Nanoscale Heterogeneities Drive Enhanced Binding and Anomalous

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Nanoscale heterogeneities drives enhanced binding and anomalous diffusion of nanoparticles in model biomembranes Roobala Chelladurai, Koushik Debnath, Nikhil R. Jana, and Jaydeep Kumar Basu Langmuir, Just Accepted Manuscript • DOI: 10.1021/acs.langmuir.7b04003 • Publication Date (Web): 10 Jan 2018 Downloaded from http://pubs.acs.org on January 12, 2018

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Nanoscale heterogeneities drives enhanced binding and anomalous diffusion of nanoparticles in model biomembranes Roobala Chelladurai,† Koushik Debnath,‡ Nikhil R. Jana,‡ and Jaydeep Kumar Basu∗,† †Department of Physics, Indian Institute of Science, Bangalore 560012, India ‡Centre for Advanced Materials, Indian Association for the Cultivation of Sciences, Kolkata 700032, India E-mail: [email protected] Phone: 080 2293 3281 Abstract Interaction of functional nanoparticles with cells and model biomembranes have been widely studied to evaluate the effectiveness of the particles as potential drug delivery vehicles and bio-imaging labels as well as in understanding nanoparticle cytotoxicity effects. Charged nanoparticles, in particular, with tunable surface charge have been found to be effective in targeting cellular membranes as well as the sub-cellular matrix. However, a microscopic understanding of the underlying physical principles which govern nanoparticle binding, uptake or diffusion on cells is lacking. Here we report the first experimental studies of nanoparticle diffusion on model biomembranes and correlate this to existence of nanoscale dynamics and structural heterogeneities using super-resolution stimulated emission depletion (STED) microscopy. Using confocal and STED microscopy coupled with fluorescence correlation spectroscopy (FCS)

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we provide novel insight on why these nanoparticles show enhanced binding on two component lipid bilayers compared to single component membranes and how binding and diffusion is correlated to sub-diffraction nanoscale dynamics and structure. The enhanced binding is also dictated, in part, by the presence of structural and dynamic heterogeneity, as revealed by STED-FCS studies, which could potentially be used to understand enhanced nanoparticle binding in raft-like domains in cell membranes. In addition, we also observe a clear correlation between the enhanced nanoparticle diffusion on membranes and the extent of membrane penetration by the nanoparticles. Our results not only have significant impact on our understanding of nanoparticle binding and uptake as well as diffusion in cell and biomembranes, but has very strong implications for uptake mechanisms and diffusion of other biomolecules, like proteins on cell membranes and their connections to functional membrane nanoscale platform.

Introduction Cell membranes are widely believed to contain dynamically organizing nanoscale domains and platforms which acts as centers of information processing between the interior and exterior environments of cells. 1,2 Most often various pathogens use these platforms as the point of first attack on cells and their subsequent endocytosis. 3,4 Various drug delivery vehicles like nanoparticles are also designed to take advantage of these nanoscale regions that are believed to exist in target cell membranes. 5–7 Different cells can have varied compositions, obtaining information about efficacy of targeting of external agents to such nanoscale regions in cell membranes and their subsequent intake and delivery to desired sub-cellular locations provide some general guiding physical principle. 8,9 These principle would, in general, be very useful in rational designing of various drug delivery vehicles as well as for their potential cellular and sub-cellular imaging applications. Various types of nanoparticles (NPs) have been used to study interaction with cell as well as model bio-membranes to understand their cytotoxic effects, their efficacy as drug delivery

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agents or for bio-imaging and sensor applications 10–15 . Some recent reports by us 16–19 and others 20–23 on various cells suggest that apart from shape and size, surface functionality and charge are important parameters which can be used to alter the mode of cellular uptake ranging from clathrin mediated to caveolae or raft mediated. The so-called raft mediated uptake of NPs is widely believed to be optimal for sub-cellular entry and occurs for zwitterionic or hydrophobic entities 19 . While model lipid membranes do not have the complexity or functionality of cell membranes these can be very effective in delineating the subtle aspects of nanoparticle - bio membrane interactions and uptake mechanisms which are otherwise difficult in cell membranes. In this context, most studies have focused on NP - membrane interactions using single phospholipid membrane. 24–28 However, there are few notable studies on multicomponent model membranes 29–31 and interestingly it has been shown that even cell functionality can be tuned with nanoparticle - membrane interaction. 32 Specifically, it was shown that partitioning of nanoparticles to different phases depending on their size which has implications for binding of nanoparticles to the membrane. 33 There are several reports on nanoparticle induced fluidity changes in the membrane 24,34,35 mostly focusing on single component membranes. In addition to this, recent studies report on nanoscale heterogeneities in the model membrane 36,37 and proteins binding preferentially in these heterogeneous regions. 38,39 We believe that these nanoscale heterogeneities acts as precursors to raft like domains in membranes and studying the interaction of nanoparticles in presence of these heterogeneities could help understand raft mediated endocytosis of nanoparticles in a controlled environment. Apart from understanding the various mechanisms of NPs and biomolecule uptake in cell membranes, it is also crucial to elaborate the dependence of membrane binding as well as diffusion of these particles or molecules. It is reported that NP binding and diffusion 40,41 is very sensitive to membrane composition suggesting that better understanding of NP / biomolecule - cell interactions can be obtained using multicomponent model membranes. These aspects can be much better explored in model membranes of controlled complexity rather than with actual cellular membranes.

