CHAPTER 20
Protein–Protein Interactions Determined by
Fluorescence Correlation Spectroscopy
J. Langowski
German Cancer Research Center
Division Biophysics of Macromolecules
Im Neuenheimer Feld 580
D-69120 Heidelberg, Germany
Abstract
I. Introduction
II. FCS Theory
A. Autocorrelation Function for One Species
B. Multiple Species
C. Triplet Contribution
III. Two-Color Cross-Correlation
IV. Protein–Protein Interactions Using FCCS and Nongenetic Labels
V. Protein–Protein Interactions In Vivo Using FCCS and Autofluorescent Proteins
References
Abstract
Fluorescence correlation spectroscopy (FCS) is an emerging technique where the
interaction between biomolecules is detected through their correlated motion.
It oVers the advantage of high (single-molecule) sensitivity; independence of molecular orientation or distance; and simultaneous measurement of molecular interactions, concentrations, and mobilities. Here we introduce the principle of the
technique and review some recent examples from the literature where FCS has
been used with autofluorescent proteins for measuring protein–protein interactions
and mobilities in living cells.
METHODS IN CELL BIOLOGY, VOL. 85
Copyright 2008, Elsevier Inc. All rights reserved.
471
0091-679X/08 $35.00
DOI: 10.1016/S0091-679X(08)85020-0
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J. Langowski
I. Introduction
The quantitative characterization of biomolecular interactions is of fundamental
importance for our understanding of cellular mechanisms. In recent years, two
developments have revolutionized the way such interactions can be measured: the
discovery of autofluorescent proteins (which this book deals with) and the possibility to detect such molecules with single-molecule sensitivity. Confocal optics, as
schematized in Fig. 1, are a typical experimental setup that allow single-molecule
detection. The fluorescent molecules are excited in a very small detection volume
(1 fl) with a laser beam focused through a microscope lens. The emitted fluorescence is detected through the same optics and a pinhole whose image in the sample
overlaps with the laser focus; excitation and emission wavelengths are separated by
dichroic mirrors and filters. This way, a few hundred photons can be detected from
a molecule during its random motion through the laser focus.
The diVusion of the fluorescent particles in and out of the detection volume causes
the fluorescence intensity in the detectors to fluctuate randomly. Fluorescence correlation spectroscopy (FCS) measures and analyzes these fluctuations, thus yielding
information about the motion of the molecules through the laser beam.
A particular intriguing application of FCS is the measurement of biomolecular
interactions. When a fluorescent ligand binds to a macromolecule, its mobility will
be restricted by the presence of the large interaction partner. The extreme case
occurs when the fluorophore is bound to large cellular structures, such as membranes, the cytoskeleton, or chromatin: it will then be immobilized, the amount of
residual mobility being determined by the binding kinetics to the structure and any
slow motion of the structure itself. Interactions of this type have often been studied
with techniques such as fluorescence recovery after photobleaching (see Chapter 14
by McNally, this volume) or related methods, such as continuous microphotolysis
(Wachsmuth et al., 2003).
If the fluorescent ligand binds to a mobile macromolecule, the mobility of the
complex will be determined by that of the largest interaction partner. This mobility
is characterized by the diVusion coeYcient D in cell biology often given in units of
mm2 s1. Measuring D will reveal eventual interactions of the fluorescent molecule
with larger macromolecules, as long as D changes significantly. Since D is in first
approximation proportional to the largest linear dimension of the macromolecule,
its dependence on the molecular mass M of a globular protein is not very strong;
for D to double, M would have to increase by 23 ¼ 8. An association between two
ligands of equal size would decrease D by only 26%, a change that can easily be
undetectable in the presence of noise.
A much more sensitive way to use the random motion of macromolecules to
detect their interaction is two-color fluorescence cross-correlation spectroscopy
(FCCS) (Ricka and Binkert, 1989; Schwille et al., 1997). Here both interaction
partners are labeled with fluorophores that can be spectrally distinguished. If they
form a complex, they will always enter and exit the laser focus at the same time;
473
20. Protein–Protein Interactions Determined by FCS
Filters
Dichroic mirrors
Objective lens
Laser
Scan lens
Dichroic
mirror
Pinhole
Detectors
Rotating scan
mirrors
Fig. 1 Schematic principle of the FCS method.
if they do not interact, their respective movements (and fluorescence fluctuations)
will be uncorrelated. In practice, the fluorophores are excited by either one or two
laser lines focused into the same spot; emitted light from the focal volume is
detected at two wavelengths, and particles that fluoresce at both wavelengths will
give simultaneous bursts of intensity in the two channels. This correlated emission
is detected by computing the cross-correlation function. FCCS is a convenient
means to show binding between two ligands labeled with diVerent fluorophores
because the complex will show correlated fluorescence at the two wavelengths. The
examples given in this chapter will concentrate on the application of FCCS to
in vivo biomolecular interactions.
