Single Molecular Observation of Hop Diffusion in a Lipid Bilayer at

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Single Molecular Observation of Hop Diffusion in a Lipid Bilayer at Metallic Nanogates B. Takimoto, H. Nabika, and K. Murakoshi* DiVision of Chemistry, Graduate School of Science, Hokkaido UniVersity, Sapporo, 060-0810, Japan ReceiVed: October 1, 2008; ReVised Manuscript ReceiVed: December 6, 2008

Single molecular tracking was carried out for a lipid molecule (TR-DHPE) in a self-spreading lipid bilayer (egg-PC) on a glass substrate with and without Ag nanoarchitectures. Without Ag architectures, the mean square displacement (MSD) analyses demonstrated that lipid molecules diffuse randomly within the selfspreading lipid bilayer with a diffusion coefficient of 3.1 µm2/s, which is comparable to the value for an artificial solid supported lipid bilayer. However, in the presence of the Ag architectures, TR-DHPE molecules undergo hop diffusion between two neighboring compartments surrounded by the Ag architectures. Smaller diffusion coefficients observed for the substrates with the Ag architectures are attributable to the suppressed diffusion at the gap between the Ag architectures. The escape probability from the initial compartment to the neighboring compartment estimated from the MSD analysis agreed well with the values estimated from the compartment configuration, which proves that the Ag nanoarchitectures act as a diffusion barrier. Introduction Molecular manipulation with microscopic obstacles is valuable from both scientific and technological viewpoints. There are various types of approaches that exploit an array of cylindrical posts,1-4 nanofilters,5-9 nanogates,10-12 asymmetric obstacles,13-17 colloids,18 and cavities.19 All of these proposed structures are aimed at causing microscopic molecular separation or filtration, characterized by low energy consumption, low dimensionality, and high molecular recognition ability. For example, the electrophoretic drift velocity of DNA molecules in a microchannel with alternating shallow regions differs depending on the molecular weight of the DNA molecules.5 Shorter DNA molecules can enter a shallow channel without any structural change or with only a change in a molecular orientation. However, longer DNA molecules should deform for passage at the cost of their internal conformational entropy. This creates a difference in the passage probability for each molecule, and they are thus effectively separated from each other. For any molecular manipulating systems, it is highly useful to carry out an in situ observation of the drift trajectory of the molecule being manipulated. In particular, single molecular tracking has frequently yielded direct information on how the molecular motion is controlled by the structural obstacles. For example, length-dependent DNA ratcheting motion in the presence of asymmetric obstacles has been clearly demonstrated by carrying out the drift trajectory tracking.14 The structural obstacles installed in the microchannels have an advantage in that they are capable of providing continuous filtration under electrophoresis. Despite these beneficial abilities in microscopic molecular manipulation, their reliance on electrophoresis imposes the severe restriction that only charged molecules can be manipulated. To overcome this limitation, a self-spreading lipid bilayer has recently attracted growing interest for its ability to transport any molecule in any desired direction.20-28 In the self-spreading process, the bilayer growth is promoted by a gain in free energy resulting from the formation of the bilayer-substrate attractive interactions, mainly due to * Corresponding author. Phone: +81-11-706-2704. Fax: +81-11-7064810. E-mail: [email protected].

