ACS Publications. Most Trusted. Most Cited. Most Read
Experimentally Probing the Effect of Confinement Geometry on Lipid Diffusion
My Activity

Figure 1Loading Img
  • Open Access
B: Biomaterials and Membranes

Experimentally Probing the Effect of Confinement Geometry on Lipid Diffusion
Click to copy article linkArticle link copied!

Open PDFSupporting Information (1)

The Journal of Physical Chemistry B

Cite this: J. Phys. Chem. B 2024, 128, 18, 4404–4413
Click to copy citationCitation copied!
https://doi.org/10.1021/acs.jpcb.3c07388
Published April 4, 2024

Copyright © 2024 The Authors. Published by American Chemical Society. This publication is licensed under

CC-BY 4.0 .

Abstract

Click to copy section linkSection link copied!

The lateral mobility of molecules within the cell membrane is ultimately governed by the local environment of the membrane. Confined regions induced by membrane structures, such as protein aggregates or the actin meshwork, occur over a wide range of length scales and can impede or steer the diffusion of membrane components. However, a detailed picture of the origins and nature of these confinement effects remains elusive. Here, we prepare model lipid systems on substrates patterned with confined domains of varying geometries constructed with different materials to explore the influences of physical boundary conditions and specific molecular interactions on diffusion. We demonstrate a platform that is capable of significantly altering and steering the long-range diffusion of lipids by using simple oxide deposition approaches, enabling us to systematically explore how confinement size and shape impact diffusion over multiple length scales. While we find that a “boundary condition” description of the system captures underlying trends in some cases, we are also able to directly compare our systems to analytical models, revealing the unexpected breakdown of several approximate solutions. Our results highlight the importance of considering the length scale dependence when discussing properties such as diffusion.

This publication is licensed under

CC-BY 4.0 .
  • cc licence
  • by licence
Copyright © 2024 The Authors. Published by American Chemical Society

Introduction

Click to copy section linkSection link copied!

The cell membrane is a crowded, continuously fluctuating environment composed of a diverse population of lipids, proteins, and small molecules (Figure 1). Characterizing and understanding the dynamics of these components and how they interact are crucial to developing an effective description of both structural and functional aspects of the membrane. This has motivated extensive experimental and theoretical efforts to probe the motion of lipids over a wide range of length scales. (1−5) However, the complex, heterogeneous nature of biological membranes makes systematic exploration of the energy landscape of systems in vivo extremely challenging. Of particular importance is understanding how the effect of confinement manifests in these systems and shapes the behavior of membrane components. Confinement can influence the diffusion of small molecules in a wide range of contexts (6−12) spanning many length scales; one important example is the localization of membrane components by actin filaments, where domain sizes range from nanometer to micrometer scales. (13−17) Hierarchical, scale-dependent behavior has been observed in these networks, (18) having a range of geometries, spanning mesh-like irregular networks (19) to ordered rings. (17) This range of length scales and geometries motivates efforts to understand the driving forces between specific molecular interactions and physical boundary conditions, how this impacts processes such as diffusion and binding, and how this depends on the length scale.

Figure 1

Figure 1. Membranes are crowded, congested environments with obstructions spanning length scales from nanometers to microns. Schematic showing a membrane domain with a transmembrane protein (green), integral and peripheral proteins (purple), cholesterol (orange/red), glycosphingolipids (red), actin meshwork (navy), an ion channel (blue), and a lipid aggregate (dark gray). In this work, we probe the effect of the confinement geometry on both local and global scales.

Diffusion plays a key role in many biological processes, ultimately determining the rate of encounter of membrane proteins and mass transport within the membrane. Developing a picture of how diffusion changes in response to different membrane perturbations is therefore of great importance, which has motivated many efforts at characterizing diffusion of systems such as particles in the presence of obstacles, (6,11,20−26) membrane flow under different conditions, (27,28) and components in curved membranes. (29−31) The ubiquity of confined environments in biological systems, such as ions in ion channels, (32) molecules in dendritic spines, (33) and ligands binding to cells, (34) has also motivated extensive theoretical descriptions of diffusion in these systems, (35−45) which have robustly demonstrated the nontrivial relationship between diffusion and geometry. (46−50) In many cases, however, the validity of these descriptions has not been experimentally tested, leaving unanswered questions about when specific molecular interactions can be neglected, when a confined membrane should be described as a continuous fluid or discrete particles, or how to treat irregularly shaped confined regions. Even in the simplest systems – a membrane composed of lipids, with no proteins or other small molecules – challenges remain. Simulating diffusion in these systems is substantially more complicated than it first appears (51,52) even in the absence of confinement because of subtle artifacts in finite-size simulations.
Here, we systematically explore how the effects of geometric confinement shape and control the motion of lipids in membranes at different length scales by using thin oxide structures to form easily fabricated obstacles. Using a combination of fluorescence recovery after photobleaching (FRAP) and fluorescence correlation spectroscopy (FCS) approaches, we characterize how these systems can be described globally in terms of “boundary conditions,” which depend on the geometry of confinement, while still locally retaining their native diffusion coefficient, revealing the complex scale-dependent behavior of these dynamics.

Materials and Methods

Click to copy section linkSection link copied!

Vesicle Preparation

Vesicles used in this study were prepared from 1,2-dilauroyl-sn-glycero-3-phosphocholine (DLPC) and the label 1,2-dipalmitoyl-sn-glycero-3-phosphoethanolamine-N-(lissamine rhodamine B sulfonyl) (Rhod-PE), both purchased from Avanti Polar Lipids (Alabaster, AL). Prior to vesicle formation, Rhod-PE was stored in chloroform, DLPC in its powder form, and all lipids were stored at −20 °C. For FRAP measurements, a 1.2 mg/mL DLPC 0.5 mol % Rhod-PE solution was prepared by mixing appropriate amounts of DLPC, Rhod-PE, and chloroform (≥99%; Sigma-Aldrich; Darmstadt, Germany) and then dried under N2 gas and placed under vacuum for 24 h. The lipid film was rehydrated with phosphate-buffered saline (Boston BioProducts Inc.; Ashland, MA), and large unilamellar vesicles (LUVs) were formed by extruding through a 0.1 μm pore size polycarbonate membrane a total of 21 times. The mini-extruder set used to form LUVs was purchased from Avanti Polar Lipids. For FCS measurements, a 0.3 mg/mL DLPC 0.0025 mol % Rhod-PE solution was prepared following the same procedure.

Substrate Preparation

SiO2 substrates were formed by dicing wet thermal oxide wafers (University Wafers; South Boston, MA) into 1 cm × 1 cm chips. Substrates were subsequently cleaned by sonicating in 1:1 IPA:acetone for 2 min and then dried under N2 gas. Residual organic contaminants were removed by exposing the substrate to oxygen plasma treatment for 2 min (Anatech SP-100 Plasma System). Supported lipid bilayers were formed on the substrates immediately after the oxygen plasma cleaning using the vesicle fusion method, described in the following section.
TiO2-patterned substrates were formed by using photolithography. First, wet thermal oxide wafers were exposed to oxygen plasma treatment for 2 min. Immediately after the oxygen plasma cleaning, they were coated in a thin layer of S1813 photoresist by spinning at 4000 rpm for 1 min and then baking at 115 °C for 1 min. A quartz mask coated in chrome was used to pattern the photoresist. A Quintel 4000 Mask Aligner was used to expose the wafer for 7 s. The pattern was developed in AZ 726 MIF for 45 s, then rinsed with DI water for 1 min, and dried under N2 gas. The developed wafer was plasma-cleaned for 1 min. Using the Savannah 100 system, ∼4 nm of TiO2 (atomic force microscopy characterization shown in Figure S1) was grown on top of the wafer through atomic layer deposition (ALD) at 105 °C. Low-temperature deposition provides a cleaner liftoff, avoiding the potential destruction or outgassing of the resist layer. (53) After ALD, the S1813 photoresist was stripped by sonicating the wafer in acetone for 2 min and then drying under N2 gas, leaving a SiO2 substrate with TiO2 obstacles ∼4 nm in height. To investigate the material dependence, Al2O3-patterned substrates were made in the same way. Immediately before bilayer formation, the substrates were cleaned in 1:1 IPA:acetone (sonicating for 2 min) and then exposed to oxygen plasma treatment for 30 s.
The patterned substrates host three families of geometries: four circular pillars in a square array (termed “four pillars”), four arcs arranged in a circular array (“four channels”), and a continuous arc forming an almost-complete circle (“one channel”). These are shown schematically in Figure 2b. SiO2, TiO2-patterned, and Al2O3-patterned substrates were adhered to a CoverWell imaging gasket (Thermo Fisher Scientific; Waltham, MA) with double-sided Scotch tape.

Figure 2

Figure 2. (a) Schematic of the fabrication process, starting with a plain SiO2 substrate. The SiO2 substrate is lithographically patterned, developed, and then 2.5–5 nm of TiO2 or Al2O3 is deposited on the surface through atomic layer deposition (ALD). The substrate is cleaned, and supported lipid bilayers (SLBs) are formed on the SiO2 surfaces with the vesicle fusion method. The bottom left panel is a fluorescence image showing a SLB (dark gray) formed on a SiO2 substrate around TiO2 structures (black circles). Scale bar is 100 μm. (b) Three classes of structures used to explore geometric aspects of confinement.

Supported Lipid Bilayers

Supported lipid bilayers (SLBs) were formed using the vesicle fusion method, described elsewhere. (54) Briefly, the TiO2-patterned substrates were placed on a hot plate held at 30 °C, well above the gel–fluid phase transition temperature of DLPC (−1 °C). (55) 30 μL of the LUVs was deposited on the substrates, followed by 90 μL of phosphate-buffered saline. The LUVs were left to incubate on the substrates for 10 min. After incubation, 700 μL of phosphate-buffered saline was added to the gasket. The substrate was washed with phosphate-buffered saline a total of 15 times. At the end of the washing step, a 30 × 22 mm coverslip (Thermo Fisher Scientific; Waltham, MA) was rolled on top of the substrate. For FCS measurements, the sides of the gasket were coated with clear nail polish (Sally Hanson Xtreme-Wear; CVS) on top to avoid sample drying. A step-by-step protocol for forming SLBs with the vesicle fusion method can be found in archived protocols online. (56) The full fabrication process is illustrated in Figure 2a.

Fluorescence Recovery After Photobleaching (FRAP)

FRAP measurements are discussed elsewhere in detail. (57) Here, SLBs were immediately imaged after formation with a Zeiss LSM 880 fluorescence microscope equipped with a 10× objective and a 561 nm diode laser (Oberkochen, Germany). Selected regions inside of the TiO2 geometries were bleached and monitored with the 561 nm laser (Figure 3; bleached radius 12.25 μm). The intensity of the bleached region was integrated and fit to obtain a characteristic diffusion time using eq 1: (58)
I(t)=I0+n(InI0)e2Τn/t(J0(2Τnt)+J1(2Τnt))
(1)

Figure 3

Figure 3. FRAP enables imaging of the bilayer morphology and fluidity. Bilayers readily form and recover on SiO2 substrates, as shown in (a)–(c). (d) Recovery of fluorescence inside (red) and outside (blue) the pattern. Outside the pattern, the diffusion coefficient is consistent with literature values for other one-phase fluid bilayers at room temperature (4,59,60) (2.92 ± 0.07 μm2/s). Inside the pattern, the diffusion coefficient decreases; in the geometry shown, it decreases to 1.61 ± 0.04 μm2/s. The scale bar is 15 μm.

where I0 is the fluorescence intensity just after the bleach, J0 and J1 are the modified Bessel functions of orders 0 and 1, respectively, In is the intensity contribution of species n at t = ∞, and Tn is the characteristic diffusion time of species n. In our experiments, satisfactory fits are given for n = 1. Eq 1 is derived for a system without obstructions; our use of this analysis here is motivated by the successful application of this equation in previous studies exploring obstructed diffusion, (21,61−63) and we note the agreement between this functional form and our experimental data (Figure 3d and Supporting Information). Diffusion coefficients were calculated using the following equation:
D=0.224×R2Tn
(2)
where R is the radius of the bleached region.
Data were collected for all geometries on one patterned substrate at a time. Each substrate had at least three repetitions of the patterns shown in Figure S2. One FRAP data collection consisted of measuring the diffusion within all geometries for each pattern; this was repeated twice for two different patterns on each substrate. Data were recorded in one batch for each substrate. Data were collected on three different substrates on three different days. All diffusion data were normalized with respect to diffusion measured outside of the patterns to account for the systematic differences in the environment.