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The fact that NP or various biomolecule binding and their diffusion mediated membrane bound aggregation are both dynamic in nature and believed to occur on length scales well below the optical diffraction limit. However, challenge is to track such processes due to paucity of techniques 42–44 which can provide simultaneous nanoscale spatial and sub millisecond temporal resolution. Advent of super-resolution techniques like stimulated emission depletion (STED) microscopy 45,46 in combination with fluorescence correlation spectroscopy (FCS) and related time resolved techniques 47–49 has provided the necessary impetus to explore nanoscale biomembrane dynamics with simultaneous high temporal resolution. Here, we report results of interaction of polymer coated charged NPs with model single and two component phase separated phospholipid membranes using time dependent FCS measurements in both confocal and STED microscopy modes. Confocal measurements suggests that fluidization was observed in single component bilayer irrespective of fluid or gel phase. However, what is interesting is the two component bilayer with co - existing phases where fluid and gel phase co - exists. Surprisingly, lipid diffusivity was significantly reduced and further fluidization was not observed in fluid phase (F) contrary to what was observed in the single component fluid-like lipid bilayer. Whereas, phase with slower diffusivity (S) fluidized on interaction with QDs. Further, we also observe enhanced QD binding in these two component phase separated bilayers compared to single component fluid or gel-like bilayers. In addition the work reports the first experimental study of diffusion of nanoparticles, on model lipid biomembranes and its correlation to underlying sub-diffraction nanoscale dynamical and structural heterogeneities using super-resolution STED microscopy. Interestingly, QD showed anomalous enhanced diffusion with decreasing lipid diffusivity in multicomponent membranes contrary to what is observed for protein diffusion on lipid membranes. Our earlier studies 36 using spot variation (sv)-FCS measurements in the STED mode suggested existence of nanoscale dynamically organizing lipid regions in these two component bilayers within each of these phases. After incubation of these bilayers with QDs, sv-FCS in STED mode showed qualitatively similar observations but revealed very subtle changes in lipid dy-

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namical response on the nanometer scale regime, especially, in the S phase due to binding of QDs. While at larger length scales, lipid diffusion is enhanced due to QD interaction suggesting fluidization, below a cross-over length scale ξ, the opposite behavior (reduction of diffusivity) is observed. These results establishes the existence and significance of dynamic nanoscale lipid domains in biomembranes in determining nanoparticle binding with potential significance for drug delivery and other biomolecule-cell interactions. It also points to the significance of membrane heterogeneity and the existence of length scale dependence in determining binding of nanoparticles and membrane reorganization even in simple model biomembranes. Our results could have far reaching consequences towards understanding of not only nanoparticle and drug-cell interactions but also for interaction and uptake of other biomolecules like proteins in cells.

Experimental section Sample Preparation i) Supported Lipid bilayer: DPPC and DLPC lipids were purchased from Avanti lipids and used without further processing. Fluorescent markers ATTO 488 DMPE,head tagged lipid (ATTO - Tec GmbH) and BODIPY C12 HPC, tail tagged lipid (Life Technologies) used for the measurements were purchased from ATTO-TEC and Invitrogen respectively. Langmuir-Blodgett technique was used in transferring the bilayers and transferred SLBs were always stored under DI water. Fluorescent dyes were mixed in dye:lipid ratio 1:10,000 / 1: 100,000. SLBs were transferred at a surface pressure of 32 mN/m (Figure S1) from a sub-phase of water maintained at T = 15o C with a dipper speed of 5 mm/min (up stroke) and 3 mm/min (down stroke). Transferred bilayers are used for measurements immediate to transfer or within few hours.

