Targeting membrane proteins to liquid-ordered phases: molecular self-organization explored by fluorescence correlation spectroscopy

Chemistry and Physics of Lipids 141 (2006) 158–168

Review

Targeting membrane proteins to liquid-ordered phases: molecular self-organization explored by fluorescence correlation spectroscopy Nicoletta Kahya ∗ Institute of Biophysics, Biotechnology Center, Dresden University of Technology, Tatzberg 47–49, D-01307 Dresden, Germany Received 17 November 2005; accepted 20 February 2006 Available online 4 April 2006

Abstract The complex and dynamic architecture of biological membranes comprises of various heterogeneities, some of which may include lipid-based and/or protein-based microdomains called “rafts”. Due to interactions among membrane components, several types of domains can form with different characteristics and mechanisms of formation. Model membranes, such as giant unilamellar vesicles (GUVs), provide a key system to study lipid–lipid and lipid–protein interactions, which are potentially relevant to raft formation, by (single-molecule) optical microscopy. Here, we review studies of combined confocal imaging and fluorescence correlation spectroscopy (FCS) on lipid dynamics and organization in domains assembled in GUVs, prepared from various lipid mixtures, which are relevant to the problem of raft formation. Finally, we summarize the results on lipid–protein interactions, which govern the targeting of several putative raft- and non-raft-associated membrane proteins to domain-exhibiting GUVs. © 2006 Published by Elsevier Ireland Ltd. Keywords: Fluorescence correlation spectroscopy; Confocal fluorescence microscopy; Giant unilamellar vesicles; Lipid rafts; Cholesterol; Phosphatidylcholine; Sphingomyelin

Contents 1. 2. 3. 4. 5.

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fluorescence correlation spectroscopy on giant unilamellar vesicles: principle and experimental details . . . . . . . . . . . . Dynamic self-organization of lipids with complex phase behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Putative raft- and non-raft-associated proteins in laterally heterogeneous membranes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Watching membrane proteins in complex lipid environments: how to get there . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction Membranes are thought as the portal to cellular life, actively maintaining an interface with a beautifully complex architecture. So far, more than 1000 structurally distinct lipids have been identified in eukaryotic cells and one-third of the proteins encoded in the entire human genome is estimated to belong to membrane proteins. Why membranes contain such a variety of components remains an unanswered question. Is there any link between the enormous chemical diversity and the spatio-temporal organization of membrane components? In other words, we expect so many different lipids and proteins to organize in a non-random fashion but how this is regulated and what are the key molecular determinants is still an open question. Even more interesting is whether this spatio-temporal order bears some relevance to biological functions that range from cellular growth, development and death to endo- and exocytosis, signaling, membrane trafficking and protein sorting. Lateral heterogeneities, such as “rafts” (Simons and van Meer, 1988; Simons and Ikonen, 1997), have received in the past decade much attention thanks to the efforts of many groups to assign biological relevance to concepts developed in other fields, in particular physical chemistry. Rafts are thought to be dynamic assemblies of lipids and proteins (Mayor and Rao, 2004; Kusumi et al., 2004) serving as functional platforms to concentrate some components and excluding others. Although the mechanisms of formation, the dynamics and, even, the definition itself have been put under severe scrutiny, there is no doubt that lateral heterogeneities are the result of a delicate balance between lipid–lipid and lipid–protein interactions. Small fluctuations induced by perturbing the system with more or less invasive techniques may alter this delicate molecular balance, thereby making rafts and, in general, lateral complexes/heterogeneities more difficult to track and characterize. Optical microscopy techniques are in many cases less invasive compared to other methodologies available. They also provide high temporal resolution to follow molecular trajectories in real time and – in some cases – at the single-molecule level. Potentially, optical microscopy (from imaging to fluorescence resonance energy transfer (FRET), fluorescence recovery after photobleaching (FRAP), single particle tracking (SPT) and FCS) has contributed to the understanding of the dynamics of membrane processes (see for instance Kusumi et al., 2004; Sharma et al., 2004; Kenworthy et al., 2004; Glebov and Nichols, 2004; Goodwin et al., 2005).

