Surface Binding Affinity Measurements from Order Transitions of Lipid

Anal. Chem. 2006, 78, 174-180

Surface Binding Affinity Measurements from Order Transitions of Lipid Membrane-Coated Colloidal Particles Esther M. Winter†,‡ and Jay T. Groves*,‡,§,|

Departments of Chemistry and Chemical Engineering, University of California, Berkeley, California 94720, and Physical Biosciences and Materials Sciences Divisions, Lawrence Berkeley National Laboratory, Berkeley, California 94720

Lipid bilayers can be assembled onto the surface of colloidal silica particles to form a continuous and fluid supported membrane coating. In this configuration, the collective behavior of the colloidal dispersion is governed by interactions between particles and exhibits a sensitive dependency on chemical features of the membrane surface. Protein binding to membrane surface receptors can trigger macroscopic changes in the colloidal order, which provides a label-free readout of such binding events. Here, the relationship between order in the colloidal dispersion and the surface concentration of bound protein is characterized quantitatively in terms of the radial pair distribution function. Using parallel fluorescence measurements for comparison, we construct a scalar measure of the distribution function that exhibits linear proportionality with surface protein binding. This is used to determine binding affinity based only on observations of the colloidal distribution. Roughly one-third of the human genome codes for proteins that naturally reside in the cell membrane.1 These include protein classes of substantial pharmacological significance such as G protein coupled receptors, ion channels, and receptor tyrosine kinases, as well as cell adhesion and immune recognition molecules. Membrane proteins are notoriously difficult to study at the molecular level due to the fact that their structure and chemical functionality is often substantially influenced by the surrounding lipid membrane environment. As a result, there is significant interest in the development of analytical technologies that enable characterization of membrane proteins in membranes. One system, which has proven successful in a number of applications, is the supported membrane.2,3 Lipid bilayer vesicles spontaneously adsorb and fuse on certain substrates, such as silica. The resulting supported bilayer forms a single, continuous membrane that uniformly coats the substrate. The membrane is typically separated from the underlying substrate by a thin film * To whom correspondence should be addressed. E-mail: [email protected]. † Department of Chemical Engineering, University of California. ‡ Physical Biosciences Division, Lawrence Berkeley National Laboratory. § Department of Chemistry, University of California. | Materials Sciences Division, Lawrence Berkeley National Laboratory. (1) Venter, J. C.; et al. Science 2001, 291, 1304-1351. (2) Sackmann, E. Science 1996, 271, 43-48. (3) Groves, J. T. Curr. Opin. Drug Discovery Dev. 2002, 5, 606-612.