Fluorescence Correlation Spectroscopy (FCS)

FCS measurements were carried out using a 532 nm diode-pumped solid-state laser (GEM, Novanta Photonics; Bedford, MA) at a power less than 100 μW in a confocal microscope setup described in the Supporting Information. Briefly, the 532 nm laser beam was used to illuminate a 100× (1.25 NA) oil immersion objective (Zeiss; Oberkochen, Germany). Fluorescence was collected through the objective, and the emission was filtered by a 550 nm long-pass dichroic mirror (Thorlabs; Newton, NJ). A single-mode fiber acted as a pinhole. The fluorescence signal was detected by an avalanche photodiode (Excelitas Technologies; Waltham, MA) coupled to the fiber and was correlated with the Time Tagger Series software from Swabian Instruments (Time Tagger Ultra, Swabian Instruments; Stuttgart, Germany). The FCS setup was calibrated using 10 nM solutions of Rhodamine 6G (TCI America; Portland, OR) (Rhod-6G) in Millipore water following previously established calibration procedures. (64,65)
Measurements were taken by positioning the laser focal spot in the center of the TiO2 geometries. Each acquisition consisted of 10 FCS measurements of 5 s duration recorded at that position. The correlation functions were fit with a 2D diffusion model:
G(Τ)=1N(1+ΤΤD)1
(3)
where N is the average molecule number in the detection volume, Τ is the lag time, and TD is the average time of molecules diffusing through the detection volume. (64,66) FCS measurements were taken in the middle of each TiO2 geometry twice at different spatial locations on the TiO2-patterned substrates. The resultant TD and N were averaged for each pattern, and the diffusion coefficient was calculated from the subsequent average TD using the following equation:
D=w24ΤD
(4)
where w = 191 ± 7.42 nm is the beam waist obtained from the Rhod-6G calibration measurements. The error in w was calculated from the standard deviation of the results of 10 calibration acquisitions.

Numerical Simulation of Obstructed Diffusion

We simulate the expected FRAP response for different geometries by numerically solving the diffusion equation using VCell. (67,68) To model the TiO2 obstacles, we applied zero-flux boundary conditions around regions matching the geometries in our experiment. We initially generate a circular profile of nonzero concentration (representing the bleached lipids) and allow this to propagate. We simulate our experiment by integrating the concentration in this region at each time step and fitting the resulting curve to extract the effective diffusion coefficient in the same way as the experimental data.

Results and Discussion

Click to copy section linkSection link copied!

Effect of Confinement Geometry

Figure 4b shows the effective diffusion of DLPC lipids as a function of unobstructed fraction for the three different geometries obtained by FRAP, which probes >μm length scales. The unobstructed fraction for each geometry is defined as
σ=nl2πR
(6)

Figure 4

Figure 4. (a) The unobstructed fraction is defined as σ=nl2πR where n is the number of escape channels, l is their arclength, and R is the radius of the bleached region (dashed gray circle). (b) The unobstructed fraction vs the effective diffusion for the three different TiO2 geometries are compared. (c) Comparison of the unobstructed fraction vs the effective diffusion for the four channel TiO2 structures on SiO2 (black circles) and for the four channel Al2O3 structures on SiO2 (green stars).

where n is the number of escape channels, l is the arc length of each escape channel, and R is the radius of the region shown schematically in Figure 4a. The FRAP data (Figure 4b) show a decrease in the effective diffusion coefficient as the unobstructed fraction decreases but also reveal a striking dependence on the geometry of the confining structure. Simply by changing the shape of the obstruction, we find that the micron-scale diffusion coefficient varies by a factor of up to four. Specifically, our FRAP data allow us to make two distinct observations: first, the pattern with a convex surface (“four pillars,” Figure 4b) shows a faster effective diffusion rate than either of the concave structures (“four channels” and “one channel,” Figure 4b); second, the arrangement of the escape channels (together in one channel or arranged regularly around the perimeter) impacts the effective diffusion, with a single larger channel giving rise to a slower effective diffusion than four smaller channels. The effect of confinement on the dynamics of membrane components has been well-studied in both experimental (15,16,19,20,22,25,57,61,69−79) and theoretical contexts. (10,80,81) Intuitively, it has been found that as the environment becomes increasingly crowded, the effective diffusion rate decreases, (7,11,25,26,82) leading to a common model for obstructed diffusion in FRAP experiments framed in terms of the average obstructed fraction. (83−85) Our observations are consistent with this general trend but also highlight the importance of not only the obstructed fraction but also the geometry of the obstacles when trying to extract the detailed dynamics of these systems.
In our experiments, we irradiated a constant area to bleach the fluorescent lipid and monitored the recovery. In the case of the four pillar geometry, however, this means that the lipid area in our probed region decreases as the radii of the pillars increase. We can consider the case where we scale the geometry and bleach radius to maintain a constant irradiated lipid area; here we expect the recovery time to scale as R2 (eq 1) for the unobstructed case, and our numerical simulations demonstrate that this also holds for our confined geometries (Supporting Information). Thus, we expect an appropriately normalized data set with varying pattern sizes to reproduce our data. However, this does lead to a potential explanation for the faster recovery of the four pillar geometry compared to the channel geometries for a given sigma; for a randomly diffusing lipid, the smaller free area of the four pillar geometry reduces the search area to find the exit.
The effect of concavity on confined dynamics has also previously been studied in silico in the context of membrane proteins and nanopores, (10,80) though the effect of specific lipid-surface interactions in these cases makes it challenging to isolate the purely geometric effects. More recently, both experimental and theoretical descriptions of colloidal particles around curved interfaces have found concavity-dependent diffusion behaviors with slower diffusion around concave structures, consistent with our FRAP observations. We emphasize that experiments performed by Modica et al. (86) probe fluorescent spheres several microns in diameter, many orders of magnitude different from our diffusing species, DLPC molecules, highlighting the broad importance of understanding the impact of geometry on confinement effects. However, even for a fixed degree of concavity, we also find that the effective diffusion coefficient depends on the arrangement of obstacles, as seen by comparing the clear variation in trends between the “four channel” and “one channel” data in Figure 4b.

Global and Local Probes of Diffusion

To probe the effect of length scale on the observed diffusion behavior in our confined systems, we also performed FCS measurements at the center of each geometry. Figure 5 shows the effective diffusion of DLPC lipids as a function of the unobstructed fraction obtained from both FRAP and FCS measurements. FRAP probes the global diffusion of lipids on the many micron-scale whereas FCS probes more localized dynamics on the hundred-nanometer scale (here, <300 nm). The FCS data were collected at the center point of the confined domains; the FRAP data were collected for the entire confined domain.

Figure 5

Figure 5. Effective diffusion for the three different TiO2 geometries (insets) obtained from FCS (diamonds) and FRAP (circles) measurements for (a) the four pillar geometry, (b) the four channel geometry, and (c) the one channel geometry.

We observed distinctly different behaviors with each of these probes. The local behavior in the center of the confined domains, as determined by FCS, does not depend on the pattern shape or the size of the channels in the pattern and is constant within our experimental precision; in all cases, the diffusion coefficient is statistically indistinguishable from a reference measurement taken far from any confining pattern (Figure 5). The FRAP data, however, show a clear decrease in the effective diffusion coefficient as the unobstructed fraction decreases. The equivalence of FCS and FRAP measurements for extracting diffusion coefficients has been demonstrated extensively, (4,87−92) leading us to interpret our data in terms of length-scale dependent dynamics rather than any systematic difference between these two measurements. We assume that the diffusion measured by both FRAP and FCS is Brownian; we fit our FRAP data with the anomalous diffusion equation determined by Pastor et al. (93) and found fit parameters converged toward α = 1 (free diffusion case; Figure S4). Additionally, the radius of the confined regions (R ∼ 12.25 μm; Figures 3a–c and 4a) is significantly larger than the FCS beam waist (w ∼ 191 nm), allowing our FCS measurements to be described by the free diffusion case. (94) Differences between FRAP and submicron probes such as FCS or single-particle tracking can be observed in systems with heterogeneous environments, such as caveolae formation (95) or picket-fence protein structures, (96−98) where microscopic obstacles introduce length-scale dependent effective diffusion coefficients. We interpret the difference in our FCS and FRAP data in terms of free, unobstructed local diffusion (probed by FCS) and longer-range mass transport between the interior of the pattern and the outer lipid reservoir limited by the transit through the escape channels (probed by FRAP). Although FCS has higher spatial resolution than FRAP, in this case, we observe that FCS measurements are insensitive to the presence of obstacles. The FRAP data is collected over a larger area that includes the space near the domain boundaries, whereas the FCS data is collected in a small area in the center of the confined regions, rendering it agnostic to the existence of the domain walls. We interpret the observed differences in the FCS and FRAP data in terms of the inclusion of the portion of the bilayer near the domain boundaries in the FRAP measurements and the exclusion of this region in the FCS measurements. The opposite effect is observed by Calizo and Scarlata, (95) where FCS measurements were sensitive to the presence of caveolae domains, but FRAP measurements were not. Here, the size of the obstacles is on the same length scale as our bleached region in the FRAP experiments (many microns) whereas caveolae domains are on the same length scale as the FCS beam diameter (hundred(s) of nanometers). (95) However, similar effects to our data have been observed by Wawrezinieck et al. in simulating fully confined lipids, where diffusion slows as the probed length scale approaches that of the confining structure. (94) This comparison highlights the importance of considering the length scale of the confinement or obstruction and choosing an approach that is sensitive to effects on that scale. Probing smaller length scales does not inherently provide greater sensitivity to all types of confinement, which will be important when considering micron-scale confinement possible with actin filaments. (17−19,23)

Observed Trends Are Not Specific to TiO2

To explore whether the observed behaviors of the lipids are dependent on specific interactions with the TiO2 obstacles, we also generated systems using Al2O3 instead of TiO2. Both surfaces inhibit bilayer formation under our experimental conditions (99,100) but differ in their surface chemistries and have different detailed interactions with supported lipid bilayers. (101−106) Patterns from both materials show the same trends (Figures 4c and S6), indicating that the behaviors we see arise from nonspecific confinement geometry effects. The lack of material dependence motivates our description of confinement in terms of a general repulsive “boundary condition” imposed by the patterned material, as discussed below.

Confinement as a Narrow-Escape Problem

The so-called “narrow escape” problem – where species confined in a region can exit only through a few discrete channels – has been studied theoretically in a vast range of different geometries, (35,36,40−43,45,107−111) yet few of these have been directly tested against experiment. The confinement geometries used in our experiments were chosen, in part, to enable comparison with a range of theoretical and numerical approaches for calculating the effective diffusion of the lipids out of this region. Here, we directly compare our experimental data to analytical and numerical models commonly employed to describe the narrow escape problem to assess the correspondence with experiment.
In general, the narrow escape problem is formulated in terms of a local diffusion coefficient that is uniform across the sample region but where boundary conditions imposed by the confining structure impose an effective “global” diffusion coefficient limited by the exchange of species with the outside reservoir, consistent with our FRAP and FCS observations. Figure 6 shows the comparison between our experimental data and commonly used models from the literature, which describe our geometries. These models all describe the system in terms of a reflecting boundary (here, our TiO2 region) and an absorbing boundary (our escape channel) but differ in their approach to determining analytical expressions for these systems. Since we compare the diffusion normalized to the unconfined case, these models have no adjustable or system-specific parameters.

Figure 6

Figure 6. Comparison of experimental data with analytical models and numerical simulations for the various geometries in this work: (a) the four pillar geometry, (b) the four channel geometry, and (c) the one channel geometry. Descriptions of the models for each geometry are given in the main text.

Interestingly, there is significant variation in the agreement between our experimental data and the results of the various models. For the four pillar geometry, we find that our numerical simulations, the perturbation theory-based approach of Mangeat et al. (107) (perturbation model), and the multipole expansion-based approach of Rayleigh (109) (Rayleigh model) agree with our FRAP data over the entire range of our experimental parameters (Figure 6a), with relatively minor variations between the expected effective diffusion coefficient. Numerical simulations of the diffusion also show the same general trend as our experimental data.
In contrast, the boundary-homogenization descriptions of Berezhkovskii and Barzykin (36) (BH model) and the narrow escape problem for a circular disk (110−112) (NET model) show marked deviations from our data both in quantitative value and in overall shape (Figure 6b,c). For the NET model, this is consistent with previous experimental observations, which show deviation from the model at σ > 0.05. (112) The boundary homogenization approach, however, is expected to hold for all values of σ, (36) but here we find that our experiments show different behavior for the entire experimental range. In contrast, our numerical simulations show qualitative agreement with the overall trend but consistently overestimate the effective diffusion of the confined lipid for the four channel and one channel structures.
One observation our comparison allows us to make is that the best agreement is observed in systems where zero-flux (Neumann) boundary conditions are employed (perturbation, Rayleigh models, and the numerical simulations), while the approach utilizing mixed boundary conditions (boundary homogenization) shows poorer agreement with our experimental results. However, the sensitivity of the boundary homogenization approach to the parametrization and functional form of the effective “trapping rate” provides an alternative explanation for the disagreement. Nevertheless, our results highlight not only the significant effect of confinement geometry on the diffusion of species in lipid membranes but also the need to experimentally test the limitations of analytical models developed, even for geometrically simple systems.

