Biotechnology Letters

, Volume 28, Issue 24, pp 1971–1982 | Cite as

Protein biosensors based on the principle of fluorescence resonance energy transfer for monitoring cellular dynamics

Review

Abstract

Genetically-coded, fluorescence resonance energy transfer (FRET) biosensors are widely used to study molecular events from single cells to whole organisms. They are unique among biosensors because of their spontaneous fluorescence and targeting specificity to both organelles and tissues. In this review, we discuss the theoretical basis of FRET with a focus on key parameters responsible for designing FRET biosensors that have the highest sensitivity. Next, we discuss recent applications that are grouped into four common biosensor design patterns—intermolecular FRET, intramolecular FRET, FRET from substrate cleavage and FRET using multiple colour fluorescent proteins. Lastly, we discuss recent progress in creating fluorescent proteins suitable for FRET purposes. Together these advances in the development of FRET biosensors are beginning to unravel the interconnected and intricate signalling processes as they are occurring in living cells and organisms.

Keywords

Fluorescence resonance energy transfer (FRET) Genetically coded biosensor Green fluorescent protein (GFP) Intermolecular FRET Intramolecular FRET Protein conformational changes Protein–substrate interaction Substrate cleavage Transgenic organisms 

Introduction

Since the discovery of fluorescent proteins (FPs) that are suitable for fluorescence resonance energy transfer (FRET) (Shaner et al. 2005), protein biosensors have rapidly become important tools for studying live cell molecular events. FRET was first described by Theodor Förster nearly sixty years ago as a non-radiative transfer of energy from a photo-excited donor to an acceptor fluorescent molecule located in close proximity (<100 Å) (Valeur 2002). The distance and orientation between the donor and acceptor governs the efficiency of the energy transfer. This efficiency can be determined by a fluorescence emission spectrum. In FRET biosensors, a biological event induces a conformational change in the biosensor, which in turn causes a detectable change in FRET efficiency as measured by the change of profile in the emission spectrum.

These FRET biosensors have many advantages over methods based on conjugating synthetic dyes: first, their fluorescence is acquired spontaneously; second, they can be constructed by simple genetic manipulations; third, they can be delivered into cells by transient transfection and subsequent expression; lastly, they can be targeted to organelles and tissues allowing imaging from single cells to whole organisms. A potential drawback of FRET biosensors is that FPs are relatively bulky (∼50 Å) compared to synthetic dyes (∼5 Å) and therefore may hinder protein activity. However, the activities of many proteins are not affected by fusion to FPs. A second potential drawback is that FPs gain fluorescence through the rate-limiting step of fluorophore maturation, which in some cases can be as long as 12 h, hindering the study of molecular events occurring during that period. To address this problem, many fast maturing FPs have been developed (Bevis and Glick 2002; Nagai et al. 2002; Pedelacq et al. 2006). A third drawback is that FPs are susceptible to photobleaching as is the case with any organic fluorescent dyes, which restricts their use in long term monitoring of cellular events. This problem can be solved by two-photon microscopy, which significantly lowers the donor FP photobleaching rate. This review will discuss recent applications of FRET biosensors.

The principle of FRET

The energy of an excited donor molecule is transferred to the acceptor by resonance coupling of the donor dipole and the acceptor dipole. The rate of this energy transfer, kFRET, is described by the equations (Valeur 2002):
$$ k_{{\hbox{FRET}}} = \frac{1} {{\tau _{\hbox{D}} }} \times \frac{{9000\ln (10)\kappa ^2 Q_{\hbox{D}} J}} {{128\pi ^5 n^4 N_{\hbox{A}} }} \times \frac{1} {{R^6 }} = \frac{1} {{\tau _{\hbox{D}} }}\left( {\frac{{R_0 }} {R}} \right)^6 $$
(1)
$$ R_0 = \left( {\frac{{9000\ln (10)\kappa ^2 Q_{\hbox{D}} J}} {{128\pi ^5 n^4 N_{\hbox{A}} }}} \right)^{\frac{1} {6}} $$
(2)
where R is the distance between the donor and the acceptor. The Förster radius, R0, is the distance between the donor and acceptor where the FRET efficiency is 50%. It is influenced by the following parameters: the quantum yield of the donor in the presence of the acceptor (QD); the refractive index (n) of the medium; Avogadro’s number (NA); the lifetime of the donor in the absence of acceptors (τD); the relative orientation factor (κ2), which will be discussed in the following text; and lastly, the spectra overlap integral J, defined by
$$ J = \int_0^\infty {F_{\hbox{D}} (\lambda )\varepsilon _{\hbox{A}} (\lambda )\lambda ^4 \,{\hbox{d}}\lambda } $$
(3)
where FD(λ) is the donor emission profile and εA(λ) is the acceptor molar extinction coefficient.
The FRET efficiency is defined by the equation:
$$ E = \frac{{k_{{\hbox{FRET}}} }} {{k_{{\hbox{FRET}}} + \tau _{\hbox{D}}^{ - 1} }} = \frac{{R_0^6 }} {{R_0^6 + R^6 }} $$
(4)
Equations 14 indicate that a higher donor quantum yield and larger overlap of donor-emission and acceptor-excitation spectra are key parameters to achieve greater energy transfer, which will result in a better FRET signal. Therefore, these are the important factors to consider when choosing FPs for FRET applications. Given a chosen FRET pair, the orientation factor (κ2) and distance (R) determines the amount of energy that is actually transferred. The orientation factor characterizes the statistical average of the relative fluorophore orientation, which determines both how well the fluorophore dipoles are coupled and how efficiently energy is transferred. The orientation factor is 2/3 for free FRET pairs, but has a different value in biosensors where the movement of the FRET pairs is restricted. The second factor that affects the FRET signal is the distance (R) between the fluorophores. The most sensitive range of R is 0.7–1.4R0, corresponding to 90–10% FRET efficiency (Fig. 1). R0 is usually between 40 Å to 70 Å, hence, protein conformational change in this range is ideal for the largest dynamic range in FRET biosensors.
Fig. 1

