pubs.acs.org/Langmuir © 2010 American Chemical Society
Direct Measurements of Protein-Stabilized Gold Nanoparticle Interactions Shannon L. Eichmann and Michael A. Bevan* Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218 Received July 10, 2010. Revised Manuscript Received August 15, 2010 We report integrated video and total internal reflection microscopy measurements of protein stabilized 110 nm Au nanoparticles confined in 280 nm gaps in physiological media. Measured potential energy profiles display quantitative agreement with Brownian dynamic simulations that include hydrodynamic interactions and camera exposure time and noise effects. Our results demonstrate agreement between measured nonspecific van der Waals and adsorbed protein interactions with theoretical potentials. Confined, lateral nanoparticle diffusivity measurements also display excellent agreement with predictions. These findings provide a basis to interrogate specific biomacromolecular interactions in similar experimental configurations and to design future improved measurement methods.
Introduction Nanoparticles with adsorbed synthetic macromolecules and biomacromolecules (e.g., proteins, carbohydrates, nucleic acids) are currently investigated for use in a broad range of emerging biomedical applications (e.g., imaging,1 diagnostics,2 drug delivery3). Such nanoparticles are also being developed in basic biophysical and biochemical research as probes of cellular and biomacromolecular structures and their interface with synthetic material systems.4,5 In many applications, nanoparticles reside near surfaces or in confined spaces as part of their fabrication, transport within various tissues, and interactions with different synthetic and biological material systems. Although protein stabilized nanoparticle interactions in confinement determine their behavior and properties in many applications, there is little precedent for directly measuring these interactions. Nanoparticles are routinely measured with spectroscopic and scattering methods in bulk ensembles,6,7 but the information obtained from such measurements is indirect, averaged over many particles, and is generally for unconfined environments. Because nanoparticles are below the optical diffraction limit, they are not generally amenable to optical microscopy measurements and instead are routinely imaged with electron microscopy in dry, vacuum environments far from biologically relevant conditions. Atomic force microscopy provides another means to interrogate nanoparticles in physiological media, by either attaching nanoparticles to cantilevers or surfaces,8-10 but is *To whom correspondence should be addressed. E-mail: mabevan@ jhu.edu. (1) Qian, X.; Peng, X.-H.; Ansari, D. O.; Yin-Goen, Q.; Chen, G. Z.; Shin, D. M.; Yang, L.; Young, A. N.; Wang, M. D.; Nie, S. Nat. Biotechnol. 2008, 26, 83–90. (2) Alivisatos, P. Nat. Biotechnol. 2004, 22, 47–52. (3) Langer, R.; Peppas, N. A. AIChE J. 2003, 49, 2990–3006. (4) Dahan, M.; Levi, S.; Luccardini, C.; Rostaing, P.; Riveau, B.; Triller, A. Science 2003, 302, 442–445. (5) Warnasooriya, N.; Joud, F.; Bun, P.; Tessier, G.; Coppey-Moisan, M.; Desbiolles, P.; Atlan, M.; Abboud, M.; Gross, M. Opt. Express 2010, 18, 3264– 3273. (6) Berne, B. J.; Pecora, R. Dynamic Light Scattering; Wiley: New York, 1976. (7) Kerker, M. The Scattering of Light and Other Electromagnetic Radiation; Academic Press: New York, 1969. (8) Li, H. Y.; Park, S. H.; Reif, J. H.; LaBean, T. H.; Yan, H. J. Am. Chem. Soc. 2004, 126, 418–419. (9) Tong, L.; Zhu, T.; Liu, Z. Appl. Phys. Lett. 2008, 92. (10) Wong, S. S.; Joselevich, E.; Woolley, A. T.; Cheung, C. L.; Lieber, C. M. Nature 1998, 394, 52–55.
