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* Laboratoire Chimie Bioinorganique Médicale, Institut Universitaire Technologique Paul Sabatier, Castres, France; Institut Curie, Laboratoire Physico Chimie Curie, Centre National de la Recherche Scientifique, Unité Mixte de Recherche 168, Paris, France; and Laboratoire de Physique Statistique–Ecole Normale Supérieure, Paris, France
Correspondence: Address reprint requests to Nelly Henry, Institut Curie, CNRS, UMR 168, Physico Chimie Curie, 11 rue P. et M. Curie, Paris, France 75005. Tel.: 33-01-42-34-6495; E-mail: nelly.henry{at}curie.fr.
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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INTRODUCTION |
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; Verkhivker et al, 2002; Pierres et al., 2002; Jung et al., 2000). Ever-increasing amounts of structural data (Stuart and Jones, 1995) and the emergence of single molecule approaches have particularly contributed to enlightening the field of molecular recognition (Merkel et al., 1999; Evans, 2001). However, in many instances, the recognition at the cell surface appears to involve much higher complexity through multiple factors such as cell surface composition and architecture, membrane mechanics, receptor dynamics and complexation, connection with the cytoskeleton network, etc. This becomes of most importance when the ligands themselves are presented to another surface, as is the case for many specific cell interactions such as those of cells in tissues (Gumbiner, 1996) or in immunological complexes such as those formed by T cells and antigen-presenting cells (van der Merwe, 2002). It now clearly appears that cell interactions engage molecular assemblies rather than unique ligand-receptor interaction (Hutchinson et al., 2003). Moreover, the cell surface is covered by the glycocalix—a hydrophilic, negatively charged, carbohydrate polymer layer whose thickness can reach up to several tens of nanometers depending on cell type (Braun and Fromherz, 1998). It very likely supports a steric repulsive barrier that avoids interactions with interfaces lacking specific complementary ligands or a sufficient number of positive charges (Chenevier et al., 2000; Ravaine et al., 2002). This surrounding layer actually creates a surface force field that superimposes the net receptor-ligand binding potential. Because of the high heterogeneity of the cell surface, this potential cannot be described by using the theoretical Derjaguin-Landau-Verwey-Overbeek approach or measured using surface-force techniques, as has been achieved with model surfaces (e.g., Leckband et al., 1994; Wong et al., 1997).
In this article, we report an experimental approach aimed to evidence a few consequences of the complex biological environment offered at the cell surface, on the formation of a well-known key-lock molecular link, the streptavidin-biotin bond. We depict the association characteristics of model macroscopic objects, constituted by streptavidin-covered micrometric beads, with the surface of a B-cell line. We chose to target the CD19 receptor, a B-cell-specific transmembrane glycoprotein of 80 kDa, which is involved in the MHC class II signaling complex (e.g., Lévéille et al., 2002; Bradbury et al., 1993) and the interaction with T cells. The link with streptavidin-covered particles is established through a biotinylated antibody specific for the CD19 receptor. The cell-to-particle binding was analyzed using a flow cytometry technique that allowed statistic and quantitative measurements of the association parameters, in parallel with optical microscopy and micropipette experiments that allowed evaluation of the characteristics of individual events. We found that the binding at the cell surface obeyed a receptor density threshold that depended both on the accessibility of the receptor within the surface layer and on the mechanics of the collision between the hard sphere and the soft material of the cell surface. We also evidenced that in the final stage of the particle adhesion on the cell surface, several thousand links were engaged in the cell-to-particle contact. The obtained results supported the idea of a collective, dynamic binding mechanism, which will be discussed. Above the better understanding of the mechanism of interaction, the question of the molecular recognition at the cell surface is also crucial in more applied situations involving protein-coated synthetic implants or in cell-sorting processes using specific colloids to select a cell subpopulation identified by a surface marker. This will be also considered in light of our results.