FRET efficiency as a function of fluorophore distance. The FRET efficiency is 50% when the distance equals to the Forster radius (R0). FRET efficiencies ranging from 10% to 90% correspond to distances from 1.4 R0 to 0.7 R0

Applications of FRET biosensors using FPs

FRET biosensors have been engineered to detect a broad range of molecular events such as protein-binding interactions, protein conformational changes, protein catalytic functions (for example, proteolysis, phosphorylation, dephosphorylation and GTPase activities), and concentration of biomolecules (for example, signalling molecules, cellular metabolites and nucleic acids). Table 1 provides examples of FRET biosensors based on their applications. They can be designed using four common patterns—intermolecular interactions, intramolecular interactions, proteolytic cleavage or multiple FPs (Fig. 2). Because the applications of FRET biosensors spans over a broader range, we will review the current advancement of FRET biosensors from the perspective of these four design patterns.
Table 1

List of protein biosensors

Application

Examples

Biosensor FRET mechanism

Sensory domain(s)

FRET pair

Source

Protein binding interaction

Multimerization of IL-17RA

Inter

IL-17RA with itself

CFP YFP

Kramer et al. (2006)

GPCR subunit association

Inter

Gα with Gβγ

CFP YFP

Azpiazu and Gautam (2004)

Transcriptional factor Erg and Jun interaction

Inter

Erg with Jun

CFP YFP

Camuzeaux et al. (2005)

Protein conformational change

Sensing membrane potential

Intra S

Potassium channel voltage sensing domain

ECFP EYFP

Sakai et al. (2001)

GTPase

Activation and signalling of rac and cdc42

Intra M Or Intra S

Cdc42 or rac with GTPase binding domains

CFP YFPECFPEYFP

Itoh et al. (2002), Seth et al. (2003)

Protease activity

Caspases

Cleavage

Caspase proteolytic substrate

CFP YFP Cerulean Venus

Chiang and Truong (2005), Jones et al. (2000), Nagai and Miyawaki (2004), Onuki et al. (2002), Xu et al. (1998)

Calpain

Cleavage

Calpain proteolytic substrate

ECFP EYFP

Stockholm et al. (2005)

Factor Xa

Cleavage

Factor Xa proteolytic substrate

BFP5 RSGFP4

Mitra et al. (1996)

Kinase/phosphotase activity

MLCK and MLCP

Intra S

RMLC (regulatory myosin light chain)

ECFP Citrine

Yamada et al. (2005)

Kinetics and potencies of 12 known PKC ligands

Intra S

PKCδ

ECFP EYFP

Braun et al. (2005)

Detection of PKC activities

Intra S

Truncated pleckstrin containing PH and DEP domains

ECFP EYFP

Schleifenbaum et al. (2004)

Phosphorylation by insulin receptor

Intra M

Phosphorylation recognition domain and its binding substrate

CFP YFP

Sato et al. (2002), Sato and Umezawa (2004)

Activities of EGFR, Src and Ab1

Intra M

SH2 with phosphorylation substrates for EGFR, Src and Ab1

CFP YFP

Ting et al. (2001)

Activation of Src

Intra M

SH2 with phosphorylation substrates for Src

CFP YFP

Wang et al. (2005)

Metabolic molecules

Glucose

Intra S

Glucose binding protein

ECFP EYFP

Fehr et al. (2004), Fehr et al. (2003), Ye and Schultz (2003)

Maltose

Intra S

Periplasmic binding proteins

ECFP EYFP

Fehr et al. (2002)

Glutamine

Intra S

Glutamate/aspartate binding protein ybeJ

ECFP Venus

Okumoto et al. (2005)

Signalling molecules

cAMP

Inter

PKA with cAMP-dependent binding substrate

CFP YFP

Zaccolo et al. (2002, 2005), Zaccolo and Pozzan (2002)

IP3

Intra S

InsP3 receptors

CFP YFP

Remus et al. (2006), Tanimura et al. (2004)

cGMP

Intra S

GKI and PDE

CFP YFP

Nikolaev et al. (2006)

Estrogen receptor ligand

Intra S

Estrogen receptor ligand binding domain

CFP YFP

De et al. (2005)

Ca2+ in ER

Intra S

apoK1-er

CFP YFP

Osibow et al. (2006)

Ca2+

Intra M

CaM M13

CFP YFP BFP GFP

Miyawaki et al. (1997)

Other molecule

Specific RNA sequence

Intra S

HIV-1 Rev protein

ECFP EYFP

Endoh et al. (2005)

Note: Intra S: Intramolecular single domain; Intra M: Intramolecular multiple domain interaction; Inter: Intermolecular interactions; Cleavage: Biosensor cleavage

Fig. 2

Schematics showing common FRET biosensor constructs. White cylinders are YFPs, grey cylinders are CFPs and black cylinders are RFPs. Arrows pointing into the FPs are excitation light and those pointing outwards from the FPs are their emission light. (A) Intermolecular FRET biosensor uses the binding between separate sensory domain and substrate to create FRET. Intramolecular FRET biosensors using (B) a sensory domain alone, and (C) the binding between a sensory domain to its substrate. (D) Cleavage biosensors detect the substrate cleavage between FRET pairs. (E) FRET using three FPs creates three FRET signals