FCS and its theoretical foundations have been described some time ago (Elson
and Magde, 1974; Magde et al., 1974; Webb, 1976), but in the early work its
application was severely limited by sensitivity issues. Meanwhile, FCS has undergone
an amazing development that now allows routine measurements of biomolecular
interactions. The recent improvements which made this possible (Qian and Elson,
1991; Rigler et al., 1993) are mostly the use of confocal optics for excitation and
detection, and avalanche photodiode detectors that oVer a quantum eYciency >50%
in the red range of the visible spectrum (a factor of 10 over most photomultipliers).
Currently, many manufacturers of confocal microscopes oVer avalanche
photodiode-based FCS accessories that substitute for the detection photomultipliers
and directly detect and analyze the confocally collected fluorescence emission from
the sample (Zeiss Confocor 3, Leica FCS accessory for the TCS SP2 AOBS; individual solutions exist for Olympus and Nikon confocal microscopes). Very small concentrations (<1 pM) may be detected because individual fluorescent particles will
give clearly distinguishable bursts of fluorescence intensity above the background
arising from detector noise, Raman scattering, and optical imperfections. At somewhat higher concentrations, in the nanomolar range, typically several molecules are
474
J. Langowski
present simultaneously in the focus. However, the instantaneous number of molecules will fluctuate: at a concentration c, the fluctuation of the number of solute
molecules N in a given volume element V is h@N2i ¼ hNi, where hNi ¼ cV is the
average number of molecules in V and h@N2i ¼ h(NhNi)2i is the mean squared
fluctuation (as an example, see Table I). The time dependence of the fluctuations is
directly related to the diVusion coeYcient of the molecule (see below). By observing
the concentration fluctuation of a solute in a very small volume of known size, one
can thus determine its concentration and its diVusion coeYcient.
Due to the small focus of the laser beam, measurements inside living cells become
possible. A typical high-resolution microscope lens has a focal spot of 300 nm
diameter and 1.5 mm length, such that diVusion processes inside cells or organelles
can be probed in a position-dependent manner. FCS has for instance been used to
probe chromatin in the cell nucleus (Sorscher et al., 1980) or to assess anomalous
diVusion of proteins (Wachsmuth et al., 2000; Weiss et al., 2003).
The primary data obtained in an FCS measurement is the time-dependent
fluorescence intensity F(t), which is proportional to the number of particles in
the observation volume at time t. The timescale of these fluctuations is determined
by the speed with which the molecules move through the laser focus (Fig. 2).
Table I
Number Fluctuations in a 1-nM Solution as a Function of Volume
Size (mm)
10
1
0.1
0.01
0.001
Volume (liter)
3
10
106
109
1012
1015
No. of particles
DN
6.023 10
6.023 108
6.023 105
602.3
0.6023
7,76,080
24,541
776
24.5
0.776
11
DN/N (%)
0.00013
0.0041
0.129
4.075
128.9
2.0
1.8
1.6
1.4
1.2
1.0
1.00E-06 1.00E-05 1.00E-04 1.00E-03 1.00E-02 1.00E-01 1.00E+00
Fig. 2 Example of fluctuating fluorescence intensity for fast (red), medium (green), and slow (blue)
moving particles, and corresponding autocorrelation functions G(t).
475
20. Protein–Protein Interactions Determined by FCS
II. FCS Theory
Let us assume that we measure the fluorescence of a 109 M rhodamine solution
in volume elements of various sizes. Table I shows the absolute and relative
number fluctuations for this case. In a classical fluorescence spectrometer, the
typical observation volume is of the order of 1 ml. It is easily seen that at this
sample size no observable fluctuation is expected. If one, however, measures the
fluorescence of the same solution in a smaller volume, the fluctuations become
increasingly important until they reach the size of the fluorescence signal itself at a
sample size of 1 fl; here, less than one molecule is present in the observation volume
on average. The characteristics of the fluorescence fluctuations and their relation to
molecular properties are summarized in the following sections.
A. Autocorrelation Function for One Species
The fluorescence fluctuations are characterized by their autocorrelation function
G(t), which describes the random motion of the fluorophores (e.g., see Figs. 2 and 3).
