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Address correspondence to John F. Hancock, Dept. of Pathology, University of Queensland Medical School, Herston Rd., Herston, Brisbane, Queensland 4006, Australia. Tel.: 61-7-3365-5288. Fax: 61-7-3365-5511. E-mail: j.hancock{at}mailbox.uq.edu.au; or Robert G. Parton, Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland 4072, Australia. Tel.: 61-7-3365-6468. Fax: 61-7-3365-4422 E-mail: r.parton{at}imb.uq.edu.au
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Abstract |
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Key Words: cholesterol; lipid rafts; immunogold; electron microscopy; statistical analysis
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Introduction |
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Ras GTPases play critical roles in transducing extracellular signals (Campbell et al., 1998). The Ras isoforms (H-ras, N-ras, and K-ras) are ubiquitously expressed in mammalian cells, and although highly homologous, these proteins generate different signal outputs (Bos, 1989; Umanoff et al., 1995; Koera et al., 1997; Yan et al., 1998). This biochemical diversity may result from differential lateral segregation of Ras proteins across plasma membrane microdomains that is regulated by their different COOH-terminal membrane anchors (Roy et al., 1999; Prior et al., 2001; Niv et al., 2002). We have directly investigated this hypothesis by mapping the cell surface distributions of Ras proteins at high resolution.
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Results and discussion |
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We tested if GFP-tH microdomains are cholesterol-dependent by treating cells with methyl-ß-cyclodextrin. Depletion of cell surface cholesterol, visualized by filipin staining (Fig. 1 b), did not cause any loss of GFP-tH from the plasma membrane, assessed qualitatively by fluorescence or quantitatively by immunogold labeling (not depicted). However, K-function analysis of the gold patterns reveals a time-dependent loss of GFP-tH clustering in cyclodextrin-treated cells (Fig. 1 c). After 60 min of cyclodextrin treatment, L(r) - r tracks at zero over most of the r range analyzed, indicating a random distribution. These results confirm that GFP-tH is localized to cholesterol-rich lipid rafts and reveals that their disruption disperses GFP-tH over the plasma membrane, rather than driving association with other microdomains. The presence of rafts in the extracellular leaflet of the plasma membrane was supported by studies showing cholesterol-dependent clustering of glycophosphatidylinositol (GPI)-anchored proteins (Friedrichson and Kurzchalia, 1998; Harder et al., 1998; Varma and Mayor, 1998), but similar data for rafts in the intracellular leaflet have been lacking until now.
Next, we examined the relationship between inner- and outer-leaflet lipid rafts using a variation of the K-function analysis. When plasma membrane sheets are labeled for two different antigens with 2 nm and 45 nm gold, colocalization can be assessed using bivariate K-functions that determine whether one gold population is clustered with respect to the other (Diggle, 1986; see Materials and methods and supplementary data). We compared the distribution of GFP-tH with the outer-leaflet raft marker GFP-GPI; Fig. 2). Both proteins are GFP-tagged, but because only one membrane surface is exposed at any point in the labeling and rip-off procedure, no leakage of gold probes occurs (unpublished data). We used two protocols to induce different degrees of GFP-GPI aggregation, as revealed by univariate K-function analysis of the 2-nm gold patterns (Fig. 2 c). The semi-patched technique induces relatively little GFP-GPI aggregation (univariate K-function shows a mean cluster radius of 50 nm), whereas the patched protocol, routinely used to visualize lipid rafts by immunofluorescence, induces very large GFP-GPI aggregates (univariate K-function shows a mean radius of 180 nm). It is not possible to completely evaluate unpatched GFP-GPI because this necessitates ripping off apical membranes from prefixed cells, a technique that has to date proven unsuccessful. The bivariate K-function shows that there is significant colocalization of GFP-tH with GFP-GPI only when GFP-GPI is aggregated into very large patches (Fig. 2 d); this is indicated by significant positive deflections of the Lbiv(r) - r curve from zero. Interestingly, despite the major reorganization of GFP-GPI, GFP-tH remains in small clusters (Fig. 2 b; univariate K-function for GFP-tH). This result indicates that inner leaflet rafts, when aggregated by cross-linking GPI-anchored proteins, still retain their modular structure. Incomplete colocalization of inner- and outer-leaflet raft markers has been observed previously (Harder et al., 1998; Prior et al., 2001), but our new data show that inner and outer leaflet rafts are only loosely associated at steady state. This offers a potential new mechanism for regulating signaling by modulating the extent of coupling between inner and outer leaflet rafts.