174 Analytical Chemistry, Vol. 78, No. 1, January 1, 2006

of hydration water, which prevents individual lipid molecules from directly adsorbing onto the substrate. In this way, the natural bilayer structure of the membrane is preserved, along with its lateral fluidity. Membrane proteins can be incorporated into supported bilayers, and their functionality has been confirmed in a number of cases.4-9 Supported membranes can be formed on monolithic substrates or on micrometer-sized colloidal silica particles, and the two configurations are essentially equivalent.10-12 Dispersions of membrane-coated colloidal particles exhibit an interesting interconnectivity between molecular-level associations on the membrane surface and the macroscopic behavior of the colloidal distribution. We have recently demonstrated that protein binding to the membrane surface receptors can trigger order transitions in colloidal monolayers.12 This process can be very sensitive due to intrinsic amplification provided by the cooperative nature of the order transition. Furthermore, the distribution of the colloid is easily measured by bright-field imaging with a simple, low-magnification optical microscope. Colloidal order transitions provide possibilities as label-free readouts for molecular binding on membrane surfaces. This is attractive in light of the fact that existing technologies for such detection, such as surface plasmon resonance,13-16 the quartz crystal microbalance,17,18 or ellipsometry,19 require relatively complex instrumentation and have limited sensitivity. (4) Brian, A. A.; McConnell, H. M. Proc. Natl. Acad. Sci. U.S.A. 1984, 81, 61596163. (5) Salafsky, J.; Groves, J. T.; Boxer, S. G. Biochemistry 1996, 35, 14773-14781. (6) Fang, Y.; Frutos, A. G.; Lahiri, J. J. Am. Chem. Soc. 2002, 124, 2394-2395. (7) Fang, Y.; Frutos, A. G.; Lahiri, J. ChemBioChem 2002, 3, 987-991. (8) Goennenwein, S.; Tanaka, M.; Hu, B.; Moroder, L.; Sackmann, E. Biophys. J. 2003, 85, 646-655. (9) Tanaka, M.; Wong, A. P.; Rehfeldt, F.; Tutus, M.; Kaufmann, S. J. Am. Chem. Soc. 2004, 126, 3257-3260. (10) Bayerl, T. M.; Bloom, M. Biophys. J. 1990, 58, 357-362. (11) Buranda, T.; Huang, J.; Ramarao, G. V.; Ista, L. K.; Larson, R. S.; Ward, T. L.; Sklar, L. A.; Lopez, G. P. Langmuir 2003, 19, 1654-1663. (12) Baksh, M. M.; Jaros, M.; Groves, J. T. Nature 2004, 427, 139-141. (13) MacKenzie, C. R.; Hirama, T.; Lee, K. K.; Altman, E.; Young, N. M. J. Biol. Chem. 1997, 272, 5533-5538. (14) Cooper, M. A.; Hansson, A.; Lofas, S.; Williams, D. H. Anal. Biochem. 2000, 277, 196-205. (15) Hoffman, T. L.; Canziani, G.; Jia, L.; Rucker, J.; Doms, R. W. Proc. Natl. Acad. Sci. U.S.A. 2000, 97, 11215-11220. (16) Karlsson, O. P.; Lofas, S. Anal. Biochem. 2002, 300, 132-138. (17) Janshoff, A.; Steinem, C.; Sieber, M.; Galla, H. J. Eur. Biophys. J. Biophys. Lett. 1996, 25, 105-113. (18) Janshoff, A.; Steinem, C.; Sieber, M.; elBaya, A.; Schmidt, M. A.; Galla, H. J. Eur. Biophys. J. Biophys. Lett. 1997, 26, 261-270. 10.1021/ac0514927 CCC: $33.50

© 2006 American Chemical Society Published on Web 11/24/2005

EXPERIMENTAL METHODS Performance of the colloid-based assay for membrane surface binding involves assembly of supported bilayer membranes containing membrane-associated receptors on silica microbeads and subsequent incubation with cognate ligands or test molecules. Beads are then dispersed underwater, into wells of a microtiter plate; 96-well plates were used for the experiments described here. Beads settle gravitationally, forming a colloidal monolayer, which can then be easily imaged using bright-field microscopy (Figure 1). Ligand binding on the membrane surface leads to measurable changes in the distribution of the colloid. Supported Lipid Bilayers. Supported lipid bilayers were formed by standard vesicle fusion techniques onto clean 6.8-µm silica beads (Bangs Laboratories, Fishers, IN).4,12 Lipids were mixed in chloroform and then dried onto round-bottom flasks followed by desiccation under vacuum for at least 2 h. Lipid films were then hydrated with 18.2 MΩ‚cm deionized (DI) water to 2 mg/mL at 4 °C overnight. Small unilamellar vesicles (SUVs) were formed by repeated extrusion through 0.1-µm polycarbonate filters using a Lipex extruder (Northern Lipids, Vancouver, Canada). A spreading solution was formed by combining equal volumes (typically 25 µL) of the SUV suspension with buffer (250 mM NaCl, 10 mM Tris at pH 7.4). Silica microbeads were used as received