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MATERIALS AND METHODS |
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Cell culture and labeling
The B-cell lymphoma cell line, line Bernard (LB), EBV-transformed, was a gift from J. Dechanet-Merville (UMR CNRS 5540, Université Bordeaux II, Bordeaux, France). Cells were cultured suspended in Dulbecco's Modified Eagle Medium supplemented with 10% fetal calf serum, 2 mM L-glutamine, 50 U/ml streptomycin, at 37°C in 5% CO2. For particle-binding experiments, the cells were labeled with biotinylated anti-CD19 as follows. The whole procedure was carried out in ice; cells from an exponentially growing culture were washed with PBS and their concentration adjusted to 5 x 106 cells/ml, then incubated with the antibody above saturating concentration (4 µg/ml) for 1 h and washed twice in PBS to remove biotinylated antibody excess. The cells were then ready to be put in contact with streptavidin particles. Titrations with anti-CD19-FITC or anti-cytokeratin-FITC were performed in the same conditions. When required and as stated below, PBS was added with 0.1% sodium azide.
Cell-particle contact
Cells were put into contact at time (t) = 0 by gentle mixing in a tube of 2 ml of cell suspension adjusted at the desired concentration with a few microliters of the particle suspension. To ensure proper mixing of the samples all along the interaction process, the tubes containing the cell-particle suspensions were placed on the radii of a rotating disk spinning at 5 rpm, either at 4°C or ambient temperature. This stirring was interrupted only to carry out regular 10-s flow cytometry acquisitions.
Flow cytometry
Flow cytometry data were acquired using a FACScan (Becton Dickinson, Le Pont de Claix, France) equipped with an air-cooled 488-nm argon-ion laser. Fluorescence measurements were collected using dichroic mirrors and filter sets: a 530/30-nm bandpass on the FL1 channel and a 650-nm longpass on the FL3 channel. Ten-thousand events were the typical number collected, except for the most diluted samples, where only 2000 events were acquired to maintain short time resolution for each sample. Data were analyzed using the multivariate analysis software CellQuest (BD Biosciences, San Diego, CA), except in a few cases where more detailed analysis was performed on list-mode data files stored in flow cytometry standard (FCS) format.
Fluorescence absolute calibration was performed using the following autocalibration method: 
, the coefficient giving the proportionality between the mean fluorescence provided by the cytometer photomultiplier and the amount of fluorescent-bound molecules per cell, was obtained directly from the slope of the titration curve giving the fluorescence per cell as a function of increasing fluorescent ligand concentration in the initial linear part. Indeed, for high affinities, the amount of free ligand may be neglected when ligand concentration is low and receptors are in excess. The amount of complex is then very closely equal to the total amount of ligand. In the range-of-affinity constant expected for the binding of an antibody to its receptor, this consisted of a maximum approximation of 1% of the signal and avoided all the drawbacks related to calibration performed with beads having different optical properties than cells.
Binding equilibrium analysis
Equilibrium data were analyzed according to the following Scatchard-like method, wherein the binding affinity of a ligand L, for a receptor R, which is present in a mean number of n copies on a cell C, is considered.
This analysis is performed on the basis of a simple binding equilibrium described by the mass action law,
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Then, it becomes
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x (FL) is plotted as a function of x (FL) with Ka and n as adjustable parameters. This method was applied to characterize the binding equilibrium of anti-CD19 on its receptor on the B-cell line, and FITC-coupled anti-CD19 was used.