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Little is known about the microlocalization of K-ras targeted by a polylysine domain and a farnesylated CAAX motif. Therefore, we investigated the distribution of GFP fused to the minimal plasma membrane targeting motifs of K-ras, GFP-tK. Analysis of the GFP-tKlabeled gold patterns reveals that they are clustered, but with different characteristics from GFP-tH (Fig. 4 a and Fig. S1 a). Modeling establishes that the GFP-tK domains have a mean radius of 16 ± 3 nm and occupy 20% of the plasma membrane (Fig. S1 c). In contrast to GFP-tH, cholesterol depletion causes a small rise in GFP-tK clustering after 60 min of cyclodextrin treatment. The subtle effect of cyclodextrin on GFP-tK microdomains may reflect a general role of cholesterol in maintaining overall plasma membrane integrity. Wild-type and constitutively active K-ras show identical clustering to GFP-tK, both in the presence or absence of cyclodextrin (unpublished data), and bivariate analysis of plasma membranes coexpressing GFP-tH and activated K-rasG12V showed no significant colocalization of the lipid raft marker with K-ras (Fig. 4 b). Clustering of GFP-tK and K-ras was unexpected, although biophysical studies have shown that myristoylated polybasic peptides can sequester negatively charged lipids to generate novel membrane domains (Murray et al., 1999). Intriguingly, clustering was strikingly reduced when the wild-type K-ras CVIM motif was replaced with CCIL, to direct geranylgeranylation rather than farnesylation (Fig. 4 c, GFP-tKCCIL). Therefore, our data show that nature of the prenoid group profoundly affects the ability of polybasic K-ras to organize into specific microdomains, an observation that may have functional implications. Overall, our results clearly demonstrate for the first time the existence of K-ras microdomains within disordered plasma membranes that are distinct from classical lipid rafts.
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A recent study has shown GFP dimerization compromises FRET analysis of proteinprotein interactions in intact cells and results in lipid rafttargeted CFP and YFP continuing to display FRET when cells are treated with cyclodextrin (Zacharias et al., 2002). In contrast, we show here that GFP-tH clustering detected by immunogold labeling is completely abolished with cyclodextrin treatment; thus, if GFP is dimerized, then the gold-labeled antibody must detect only one GFP epitope in the dimer. Therefore, this loss of clustering is an important validation of the methodology, and clearly demonstrates that potential antibody or GFP dimerizationinduced artifacts are not being observed. A similar argument applies to the loss of GFP-tK and GFPH-rasG12V clustering induced by changing the prenoid group or reducing galectin expression, respectively, and an analysis of non-GFP tagged Ras clustering with anti-Ras5 nm gold that yields very similar results to those observed with GFP-tagged Ras (unpublished data). Finally, we can also detect marked differences in the clustering of different transmembrane-anchored plasma membrane proteins, e.g., GFP-tagged EGF receptor is not clustered when expressed at relatively low levels (Fig. S3 c), whereas GFP-tagged Angiotensin II, Type 1 receptor, a G proteincoupled receptor, is highly clustered when expressed at comparable levels (unpublished data).
In summary, we have integrated powerful statistical analysis tools with high resolution imaging of morphologically featureless plasma membrane microdomains. This has allowed us to characterize both the size and distribution of lipid rafts and Ras signaling domains at steady state. Ras proteins are clustered irrespective of activation state, and occupy multiple raft and nonraft microdomains that are extensively distributed over the cell surface. K-ras requires the farnesyl moiety for correct microlocalization, whereas galectin-1 stabilizes H-ras interactions with nonraft microdomains. Our analysis shows that activated H-ras and activated K-ras largely occupy spatially distinct plasma membrane signaling domains; an observation that could account for differential signal outputs from these highly homologous proteins. Finally, the technique we have described has the potential to spatially map any epitope-tagged or endogenous signaling protein with respect to validated microdomain signposts. This will greatly facilitate studies dissecting the spatial organization and regulation of plasma membranebased signaling pathways.