Conclusion

Click to copy section linkSection link copied!

We developed a novel lithography-based platform to study the diffusion of DLPC lipids inside confined regions. Global diffusion of lipids, probed by FRAP, was impeded inside the confined domains; the degree of this effect depends on the geometry of the domain but not on the material with which the domains were formed. This observation implies that the obstructed fraction is not the only parameter that determines how molecules diffuse in a crowded environment─the shape and form of the obstacles play a significant role in the diffusive behavior as well. We also demonstrate that the local effective diffusion of lipids in the center of the confined domain is the same as the effective diffusion of lipids in an obstacle-free membrane, emphasizing the importance of explicitly considering the length scale when describing dynamics. Lastly, the effective diffusion obtained from FRAP was compared with a range of theoretical models describing the system in terms of a repulsive “boundary condition.” Disagreement between the model for the concave geometries and the FRAP data indicates that there is underlying behavior not fully explained by the theoretical models.

Data Availability

Click to copy section linkSection link copied!

The data associated with this work are available at 10.5281/zenodo.10830128.

Supporting Information

Click to copy section linkSection link copied!

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jpcb.3c07388

  • Additional information regarding the substrate preparation (Figures S1 and S2), FCS setup (Figure S3), appropriateness of FRAP fit (Figures S4 and S5), comparison between FRAP measurements for all TiO2 and Al2O3 geometries (Figure S6), and numerical simulations (Figures S7 and S8) (PDF)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

Click to copy section linkSection link copied!

  • Corresponding Author
  • Author
    • Nicole Voce - Department of Physics, Northeastern University, Boston, Massachusetts 02115, United States
  • Notes
    The authors declare no competing financial interest.

Acknowledgments

Click to copy section linkSection link copied!

P.S. acknowledges support from Northeastern University Provost’s Office and TIER 1 Internal Seed Grant Program. P.S. thanks the Institute for Chemical Imaging of Living Systems at Northeastern University for consultation and imaging support

References

Click to copy section linkSection link copied!

This article references 112 other publications.