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Given the complexity of cellular systems, it is still meaningful to choose a bottom-up approach, in which a minimal system is exploited to understand simple few-body interactions. Within this reductionistic strategy, model membranes are invaluable tools to investigate the key mechanisms by which lipid–lipid and lipid–protein interactions shape up the membrane architecture. In particular, the so-called giant unilamellar vesicles (GUVs) (Angelova and Dimitrov, 1986; Menger and Keiper, 1998) have become of increasingly high interest for this purpose, because of their stability, their artifact-free structure and suitability for (singlemolecule) optical microscopy. We and other groups have applied various optical methodologies to GUVs made of either synthetic or natural mixtures to mimic and characterize raft-assembly (Samsonov et al., 2001; Dietrich et al., 2001; Kahya et al., 2003; Scherfeld et al., 2003). In this review, we first introduce the principle and experimental details of FCS (Section 2) and how this technique can be applied to membranes. Then, we will introduce GUVs as a model membrane system, and review the current knowledge on applying FCS to GUVs, in particular to domain-forming GUVs, in both pure lipid and lipid–protein systems (Sections 3 and 4). Finally, we will briefly list some promising developments of FCS, which are of potential relevance to membrane research, in general, and to lipid raft issues, in particular (Section 5). 2. Fluorescence correlation spectroscopy on giant unilamellar vesicles: principle and experimental details Fluorescence correlation spectroscopy (FCS) relies on the temporal analysis of fluorescence fluctuations coming from a small open detection volume defined by a focused laser beam in the sample (Rigler and Elson, 2001). Fluorescence fluctuations can arise from the diffusion of the optical species in and out of focus, as well as from any other process (chemical reaction, association/dissociation event, photodynamic process, conformational change), which converts the optical species between states with different emission properties. Hence, the autocorrelation function represents an ideal method for studying diffusion and dynamics of molecules at nanomolar concentrations (Schwille, 2001). It provides a statistically accurate quantification of local concentrations and various dynamic parameters (e.g. diffusion coefficients, kinetic rate constants, association/dissociation constants, triplet lifetimes) (Magde et al., 1972).

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The normalized autocorrelation function G(τ) of the temporal fluctuations of the fluorescence F(t) is given by (Schwille, 2001): G(τ) =

δF (t + τ)δF (t) δF (τ)δF (0) = F 2 F (t)2

G(τ) corresponds to the probability that, provided a particle is emitting inside the focal volume at a time 0, the same particle still emits inside the focal volume at a later time τ. If the optical species diffuses through the focus, this probability will decay over time in a fashion that is related to the mobility of the particle itself. Faster particles yield more rapid fluorescence fluctuations, thereby resulting into a faster decay of the probability G(τ). The shape of the decay contains information on the type of motion of the particle, whether it is a two- or three-dimensional Brownian diffusion or it is an active transport mode. When the laser focus is positioned on a membrane bilayer, as in Fig. 1, and the optical species are confined to a two-dimensional Brownian motion in the plane of the membrane with diffusion coefficient Di , then the autocorrelation function can be written as follows:
Ci (1/(1 + τ/τd,i )) G(τ) = i (1)
Aeff ( i Ci )2 where Ci  is the two-dimensional time average concentration of the species i in the detection area Aeff = πr02 (∼0.1 ␮m2 ), and τ d,i is the average residence time of the species i. The focal volume is assumed to be a Gaussian function with 1/e2 dimension r0 , the autocorrelation function can be written as follows (Magde et al., 1972). The diffusion coefficient Di for the species i is inversely proportional to τ d,i with τd,i = r02 /4Di . A typical FCS scheme is shown in Fig. 1. A tight spatial confinement of the sample is needed for a resolution of small molecular ensembles. Therefore, the core of a microscope setup for FCS is a high numerical aperture objective and a confocal geometry (for onephoton absorption). The confocal geometry is highly recommended even in the case of two-dimensional samples such as bilayers, as it minimizes the out-of-focus background and maximizes the signal-to-noise ratio. For membrane applications, the position of the focus with respect to the source of signal is critical for the recording of correlation curves without artifacts. Therefore, it is always recommended to combine a FCS setup with a laser scanning module. In general, a compromise in the concentration range can be found that supports both applications, i.e. an acceptable contrast quality in confocal imaging and a good amplitude of correlation function in FCS.

