A Continuous Network of Lipid Nanotubes Fabricated from the Gliding

Feb 7, 2013 - A Continuous Network of Lipid Nanotubes Fabricated from the. Gliding Motility of Kinesin Powered Microtubule Filaments. Nathan F. Bouxse...
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A Continuous Network of Lipid Nanotubes Fabricated from the Gliding Motility of Kinesin Powered Microtubule Filaments Nathan F. Bouxsein,† Amanda Carroll-Portillo,† Marlene Bachand,† Darryl Y. Sasaki,‡ and George D. Bachand*,† †

Center for Integrated Nanotechnology, Sandia National Laboratories, Albuquerque, New Mexico 87123, United States Department of Biotechnology and Bioengineering, Sandia National Laboratories, Livermore, California 94550, United States

‡

S Supporting Information *

ABSTRACT: Synthetic interconnected lipid nanotube networks were fabricated on the millimeter scale based on the simple, cooperative interaction between phospholipid vesicles and kinesin−microtubule (MT) transport systems. More specifically, taxol-stabilized MTs, in constant 2D motion via surface absorbed kinesin, extracted and extended lipid nanotube networks from large Lα phase multilamellar liposomes (5−25 μm). Based on the properties of the inverted motility geometry, the total size of these nanofluidic networks was limited by MT surface density, molecular motor energy source (ATP), and total amount and physical properties of lipid source material. Interactions between MTs and extended lipid nanotubes resulted in bifurcation of the nanotubes and ultimately the generation of highly branched networks of fluidically connected nanotubes. The network bifurcation was easily tuned by changing the density of microtubules on the surface to increase or decrease the frequency of branching. The ability of these networks to capture nanomaterials at the membrane surface with high fidelity was subsequently demonstrated using quantum dots as a model system. The diffusive transport of quantum dots was also characterized with respect to using these nanotube networks for mass transport applications.

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of ∼50%.9 Based on these properties, biological active transport systems have been reconstructed ex vivo for a wide array of applications including supramolecular self-assembly, analyte concentration, advanced molecular detection, and microfluidic pumping.3,5,10,11 Collectively, these advances in applying active transport systems in engineered nanofluidic devices establish a precedent for considering other bio-inspired and mimetic mechanisms for developing novel nanofluidic systems. In cells, nanofluidic transport by diffusion and osmosis often involves membranes and/or membrane-based organelles that are capable of separating molecular species (osmosis) or limiting the path length (diffusion). For example, the endoplasmic reticulum (ER) and Golgi body play critical roles in a wide variety in nanofluidic transport systems. Within these organelles, diffusive transport may occur not only within the lumen12 but also within the fluid lipid membranes that compose these organelles.13 From an engineering perspective, developing a system that mimics the structure and function of the ER/Golgi body offers a novel route to complex, highly interconnect nanofluidic systems based on osmotic or diffusive transport. The highly networked, tubular structure of the ER and Golgi body are assembled and reorganized through the mechanical

INTRODUCTION The area of nanofluidics involves transport phenomena of fluids at nanometer scales and is driven, at least in part, by continued efforts to miniaturize bioanalytical, lab-on-a-chip systems.1,2 Decreasing channel dimensions to submicrometer length scales, however, raises critical challenges with fluid transport including the need for high pressures and external pumps.3 In addition, significant increases in the surface-to-volume ratio occur when device features are reduced to the nanometer scale, which in turn leads to an increased dominance of surface-related phenomena,2 many of which create critical barriers to developing specific nanofluidic applications. While these challenges are often being addressed independently as they arise, looking to nature enables us to borrow from billions of year of evolution to develop unique and more broadly applicable solutions. At a primary level, nanofluidic transport is foundational to a wide array of cellular functions in biological organisms and may be broadly categorized in terms of active transport, diffusion, and osmosis. Active transport systems, in particular those involved in cytoskeletal transport, have received considerable attention with regard to nanofluidic transport in engineered devices and systems.3−6 Cytoskeletal biomolecular motors such as kinesin are responsible for transporting macromolecules and organelles within cell’s cytoplasm and exhibit extremely attractive biophysical properties including high processivity (∼1000 nm),7 stall forces of 6−7 pN,8 and catalytic efficiencies © XXXX American Chemical Society