at 10 wt % solids in DI water. Bilayers were assembled on beads by combining equal volumes (typically 25 µL) of bead stock solution with spreading solution in a small centrifuge tube, followed by pulse vortexing of the mixture. Excess vesicles were removed by rinsing several times with phosphate-buffered saline (PBS), diluted 0.5× at pH 7.4. The following lipids used were purchased from Avanti Polar Lipids (Alabaster, AL): 1,2-dimyristoleoyl-sn-glycero-3-phosphocholine (DMOPC), 1,2-dioleoyl-sn-glycero-3-ethylphosphocholine (DOEPC), and 1,2-dimyristoyl-sn-glycero-3-(phospho-L-serine) (DMPS). The fluorescent lipid probe Texas Red 1,2-dipalmitoylsn-glycero-3- phosphoethanolamine (Texas Red DPPE) was purchased from Molecular Probes (Eugene, OR), and the membraneassociated receptor monosialoganglioside GM1 (GM1) was purchased from Matreya, Inc. (Pleasant Gap, PA). The compositions (mole fraction) of the lipid bilayers studied are as follows: For binding experiments, the supported bilayer composition was 97.5% DMOPC, 1.5% DOEPC, 0.5% Texas Red DPPE, and 0.5% GM1; for control experiments for nonspecific binding, the supported bilayer composition was 98% DMOPC, 1.5% DOEPC, and 0.5% Texas Red DPPE; for size and membrane composition experiments, the supported bilayers were composed of varying amounts of DMOPC, DOEPC, DMPS, and 0.5% Texas Red DPPE. Protein Binding Assay. Lipid membrane-coated beads were incubated in 0.5× PBS (pH 7.4) with varying concentrations of fluorescently labeled (FITC) cholera toxin subunit B (CTB; Sigma, St. Louis, MO) for 45 min under continuous gentle mixing. All incubations were performed in the dark at 22 °C. Concentrations of CTB were varied over 3 orders of magnitude. A typical sample contained ∼8.0 × 106 beads, giving a total bilayer area of 1.18 × 109 µm2 and ∼8 × 103 molecules of GM1/µm2. Maximum protein coverage would occur if there were one to one binding between a CTB pentamer and GM1; however, it is more likely that each CTB pentamer binds to multiple membrane-associated GM1 molecules thereby reducing the actual surface concentration of CTB below maximal coverage. On average, three GM1 molecules are bound to each CTB pentamer at low incubation concentrations.20 After incubation, the beads were rinsed in DI water, diluted to desired bead concentration, and pipetted into Costar 96-well plates (Corning Inc., 3997). Well plates were left undisturbed on the microscope stage for 15 min before imaging to allow the beads to settle, gravitationally, to an equilibrium height21 above the underlying substrate and for the resultant colloidal monolayer to attain its near-equilibrium lateral distribution. Alternatively, protein binding to the membrane-coated beads can also be accomplished through addition of protein solution to preformed colloids; however, this format has proven to be less streamlined and consequently is not used in these studies. Microscopy and Image Analysis. Bright-field and fluorescence images were acquired with 10× air and 100× oil objectives, respectively, at room temperature using a Nikon TE-300 inverted fluorescence microscope with a Hamamatsu ORCA 2 chargecoupled device camera (Hamamatsu, Tokyo, Japan) and Simple PCI acquisition software (Compix, Inc.). Bright-field movies of dilute solutions of beads were also acquired with a 40× air objective for bead diffusion measurements. Images and movies

(19) Spaargaren, J.; Giesen, P. L. A.; Janssen, M. P.; Voorberg, J.; Willems, G. M.; Vanmourik, J. A. Biochem. J. 1995, 310, 539-545.

(20) Lauer, S.; Goldstein, B.; Nolan, R. L.; Nolan, J. P. Biochemistry 2002, 41, 1742-1751. (21) Clack, N.; Groves, J. T. Langmuir 2005, 21, 6430-6435.

Figure 1. Illustration of colloid assay. Protein binding to membrane receptors on membrane-coated colloids triggers an order transition from a condensed state to a more disordered state.

In the experiments described here, the relationship between order in the colloidal dispersion and the surface concentration of bound protein is characterized quantitatively. By using a fluorescently labeled protein, parallel fluorescence measurements provide an independent determination of surface-bound protein concentration for comparison with the corresponding order in the colloidal distribution. Image correlation is employed to determine the spatial positions of each particle in the colloidal distribution, and these are compiled into radial pair distribution functions. We then construct a scalar measure of the distribution function that exhibits linear proportionality with surface protein binding. This construction enables determination of protein-membrane ligand binding affinity from observations of the colloidal distribution alone.