the summation of the van der Waals, electrostatic, and hydration interactions.28 Previous reports have already proven that even noncharged molecules can be transported by the self-spreading lipid bilayer without any external biases.21 If we can control the transport velocity for each molecule doped in the selfspreading lipid bilayer, then we can filter these molecules similar to the electrophoretic approaches but without any external biases. The first successful example of this concept was reported with the use of a periodic array of nanogates.10 In this system, they used a glass substrate coated with metallic triangle nanoarchitectures. The bilayer spread only through a tiny gate created between two neighboring nanoarchitectures. During the selfspreading on these substrates, the transport velocity for a dyelabeled lipid molecule, TR-DHPE, in the self-spreading lipid bilayer was found to be tunable by changing the gate width. By using the narrowest gate with a 75 nm width, TR-DHPE was successfully filtered almost completely from the selfspreading edge within a few minutes. The suggested mechanism was based on the formation of a chemical potential barrier at the tiny gate due to the formation of a densely packed phase at the gate region. As is well-known in a supported bilayer or bilayer vesicle, bulky TR-DHPE molecules are likely excluded from the densely packed phase and accumulated at the surrounding less dense phase because of the chemical potential difference.29 Thus, the entrance probability of TR-DHPE into the gate with the densely packed phase was thought to be selectively reduced. A similar result was also found by using a single gate system.12 They found a strong dye-dependency, which was also discussed in terms of a molecular-dependent chemical potential barrier. However, there are no reports to date that consider how the diffusive motion of the lipid molecule is altered in the presence the nanogate. In the present report, we have designed a single molecular tracking experiment using a molecule diffusing in the selfspreading lipid bilayer on a substrate with metallic nanogates having the same configuration as the molecular filter system mentioned above.10 We clearly demonstrated that the long-time diffusivity was significantly reduced due to a bouncing event at the metal nanoarchitectures. The molecule diffused between the compartments, each consisting of six metal nanoarchitec-

10.1021/jp808681d CCC: $40.75  2009 American Chemical Society Published on Web 02/04/2009

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Figure 1. SEM images of the NSL substrates: (a, b) NSL-3000 and (c) NSL-1000 substrates.

tures, only through the nanogates. The escaping probability from the initial compartment was found to be controllable via control of the compartment configuration. Furthermore, numerical analysis also revealed that the self-spreading lipid bilayers were capable of covering tiny and complicated vacancies a few tens of nanometers in size. It also should be noted that the present system consisting of a lipid bilayer and structural obstacles was suggested to be an appropriate model system for mimicking a biological cell membrane. As has been vigorously investigated, the diffusivity of lipid molecules in cell membranes is reduced compared with those in artificial lipid bilayers.30,31 Recent single molecular tracking experiments with high time and space resolution have made clear the presence of a compartment in the cell membrane, which results in a hop diffusion and reduction in overall diffusivity.32-36 Simulation studies also support this model that the change in the lipid diffusivity is due to compartmentalization.37,38 However, no model experiment based on a well-designed compartment and bilayer system has yet been reported. This kind of model experiment would play an important role in bridging between realistic biological experiments and computational simulations. Our system was suggested as one possible candidate for this purpose because of the ability to fabricate well-defined compartments and a bilayer configuration, primarily owing to the advantageous nature of the self-spreading lipid bilayer.

chemically by immersion in a solution of 1 mM thioacetamide (TAA) in ethanol for 30 min to reduce background emission. Preparation of Lipid Bilayer. Egg-PC was dissolved in chloroform to a concentration of 1.0 mg/mL. Dye-labeled lipid molecules of TR-DHPE were mixed with egg-PC to a final concentration of 1.0 × 10-4 mol %. A 5 µL solution containing egg-PC and TR-DHPE was dropped onto the substrate and then dried to evaporate the chloroform. The lipid aggregate left after the chloroform evaporation contains 4 × 1015 lipids, assuming the molecular weight of egg-PC as 770. By dropping 100 mM Na2SO4 aqueous solution onto the NSL substrate, the lipid bilayer of egg-PC and TR-DHPE mixture spread spontaneously from the lipid lump. If the whole lipids in the aggregate spread, the bilayer with an area of 1 × 104 mm would be obtained, which is relatively large compared with a lipid micropattern prepared via the self-spreading.40 The use of a large bilayer is important for our system to reduce the edge fraction of the bilayer, because the bilayer edge will alter the diffusion dynamics as the structural defect. Single Molecule Tracking with TIRFM and Analysis of the Trajectory. Objective-type TIRFM was applied to track individual TR-DHPE molecules using an IX-71 inverted microscope (Olympus). The 532 nm excitation laser was used, and the laser power was ca. 10 mW at the objective lens. The image was projected on a CCD camera (C9018; Hamamatsu Photonics). The images were recorded at video rate (30 frames/ s).