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ii) Quantum Dots: Hydrophobic red emissive QD (CdSe/ZnS) of average hydrodynamic diameter 10 nm was synthesized by our previously reported method 50 . In brief, CdSe-ZnS was synthesized via ZnS shelling of a CdSe core. First, high quality red fluorescent CdSe nanoparticles were synthesized by injection of Se solution into cadmium stearate solution in 1-octadecene at 280◦ C, and then shelling of ZnS was performed through consecutive injection of zinc stearate solution and S solution at 200◦ C. The hydrophobic QD was converted into hydrophilic QD by polyacrylate coating. Characterization details of the QD sample used in this study is given in SI (Figure S2).

iii) Incubation of QDs to the membrane: In the liquid cell containing the bilayer, 5ul of 4 nM solution is added to 1ml of buffer present on top of the bilayer. This solution is then mixed well for homogeneity in the system through pipette aspiration. Immediate to this step, changes in the membrane were monitored under confocal microscope or for other measurements.

Confocal and STED FCS Confocal and STED FCS measurements were performed using a commercial setup (LEICA TCS SP5 II, GmbH, Mannheim, Germany). Excitation was done with Argon laser of 488nm with its master power set to 40 % and used 63x water immersion objective for all confocal related measurements. Images were captured using PMT or APD. For FCS measurements, APD signal was collected using a TCSPC unit (PicoQuant). Measurements were controlled using the microscope software (LEICA LAS AF) which is interfaced with the PicoQuant SymPho Time software. Each measurements were carried out for 10s and 30-40 points were chosen in a given sample. Each sample were repeated at least 3 times. Correlation curves obtained from this measurements provides information on transit time τd , time taken for a molecule to diffuse through the focal spot. In case of 2D diffusion in membrane, 2D model 6


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(SI Eqn 1) is used. For bulk QD diffusion, single component 3D model (SI Eqn 2) was used for fitting and τd = 0.7ms was obtained as shown in Figure S4. For membrane bound QD, two component - 2D model (general ’i’ component model given in SI Eqn 3) was employed with one component fixed to 0.7 ms (bulk diffusion) during fitting as shown in Figure S5. τd obtained from this fits can be used in calculating the diffusion co-efficient given by,

D= where D - diffusion co-efficient and ω -

1 e2

ω2 , 4 ∗ τd

(1)

of beam size.

STED - FCS was performed using a 488 nm excitation laser and 592 nm depletion laser. Laser beam size in a STED microscope can be tuned by changing the STED (592 nm) laser power 46 ,

ω≈

λ q 2nsin(α) 1 +

,

(2)

I Is

where ω is 1/e2 beam radius in lateral direction, λ is the wavelength of laser line , nsinα is the numerical aperture of objective with n corresponding to refractive index and α is the maximal half angle of the cone of light, I for intensity of the laser and Is denotes saturation intensity (depends on the dye). ω 2 (probe area of the beam) is tuned by varying the laser power (592 nm) from 0-100 % (0 - 285 mW ) using Acousto - optical tunable filter (AOTF). STED - FCS measurements were performed for a range of power (0-100 % ) in each position and the corresponding ω 2 ranges from 0.04 - 0.01 µm2 . For each series of STED-FCS measurements, auto alignment was performed to overlap the excitation and STED beam. 10-15 points were chosen in a given sample and 3 samples were studied for a given composition. For each probe area, transit time is calculated. Dependence of τd on ω 2 is interpreted using FCS diffusion law 51 given by,

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τd =

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ω2 + t0 . 4Def f

(3)

Here, Def f - effective diffusion co-efficient (slope), t0 - intercept. Zero intercepts indicate pure diffusion and a non - zero intercept indicates presence of nanoscale heterogeneities in the sample. On a larger length scale, averaged out diffusion is observed. As the probe area is reduced we are able to resolve the heterogeneity in dynamics and sensitive to the environment. As we reduce the size of the focal area, we can detect the change of diffusivity of lipid in such regions.Positive t0 was interpreted as diffusion in a mesh work like structure and negative t0 as presence of domains within the probe area. In simple model system, negative intercept was interpreted as signature of gel like nanodomains 52 . Cross over of slope at a certain dynamical length scale 36 , ξ, indicates the scales of heterogeneities in the membrane. Through the observation of cross-over in the FCS diffusion law, we suggest that as dynamical cross-overs indicating that motion of lipids below and above are different.

Results and Discussion Table 1: Description of samples

†X DP P C

SAMPLE LABEL

DLPC:DPPC

XDP P C in%†

L1P0 L1P1 L0P1

100:0 50:50 0:100

0 50 100

denotes mole fraction of DPPC

We first discuss how the response of bilayer membrane to interaction with charged polymer coated NPs varied with their composition. For this purpose we performed time dependent FCS measurements in confocal mode to observe changes in lipid dynamics due to inter-