Analytical Chemistry, Vol. 78, No. 1, January 1, 2006

175

were analyzed using Adobe Photoshop (Adobe Systems), Metamorph (Universal Imaging), IDL (Research Systems, Inc.), and Matlab v. 6.5 (Mathworks) software. Data processing of the bright-field images consisted of two steps. First, rough estimates of the lateral positions of individual particles within the field of view were determined by applying a threshold. Next, intensity surfaces of individual particles were bicubicly interpolated to get a continuous surface, which was then used to refine the positions of the individual particles through a maximization procedure. The precision of the particle location algorithm was determined through experimental and computational techniques. Position measurements of particles in a high ionic strength (250 mM NaCl) solution, where they are essentially immobile due to electrostatic interactions with the underlying substrate, varied by 80 nm. This represents an upper bound on error as adherent particles also have some real thermal motion. Error estimates using computational bootstrapping and the introduction of zero-mean Gaussian noise (1% variance) both indicated that the positions could be resolved with a precision of 25 nm. Fluorescence image analysis was performed by first thresholding images for bright objects. The average intensity of individual thresholded particles was then determined. Finally, average per particle intensities were determined by averaging the intensities of ∼100 individual particles from each sample. There is low, but measurable (5% of maximum signal), nonspecific binding to membrane without GM1; this background was subtracted from sample measurements to obtain the net signal from specific binding of CTB to membrane-associated GM1 (0.5%). RESULTS AND DISCUSSION Colloid Assay. It has recently been established that protein binding to cognate membrane-associated receptor on membranecoated beads alters the distribution of a colloidal monolayer.12 Here, we quantitatively analyze the relationship between order within the colloidal distribution and the degree of binding. This information is then interpreted to determine the binding affinity between a membrane-associated receptor-ligand pair. Binding of soluble cholera toxin to membrane-associated GM1 is chosen as a test case to characterize the colloidal assay. Cholera toxin, which is produced by Vibrio cholerae, is an AB5 hexameric protein. The B-pentamer (CTB) consists of five binding sites, each of which can bind specifically to the pentasaccharide headgroup of membrane-associated GM1, resulting in multivalent interactions and enhanced affinity.22 Dispersions of beads, in which the coating membrane has a net content of ionizable groups near neutral or negative, form condensed distributions in the absence of bound protein. At colloidal monolayer area fractions, φ, greater than 0.15, hexagonally packed crystallites predominate the dispersion. Inclusion of 0.5% GM1 does not effect this behavior. In contrast, CTB binding to the membrane surface triggers the formation of more dispersed monolayers; several representative colloidal distributions are illustrated in Figure 2A. Control experiments using beads coated with membranes that lack membrane-associated GM1 confirm that condensed structures persist after CTB incubation (see Supporting Information). The observed transitions are driven by membrane surface-bound CTB and exhibit ligand specificity. (22) Spangler, B. D. Microbiol. Rev. 1992, 56, 622-647.

176

Analytical Chemistry, Vol. 78, No. 1, January 1, 2006

Figure 2. Colloid binding assay for 0.5 mol % GM1 membranecoated silica microbeads. (A) Images depict varying states of clustering upon binding of CTB to GM1 at specified incubation concentrations of CTB. (B) Plots of corresponding g(r). (C) G versus CTB concentration. The solid line represents a least-squares fit of the data.