Micropipette experiments
Pipettes with a 0.5–1-µm inner radius, rp, were used to manipulate the cell and the bead. The experimental approach consisted of micromanipulating them to ensure contact and then holding them together for a few seconds to allow bond formation. The pipettes were then moved apart over a few micrometers. During this process, the cell was enduring an axisymmetric stretch. The analysis of the equilibrium geometry allowed us to evaluate the adhesion energy, inasmuch as the local tension 
around the contact line was known. Neglecting the pressure difference between the inside of the cell and the solution, can be deduced (Tozeren et al, 1989) from the angle 
1 that the cell makes with the radial direction at the tip of the pipette, as
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P is the aspiration pressure inside the pipette, rc is the contact radius, and 
1 is the angle formed by the cell and the radial direction at particle contact. Assuming adhesion is uniform, the adhesive energy per unit area, wa, is given by Young's equation (Berk and Evans, 1991), as
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c is the contact angle between the bead and the cell. |
RESULTS |
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Cell-particle binding profile: evidence for a subpopulation selection
On this basis, our purpose has been to characterize the specific binding on the cell surface of micrometric particles under passive conditions, i.e., low temperature and poor physiological buffer. Cells labeled with biotinylated anti-CD19 were put into contact with streptavidin-grafted particles (t = 0). Immediately 10-s flow cytometry acquisitions were initiated and regularly recorded all along the binding process, providing sequential snapshots of the situation within the cell-particle suspension. Fig. 3 shows the biparametric dot-plots acquired on mixtures of 1.5 x 105 cells/ml and to 1.5 x 106 particles/ml, i.e., 10 particles per cell, at 1 and 30 min of contact, together with the dot-plots acquired before any particle contact and after 30-min contact between unlabeled cells and streptavidin particles. Forward-light scatter (FSC) versus side-light scatter (SSC) and fluorescence emission at the highest wavelength (FL3) are shown. Dead cells and debris were gated out. The dot cluster of the living cells, initially concentrated in the lower-left region of the scatter plot extended toward the region of higher side scatter, revealed the capture of particles by the cells. This particle-bearing cell population was also clearly identified by its higher fluorescence in the FL3 vs. FSC plots. The number of events associated with this cluster increased with time. Control plots did not undergo significant alteration. It can be seen that particle binding onto the cell did not affect their size-related forward-scatter parameter. Unbound particles, at least a fraction, appeared in cytograms at lowest-forward-scatter values as expected from their 2.8-µm diameter. They displayed rather high values of side scatter and FL3, due to their iron oxide payload, which conferred to the particle both a high optical index and a large fluorescence spectrum as confirmed using fluorescence microscopy. Each cytogram also allowed us to discriminate and quantify free particles (small size, high SSC, and FL3), and free cells (defined size and low SSC) from cells having bound particles (same defined size as free cells and increased FL3 and SSC) in any suspension. A crucial point to underline, in these results, was the splitting of the cell population into two classes—cells with bound particles and cells without. The particles actually operated a selection within the cell population, despite the one-mode distribution, in regard to CD19 receptor occurrence for this cell line. In the following, the parameter 
c, the fraction of cells holding at least one particle, will be used to characterize this selection. On the basis of the fluorescence data, it was determined as the ratio of the events acquired in the upper-left region of the FL3 vs. FSC plot (living cells of higher fluorescence) to the total number of events acquired in both the upper and lower regions (all living cells). The number of events comprised in the upper-left region at t = 0 constituted the background and was subtracted from all numbers. Fig. 4 shows the evolution of the fraction c as a function of time of cell/particle contact. Obviously, the fraction of cells having bound particles reached a plateau value equal to 0.4 after 30 min of cell/particle contact. At this plateau, using optical microscope observations, we checked that the samples still exhibited significant amounts of free cells and free particles together with cells having bound particles (Fig. 5).
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c, the fraction of captured cells when the binding is achieved (i.e., binding plateau), and 2), the apparent characteristic time 
(i.e., the time to reach a fraction of captured cells equal to 
). In the previous experimental conditions (1.5 x 105 cells/ml; 1.5 x 106 particles/ml; 5 rpm stirring; standard molecular link), we found c = 0.42 and = 210 s.
