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Spectroscopic techniques for m
onitoring protein interactionsin living cellsJacob PiehlerQuantitative protein interaction analysis in living cells remains
highly challenging as concentrations of interactions partners
are difficult to quantify and to temporally modulate. In this
review, the fundamental concepts for monitoring protein
interactions in cells are discussed. Next to already well-
established resonance energy transfer-based techniques,
recent developments of approaches based on single molecule
fluctuation and localization are presented. Moreover, the
application of surface micropatterning and functionalized
nanoparticles for solid phase type of protein interaction
analysis in living cells are introduced. The complementary
capabilities and limitations of these techniques and future
directions based technological developments are discussed.
Addresses
Department of Biology, University of Osnabruck, Barbarastr. 11, 49076
Osnabruck, Germany
Corresponding author: Piehler, Jacob ([email protected])
Current Opinion in Structural Biology 2014, 24:54–62
This review comes from a themed issue on Folding and binding
Edited by James Bardwell and Gideon Schreiber
0959-440X/$ – see front matter, # 2013 Elsevier Ltd. All rights
reserved.
http://dx.doi.org/10.1016/j.sbi.2013.11.008
IntroductionDuring the past decade, the scientific community has
witnessed an enormous progress in our knowledge on
protein–protein interactions. Proteomic methods for iden-
tifying interaction partners based on mass spectrometry,
protein arrays and genome-wide screening by two-hybrid
techniques have established detailed protein–protein
interactions networks [1]. Moreover, elegant techniques
for validating interactions are available including a broad
spectrum of protein complementation techniques [2,3].
These techniques have proven powerful for confirming
and visualizing interactions in the context of living cells
and even in entire organisms [4]. Quantitative character-
ization of interactions, that is, the determination of equi-
librium dissociation and kinetic rate constants, however,
cannot be achieved by protein complementation assays
because complexes are irreversibly trapped by these tech-
niques. Truly mechanistic description of cellular processes
requires precise parameterization of the formation and the
stability of protein complexes within the cellular context.
Current Opinion in Structural Biology 2014, 24:54–62
For this reason, techniques for monitoring reversible
protein–protein interactions are required.
A variety of powerful methods are available for quanti-
tative protein interaction analysis in vitro, which are based
on monitoring complex formation upon mixing inter-
action partners in defined concentrations. Thus, rate
constants and/or equilibrium constants (association rate
constant ka, dissociation rate constant kd and equilibrium
dissociation constant KD) can be readily obtained. How-
ever, protein interactions are intricately regulated by the
local environment within the cell, which controls post-
translational modifications and may provide additional
proteins, lipids, carbohydrates or nucleic acids modulat-
ing complex formation. Monitoring protein interactions
within their physiological context of an intact, viable cell
is substantially more challenging for several reasons. First,
formation of protein complexes needs to be detected
highly specifically in the presence of the whole cellular
proteome. Second, protein concentrations within a living
cell are difficult to quantify and cannot be varied readily,
as required for probing the kinetics of protein inter-
actions. Thus, most conventional approaches for quanti-
tative protein interaction analysis cannot be applied for
studying protein–protein interactions within cell. During
the past decade, novel techniques to overcome these
challenges have emerged, which exploit recent develop-
ments in ultrasensitive fluorescence detection, life cell
fluorescence probe development as well as soft nanoma-
terial and micromaterial sciences. I will briefly introduce
these concepts and then focus on exemplary applications
and discuss current capabilities, limitations and future
developments.