  1. 1
    Enkavi, G.; Javanainen, M.; Kulig, W.; Róg, T.; Vattulainen, I. Multiscale Simulations of Biological Membranes: The Challenge To Understand Biological Phenomena in a Living Substance. Chem. Rev. 2019, 119 (9), 56075774,  DOI: 10.1021/acs.chemrev.8b00538
  2. 2
    Bennett, W. F. D.; Tieleman, D. P. Computer Simulations of Lipid Membrane Domains. Biochim. Biophys. Acta, Biomembr. 2013, 1828 (8), 17651776,  DOI: 10.1016/j.bbamem.2013.03.004
  3. 3
    Camley, B. A.; Brown, F. L. H. Dynamic Simulations of Multicomponent Lipid Membranes over Long Length and Time Scales. Phys. Rev. Lett. 2010, 105 (14), 148102,  DOI: 10.1103/PhysRevLett.105.148102
  4. 4
    Guo, L.; Har, J. Y.; Sankaran, J.; Hong, Y.; Kannan, B.; Wohland, T. Molecular Diffusion Measurement in Lipid Bilayers over Wide Concentration Ranges: A Comparative Study. ChemPhyschem 2008, 9 (5), 721728,  DOI: 10.1002/cphc.200700611
  5. 5
    Dolainsky, C.; Karakatsanis, P.; Bayerl, T. M. Lipid Domains as Obstacles for Lateral Diffusion in Supported Bilayers Probed at Different Time and Length Scales by Two-Dimensional Exchange and Field Gradient Solid State NMR. Phys. Rev. E 1997, 55 (4), 45124521,  DOI: 10.1103/PhysRevE.55.4512
  6. 6
    Minton, A. P. Confinement as a Determinant of Macromolecular Structure and Reactivity. Biophys. J. 1992, 63 (4), 10901100,  DOI: 10.1016/S0006-3495(92)81663-6
  7. 7
    Löwe, M.; Kalacheva, M.; Boersma, A. J.; Kedrov, A. The More the Merrier: Effects of Macromolecular Crowding on the Structure and Dynamics of Biological Membranes. FEBS J. 2020, 287 (23), 50395067,  DOI: 10.1111/febs.15429
  8. 8
    Kuznetsova, I. M.; Zaslavsky, B. Y.; Breydo, L.; Turoverov, K. K.; Uversky, V. N. Beyond the Excluded Volume Effects: Mechanistic Complexity of the Crowded Milieu. Molecules 2015, 20 (1), 13771409,  DOI: 10.3390/molecules20011377
  9. 9
    Jacobson, K.; Liu, P.; Lagerholm, B. C. The Lateral Organization and Mobility of Plasma Membrane Components. Cell 2019, 177 (4), 806819,  DOI: 10.1016/j.cell.2019.04.018
  10. 10
    Goose, J. E.; Sansom, M. S. P. Reduced Lateral Mobility of Lipids and Proteins in Crowded Membranes. PLoS Comput. Biol. 2013, 9, e1003033  DOI: 10.1371/journal.pcbi.1003033
  11. 11
    Ellis, R. J. Macromolecular Crowding: Obvious but Underappreciated. Trends Biochem. Sci. 2001, 26 (10), 597604,  DOI: 10.1016/S0968-0004(01)01938-7
  12. 12
    Sadjadi, Z.; Vesperini, D.; Laurent, A. M.; Barnefske, L.; Terriac, E.; Lautenschläger, F.; Rieger, H. Ameboid Cell Migration through Regular Arrays of Micropillars under Confinement. Biophys. J. 2022, 121 (23), 46154623,  DOI: 10.1016/j.bpj.2022.10.030
  13. 13
    Narhi, L. O.; Schmit, J.; Bechtold-Peters, K.; Sharma, D. Classification of Protein Aggregates. J. Pharm. Sci. 2012, 101 (2), 493498,  DOI: 10.1002/jps.22790
  14. 14
    Mahler, H.-C.; Friess, W.; Grauschopf, U.; Kiese, S. Protein Aggregation: Pathways, Induction Factors and Analysis. J. Pharm. Sci. 2009, 98 (9), 29092934,  DOI: 10.1002/jps.21566
  15. 15
    Albrecht, D.; Winterflood, C. M.; Sadeghi, M.; Tschager, T.; Noé, F.; Ewers, H. Nanoscopic Compartmentalization of Membrane Protein Motion at the Axon Initial Segment. J. Cell Biol. 2016, 215 (1), 3746,  DOI: 10.1083/jcb.201603108
  16. 16
    Kusumi, A.; Nakada, C.; Ritchie, K.; Murase, K.; Suzuki, K.; Murakoshi, H.; Kasai, R. S.; Kondo, J.; Fujiwara, T. Paradigm Shift of the Plasma Membrane Concept from the Two-Dimensional Continuum Fluid to the Partitioned Fluid: High-Speed Single-Molecule Tracking of Membrane Molecules. Annu. Rev. Biophys. Biomol. Struct. 2005, 34, 351378,  DOI: 10.1146/annurev.biophys.34.040204.144637
  17. 17
    Rentsch, J.; Bandstra, S.; Sezen, B.; Sigrist, P. S.; Bottanelli, F.; Schmerl, B.; Shoichet, S.; Noé, F.; Sadeghi, M.; Ewers, H. Sub-Membrane Actin Rings Compartmentalize the Plasma Membrane. J. Cell Biol. 2024, 223 (4), e202310138  DOI: 10.1083/jcb.202310138
  18. 18
    Sadegh, S.; Higgins, J. L.; Mannion, P. C.; Tamkun, M. M.; Krapf, D. Plasma Membrane Is Compartmentalized by a Self-Similar Cortical Actin Meshwork. Phys. Rev. X 2017, 7 (1), 011031,  DOI: 10.1103/PhysRevX.7.011031
  19. 19
    Andrade, D. M.; Clausen, M. P.; Keller, J.; Mueller, V.; Wu, C.; Bear, J. E.; Hell, S. W.; Lagerholm, B. C.; Eggeling, C. Cortical Actin Networks Induce Spatio-Temporal Confinement of Phospholipids in the Plasma Membrane -A Minimally Invasive Investigation by STED-FCS. Sci. Rep. 2015, 5, 11454,  DOI: 10.1038/srep11454
  20. 20
    Deverall, M. A.; Gindl, E.; Sinner, E.-K.; Besir, H.; Ruehe, J.; Saxton, M. J.; Naumann, C. A. Membrane Lateral Mobility Obstructed by Polymer-Tethered Lipids Studied at the Single Molecule Level. Biophys. J. 2005, 88 (3), 18751886,  DOI: 10.1529/biophysj.104.050559
  21. 21
    Ratto, T. V.; Longo, M. L. Obstructed Diffusion in Phase-Separated Supported Lipid Bilayers: A Combined Atomic Force Microscopy and Fluorescence Recovery after Photobleaching Approach. Biophys. J. 2002, 83 (6), 33803392,  DOI: 10.1016/S0006-3495(02)75338-1
  22. 22
    Brown, F. L. H.; Leitner, D. M.; McCammon, J. A.; Wilson, K. R. Lateral Diffusion of Membrane Proteins in the Presence of Static and Dynamic Corrals: Suggestions for Appropriate Observables. Biophys. J. 2000, 78 (5), 22572269,  DOI: 10.1016/S0006-3495(00)76772-5
  23. 23
    Heinemann, F.; Vogel, S. K.; Schwille, P. Lateral Membrane Diffusion Modulated by a Minimal Actin Cortex. Biophys. J. 2013, 104 (7), 14651475,  DOI: 10.1016/j.bpj.2013.02.042
  24. 24
    Polanowski, P.; Sikorski, A. Motion in a Crowded Environment: The Influence of Obstacles’ Size and Shape and Model of Transport. J. Mol. Model. 2019, 25 (3), 84,  DOI: 10.1007/s00894-019-3968-9
  25. 25
    Javanainen, M.; Hammaren, H.; Monticelli, L.; Jeon, J.-H.; S. Miettinen, M.; Martinez-Seara, H.; Metzler, R.; Vattulainen, I. Anomalous and Normal Diffusion of Proteins and Lipids in Crowded Lipid Membranes. Faraday Discuss. 2013, 161, 397417,  DOI: 10.1039/C2FD20085F
  26. 26
    Zhou, H.-X.; Rivas, G.; Minton, A. P. Macromolecular Crowding and Confinement: Biochemical, Biophysical, and Potential Physiological Consequences. Annu. Rev. Biophys. 2008, 37, 375397,  DOI: 10.1146/annurev.biophys.37.032807.125817
  27. 27
    Cremer, P. S.; Boxer, S. G. Formation and Spreading of Lipid Bilayers on Planar Glass Supports. J. Phys. Chem. B 1999, 103 (13), 25542559,  DOI: 10.1021/jp983996x
  28. 28
    Kam, L.; Boxer, S. G. Spatially Selective Manipulation of Supported Lipid Bilayers by Laminar Flow: Steps Toward Biomembrane Microfluidics. Langmuir 2003, 19 (5), 16241631,  DOI: 10.1021/la0263413
  29. 29
    Iversen, L.; Mathiasen, S.; Larsen, J. B.; Stamou, D. Membrane Curvature Bends the Laws of Physics and Chemistry. Nat. Chem. Biol. 2015, 11 (11), 822825,  DOI: 10.1038/nchembio.1941
  30. 30
    Woodward, X.; Stimpson, E. E.; Kelly, C. V. Single-Lipid Tracking on Nanoscale Membrane Buds: The Effects of Curvature on Lipid Diffusion and Sorting. Biochim. Biophys. Acta, Biomembr. 2018, 1860 (10), 20642075,  DOI: 10.1016/j.bbamem.2018.05.009
  31. 31
    Kusters, R.; Kapitein, L. C.; Hoogenraad, C. C.; Storm, C. Shape-Induced Asymmetric Diffusion in Dendritic Spines Allows Efficient Synaptic AMPA Receptor Trapping. Biophys. J. 2013, 105 (12), 27432750,  DOI: 10.1016/j.bpj.2013.11.016
  32. 32
    Bressloff, P. C.; Newby, J. M. Stochastic Models of Intracellular Transport. Rev. Mod. Phys. 2013, 85 (1), 135196,  DOI: 10.1103/RevModPhys.85.135
  33. 33
    Holcman, D.; Marchewka, A.; Schuss, Z. Survival Probability of Diffusion with Trapping in Cellular Neurobiology. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2005, 72 (3), 031910,  DOI: 10.1103/PhysRevE.72.031910
  34. 34
    Northrup, S. H. Diffusion-Controlled Ligand Binding to Multiple Competing Cell-Bound Receptors. J. Phys. Chem. 1988, 92 (20), 58475850,  DOI: 10.1021/j100331a060
  35. 35
    Holcman, D.; Hoze, N.; Schuss, Z. Narrow Escape through a Funnel and Effective Diffusion on a Crowded Membrane. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2011, 84 (2), 039903,  DOI: 10.1103/PhysRevE.84.021906
  36. 36
    Berezhkovskii, A. M.; Barzykin, A. V. Extended Narrow Escape Problem: Boundary Homogenization-Based Analysis. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2010, 82 (1), 011114,  DOI: 10.1103/PhysRevE.82.011114
  37. 37
    Ammari, H.; Kalimeris, K.; Kang, H.; Lee, H. Layer Potential Techniques for the Narrow Escape Problem. J. Math. Pures Appl. 2012, 97 (1), 6684,  DOI: 10.1016/j.matpur.2011.09.011
  38. 38
    Mangeat, M.; Rieger, H. The Narrow Escape Problem in a Circular Domain with Radial Piecewise Constant Diffusivity. J. Phys. Math. Theor. 2019, 52, 424002,  DOI: 10.1088/1751-8121/ab4348
  39. 39
    Caginalp, C.; Chen, X. Analytical and Numerical Results for an Escape Problem. Arch. Ration. Mech. Anal. 2012, 203 (1), 329342,  DOI: 10.1007/s00205-011-0455-6
  40. 40
    Skvortsov, A. Mean First Passage Time for a Particle Diffusing on a Disk with Two Absorbing Traps at the Boundary. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2020, 102 (1), 012123,  DOI: 10.1103/PhysRevE.102.012123
  41. 41
    Berezhkovskii, A. M.; Monine, M. I.; Muratov, C. B.; Shvartsman, S. Y. Homogenization of Boundary Conditions for Surfaces with Regular Arrays of Traps. J. Chem. Phys. 2006, 124 (3), 122125,  DOI: 10.1063/1.2161196
  42. 42
    Berezhkovskii, A. M.; Makhnovskii, Y. A.; Monine, M. I.; Zitserman, V. Y.; Shvartsman, S. Y. Boundary Homogenization for Trapping by Patchy Surfaces. J. Chem. Phys. 2004, 121 (22), 1139011394,  DOI: 10.1063/1.1814351
  43. 43
    Berezhkovskii, A. M.; Barzykin, A. V.; Zitserman, V. Y. One-Dimensional Description of Diffusion in a Tube of Abruptly Changing Diameter: Boundary Homogenization Based Approach. J. Chem. Phys. 2009, 131, 224110,  DOI: 10.1063/1.3271998
  44. 44
    Rupprecht, J. F.; Bénichou, O.; Grebenkov, D. S.; Voituriez, R. Exit Time Distribution in Spherically Symmetric Two-Dimensional Domains. J. Stat. Phys. 2015, 158 (1), 192230,  DOI: 10.1007/s10955-014-1116-6
  45. 45
    Holcman, D.; Schuss, Z. Diffusion through a Cluster of Small Windows and Flux Regulation in Microdomains. Phys. Lett. A 2008, 372 (21), 37683772,  DOI: 10.1016/j.physleta.2008.02.076
  46. 46
    Yang, X.; Liu, C.; Li, Y.; Marchesoni, F.; Hänggi, P.; Zhang, H. P. Hydrodynamic and Entropic Effects on Colloidal Diffusion in Corrugated Channels. Proc. Natl. Acad. Sci. U. S. A. 2017, 114 (36), 95649569,  DOI: 10.1073/pnas.1707815114
  47. 47
    Malgaretti, P.; Pagonabarraga, I.; Miguel Rubi, J. Entropically Induced Asymmetric Passage Times of Charged Tracers across Corrugated Channels. J. Chem. Phys. 2016, 144, 3034901,  DOI: 10.1063/1.4939799
  48. 48
    Malgaretti, P.; Pagonabarraga, I.; Rubi, J. M. Entropic Transport in Confined Media: A Challenge for Computational Studies in Biological and Soft-Matter Systems. Front. Phys. 2013, 1, 21.  DOI: 10.3389/fphy.2013.00021 .
  49. 49
    Burada, P. S.; Schmid, G.; Talkner, P.; Hänggi, P.; Reguera, D.; Rubí, J. M. Entropic Particle Transport in Periodic Channels. Biosystems 2008, 93 (1–2), 1622,  DOI: 10.1016/j.biosystems.2008.03.006
  50. 50
    Kalinay, P.; Percus, J. K. Corrections to the Fick-Jacobs Equation. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2006, 74 (4), 041203,  DOI: 10.1103/PhysRevE.74.041203
  51. 51
    Bullerjahn, J. T.; Von Bülow, S.; Hummer, G. Optimal Estimates of Self-Diffusion Coefficients from Molecular Dynamics Simulations. J. Chem. Phys. 2020, 153, 2024116,  DOI: 10.1063/5.0008312
  52. 52
    Vögele, M.; Hummer, G. Divergent Diffusion Coefficients in Simulations of Fluids and Lipid Membranes. J. Phys. Chem. B 2016, 120 (33), 87228732,  DOI: 10.1021/acs.jpcb.6b05102
  53. 53
    Biercuk, M. J.; Monsma, D. J.; Marcus, C. M.; Becker, J. S.; Gordon, R. G. Low-Temperature Atomic-Layer-Deposition Lift-off Method for Microelectronic and Nanoelectronic Applications. Appl. Phys. Lett. 2003, 83 (12), 24052407,  DOI: 10.1063/1.1612904
  54. 54
    Hope, M. J.; Bally, M. B.; Webb, G.; Cullis, P. R. Production of Large Unilamellar Vesicles by a Rapid Extrusion Procedure. Characterization of Size Distribution, Trapped Volume and Ability to Maintain a Membrane Potential. Biochim. Biophys. Acta, Biomembr. 1985, 812 (1), 5565,  DOI: 10.1016/0005-2736(85)90521-8
  55. 55
    Kučerka, N.; Nieh, M.-P.; Katsaras, J. Fluid Phase Lipid Areas and Bilayer Thicknesses of Commonly Used Phosphatidylcholines as a Function of Temperature. Biochim. Biophys. Acta, Biomembr. 2011, 1808 (11), 27612771,  DOI: 10.1016/j.bbamem.2011.07.022
  56. 56
    Voce, N.; Stevenson, P. Vesicle Fusion on SiO2 Substrates. VERSION 22023.  DOI: 10.17504/protocols.io.36wgq3b4ylk5/v2 .
  57. 57