Density threshold
We then aimed to gain more insight into the understanding of the binding profile and focused our interest on the origin of the selection operated by the particles within the cell population. We took advantage of the paramagnetic properties of the particles used in this study to physically separate cells that were without particles from cells with at least one particle under a magnetic field gradient. The streptavidin-binding sites of the particle-free cells were then probed on the flow cytometer using streptavidin-FITC. Fig. 6 shows the fluorescence distribution obtained on these particle-free cells together with the distribution acquired on the initial whole-cell population before any contact with particles. It appeared that those cells (which did not capture particles) displayed a binding-sites distribution that was shifted to the lower values, indicating a lower binding-site density exposed on the cell surface by these cells (Table 1). The histograms were converted into number of binding sites per cell and normalized to the same number of cells. The particle-free cell distribution was then multiplied by 0.6 to account for the fraction of discriminated cells previously measured by flow cytometry. This fraction was also corroborated by the results of the magnetic separation, which gave 43 ± 3% of cells in the pellet and 57 ± 2% of cells remaining in the supernatant. We then subtracted the calculated histogram from the histogram of the entire population. The result is shown in Fig. 7. The ascending part of the curve gives the surface density cutoff for the binding of a particle onto the cell surface. It shows that, below 2.9 x 105 receptors per cell, no particle may adhere steadily onto the cell surface; the probability to stabilize at least one particle on the surface then becomes 1, inasmuch as the mean number of binding sites attains the value of 3.8 x 105 per cell.
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, the frequency of cells bearing n particles, as a function of n when the binding was achieved (Fig. 8). The curve obtained was adjustable to an exponential decrease like
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accounts for the energy barrier increase occurring between the binding of a particle at the order (n+1) and the binding of a particle at the order n.
Parameters affecting the binding profile
The effect of four experimental parameters on the particle-binding characteristics c and were tried.
The role of particle/cell ratio
The results shown in Fig. 9 demonstrated that the decrease of the particle/cell number ratio did not affect c, the fraction of particle-bearing cells, but induced the increase of the kinetic parameter . These results evidenced an irreversible binding, the kinetics of which was determined by the number of collisions per time unit.
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c in the lengthened configuration. It also appears that the characteristic binding time was increased with this longer link.
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c and . The value c increased with the stirring speed, whereas decreased as shown in Fig. 11. Actually, this stirring mode induced the sample to flow from bottom to top of the tube twice per rotation. Each liquid inversion occurred on a rather small angle ('10° for a 2-ml sample at 5 rpm), submitting the sample to shear flows, the intensity of which depended upon the disk rotation speed. The speeding-up of the stirring induces an increase of: the collision probability per unit of time; the shear; and the kinetic energy of the particles.
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Estimation of density of molecular links in a contact
To estimate the number of links actually participating in a contact, we carried out micropipette experiments on a single cell. The binding energy stored in a contact was evaluated from the mechanic equilibrium obtained after pulling apart cell and particle in the axis of the contact (Fig. 14). We measured the contact angles using an automatic-edges research program. However, we should mention that the contact-angle measurements at the cell surface were rather inaccurate due to halo effects, inducing high standard error on the value of Wa. Following the analysis of Tozeren et al. (1989), explained in Materials and Methods, we obtained a density of energy of the order of (1 ± 0.5) x 105 kT/µm2, i.e., according to the mean area of a contact (2.5 ± 1.25) x 105 kT per contact. At this point, it is difficult to straightforwardly extract a defined number of links, N, from this energy of adhesion. The first reason is that we do not exactly know the energy of such a link within the contact at the cell surface. Indeed, it has been shown, for instance in Pérez-Luna et al. (1999), that the kinetic constants for the dissociation of the streptavidin-biotin link at an interface were affected by the structure of the surface itself. The second reason is that we have no precise description of the thermodynamics of the contact. However, it can be reasonably accepted (Brochard-Wyart and de Gennes, 2003) that this energy of adhesion is comprised between N x (kT) and N x 
b x (kT), where b is the energy of a link. Then using the energy of streptavidin-biotin link formed in solution, we found that the number of links within a 2.5-µm2 contact should be comprised between (7 ± 0.35) x 103 and (2.5 ± 1) x 105.
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DISCUSSION |
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), rather than in more technological situations such as specific cell sorting using magnetic colloids (Chalmers et al., 1998).