FRET using intermolecular interactions

Binding interactions between proteins are essential for signal transduction and catalytic activation. For instance, many protein pathways are activated by the association of membrane receptors, which activate enzymes that propagates the signals. To study these binding interactions, FRET biosensors are created by fusing the donor and acceptor FP separately to the interacting proteins of interest. When the intermolecular interaction of these separate fusion proteins occur, the donor and acceptor are consequently brought closer together to create an intermolecular FRET signal corresponding to the location and time of the interaction (see Fig. 2A). Biosensors of this class have proven to be as invaluable tools to study the association mechanisms of membrane receptors as well as the cytoplasmic proteins. Recently, an inflammatory cytokine receptor subunit, IL-17RA, was shown to multimerize and preassemble in the plasma membrane of HEK293 cells. When HEK293 cells were coexpressed with IL-17RA fused to CFP and YFP, the plasma membrane of transfected HEK293 cells displayed a strong FRET signal indicating the association of CFP and YFP tagged IL-17RA (Kramer et al. 2006). Using a similar approach, a biosensor was constructed to study the association of G-protein coupled receptors (GPCRs) with G-proteins. Gα was tagged with CFP and Gβγ with YFP (Azpiazu and Gautam 2004). Before activation by GPCRs, Gα-CFP and Gβγ-YFP subunits bind together, producing a high FRET signal. After activation, Gα-CFP and Gβγ-YFP separate, resulting in the loss of FRET. Studies using this biosensor suggested the mechanism behind the specificity of G-protein signalling pathways was the stochastic collision between GPCRs and G-proteins. In another example, the direct interaction of transcription factors Erg and Jun was demonstrated by fusing them to YFP and CFP respectively (Camuzeaux et al. 2005).

In addition to studying protein associations, intermolecular FRET has also been applied to probe the effects of small molecules in signal transduction pathways. Here, the biosensor mechanism usually relies on the small-molecule-dependent binding of two separate domains that bring a FRET pair into close proximity and consequently, increasing the FRET signal. This mechanism has been used to construct biosensors for cAMP where YFP is fused to protein kinase A (PKA) and CFP to its cAMP-dependent binding substrate (Lissandron et al. 2005; Zaccolo et al. 2005). Using this biosensor, it was discovered that cAMP is generated by cells in discrete functional compartments inside cardiac myocytes (Zaccolo et al. 2002, 2005; Zaccolo and Pozzan 2002). We have seen from the examples described above that intermolecular FRET biosensors provide direct evidence of protein binding events, which is ideal for studying protein associations in vivo. The available intermolecular biosensors to this date cover only a small fraction of the total protein binding interactions, hence, we expect to see a broader the range of protein binding interactions being detected with new biosensors in the near future. Currently, one of the limitations of intermolecular FRET biosensors is that using one FP pair can detect only one pair of protein association. The solution to the problem is to use multiple FRET pair as will be discussed below.

FRET using intramolecular interactions

The changes of protein conformation are usually resulted from biochemical stimulation, including either environmental changes such as the concentration of small molecules and membrane voltage or modifications performed by other proteins such as kinases and phosphatases. By monitoring these protein conformation changes with FRET, we can indirectly detect their inducing molecular events (see Fig. 2B). Indeed, sensory proteins with relatively large conformation changes such as IP3-receptor, cGMP-dependent protein kinase I, estrogen receptor ligand binding domain, HIV-1 Rev protein, glucose-binding protein, periplasmic binding protein and glutamine-binding protein ybeJ exhibit sufficient conformational changes to be used to detect concentration changes of IP3 (Remus et al. 2006; Tanimura et al. 2004), cGMP (Nikolaev et al. 2006), estrogen receptor ligand (De et al. 2005), specific RNA sequence (Endoh et al. 2005), glucose (Fehr et al. 2003, 2004; Ye and Schultz 2003), maltose (Fehr et al. 2002) and glutamate (Okumoto et al. 2005), respectively. These small molecules are important in cellular signalling and metabolic pathways and hence, these biosensors will help to study their dynamics in the cell. For example, a glucose sensor capable of sensing glucose concentration within the physiological range was employed to study the dynamics of glucose metabolism in COS-7 cells (Fehr et al. 2003). The study showed the rapid glucose consumption in COS-7 cells by probing glucose concentration in the cytosol while changing the extracellular glucose concentration or inhibiting glucose transport by cytochalasin B.

In the above examples, target molecules are detected by directly binding to the biosensors. Other intramolecular FRET biosensors are constructed such that instead of binding, the sensory domain is modified by enzymes and is thus able to detect molecular events associated with the activities of the modifying enzyme. For example, a biosensor able to detect phosphorylation of regulatory myosin light chain (RMLC) was constructed utilizing the conformational change of RMLC upon phosphorylation and dephosphorylation by myosin light chain kinase (MLCK) and myosin light chain phosphatase (MLCP), respectively (Yamada et al. 2005). In another example, a Ca2+ biosensor without the parasitic Ca2+-buffering properties was created using the apoK1-er domain, which undergoes a reversible conformational change in a Ca2+-dependent reaction with calreticulin and a protein disulfide isomerase (Osibow et al. 2006). Because of this unique Ca2+-dependent reaction, the biosensor is sensitive to Ca2+ concentration levels in the physiological range of the endoplasmic reticulum (10–700 μM). Proteins may undergo conformational changes without chemically reacting with other molecules. For instance, the voltage-sensing domain of a potassium channel protein changes conformation when triggered with changes in membrane potential. A membrane potential biosensor was cleverly constructed utilizing the rotational conformation change of this protein that causes changes in the orientation factor rather than separation between the FRET pair (Sakai et al. 2001).