Received: October 25, 2012 Revised: February 6, 2013

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MLVs containing 5% biotin−PE and 0.5% Texas Red−DHPE were then incubated inside the flow cell, and after 30 min of incubation, millimeter-sized continuous networks of LNT were observed, originating from the large seed MLVs as can be seen in the fluorescent TIRF image of Figure 2.

work of biomolecular motors operating on cytoskeletal filaments.14 The fundamental elements of this system have been replicated in vitro. Here, movement of kinesin motors along surface-bound microtubules (MTs) generates lipid nanotube networks with length of ∼10 μm and minimal branching.15−17 While these lengths are consistent with cellular architectures, application of lipid-based networks for nanofluidic transport in lab-on-a-chip systems ideally requires fabrication of highly branched structures with lengths of hundreds of micrometers. In the inverted motility system, MTs “glide” along a monolayer of surface-adsorbed kinesin with run lengths are nearly infinite and mainly limited by the amount of ATP present in the system. Thus, we hypothesized that application of the inverted motility assay (IMA) can be used to fabricate large-scale, high-bifurcate networks of lipid nanotubes (Figure 1), which in turn could be used as

Figure 2. Example network formed from MLV and motile MTs. TIRF with 40× oil immersion objective. DOPC MLV with 0.5% Texas Red− DHPE and 5% biotin−PE (red). Free-standing kinked LNT highlighted with white arrow and shown in magnifed inset. Initial size of MLV ≈ 25 μm. Scale bar = 50 μm.

MLV concentration added to the flow cell was kept low such that neighboring LNT networks did not overlap. It is evident that the origin of the LNT networks arises from the motility of biotinylated MT which are bridged to the surface of the biotinylated MLV though the streptavidin linker and are able to extract LNT from the dense lipid reservoir. The radial pattern of LNT extending from the MLV is due to the stochastic surface motion of the MTs that attach to the MLV. LNT junctions close to the source MLV may arise from coalescence of near-neighbor LNT as seen for pipet-assisted manual extraction of LNT but are unlikely for junctions seen much further from the source MLV.21 We observe LNTs extracted from already formed LNTs, as shown in Figure 3, creating bifurcations and junctions even at the extremities of the network. Another unique feature highlighted in Figure 2 is the presence of LNT “kinks” that appear to be surface supported as they exist in the absence of neighboring MTs. We attribute this observation to free, surface-adsorbed streptavidin deposited during the streptavidin MT coating step. The surface adsorbed streptavidin not only acts to adhere the MLVs to the surface but also acts as an anchor point for biotin lipids in extended LNTs. Notably, when using reduced concentrations of streptavidin during MT coating, LNTs do not exhibit kinking, and the LNT network, which is normally stable for 1 day at room temperature, is increasingly susceptible to thermal fluctuations. Force Requirements. Formation of LNTs from the MLV reservoir requires that the mechanical work done by the kinesin motors on the motile MTs exceeds the force requirements for tube extraction and extension. The bending rigidity of the lipid membranes, κ, and the membrane tension, σ (constant for vesicles with a membrane reservoir such as MLVs),22 set the free energy of a LNT tether with length L and radius R pulled with a force f as FLNT = [κ/2R2 + σ]2πRL − f L, which can be minimized with respect to L and R to give the equilibrium tether force, f 0 = 2π(2κσ)1/2.23 Unlike free unilamellar vesicles,

Figure 1. In the presence of ATP, surface-absorbed kinesin motors power the motility of biotinylated microtubule filaments. Biotinylated multilamellar vesicles are attached to the motile microtubules using free streptavidin.

nanofluidic highways for materials transport. Such networks would serve as a model system for studying 1D diffusion in lipid structures that mimic the ER and Golgi and serve as a platform for developing nanofluidic systems in which transport occurs via thermal motion.

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RESULTS AND DISCUSSION LNT Network Formation. MTs polymerized with 5% biotinylated tubulin were added to a capillary flow chamber containing kinesin-coated surfaces kept at 23 °C. Commonly, this setup is referred to as the IMA for kinesin and actin molecular motors. A 1.7 μM of streptavidin was then added into the flow cell to create fully coated streptavidin MTs.18 After excess streptavidin was removed, MTs were observed to undergo normal motile behavior and translocated on the surface of the chamber with an average velocity of 0.71 ± 0.02 μm s−1. Here, the concentration of kinesin motors (325 nM) and length of the MTs (>2 μm) satisfy the criteria for near perpetual MT surface motion.19 There is no initial preferential orientation of the surface-absorbed MT, and their trajectories follow random walk statistics at sufficient time scales.20 DOPC B