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not generally capable of interrogating diffusing nanoparticles experiencing weak interactions with biomacromolecules. In this Letter, we describe integrated total internal reflection (TIRM) and video (VM) microscopy methods to measure bovine serum albumin (BSA)-stabilized 110 nm Au nanoparticles confined in 280 nm gaps between glass microscope slides (Figure 1). This builds on a precedent of using variants of TIRM to measure synthetic macromolecular interactions11-13 and nonspecific biomacromolecular interactions14 between micron-sized colloids and surfaces in physiological ionic strength media. More recently, TIRM/VM has been used to measure interactions in ensembles of micron-sized colloids,15-18 which was adapted to measure electrostatic interactions of 50-250 nm Au nanoparticles confined between parallel microscope slides.19 In the following, we use Brownian dynamics (BD) simulations to address a number of challenges associated with TIRM/VM measurements of proteinstabilized nanoparticle interactions, which arise from a combination of fast dynamics, strong van der Waals forces, and camera acquisition limits.
Materials and Methods BSA solutions (1 mg/mL in DI water) were filtered through 0.2 μm filters (Fisher Scientific, Pittsburgh, PA) and adsorbed to coverslips for >6 h. Bare 110 nm Au nanoparticles (Ted Pella, Inc., Redding, CA) were dispersed in BSA solutions for >6 h and then centrifuged to remove excess BSA. Bare 280 nm SiO2 spacers (Microspheres-Nanospheres, Cold Spring, NY). Colloid sizes were measured using dynamic light scattering (Malvern Instruments, Worcestershire, U.K.) (see Table 1). TIRM/VM measurements were performed as in our previous paper on Au nanoparticle interactions19 with several exceptions. Prior to cell assembly, BSAcoated Au nanoparticles and SiO2 spacers were dispersed in 0.15 M (11) Bevan, M. A. Ph.D. Dissertation, Carnegie Mellon University, Pittsburgh, PA, 1999. (12) Bevan, M. A.; Prieve, D. C. Langmuir 2000, 16, 9274–9281. (13) Fernandes, G. E.; Bevan, M. A. Langmuir 2007, 23, 1500–1506. (14) Everett, W. N.; Wu, H.-J.; Anekal, S. G.; Sue, H.-J.; Bevan, M. A. Biophys. J. 2007, 92, 1005–1013. (15) Wu, H. J.; Bevan, M. A. Langmuir 2005, 21, 1244–1254. (16) Wu, H.-J.; Pangburn, T. O.; Beckham, R. E.; Bevan, M. A. Langmuir 2005, 21, 9879–9888. (17) Wu, H.-J.; Everett, W. N.; Anekal, S. G.; Bevan, M. A. Langmuir 2006, 22, 6826–6836. (18) Wu, H.; Shah, S.; Beckham, R. E.; Meissner, K.; Bevan, M. A. Langmuir 2008, 24, 13790–13795. (19) Eichmann, S. L.; Anekal, S. G.; Bevan, M. A. Langmuir 2008, 24, 714–721.
Published on Web 08/24/2010
DOI: 10.1021/la1027674
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where the exponential macromolecular repulsion is an empirical model where Γ is the intercept at an effective hard wall at hw=(Δþδ)-4(γ-1), Δ and δ are the adsorbed protein layer thickness on the walls and particle, and γ-1 is a decay length associated with interpenetration and compression of adsorbed layers. The inverse power law van der Waals potentials are fits to the screened, retarded Lifshitz theory with a Hamaker geometric correction.20 In contrast to measurements of micron scale colloids near surfaces,15 the nanoparticle buoyant weight is negligible in this work as the result of their small dimensions (despite the high Au density), which is discussed in detail in our previous study of Au nanoparticles.19 The role of hydrodynamic interactions for motion of particles normal to confining walls are taken into account by modifying the Stokes-Einstein diffusivity, D0=kT/(6πμa), of an unbounded particle by a correction factor as Dðz, aÞ
¼
D0 f2 ðz, L, aÞ
f2 ðz, L, aÞ
¼
½ f1 ðz, aÞ - 1 þ f1 ðL - z, aÞ - 1 - 1 - 1
f1 ðz, aÞ Figure 1. Confined BSA-stabilized Au nanoparticle schematic. Variable labels as defined in theory section with values in Table 1. Materials correspond to (1) Au, (2) BSA, (3) 150 mM phosphate buffer solution, and (4) SiO2 coverslips. Table 1. Parameters in Measurements and Simulations experimental
simulation
eq
2a/nma 110.0 ( 9.3 110 1-3 262.7 ( 33.7 280 1-3 L/nma 1 1 2 γ/nm-1b b 10 10 2 Γ/kT c 9 9 δ/nm 5.5 5.5 Δ/nmc 2.5792 2.5792 2 A/kTd d 1.922 1.922 2 p e 12877 12877 4,6 I0 6.5 6.5 σ/nm f 36 36 τF/msg g 0.4 0.4 τE/nm a Obtained from DLS. b Obtained from TIRM.14 c Obtained from literature BSA adsorbed dimensions.24,25 d Obtained from power law fit to Lifshitz theory.17,26 e Obtained from TIRM/VM experiment in Figure 3. f Obtained from irreversibly deposited particle standard deviation. g Obtained from CCD camera setting.
