. (2013) have visualised homo- and heteromeric complex formation by multiparameter

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fluorescence image spectroscopy (MFIS) analysis. The unique feature of this method is the simultaneous recording of the fluorescence intensity, lifetime and anisotropy (Stahl et al. 2013). In this study, donor fluorescence lifetimes were measured for elucidating hetero-oligomer protein complexes. In addition, fluorescence anisotropy data was also acquired, which allowed for the detection of homo-FRET between identically labeled molecules as shown by Bader et al. (2009) in mammalian cells (Bader et al. 2009) and in plant plasmodesmata by Stahl et al. (2013). MFIS analysis of (time resolved) fluorescence anisotropy microscopy data provides a straightforward way of studying multiple combinations of molecular interactions in living tissue. Here, the locationdependent formation of homo- and heterodimers of CLV1 and ACR4 was determined in N. benthamiana leaves, respectively. This study revealed restricted CLV1 homodimerisation at plasmodesmata, whereas hetero-oligomerisation with ACR4 was observed along the PM (Stahl et al. 2013). Future perspectives In the last decade, many fluorescence imaging tools have been developed for visualising biological processes in living cells. Confocal microscopy is still the method of choice for gaining information of subcellular structures, but lacks spatial resolution to obtain molecular information. Various superresolution imaging techniques allow resolving molecular structures with high precision, as recently has been shown for membrane domains (nanodomains) labeled by remorin proteins using STED microscopy. However, high-resolution imaging does not allow visualisation of proteins at the molecular scale or to visualise protein interactions in their natural habitat. As discussed in this review, FRET is a unique methodology with a high potential to detect in vivo protein interactions. Although a large number of FRET techniques have already been developed and implemented, there is still the need to improve and extend current experimental setups. In most cases, FRET is mainly used for detecting two interacting molecules. Next to MFIS for revealing higher order complexes, it is also possible to combine BiFC with FRET analysis. This approach allows visualising simultaneous interactions between three fluorophore-tagged proteins as has been demonstrated by Kwaaitaal et al. (2010). In this study, the ternary SNARE complex formation was determined in barley leaf epidermal cells by expressing one SNARE protein as a translational fusion to CrFP (cerulean) and the other two as a split-YFP couple (translationally fused to NYFP and CYFP, respectively) (Kwaaitaal et al. 2010). FRET-FLIM analysis of CrFP resulted in a reduction of the fluorescence lifetime yielding a FRET efficiency of 13 % (Kwaaitaal et al. 2010) demonstrating that ternary SNARE complexes are formed. Another interesting possibility providing new insights in protein complex formation in plant cells is the combination of

C.A. Bücherl et al.

TCSPC and FCS, the so-called fluorescence lifetime correlation spectroscopy (FLCS) (Kapusta et al. 2007). FLCS is a powerful tool to improve the quality of FCS data using fluorescence lifetime components, assuming that the various components have distinct and non-changing lifetime signatures (Kapusta et al. 2007). Moreover, there are alternative ways to detect FRET by FLIM. We have elaborated on donor fluorescence lifetime imaging for the detection of FRET. However, an intriguing approach is described by Laptenok et al. (2010), where timedependent formation of acceptor fluorescence is monitored (Laptenok et al. 2010). In this study, plant protoplasts were transfected with fusions of VFPs and it was demonstrated that the distances estimated with this method are substantially smaller than those estimated from average donor fluorescence lifetime measurements. Because this method reports only those molecules that undergo FRET, the fraction of nontransferring donor molecules is excluded in this analysis. These experiments were performed in a sequential manner, but future integration of multiple FLIM detectors will allow simultaneous monitoring of donor quenching and of acceptor fluorescence increase, yielding quantitative interaction results. Some novel applications of FRET that minimise the complexity of the instrumentation and methodology have been developed as well. For example, intramolecular FRET-based biosensor technology is used to report on specific biological processes. Expression of these sensors results in a 1:1 ratio of donor–acceptor moieties and crosstalk factors hardly affect FRET signal output. Therefore, simple ratiometric measurements of FRET signals are sufficient and can be easily detected with most high-content bio-imaging devices.

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

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