Quantitative comparison of colloidal dispersions, which had been exposed to a range of solution protein concentrations, was performed by optically imaging the colloidal monolayer and determining the radial pair distribution function, g(r). This function represents the correlated probability of finding the center of a particle at a given distance from the center of another particle. An object-locating algorithm was used to measure bead positions to a precision of 25 nm from bright-field images (1020 × 816 µm2) containing ∼7000 individual beads per image, corresponding to a bead area fraction of φ ) 0.30; standard deviations in φ from independent images were typically less than 10%. Distribution functions were generated by averaging 5-10 independent images. For a finite rectangular window of spatial dimensions X by Y, g(r) can be computed from

g(r) )

η(r)X2Y2 N(N - 1) δr[πXYr - 2(X + Y)r2 + r3]

(1)

where η(r) is the number of bead pairs with separation distance r ( δr/2 and N is the total number of beads.12 Plots of g(r) for a range of solution CTB incubation concentrations are shown in Figure 2B. The magnitudes of the peaks in g(r), which provide a measure of spatial order in the dispersion, vary continuously with the concentration of CTB bound to the membrane surface. A range of intermediate distributions, corresponding to differing degrees of binding site saturation, are readily

observable. The ability to distinguish intermediate distributions may be a result of an equilibrated two-phase system, where a range of distributions would be observable based on the proportion of dispersed (gas) to condensed (liquid or crystalline) phases of the colloidal monolayer. Alternatively, density fluctuations of a monophase distribution may be responsible for the observed structure. Though interesting, distinction between these physical nuances of the system is not essential for its development as an assay. When designing an assay to measure ligand-receptor binding, the observable must exhibit predictable scaling behavior that can be related to the fraction of occupied surface sites. For the case of the colloidal assay, the peaks in g(r) satisfy this criterion, as can be seen clearly by looking at the peak magnitudes in Figure 2B. To quantify the relationship between g(r) and the concentration of surface-bound CTB, we use fluorescently labeled CTB so that an independent measure of surface-bound CTB can be made by fluorescence imaging and intensity analysis. Both assays were performed simultaneously on the same beads, and as described below, the results yield comparable measures of binding affinity. Surface binding data can be fit to a Langmuir isotherm providing a reasonable approximation for the equilibrium dissociation constant for single-site interactions. For example, the binding between a monovalent ligand, L, in solution with a membrane surface receptor binding site, R, results in the formation of a membrane-associated complex, LR, which can be described by the following dynamic equilibrium

[L] + [R]s h [LR]s

(2)

For this interaction, the fraction of bound sites can be expressed as

[LR]s [S]s

)

[L] KD + [L]

first peak in g(r):

∫ g(r)rdr G≡ ∫ rdr r1

d

r1

(4)

d

where d is the mean bead diameter and r1 is the outer radius of the first coordination shell of a central bead. Physically, G represents the degree of correlation in the first coordination shell and is immediately sensitive to interactions between beads. For the data presented here, d ) 6.8 µm and r1 ) 10.9 µm. We chose r1 to be the average distance at which the local minimum value in the g(r) function is found between the first and second peaks for samples that condense strongly (incubation [CTB] e 10 nM). For consistency, r1 is kept constant throughout the analysis. Changing r1 shifts the values of G for all samples in the same direction but does not affect the general trend in the data. G varies inversely with the bound CTB concentration due to the fact that the colloidal phase is more condensed at lower concentrations of bound CTB, as seen in Figure 2C. For convenience in binding data analysis, we define G ˜ to be the normalization of G: G ˜ ) (G - Go)/G∞ where G∞ is a scaling factor representing the value of G at saturation of the surface sites and Go is an offset constant to account for the nonzero value of G for the unbound state. To relate G ˜ to an independent measure of surface-bound CTB, quantitative measurements of fluorescence from the FITC label on CTB were performed. Typical fluorescence images are shown in Figure 3A. Binding of CTB to GM1 shows characteristic saturation of surface binding sites. In the absence of GM1, nonspecific binding of CTB to the membrane surface is low. At the low surface densities of GM1 that were used, the measured