    Pincet, F.; Adrien, V.; Yang, R.; Delacotte, J.; Rothman, J. E.; Urbach, W.; Tareste, D. FRAP to Characterize Molecular Diffusion and Interaction in Various Membrane Environments. PLoS One 2016, 11 (7), e0158457  DOI: 10.1371/journal.pone.0158457
  58. 58
    Soumpasis, D. M. Theoretical Analysis of Fluorescence Photobleaching Recovery Experiments. Biophys. J. 1983, 41 (1), 9597,  DOI: 10.1016/S0006-3495(83)84410-5
  59. 59
    Korlach, J.; Schwille, P.; Webb, W. W.; Feigenson, G. W. Characterization of lipid bilayer phases by confocal microscopy and fluorescence correlation spectroscopy. PNAS 1999, 96 (15), 84618466,  DOI: 10.1073/pnas.96.15.8461
  60. 60
    Almeida, P. F. F.; Vaz, W. L. C.; Thompson, T. E. Lateral diffusion in the liquid phases of dimyristoylphosphatidylcholine/cholesterol lipid bilayers: a free volume analysis. Biochemistry 1992, 31 (29), 67396747,  DOI: 10.1021/bi00144a013
  61. 61
    Jung, M.; Vogel, N.; Köper, I. Nanoscale Patterning of Solid-Supported Membranes by Integrated Diffusion Barriers. Langmuir 2011, 27 (11), 70087015,  DOI: 10.1021/la200027e
  62. 62
    Motegi, T.; Takiguchi, K.; Tanaka-Takiguchi, Y.; Itoh, T.; Tero, R. Physical Properties and Reactivity of Microdomains in Phosphatidylinositol-Containing Supported Lipid Bilayer. Membranes 2021, 11 (5), 339,  DOI: 10.3390/membranes11050339
  63. 63
    Morigaki, K.; Kiyosue, K.; Taguchi, T. Micropatterned Composite Membranes of Polymerized and Fluid Lipid Bilayers. Langmuir 2004, 20 (18), 77297735,  DOI: 10.1021/la049340e
  64. 64
    Gao, Y.; Zhong, Z.; Geng, M. L. Calibration of Probe Volume in Fluorescence Correlation Spectroscopy. Appl. Spectrosc. 2007, 61 (9), 956962,  DOI: 10.1366/000370207781745883
  65. 65
    Majer, G.; Melchior, J. P. Characterization of the Fluorescence Correlation Spectroscopy (FCS) Standard Rhodamine 6G and Calibration of Its Diffusion Coefficient in Aqueous Solutions. J. Chem. Phys. 2014, 140, 094201,  DOI: 10.1063/1.4867096
  66. 66
    Yu, L.; Lei, Y.; Ma, Y.; Liu, M.; Zheng, J.; Dan, D.; Gao, P. A Comprehensive Review of Fluorescence Correlation Spectroscopy. Front. Phys. 2021, 9, 644450,  DOI: 10.3389/fphy.2021.644450
  67. 67
    Schaff, J.; Fink, C. C.; Slepchenko, B.; Carson, J. H.; Loew, L. M. A General Computational Framework for Modeling Cellular Structure and Function. Biophys. J. 1997, 73 (3), 11351146,  DOI: 10.1016/S0006-3495(97)78146-3
  68. 68
    Cowan, A. E.; Moraru, I. I.; Schaff, J. C.; Slepchenko, B. M.; Loew, L. M. Spatial Modeling of Cell Signaling Networks. Methods Cell Biol. 2012, 110, 195221,  DOI: 10.1016/B978-0-12-388403-9.00008-4
  69. 69
    Okazaki, T.; Inaba, T.; Tatsu, Y.; Tero, R.; Urisu, T.; Morigaki, K. Polymerized Lipid Bilayers on a Solid Substrate: Morphologies and Obstruction of Lateral Diffusion. Langmuir 2009, 25 (1), 345351,  DOI: 10.1021/la802670t
  70. 70
    Groves, J. T.; Ulman, N.; Boxer, S. G. Micropatterning Fluid Lipid Bilayers on Solid Supports. Science 1997, 275 (5300), 651653,  DOI: 10.1126/science.275.5300.651
  71. 71
    Kung, L. A.; Kam, L.; Hovis, J. S.; Boxer, S. G. Patterning Hybrid Surfaces of Proteins and Supported Lipid Bilayers. Langmuir 2000, 16 (17), 67736776,  DOI: 10.1021/la000653t
  72. 72
    Morigaki, K.; Baumgart, T.; Offenhäusser, A.; Knoll, W. Patterning Solid-Supported Lipid Bilayer Membranes by Lithographic Polymerization of a Diacetylene Lipid. Angew. Chem., Int. Ed. 2001, 40 (1), 172174,  DOI: 10.1002/1521-3773(20010105)40:1<172::AID-ANIE172>3.0.CO;2-G
  73. 73
    Hovis, J. S.; Boxer, S. G. Patterning Barriers to Lateral Diffusion in Supported Lipid Bilayer Membranes by Blotting and Stamping. Langmuir 2000, 16 (3), 894897,  DOI: 10.1021/la991175t
  74. 74
    Hovis, J. S.; Boxer, S. G. Patterning and Composition Arrays of Supported Lipid Bilayers by Microcontact Printing. Langmuir 2001, 17 (11), 34003405,  DOI: 10.1021/la0017577
  75. 75
    Etoc, F.; Balloul, E.; Vicario, C.; Normanno, D.; Liße, D.; Sittner, A.; Piehler, J.; Dahan, M.; Coppey, M. Non-Specific Interactions Govern Cytosolic Diffusion of Nanosized Objects in Mammalian Cells. Nat. Mater. 2018, 17 (8), 740746,  DOI: 10.1038/s41563-018-0120-7
  76. 76
    Stylianopoulos, T.; Poh, M.-Z.; Insin, N.; Bawendi, M. G.; Fukumura, D.; Munn, L. L.; Jain, R. K. Diffusion of Particles in the Extracellular Matrix: The Effect of Repulsive Electrostatic Interactions. Biophys. J. 2010, 99 (5), 13421349,  DOI: 10.1016/j.bpj.2010.06.016
  77. 77
    Ando, T.; Skolnick, J. Crowding and Hydrodynamic Interactions Likely Dominate in Vivo Macromolecular Motion. Proc. Natl. Acad. Sci. U. S. A. 2010, 107 (43), 1845718462,  DOI: 10.1073/pnas.1011354107
  78. 78
    Lizana, L.; Bauer, B.; Orwar, O. Controlling the Rates of Biochemical Reactions and Signaling Networks by Shape and Volume Changes. Proc. Natl. Acad. Sci. U. S. A. 2008, 105 (11), 40994104,  DOI: 10.1073/pnas.0709932105
  79. 79
    Eggeling, C.; Ringemann, C.; Medda, R.; Schwarzmann, G.; Sandhoff, K.; Polyakova, S.; Belov, V. N.; Hein, B.; Von Middendorff, C.; Schönle, A.; Hell, S. W. Direct Observation of the Nanoscale Dynamics of Membrane Lipids in a Living Cell. Nature 2009, 457 (7233), 11591162,  DOI: 10.1038/nature07596
  80. 80
    Garcia-Fandino, R.; Pineiro, A.; Trick, J. L.; Sansom, M. S. P. Lipid Bilayer Membrane Perturbation by Embedded Nanopores: A Simulation Study. ACS Nano 2016, 2016 (10), 36933701,  DOI: 10.1021/acsnano.6b00202
  81. 81
    Niemelä, P. S.; Miettinen, M. S.; Monticelli, L.; Hammaren, H.; Bjelkmar, P.; Murtola, T.; Lindahl, E.; Vattulainen, I. Membrane Proteins Diffuse as Dynamic Complexes with Lipids. J. Am. Chem. Soc. 2010, 132 (22), 75747575,  DOI: 10.1021/ja101481b
  82. 82
    Długosz, M.; Trylska, J. Diffusion in Crowded Biological Environments: Applications of Brownian Dynamics. BMC Biophys. 2011, 4 (1), 3,  DOI: 10.1186/2046-1682-4-3
  83. 83
    Netz, P. A.; Dorfmüller, T. Computer Simulation Studies of Diffusion in Gels: Model Structures. J. Chem. Phys. 1997, 107 (21), 92219233,  DOI: 10.1063/1.475214
  84. 84
    Saxton, M. J. Anomalous Diffusion Due to Obstacles: A Monte Carlo Study. Biophys. J. 1994, 66 (2), 394401,  DOI: 10.1016/S0006-3495(94)80789-1
  85. 85
    Saxton, M. J. Lateral Diffusion in an Archipelago. The Effect of Mobile Obstacles. Biophys. J. 1987, 52 (6), 989997,  DOI: 10.1016/S0006-3495(87)83291-5
  86. 86
    Modica, K. J.; Xi, Y.; Takatori, S. C. Porous Media Microstructure Determines the Diffusion of Active Matter: Experiments and Simulations. Front. Phys. 2022, 10, 869175,  DOI: 10.3389/fphy.2022.869175
  87. 87
    He, K.; Babaye Khorasani, F.; Retterer, S. T.; Thomas, D. K.; Conrad, J. C.; Krishnamoorti, R. Diffusive Dynamics of Nanoparticles in Arrays of Nanoposts. ACS Nano 2013, 7 (6), 51225130,  DOI: 10.1021/nn4007303
  88. 88
    Macháň, R.; Foo, Y. H.; Wohland, T. On the Equivalence of FCS and FRAP: Simultaneous Lipid Membrane Measurements. Biophys. J. 2016, 111 (1), 152161,  DOI: 10.1016/j.bpj.2016.06.001
  89. 89
    Stasevich, T. J.; Mueller, F.; Michelman-Ribeiro, A.; Rosales, T.; Knutson, J. R.; McNally, J. G. Cross-Validating FRAP and FCS to Quantify the Impact of Photobleaching on In Vivo Binding Estimates. Biophys. J. 2010, 99 (9), 30933101,  DOI: 10.1016/j.bpj.2010.08.059
  90. 90
    Reitan, N. K.; Juthajan, A.; Lindmo, T.; de Lange Davies, C. Macromolecular Diffusion in the Extracellular Matrix Measured by Fluorescence Correlation Spectroscopy. J. Biomed. Opt. 2008, 13 (5), 054040,  DOI: 10.1117/1.2982530
  91. 91
    Mazza, D.; Abernathy, A.; Golob, N.; Morisaki, T.; McNally, J. G. A Benchmark for Chromatin Binding Measurements in Live Cells. Nucleic Acids Res. 2012, 40, e119  DOI: 10.1093/nar/gks701
  92. 92
    Goksu, E. I.; Nellis, B. A.; Lin, W.-C.; Jr, J. H. S.; Groves, J. T.; Risbud, S. H.; Longo, M. L. Effect of Support Corrugation on Silica Xerogel–Supported Phase-Separated Lipid Bilayers. Langmuir 2009, 25, 37133717,  DOI: 10.1021/la803851b
  93. 93
    Pastor, I.; Vilaseca, E.; Madurga, S.; Garcés, J. L.; Cascante, M.; Mas, F. Diffusion of α-Chymotrypsin in Solution-Crowded Media. A Fluorescence Recovery after Photobleaching Study. J. Phys. Chem. B 2010, 114 (11), 40284034,  DOI: 10.1021/jp910811j
  94. 94
    Wawrezinieck, L.; Rigneault, H.; Marguet, D.; Lenne, P.-F. Fluorescence Correlation Spectroscopy Diffusion Laws to Probe the Submicron Cell Membrane Organization. Biophys. J. 2005, 89 (6), 40294042,  DOI: 10.1529/biophysj.105.067959
  95. 95
    Calizo, R. C.; Scarlata, S. Discrepancy between Fluorescence Correlation Spectroscopy and Fluorescence Recovery after Photobleaching Diffusion Measurements of G-Protein-Coupled Receptors. Anal. Biochem. 2013, 440 (1), 4048,  DOI: 10.1016/j.ab.2013.04.033
  96. 96
    Müller, K. P.; Erdel, F.; Caudron-Herger, M.; Marth, C.; Fodor, B. D.; Richter, M.; Scaranaro, M.; Beaudouin, J.; Wachsmuth, M.; Rippe, K. Multiscale Analysis of Dynamics and Interactions of Heterochromatin Protein 1 by Fluorescence Fluctuation Microscopy. Biophys. J. 2009, 97 (11), 28762885,  DOI: 10.1016/j.bpj.2009.08.057
  97. 97
    Adkins, E. M.; Samuvel, D. J.; Fog, J. U.; Eriksen, J.; Jayanthi, L. D.; Vaegter, C. B.; Ramamoorthy, S.; Gether, U. Membrane Mobility and Microdomain Association of the Dopamine Transporter Studied with Fluorescence Correlation Spectroscopy and Fluorescence Recovery after Photobleaching. ACS Biochem. 2007, 46, 1048410497,  DOI: 10.1021/bi700429z
  98. 98
    Renz, M.; Langowski, J. Dynamics of the CapG Actin-Binding Protein in the Cell Nucleus Studied by FRAP and FCS. Chromosome Res. 2008, 16 (3), 427437,  DOI: 10.1007/s10577-008-1234-6
  99. 99
    Rossetti, F. F.; Bally, M.; Michel, R.; Textor, M.; Reviakine, I. Interactions between Titanium Dioxide and Phosphatidyl Serine-Containing Liposomes: Formation and Patterning of Supported Phospholipid Bilayers on the Surface of a Medically Relevant Material. Langmuir 2005, 21 (14), 64436450,  DOI: 10.1021/la0509100
  100. 100
    Groves, J. T.; Ulman, N.; Cremer, P. S.; Boxer, S. G. Substrate–Membrane Interactions: Mechanisms for Imposing Patterns on a Fluid Bilayer Membrane. Langmuir 1998, 14 (12), 33473350,  DOI: 10.1021/la9711701
  101. 101
    Mager, M. D.; Almquist, B.; Melosh, N. A. Formation and Characterization of Fluid Lipid Bilayers on Alumina. Langmuir 2008, 24 (22), 1273412737,  DOI: 10.1021/la802726u
  102. 102
    Jackman, J. A.; Tabaei, S. R.; Zhao, Z.; Yorulmaz, S.; Cho, N.-J. Self-Assembly Formation of Lipid Bilayer Coatings on Bare Aluminum Oxide: Overcoming the Force of Interfacial Water. ACS Appl. Mater. Interfaces 2015, 7 (1), 959968,  DOI: 10.1021/am507651h
  103. 103
    Tabaei, S. R.; Vafaei, S.; Cho, N. J. Fabrication of Charged Membranes by the Solvent-Assisted Lipid Bilayer (SALB) Formation Method on SiO2 and Al2O3. Phys. Chem. Chem. Phys. 2015, 17 (17), 1154611552,  DOI: 10.1039/C5CP01428J
  104. 104
    Tero, R.; Ujihara, T.; Urisu, T. Lipid Bilayer Membrane with Atomic Step Structure: Supported Bilayer on a Step-and-Terrace TiO2(100) Surface. Langmuir 2008, 24 (20), 1156711576,  DOI: 10.1021/la801080f
  105. 105
    Rossetti, F. F.; Textor, M.; Reviakine, I. Asymmetric Distribution of Phosphatidyl Serine in Supported Phospholipid Bilayers on Titanium Dioxide. Langmuir 2006, 22 (8), 34673473,  DOI: 10.1021/la053000r
  106. 106
    Reimhult, E.; Höök, F.; Kasemo, B. Intact Vesicle Adsorption and Supported Biomembrane Formation from Vesicles in Solution: Influence of Surface Chemistry, Vesicle Size, Temperature, and Osmotic Pressure. Langmuir 2003, 19 (5), 16811691,  DOI: 10.1021/la0263920
  107. 107
    Mangeat, M.; Guérin, T.; Dean, D. S. Effective Diffusivity of Brownian Particles in a Two Dimensional Square Lattice of Hard Disks. J. Chem. Phys. 2020, 152, 234109,  DOI: 10.1063/5.0009095
  108. 108
    Holcman, D.; Schuss, Z. Diffusion through a Cluster of Small Windows and Flux Regulation in Microdomains. Phys. Lett. A 2008, 372 (21), 37683772,  DOI: 10.1016/j.physleta.2008.02.076
  109. 109
    Jóhannesson, H.; Halle, B. Solvent Diffusion in Ordered Macrofluids: A Stochastic Simulation Study of the Obstruction Effect. J. Chem. Phys. 1996, 104 (17), 68076817,  DOI: 10.1063/1.471347
  110. 110
    Singer, A.; Schuss, Z.; Holcman, D. Narrow Escape, Part II: The Circular Disk. J. Stat. Phys. 2006, 122 (3), 465489,  DOI: 10.1007/s10955-005-8027-5
  111. 111
    Holcman, D.; Schuss, Z. Control of Flux by Narrow Passages and Hidden Targets in Cellular Biology. Rep. Prog. Phys. 2013, 76 (7), 074601,  DOI: 10.1088/0034-4885/76/7/074601
  112. 112
    Meiser, E.; Mohammadi, R.; Vogel, N.; Holcman, D.; Fenz, S. F. Experiments in Micro-Patterned Model Membranes Support the Narrow Escape Theory. Commun. Phys. 2023, 6 (1), 330,  DOI: 10.1038/s42005-023-01443-2