In contrast to what was described above, not all proteins undergo significant conformational changes to induce observable FRET differences. In these cases, a sensory domain is fused with its binding substrate and sandwiched between a FRET pair. When the sensory domain is stimulated by the molecular event of interest, it binds to the substrate protein inducing a large overall conformational change that changes the FRET signal (see Fig. 2C). This substrate-binding mechanism has been demonstrated in early studies on Ca2+ biosensors using calmodulin and its binding peptide (Miyawaki et al. 1997). The binding of calmodulin to the binding peptide is reversible depending on the Ca2+ concentration. When Ca2+ concentration rises to 1 μM, calmodulin binds to the peptide to bring CFP and YFP closer, increasing FRET; when Ca2+ concentration drops below 0.1 μM, calmodulin dissociates from the peptide, decreasing FRET. Further improved variants of Ca2+ biosensors have been created to monitor cellular Ca2+ dynamics and signalling (Mank et al. 2006; Miyawaki et al. 1999; Truong et al. 2001). For example, by targeting mitochondria with a yellow cameleon Ca2+ biosensor (Miyawaki et al. 1997), researchers have shown that mitochondria in mouse skeletal muscle cells take up Ca2+ during neuron stimulated contraction and release Ca2+ during relaxation. Furthermore, the Ca2+ dynamics in the mitochondria is several milliseconds behind that of the cytosol (Rudolf et al. 2004). The cameleon biosensors (Miyawaki et al. 1997, 1999) have also been used in transgenic flies (Diegelmann et al. 2002; Fiala and Spall 2003; Fiala et al. 2002; Mank et al. 2006), mice (Hara et al. 2004; Nyqvist et al. 2005; Tsujino et al. 2005), zebrafish (Higashijima et al. 2003) and nematodes (Kerr et al. 2000) to detect cellular activities under external stimulations. For instance, researchers were able to visualize Ca2+ concentration changes in olfactory projection neurons of Drosophila brain when stimulated by odorant, providing a system to model olfaction (Fiala et al. 2002). Biosensors using a similar substrate-binding mechanism have also been created to detect protein activities such as kinases (Braun et al. 2005; Sato et al. 2002; Sato and Umezawa 2004; Schleifenbaum et al. 2004; Ting et al. 2001; Wang et al. 2005) and GTPases (Itoh et al. 2002; Seth et al. 2003).

Presently, the major application of intramolecular biosensors is to detect enzyme activities and the concentration of small molecules as we have seen in the examples discussed above. In the coming years, we expect to see biosensors for most of the important signalling and metabolic molecules. On the technical side, it is easier to characterize the FRET signal from intramolecular biosensors because it is less sensitive to the relative concentration of the biosensor than intermolecular biosensors. Hence, this type of biosensors is preferred in quantitative molecular cell biology studies. However, one of the challenges is to improve the dynamic range of intracellular biosensors in order to achieve higher signal-to-noise ratio. At the moment, intramolecular biosensors having the largest dynamic range is constructed using interacting sensory domains, which could be further optimized by circularly permutated FPs (cpFPs) as will be discussed below.

FRET detecting proteolytic cleavage

Proteases belong to a major class of enzymes essential to many cellular processes such as the initiation and propagation of the apoptosis pathway. Because proteases usually cleave their substrate irreversibly, the detection of protease activity inevitably requires the cleavage of the biosensor (see Fig. 2D). A frequent target for this class of FRET biosensors are caspases due to their importance in the apoptosis pathway and high peptidal substrate specificity (Chiang and Truong 2005; Jones et al. 2000; Nagai and Miyawaki 2004; Onuki et al. 2002; Xu et al. 1998). The caspase biosensors is constructed by fusing a FRET pair to the N- and C-terminal of their peptide substrates, which is usually four residues with the sequence specific to the caspase of interest. When caspase cleaves the substrate in the biosensor, the FRET signal dramatically decreases as the FP pair is separated. By monitoring the FRET signal, the dynamics of caspase-8 activation in single cell was studied showing that caspase-8 activation occurs earlier than caspase-3 during apoptosis, which suggested along with other evidences that caspase-3 activation is dependent on caspase-8 (Luo et al. 2003). Recently, by using both caspase-3 and Ca2+ biosensors in a cell co-culture, the two molecular events were imaged simultaneously showing that the activation of caspase-3 during apoptosis is accompanied by a cytosolic Ca2+ concentration rise and fall (Chiang and Truong 2005). Since the discovery of caspases in the early 90s, caspase biosensors have played critical roles in the discovery of the biochemical properties of caspases and their biological roles inside cells. Beyond caspases, biosensors for other proteases, such as calpain (Stockholm et al. 2005) and Factor Xa protease (Mitra et al. 1996), were also made based on the same principle to image in vivo protease activities.

FRET using multiple FPs

As was mentioned in the previous sections, FRET experiments using one pair of FPs provide information on a single molecular event, however in many cases signalling and metabolic pathways are composed of multiple simultaneous molecular events. To image two molecular events, FRET biosensors using multiple FPs have been used. As each FRET pair occupies a large portion of the visible spectrum, it is difficult to introduce a second distinct FRET pair. To solve the problem, a triplet of CFP-YFP-mRFP was used to measure three distinct FRET signals—CFP to YFP, CFP to mRFP, YFP to mRFP (see Fig. 2E) (Galperin et al. 2004; He et al. 2005). As a proof-of-concept experiment to simultaneously monitor the three FRET events, the three-domain complex of Rab5 and EEA.1sh was detected and the interaction between EGFR with Grb2 and Cbl was confirmed (Galperin et al. 2004). In a similar experiment, the trimerization of TRAF2 was confirmed in living cells (He et al. 2005). Currently, the only triplet FPs for multiple FRET is CFP-YFP-RFP. Since the CFP-YFP FRET is very high and the YFP-RFP FRET is barely detectable, only a handful of studies were conducted using multiple FRET to this date. We expect multiple FRET to become more popular as more RFP mutants are created with better properties as FRET acceptors.