FCS represents a very sensitive method to study intermolecular interactions. For freely diffusing particles in 3D, the translational diffusion coefficient scales with the hydrodynamic radius of the particle, according to the Stokes–Einstein equation. So, in the case of threedimensional Brownian diffusion, in order to resolve two distinct components in the FCS curve the particles should have a mass which differ by a factor of at least 3–4. In principle, the diffusion coefficient can then be used to detect lipid–protein or protein–protein interactions. From a theoretical point of view, the Brownian motion in biological membranes has been first analyzed by Saffman and Delbruck (1975), followed by other groups who extended their model. As a result of these studies, in the case of particles freely diffusing in 2D, the diffusion coefficient scales with the logarithm of the hydrodynamic radius (defined as the cross-section of the particle along the direction perpendicular to the plane of the membrane). Given the weak dependence of the diffusion coefficient upon the crosssection of the particle, in order to resolve two distinct components in the FCS curves for the case of twodimensional diffusion, the mass of the particles should differs by at least a factor of 15–20. However, this holds within the assumptions of the Saffman–Delbr¨uck model, that is: (i) the particle is regarded as a cylinder, with axis perpendicular to the plane of the membrane sheet (i.e. spanning both leaflets), moving about in the sheet in a Brownian fashion; (ii) the viscosity of the medium around the membrane is much lower than that of the membrane; (iii) the membrane is regarded as a continuous medium (Saffman and Delbruck, 1975). In many cases, some assumptions, such as the last one, may be too strict and the particle diffusion coefficient may become sensitive to specific lipid–protein interactions, which produce a large effect on the particle dynamics. For the sake of accuracy in the data analysis, the focal spot is positioned at the top/bottom of a GUV, which is large enough to be considered as approximately planar in the focal plane (0.1 ␮m2 ). During the data acquisition, the fluorescence count rate is monitored online ensuring that no fluorescence fluctuations originate from movements of the whole membrane. Finally, we prepare fresh vesicles for every FCS experiment. When freshly prepared, GUVs stay nicely in tension for approximately 2–3 h, thereby providing a planar tense bilayer in the focal spot. The consistency of the diffusion times in the FCS curves sufficiently proves that no artifacts compromise the data. Sometimes, thermal undulations of the bilayer in focus induce an additional component in the FCS curve, characterized by

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Fig. 1. Top view of the focal plane and detection volume (top left). Optical species are spotted by the detector only if they diffuse into the detection volume. Scheme of a confocal microscope for dual-color fluorescence correlation spectroscopy (top right). Fluorescence fluctuations are recorded over time and the correlation algorithm gives rise to the correlation function G(τ) (bottom left). An accurate positioning of the focal volume on the membrane bilayer is crucial to record artifact-free correlation curves. G(τ) strongly changes as a function of the distance of the focal plane with respect to the bilayer along the optical axis z (bottom right).

long (with respect to the real lipid diffusion time) diffusion times. Data affected by such artifacts should be rejected. Furthermore, precautions need to be taken to rule out potential artifacts arising from an inaccurate positioning

of the detection volume with respect to the bilayer. First of all, the optimal x, y, z positions of the top/bottom side of GUVs are chosen by high resolution confocal scanning microscopy. Furthermore, it is known that, in planar systems, the diffusion times, τ D , and the particle num-

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ber, N, depend on the position of the focus, as follows: r2 τD = 0 4D



λ2 z2 1 + 02 2 2 π n r0

 N = πcr02

λ2 z2 1 + 02 2 2 π n r0





We highly recommend to perform routine tests to calibrate for the detection area at the membrane and to check the lipid diffusion times as a function of the bilayer zposition (see for instance Benda et al., 2003; Milon et al., 2003). 3. Dynamic self-organization of lipids with complex phase behavior Relatively simple mixtures of a low melting temperature (Tm ) lipid, a high Tm , such as sphingomyelin, and cholesterol, exhibit a very complex phase behavior, as demonstrated by the several versions of phase diagrams composed with different techniques (see for instance De Almeida et al., 2003; Veatch and Keller, 2003). Liquid–liquid immiscibility has been observed over a wide range of lipid compositions and temperatures in the form of round micrometer-sized domains (Veatch and Keller, 2003; Bagatolli and Gratton, 1999). Domain patterns as well as size can vary, depending on the temperature and lipid ratio. We have explored several regions of the phase diagram for dioleoylphosphatidylcholine (DOPC), sphingomyelin (SM) and cholesterol (see Fig. 2A) by combining confocal imaging and FCS (Kahya et al., 2003). On one hand, confocal imaging would give a static picture of the domain morphology, provided that sufficient contrast is reached by the preferential partitioning of the fluorescent probe in one lipid phase over the other. On the other hand, FCS would give information on the lateral lipid diffusion and, hence, phase assignment and composition. It would also help identify distinct phases, in case of low signal contrast given by the fluorescent probe. In particular, we studied the effect of cholesterol on lipid mobility for equimolar mixtures of DOPC and SM. Here, cholesterol induces formation of different liquid phases, one disordered (characterized by high lipid mobility) and one ordered (with low lipid mobility). Within this two-phase region, a change in the lipid diffusion coefficient along the line with equimolar amount of DOPC and SM suggests a switch between tie lines, hence of composition of the liquid domains, even if the domain morphology remains qualitatively unchanged in the imaging mode.