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Figure 4. Velocity distribution of MTs bound to and extending a LNT. MT velocity versus MT length for MT bound to a LNT. Inset: MT velocity vs MT length for unbound MT, average = 0.71 ± 0.02 μm s−1. Figure 3. Formation of LNT bifurcation from motile MTs. 100× oil immersion objective. DOPC MLV with 0.5% Texas Red−DHPE and 5% biotin−PE (red). MT with 15% Hylite tubulin (green). Arrows a and b indicate motility direction of two MT participating in junction formation. Left image: LNT extension from MT b before interaction with MT a. Right image: junction formation though the interaction between MT a and the LNT. Scale bar = 15 μm.

surface bound kinesin act cooperatively when working on MT motility. The cooperative processivity of molecular motors has previously been reported on for the extraction of LNT from giant unilamellar vesicles coated with surface bound kinesin.16 Additionally, a similar velocity−length relationship for MT in the IMA, manipulated by magnetic fields (force applied to a magnetic bead attached to the trailing end of a MT), was attributed to the number of bound kinesin motors.35 In contrast, actin filaments conjugated to rhodamine antibodies show no velocity dependence on load density in cell free actin/ myosin motility assays.36 Measuring velocities for MTs bound to LNT at lengths 10 mm in an arguably short period of time (30 min). While we do not claim that our system represents a true biological mimic, it is interesting to note that highly reticulated intracellular membrane structures, such as the endoplasmic reticulum and Golgi, are formed via the cooperation between molecular motors and dense cytoskeletal filaments.14 Realistically, these synthetic LNT networks have nanoengineering applications for confined materials capture and transport, as we show in the next section. σMT is thus an example of a parameter that can allow for input control over network configuration, providing simple adaptability during the fabrication step. Nanomaterial Capture on LNT. The surface of the LNT network is an excellent platform for the specific capture of nanoparticle analytes, demonstrated by the binding of streptavidin functionalized quantum dot (Qdot) nanocrystals to free biotin lipid throughout the entirety of the network as shown in Figure 6. The membrane bound Qdots undergo

LNT (for reference, the biotin−streptavidin bond strength is ≈140 pN).39 While the individual lipid adhesion strength should be independent of MT length, we note that longer MT can support multiple biotin lipid attachments and faster extraction ratesthey do not slow or stallwhich are properties that can increase the force requirements for pulling lipids out of the membrane.40,41 We also confirm that in a regime where the buildup of membrane tension quickly surpasses the lipid adhesion strength (in the case for very small DOPC MLVs), MTs of any length are not able to support network formation (see Supporting Information Figure S2). MT Surface Density. Criteria for LNT network formation established in this paper primarily depend on intrinsic properties such as κ and σeff of the lipid vesicle and the MT velocity and cumulative force. Once these conditions are met, the MT surface density, σMT, may be varied, allowing for direct tunability of macroscopic LNT network properties such as junction frequency, branch length, and total network length. σMT is calculated by taking the ratio of the projected total MT area per field of view AMT to the area of the field of view Af. AMT = ∑LidMT, where Li is the contour length of MT i, dMT is the diameter of a MT = 25 nm, and Af is determined by the capture setting for the camera. σMT is averaged over 10 images. Thirty minutes after the formation of an initial LNT from an MLV, nonhydrolyzable ATP analogue is added to the system to stop network growth.42 Binary images of typical LNT networks for low and high σMT are shown in Figure 5.

Figure 6. Nanomaterials capture by LNT network. Red (605 nm) and green (525 nm) streptavidin quantum dots attached to biotinylated lipids in the LNT network. Scale bar is 10 μm.