Experimental Methods Materials. Egg-yolk phosphatidylcholine (egg-PC) (99%, Sigma-Aldrich), Texas Red 1,2-dihexadecanoyl-sn-glycero-3phosphoethanolamine (TR-DHPE) triethylammonium salt (Molecular Probes), Na2SO4 (99.5+%; Wako Pure Chemical Industries), nitric acid (Wako Pure Chemical Industries), sulfuric acid (Wako Pure Chemical Industries), and silver wire (Nilaco) were used without further purification. Water used in all the experiments was purified by a Milli-Q system. Coverslips (Iwaki Glass) used as substrates were cleaned by being immersed in a concentrated sulfuric acid/nitric acid mixture (1:1 v/v) for 5 min, then rinsed with water, and dried with dry nitrogen just before use. Aqueous suspensions of monodispersed polystyrene (PS) beads with diameters of 1.0 and 3.0 µm (Polyscience) were centrifuged and resuspended in the same amount of Milli Q water to remove excess surfactant. Preparation and Characterization of Metal Nanoarchitectures. Periodically ordered nanoarchitectures were fabricated via the nanosphere lithography (NSL) method.39 The substrates prepared by using the PS beads with diameters of 1.0 and 3.0 µm were denoted as NSL-1000 and NSL-3000, respectively. The obtained NSL substrate was observed by scanning electron microscope to evaluate the compartment size and gate width. Before total internal reflection fluorescence microscopy (TIRFM) observation, the surfaces of nanostructures were stabilized

Results and Discussion Structural characteristics of an ordered Ag triangle array were evaluated by SEM observations (Figure 1). Enlarged images depict a single compartment surrounded by six triangle nanoarchitectures. As is clear from these images, each Ag nanoarchitecture has a particulate structure composed of small nanoparticles with different sizes. The size of the particles was smaller at the edges. We defined the outline of each triangle as the outermost edge of the constituent nanoparticles. An averaged inner radius of the compartment, r, was estimated to be 1260 and 450 nm for NSL-3000 and NSL-1000, respectively. The gate width, the distance between the edges of two neighboring triangles, dgate, was obtained to be 210 and 90 nm for NSL-3000 and NSL-1000, respectively. The height of each Ag triangle was estimated to be ca. 30 nm by AFM observation, which is consistent with the evaporation thickness. Figure 2 shows the diffusion trajectories of the TR-DHPE molecules in the self-spreading lipid bilayer. On the bare glass substrate, it was clearly shown that the diffusing distance of each trajectory was dependent upon the observed spot. When the two trajectories, with an observation duration of 4.97 and 4.83 s, in Figure 2a were compared, the former diffuses only a few micrometers whereas the latter travels more than 10

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Figure 2. Molecular diffusion trajectories on (a) glass, (b) NSL-3000, and (c) NSL-1000 substrates. (d) Superimposition of the scattering image of the NSL-3000 substrate and the trajectory of a molecule diffusing on the same area. The scale bar is 10 µm.

Figure 3. MSD plot for all acquired trajectories for (a) glass, (b) NSL-3000, and (c) NSL-1000 substrates.

µm within a similar time duration. A wide variety in the diffusivity is an intrinsic characteristic of the trajectories caused by random Brownian diffusion.41 On the NSL substrate, the TR-DHPE molecules can diffuse only through the nanogate (Figure 2d). This behavior is reasonable, because the metal surface is known to be an effective barrier for lateral lipid diffusion.42 Although the trajectories on the NSL substrates shown in Figure 2c,d demonstrated a similar variety in the diffusivity to those on the glass substrate, the number of spots showing relatively short trajectories seems to be larger on the NSL substrates than on the glass substrate. For further qualitative estimation of the molecular diffusivities on the glass and NSL substrates, MSD were plotted for all acquired trajectories (Figure 3). On the bare glass substrate, most of the MSD plots show linear increments because of random Brownian diffusion. The values of 〈r2〉 for the linear plots are in the range of 2-15 µm2 at 0.6 s. Several plots deviate from those with linear behavior. One MSD plot shows extremely high diffusivity, which reached almost 20 µm2 at 0.4 s. Judging from its parabolic increase in the MSD plot, this molecule diffuses with directed motion.43 On the other hand, several MSD remain at almost zero even after 0.6 s. The apparent nondiffusive nature of these molecules is attributed to the molecules strongly interacting or adsorbing onto the solid surface. On the NSL substrates, distinctive architectures, which may be attributable to directed motion or nondiffusivity of the molecules, are also found for several plots. The number of these plots is smaller than those of other plots showing linearity. To eliminate these peculiar diffusivities, we extracted major fractions in the MSD plot by considering a displacement histogram at 0.4 s (Figure 4). The solid lines in Figure 4 are the best fit for the Gaussian fitting. As expected from the MSD plot on the bare glass substrate, a nondiffusive fraction with 〈r2〉 ) 0 appeared out of the Gaussian distribution. For further analysis, the fraction in µ ( σ, where µ and σ are the average and standard deviation of the Gaussian distribution, respectively, were extracted.