Cholesterol largely affects the lipid packing of the liquidordered (Lo ) phase, enriched in SM, as shown by the steep increase of the lipid lateral diffusion rate by almost one order of magnitude (Fig. 2). By contrast, the mobility in the liquid-disordered (Ld ) phase hardly decreases. By comparison with the effect of cholesterol on lipid mobility in membranes either of pure DOPC or pure SM, an indication can be obtained of the composition of the two phases. Furthermore, by increasing the SM versus DOPC ratio (and keeping the cholesterol level constant), the lipid mobility of both phases remains roughly constant, suggesting that the tie lines lie pretty flat in this phase region. This is consistent with what observed by other groups, which have built similar phase diagrams by applying optical imaging, FRET and differential scanning calorimetry (De Almeida et al., 2003; Veatch and Keller, 2003). Remarkably, we found a large difference in lipid mobility between Ld - and Lo -phase (Kahya et al., 2003). This difference was cholesterol-dependent and varied between 40-fold (at 10 mol% of cholesterol) and 8-fold (at 33 mol%), in contrast to earlier FRAP data on supported bilayers and GUVs (Dietrich et al., 2001), which show a 2-fold difference in the diffusion constant. Optical imaging necessarily relies on the ability of lipid probes to associate with one phase rather than with the other. However, the phase assignment is not straightforward as the partition coefficient of a probe strongly depends on the lipid composition of the bilayer. For example, the lipid analog DiI-C18 prefers the Ld - to the Lo -phase in DOPC/SM/cholesterol mixtures but it mainly associates with the Lo -phase in DOPC/DSPC/cholesterol mixtures (cf. Fig. 2B). In this respect, FCS offers an independent and reliable tool to assign lipid phases. Alternatively, one can exploit the spectral differences in the emission spectrum of a dye, LAURDAN, which easily partitions in all of the lipid phases. Its emission spectrum is sensitive to the alignment of the acyl chains within a bilayer, thereby allowing for discriminating a solid phase (relatively blue emission) from a Ld -phase (relatively red emission) (Parasassi and Gratton, 1995). To date, we have not found yet any lipid analog that strongly associates with the SM-enriched Lo -phase in the SM/DOPC/cholesterol mixture. Actually, the simple strategy of attaching a choromophore as small as NBD to a certain lipid might turn out to modify the lipid molecular packing in such a way that the lipid analogue does not behave as the unlabeled lipid. For example, this is the case of NBD-labeled SM, which colocalizes with DiIC18 and prefers the Ld -phase in domain-forming GUVs composed of ternary SM/DOPC/cholesterol mixtures

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Fig. 2. (A) Ternary phase diagram of the SM/DOPC/cholesterol mixture. Numbers represent the diffusion coefficients (×10−8 cm2 /s) as measured by FCS in GUVs of the corresponding compositions. In the region of Lo –Ld coexistence, diffusion coefficients are measured in each lipid phase (Lo : green symbols, Ld : red dots, as shown in the 3D projection of a domain-forming GUV) and reported as a function of cholesterol concentration (for SM/DOPC 1:1, as shown by the yellow arrow). (B) 3D projection of a GUV composed of SM/DOPC/cholesterol 1:1:1 (left) and DSPC/DOPC 1:1 and 20% of cholesterol (right), labeled with 0.1% of DiI-C18 . Note that the lipid probe prefers the Ld -phase in the GUV on the left side and the Lo -phase in the GUV on the right side. (For interpretation of the references to color in this figure caption, the reader is referred to the web version of the article.)