(3)

where [S]s ) [R]s + [LR]s is the total concentration of binding sites and KD is the dissociation constant for the reaction. For multivalent ligands, such as cholera toxin (or antibodies), the overall binding affinity depends on the valency of the interaction as well as on the receptor concentration in the membrane.20,23,24 Nevertheless, the Langmuir isotherm approach can be adopted for multivalent interactions, with the resulting parameter of interest being an effective dissociation constant. This corresponds to the bulk concentration of protein at which halfmaximal coverage of the surface binding sites is achieved and provides a description of the binding avidity of multivalent interactions. For multivalent analysis, KD in eq 3 would correspond to an effective dissociation constant. This approach is used in the present analysis. Numerous scalar measures of the two-dimensional function g(r) may be constructed, and each will exhibit a characteristic scaling with the concentration of surface-bound protein. As a simple choice of measure, we calculated a radial integral over the (23) Pisarchick, M. L.; Thompson, N. L. Biophys. J. 1990, 58, 1235-1249. (24) Yang, T. L.; Baryshnikova, O. K.; Mao, H. B.; Holden, M. A.; Cremer, P. S. J. Am. Chem. Soc. 2003, 125, 4779-4784.

Figure 3. Comparison of colloid assay to fluorescence read-out. (A) Fluorescence images of labeled CTB binding to bilayers containing 0.5% GM1. (B) Normalized colloid (circles, red) and fluorescence (squares, black) binding data for CTB binding to GM1. The solid lines represent least-squares fits of the fluorescence and colloid data to eqs 5 and 6 giving effective KD values of 30 ( 6 and 31 ( 9 nM, respectively. The effective KD is the bulk CTB concentration at which half-maximal binding is achieved.

Analytical Chemistry, Vol. 78, No. 1, January 1, 2006

177

fluorescence intensity is expected to be directly proportional to the amount of protein bound to the membrane surface, even at saturation. The surface-normalized fluorescence, F˜ ) (F - Fo)/ F∞, which corresponds to the fraction of bound sites, can be used to calculate the effective KD from

F ˜)

[L] KD + [L]

(5)

where F is the fluorescence intensity at a solution concentration of fluorescently labeled ligand L, Fo is the background intensity, and F∞ is the maximum fluorescence intensity at saturation of the membrane with labeled protein. At low incubation concentrations, three GM1 molecules on average are bound to each CTB pentamer.20 Free CTB concentrations were calculated from the fraction of bound sites by assuming that each CTB bound to three GM1 molecules. This method provides an estimate for the bulk CTB solution concentration. A more accurate effective KD could be obtained using a more involved treatment of multivalency. However, this has no impact on the comparison of colloidal distribution and fluorescence binding data, which is our primary purpose here and therefore was not considered further. The Levenberg-Marquardt algorithm was used to obtain a least-squares fit of the fluorescence data to eq 5, where [L] is the free CTB concentration, giving an effective KD of 30 ( 6 nM and shown in Figure 3B as a solid, black line. To obtain the effective KD from the distribution functions, a relation between G ˜ and the fraction of occupied surface sites is required. Although there is no a priori reason to assume a linear scaling, this is the simplest mapping and yields the trivial equivalency: G ˜ ) F˜ . Comparison of experimental values of F˜ and G ˜ confirm this equivalency, as shown in Figure 3B. A nonlinear least-squares fit of the calculated G ˜ data to [L]/(KD + [L]) yields an effective KD of 31 ( 9 nM, shown in Figure 3B as a solid, red line. Various nonlinear scaling relations were explored, but the simple linear equivalency proved most accurate. Therefore, KD may be determined from measurements of the colloidal distribution by

G ˜ )

[L] KD + [L]

(6)

Tighter effective KD values have been reported for CTB-GM1 binding by surface plasmon resonance13 (effective KD ) 1.7 nM at 2% GM1) and flow cytometry20 (effective KD ∼5 nM at 0.5% GM1) studies than those obtained by the fluorescence and colloid assays outlined here. Because CTB is a multivalent molecule, the effective KD will depend on the receptor density used. Therefore, it is possible that some of the discrepancies in effective KD values may be due to the fact that the assays were performed at various surface densities of GM1. Other factors, such as buffer conditions and membrane surface charge, can also modulate the effective KD. In general, the values of KD measured here are consistent for this system. Bead Mobility Analysis. Further experiments were conducted to examine the mechanisms by which protein binding influences g(r). In particular, we explored whether protein binding 178