Figure 4. Square displacement histogram at t ) 0.4 in the MSD plot shown in Figure 3: (upper) glass, (middle) NSL-3000, and (bottom) NSL-1000 substrate. Red lines depict the results of a Gaussian fit for each histogram.

Figure 5a-c shows the averaged MSD obtained from the extracted MSD plots on respective substrates. Although MSD plots still have a wide distribution, apparently low- and highdiffusive plots were omitted from Figure 3. The 〈r2〉 at 1.0 s distributes homogeneously ranging from 5 to 25 µm2 on the bare glass substrate, whereas the fraction below 10 µm2 becomes higher on the NSL substrates. This result indicates that the Ag nanoarchitectures reduce the long-time diffusivity of TR-DHPE in the lipid bilayer. Comparison between the averaged MSD plots shown in Figure 5d clearly demonstrates the reduction in the diffusivity due to the presence of the Ag nanoarchitectures. Smaller values of 〈r2〉 at NSL-1000 than NSL-3000 reflect the difference in the molecular diffusion rectification depending on r and dgate. The diffusivity is quantitatively discussed based on a diffusion coefficient,

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Figure 5. (a-c) Extracted MSD plots from a Gaussian fit shown in Figure 4: (a) glass, (b) NSL-3000, and (c) NSL-1000 substrates. Open circles are the averaged MSD. (d) The averaged MSD plot for (black circle) glass, (red square) NSL-3000, and (blue triangle) NSL-1000 substrates. The inset is the magnified averaged MSD plots for the short-time region.

˜ (t) and t for (circle) glass, Figure 6. Double-logarithmic plot between D (square) NSL-3000, and (triangle) NSL-1000 substrates. Dashed lines are to guide the eye. The arrows depict the cross-over time from the ˜ (t). decreasing to constant D

D. For two-dimensional diffusion, the relationship between 〈r2〉 and D is written as follows.37

〈r2 〉 ) 4DtR

(R e 1)

where R is the anomalous diffusion exponent. This equation can be rewritten as

˜ (t)t 〈r2 〉 ) 4(DtR-1)t ) 4D

(1)

˜ (t) ) 〈r 〉 D 4t

(2)

then 2

˜ (t) ) DtR-1 is the time-dependent diffusion constant. where D ˜ (t) is constant at all time For random diffusion, R ) 1 and D ˜ (t) as a function of time are plotted in intervals. Changes in D ˜ (t) is almost constant Figure 6. On the bare glass substrate, D for the observed time region, indicating the dominance of ˜ (t)was 3.1 µm2/ random Brownian motion. The time averaged D s, which is in good agreement with the previously reported value ˜ (t) shows a for a supported lipid bilayer.44 On the other hand, D gradual decrease at a short-time region on the NSL substrate. ˜ (t) indicates that the molecules were bounced The decrease in D back into their initial compartment by the Ag nanoarchitectures. The bouncing event reduces an apparent displacement, leading ˜ (t) is brought to less diffusivity. Time-dependent decrease in D