(Fig. 3A; Kahya, unpublished results). On the other hand, the use of a ganglioside, GM1, which is known as a raft marker, turns out to be very effective at identifying SMenriched phases in SM/DOPC/cholesterol GUVs (Kahya et al., 2003). Rather than linking a chromophore directly to the lipid, the strategy here is to localize GM1 by binding of the cholera toxin B subunit (CTB), for which GM1 is the receptor. A fluorescent label on CTB is shown not to affect the lipid distribution and the phase coexistence (Fig. 3B). In conclusion, FCS provides a reliable methodology to assign lipid phases and gives an indication of the chem-

ical composition of different membrane regions. Unlike SPT, it offers a reliable and highly accurate statistics in short time and, unlike FRAP, it does not require heavy labeling load, which could alter the lipid organization in the bilayer. However, in the conventional experimental geometry, FCS is bound to the diffraction-limited resolution and may be therefore not sufficient to detect and characterize domains smaller than the optical resolution. As briefly discussed in the last section of this paper, new strategies are needed to tailor the detection area of FCS acquisition and, thereby, improve the resolving power of the technique.

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Fig. 3. (A) Confocal images of GUVs composed of SM/DOPC/cholesterol 1:1:1. Red channel: fluorescence from DiI-C18 , which prefers the Ld phase. Green channel: NBD-labeled SM, which colocalizes with DiI-C18 . (B) Confocal images of GUVs composed of SM/DOPC/cholesterol 1:1:1. Red channel: fluorescence from DiI-C18 , which prefers the Ld -phase. Green channel: GM1-bound AlexaFluor488-labeled cholera toxin B subumit (AF488-CTB), which partitions in the Lo -phase. (For interpretation of the references to color in this figure caption, the reader is referred to the web version of the article.)

4. Putative raft- and non-raft-associated proteins in laterally heterogeneous membranes Lipid–protein interactions have been shown to play a key role in regulating the function of biological machinery’s, such as transport complexes (see for instance O’Keeffe et al., 2000; Powl et al., 2003). The existence of cellular rafts is most likely dependent on specific lipid–protein interactions, which balance protein–protein associations in a beautifully orchestrated spatio-temporal organization (Mayor and Rao, 2004; Helms and Zurzolo, 2004). Cellular membranes are very crowded and, on average, proteins populate the bilayer up to one-half of the total membrane mass (Branden and Tooze, 1991). It is, therefore, simply not enough to examine pure lipid systems. With model membranes, we are now at the stage of taking a step forward in increasing the membrane complexity. We can then begin to answer questions such as the following: To which extent do proteins affect lipid organization in membranes of complex phase behavior? Do structurally different proteins modulate membrane organization in different ways? Do they partition differently in distinct lipid phases and, if yes, how? We set out to develop a biophysical tool to test some ideas concerning rules and structural requirements, which are responsible for targeting membrane proteins to

lipid environments of specific chemistry. We can then test whether and how the lipid matrix influences the mechanisms of function of membrane proteins. Putative raft-associated and non-raft proteins were reconstituted into domain-exhibiting GUVs (Bacia et al., 2004; Kahya et al., 2005). Their spatial organization was observed by optical imaging and FCS. The human placental alkaline phosphatase (PLAP) was abundantly found in detergent resistant membranes (DRMs) after treatment with Triton X-100 at 4 ◦ C (Brown and Rose, 1992; Schroeder et al., 1998). However, it mainly associated with Ld -phases in GUVs composed of DOPC/SM/cholesterol (1/1/1), as shown by FCS and by counterstaining the Lo -phase with GM1-bound fluorescent cholera toxin (see Fig. 4A). The same spatial organization was found for a similar GPI-anchored protein, the bovine intestine alkaline phosphatase (N. Kahya and S. Morandat, unpublished results). FCS measurements of local protein density in distinct phases revealed that at most 25–30% of PLAP partitioned into the Lo -phase. Furthermore, antibody-mediated cross-linking caused the protein to associate more (up to 50%) with the ordered phase (Kahya et al., 2005). Although this data may seem surprisingly different from the expectations of a putative raft-associated protein, PLAP showed a higher affinity for Lo -phase compared to other mem-