General considerationsGeneric approaches for specifically detecting protein
complex formation among the thousands of proteins
and other biomolecules of the living cell are based on
co-localization of the interaction partners on the molecu-
lar scale, that is, within dimension of 5–50 nm. Specific,
non-invasive detection of proteins in cells is readily
achieved by means of fluorescence microscopy with
extremely high sensitivity down to the single molecule
level. However, direct co-localization of proteins within
protein complexes is obstructed by (i) the limitation of
the spatial resolution by diffraction of light and (ii) rapid
diffusion of proteins in the various compartments of the
cell. These problems were very elegantly overcome by
using Forster resonance energy transfer (FRET) as a
direct spectroscopic reporter for proximity between a
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Monitoring protein interactions in living cells Piehler 55
Figure 1
Protein interaction analysis by FRET. (a) Spectra of two proteins labeled with a donor (green) and an acceptor (orange) dye. In the absence of
interaction (blue spectrum) emission mainly from the donor is detected. Upon complex formation, energy is transferred to the acceptor, resulting into
quenching of the donor. (b) Function of the FRET efficiency as a function of distance for a Forster radius of 5.6 nm, which is typical for FRET pairs
based on fluorescent proteins. The grey bar marks the region with a FRET efficiency higher than 5%, which is required for reliable detection. (c)
Example of a typical globular protein complex (CDK2/cyclin A, pdb-entry 1JST) labeled with fluorescent proteins. The distance between the C-termini
of both proteins marked with a black bar is already 6.8 nm. Additional 2–3 nm distance has to be considered for the fluorescent proteins shown as GFP
(green) and mCherry (red), which are shown in an arbitrary position.
donor and an acceptor chromophor on the nanometer scale
(Figure 1). Co-localization of proteins can also be achieved
by correlating fluctuations of molecules within diffraction-
limited confocal volumes (Figure 2). Temporal as well as
spatial cross-correlation of interaction partners labeled with
spectrally different fluorophores allows quantifying inter-
action and dynamics of protein complexes. For proteins
interacting at low concentrations, localization with a pre-
cision down to several nanometer is possible, thus provid-
ing the possibility to co-localize interaction partners within
molecular dimensions (Figure 3). Alternatively, rather than
trying to analyse homogeneously distributed proteins con-
stantly diffusing in membranes or cellular compartments,
microtechnological or nanotechnological approaches can
be employed for locally enriching and immobilizing bait
proteins within the cells as platforms for probing the
interactions with a prey protein (Figure 4).
Resonance energy transfer techniquesFRET is based on the spontaneous transfer of energy
from an excited state of one chromophor (the donor) via
dipolar coupling to another chromophor (the acceptor),
which emits the photon at a red-shifted wavelength
(Figure 1a). The propensity for FRET strongly depends
on the distance r (�r�6), thus requiring very close
(molecular) proximity between donor and acceptor chro-
mophor (Figure 1b). Accompanied by the development
of genetically encoded fluorescence labeling, FRET has
been established as a key approach for protein inter-
action analysis in living cells with numerous applications
and technical advancements during the past decade [5].
In particular the ability to probe FRET by fluorescence
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life time imaging (FLIM), which is becoming a standard
feature in confocal microscopy, has contributed to much
more reliable quantification of FRET signals down to a
few percent. But even with FLIM, quantitative assess-
ment of equilibria remains challenging as the FRET
efficiency is not readily converted into concentration of
protein complexes, which is required for the determi-
nation of binding constants. However, by combination
with microinjection, the formation of protein complexes
could be imaged by FRET and association rate constants
in the cytosol could be quantified [6�]. Recently, multi-
parameter fluorescence imaging has been applied for
studying protein–protein interactions in living cells
[7��]. By integrating information about fluorescence ani-
sotropy, lifetime and intensity for donor and acceptor
dye, much more reliable quantification of FRET effi-
ciencies is possible. This method exploits the possibility
to obtain information from individual molecules and
complexes. Another fundamental limitation of FRET
application for studying protein complexes arise from the
high proximity required for efficient energy transfer.