phosphate buffer solution. A ∼15 μL drop of the nanoparticle/ spacer mixture was placed on large BSA-coated coverslip (24 40 mm), and then a smaller BSA-coated coverslip (18 18 mm) was placed on top of the drop before sealing with epoxy.
Theory and Simulations The net potential energy of a nanoparticle with radius, a, as a function of elevation, z, between parallel walls separated by a distance, L, (Figure 1) is given by the superposition of interactions with the bottom wall, uBW, and top wall, uTW, as uðz, a, LÞ ¼ uTW ðz, a, LÞ þ uBW ðz, a, LÞ
ð1Þ
ð2Þ 14410 DOI: 10.1021/la1027674
ð3Þ
6ðz - aÞ2 þ 9aðz - aÞ þ 2a2
where the two-wall correction function, f2(z,L,a), is based on the linear superposition approximation19 and one-wall correction, f1(z,L,a).21 A similar expression for parallel motion between confining walls was reported previously using different correction functions.19 Using eqs 1-3, BD simulations were performed as described in extensive detail in previous papers.19,22,23 Interactions between nanoparticles are neglected in BD simulations because of the dilute conditions investigated in measurements, which also rigorously exclude nanoparticle interactions when they do rarely occur.16 Simulations were performed for 720 106 time steps with an integration time of 0.0005 ms, which is larger than momentum relaxation time and smaller than the diffusive time scale for Au nanoparticles with 2a = 110 nm. The data from BD simulations were 36 106 time dependent elevations of single Au nanoparticles with Brownian time steps, τB = 0.01 ms. To investigate effects of measurement noise and camera exposure time, elevations were first converted to time dependent evanescent wave scattering intensities, IS(t), using IS ðtÞ ¼ I0 exp½ - βðzðtÞ - aÞ
ð4Þ
where I0 is the scattering intensity at z - a = 0 for a particle irreversibly deposited on the bottom wall without adsorbed macromolecules, and β-1 is the evanescent wave decay length. Because camera exposure times, τE, are longer than the time for nanoparticle Brownian excursions (τE > τB), the exposure time averaged signal is obtained as IE(t) ≈ ÆIS(t)æ at the end of each frame period, τF, which is much longer than the exposure time (τF > τE). Gaussian-distributed noise with mean, ÆIN(t)æ = 0 and relative standard deviation, σ/I0, was added to BD-simulated intensities. A time independent background intensity, IB, was also included to simulate camera intensities, IC(t), as IC ðtÞ ¼ IE ðtÞ þ IN ðtÞ þ IB
where interactions with the top and bottom wall include steric and van der Waals contributions given by uBW ðz, a, LÞ ¼ Γ exp½ - γðz - a - hwÞ - A2aðz - aÞ - p uTW ðz, a, LÞ ¼ Γ exp½ - γðL - z - a - hwÞ - A2aðL - z - aÞ - p
¼
6ðz - aÞ2 þ 2aðz - aÞ
(20) (21) (22) (23)
ð5Þ
Bevan, M. A.; Petris, S. N.; Chan, D. Y. C. Langmuir 2002, 18, 7845–7852. Bevan, M. A.; Prieve, D. C. J. Chem. Phys. 2000, 113, 1228–1236. Anekal, S.; Bevan, M. A. J. Chem. Phys. 2005, 122, 034903. Anekal, S.; Bevan, M. A. J. Chem. Phys. 2006, 125, 034906.