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Fig. 4. Confocal images of GUVs composed of SM/DOPC/cholesterol 1:1:1 and 0.1% of GM1 containing the GPI-anchored rhodamine-labeled PLAP (Rh-PLAP) (top panels). Red channel: fluorescence signal from Rh-PLAP, which partitions for 25% in the Lo -phase. Green channel: fluorescence signal from AF488-labeled cholera toxin (AF488-CTB), which partitions for 99% in the Lo -phase. Confocal images of GUVs composed of SM/DOPC/cholesterol 1:1:1 and 0.1% of GM1 containing the multi-spanning protein AF488-labeled bacteriorhodopsin (AF488-BRh) (middle panels). Red channel: fluorescence signal from AF633-labeled choera toxin (AF633-CTB), which partitions for 99% in the Lo -phase. Green channel: fluorescence signal from AF488-labeled bacteriorhodopsin (AF488-BRh), which partitions for 99% in the Ld -phase. Confocal images of GUVs composed of SM/DOPC/cholesterol 1:1:1 and 0.1% of GM1 containing the single-span Cy5-labeled BACE (Cy5-BACE) (bottom panels). Red channel: fluorescence signal from Cy5-BACE, which partitions for 20% in the Lo -phase. Green channel: fluorescence signal from AF488-labeled cholera toxin (AF488-CTB), which partitions for 99% in the Lo -phase. (For interpretation of the references to color in this figure caption, the reader is referred to the web version of the article.)

brane proteins, e.g. syntaxin, synaptobrevin (Bacia et al., 2004) and bacteriorhodopsin (Fig. 4B). However, we have too little statistics to bring forward the hypothesis that GPI-anchored proteins are targeted to Lo -phase more than transmembrane proteins. Furthermore, there are clearly differences between partition coefficients of Lo -phase of single-span membrane proteins. A protease (BACE) responsible for the cleavage of the amyloid precursor protein (APP) at its ␤-site was reconstituted into GUVs containing coexisting liquid domains (Fig. 4C) (Kalvodova et al., 2005). FCS measurements and confocal microscopy showed that 15–20% of BACE associated with Lo -phase, still more than syntaxin/synaptobrevin do. Interestingly, when GM1 was included in the lipid composition and was cross-linked with cholera toxin, BACE shifted more towards the Lo -phase. On the confocal microscope, BACE equally distributed on the vesicle surface, although distinct lipid phases still coexisted, as

demonstrated by CTB partitioning into one phase. This suggests either a specific GM1-BACE interaction, which drags BACE towards the Lo -phase as a result of the GM1 cross-linking or a rearrangement of the lipid phases that increases BACE affinity for the ordered phase. On the other hand, some membrane proteins were found to associate with Lo -phases almost exclusively, such as cholera toxin (Kahya et al., 2003) or the GPI-anchored human prion (N. Kahya, unpublished results). By collecting data from various membrane proteins of distinct topology, we begin to look for structural determinants, which may play a key role in modulating the affinity of a protein for one lipid environment versus another. This information can potentially shape up a distribution histogram of partition coefficients for Lo phase (Fig. 5), which visualizes trends of lipid–protein interactions and will help to identify general rules on lipid–protein interactions. With these results, we do not

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Fig. 5. Frequency histogram of the partition coefficients for the tested membrane proteins. After antibody-mediated cross-linking, the tested proteins, which had intermediate partition coefficients, were more targeted to the Lo -phase.

imply that Lo -phases should be regarded as cellular rafts, although it is thought that rafts are in a liquid state. However, this data help relate structural factors of membrane proteins to their affinity for ordered versus disordered lipid environment and give us information on the thermodynamic stability of lipid–protein interactions. New interesting developments in membrane studies take the problem to the next level of complexity and propose the idea of isolate the native membrane and prepare GUVs, which would then contain the full natural lipid and protein composition. The first study following this strategy deals with the pulmonary surfactant, the lipid–protein material, which stabilizes the respiratory surface of the lungs and which mainly contains equimolar amounts of unsaturated and saturated phospholipids and cholesterol. Bagatolli et al. prepared GUVs from native pulmonary surfactant to study the lateral organization and found the coexistence of two distinct micrometer-sized fluid phases (De la Serna et al., 2004). The lateral domain pattern was shown to be dependent on the presence of cholesterol. The fact that the spreading properties of the native pulmonary surfactant were also greatly affected by cholesterol extraction was regarded as a link between the observed domain-assembly and the physiological function of the material. 5. Watching membrane proteins in complex lipid environments: how to get there Model membranes allow us to answer very specific questions on lipid–protein interactions. How selective are membrane proteins for recruiting lipids from their