Analytical Chemistry, Vol. 78, No. 1, January 1, 2006

Figure 4. Diffusion of membrane-coated microbeads. (A) Trajectory of a lipid membrane-coated bead. Distributions of lateral measured diffusion coefficients for 0.5% GM1 membrane-coated beads without bound CTB (B) and with 500 nM CTB incubation (C). The mean diffusion coefficients are comparable with (0.034 µm2/s) and without (0.035 µm2/s) bound protein. Solid curves represent the calculated probability distributions of measured diffusion coefficients from finite trajectories and are calculated with N ) 70. The observed distributions are consistent with a single infinite-time diffusion coefficient for the populations.

to the bead surface alters interactions between the bead and the underlying substrate by characterizing the Brownian motion of individual beads. Detailed analysis of the distribution of measured diffusion coefficients, Dm, from individual Brownian trajectories allows characterization of the heterogeneity in the true mean diffusion coefficient, Do. The probability distribution for the measured diffusion coefficient of a random two-dimensional walk consisting of N steps is given by25,26

p(Dm) )

()

( )

-NDm N N N-1 1 Dm exp D Do (N - 1)! o

(7)

For each trajectory, the mean squared displacement was calculated by averaging over independent steps for a given time lag. The experimental diffusion coefficient for each trajectory was calculated from Dm ) 〈r2〉/4t and used to generate histograms of diffusion coefficients. A representative trajectory, histograms, and probability distributions of diffusion coefficients of lipid-coated beads with and without bound CTB are shown in Figure 4. Observations (25) Saxton, M. J. Biophys. J. 1997, 72, 1744-1753. (26) Vrljic, M.; Nishimura, S. Y.; Brasselet, S.; Moerner, W. E.; McConnell, H. M. Biophys. J. 2002, 83, 2681-2692.

Figure 5. (A) Bright-field images of colloidal distributions of 2.3-, 5.1-, and 6.8-µm-diameter beads. (B) Corresponding plots of g(r).

indicate a homogeneous value for Do of 0.035 µm2/s, which is not influenced by protein binding. Do is lower than predicted by the Stokes-Einstein relation (0.066 µm2/s for 6.8-µm beads). In these experiments, lower mobilities likely reflect weak hydrodynamic drag on the underlying substrate, which is not appreciably altered by ligand binding to the membrane surface. Thus, we conclude that effects of protein binding on g(r) are due to interactions between the bead membrane surfaces and not the underlying substrate. Parameter Optimization. The assay described here hinges on preparation of a colloidal distribution that exhibits marked changes in order as a function of molecular binding on the particle surfaces. A variety of parameters can influence the colloidal behavior. Effects of bead size, monolayer area fraction, and the composition of the coating lipid membrane are discussed below. In its present format, the assay utilizes two-dimensional colloidal dispersions formed by gravitational settling. As such, beads must be sufficiently massive to form a well-confined monolayer while not being so heavy that they overwhelm electrostatic repulsion and adhere directly to the underlying substrate. Solid silica beads with diameters in the 2-7-µm range proved most useful. Within this range, the larger beads more readily form condensed structures for a given membrane composition (Figure 5). This trend is expected in light of that fact that pair interaction energies scale with particle size while the entropy-driven tendency to disperse is an essentially sizeindependent function of temperature. For the systems studied, including bacterial toxins and antibodies, protein binding to the membrane surface favors more dispersed distributions. Thus, greater dynamic range in measurement is achieved using larger beads (6.8 µm), to maximize the degree of order in the proteinfree state. The radial pair distribution function, g(r), for a particular colloidal dispersion is compiled from a histogram of pair separation distances. As such, the noise in its determination scales inversely with xN. For the laboratory conditions under which the assays described here were performed, colloidal monolayers covering

Figure 6. Colloid binding assay at φ ∼ 0.55. (A) Bright-field images of varying states of clustering upon binding of CTB to GM1 at 1 and 1000 nM incubation concentrations of CTB. (B) Plots of corresponding g(r). The inset depicts an expanded view of the higher order peaks.