about by an increase in the number of bouncing events with time. It should be noted here that a time-independent plateau has ˜ (t) in both been found before the time-dependent decrease in D simulations38 and the observation of cell membranes.30 The absence of the plateau at a shorter time region in Figure 6 is due to our time resolution of 33 ms. For the time duration of 33 ms, the molecule with D ) 3.1 µm2/s can diffuse to cover an area of 650 nm in radius, if there is no obstacle to act as a diffusion barrier. This expected diffusion area is comparable or larger than those of the NSL-3000 and NSL-1000 compartments (r ) 1260 nm for NSL-3000 and 450 nm for NSL-1000). Thus, diffusion within the compartment with time-independent ˜ (t) was not resolved in the present system. However, the timeD ˜ (t)was clearly observed as shown in dependent decrease in D Figure 6. Thus, the transition of the diffusivity from timeindependent to time-dependent can be quantitatively discussed under out experimental condition. ˜ (t) reached a constant value. After the gradual decrease, D ˜ (t), the molecule diffuses Over this long region with constant D between the compartments through the nanogates after several bouncing events. This is known as hop diffusion, and longtime observation will reach macroscopic random diffusion with ˜ (t)macro. This transition was apparent for the NSLconstant D 3000 substrate, in which the transition appeared at around 0.47 s, ˜ (t)macro was estimated as 2 µm2/s. Although the NSL-1000 and D substrate also showed the transition at 0.13 s, a further decrease ˜ (t) was observed after 0.3 s. This unexpected decrease was in D due to structural inhomogeneity of the NSL-1000 substrate, and ˜ (t)macro was estimated as 1.7 µm2/s by using the values between D ˜ (t) of both the decrease regions 0.1 and 0.2 s. By comparing the D ˜ (t)macro between the two NSL substrates, it was found and the D ˜ (t) for NSL-3000 was higher by about 1.2 times than that that D for the NSL-1000 system, with the exception of the region after 0.3 s. This difference is due to the presence of higher density nanoarchitectures on the NSL-1000 substrate, which hinder the molecular diffusion more frequently than NSL-3000. The transition time from the gradual decrease to the constant ˜ (t)macro corresponds to an averaged escape time from the initial D compartment to the next.36 The averaged escape time, tesc, can be estimated by37

tesc 2

πr

h)

4 + hr 2πhr

(3)

P l(1 - P)

(4)

)

where r is the radius of the circular compartment estimated from SEM images and l2 ) 4D/30. P denotes the probability to escape

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Figure 7. Enlarged SEM images and comparison between dgate and δgate: (a) NSL-3000 and (b) NSL-1000 substrates.

from the compartment when the diffusing molecule reaches the compartment boundary. For the above calculations, D ) 3.1 µm2/s was used because the molecular diffusivity within the compartment is essentially the same as that on a bare glass substrate. From these equations, P was obtained as 0.24 and 0.26 for NSL-3000 and NSL-1000, respectively. These values indicate that approximately one-fourth of the molecules reaching the boundary will escape from the compartment. In other words, one forth of the compartment circumference length acts as an effective opening for the molecular diffusion. P is equivalent to the fraction of the opening periphery in the compartment circumference

P)

6δgate 2πr

(5)

where δgate is the gate distance estimated from P. Since there are six gates in each compartment as shown in Figure 1, the total length of the opening periphery is 6δgate. Equation 5 yielded δgate as 310 and 130 nm for the NSL-3000 and NSL-1000 substrates, respectively. Both δgate are slightly larger than the respective dgate obtained from SEM observations (Figure 7). As described previously, the outermost region of the Ag nanoarchitecture is composed of small nanoparticles, and we defined dgate as the distance between two adjacent nanoarchitecture edges that are at the outermost position of the constituent nanoparticles. Therefore, there are several vacancies exposing a bare glass surface at the out-most region. The results that δgate was larger than dgate suggest that lipid molecules recognize these vacancies as diffusion pathways. The interparticle distance at the outermost region is a few tens of nanometers. Retardation of the diffusion depending on the size of the Ag nanoarchitectures is well explained by hop diffusion between the compartments through the nanogate. The observed correlation between δgate and dgate proves that the hop diffusion of target molecules in the spreading bilayer can be tuned by the size of the nanogap prepared on the substrate. The present results strongly suggest that the self-spreading lipid bilayer is capable of spreading and covering these structured surfaces. Taking advantage of the self-spreading lipid bilayer, we can obtain a glass substrate consisting of wellpatterned metallic nanoarchitectures and lipid bilayer with nanometer space-resolution. Different from a vesicle fusion process, in which the bilayer covers both the glass and metal nanoparticle surfaces,45 the self-spreading process allows the bilayer to cover only the bare glass region with the metal surface remaining as an effective barrier to lipid diffusion. Thus the lipid molecule only diffuses through the gap after microscopic diffusion in the compartment. The situation in which the lipid molecule undergoes hop diffusion with a certain escape probability is quite similar to the phenomenon observed in a cell