As a tool, we have employed well-defined micrometric particles bearing streptavidin, and a biotinylated antibody targeted to the B-cell specific receptor CD19, and then followed the phenomenology of the particles binding to the labeled cells. The most striking feature of this binding process was that only a fraction of the cell population appeared to be competent for particle binding, even though the cell line was probed for its CD19 surface expression and proved to display a monomodal CD19 distribution with a mean value of 4 x 105 receptors per cell. We have shown here that this cell selection originated in the existence of a receptor surface density threshold governing the binding. The association of a particle to a cell occurred only if the receptor surface density reached a minimal limiting value that was found equal to 1.6 x 103 receptors/µm2. Depending on the receptor distribution, this threshold value determined the fraction c of cells that were able to bind one or more particles. Similar behavior was also observed with another B-cell line (JY) and with a T-cell line (Jurkat) labeled using a biotinylated anti-CD3 antibody (data not shown). Still, we have shown that the binding threshold shifted toward the lower density values when the molecular link was lengthened, suggesting that steric hindrance created by the glycocalix restrained the binding site's accessibility.
Considering that the mean surface densities of receptors and ligands on cell and particle, together with the estimation of the contact area, were found to be 
2.5 µm2, it can be calculated that a contact may potentially assemble 4 x 103 links. This is the mean number of receptors presented by the cell over a 2.5-µm2 area. On the particle side, the same area presents 5 x 105 binding sites. The micropipette experiments have provided limit-values telling us that the number of links within a contact should be comprised between 7 x 103 and 2.5 x 105. Despite the large values-interval provided, these experiments indicated that a high number of sites were, in fine, actually connected between cell and particle; these numbers are much higher than we would have expected for the retaining of such a particle in a hydrodynamic flow. For a given system in which the nature of the molecular link and the surface densities of receptor and ligand are fixed, the two parameters governing the number of links, N, necessary to retain a particle on a surface are , the shear stress and rc, the radius of the contact area. Indeed, the theoretical framework introduced by Bell (1978) and detailed in Cozens-Roberts et al. (1990) allowed us to calculate N from the expression
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is the range of the interaction; kB is the Boltzmann constant; T is the temperature; is the shear stress; Ka is the two-dimensional association constant of the binding link; 
L is the ligand surface density; and rb is the radius of the particle. Here, to evaluate N from Eq. 1, we took to be equal to 5 x 10–8 cm, just as for the antigen-antibody bond (Cozens-Roberts et al., 1990). Considering that the rupture will take place at the site of the weakest junction (Saterbak and Lauffenburger, 1996), we took for Ka the affinity constant of the association of anti-CD19 with its CD19 target determined above (8.2 x 108 M–1). At this point, it should be noted that the constant entering in Eq. 1 is a two-dimensional constant whereas the constant we have determined is a three-dimensional constant. We made the volume-to-surface conversion following the considerations of Dustin et al. (1997), i.e., introducing a characteristic length of the order of the molecule size (10 nm). The ligand surface density, L, was equal to 2 x 1013/cm2, the streptavidin surface density of the particles, and rb was equal to 1.4 x 10–4 cm. The shear stress applied in our experimental setup was of course strongly heterogeneous but we have been able to estimate from the fluid volume in the tube and from the speed of liquid inversion that reasonably ranged between 1 and 10 dyn/cm2. We then calculated N for a range of rc-values providing contact areas comprised between 103 and 104 nm2. Afterwards we calculated the number of links offered by the cell surface for the same range of contact area considering the surface density at the binding threshold. We then obtained that, at the threshold density, the cell was able to bind a particle (i.e., to gather enough receptor for the particle not to be detached by the fluid) only if the contact area was at least equal to 5000 nm2, the number of necessary links being equal to 8. At lower receptor densities, the number of links presented by the cell for this contact area decreases below this limiting stabilizing number—explaining why the cells could no longer bind any particles, with those being immediately detached by the shear flow.