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Figure 2. (top half) (A) schematic of blurred evanescent wave scattering due to exposure time greater than time scale of Brownian motion (τE > τB), (B) noise free intensity vs time, IS(t), (gray) from BD simulated elevations in eq 4 with red vertical lines showing exposure time period, τE, red horizontal line and red points showing average intensity in exposure time period, IE(t) = ÆIS(t)æ, and blue triangles showing combined effects of exposure time and system noise on CCD measured points, IC(t), and (C) equilibrium intensity histogram with and without CCD camera effects. (bottom half) (A) schematic of “stuck” particle with no Brownian motion and intensity change only due to net system noise, (B) intensity fluctuations for stuck nanoparticle, and (C) intensity histogram for a stuck nanoparticle.
which can be transformed to nanoparticle height excursions via an inversion of eq 4 as hðtÞ ¼ zðtÞ - a ¼ β - 1 ln½I0 =IC ðtÞ
ð6Þ
These heights were used to create time averaged height histograms, p(h), for analysis as effective potential energy profiles, u(h), via a Boltzmann inversion (i.e., inversion of Boltzmann’s equation) as uðhÞ - uðhm Þ ¼ kT ln½ pðhm Þ=pðhÞ
ð7Þ
where hm is the most probable height.
Results and Discussion The goal of this work is to track the 3D coordinates of ensembles of 2a = 110 nm Au nanoparticles confined in L = 280 nm gaps between glass microscope slides using integrated TIRM/VM.19 Because Au nanoparticles have a optical contrast compared to aqueous media, the evanescent wave scattering intensity from such particles is comparable to micron-sized polymer and SiO2 colloids. This allows Au nanoparticles to be plainly visible and trackable under evanescent wave illumination in an optical microscope despite their subdiffraction limit dimensions The small dimensions of nanoparticles also cause them to diffuse rapidly (even with confined hydrodynamic hindrance, that is eq 3), which ultimately becomes an issue as rates of particle motion become comparable to characteristic camera dynamics. One critical camera parameter is the exposure time, τE, or the period during which camera shutter is open to collect scattered (24) Brewer, S. H.; Glomm, W. R.; Johnson, M. C.; Knag, M. K.; Franzen, S. Langmuir 2005, 21, 9303–9307. (25) Su, T. J.; Lu, J. R.; Thomas, R. K.; Cui, Z. F.; Penfold, J. J. Phys. Chem. B 1998, 102, 8100–8108. (26) Bevan, M. A.; Prieve, D. C. Langmuir 1999, 15, 7925–7936.
