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Spectroscopic techniques for m
onitoring protein interactionsin living cellsJacob Piehler
Quantitative 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.


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


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


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.


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)



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


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



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


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



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


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



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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