Despite tremendous progress in fluorescent probe de-
velopment [8], reliable detection of FRET is not
possible for donor–acceptor distances beyond �9 nm
(Figure 1c), thus limiting this approach for relatively
small proteins and protein complexes. Larger distances
are accessible by bioluminescent resonance energy
transfer (BRET), which moreover eliminates back-
ground excitation of the acceptor fluorophore. For
protein interactions in the plasma membrane, time-
resolved FRET (trFRET) has emerged as a powerful
alternative. This method is based on lanthanide ions as
Current Opinion in Structural Biology 2014, 24:54–62
56 Folding and binding
Figure 2
Interaction analysis by temporal and spatial cross-correlation of single molecule fluctuations. (a)–(c) Individual molecules diffusion into and out of a
confocal volume (a) lead to fluorescence fluctuation, which are correlated between the two channels upon complex formation (grey bar) (b). Auto-
correlation and cross-correlation of these intensity fluctuations (c) yield the concentration of the interaction partners and the relative number of
complexes, respectively, as well as their diffusion properties (tD). (d)–(f) For image correlation techniques, the intensities within each pixel of the image
(schematically indicated by the white circles) are correlated (d). If the image is acquired by sequential scanning, spatial correlation also includes
temporal information, which can be extracted by RICS. Image cross-correlation reveals the number and dynamics of protein complexes. (e) Image
auto-correlation (left) and cross-correlation (right) functions. (f) Image cross-correlation function for fast (left) and slow (right) diffusing complexes.
Panels e and f are taken from Ref. [26��] with permission.
FRET donors, which exhibit much longer fluorescence
lifetimes than traditional fluorophores (millisecond vs.
nanosecond regime) and very narrow emission bands.
These features provide larger Forster radii (up to 10 nm)
and very sensitive detection of sensitized fluorescence
by time-gated detection. Thus, G-protein coupled re-
ceptor (GPCR) dimerization could be reliably detected
in cells and in tissues [9�,10]. Though BRET and
trFRET are highly sensitive for detecting interactions,
they are not useful for quantifying and for imaging
interactions on the single cell level as the overall yield
of light is relatively low. However, nanoparticles with
engineered photophysical properties are emerging as
highly sensitive probes for FRET applications [11].
Thus, gold particles are very efficient fluorescence
quenchers over relatively long distances, while Lantha-
nide ion-based upconversion nanoparticles can be
employed as background-free luminescence donors.
Current Opinion in Structural Biology 2014, 24:54–62
Correlation techniquesProtein complex formation results into a correlated move-
ment of the interaction partners. This readout is exploited
by various cross-correlation techniques, which map the
motion of proteins by analyzing fluctuations due to vari-
ation in the number of proteins diffusing in and out of
each volume element of the cell (Figure 2a). Fluor-
escence correlation spectroscopy is based on the auto-
correlation of such fluctuations within a diffraction-
limited (confocal) detection volume (approximately
0.2 fL), thus obtaining information about the diffusion
dynamics of fluorescent molecules, as well as their con-
centration [12] (Figure 2b,c). As fluctuations result from
individual molecules, the detection volume must not be
overcrowded. For protein–protein interaction analysis,
cross-correlation of the fluctuations obtained for two
interacting partners labeled with spectrally distinct dyes
(fluorescence cross-correlation spectroscopy, FCCS)
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Monitoring protein interactions in living cells Piehler 57
Figure 3
Protein interaction analysis by single molecule localization. (a) Diffraction-limited images of individual fluorescent molecules (left) and localization with
nanometer precision by fitting the intensity distribution with a Gaussian function (right). (b) Frame-by-frame localization of interaction partners detected
simultaneously in two different channels for co-localization/co-tracking (c) or PICCS (d) analysis. (c) Trajectories obtained by single molecule tracking
providing information on the diffusion properties (left). Frame-by-frame co-localization within both channels (right, top) followed by co-tracking (right,
bottom) yields the trajectories of protein complexes. (d) Spatial correlation of individual molecules by plotting the cumulative number of molecules B
(red) detected in a distance r from molecules A (blue). The fraction of complexes can be estimated from the exponential contribution in the resulting
cross-correlation function.
allows to quantify complex formation [13,14]. In combi-
nation with the ability to determine the absolute concen-
trations of the interaction partners from the autocorrelation
curves, equilibrium constants are readily determined. In
the past 5 years, several successful applications of FCCS to
quantify interactions have been reported including tran-
scription factors [15,16] and ligand–receptor interactions
[17,18]. Even in living zebra fish embryos, the Kd of a
protein complex could be determined [19]. Despite
improved detection schemes, for example, by including
fluorescence lifetimes (lifetime cross-correlation) [20],
absolute quantification by FCCS still remains technically
challenging, as very precisely overlapping confocal
volumes are required [21]. Relatively high brightness
and photostabilities as well as fast maturation of fluoro-
phores are required for reliable quantification. Combi-
nation of EGFP with mCherry is typically applied for
live cell FCCS measurements.