Cited By

Click to copy section linkSection link copied!
Citation Statements
Explore this article's citation statements on scite.ai

This article is cited by 1 publications.

  1. Luis S. Mayorga, Maria L. Mascotti, Bart M. H. Bruininks, Diego Masone. Confinement Induces Morphological and Topological Transitions in Multivesicles. ACS Nano 2025, 19 (4) , 4515-4527. https://doi.org/10.1021/acsnano.4c14171

The Journal of Physical Chemistry B

Cite this: J. Phys. Chem. B 2024, 128, 18, 4404–4413
Click to copy citationCitation copied!
https://doi.org/10.1021/acs.jpcb.3c07388
Published April 4, 2024

Copyright © 2024 The Authors. Published by American Chemical Society. This publication is licensed under

CC-BY 4.0 .

Article Views

1033

Altmetric

-

Citations

Learn about these metrics

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.

  • Abstract

    Figure 1

    Figure 1. Membranes are crowded, congested environments with obstructions spanning length scales from nanometers to microns. Schematic showing a membrane domain with a transmembrane protein (green), integral and peripheral proteins (purple), cholesterol (orange/red), glycosphingolipids (red), actin meshwork (navy), an ion channel (blue), and a lipid aggregate (dark gray). In this work, we probe the effect of the confinement geometry on both local and global scales.

    Figure 2

    Figure 2. (a) Schematic of the fabrication process, starting with a plain SiO2 substrate. The SiO2 substrate is lithographically patterned, developed, and then 2.5–5 nm of TiO2 or Al2O3 is deposited on the surface through atomic layer deposition (ALD). The substrate is cleaned, and supported lipid bilayers (SLBs) are formed on the SiO2 surfaces with the vesicle fusion method. The bottom left panel is a fluorescence image showing a SLB (dark gray) formed on a SiO2 substrate around TiO2 structures (black circles). Scale bar is 100 μm. (b) Three classes of structures used to explore geometric aspects of confinement.

    Figure 3

    Figure 3. FRAP enables imaging of the bilayer morphology and fluidity. Bilayers readily form and recover on SiO2 substrates, as shown in (a)–(c). (d) Recovery of fluorescence inside (red) and outside (blue) the pattern. Outside the pattern, the diffusion coefficient is consistent with literature values for other one-phase fluid bilayers at room temperature (4,59,60) (2.92 ± 0.07 μm2/s). Inside the pattern, the diffusion coefficient decreases; in the geometry shown, it decreases to 1.61 ± 0.04 μm2/s. The scale bar is 15 μm.

    Figure 4

    Figure 4. (a) The unobstructed fraction is defined as σ=nl2πR where n is the number of escape channels, l is their arclength, and R is the radius of the bleached region (dashed gray circle). (b) The unobstructed fraction vs the effective diffusion for the three different TiO2 geometries are compared. (c) Comparison of the unobstructed fraction vs the effective diffusion for the four channel TiO2 structures on SiO2 (black circles) and for the four channel Al2O3 structures on SiO2 (green stars).

    Figure 5

    Figure 5. Effective diffusion for the three different TiO2 geometries (insets) obtained from FCS (diamonds) and FRAP (circles) measurements for (a) the four pillar geometry, (b) the four channel geometry, and (c) the one channel geometry.

    Figure 6

    Figure 6. Comparison of experimental data with analytical models and numerical simulations for the various geometries in this work: (a) the four pillar geometry, (b) the four channel geometry, and (c) the one channel geometry. Descriptions of the models for each geometry are given in the main text.

  • References


    This article references 112 other publications.