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Materials and methods |
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Electron microscopy and image analysis
LipofectAMINETM-transfected cells were incubated overnight in serum-free medium and, if indicated, treated with 1% methyl-ß-cyclodextrin. Plasma membrane sheets were prepared, fixed with 4% PFA, 0.1% glutaraldehyde, labeled as described previously (Parton and Hancock, 2001), and photographed in an electron microscope (model 1010; JEOL USA, Inc.). 1.45-µm2 areas (for univariate analysis) or 0.725-µm2 areas (for bivariate analysis) of digitized negatives were processed using Adobe Photoshop® 5.0 Curves, Brightness/Contrast, and Airbrush tools to remove background. To ensure that all gold particles were counted, a 1-pixel line was inserted between conjoined particles. The coordinates of the gold particles were determined using NIH Image v1.82. For double-labeled areas, small gold was discerned from large gold by setting the limits for counting in NIH Image to values determined by precalibrating the gold fractions (see supplementary data). Gold densities were comparable within experiments, typically 500800 gold/µm2 for single-labeled and 100200 gold/µm2 for each gold probe in double-labeled areas.
GFP-GPI clustering
Cells were cotransfected with GFP-GPI and GFP-tH constructs. For semi-patching, cells on coverslips were incubated with anti-GFP2 nm gold for 45 min at 1012°C. To induce extensive aggregation (patched protocol), washed cells were then further incubated with antirabbit-CY3 for 45 min at 1012°C. After further washing, plasma membrane sheets were prepared and labeled with anti-GFP5 nm.
Statistics
The first order property of a point pattern is its intensity (=N/A), where N = number of points in the study area A. The second order property is characterized by Ripley's K-function K(r), where the expected number of neighbors N(r) within a distance r of any point in A is given by N(r) =
K(r). Thus, K(r) = N(r)/
, and it normalizes N(r) for the density of the pattern. Our question is whether gold particles are clustered or exhibit CSR. In random patterns, at any distance r, the expected value of N(r) is
r2, so K(r) =
r2. If K(r) >
r2, the gold particles have more neighbors than expected from CSR, i.e., are clustered. We use a linear transformation of K(r) where L(r) =
K(r)/
(Besag, 1977). L(r) - r is readily interpreted because under CSR, the expected value of L(r) - r = 0 for all values of r. We estimate N(r) as the mean value calculated over A, taking into account border effects (Ripley, 1977), and calculate L(r) - r from the above equations. When there are big gold (Nb) and small gold (Ns) in A, their distributions are described by three K-functions. The univariate functions Kb(r) and Ks(r) are used as above. The bivariate function Kbiv(r) maps distances from each big to each small gold particle, and vice versa. Kbiv(r) examines whether either gold particle population, at a distance r, is clustered around the other; a test of colocalization (Diggle, 1986). Significance tests of L(r) - r and Lbiv(r) - r are performed by Monte Carlo methods (Besag and Diggle, 1977; for review see supplementary data).
Online supplemental material
Online supplemental material includes additional information on statistical methods, modeling of gold point patterns, and calibration of gold sizes. Figs. S1 and S2 show modeling data to estimate the size and distribution of GFP-tH and GFP-tK microdomains. Fig. S3 shows that glutaraldehyde fixation abolishes antibody-induced clustering, that probe size is important for detecting clustering, and that GFP-tagged EGF receptor is unclustered. Fig. S4 illustrates an example of gold size calibration, necessary for double-labeling studies. Online supplemental material available at http://www.jcb.org/cgi/content/full/jcb.200209091/DC1.
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Footnotes |
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* Abbreviations used in this paper: CSR, complete spatial randomness; GPI, glycophosphatidylinositol.
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Acknowledgments |
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This work was supported by grants from the National Health and Medical Research Council (NHMRC) of Australia to J.F. Hancock and R.G. Parton, and a UQ Early Career Research Grant to I.A. Prior. R.G. Parton is a Principal Research Fellow of the NHMRC. The Institute for Molecular Bioscience is a Special Research Centre of the Australian Research Council.
Submitted: 18 September 2002
Revised: 4 December 2002
Accepted: 4 December 2002
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