relatively large areas (5-10 mm2) could easily be imaged and noise in the determination of g(r) was not a limiting factor. Using the first peak in g(r) as a measure (as defined in eq 4), the maximum dynamic range is achieved at intermediate area fractions of roughly φ ∼ 0.3. For implementations of the assay in which small imaging areas are more desirable, using higher φ can yield more precise determination of g(r). However, the colloidal distribution itself, as well its sensitivity to binding events on the membrane surface, also exhibits a functional dependence on φ. In a dense monolayer, crowding effects can lead to ordering, giving rise to a strong primary peak in g(r) that exhibits reduced variation with protein binding. The data depicted in Figure 6 illustrate this effect at φ ∼ 0.55. At this density, the higher order peaks in g(r) show a stronger dependence on protein binding and can be used as alternative measures; see the inset in Figure 6B. The most critical parameter governing the structure of the colloidal dispersion is the membrane composition itself. This determines the baseline behavior of the system, around which perturbations due to molecular binding events can be observed. It is important to poise the system near an order transition. Finetuning of the system can be achieved by adjusting the net charge content in the membrane. Baseline g(r) plots, without bound protein, for a series of colloidal dispersions with differing membrane composition are illustrated in Figure 7, along with several representative images. The membrane composition was varied from net 5% negative ionizable lipids to net 3% positive ionizable lipids by adjusting the concentration of negative DMPS and positive DOEPC lipids in the membrane. The actual surface charge of the membrane is determined by a number of factors including the ionization state of the lipids and charge contributions from the underlying substrate. The total surface charge for all of the compositions studied is negative, as determined by the fact that the colloids are electrostatically repelled from and levitate above negatively charged glass surfaces. Nonetheless, adding Analytical Chemistry, Vol. 78, No. 1, January 1, 2006

179

composition of 0.5% positive to maximize the sensitivity of the distribution to perturbations resulting from protein binding. CONCLUSION Several characteristics of the dispersions of membrane-coated particles described here are unusual among colloidal systems. Most importantly, we note that the particles do not irreversibly aggregate. Although the more ordered states consist of hexagonal crystallites, their structures are dynamic. Brownian movement of individual particles within crystallites is clearly visible, and binding of protein to the membrane surface of particles in the clustered state triggers a dynamic transition to more dispersed configurations. This tendency of micrometer-sized membrane-coated particles to interact weakly is likely to be an important contributor to the wide range in the degree of ordering experimentally observed here. Our analysis of the quantitative relationship between colloidal order and protein binding on the membrane surface reveals that molecular binding affinities can be accurately determined from observations of the colloidal structure. Although the physical origins of the system behavior are not fully understood, order transitions in membrane-coated colloids provide a simple and label-free strategy to assay protein binding on membrane surfaces. Figure 7. Membrane composition panel. The composition of the membrane was changed by varying the mole percentage of negatively charged DMPS and positively charged DOEPC in the membrane coating the beads. The compositions of the membranes are reported in mole percent of net ionizable lipid, z. (A) Representative images of membrane-coated beads at different membrane compositions. (B) Corresponding plot of g(r).

DMPS makes the surface more negative while adding DOEPC makes it less negative. These effects may be highly nonlinear, but are monotonic.21 Irrespective of details of the actual surface charge, the behavior of the colloid can be adjusted over a wide range with these composition modulations. A sharp order transition occurs between 0 and 1% net positive composition under these experimental conditions. Accordingly, we chose a membrane

180 Analytical Chemistry, Vol. 78, No. 1, January 1, 2006

ACKNOWLEDGMENT This work was supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract DE-AC03-76SF00098. We thank Michael Baksh and Nathan Clack for assistance with the size panel and precision analysis, respectively. SUPPORTING INFORMATION AVAILABLE Colloid binding data for membranes without GM1. This material is available free of charge via the Internet at http://pubs.acs.org. Received for review August 18, 2005. Accepted October 11, 2005. AC0514927