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light. Although inversion of eq 4 provides the potential capability to resolve nanometer changes in instantaneous nanoparticle elevations by measuring instantaneous intensities, finite τE results in integration of intensities from nanoparticles sampling a range of elevations and thus “blurs” this resolution (Figure 2A, top). The top portion of Figure 2 shows effects of camera exposure time in a single BD simulated nanoparticle trajectory (using parameters in Table 1), which has been converted to artifact free scattering intensities using eq 4. The gray line in top portion of Figure 2B represents intensity fluctuations due to Brownian excursions as a function of time, IS(t). The red vertical dashed lines represent the experimental exposure time period, τE = 0.4 ms, during which a time averaged intensity, IE(t) ≈ ÆIS(t)æ, is obtained. Red points are average intensities obtained during τE, which are separated by the τF = 36 ms frame period. To analyze the scattering intensities as nanoparticle-surface interactions using eq 7, it is necessary to construct a time-averaged equilibrium intensity histogram (or height histogram). The top portion of Figure 2C also shows how the exposure time affects the resulting intensity (and therefore, height) distribution acquired by the CCD camera compared to intensities distributions without “blurring”. The BD simulated results in the top portion of Figure 2 clearly show that small temporal and spatial changes are not resolved, but rather many dynamically sampled heights are averaged together during the exposure time. In addition to CCD camera exposure time effects, noise is also present in the intensity measurements based on laser fluctuations, mechanical vibrations in all optical and microscopy components, and in the CCD camera detection. Misinterpretation of intensity noise as nanoparticle Brownian motion will distort both the static and dynamic interpretation of such motion as a probe of conservative and dissipative forces between nanoparticles and surfaces. The net effect of all noise sources can be characterized directly from intensity variations of irreversibly deposited particles not experiencing Brownian motion (bottom, Figure 2A). DOI: 10.1021/la1027674
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Figure 4. Lateral MSD results from experiments from orthogonal 1D random walks (Æx2æ or Æy2æ, gray points), average of orthogonal 1D random walks (Ær2æ, black points), fit to experimental points (black line), and theory (blue line)19 with hydrodynamic radius, 2ah = 2(a þ δ) = 128 nm, and hydrodynamic gap, Lh = L - 2Δ = 269 nm, based on values in Table 1.
The bottom of Figure 2B shows representative intensity fluctuations (black line) obtained for this particle due to noise. Figure 2C bottom shows a representation of how noise affects the intensities acquired by the CCD camera compared to the single intensity expected for a particle stuck to the surface. As expected for random noise sources, the time averaged intensity distribution of deposited nanoparticles is a Gaussian with a mean, I0, and relative standard deviation, σ/I0. Although various instrument settings (e.g., integration area, CCD binning, laser power, etc.) and physical characteristics (e.g., nanoparticle size, refractive indices, etc.) determine the absolute intensities obtained for nanoparticle evanescent wave scattering,15 intensities measured relative to I0 are sufficient to obtain potential energy profiles via eqs 4-7. For the sake of comparing BD simulations and microscopy experiments, we choose a value of I0 = 12 877, which happens to be the highest integrated intensity value obtained in a Au nanoparticle TIRM/VM measurement to follow in Figure 3. A value of σ/I0 = 0.058 (i.e., 750/12877) was obtained from a Gaussian fit to the deposited nanoparticle intensity histogram. Figure 3 shows in experiment and simulation the net effects of exposure time and noise on potential profile measurements for confined Au nanoparticles with near hard sphere macromolecular repulsion between adsorbed protein layers. Figure 3 shows (1) a black line indicating the theoretical net potential energy profile given by eqs 1 and 2, gray points indicating the potential energy profile obtained from inversion of an equilibrium histogram constructed from the clean BD data in Figure 2B, top, (3) blue points constructed from the BD data in Figure 2B, top that include both exposure time and noise effects, and (4) black points from measured microscopy data with the same parameters as the BD simulations (Table 1). Because microscopy experiments and BD simulation results in Figure 3 use all of the same unadjustable parameters obtained from independent measurements, these results provide an unambiguous comparison between experiment, simulation, and theory including camera limitations. The excellent agreement between the experimentally measured and simulated potential energy profiles in Figure 3 demonstrates the validity of (1) the measurement/analysis (including camera effects) and (2) the theoretical colloidal potentials (eqs 1, 2) for confined, protein-coated 110 nm Au nanoparticles. The measured potential in Figure 3 clearly shows weaker forces from exposure