membrane.31 As has been vigorously investigated by Kusumi et al., a cell membrane is physically compartmentalized by the presence of a membrane skeleton, in which the compartment size ranges from tens to thousands of nanometers.30 By tuning the compartment size and the gap distance through a more accurate fabrication process, such as electron beam lithography, the substrate will be applicable as an artificial model system, mimicking these cell membranes with complicated compartments. Although the present system can provide one of the models of a physical barrier, another kind of interaction may be involved in the cell membrane, such as a specific intermolecular interaction between biomolecules. Further study using surface modified metallic architectures with biomolecules would offer information on both physical and chemical interactions in the cell membrane, which are controlling the diffusivity of target molecules. Conclusion In the present study, we have successfully estimated the diffusivity of dye-labeled lipid molecules in a self-spreading lipid bilayer. In particular, metal nanoarchitectures were found to be an effective barrier to lipid diffusion. By constructing a compartment surrounded by six Ag nanoarchitectures, hop diffusion between the compartments was observed. The escape probability was tunable by precise control of the metallic compartment. Our findings are expected to benefit the potential application for biomimetic artificial surfaces bearing nanoscopically complicated compartments. Acknowledgment. This work was partially supported by KAKENHI (Grant-in-Aid for Scientific Reserch) No. 19049003 on priority area “Strong Photon-Molecule Coupling Fields (470)” and No. 18750001 of Young Scientists (B) from MEXT, Japan. References and Notes (1) Volkmuth, W. D.; Austin, R. H. Nature 1992, 358, 600. (2) Huang, L. R.; Tegenfeldt, J. O.; Kraeft, J. J.; Sturm, J. C.; Austin, R. H.; Cox, E. E. Nat. Biotechnol. 2002, 20, 1048. (3) Turner, S. W. P.; Cabodi, M.; Craighead, H. G. Phys. ReV. Lett. 2002, 88, 128103. (4) Kaji, N.; Tezuka, Y.; Takamura, Y.; Ueda, M.; Nishimoto, T.; Nakanishi, H.; Horiike, Y.; Baba, Y. Anal. Chem. 2004, 76, 15. (5) Fu, J.; Schoch, R. B.; Stevens, A. L.; Tannenbaum, S. R.; Han, J. Nature Nanotechnol. 2007, 2, 121. (6) Han, J.; Craighead, H. G. Science 2000, 288, 1026. (7) Fu, J.; Yoo, J.; Han, J. Phys. ReV. Lett. 2006, 97, 018103. (8) Tessier, F.; Labrie, J.; Slater, G. W. Macromolecules 2002, 35, 4791. (9) Fu, J.; Mao, P.; Han, J. Appl. Phys. Lett. 2005, 87, 263902. (10) Nabika, H.; Sasaki, A.; Takimoto, B.; Sawai, H.; He, S.; Murakoshi, K. J. Am. Chem. Soc. 2005, 127, 16786. (11) Nabika, H.; Takimoto, B.; Iijima, N.; Murakoshi, K. Electrochim. Acta 2008, 53, 6278. (12) Kashimura, Y.; Durao, J.; Furukawa, K.; Torimitsu, K. Jpn. J. Appl. Phys. 2008, 47, 3248. (13) van Ondenaarden, A.; Boxer, S. G. Science 1999, 285, 1046. (14) Chou, C.-F.; Bakajin, O.; Turner, S. W. P.; Duke, T. A. J.; Chan, S. S.; Cox, E. C.; Craighead, H. G.; Austin, R. H. Proc. Natl. Acad. Sci. 1999, 96, 13762. (15) Etras¸, D. Phys. ReV. Lett. 1998, 80, 1548. (16) Duke, T. A. J.; Austin, R. H. Phys. ReV. Lett. 1998, 80, 1552. (17) Huang, L. R.; Silberzan, P.; Tegenfeldt, J. O.; Cox, E. C.; Sturm, J. C.; Austin, R. H.; Craighead, H. Phys. ReV. Lett. 2002, 89, 178301. (18) Zeng, Y.; Harrison, D. J. Anal. Chem. 2007, 79, 2289. (19) Nykypanchuk, D.; Strey, H. H.; Hoadland, D. A. Science 2002, 297, 987. (20) Ra¨dler, J.; Strey, H.; Sackmann, E. Langmuir 1995, 11, 4539. (21) Nissen, J.; Gritsch, S.; Wiegand, G.; Ra¨dler, J. O. Eur. Phys. J. B 1999, 10, 335. (22) Nissen, J.; Jacobs, K.; Ra¨dler, J. O. Phys. ReV. Lett. 2001, 86, 1904. (23) Suzuki, K.; Masuhara, H. Langmuir 2005, 21, 537.