It is defined as
GðtÞ ¼
hF ðtÞF ðt þ tÞi
ð1Þ
hF ðtÞi2
For obtaining quantities such as diVusion coeYcients, concentrations, or reaction rate constants, one has to fit a theoretical correlation function to the measured
G(t) which is based on a model that contains these quantities as free parameters.
For a solution of a single fluorescent species with diVusion coeYcient D and molar
1.0
g (t)
0.8
0.6
0.4
0.2
0.0
1 4
−3
−2
−1
0
1000
1
2
log (t)
Fig. 3 Influence of the structure factor k on the FCS autocorrelation function. Three curves are
displayed for the same diVusion time t and k ¼ 1, 4, 1000. Since k 4 for typical confocal optics, the
relevant range in practice is between the rightmost two curves.
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J. Langowski
concentration c and for Gaussian profiles for the excitation intensity and detection
eYciency, G(t) evaluates to (Rigler et al., 1993):
1
GðtÞ ¼
cV eff
4Dt 1
4Dt 1=2
1þ 2
1þ 2
þ1
w0
z0
ð2Þ
Here VeV is the eVective observation volume which depends on the geometry of
the focus for excitation and emission, w0 and z0 are the half-widths of the focus in
the x-y plane (the observation plane of the lens) and in the z-direction, respectively.
VeV, w0, and z0 can be measured independently by calibration with a solution of
a fluorophore of known concentration and diVusion coeYcient. If only relative
changes are of interest, one can use the average particle number N ¼ cVeV and an
eVective diVusion time tdiV ¼ w02/4D as parameters:
GðtÞ ¼
1=2
1
t 1
t
1þ
1þ
þ1
N
tdiff
tdiff k2
ð3Þ
k (also called the structure factor) is the axial ratio of the observation volume z0/w0
(see Fig. 3).
The intercept of the FCS autocorrelation function G(t) is inversely proportional to
the number of particles in the focal volume, and thus to their concentration.
In practice, deviations from this ideal behavior are found at very high and very low
concentrations. At low concentrations these deviations are due to the background
which becomes comparable to the fluorescence signal, and which is caused by
incomplete suppression of the excitation light, detector dark counts, and background
fluorescence. At a particle concentration c, the measured particle number N in
the presence of background is then
n 2
N ¼ cV eff 1 þ
c
ð4Þ
where v ¼ hUi=Fe is the ratio of the background signal to the normalized fluorescence intensity of the fluorophor.
B. Multiple Species
In a mixture of molecules with diVerent diVusion coeYcients, the fluorescence
intensity autocorrelation function is a sum of the contributions of the individual
species. The general form of G(t) for a mixture m of diVerent fluorescent species
with diVusion times tdiV,i is then given by the following expression:
GðtÞ ¼
m
1X
r gi ðtÞ þ 1
N i¼1 i
1 1=2
t
with gi ðtÞ ¼ 1 þ tdiff;i
1 þ tdiff;it k2
:
ð5Þ
477
20. Protein–Protein Interactions Determined by FCS
The ri are the relative amplitudes corresponding to molecules with distinct
diVusion coeYcients; they are related to their concentrations ci by the following
expression:
ri ¼
f2i ci
m
X
f2i ci
ð6Þ
i¼1
where fi is the quantum yield of species i.
C. Triplet Contribution
Up to now only number fluctuations in the detection volume have been considered to contribute to the fluctuations of the light intensity at the detector, under the
simplifying assumption that an excited fluorophor will emit a constant light flux.
Because of the quantum nature of light and the photophysics of fluorescent
molecules, this is not the case. The most important eVect that has to be considered
is a transition of the excited molecule into the triplet state. This will ‘‘interrupt’’ the
stream of photons for approximately the triplet lifetime of the fluorophor and add
another contribution to the autocorrelation function, which in good approximation
is then (Widengren et al., 1995):
GðtÞ ¼ ð1 þ be
lt
!
m
1X
Þ
r gi ðtÞ þ 1
N i¼1 i
ð7Þ
The amplitude of the triplet term b and its relaxation time l increase with the
excitation light intensity up to a limit given by the excitation, emission, and
intersystem crossing probabilities of the fluorophore. Practically, b can reach
amplitudes higher than the number correlation function itself. Since relaxation
time of the triplet term is of the same order as the diVusion times of small molecules
(some microseconds), it is important to conduct the FCS experiment with a laser
intensity that keeps b as small as possible.