Figure 5. Configurable LNT network properties by MT surface density (σMT). Binary mosaics created from stitching together multiple fluorescent images (40× oil immersion) for two LNT networks at 30 min after initial LNT formation (MT are not shown): Left: network formed from σMT = 0.20. Right: network formed from σMT = 0.07. Initial MLV size ≈ 25 μm. Scale bars are 100 μm.

thermal transport and demonstrate the fluidity of the junctions as seen in Figure 7. The use of Qdot nanocrystals in cell free systems has proven to be a vital tool in tracking various components in motor protein assays.43−46 The location of a Qdot around a distal junction over time is equally distributed and may indicate uniformity of the underlying lipid structure at junction vertices. For isolated Qdots on distal straight segments of LNTs, we measured the mean squared displacement as a function of time from Qdot positional data as described in the experimental section. One example is shown in the top of Figure 8. The linear fits to normal 1D diffusion (ND) results in a diffusivity coefficient of D = 2.278 ± 0.36 μm2 s−1 (averaged over 10 Qdots). Here, the diffusion of the Qdot is coupled to the underlying diffusivity of the lipids in the LNT membrane. This value is significantly smaller than reported values of the self-diffusion coefficient of DOPC in flat membranes (for example, pulse gradient NMR gives 9.32 μm2 s−1 at room temperature).47 Indeed, for curved membranes where the membrane viscosity is much greater than the viscosity of the surrounding media, the geometry plays a significant role for

Network analysis results are displayed in Table 1. The inverse relationship between the number of branches and the average branch length as a function of σMT is not surprising Table 1. Network Analysis at 30 min for LNT Networks Formed from Three Different Surface Fraction Densities, σMT, of MTs σMTa no. of junctions no. of branches mean branch length (μm)

0.07 30 62 47.4

0.16 104 182 31.8

0.25 465 757 14.8

σMT is calculated by taking the ratio of the projected total MT area, AMT, to the area of the field of view, Af. AMT = ∑LidMT, where Li is the contour length of MT i and dMT is the diameter of a MT = 25 nm. Junctions are counted as verticies where three or more LNT intersect. Branches are counted as the sum of all LNT between junctions. a

D

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Figure 7. Fluidity of LNT junctions as indicated by the diffusive position of a bound Qdot. A single red (605 nm) streptavidin quantum dot (indicated at various time points by the arrow) attached to biotinylated lipids in LNT network (green) at indicated time in seconds. Lower right graph shows positional distribution of the Qdot for all time points. Scale bar is 10 μm.

the LNT radius, r, and properties of the lipid membrane such that D = kBT(4πη)−1 log(r/a), where η is the intrinsic 2D membrane viscosity and a is the radius of an individual lipid or membrane protein inclusion.48 Recent experimental evidence using Qdots on egg phosphatidylcholine (egg-PC) LNTs confirms this relationship,49 showing diffusivity to scale with LNT diameter. Using published values for η of DOPC (5.4 × 10−10 N m−1 s), we find a tube diameter of 41 nm for our measured diffusivity.50 The narrow distribution of measured D suggests some uniformity in the fabricated LNT networks, a result that is not surprising since LNT radius, r, depends on the pulling force, r = 2πκ/f 0, of the MTs, which for long MT at similar velocities remains most likely constant.30 At high capture densities, the thermal motion of a Qdot is confined by near-neighbor Qdots. Straight line fits (normal diffusion) to MSD data for Qdots with a capture density of 2.4 per μm of LNT fail to adequately describe the diffusivity (Figure 8, bottom). In the limit where Qdots are not allowed to slip past one another, the motion is considered single file, and at long times, the MSD of singe file diffusion (SFD) scales as the square root of the time interval.51 Exact solutions for the MSD of finite SFD systems have recently been proposed giving MSD(t) = [(1 − (N/L)d)/(N/L)][(4Dt/π)1/2], where N is the number of Qdots with diameter d on a straight segment of LNT of length L for neighbor confined Qdot mobility.52 Figure 8 (bottom) is from a sample with N = 47 and L = 20 μm; here SFD fits are more appropriate when describing the nature of the tracer Qdot mobility. Errors to this fit may arise from Qdot neighbor interactions or occasional slipping of Qdots past one another. Generally, one must consider these effects when fabricating LNT for the purpose of nanomaterials capture and transport. While the 1D networks allow for more controlled transport, the limit of single file interactions can inhibit longrange motion, even in the case where external control (such as magnetic or electrical fields) drives captured particle transport beyond thermal diffusivity. Simple changes to parameters such at σMT can increase LNT junction frequency and segment

Figure 8. MSD as a function of time for low capture (top) and high capture (bottom) of Qdots. ND: normal diffusion fit with linear relationship between MDS and Δt. SFD: single file diffusion fit with square root relationship between MSD and Δt. Open circles are measured data points. Lines are linear fits or SFD fit to the data.

lipid diffusion. For membrane tubes, an extension of the Saffman−Delbrück theory relates the diffusivity coefficient to E