While FCS and FCCS are based on a temporal correlation
of fluorescence fluctuations within a given detection
volume, spatial correlation within images is also possible.
Image correlation spectroscopy (ICS) [22] was originally
developed in order to analyse oligomerization (spatial
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ICS) or relatively slow concerted movements (temporal
ICS), while image cross-correlation spectroscopy was
applied for quantifying protein co-localization at diffrac-
tion-limited resolution (Figure 2d). With fast, sensitive
image acquisitions becoming possible with confocal scan-
ning microscopes, raster ICS (RICS) has been established
as a method to analyse spatial and temporal single mol-
ecule fluctuations in living cells [23]. The principles of
these methods have been recently summarized [24,25].
As for FCCS, cross-correlation of images from two protein
species detected in different channels (cross-correlation
raster image correlation spectroscopy, ccRICS, and spa-
tio-temporal ICCS, STICCS) allows for quantitatively
analysing the spatio-temporal dynamics of protein com-
plexes [26��,27�,28,29] (Figure 2e,f). This technique can
resolve diffusion and interaction dynamics in the sub-
second regime and has been successfully applied for
unraveling complex formation at focal adhesions [26��].In combination with number and brightness analysis, the
stoichiometry of protein complexes can be determined
[27�,30,31]. While these single molecule fluctuation tech-
niques have so far been mostly applied based on confocal
detection, fast and ultrasensitive camera technology as
well as novel detection schemes such as single plane
Current Opinion in Structural Biology 2014, 24:54–62
58 Folding and binding
Figure 4
Probing the stability of protein complexes by micropatterning techniques. (a) By using a micropatterned functionalized support, a bait protein (blue)
anchored in the plasma membrane is spatially redistributed. A prey protein (green) interacting with the bait will be redistributed accordingly. (b) Upon
bleaching of the prey protein, recovery within the enriched area (rectangular ROI) reports the exchange of bleached by the non-bleached prey in the
cytosol. The exchange kinetics following an exponential function is determined by the dissociation rate constant kd in the case of an large excess of
non-bound protein.
illumination microscopy (SPIM) opens new exciting pos-
sibilities for their application to protein interaction
analysis in living cells.
Single molecule localization techniquesImaging of individual fluorescent molecules by far-field
microscopy yields diffraction-limited intensity distri-
butions, which can be fitted to determine the center of
gravity with a precision far beyond the resolution of the
image [32] (Figure 3a,b). The localization precision
mainly depends on the number of detected photons
versus the background signal, yielding typical accuracies
within molecular dimensions (5–30 nm) and even below
[33]. As a diffusive molecule will continuously change its
position, high image acquisition speed is needed for
precise localization. Localization of soluble molecules
is only possible for relatively large species or within very
crowded environments such as the nucleus [34]. For this
reason, single molecule localization can discriminate
binding of proteins to membranes or other structures of
the cell. Thus, binding of cytosolic proteins to interaction
partners at membranes can be visualized and the life-time
of complexes can be quantified [35].
Since the propensity of statistic co-localization within
distances below 100 nm is rather low, the formation of
proteins complexes can be quantified by dual-color
single molecule imaging [36]. Statistical co-localization
of molecules within these dimensions depends on
the density — i.e. the concentration — and therefore
methods for reliably discriminating protein complexes
from statistically co-localized molecules is required. For
homogeneously distributed interaction partners, this is
possible by simply correcting the observed co-localiz-
ations for the theoretical number of statistic co-localiz-
ations [37]. Since molecules are often heterogeneously
organized within cells, other means have been developed
Current Opinion in Structural Biology 2014, 24:54–62
for unambiguously identifying protein complexes. In
case of diffusive molecules, this is possible by tracking
co-localized molecules over multiple frames [38]
(Figure 3c). This method has been very successfully
employed to monitor and quantify receptor dimerization
in the plasma membrane of living cells [39��,40�]. While
the quantification of complexed versus the free inter-
action partners allows to directly determine equilibrium
dissociation constants, co-tracking also has the potential
to assess the dynamics of protein interaction as the length
of co-trajectories correspond to the lifetime of a complex.