III. Two-Color Cross-Correlation
The detection of specific binding between biomolecules by FCS depends on a
change in molecular size: when the diVusion coeYcient D changes suYciently on
binding, the complex can be distinguished in G(t) as a second species and its
concentration determined [Eqs. (5) and (6)]. However, in cases when D changes
only very slightly or not at all, that is, when a nonfluorescent ligand binds to a
larger fluorescent particle (see above), this approach is not practicable anymore.
478
J. Langowski
Schwille et al. (1997) were the first to show the feasibility of two-color FCCS.
In this method the fluorescence is detected simultaneously at two distinct wavelengths in the same detection volume. The signals from the two detectors are
analyzed by computing their cross-correlation function. It is easily seen that in a
mixture of two fluorescent molecules emitting at the two wavelengths but not
interacting with each other the particles will diVuse independently and the amplitude of the cross-correlation function will be zero. On the contrary, when the
particle is labeled with two dyes and emits simultaneously at the two detection
wavelengths, the cross-correlation function is equal to the autocorrelation function
for single-color FCS (assuming equal detection eYciencies and exact overlap of the
detection volumes for the two channels). This latter case occurs when the two
fluorescent species form a complex.
In FCCS, therefore, the amount of complex formation between two fluorescently
labeled biomolecules can be obtained simply by measuring the cross-correlation
amplitude. A comprehensive summary of the theory of FCCS of associating species
has been given by Weidemann et al. (2002); a recent review summarizes some of
the applications of FCCS in vivo (Bacia et al., 2006).
IV. Protein–Protein Interactions Using FCCS and
Nongenetic Labels
While the possibility of determining biomolecular interactions in vivo by FCCS
has been evoked in the earlier FCS literature [see, e.g., Hink et al. (2002) for a
review], only recently have attempts been successful to show these interactions in
living cells. The first results were obtained using chemically labeled proteins. Bacia
et al. (2002) followed the fate of cholera toxin labeled with Cy2 on one subunit and
Cy5 on the other during endocytosis and could show the separation of the two
subunits in the Golgi apparatus. Furthermore, they could demonstrate crosscorrelation after endocytosis of a mixture of Cy2- and Cy5-labeled holotoxins,
showing the tight association between diVerent protein molecules in the same
endocytic pathway. For analyzing the motion of the proteins bound to the membrane, photobleaching was a problem (as it is in most FCS studies of slowly
moving biomolecules); in cases of strong bleaching, data was only taken after the
strongly immobilized molecules had been bleached out and the fluorescence was
approximately constant. Proteins that were free to move in the cytosol (during
movement from the plasma membrane to the Golgi apparatus) did not show this
strong bleaching eVect.
Chemical labeling of viral capsid proteins with Alexa 568, together with labeling
of the viral genome through a GFP-histone fusion protein was used by Bernacchi
et al. (2004) to investigate the infectious pathway of Simian virus 40. Through a
mobility analysis of the two fluorescent components it could be shown that the
virus disintegrates close to the nucleus, before being transported through the
20. Protein–Protein Interactions Determined by FCS
479
nuclear membrane. Simultaneously, cross-correlation was lost on virus disassembly. FCS also showed that some nonpermissive cells incorporate the virus, but do
not transport it into the nucleus.
In a recent FCCS study, Oyama et al. (Oyama and Yanagawa, 2004; Oyama
et al., 2006) used an in vitro translation system (Doi et al., 2002) to attach a
fluorescent puromycin derivative to the C-terminus of the protein. They measured
in vitro the diVusion coeYcients and association constants of c-Fos and c-Jun
proteins, calmodulin and calmodulin-binding proteins, and various members of
the Polycomb group protein family. Another intriguing new possibility for specific
labeling of proteins in FCCS was recently presented by Becker et al. (2006). They
used proteins recombinantly expressed in Escherichia coli and modified at the
C-terminal end with a thioester which were then chemically ligated to a peptide
containing the fluorescent dye. Although both these latter strategies have not been
applied for in vivo studies yet, they constitute a promising route for characterizing
protein–protein interactions by FCCS and other single-molecule fluorescence
techniques.