    1. 1
      Enkavi, G.; Javanainen, M.; Kulig, W.; Róg, T.; Vattulainen, I. Multiscale Simulations of Biological Membranes: The Challenge To Understand Biological Phenomena in a Living Substance. Chem. Rev. 2019, 119 (9), 56075774,  DOI: 10.1021/acs.chemrev.8b00538
    2. 2
      Bennett, W. F. D.; Tieleman, D. P. Computer Simulations of Lipid Membrane Domains. Biochim. Biophys. Acta, Biomembr. 2013, 1828 (8), 17651776,  DOI: 10.1016/j.bbamem.2013.03.004
    3. 3
      Camley, B. A.; Brown, F. L. H. Dynamic Simulations of Multicomponent Lipid Membranes over Long Length and Time Scales. Phys. Rev. Lett. 2010, 105 (14), 148102,  DOI: 10.1103/PhysRevLett.105.148102
    4. 4
      Guo, L.; Har, J. Y.; Sankaran, J.; Hong, Y.; Kannan, B.; Wohland, T. Molecular Diffusion Measurement in Lipid Bilayers over Wide Concentration Ranges: A Comparative Study. ChemPhyschem 2008, 9 (5), 721728,  DOI: 10.1002/cphc.200700611
    5. 5
      Dolainsky, C.; Karakatsanis, P.; Bayerl, T. M. Lipid Domains as Obstacles for Lateral Diffusion in Supported Bilayers Probed at Different Time and Length Scales by Two-Dimensional Exchange and Field Gradient Solid State NMR. Phys. Rev. E 1997, 55 (4), 45124521,  DOI: 10.1103/PhysRevE.55.4512
    6. 6
      Minton, A. P. Confinement as a Determinant of Macromolecular Structure and Reactivity. Biophys. J. 1992, 63 (4), 10901100,  DOI: 10.1016/S0006-3495(92)81663-6
    7. 7
      Löwe, M.; Kalacheva, M.; Boersma, A. J.; Kedrov, A. The More the Merrier: Effects of Macromolecular Crowding on the Structure and Dynamics of Biological Membranes. FEBS J. 2020, 287 (23), 50395067,  DOI: 10.1111/febs.15429
    8. 8
      Kuznetsova, I. M.; Zaslavsky, B. Y.; Breydo, L.; Turoverov, K. K.; Uversky, V. N. Beyond the Excluded Volume Effects: Mechanistic Complexity of the Crowded Milieu. Molecules 2015, 20 (1), 13771409,  DOI: 10.3390/molecules20011377
    9. 9
      Jacobson, K.; Liu, P.; Lagerholm, B. C. The Lateral Organization and Mobility of Plasma Membrane Components. Cell 2019, 177 (4), 806819,  DOI: 10.1016/j.cell.2019.04.018
    10. 10
      Goose, J. E.; Sansom, M. S. P. Reduced Lateral Mobility of Lipids and Proteins in Crowded Membranes. PLoS Comput. Biol. 2013, 9, e1003033  DOI: 10.1371/journal.pcbi.1003033
    11. 11
      Ellis, R. J. Macromolecular Crowding: Obvious but Underappreciated. Trends Biochem. Sci. 2001, 26 (10), 597604,  DOI: 10.1016/S0968-0004(01)01938-7
    12. 12
      Sadjadi, Z.; Vesperini, D.; Laurent, A. M.; Barnefske, L.; Terriac, E.; Lautenschläger, F.; Rieger, H. Ameboid Cell Migration through Regular Arrays of Micropillars under Confinement. Biophys. J. 2022, 121 (23), 46154623,  DOI: 10.1016/j.bpj.2022.10.030
    13. 13
      Narhi, L. O.; Schmit, J.; Bechtold-Peters, K.; Sharma, D. Classification of Protein Aggregates. J. Pharm. Sci. 2012, 101 (2), 493498,  DOI: 10.1002/jps.22790
    14. 14
      Mahler, H.-C.; Friess, W.; Grauschopf, U.; Kiese, S. Protein Aggregation: Pathways, Induction Factors and Analysis. J. Pharm. Sci. 2009, 98 (9), 29092934,  DOI: 10.1002/jps.21566
    15. 15
      Albrecht, D.; Winterflood, C. M.; Sadeghi, M.; Tschager, T.; Noé, F.; Ewers, H. Nanoscopic Compartmentalization of Membrane Protein Motion at the Axon Initial Segment. J. Cell Biol. 2016, 215 (1), 3746,  DOI: 10.1083/jcb.201603108
    16. 16
      Kusumi, A.; Nakada, C.; Ritchie, K.; Murase, K.; Suzuki, K.; Murakoshi, H.; Kasai, R. S.; Kondo, J.; Fujiwara, T. Paradigm Shift of the Plasma Membrane Concept from the Two-Dimensional Continuum Fluid to the Partitioned Fluid: High-Speed Single-Molecule Tracking of Membrane Molecules. Annu. Rev. Biophys. Biomol. Struct. 2005, 34, 351378,  DOI: 10.1146/annurev.biophys.34.040204.144637
    17. 17
      Rentsch, J.; Bandstra, S.; Sezen, B.; Sigrist, P. S.; Bottanelli, F.; Schmerl, B.; Shoichet, S.; Noé, F.; Sadeghi, M.; Ewers, H. Sub-Membrane Actin Rings Compartmentalize the Plasma Membrane. J. Cell Biol. 2024, 223 (4), e202310138  DOI: 10.1083/jcb.202310138
    18. 18
      Sadegh, S.; Higgins, J. L.; Mannion, P. C.; Tamkun, M. M.; Krapf, D. Plasma Membrane Is Compartmentalized by a Self-Similar Cortical Actin Meshwork. Phys. Rev. X 2017, 7 (1), 011031,  DOI: 10.1103/PhysRevX.7.011031
    19. 19
      Andrade, D. M.; Clausen, M. P.; Keller, J.; Mueller, V.; Wu, C.; Bear, J. E.; Hell, S. W.; Lagerholm, B. C.; Eggeling, C. Cortical Actin Networks Induce Spatio-Temporal Confinement of Phospholipids in the Plasma Membrane -A Minimally Invasive Investigation by STED-FCS. Sci. Rep. 2015, 5, 11454,  DOI: 10.1038/srep11454
    20. 20
      Deverall, M. A.; Gindl, E.; Sinner, E.-K.; Besir, H.; Ruehe, J.; Saxton, M. J.; Naumann, C. A. Membrane Lateral Mobility Obstructed by Polymer-Tethered Lipids Studied at the Single Molecule Level. Biophys. J. 2005, 88 (3), 18751886,  DOI: 10.1529/biophysj.104.050559
    21. 21
      Ratto, T. V.; Longo, M. L. Obstructed Diffusion in Phase-Separated Supported Lipid Bilayers: A Combined Atomic Force Microscopy and Fluorescence Recovery after Photobleaching Approach. Biophys. J. 2002, 83 (6), 33803392,  DOI: 10.1016/S0006-3495(02)75338-1
    22. 22
      Brown, F. L. H.; Leitner, D. M.; McCammon, J. A.; Wilson, K. R. Lateral Diffusion of Membrane Proteins in the Presence of Static and Dynamic Corrals: Suggestions for Appropriate Observables. Biophys. J. 2000, 78 (5), 22572269,  DOI: 10.1016/S0006-3495(00)76772-5
    23. 23
      Heinemann, F.; Vogel, S. K.; Schwille, P. Lateral Membrane Diffusion Modulated by a Minimal Actin Cortex. Biophys. J. 2013, 104 (7), 14651475,  DOI: 10.1016/j.bpj.2013.02.042
    24. 24
      Polanowski, P.; Sikorski, A. Motion in a Crowded Environment: The Influence of Obstacles’ Size and Shape and Model of Transport. J. Mol. Model. 2019, 25 (3), 84,  DOI: 10.1007/s00894-019-3968-9
    25. 25
      Javanainen, M.; Hammaren, H.; Monticelli, L.; Jeon, J.-H.; S. Miettinen, M.; Martinez-Seara, H.; Metzler, R.; Vattulainen, I. Anomalous and Normal Diffusion of Proteins and Lipids in Crowded Lipid Membranes. Faraday Discuss. 2013, 161, 397417,  DOI: 10.1039/C2FD20085F
    26. 26
      Zhou, H.-X.; Rivas, G.; Minton, A. P. Macromolecular Crowding and Confinement: Biochemical, Biophysical, and Potential Physiological Consequences. Annu. Rev. Biophys. 2008, 37, 375397,  DOI: 10.1146/annurev.biophys.37.032807.125817
    27. 27
      Cremer, P. S.; Boxer, S. G. Formation and Spreading of Lipid Bilayers on Planar Glass Supports. J. Phys. Chem. B 1999, 103 (13), 25542559,  DOI: 10.1021/jp983996x
    28. 28
      Kam, L.; Boxer, S. G. Spatially Selective Manipulation of Supported Lipid Bilayers by Laminar Flow: Steps Toward Biomembrane Microfluidics. Langmuir 2003, 19 (5), 16241631,  DOI: 10.1021/la0263413
    29. 29
      Iversen, L.; Mathiasen, S.; Larsen, J. B.; Stamou, D. Membrane Curvature Bends the Laws of Physics and Chemistry. Nat. Chem. Biol. 2015, 11 (11), 822825,  DOI: 10.1038/nchembio.1941
    30. 30
      Woodward, X.; Stimpson, E. E.; Kelly, C. V. Single-Lipid Tracking on Nanoscale Membrane Buds: The Effects of Curvature on Lipid Diffusion and Sorting. Biochim. Biophys. Acta, Biomembr. 2018, 1860 (10), 20642075,  DOI: 10.1016/j.bbamem.2018.05.009
    31. 31
      Kusters, R.; Kapitein, L. C.; Hoogenraad, C. C.; Storm, C. Shape-Induced Asymmetric Diffusion in Dendritic Spines Allows Efficient Synaptic AMPA Receptor Trapping. Biophys. J. 2013, 105 (12), 27432750,  DOI: 10.1016/j.bpj.2013.11.016
    32. 32
      Bressloff, P. C.; Newby, J. M. Stochastic Models of Intracellular Transport. Rev. Mod. Phys. 2013, 85 (1), 135196,  DOI: 10.1103/RevModPhys.85.135
    33. 33
      Holcman, D.; Marchewka, A.; Schuss, Z. Survival Probability of Diffusion with Trapping in Cellular Neurobiology. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2005, 72 (3), 031910,  DOI: 10.1103/PhysRevE.72.031910
    34. 34
      Northrup, S. H. Diffusion-Controlled Ligand Binding to Multiple Competing Cell-Bound Receptors. J. Phys. Chem. 1988, 92 (20), 58475850,  DOI: 10.1021/j100331a060
    35. 35
      Holcman, D.; Hoze, N.; Schuss, Z. Narrow Escape through a Funnel and Effective Diffusion on a Crowded Membrane. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2011, 84 (2), 039903,  DOI: 10.1103/PhysRevE.84.021906
    36. 36
      Berezhkovskii, A. M.; Barzykin, A. V. Extended Narrow Escape Problem: Boundary Homogenization-Based Analysis. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2010, 82 (1), 011114,  DOI: 10.1103/PhysRevE.82.011114
    37. 37
      Ammari, H.; Kalimeris, K.; Kang, H.; Lee, H. Layer Potential Techniques for the Narrow Escape Problem. J. Math. Pures Appl. 2012, 97 (1), 6684,  DOI: 10.1016/j.matpur.2011.09.011
    38. 38
      Mangeat, M.; Rieger, H. The Narrow Escape Problem in a Circular Domain with Radial Piecewise Constant Diffusivity. J. Phys. Math. Theor. 2019, 52, 424002,  DOI: 10.1088/1751-8121/ab4348
    39. 39
      Caginalp, C.; Chen, X. Analytical and Numerical Results for an Escape Problem. Arch. Ration. Mech. Anal. 2012, 203 (1), 329342,  DOI: 10.1007/s00205-011-0455-6
    40. 40
      Skvortsov, A. Mean First Passage Time for a Particle Diffusing on a Disk with Two Absorbing Traps at the Boundary. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2020, 102 (1), 012123,  DOI: 10.1103/PhysRevE.102.012123
    41. 41
      Berezhkovskii, A. M.; Monine, M. I.; Muratov, C. B.; Shvartsman, S. Y. Homogenization of Boundary Conditions for Surfaces with Regular Arrays of Traps. J. Chem. Phys. 2006, 124 (3), 122125,  DOI: 10.1063/1.2161196
    42. 42
      Berezhkovskii, A. M.; Makhnovskii, Y. A.; Monine, M. I.; Zitserman, V. Y.; Shvartsman, S. Y. Boundary Homogenization for Trapping by Patchy Surfaces. J. Chem. Phys. 2004, 121 (22), 1139011394,  DOI: 10.1063/1.1814351
    43. 43
      Berezhkovskii, A. M.; Barzykin, A. V.; Zitserman, V. Y. One-Dimensional Description of Diffusion in a Tube of Abruptly Changing Diameter: Boundary Homogenization Based Approach. J. Chem. Phys. 2009, 131, 224110,  DOI: 10.1063/1.3271998
    44. 44
      Rupprecht, J. F.; Bénichou, O.; Grebenkov, D. S.; Voituriez, R. Exit Time Distribution in Spherically Symmetric Two-Dimensional Domains. J. Stat. Phys. 2015, 158 (1), 192230,  DOI: 10.1007/s10955-014-1116-6
    45. 45
      Holcman, D.; Schuss, Z. Diffusion through a Cluster of Small Windows and Flux Regulation in Microdomains. Phys. Lett. A 2008, 372 (21), 37683772,  DOI: 10.1016/j.physleta.2008.02.076
    46. 46
      Yang, X.; Liu, C.; Li, Y.; Marchesoni, F.; Hänggi, P.; Zhang, H. P. Hydrodynamic and Entropic Effects on Colloidal Diffusion in Corrugated Channels. Proc. Natl. Acad. Sci. U. S. A. 2017, 114 (36), 95649569,  DOI: 10.1073/pnas.1707815114
    47. 47
      Malgaretti, P.; Pagonabarraga, I.; Miguel Rubi, J. Entropically Induced Asymmetric Passage Times of Charged Tracers across Corrugated Channels. J. Chem. Phys. 2016, 144, 3034901,  DOI: 10.1063/1.4939799
    48. 48
      Malgaretti, P.; Pagonabarraga, I.; Rubi, J. M. Entropic Transport in Confined Media: A Challenge for Computational Studies in Biological and Soft-Matter Systems. Front. Phys. 2013, 1, 21.  DOI: 10.3389/fphy.2013.00021 .
    49. 49
      Burada, P. S.; Schmid, G.; Talkner, P.; Hänggi, P.; Reguera, D.; Rubí, J. M. Entropic Particle Transport in Periodic Channels. Biosystems 2008, 93 (1–2), 1622,  DOI: 10.1016/j.biosystems.2008.03.006
    50. 50
      Kalinay, P.; Percus, J. K. Corrections to the Fick-Jacobs Equation. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2006, 74 (4), 041203,  DOI: 10.1103/PhysRevE.74.041203
    51. 51
      Bullerjahn, J. T.; Von Bülow, S.; Hummer, G. Optimal Estimates of Self-Diffusion Coefficients from Molecular Dynamics Simulations. J. Chem. Phys. 2020, 153, 2024116,  DOI: 10.1063/5.0008312
    52. 52
      Vögele, M.; Hummer, G. Divergent Diffusion Coefficients in Simulations of Fluids and Lipid Membranes. J. Phys. Chem. B 2016, 120 (33), 87228732,  DOI: 10.1021/acs.jpcb.6b05102
    53. 53
      Biercuk, M. J.; Monsma, D. J.; Marcus, C. M.; Becker, J. S.; Gordon, R. G. Low-Temperature Atomic-Layer-Deposition Lift-off Method for Microelectronic and Nanoelectronic Applications. Appl. Phys. Lett. 2003, 83 (12), 24052407,  DOI: 10.1063/1.1612904
    54. 54
      Hope, M. J.; Bally, M. B.; Webb, G.; Cullis, P. R. Production of Large Unilamellar Vesicles by a Rapid Extrusion Procedure. Characterization of Size Distribution, Trapped Volume and Ability to Maintain a Membrane Potential. Biochim. Biophys. Acta, Biomembr. 1985, 812 (1), 5565,  DOI: 10.1016/0005-2736(85)90521-8
    55. 55
      Kučerka, N.; Nieh, M.-P.; Katsaras, J. Fluid Phase Lipid Areas and Bilayer Thicknesses of Commonly Used Phosphatidylcholines as a Function of Temperature. Biochim. Biophys. Acta, Biomembr. 2011, 1808 (11), 27612771,  DOI: 10.1016/j.bbamem.2011.07.022
    56. 56
      Voce, N.; Stevenson, P. Vesicle Fusion on SiO2 Substrates. VERSION 22023.  DOI: 10.17504/protocols.io.36wgq3b4ylk5/v2 .
    57. 57
      Pincet, F.; Adrien, V.; Yang, R.; Delacotte, J.; Rothman, J. E.; Urbach, W.; Tareste, D. FRAP to Characterize Molecular Diffusion and Interaction in Various Membrane Environments. PLoS One 2016, 11 (7), e0158457  DOI: 10.1371/journal.pone.0158457
    58. 58