On the other hand, we observed that increasing the rotation speed of the stirring machine decreased the binding threshold value and we attributed this effect to the increase of the shear stress. Now, Eq. 1 predicts that the threshold should increase, inasmuch as N is proportional to the shear stress but only if and rc are independent variables—which is the case for solid surfaces as described in Cozens-Roberts et al. (1990), Saterbak et al., (1993), and Pierres et al. (1998, 2001). Here, the cell surface is a soft material offering a viscoelastic layer, having a plastic response in the collision with the particle, the extent of which should depend strongly on the torque and force imposed by the fluid on the particle, and then from the shear stress. Our working hypothesis is now that the shear stress has two counteracting effects in the binding. On the one hand, it increases the detaching force; on the other hand, it increases the contact area between cell and particle—decreasing the number of required links to stabilize the particle at the surface, thereby decreasing the threshold value. This idea is now under investigation in our group using homogeneous shearing in a cone-plate setup. At this point, we guess that the characteristics of the binding profile originate in the existence and the properties of the glycocalyx and that describing the response to the shear will help us to better understand the role of this structure in the regulation of the cell-surface interactions. Sabri et al. (2000) has already shown in activated human monocytes that such a regulating role could take place through compression or displacement of the bulky structures of this layer.
The binding scheme proposed above entails that the contact area delineated by the collision may grow from approximately eight links, distributed over 500 nm2, to reach a few thousand occupying a few µm2—as was measured at the plateau of the interaction. This contact area may simply grow as the particle locally rolls over the cell with an amplitude depending on the cell membrane and the initial link's elasticity (Schmid-Schonbein et al., 1981; Dong et al., 1988). Then, the ligands and receptors align, allowing much additional molecular binding. In a static vision of ligands and receptors distribution, the particle connects only the locally facing receptors. However, in this hypothesis, the cell should accommodate particles almost up to close packing, whereas only cells having bound a few particles (10 at the very most) were evidenced. Moreover, we have shown in this report that the probability for a cell-particle contact to occur decreased exponentially with the particle order, suggesting that the binding of the particle nth affected the binding of the particle (n+1)th. This might have something to do with some spatial orientation effect induced by the already bound particles. Indeed, once a cell has bound one particle, it is no longer a spherical object and in a shear field, it might well adopt a preferential orientation that would affect the subsequent binding events. However, this is rather difficult to evaluate and our preferred hypothesis is that of a dynamic process where the binding initiation would induce migration of receptors toward the contact. This receptor migration would then also drive the arrest of the binding of particles of higher order by decreasing the mean receptors surface density below the binding threshold. This implies that migration of receptors occurs with a characteristic time significantly lower than the characteristic time of particle binding. The ratio of these two times is contained in the constant , which gives the exponential decrease of the binding probability with the binding order of the particle. This migration of receptors toward the contact area seems to be a mainly passive process driven by the thermodynamic equilibrium of the unbound receptors at the cell surface. Indeed, the energetic poisoning of the cell only slightly affected the interaction characteristics. A small density threshold decrease and a binding inhibition retardation were observed using sodium azide treatment. This suggests that cell active processes, which were likely of very low level in the conditions we used, only tended to facilitate or accelerate the migration of the receptors but not to trigger it. CD19 is actually a co-receptor of the B-cell receptor engagement and is known for being able to translocate in lipid rafts upon stimulation (Cherukuri et al., 2001). It forms transitory noncovalent complexes with CD21 and CD81, obviously holding the intrinsic aptitude to diffuse freely in the membrane.
To summarize the main findings of this work, we propose a model for the specific cell-surface interaction where:
The receptor clustering in adhesive phenomena has often been observed, both experimentally and theoretically, as the result of spontaneous thermodynamic equilibrium upon binding mostly in biophysical model systems (e.g., Torney et al., 1986; Albersdörfer et al., 1997; Bruinsma et al., 2000; Brochard-Wyart and de Gennes, 2002). Depending upon the physical and chemical conditions offered, this clustering might provide to the cell some sort of basic means for regulating its interaction with the environment.
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ACKNOWLEDGEMENTS |
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The Fonds Européen de Développement Régional Objectif 2 and Bioengineering Program from the French Ministry of Research and Technology have contributed financial support for this work.
Submitted on June 23, 2003; accepted for publication January 2, 2004.
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which provided a mean value of 47.
) with biotinylated anti-CD19. Same experimental conditions as in 
), 7 (•), and 15 (
).
). Data obtained with the shortest link, i.e., biotinylated anti-CD19 (•).
) or without (•) 0.1% sodium azide. The normalized frequency for a subpopulation of cells bearing i particles is equal to 
where 