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number to alleviate “traffic jam” scenarios or critical pathway failures (“collapsed bridge”) to make the networks more suitable for particle transport or materials separation. Other relevant systems have observed kinesin traffic jams on MTs in crowded environments, such as would be found inside cells.53,54 While these cases describe active transport, in contrast to our purely diffusive mechanism, it is nonetheless interesting to consider the potential biological relevance for higher ordered MT arrays such as bundles and asters to use network organization as an additional pathway for alleviating kinesin jamming.55−57

Kinesin. Full-length D. melanogaster kinesin-1 was expressed in E. coli from the recombinant kinesin heavy chain expression vector pPK113 and purified by Ni-NTA chromatography (Invitrogen).58 Protein concentration was determined by standard Bradford assay to be 1.08 μM. Aliquots of the protein were snap frozen in liquid nitrogen and stored at −80 °C MLV Preparation. All lipids were dissolved in chloroform and stored under nitrogen at −20 °C. A 5 mM solution of 94.5 mol % DOPC, 5 mol % Biotin-PE, and 0.5 mol % TR-DHPE was prepared in 900 μL of chloroform and 100 μL of methanol in a 20 mL roundbottom flask. 7 mL of BRB80 was gently added to the solvent mixture to preserve an aqueous/solvent interface. The solvent was slowly removed on a rotovap at 60 rpm and 40 °C, holding the pressure just below the chloroform and then the methanol boiling points. The pressure was reduced further to 70 mbar and held to ensure solvent removal. The aqueous lipid suspension was then dialyzed against BRB80 for 24 h at 4 °C in a custom dialysis cassette using 3 μm track etched membranes to remove smaller MLVs. MLVs were stored at 4 °C under nitrogen. Motility Assays. A capillary flow chamber was constructed on a glass slide using double-sided tape and a coverslip. The average dimensions of the flow chamber were 22 mm × 3 mm × 40 μm. 5 mg/ mL casein protein diluted in BRB80 was added to fill the flow chamber and incubated for 5 min. Kinesin was diluted to 325 nM in BRB80 with 1 mg/mL casein and 1 mM ATP and then added to the flow chamber and incubated for 5 min. Paclitaxel stabilized MTs were diluted to 0.05 μM in motility solution (BRB80 containing 5 mg/mL casein, 3 mM ATP, 0.04 mg/mL glucose oxidase, 0.016 mg/mL catalase, 1 mM DTT, and 40 mM D-glucose) and added to the flow chamber. After 5 min, the flow chamber was imaged on an inverted Olympus IX-71 microscope equipped with a 100× oil immersion objective and a Hamamatsu color CDD camera (ORCA-3CCD). MT surface densities (σMT) were calculated by dividing the average MT projected surface by the image field of view for 10 different images before the addition of MLVs. All images and data were captured at 23 °C. LNT Fabrication. Flow chambers as described above were checked for the presence of MT motility and the calculation of σMT. Streptavidin diluted to 0.01 mg/mL in motility solution was added to the flow chamber and incubated for 10 min. Motility solution was added to the flow chamber to wash out excess streptavidin. 1:10 dilution of MLVs in motility solution was added to the flow chambers and immediately imaged on the microscope. At the initial observation of the formation of a LNT network a 30 min timer was started. At the end of this period, motility solution with adenosine 5′-(β,γimido)triphosphate (AMP-PNP) substituted for ATP was added to the flow chamber to inhibit MT motility and preserve the properties of the 30 min network. Images were taken during the LNT network growth and at the 30 min stopped condition with a 40×, 60×, and 100× oil immersion objectives. Many large networks spanned multiple fields of view, and final network images were composed of a mosaic of individually capture images stitched together. Velocities of bound and unbound MT were measured from positional data of the leading MT end as a function of time. Nanoparticle Capture and Diffusivity. To the stopped networks, 1:100 and 1:10 000 diluted concentration of Qdot 525 and/or Qdot 605 streptavidin coated nanocryastals in AMP-PNP motility solution were added to the flow chambers and incubated for 10 min. AMP-PNP motility solution was added to the flow chambers to wash out excess Qdots. Qdot diffusivity was observed at 60 frames/s. Image processing and particle tracking were performed in Fiji released under the General Public License.59,60 Mean-squared displacements were measured from Qdot positional data on perfectly straight segments of 2 2 LNT using [1/(N − τ)]∑N−τ j=1 (xj+τ − xj) + (yj+τ − yj) , where N is the total number of frames in the image sequence and τ is the instantaneous fame number to which a particle moves from initial position xj, yj. The time interval Δt is τ times 1/(frame rate). MSD was calculated using a Matlab script. Diffusivity coefficient D was calculated by fitting the MSD plot versus time with MSD(t) = 2Dt for isolated Qdot mobility or with MSD(t) = [(1 − (N/L)d)/(N/L)][(4Dt/