Fig. 6. Plot of the temporal resolution versus the spatial resolution for various methodologies to investigate lipid and protein organization and dynamics in membranes. Techniques on the upper right corner are characterized by high temporal resolution, whereas their spatial resolution goes beyond the diffraction limit, thereby achieving molecular specificity. (a) The minimal temporal resolution is on the picosecond time-scale (although it can be extended to the second time-scale). Some NMR applications (diffusion NMR, see Filippov et al., 2004). (b) The minimal spatial resolution is dependent on the wavelength (diffractionlimited), although it can be extended to tens of micrometers. EM, electron microscopy. AFM, atomic force microscopy; SNOM, scanning near-field optical microscopy; FPR, fluorescence photobleaching after recovery; NMR, nuclear magnetic resonance; EPR, electron paramagnetic resonance; FRET, fluorescence resonance energy transfer; SPT, single particle tracking; FCCS, fluorescence cross-correlation spectroscopy.

environment? To what extent does this affect protein function? How important are “bulk” properties of the membrane versus “specific” lipid–protein interactions for species, signals and reactions to be formed, transmitted and consumed? Interesting avenues of model membranes concern a few strategies to increase their complexity and mimic more closely cellular membranes. When looking at the pool of techniques available nowadays for detecting and tracking membrane components, one realizes that there is a trade off between techniques which provide excellent spatial resolution, but rather poor temporal resolution and techniques which offer high temporal resolution (and they are mostly noninvasive) but lack spatial resolution. In Fig. 6, we plot the reciprocal of the temporal resolution versus the reciprocal of the spatial resolution. Techniques such as electron microscopy or X-ray crystallography appear at the bottom right of the diagram, whereas optical microscopy

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techniques up to NMR and EPR will line up towards the upper left corner. In order to gain insight into membrane fluctuations and molecular interactions, we clearly need to improve both temporal and spatial resolution. In other words, we need to develop forefront methodologies, which gain the upper right region of the graph in Fig. 6. Actually, there is a small pool of techniques already available – SPT, FRET and FCS, especially its variant cross-correlation spectroscopy – which provide both high spatial and temporal resolution. They offer an intrinsic spatial resolution, which goes beyond the diffraction limit, thereby gaining precision at the molecular scale. In particular, dual-color cross-correlation spectroscopy (Heinze et al., 2000) is specifically sensitive to the co-movement of particles which carry different fluorescent labels through the focus, thereby following the trajectory of interacting molecules. Advances in optical microscopy have paved the way to new prospects in FCS applications (see the review Kim and Schwille, 2003). Coincidence analysis (i.e. cross-correlation amplitudes) improves the time resolution when looking at fast binding kinetics, for instance, of signaling processes (Winkler et al., 1999; Heinze et al., 2002). Scanning FCS (Ruan et al., 2004) may offer a solution to photobleaching damages and provides interesting novel prospects on spatial cross-correlation. These forefront technologies are still under development and other still need to be developed in the direction of tailoring the detection volume of FCS, by restricting the data acquisition beyond the diffraction limit. For instance, this can be achieved by combining FCS with total internal reflection fluorescence (TIRF) (Lieto et al., 2003), stimulated emission depletion (STED) (Kastrup et al., 2005) or exploiting other ways of illumination (near field scaning optical microscopy, NSOM). In conclusion, the advancements of FCS along with other optical microscopy techniques hold great promise in gaining more detailed knowledge of the biochemistry of membranes, from model up to cellular ones. Acknowledgments We thank Dick Hoekstra, Kai Simons and Lucie Kalvodova for useful discussions. References Angelova, M.I., Dimitrov, D.S., 1986. Liposome electroformation. Faraday Discuss. Chem. Soc. 81, 303–308. Bacia, K., Schuette, C.G., Kahya, N., Jahn, R., Schwille, P., 2004. SNAREs prefer liquid-disordered over “raft” (liquid-ordered) domains when reconstituted intogiant unilamellar vesicles. J. Biol. Chem. 279, 37951–37955.

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