Fluorescent proteins for FRET

One of the challenges in using and designing FRET biosensors is the signal-to-noise ratio (SNR). One way to achieve high SNR is by choosing the right FPs or circularly permutated FPs (cpFPs) for FRET. Since the discovery of green fluorescent protein (GFP) in Aequorea Victoria (jellyfish), many FP variants have been designed or discovered. These FPs can be classified into five groups according to their peak emission wavelengths: blue, cyan, green, yellow, and orange-red. Based on colour, there are three types of common FRET pairs: blue donor to green acceptor, cyan to yellow/orange and green to orange/red. Optimal FRET pairs have high spectra overlap, acceptor quantum yield and donor molar extinction coefficient (Table 2). Recently, FP pairs such as CyPET and YPET have been created specifically for FRET (Nguyen and Daugherty 2005). In this case, random mutagenesis and gene shuffling were used to create variants of CFP and YFP. These variants were then fused together and screened for improved FRET signals. The resulting CyPET and YPET exhibited a 20-fold ratiometric FRET change when used in caspase-3 biosensors comparing to only threefold using the parental CFP-YFP pair. In addition to designing FRET pairs with better spectral properties, most of the FPs used in FRET are engineered to be monomeric or weakly dimeric to avoid the oligomerization of FPs, which in biosensors may cause interactions that result in parasitic FRET signals. Furthermore, oligomerization may constrain the kinetics of the biosensor or even cause aggregation, making strongly oligomerizing FPs such as DsRed (Clontech) and MiCy (Karasawa et al. 2004) relatively difficult to use. Recently, cpFPs were used to optimize the FRET dynamic range of biosensors (Mank et al. 2006; Nagai et al. 2004; Zapata-Hommer and Griesbeck 2003). Since the circular permutation of a FP creates new N- and C-termini for protein fusion, it has a different FRET orientation factor when spliced into biosensors. Particular cpFPs will have a large gain in dynamic range as a result of favourable changes in both the distance and orientation factors for FRET. The YC3.60 calcium biosensor was created by replacing the FRET acceptor Venus with its circular permutation variant at Asp-173. The variant showed a dynamic range of 600% in comparison to the 120% of the original YC2.12 and YC3.12 (Nagai et al. 2004). The improvement of dynamic range by circularly permutated FPs is case-dependent because certain FP circular permutations that benefit one biosensor may not improve other biosensors. Hence, circular permutation of FP must be carried out on a case-by-case basis to optimize biosensor dynamic range.
Table 2

Spectral properties of several FRET pairs

Proteins

Donor/Acceptor

Ex (Steinmeyer et al. 2005)

Em (Steinmeyer et al. 2005)

Extinction coefficient (EC)

Quantum yield (QY)

Relative brightness (ECxQY)

pKa

R0

Oligomerization

Source

Blue-Green

EBFP (Blue)

D

380

440

29,000

0.31

9.0

N/A

 

Weakly dimeric

Clontech

EGFP (Green)

A

484

507

56,000

0.60

33.6

N/A

4.5 nm

Weakly dimeric

Clontech

Cyan-Yellow/Orange

Cerulean (Cyan)

D

433

475

43,000

0.62

26.7

4.7

 

Weakly dimeric

Rizzo et al. (2004)

Venus (Yellow)

A

515

528

92,200

0.57

52.5

6.0

5.3 nm

Weakly dimeric

Nagai et al. (2002)

mCitrine (Yellow)

A

516

529

77,000

0.76

58.5

5.7

5.2 nm

Monomeric

Griesbeck et al. (2001)

CyPet (Cyan)

D

435

477

35,000

0.51

17.9

5.0

 

Weakly dimeric

Nguyen and Daugherty (2005)

YPet (Yellow)

A

517

530

104,000

0.77

80.1

5.6

N/A

Weakly dimeric

Nguyen and Daugherty (2005)

MiCy (Green-Cyan)

D

472

495

27,250

0.90

24.5

6.6

 

Dimeric

Karasawa et al. (2004)

mKO (Orange)

A

548

559

51,600

0.60

31.0

5.0

5.4 nm

Monomeric

Karasawa et al. (2004)

Green-Orange/Red

T-Sapphire (Green)

D

399

511

44,000

0.60

26.4

4.9

 

Weakly dimeric

Zapata-Hommer and Griesbeck (2003)

mOrange (Orange)

A

548

562

71,000

0.69

49.0

<6.5

5.7 nm

Monomeric

Shaner et al. (2004)

TDimer2 (Red)

A

552

579

120,000

0.68

81.6

4.8

6.3 nm

Functionally monomeric

Yang et al. (2005)

Conclusion and perspectives

FRET biosensors are valuable tools in monitoring molecular events in living cells. Here, we have reviewed the concepts and applications of FRET biosensors. They are widely used for their unique advantages in cellular imaging, including spontaneous fluorescence, simple genetic manipulations, ease of delivery inside cells, and flexibility in targeting to organelles and tissues. One of the challenges in FRET biosensor design is the availability of good FRET pairs. Consequently, many recent studies on FPs are focused on creating variants that are especially designed for FRET applications (Karasawa et al. 2004; Nguyen and Daugherty 2005; Rizzo et al. 2004). Another challenge in biosensor design is ensuring that the sensory domain traverses conformations with distance and orientation factors favourable for the FRET signals. Looking into the future, we anticipate FRET biosensors will allow us to simultaneously image the many coordinated and complex molecular events of signalling and metabolic pathways within transgenic animals.

Notes

Acknowledgements

This work was supported by grants from the Canadian Foundation of Innovation (CFI) and the National Science and Engineering Research Council (NSERC).