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CONCLUSION In this work, we couple a soft source material (multilamellar lipid vesicles) to a biological transport phenomenon (MTs and associated motor proteins) to fabricate very large scale (>10 mm) networks of lipid nanotubes in a relatively short time frame. Because of the stochastic nature of gliding MTs in this application, we do not have precise control over the generated nanostructured patterns; however, we do discuss a number of simple parameters that can be changed to tune properties of the LNT networks. MT length and surface density can dictate whether LNT will form and the frequency of network branching. Additionally, careful consideration of the structure of the source material (multilamellar vs unilamellar) along with intrinsic source material properties such as lipid membrane bending rigidity, membrane viscosity, and surface tension plays a role in the critical requirements for network formation, surface transport properties, and LNT size. Tubular systems are quite common in biology and are useful as conduits for the diffusive transport of small molecules. We explored LNT networks as platforms to capture and transport functionalized nanomaterials along the confined 1D tracks of the nanotubes. At high capture densities, model Qdots experience single file diffusive motion, limiting large scale thermal transport. However, at low capture densities, Qdots motion can be described by a simple 1D diffusive mechanism that depends on the LNT membrane viscosity and tube size. We are currently working toward directed transport of the capture nanomaterials by application of applied external fields and propose that LNT network bifurcation frequency will aid in the transport of densely capture nanomaterial analytes.

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METHODS

All chemicals were purchased from Sigma-Aldrich unless indicated otherwise. Lyophilized unlabeled tubulin, Hilyte 488 (Anaspec Inc., Fremont, CA) labeled tubulin, and biotin labeled tubulin from porcine brain were purchased from Cytoskeleton Inc. (Denver, CO) and used without further purification. 1,2-Dioleoyl-sn-glycero-3-phosphocholine (DOPC) and 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine-N-(biotinyl) (Biotin-PE) were purchased from Avanti Polar Lipids (Alabaster, AL). Texas Red, 1,2-dihexadecanoyl-sn-glycero-3-phosphoethanolamine (TR-DHPE), Qdot 525 streptavidin conjugate, and Qdot 605 streptavidin conjugate were purchased from Invitrogen (Life Technologies, Grand Island, NY). MT Polymerization. An ice cold solution of 1 mM GTP and 15% glycerol dissolved in 80 mM piperazine-N,N′-bis(2-ethanesulfonic acid), 2 mM MgCl2, and 1 mM EDTA at pH 6.9 (BRB80) was used to resuspend the tubulin proteins to 22 μM. Biotin tubulin, Hilyte 488 tubulin, and unlabeled tubulin were mixed at a molar ratio of 5:15:80, respectively, and polymerized at 37 °C for 20 min. The polymerized MTs were then diluted to 0.5 μM and stabilized against depolymerization by using a solution of BRB80 containing 10 μM paclitaxel and stored at room temperature. F

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π)1/2], where N is the number of Qdots with diameter d on a straight segment of LNT of length L for neighbor-confined Qdot mobility.