V. Protein–Protein Interactions In Vivo Using FCCS and
Autofluorescent Proteins
At the time of this writing, only few studies have been published where protein–
protein interactions have been observed in vivo by FCCS using two autofluorescent
protein-labeled interaction partners. Baudendistel et al. (2005) have used the
Fos-Jun system to demonstrate protein–protein interaction. Fos and Jun are two
components of the AP-1 general transcription activator; they are known to exert
their function as a dimer and can therefore serve as a reference for dimer formation
(Allegretto et al., 1990; Kerppola and Curran, 1991; Leonard et al., 1997). FCCS
was measured in cells expressing equal levels of Fos-EGFP and Jun-mRFP1
fusion proteins. As a control, the deletion mutants FosDdimDDNA-EGFP and
JunDdimDDNA-mRFP1 were used in which the dimerization (Ddim) as well as the
DNA-binding (DDNA) bZip domains were removed to abolish the dimerization
and DNA-binding reactions.
Figure 4 summarizes the results. The negative control, in which free EGFP and
mRFP1 were expressed at equimolar amounts in HeLa cells, shows autocorrelation decays with diVusion coeYcients corresponding to intracellular diVusion of
typical small proteins. The cross-correlation function (in blue) shows only a small
background amplitude due to spectral cross talk, with a decay on the same
timescale as the single species autocorrelation functions. The positive control for
maximum cross-correlation, using a fusion protein of EGFP and mRFP1
expressed in HeLa cells, is shown in Fig. 4B. Again, the decay curves for the
red and green channel autocorrelation functions give a diVusion coeYcient in
the range expected for a small protein of this size; the amplitude of the
480
J. Langowski
B
A
1.2
1.2
EGFP and mRFP1
EGFP-mRFP1
10 mm
1.0
1.0
0.8
0.8
G(t) N
G(t) N
10 mm
0.6
0.4
0.4
0.2
0.2
0.0
−3
−2
−1
0
1
2
0.0
3
log t/ms
C
−2
−1
0
log t/ms
2
3
2
3
c-Fos-EGFP + c-Jun-mRFP1
10 mm
1.0
1
1.2
c-FosΔΔ-EGFP + c-JunΔΔ-mRFP1
10 mm
1.0
0.8
G(t) N
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
−3
−3
D
1.2
G(t) N
0.6
−2
−1
0
log t/ms
1
2
3
0.0
−3
−2
−1
0
log t/ms
1
Fig. 4 A, B: In vivo FCCS controls for independent (A) and covalently tethered (B) fluorophores.
Normalized autocorrelation amplitudes G(t)N in the red (red line) and the green (green line) channels
measured in HeLa cells expressing (A) EGFP and mRFP1 separately; (B) a two-color fusion construct
of EGFP and mRFP1. Cross-correlation functions are shown in blue. Rhombi represent measured data,
lines fitted curves. Inset: confocal images of cells in the EGFP (green) and the mRFP1 (red) channels.
The white crosses indicate the measurement points. C, D: Protein–protein interactions in the AP-1
system. Normalized autocorrelation amplitudes G(t)N in the red (red line) and the green (green line)
channels measured in HeLa cells expressing (C) the AP-1 deletion mutants c-FosDdimDDNA-EGFP
and c-JunDdimDDNA-mRFP1; (D) the AP-1 wildtype proteins c-Fos-EGFP and c-Jun-mRFP1.
cross-correlation function is almost half of the maximum value of 100% expected
in the ideal case. Due to imperfect overlap of the focus volumes for excitation and
detection of the two chromophores used, this maximum value is practically never
reached in an FCCS experiments and the 45% obtained here were taken as the
reference for complete binding of the two proteins.
20. Protein–Protein Interactions Determined by FCS
481
Intracellular FCCS measurements with the Fos and Jun deletion mutants that
lacked DNA binding and dimerization domains gave a cross-correlation amplitude
not significantly exceeding the background level (Fig. 4C). Again, the diVusion
coeYcients are in the range expected for freely diVusing proteins without DNA
binding.
The demonstration of the direct interaction and DNA binding of c-Fos and
c-Jun in vivo finally followed by expressing the AP-1 wild-type fusion proteins
c-Fos-EGFP and c-Jun-mRFP1 together in the cells. These cells showed fluorescence only in the nuclei (Fig. 4D) due to the nuclear localization sites present in fulllength AP-1 proteins. The cross-correlation amplitude in these cells is strongly
increased over the background level and clearly indicates the Fos-Jun dimerization
(Fig. 4D). The autocorrelation functions showed a fast component corresponding
to freely diVusing c-Fos in the green and c-Jun protein in the red channel. Additionally, a second slow component of about equal amplitude as the fast one, but a
diVusion coeYcient two orders of magnitude smaller, was detected in both the red
and the green channel. This slow component had a much higher amplitude for the
wild-type proteins than for the nonbinding controls, indicating a strong retention or
immobilization of the AP-1 proteins. For the cross-correlation function, only about
25% could be attributed to faster diVusive processes, corresponding to free Fos-Jun
dimers. The main decay component of the cross-correlation curve was slow, with a
diVusion coeYcient equal to the slow component of the autocorrelation functions and a relative amplitude of 75%. This result showed directly that binding
of c-Fos and c-Jun to DNA strongly favors the formation of the heterodimer and
that a significant amount of the free proteins exists as monomer in vivo, showing no
cross-correlation.