time and noise effects “blurring” the measurement of instantaneous particle positions, which limits the measurement of strong hard wall repulsion between adsorbed protein layers and strong van der Waals attraction (at low energies). Uncertainty in changes in nanoparticle position due to exposure time averaging affects strong forces to a greater extent than weak forces since strong forces correspond to large energy changes with small changes in nanoparticle position. Theoretical potentials also appear to be validated by the results in Figure 3 including (1) the Au nanoparticle-silica surface van der Waals attraction predicted by Lifshitz theory, and (2) the near hard wall repulsion between adsorbed BSA layers with dimensions of ∼δ = 9 nm on the Au nanoparticle surface24 and ∼Δ = 5.5 nm on the flat silica substrates.25 Finally to probe the confined diffusion of protein-stabilized Au nanoparticles, Figure 4 shows mean-squared displacements (MSD), Ær2æ, of particles in a given direction, r, as a function of time, t,23 to characterize the average lateral diffusion, ÆD2w æ, which is compared to theoretical predictions.19,21 To compare experimental and theoretical diffusivities, the hydrodynamic particle size, 2ah = 2(a þ δ) = 128 nm, and hydrodynamic gap spacing, Lh = L - 2Δ = 269 nm, are modeled assuming adsorbed protein layers are impermeable. Comparison between the blue and black lines shows excellent agreement and no evidence of camera issues important to normal particle-wall measurements (Figure 3) or electroviscous effects found at lower ionic strengths.19 Agreement between experiment and theory for lateral diffusion also suggests the validity of the equation of motion in BD simulations when investigating exposure time effects on potential energy profiles in Figure 3. Our previous low ionic strength measurements of 50-250 nm Au nanoparticles are validated by, and consistent with, our present findings (Figures 3 and 4) that indicate (1) much softer electrostatic interactions that do not require consideration of camera exposure time and noise effects, and (2) much thicker electric double layers with strong overlap appear to produce electroviscous effects that alter dissipative forces and confined particle diffusion at low ionic strengths. These conclusions are supported by using BD simulations including exposure time and noise to quantitatively reproduce the low ionic strength potential energy profiles while still overestimating low ionic strength confined, lateral diffusivities.
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)
Figure 3. Potential energy profiles from theory (eqs 1, 2) (black line), inversion of noise-free BD simulated trajectories (gray points), measured data (black points), and BD simulated trajectories including CCD camera exposure time and system noise effects (blue triangles) with potential and simulation parameters in Table 1.
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In summary, the results in Figures 3 and 4 demonstrate the ability to directly measure interactions between single 110 nmsized Au nanoparticles and glass microscope slide surfaces both bearing adsorbed protein layers. This approach can now be used to confidently measure potentials in similar confined nanoparticle systems in the presence of adsorbed biomacromolecules where the potential are not known ahead of time. To analyze such measured potentials, our results demonstrate that “inverse” BD simulations incorporating independently characterized exposure time and noise effects can be used to compare theoretical predictions against measured potentials that suffer from such effects. Although such inverse analyses could potentially involve issues of “uniqueness” (i.e., several “blurred” theoretical potentials could agree with same measured potential), performing independent measurements of theoretical potential parameters practically provides for the formulation of well posed inverse problems.
nanoparticles are well below the optical diffraction limit, such nanoparticles were successfully tracked in three dimensions in real space and real time using integrated VM and TIRM methods. CCD camera exposure time and instrument noise effects do not allow nanometer spatial resolution of Brownian excursions normal to parallel confining walls. However, comparison of TIRM measurements to BD simulations that use the same physical, material, and instrument acquisition parameters produce nearly identical potential energy profiles. These results demonstrate a capability to directly measure nonspecific protein interactions and lateral diffusion in confined nanoparticle systems, which provides a foundation for future quantitative measurements of specific biomacromolecular interactions in similar experimental configurations. Agreement between experiments and simulations also provides a basis to quantify the degree to which instrument advancements will improve measurements in such systems.
Conclusion Our direct TIRM measurements and BD simulations of BSAstabilized 110 nm Au nanoparticles demonstrate the ability to quantify van der Waals and biomacromolecular interactions even in the presence of CCD camera limitations. Although 110 nm
Acknowledgment. We acknowledge financial support by the National Science Foundation (CTS-0346473, CBET-0834125) and the Robert A. Welch Foundation (A-1567). We thank Daniel J. Beltran-Villegas for assistance incorporating camera and noise effects into BD simulations.
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DOI: 10.1021/la1027674
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