References

  1. Azpiazu I, Gautam N (2004) A fluorescence resonance energy transfer-based sensor indicates that receptor access to a G protein is unrestricted in a living mammalian cell. J Biol Chem 279(26):27709–27718PubMedCrossRefGoogle Scholar
  2. Bevis BJ, Glick BS (2002) Rapidly maturing variants of the Discosoma red fluorescent protein (DsRed). Nat Biotechnol 20(1):83–87PubMedCrossRefGoogle Scholar
  3. Braun DC, Garfield SH, Blumberg PM (2005) Analysis by fluorescence resonance energy transfer of the interaction between ligands and protein kinase Cdelta in the intact cell. J Biol Chem 280(9):8164–8171PubMedCrossRefGoogle Scholar
  4. Camuzeaux B, Spriet C, Heliot L, Coll J, Duterque-Coquillaud M (2005) Imaging Erg and Jun transcription factor interaction in living cells using fluorescence resonance energy transfer analyses. Biochem Biophys Res Commun 332(4):1107–1114PubMedCrossRefGoogle Scholar
  5. Chiang JJ, Truong K (2005) Using co-cultures expressing fluorescence resonance energy transfer based protein biosensors to simultaneously image caspase-3 and Ca2+ signaling. Biotechnol Lett 27(16):1219–1227PubMedCrossRefGoogle Scholar
  6. De S, Macara IG, Lannigan DA (2005) Novel biosensors for the detection of estrogen receptor ligands. J Steroid Biochem Mol Biol 96(3–4):235–244PubMedCrossRefGoogle Scholar
  7. Diegelmann S, Fiala A, Leibold C, Spall T, Buchner E (2002) Transgenic flies expressing the fluorescence calcium sensor Cameleon 2.1 under UAS control. Genesis 34(1–2):95–98PubMedCrossRefGoogle Scholar
  8. Endoh T, Funabashi H, Mie M, Kobatake E (2005) Method for detection of specific nucleic acids by recombinant protein with fluorescent resonance energy transfer. Anal Chem 77(14):4308–4314PubMedCrossRefGoogle Scholar
  9. Fehr M, Frommer WB, Lalonde S (2002) Visualization of maltose uptake in living yeast cells by fluorescent nanosensors. Proc Natl Acad Sci USA 99(15):9846–9851PubMedCrossRefGoogle Scholar
  10. Fehr M, Lalonde S, Ehrhardt DW, Frommer WB (2004) Live imaging of glucose homeostasis in nuclei of COS-7 cells. J Fluoresc 14(5):603–609PubMedCrossRefGoogle Scholar
  11. Fehr M, Lalonde S, Lager I, Wolff MW, Frommer WB (2003) In vivo imaging of the dynamics of glucose uptake in the cytosol of COS-7 cells by fluorescent nanosensors. J Biol Chem 278(21):19127–19133PubMedCrossRefGoogle Scholar
  12. Fiala A, Spall T (2003) In vivo calcium imaging of brain activity in Drosophila by transgenic cameleon expression. Sci STKE 2003(174):PL6PubMedGoogle Scholar
  13. Fiala A, Spall T, Diegelmann S, Eisermann B, Sachse S, Devaud JM, Buchner E, Galizia CG (2002) Genetically expressed cameleon in Drosophila melanogaster is used to visualize olfactory information in projection neurons. Curr Biol 12(21):1877–1884PubMedCrossRefGoogle Scholar
  14. Galperin E, Verkhusha VV, Sorkin A (2004) Three-chromophore FRET microscopy to analyze multiprotein interactions in living cells. Nat Meth 1(3):209–217CrossRefGoogle Scholar
  15. Griesbeck O, Baird GS, Campbell RE, Zacharias DA, Tsien RY (2001) Reducing the environmental sensitivity of yellow fluorescent protein. Mechanism and applications. J Biol Chem 276(31):29188–29194PubMedCrossRefGoogle Scholar
  16. Hara M, Bindokas V, Lopez JP, Kaihara K, Landa LR Jr, Harbeck M, Roe MW (2004) Imaging endoplasmic reticulum calcium with a fluorescent biosensor in transgenic mice. Am J Physiol Cell Physiol 287(4):C932–C938PubMedCrossRefGoogle Scholar
  17. He L, Wu X, Simone J, Hewgill D, Lipsky PE (2005) Determination of tumor necrosis factor receptor-associated factor trimerization in living cells by CFP→YFP→mRFP FRET detected by flow cytometry. Nucleic Acids Res 33(6):e61PubMedCrossRefGoogle Scholar
  18. Higashijima S, Masino MA, Mandel G, Fetcho JR (2003) Imaging neuronal activity during zebrafish behavior with a genetically encoded calcium indicator. J Neurophysiol 90(6):3986–3997PubMedCrossRefGoogle Scholar
  19. Itoh RE, Kurokawa K, Ohba Y, Yoshizaki H, Mochizuki N, Matsuda M (2002) Activation of rac and cdc42 video imaged by fluorescent resonance energy transfer-based single-molecule probes in the membrane of living cells. Mol Cell Biol 22(18):6582–6591PubMedCrossRefGoogle Scholar
  20. Jones J, Heim R, Hare E, Stack J, Pollok BA (2000) Development and application of a GFP-FRET intracellular caspase assay for drug screening. J Biomol Screen 5(5):307–318PubMedCrossRefGoogle Scholar
  21. Karasawa S, Araki T, Nagai T, Mizuno H, Miyawaki A (2004) Cyan-emitting and orange-emitting fluorescent proteins as a donor/acceptor pair for fluorescence resonance energy transfer. Biochem J 381(Pt 1):307–312PubMedGoogle Scholar