Thus, the dynamic equilibrium of protein complex for-
mation can potentially by fully analysed. Although this
strategy has been successfully applied for short-lived
complexes (kd > 1 s�1) [39��,40�], effective co-tracking
is limited by photobleaching and tracking fidelity.
Highly photostable fluorescent probes such as quantum
dots (QDs) have been demonstrated to allow co-tracking
over extended time periods with very high spatial and
temporal resolution [41��]. For quantitative protein
interaction analysis, however, monofunctional QD are
crucial [42], in order not to bias complex formation by
avidity effects.
While single molecule co-localization and co-tracking is
readily applicable up to densities of�5 molecules/mm2, at
higher protein concentrations single molecule localization
is obstructed by the convolution of the individual signals.
Reducing the degree of labeling improves localization and
tracking, but at the same time reduces the propensity for
co-localization of labeled molecules. This problem can be
overcome by combining dual-color co-tracking with local,
intensive photobleaching. Labeled proteins and protein
complexes diffusing into the photobleached area show
conserved stoichiometry of labeling yet are thinned out
to a level, which allows precise localization and tracking
(‘thinning out clusters while conserving stoichiometry of
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Monitoring protein interactions in living cells Piehler 59
labeling’ [43]). Using a model system, interaction prob-
abilities down to 2.5% have been shown to be reliably
quantified by this method [44].
However, out the large spectrum of available fluorescent
proteins, only few are sufficiently bright and photostable
for reliable single molecule tracking [45]. Particle image
cross-correlation spectroscopy (PICCS) provides an
alternative, robust strategy for quantifying proteins com-
plex formation in living cells without the need for track-
ing individual complexes (Figure 3d). By analysing the
cumulative probability of detecting interaction partner B
in a distance r of interaction partner A, statistic and
interaction-mediated co-localization can be efficiently
separated [46�]. Thus, the fraction of molecules in com-
plexes and the (local) concentration of interaction part-
ners are readily determined, providing the possibility to
determine Kd values. Though tracking is not required,
this method still requires the ability to localize the entire
ensemble of both interaction partners, which can be
achieved with densities up to 5–10 molecules/mm2.
Higher densities require to localize molecules sequen-
tially rather than simultaneously, which can be achieved
by photoactivation localization microscopy (PALM) in
fixed cells [47]. On this basis, pair-correlation PALM
was recently established as a method for quantifying
protein co-clustering [48].
Spatial protein redistributionRather than trying to analyse the dynamic equilibrium of
interaction partners statistically distributed within cellu-
lar membranes and the cytoplasm, recent approaches
have exploited microtechnological and nanotechnological
techniques for redistributing and immobilizing proteins
within living cells. This has been achieved by patterning
proteins within the plasma membrane using micropat-
terned functionalized substrates, or by nanoparticles
injected into the cytoplasm.
The concept of protein interaction analysis by patterning
proteins in the plasma membrane is depicted in Figure 4a.
Cells expressing a bait protein in the plasma membrane are
cultivated on a support presenting spatially resolved func-
tionalities for capturing the bait protein via its extracellular
domain. Thus, the bait protein is enriched and immobil-
ized within a predefined micropattern. Interaction of prey
proteins carrying a fluorescent tag thus can be quantified
via the fluorescence contrast, preferably by surface-selec-
tive imaging using total internal reflection fluorescence
microscopy. Moreover, the interaction dynamics of protein
complexes can be probed by fluorescence recovery of
photobleaching (FRAP) experiments: since the bait
protein is immobilized, FRAP in the functionalized zones
can be attributed to the exchange of bleached prey protein
bound to the micropatterned bait by the non-bleached
species in the cytosol, which is typically limited by
complex stability (Figure 4b,c). Thus, dissociation rate
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constants can be readily quantified within a range of
�1 s�1 to 10�3 s�1, which is very relevant for many protein
complexes. Pioneering work in this field was carried out
based on capturing of the membrane receptors such as CD4
by means of micropatterned antibodies [49��]. Spatial
reorganization of Lck, a cytosolic, membrane-anchored
interaction partner, was observed and the interaction
dynamics was further explored by FRAP and single mol-
ecule tracking (SMT) [49��,50]. This method proved
powerful for probing interactions with other transmem-
brane receptors [51]. Recently, the concept was extended
towards more general and multiplexed bait patterning
strategies, allowing to monitor signal activation in living
cells [52�].