      Soumpasis, D. M. Theoretical Analysis of Fluorescence Photobleaching Recovery Experiments. Biophys. J. 1983, 41 (1), 9597,  DOI: 10.1016/S0006-3495(83)84410-5
    59. 59
      Korlach, J.; Schwille, P.; Webb, W. W.; Feigenson, G. W. Characterization of lipid bilayer phases by confocal microscopy and fluorescence correlation spectroscopy. PNAS 1999, 96 (15), 84618466,  DOI: 10.1073/pnas.96.15.8461
    60. 60
      Almeida, P. F. F.; Vaz, W. L. C.; Thompson, T. E. Lateral diffusion in the liquid phases of dimyristoylphosphatidylcholine/cholesterol lipid bilayers: a free volume analysis. Biochemistry 1992, 31 (29), 67396747,  DOI: 10.1021/bi00144a013
    61. 61
      Jung, M.; Vogel, N.; Köper, I. Nanoscale Patterning of Solid-Supported Membranes by Integrated Diffusion Barriers. Langmuir 2011, 27 (11), 70087015,  DOI: 10.1021/la200027e
    62. 62
      Motegi, T.; Takiguchi, K.; Tanaka-Takiguchi, Y.; Itoh, T.; Tero, R. Physical Properties and Reactivity of Microdomains in Phosphatidylinositol-Containing Supported Lipid Bilayer. Membranes 2021, 11 (5), 339,  DOI: 10.3390/membranes11050339
    63. 63
      Morigaki, K.; Kiyosue, K.; Taguchi, T. Micropatterned Composite Membranes of Polymerized and Fluid Lipid Bilayers. Langmuir 2004, 20 (18), 77297735,  DOI: 10.1021/la049340e
    64. 64
      Gao, Y.; Zhong, Z.; Geng, M. L. Calibration of Probe Volume in Fluorescence Correlation Spectroscopy. Appl. Spectrosc. 2007, 61 (9), 956962,  DOI: 10.1366/000370207781745883
    65. 65
      Majer, G.; Melchior, J. P. Characterization of the Fluorescence Correlation Spectroscopy (FCS) Standard Rhodamine 6G and Calibration of Its Diffusion Coefficient in Aqueous Solutions. J. Chem. Phys. 2014, 140, 094201,  DOI: 10.1063/1.4867096
    66. 66
      Yu, L.; Lei, Y.; Ma, Y.; Liu, M.; Zheng, J.; Dan, D.; Gao, P. A Comprehensive Review of Fluorescence Correlation Spectroscopy. Front. Phys. 2021, 9, 644450,  DOI: 10.3389/fphy.2021.644450
    67. 67
      Schaff, J.; Fink, C. C.; Slepchenko, B.; Carson, J. H.; Loew, L. M. A General Computational Framework for Modeling Cellular Structure and Function. Biophys. J. 1997, 73 (3), 11351146,  DOI: 10.1016/S0006-3495(97)78146-3
    68. 68
      Cowan, A. E.; Moraru, I. I.; Schaff, J. C.; Slepchenko, B. M.; Loew, L. M. Spatial Modeling of Cell Signaling Networks. Methods Cell Biol. 2012, 110, 195221,  DOI: 10.1016/B978-0-12-388403-9.00008-4
    69. 69
      Okazaki, T.; Inaba, T.; Tatsu, Y.; Tero, R.; Urisu, T.; Morigaki, K. Polymerized Lipid Bilayers on a Solid Substrate: Morphologies and Obstruction of Lateral Diffusion. Langmuir 2009, 25 (1), 345351,  DOI: 10.1021/la802670t
    70. 70
      Groves, J. T.; Ulman, N.; Boxer, S. G. Micropatterning Fluid Lipid Bilayers on Solid Supports. Science 1997, 275 (5300), 651653,  DOI: 10.1126/science.275.5300.651
    71. 71
      Kung, L. A.; Kam, L.; Hovis, J. S.; Boxer, S. G. Patterning Hybrid Surfaces of Proteins and Supported Lipid Bilayers. Langmuir 2000, 16 (17), 67736776,  DOI: 10.1021/la000653t
    72. 72
      Morigaki, K.; Baumgart, T.; Offenhäusser, A.; Knoll, W. Patterning Solid-Supported Lipid Bilayer Membranes by Lithographic Polymerization of a Diacetylene Lipid. Angew. Chem., Int. Ed. 2001, 40 (1), 172174,  DOI: 10.1002/1521-3773(20010105)40:1<172::AID-ANIE172>3.0.CO;2-G
    73. 73
      Hovis, J. S.; Boxer, S. G. Patterning Barriers to Lateral Diffusion in Supported Lipid Bilayer Membranes by Blotting and Stamping. Langmuir 2000, 16 (3), 894897,  DOI: 10.1021/la991175t
    74. 74
      Hovis, J. S.; Boxer, S. G. Patterning and Composition Arrays of Supported Lipid Bilayers by Microcontact Printing. Langmuir 2001, 17 (11), 34003405,  DOI: 10.1021/la0017577
    75. 75
      Etoc, F.; Balloul, E.; Vicario, C.; Normanno, D.; Liße, D.; Sittner, A.; Piehler, J.; Dahan, M.; Coppey, M. Non-Specific Interactions Govern Cytosolic Diffusion of Nanosized Objects in Mammalian Cells. Nat. Mater. 2018, 17 (8), 740746,  DOI: 10.1038/s41563-018-0120-7
    76. 76
      Stylianopoulos, T.; Poh, M.-Z.; Insin, N.; Bawendi, M. G.; Fukumura, D.; Munn, L. L.; Jain, R. K. Diffusion of Particles in the Extracellular Matrix: The Effect of Repulsive Electrostatic Interactions. Biophys. J. 2010, 99 (5), 13421349,  DOI: 10.1016/j.bpj.2010.06.016
    77. 77
      Ando, T.; Skolnick, J. Crowding and Hydrodynamic Interactions Likely Dominate in Vivo Macromolecular Motion. Proc. Natl. Acad. Sci. U. S. A. 2010, 107 (43), 1845718462,  DOI: 10.1073/pnas.1011354107
    78. 78
      Lizana, L.; Bauer, B.; Orwar, O. Controlling the Rates of Biochemical Reactions and Signaling Networks by Shape and Volume Changes. Proc. Natl. Acad. Sci. U. S. A. 2008, 105 (11), 40994104,  DOI: 10.1073/pnas.0709932105
    79. 79
      Eggeling, C.; Ringemann, C.; Medda, R.; Schwarzmann, G.; Sandhoff, K.; Polyakova, S.; Belov, V. N.; Hein, B.; Von Middendorff, C.; Schönle, A.; Hell, S. W. Direct Observation of the Nanoscale Dynamics of Membrane Lipids in a Living Cell. Nature 2009, 457 (7233), 11591162,  DOI: 10.1038/nature07596
    80. 80
      Garcia-Fandino, R.; Pineiro, A.; Trick, J. L.; Sansom, M. S. P. Lipid Bilayer Membrane Perturbation by Embedded Nanopores: A Simulation Study. ACS Nano 2016, 2016 (10), 36933701,  DOI: 10.1021/acsnano.6b00202
    81. 81
      Niemelä, P. S.; Miettinen, M. S.; Monticelli, L.; Hammaren, H.; Bjelkmar, P.; Murtola, T.; Lindahl, E.; Vattulainen, I. Membrane Proteins Diffuse as Dynamic Complexes with Lipids. J. Am. Chem. Soc. 2010, 132 (22), 75747575,  DOI: 10.1021/ja101481b
    82. 82
      Długosz, M.; Trylska, J. Diffusion in Crowded Biological Environments: Applications of Brownian Dynamics. BMC Biophys. 2011, 4 (1), 3,  DOI: 10.1186/2046-1682-4-3
    83. 83
      Netz, P. A.; Dorfmüller, T. Computer Simulation Studies of Diffusion in Gels: Model Structures. J. Chem. Phys. 1997, 107 (21), 92219233,  DOI: 10.1063/1.475214
    84. 84
      Saxton, M. J. Anomalous Diffusion Due to Obstacles: A Monte Carlo Study. Biophys. J. 1994, 66 (2), 394401,  DOI: 10.1016/S0006-3495(94)80789-1
    85. 85
      Saxton, M. J. Lateral Diffusion in an Archipelago. The Effect of Mobile Obstacles. Biophys. J. 1987, 52 (6), 989997,  DOI: 10.1016/S0006-3495(87)83291-5
    86. 86
      Modica, K. J.; Xi, Y.; Takatori, S. C. Porous Media Microstructure Determines the Diffusion of Active Matter: Experiments and Simulations. Front. Phys. 2022, 10, 869175,  DOI: 10.3389/fphy.2022.869175
    87. 87
      He, K.; Babaye Khorasani, F.; Retterer, S. T.; Thomas, D. K.; Conrad, J. C.; Krishnamoorti, R. Diffusive Dynamics of Nanoparticles in Arrays of Nanoposts. ACS Nano 2013, 7 (6), 51225130,  DOI: 10.1021/nn4007303
    88. 88
      Macháň, R.; Foo, Y. H.; Wohland, T. On the Equivalence of FCS and FRAP: Simultaneous Lipid Membrane Measurements. Biophys. J. 2016, 111 (1), 152161,  DOI: 10.1016/j.bpj.2016.06.001
    89. 89
      Stasevich, T. J.; Mueller, F.; Michelman-Ribeiro, A.; Rosales, T.; Knutson, J. R.; McNally, J. G. Cross-Validating FRAP and FCS to Quantify the Impact of Photobleaching on In Vivo Binding Estimates. Biophys. J. 2010, 99 (9), 30933101,  DOI: 10.1016/j.bpj.2010.08.059
    90. 90
      Reitan, N. K.; Juthajan, A.; Lindmo, T.; de Lange Davies, C. Macromolecular Diffusion in the Extracellular Matrix Measured by Fluorescence Correlation Spectroscopy. J. Biomed. Opt. 2008, 13 (5), 054040,  DOI: 10.1117/1.2982530
    91. 91
      Mazza, D.; Abernathy, A.; Golob, N.; Morisaki, T.; McNally, J. G. A Benchmark for Chromatin Binding Measurements in Live Cells. Nucleic Acids Res. 2012, 40, e119  DOI: 10.1093/nar/gks701
    92. 92
      Goksu, E. I.; Nellis, B. A.; Lin, W.-C.; Jr, J. H. S.; Groves, J. T.; Risbud, S. H.; Longo, M. L. Effect of Support Corrugation on Silica Xerogel–Supported Phase-Separated Lipid Bilayers. Langmuir 2009, 25, 37133717,  DOI: 10.1021/la803851b
    93. 93
      Pastor, I.; Vilaseca, E.; Madurga, S.; Garcés, J. L.; Cascante, M.; Mas, F. Diffusion of α-Chymotrypsin in Solution-Crowded Media. A Fluorescence Recovery after Photobleaching Study. J. Phys. Chem. B 2010, 114 (11), 40284034,  DOI: 10.1021/jp910811j
    94. 94
      Wawrezinieck, L.; Rigneault, H.; Marguet, D.; Lenne, P.-F. Fluorescence Correlation Spectroscopy Diffusion Laws to Probe the Submicron Cell Membrane Organization. Biophys. J. 2005, 89 (6), 40294042,  DOI: 10.1529/biophysj.105.067959
    95. 95
      Calizo, R. C.; Scarlata, S. Discrepancy between Fluorescence Correlation Spectroscopy and Fluorescence Recovery after Photobleaching Diffusion Measurements of G-Protein-Coupled Receptors. Anal. Biochem. 2013, 440 (1), 4048,  DOI: 10.1016/j.ab.2013.04.033
    96. 96
      Müller, K. P.; Erdel, F.; Caudron-Herger, M.; Marth, C.; Fodor, B. D.; Richter, M.; Scaranaro, M.; Beaudouin, J.; Wachsmuth, M.; Rippe, K. Multiscale Analysis of Dynamics and Interactions of Heterochromatin Protein 1 by Fluorescence Fluctuation Microscopy. Biophys. J. 2009, 97 (11), 28762885,  DOI: 10.1016/j.bpj.2009.08.057
    97. 97
      Adkins, E. M.; Samuvel, D. J.; Fog, J. U.; Eriksen, J.; Jayanthi, L. D.; Vaegter, C. B.; Ramamoorthy, S.; Gether, U. Membrane Mobility and Microdomain Association of the Dopamine Transporter Studied with Fluorescence Correlation Spectroscopy and Fluorescence Recovery after Photobleaching. ACS Biochem. 2007, 46, 1048410497,  DOI: 10.1021/bi700429z
    98. 98
      Renz, M.; Langowski, J. Dynamics of the CapG Actin-Binding Protein in the Cell Nucleus Studied by FRAP and FCS. Chromosome Res. 2008, 16 (3), 427437,  DOI: 10.1007/s10577-008-1234-6
    99. 99
      Rossetti, F. F.; Bally, M.; Michel, R.; Textor, M.; Reviakine, I. Interactions between Titanium Dioxide and Phosphatidyl Serine-Containing Liposomes: Formation and Patterning of Supported Phospholipid Bilayers on the Surface of a Medically Relevant Material. Langmuir 2005, 21 (14), 64436450,  DOI: 10.1021/la0509100
    100. 100
      Groves, J. T.; Ulman, N.; Cremer, P. S.; Boxer, S. G. Substrate–Membrane Interactions: Mechanisms for Imposing Patterns on a Fluid Bilayer Membrane. Langmuir 1998, 14 (12), 33473350,  DOI: 10.1021/la9711701
    101. 101
      Mager, M. D.; Almquist, B.; Melosh, N. A. Formation and Characterization of Fluid Lipid Bilayers on Alumina. Langmuir 2008, 24 (22), 1273412737,  DOI: 10.1021/la802726u
    102. 102
      Jackman, J. A.; Tabaei, S. R.; Zhao, Z.; Yorulmaz, S.; Cho, N.-J. Self-Assembly Formation of Lipid Bilayer Coatings on Bare Aluminum Oxide: Overcoming the Force of Interfacial Water. ACS Appl. Mater. Interfaces 2015, 7 (1), 959968,  DOI: 10.1021/am507651h
    103. 103
      Tabaei, S. R.; Vafaei, S.; Cho, N. J. Fabrication of Charged Membranes by the Solvent-Assisted Lipid Bilayer (SALB) Formation Method on SiO2 and Al2O3. Phys. Chem. Chem. Phys. 2015, 17 (17), 1154611552,  DOI: 10.1039/C5CP01428J
    104. 104
      Tero, R.; Ujihara, T.; Urisu, T. Lipid Bilayer Membrane with Atomic Step Structure: Supported Bilayer on a Step-and-Terrace TiO2(100) Surface. Langmuir 2008, 24 (20), 1156711576,  DOI: 10.1021/la801080f
    105. 105
      Rossetti, F. F.; Textor, M.; Reviakine, I. Asymmetric Distribution of Phosphatidyl Serine in Supported Phospholipid Bilayers on Titanium Dioxide. Langmuir 2006, 22 (8), 34673473,  DOI: 10.1021/la053000r
    106. 106
      Reimhult, E.; Höök, F.; Kasemo, B. Intact Vesicle Adsorption and Supported Biomembrane Formation from Vesicles in Solution: Influence of Surface Chemistry, Vesicle Size, Temperature, and Osmotic Pressure. Langmuir 2003, 19 (5), 16811691,  DOI: 10.1021/la0263920
    107. 107
      Mangeat, M.; Guérin, T.; Dean, D. S. Effective Diffusivity of Brownian Particles in a Two Dimensional Square Lattice of Hard Disks. J. Chem. Phys. 2020, 152, 234109,  DOI: 10.1063/5.0009095
    108. 108
      Holcman, D.; Schuss, Z. Diffusion through a Cluster of Small Windows and Flux Regulation in Microdomains. Phys. Lett. A 2008, 372 (21), 37683772,  DOI: 10.1016/j.physleta.2008.02.076
    109. 109
      Jóhannesson, H.; Halle, B. Solvent Diffusion in Ordered Macrofluids: A Stochastic Simulation Study of the Obstruction Effect. J. Chem. Phys. 1996, 104 (17), 68076817,  DOI: 10.1063/1.471347
    110. 110
      Singer, A.; Schuss, Z.; Holcman, D. Narrow Escape, Part II: The Circular Disk. J. Stat. Phys. 2006, 122 (3), 465489,  DOI: 10.1007/s10955-005-8027-5
    111. 111
      Holcman, D.; Schuss, Z. Control of Flux by Narrow Passages and Hidden Targets in Cellular Biology. Rep. Prog. Phys. 2013, 76 (7), 074601,  DOI: 10.1088/0034-4885/76/7/074601
    112. 112
      Meiser, E.; Mohammadi, R.; Vogel, N.; Holcman, D.; Fenz, S. F. Experiments in Micro-Patterned Model Membranes Support the Narrow Escape Theory. Commun. Phys. 2023, 6 (1), 330,  DOI: 10.1038/s42005-023-01443-2
  • Supporting Information

    Supporting Information


    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jpcb.3c07388

    • Additional information regarding the substrate preparation (Figures S1 and S2), FCS setup (Figure S3), appropriateness of FRAP fit (Figures S4 and S5), comparison between FRAP measurements for all TiO2 and Al2O3 geometries (Figure S6), and numerical simulations (Figures S7 and S8) (PDF)


    Terms & Conditions

    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.