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(16) Leduc, C.; Campàs, O.; Zeldovich, K. B.; Roux, A.; Jolimaitre, P.; Bourel-Bonnet, L.; Goud, B.; Joanny, J. F.; Bassereau, P.; Prost, J. Cooperative extraction of membrane nanotubes by molecular motors. Proc. Natl. Acad. Sci. U. S. A. 2004, 101, 17096. (17) Roux, A.; Cappello, G.; Cartaud, J.; Prost, J.; Goud, B.; Bassereau, P. A minimal system allowing tubulation with molecular motors pulling on giant liposomes. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 5394. (18) He, S.; Lam, A. T. C.; Jeune-Smith, Y.; Hess, H. Modeling negative cooperativity in streptavidin adsorption onto biotinylated microtubules. Langmuir 2012, 28, 10635−10639. (19) Howard, J.; Hudspeth, A.; Vale, R. Movement of microtubules by single kinesin molecules. Nature 1989, 342, 154. (20) Duke, T.; Holy, T. E.; Leibler, S. “Gliding assays” for motor proteins: a theoretical analysis. Phys. Rev. Lett. 1995, 74, 330−333. (21) Karlsson, M.; Sott, K.; Davidson, M.; Cans, A.-S.; Linderholm, P.; Chiu, D.; Orwar, O. Formation of geometrically complex lipid nanotube-vesicle networks of higher-order topologies. Proc. Natl. Acad. Sci. U. S. A. 2002, 99, 11573−8. (22) Raucher, D.; Sheetz, M. P. Characteristics of a membrane reservoir buffering membrane tension. Biophys. J. 1999, 77, 1992− 2002. (23) Derényi, I.; Jülicher, F.; Prost, J. Formation and interaction of membrane tubes. Phys. Rev. Lett. 2002, 88, 238101. (24) de Gennes, P.-G.; Prost, J. The Physics of Liquid Crystals; Clarendon Press: Oxford, 1995. (25) Van der Linden, E.; Dröge, J. Deformability of lamellar droplets. Physica A 1993, 193, 439−447. (26) Helfrich, W. Steric interaction of fluid membranes in multilayer systems. Z. Naturforsch., A 1978, 33, 305−315. (27) Nallet, F.; Roux, D.; Prost, J. Dynamic light scattering study of dilute lamellar phases. Phys. Rev. Lett. 1989, 62, 276−279. (28) Tristram-Nagle, S.; Petrache, H. I.; Nagle, J. F. Structure and interactions of fully hydrated dioleoylphosphatidylcholine bilayers. Biophys. J. 1998, 75, 917−925. (29) Pan, J.; Tristram-Nagle, S.; Kučerka, N.; Nagle, J. F. Temperature dependence of structure, bending rigidity, and bilayer interactions of dioleoylphosphatidylcholine bilayers. Biophys. J. 2008, 94, 117−124. (30) Koster, G.; Cacciuto, A.; Derényi, I.; Frenkel, D.; Dogterom, M. Force barriers for membrane tube formation. Phys. Rev. Lett. 2005, 94, 68101. (31) Koster, G.; VanDuijn, M.; Hofs, B.; Dogterom, M. Membrane tube formation from giant vesicles by dynamic association of motor proteins. Proc. Natl. Acad. Sci. U. S. A. 2003, 100, 15583. (32) Schnitzer, M. J.; Visscher, K.; Block, S. M.; et al. Force production by single kinesin motors. Nat. Cell Biol. 2000, 2, 718−723. (33) Marsh, D.; King, M. D. Prediction of the critical micelle concentrations of mono- and di-acyl phospholipids. Chem. Phys. Lipids 1986, 42, 271−277. (34) Cevc, G.; Marsh, D. Phospholipid Bilayers: Physical Principles and Models; John Wiley and Sons Ltd.: New York, 1987. (35) Fallesen, T. L.; Macosko, J. C.; Holzwarth, G. Force-velocity relationship for multiple kinesin motors pulling a magnetic bead. Eur. Biophys. J. 2011, 1−9. (36) Kumar, S.; ten Siethoff, L.; Persson, M.; Lard, M.; te Kronnie, G.; Linke, H.; Månsson, A. Antibodies covalently immobilized on actin filaments for fast myosin driven analyte transport. PLoS One 2012, 7, e46298. (37) Cuvelier, D.; Chiaruttini, N.; Bassereau, P.; Nassoy, P. Pulling long tubes from firmly adhered vesicles. Europhys. Lett. 2005, 71, 1015. (38) Shenoy, S.; Moldovan, R.; Fitzpatrick, J.; Vanderah, D. J.; Deserno, M.; Lösche, M. In-plane homogeneity and lipid dynamics in tethered bilayer lipid membranes (tBLMs). Soft Matter 2010, 6, 1263− 1274. (39) Wong, J.; Chilkoti, A.; Moy, V. T. Direct force measurements of the streptavidin-biotin interaction. Biomol. Eng. 1999, 16, 45−55.

ASSOCIATED CONTENT

S Supporting Information *

Additional figures of network formation and Qdot mobility. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research was supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering, Project KC0203010. Sandia National Laboratories is a multiprogram laboratory operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Company, for the U.S. Department of Energy’s National Nuclear Security Administration under Contract DE-AC0494AL85000.

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