  22. Kerr R, Lev-Ram V, Baird G, Vincent P, Tsien RY, Schafer WR (2000) Optical imaging of calcium transients in neurons and pharyngeal muscle of C. elegans. Neuron 26(3):583–594PubMedCrossRefGoogle Scholar
  23. Kramer JM, Yi L, Shen F, Maitra A, Jiao X, Jin T, Gaffen SL (2006) Evidence for ligand-independent multimerization of the IL-17 receptor. J Immunol 176(2):711–715PubMedGoogle Scholar
  24. Lissandron V, Terrin A, Collini M, D’Alfonso L, Chirico G, Pantano S, Zaccolo M (2005) Improvement of a FRET-based indicator for cAMP by linker design and stabilization of donor-acceptor interaction. J Mol Biol 354(3):546–555PubMedCrossRefGoogle Scholar
  25. Luo KQ, Yu VC, Pu Y, Chang DC (2003) Measuring dynamics of caspase-8 activation in a single living HeLa cell during TNFalpha-induced apoptosis. Biochem Biophys Res Commun 304(2):217–222PubMedCrossRefGoogle Scholar
  26. Mank M, Reiff DF, Heim N, Friedrich MW, Borst A, Griesbeck O (2006) A FRET-based calcium biosensor with fast signal kinetics and high fluorescence change. Biophys J 90(5):1790–1796PubMedCrossRefGoogle Scholar
  27. Mitra RD, Silva CM, Youvan DC (1996) Fluorescence resonance energy transfer between blue-emitting and red-shifted excitation derivatives of the green fluorescent protein. Gene 173(1 Spec No):13–17PubMedCrossRefGoogle Scholar
  28. Miyawaki A, Griesbeck O, Heim R, Tsien RY (1999) Dynamic and quantitative Ca2+ measurements using improved cameleons. Proc Natl Acad Sci USA 96(5):2135–2140PubMedCrossRefGoogle Scholar
  29. Miyawaki A, Llopis J, Heim R, McCaffery JM, Adams JA, Ikura M, Tsien RY (1997) Fluorescent indicators for Ca2+ based on green fluorescent proteins and calmodulin. Nature 388(6645):882–887PubMedCrossRefGoogle Scholar
  30. Nagai T, Ibata K, Park ES, Kubota M, Mikoshiba K, Miyawaki A (2002) A variant of yellow fluorescent protein with fast and efficient maturation for cell-biological applications. Nat Biotechnol 20(1):87–90PubMedCrossRefGoogle Scholar
  31. Nagai T, Miyawaki A (2004) A high-throughput method for development of FRET-based indicators for proteolysis. Biochem Biophys Res Commun 319(1):72–77PubMedCrossRefGoogle Scholar
  32. Nagai T, Yamada S, Tominaga T, Ichikawa M, Miyawaki A (2004) Expanded dynamic range of fluorescent indicators for Ca(2+) by circularly permuted yellow fluorescent proteins. Proc Natl Acad Sci USA 101(29):10554–10559PubMedCrossRefGoogle Scholar
  33. Nguyen AW, Daugherty PS (2005) Evolutionary optimization of fluorescent proteins for intracellular FRET. Nat Biotechnol 23(3):355–360PubMedCrossRefGoogle Scholar
  34. Nikolaev VO, Gambaryan S, Lohse MJ (2006) Fluorescent sensors for rapid monitoring of intracellular cGMP. Nat Meth 3(1):23–25CrossRefGoogle Scholar
  35. Nyqvist D, Mattsson G, Kohler M, Lev-Ram V, Andersson A, Carlsson PO, Nordin A, Berggren PO, Jansson L (2005) Pancreatic islet function in a transgenic mouse expressing fluorescent protein. J Endocrinol 186(2):333–341PubMedCrossRefGoogle Scholar
  36. Okumoto S, Looger LL, Micheva KD, Reimer RJ, Smith SJ, Frommer WB (2005) Detection of glutamate release from neurons by genetically encoded surface-displayed FRET nanosensors. Proc Natl Acad Sci USA 102(24):8740–8745PubMedCrossRefGoogle Scholar
  37. Onuki R, Nagasaki A, Kawasaki H, Baba T, Uyeda TQ, Taira K (2002) Confirmation by FRET in individual living cells of the absence of significant amyloid beta -mediated caspase 8 activation. Proc Natl Acad Sci USA 99(23):14716–14721PubMedCrossRefGoogle Scholar
  38. Osibow K, Malli R, Kostner GM, Graier WF (2006) A new type of non-Ca2+-buffering Apo(a)-based fluorescent indicator for intraluminal Ca2+ in the endoplasmic reticulum. J Biol Chem 281(8):5017–5025PubMedCrossRefGoogle Scholar
  39. Pedelacq JD, Cabantous S, Tran T, Terwilliger TC, Waldo GS (2006) Engineering and characterization of a superfolder green fluorescent protein. Nat Biotechnol 24(1):79–88PubMedCrossRefGoogle Scholar
  40. Remus TP, Zima AV, Bossuyt J, Bare DJ, Martin JL, Blatter LA, Bers DM, Mignery GA (2006) Biosensors to measure inositol 1,4,5-trisphosphate concentration in living cells with spatiotemporal resolution. J Biol Chem 281(1):608–616PubMedCrossRefGoogle Scholar
  41. Rizzo MA, Springer GH, Granada B, Piston DW (2004) An improved cyan fluorescent protein variant useful for FRET. Nat Biotechnol 22(4):445–449PubMedCrossRefGoogle Scholar
  42. Rudolf R, Mongillo M, Magalhaes PJ, Pozzan T (2004) In vivo monitoring of Ca(2+) uptake into mitochondria of mouse skeletal muscle during contraction. J Cell Biol 166(4):527–536PubMedCrossRefGoogle Scholar
  43. Sakai R, Repunte-Canonigo V, Raj CD, Knopfel T (2001) Design and characterization of a DNA-encoded, voltage-sensitive fluorescent protein. Eur J Neurosci 13(12):2314–2318PubMedCrossRefGoogle Scholar