Alternatively, spatial rearrangement can be achieved by
injection of relatively large, functionalized nanoparticles
into the cytosol of living cells. Bait protein can either be
attached to the nanoparticle before injection, or com-
plexes are captured directly from the cytoplasm by using
suitable capturing methods [53]. In combination with
magnetic control of the functionalized nanoparticle, this
method was recently established for probing the stability
of protein complexes in the cell by FRAP on the nano-
particle [54��]. Thus, an in-cell solid-phase binding assay
was achieved.
Concluding remarksMonitoring and quantifying protein interactions in the
living cell remains challenging, as not only demanding
and specialized experimental equipment, but also sophis-
ticated data evaluation is required. Out of the four princi-
pal, highly complementary approaches I have presented
here, the most suitable has to be carefully chosen to address
a specific interaction in the cell. Energy transfer techniques
are promising for probing relatively small and structurally
well-defined protein complex, and quantification is
possible at both high and low protein concentrations.
Single molecule fluctuation-based techniques can detect
and quantify interactions at low and medium expression
levels in solution and at membranes, independent on the
size and the stoichiometry of complexes. Single molecule
localization-based techniques are particularly powerful at
very low concentrations and with relatively slowly diffusing
complexes. Interactions can be directly visualized, allow-
ing much more intuitive data evaluation compared to
correlation techniques. Moreover, the dynamic equi-
librium of association and dissociation can potentially be
fully characterized. Yet, optimization of labeling and ima-
ging is required, as background and photobleaching is a
major problem for single molecule imaging techniques.
Importantly, all single molecule-based techniques directly
provide concentrations, which is a prerequisite for quan-
titative analysis. The dynamics of protein complexes can
be studied for relatively transient interactions (<1 s). By
contrast, protein rearrangement by micropatterning or
nanoparticles allows probing the stability of high-affinity
Current Opinion in Structural Biology 2014, 24:54–62
60 Folding and binding
protein complexes in cells. Measurements are relatively
simple and intuitive, but currently the required microma-
terials and nanomaterials are not commercially available.
All these approaches profit from the rapid development in
the field of microscopy techniques and fluorescence probe
development. Fast cameras for localization-based super-
resolution imaging [55], novel approaches for optical sec-
tioning for three-dimensional single molecule localization
and tracking [56] as well as fast confocal imaging beyond
the diffraction limit by stimulated emission depletion
(STED) [57] will boost protein-interaction analysis on
the single molecule level. Maybe even more importantly,
efficient proteins labeling with bright, photophysically
well-defined fluorescent probes is required. Next to opti-
mized fluorescent proteins and organic dyes, nanoparticles
providing a broad spectrum of photophysical properties are
emerging as promising probes for protein interaction
analysis. In this context, more efficient posttranslational
labeling techniques will play a pivotal role, as well as
suitable membrane-permeable probes [58]. The appli-
cation of nanoparticles for unbiased protein interaction
analysis requires strategies for efficient conjugation to
target proteins in a defined 1:1 stoichiometry without
affecting their function [53,59]. On the basis of recent
and future developments of nanomaterials as selective,
multifunctional probes for protein interactions in combi-
nation with in-cell delivery and targeting, novel tools for
probing protein interactions in living cells will soon be
available. With these powerful emerging techniques, the
future of protein interaction analysis in living cells is bright.
AcknowledgementsThe author thanks C. Richter and D. Schmedt for graphic material. Thiswork has been supported by the Deutsche Forschungsgemeinschaft (PI405/6 and SFB 944).
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