3132 J. Phys. Chem. C, Vol. 113, No. 8, 2009 (24) Suzuki, K.; Masuhara, H. Langmuir 2005, 21, 6487. (25) Furukawa, K.; Nakashima, H.; Kashimura, Y.; Torimitsu, K. Lab Chip 2006, 6, 1001. (26) Nabika, H.; Fukasawa, A.; Murakoshi, K. Langmuir 2006, 22, 10927. (27) Furukawa, K.; Sumitomo, K.; Nakashima, H.; Kashimura, Y.; Torimitsu, K. Langmuir 2007, 23, 367. (28) Nabika, H.; Fukasawa, A.; Murakoshi, K. Phys. Chem. Chem. Phys. 2008, 10, 2243. (29) Dietrich, C.; Bagatolli, L. A.; Volovyk, Z. N.; Thompson, N. L.; Levi, M.; Jacobson, K.; Gratton, E. Biophys. J. 2001, 80, 1417. (30) Murase, K.; Fujiwara, T.; Umemura, Y.; Suzuki, S.; Iino, R.; Yamashita, H.; Saito, M.; Murakoshi, H.; Ritchie, K.; Kusumi, A. Biophys. J. 2004, 86, 4075. (31) Fujiwara, T.; Ritchie, K.; Murakoshi, H.; Jacobson, K.; Kusumi, A. J. Cell Biol. 2002, 157, 1071. (32) Sheets, E. D.; Lee, G. M.; Simson, R.; Jacobson, K. Biochemistry 1997, 36, 12449. (33) Simson, R.; Yang, B.; Moore, S. E.; Doherty, P.; Walsh, F. S.; Jacobson, K. A. Biophys. J. 1998, 74, 297. (34) Dietrich, C.; Yang, B.; Fujiwara, T.; Kusumi, A.; Jacobson, K. Biophys. J. 2002, 82, 274.

Takimoto et al. (35) Ritchie, K.; Iino, R.; Fujiwara, T.; Murase, K.; Kusumi, A. Mol. Membr. Biol. 2003, 20, 13. (36) Ritchie, K.; Shan, X.-Y.; Kondo, J.; Iwashita, K.; Fujiwara, T.; Kusumi, A. Biophys. J. 2005, 88, 2266. (37) Saxton, M. J. Biophys. J. 1995, 69, 389. (38) Niehaus, A. M. S.; Vlachos, D. G.; Edwards, J. S.; Plechac, P.; Tribe, R. Biophys. J. 2008, 94, 1551. (39) Hulteen, J. C.; Van Duyne, R. P. J. Vac. Sci. Technol. A 1995, 13, 1553. (40) Fang, Y. J. Am. Chem. Soc. 2006, 128, 3158. (41) Lee, G. M.; Ishihara, A.; Jacobson, K. A. Proc. Natl. Acad. Sci. U.S.A. 1991, 88, 6274. (42) Groves, J. T.; Ulman, N.; Boxer, S. G. Science 1997, 275, 651. (43) Saxton, M. J.; Jacobson, K. Annu. ReV. Biophys. Biomol. Struct. 1997, 26, 373. (44) Guo, L.; Har, J. Y.; Sankaran, J.; Hong, Y.; Kannan, B.; Wohland, T. ChemPhysChem 2008, 9, 721. (45) Roiter, Y.; Ornatska, M.; Rammohan, A. R.; Balakrishna, J.; Heine, D. R.; Minko, S. Nano Lett. 2008, 8, 941.

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