In a more recent study, Muto et al. (2006) showed the interaction between
Arabidopsis thaliana auxin response factors MP/ARF5 or NPH4/ARF7 and their
repressive regulator, MSG2/IAA19, using FCCS with autofluorescent labeling.
They transiently expressed GFP and mRFP1 fusions of these proteins in HeLa
cells, measured FCS auto- and cross-correlation functions in vivo and compared
the FCCS amplitudes with positive and negative controls similar to those used by
Baudendistel et al. (2005) The diVerent interaction pairs investigated fell into two
classes, with either about 80% or about 20% of the proteins bound in a complex.
However, these data were not corrected for diVerences in the expression level of the
two proteins.
The cross-correlation amplitudes in this work were significantly higher than
those obtained in the work by Baudendistel et al. (2005); however, both studies
show about a factor of 3 in amplitude of the positive control over the background.
This seems therefore to define the maximum dynamic range that can be obtained
with FCCS for quantitative binding studies in vivo.
Some studies have looked at the interaction of membrane-bound proteins by
FCCS. Vamosi et al. (2004) studied the membrane localization and interaction of
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J. Langowski
subunits of the IL-2 and IL-15 interleukin receptors and major histocompatibility
complex (MHC) class I by fluorescence resonance energy transfer (FRET) and
FCCS. In this case, the proteins were labeled chemically or with fluorescent
antibodies. A strong decrease by about three orders of magnitude of the diVusional
mobility of the Alexa 488-labeled antibody was detected on binding to the IL-2 a
subunit. Fluorescence cross-correlation on the same timescale as the immobilized
antibody could then be shown between Cy5-labeled MHC class I and the
Alexa 488-labeled antibody, indicating that MHC and IL-2 diVuse in the same
membrane subdomain (raft).
Autofluorescent protein labeling was employed in the work of Larson et al.
(2005) who studied the interaction of the IgE receptor FcERI with Lyn kinase,
the enzyme responsible for initiating cell signaling by phosphorylating the
IgE-associated FceRI. Here IgE was chemically labeled with Alexa 546 and
the Lyn kinase fused to EGFP. In an approach similar to the work by Vamosi
et al. (2004), the authors showed a slowing down of the diVusion of the protein
components after antigen stimulation, indicating association in the membrane.
This association could then be proved unequivocally by the appearance of a crosscorrelation signal 6 min after antigen stimulation, which decreased again after
30 min. Thus, the association between Lyn kinase and IgE receptor is transient.
These examples, albeit few, demonstrate that FCCS is a very powerful method to
characterize association between fluorescent proteins in living cells. Both in the
papers by Baudendistel et al. (2005) and Muto et al. (2006) the analogy between
yeast two-hybrid techniques (YTH) and FCCS with double autofluorescent
fusion proteins (‘‘two-hybrid FCCS,’’ THFCCS) has been evoked. In both cases,
a reporter protein is genetically linked to the protein whose association is to be
measured: a signal is generated by the association between the reporter groups that
are brought together by the binding of the proteins under study. While YTH does
not require advanced (and expensive) optical equipment, optical techniques have
several advantages: first, since YTH uses transcriptional activators for generating a
signal, it is diYcult to use for measuring interactions between transcription factors
themselves. Also, for in vivo interactions in higher eukaryotes, evidently other cell
lines must be available. Alternatives to THFCCS are FRET and fluorescent protein
complementation, both of which require close spatial proximity of the interaction
partners. THFCCS has the great advantage that binding is detected through the
correlated motion of the two proteins, which does not restrict the technique to
interactions within the range of the Förster radius.
It is conceivable that THFCCS combined with a scanning system may be applied
to screen protein–protein interaction in a large number of cells and/or in a positiondependent manner. Thus, automatic detection of such interactions and of the cellular
compartment in which they occur becomes feasible, making this eVectively a highthroughput technique usable in automated microscopy setups where biomolecular
interactions and cell morphology are monitored at the same time.
20. Protein–Protein Interactions Determined by FCS
483
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