  44. Sato M, Ozawa T, Inukai K, Asano T, Umezawa Y (2002) Fluorescent indicators for imaging protein phosphorylation in single living cells. Nat Biotechnol 20(3):287–294PubMedCrossRefGoogle Scholar
  45. Sato M, Umezawa Y (2004) Imaging protein phosphorylation by fluorescence in single living cells. Methods 32(4):451–455PubMedCrossRefGoogle Scholar
  46. Schleifenbaum A, Stier G, Gasch A, Sattler M, Schultz C (2004) Genetically encoded FRET probe for PKC activity based on pleckstrin. J Am Chem Soc 126(38):11786–11787PubMedCrossRefGoogle Scholar
  47. Seth A, Otomo T, Yin HL, Rosen MK (2003) Rational design of genetically encoded fluorescence resonance energy transfer-based sensors of cellular Cdc42 signaling. Biochemistry 42(14):3997–4008PubMedCrossRefGoogle Scholar
  48. Shaner NC, Campbell RE, Steinbach PA, Giepmans BN, Palmer AE, Tsien RY (2004) Improved monomeric red, orange and yellow fluorescent proteins derived from Discosoma sp. red fluorescent protein. Nat Biotechnol 22(12):1567–1572PubMedCrossRefGoogle Scholar
  49. Shaner NC, Steinbach PA, Tsien RY (2005) A guide to choosing fluorescent proteins. Nat Meth 2(12):905–909CrossRefGoogle Scholar
  50. Steinmeyer R, Noskov A, Krasel C, Weber I, Dees C, Harms GS (2005) Improved fluorescent proteins for single-molecule research in molecular tracking and co-localization. J Fluoresc 15(5):707–721PubMedCrossRefGoogle Scholar
  51. Stockholm D, Bartoli M, Sillon G, Bourg N, Davoust J, Richard I (2005) Imaging calpain protease activity by multiphoton FRET in living mice. J Mol Biol 346(1):215–222PubMedCrossRefGoogle Scholar
  52. Tanimura A, Nezu A, Morita T, Turner RJ, Tojyo Y (2004) Fluorescent biosensor for quantitative real-time measurements of inositol 1,4,5-trisphosphate in single living cells. J Biol Chem 279(37):38095–38098PubMedCrossRefGoogle Scholar
  53. Ting AY, Kain KH, Klemke RL, Tsien RY (2001) Genetically encoded fluorescent reporters of protein tyrosine kinase activities in living cells. Proc Natl Acad Sci USA 98(26):15003–15008PubMedCrossRefGoogle Scholar
  54. Truong K, Sawano A, Mizuno H, Hama H, Tong KI, Mal TK, Miyawaki A, Ikura M (2001) FRET-based in vivo Ca2+ imaging by a new calmodulin-GFP fusion molecule. Nat Struct Biol 8(12):1069–1073PubMedCrossRefGoogle Scholar
  55. Tsujino N, Yamanaka A, Ichiki K, Muraki Y, Kilduff TS, Yagami K, Takahashi S, Goto K, Sakurai T (2005) Cholecystokinin activates orexin/hypocretin neurons through the cholecystokinin A receptor. J Neurosci 25(32):7459–7469PubMedCrossRefGoogle Scholar
  56. Valeur B (2002). Molecular fluorescence: principles and applications. Wiley-VCH, WeinheimGoogle Scholar
  57. Wang Y, Botvinick EL, Zhao Y, Berns MW, Usami S, Tsien RY, Chien S (2005) Visualizing the mechanical activation of Src. Nature 434(7036):1040–1045PubMedCrossRefGoogle Scholar
  58. Xu X, Gerard AL, Huang BC, Anderson DC, Payan DG, Luo Y (1998) Detection of programmed cell death using fluorescence energy transfer. Nucleic Acids Res 26(8):2034–2035PubMedCrossRefGoogle Scholar
  59. Yamada A, Hirose K, Hashimoto A, Iino M (2005) Real-time imaging of myosin II regulatory light-chain phosphorylation using a new protein biosensor. Biochem J 385(Pt 2):589–594PubMedGoogle Scholar
  60. Yang X, Xu P, Xu T (2005) A new pair for inter- and intra-molecular FRET measurement. Biochem Biophys Res Commun 330(3):914–920PubMedCrossRefGoogle Scholar
  61. Ye K, Schultz JS (2003) Genetic engineering of an allosterically based glucose indicator protein for continuous glucose monitoring by fluorescence resonance energy transfer. Anal Chem 75(14):3451–3459PubMedCrossRefGoogle Scholar
  62. Zaccolo M, Cesetti T, Di Benedetto G, Mongillo M, Lissandron V, Terrin A, Zamparo I (2005) Imaging the cAMP-dependent signal transduction pathway. Biochem Soc Trans 33(Pt 6):1323–1326PubMedGoogle Scholar
  63. Zaccolo M, Magalhaes P, Pozzan T (2002) Compartmentalisation of cAMP and Ca(2+) signals. Curr Opin Cell Biol 14(2):160–166PubMedCrossRefGoogle Scholar
  64. Zaccolo M, Pozzan T (2002) Discrete microdomains with high concentration of cAMP in stimulated rat neonatal cardiac myocytes. Science 295(5560):1711–1715PubMedCrossRefGoogle Scholar
  65. Zapata-Hommer O, Griesbeck O (2003) Efficiently folding and circularly permuted variants of the Sapphire mutant of GFP. BMC Biotechnol 3:5PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2006

Authors and Affiliations

  1. 1.Institute of Biomaterials and Biomedical EngineeringUniversity of TorontoTorontoCanada
  2. 2.Edward S. Rogers Sr. Department of Electrical and Computer EngineeringUniversity of TorontoTorontoCanada

Personalised recommendations