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action with the cationic NP. Table 1 summarizes description of samples used in this study. Bilayers of required composition and packing were transferred using Langmuir-Blodgett (LB) technique (Fig S1). Figure 1 shows typical FCS correlation curves obtained at random sampling time interval after introducing the CdSe/ZnS QDs to the respective membranes. Details of different models used in fitting FCS data are given in SI Section 2. Figure 1a shows the correlation curve measured on DLPC (L1P0) membranes. Interestingly, a non-monotonic variation of the transit time ( / lipid relaxation time), τd , with elapsed time after QD incubation was observed. Surprisingly, L1P0 membranes which are expected to be fluid-like under ambient temperature showed ∼ 2 times faster lipid relaxation after NP incubation compared to its pristine. This is contrary to expectation and some earlier reports on single component membranes interacting with cationic NPs. 24 Initial slowing down of lipids and the nature of time dependent changes in τd will be discussed in the following section. We next discuss the impact of charged NP interaction with two component membranes which is more relevant for understanding of NP-cell membrane interactions but has not been extensively studied. In particular our interest was to investigate two component membranes exhibiting phase co - existence. We chose DLPC : DPPC (1 : 1) for this purpose, indicated as L1P1 and represented in Table 1. This is a widely studied system and recently we have shown that supported bilayer membranes made up of these lipids, for various compositions, shows interesting dynamical heterogeneity in the co-existing F and S phases 36 . The F (or relatively fluid and enriched in DLPC) phase shows ∼ 2 times faster diffusion compared to the S phase (enriched in DPPC compared to the F phase). Figure 1b shows time dependent FCS data obtained from the S phase of L1P1 bilayer. Expectedly, τd is larger than that of the L1P0 membranes but interestingly shows similar non-monotonic trends in its variation with time. Similar measurements on F phase (Figure 1c), showed that although the initial τd is similar compared to pristine L1P0, it showed drastic enhancement in the relaxation time in a monotonic manner with elapsed time after QD incubation. While the F phase lipid

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relaxation time for L1P1 samples is very similar to that of the completely fluid-like L1P0 pristine case, the behavior after NP incubation is counter intuitive. Interestingly, the trend observed in the S phase (Figure 1b) is closer to the L1P0 data (Figure 1a) although the pristine τd values are quite different in these two systems. We also studied lipid dynamics after QD incubation in L0P1 membranes, which are expected to be in the gel phase at ambient temperatures. L0P1 bilayers inherently do not show measurable diffusion at ambient temperature and shows no change in lipid relaxation time upon addition of QDs. Since the τd values extracted from FCS measurements after QD incubation shows a non- monotonic time dependence for L1P0 and S phase of L1P1 bilayers while it is monotonic for F phase of L1P1, we performed continuous monitoring to investigate the systematics of changes in membrane dynamics.

Figure 1: FCS correlation curves measured from bilayers of a) L1P0, b) L1P1 S and c) L1P1 F. Transit time, τd , extracted from each correlation curve is provided next to the respective data label. Representative fits to the respective correlation data are shown as solid black lines in panel b. In Figure 2, we discuss time dependence of τd for the various membrane systems before 10


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and after incubation with QDs. As was discussed earlier. τd shows non-monotonic behavior with elapsed time for the L1P0 samples. However from this plot we were able to extract a time scale for the taken for lipid re - arrangement as indicated in Figure 2a. In order to quantify these effects further, we used Boltzmann-Sigmoid (BS) function of the form,

τ = τmin +

τmax − τmin , c 1 + exp( t−t ) ti

(4)

where τmin corresponds to initial value and τmax corresponds to final value, tc is the reference for the BS function while ti corresponds to the time constants for growth (i = R) or decay (i = F ) of τ values as depicted in Figure S6. Figure 2a shows that τd increases and drops with an equal time constants of (tR , tF ) 19 sec in case of L1P0 bilayer. Figure 2b shows that for the F phase of L1P1 sample, τd increases after QD incubation but does not come down as in L1P0 membrane although the rise time is very similar in both cases (tR = 19 sec and 16 sec respectively). Figure 2c shows the same data for the S phase of the L1P1 sample. We observe non-monotonic trend similar to L1P0 sample as was discussed earlier. However, data corresponding to S phase of L1P1 membranes reveals an interesting difference as the time constants tR = 7 & tF = 11 are comparatively faster than L1P0 (tR = 19). This indicates that lipids reorganize much faster in the multi-component bilayers as compared to single component bilayers under exactly similar conditions of QD incubation. Our earlier study with these cationic NPs on single component DMPC bilayers showed significant disruption after their addition suggesting possible changes in lipid density. 26 One method to explore this is to monitor the fluorescence intensity in the lipid bilayers. In Figure 2 (right panels, d-f) time dependent changes in the fluorescence intensities of all the bilayers before and after addition of QDs are depicted. The intensity profiles for both L1P0 and S phase of L1P1 samples seem to show similar trend suggestive of reduction in lipid density while that of the F phase of the L1P1 sample shows increase in intensity with elapsed time after QD incubation. This seems to suggest that eventual fluidization of the L1P0 membranes or S phase of L1P1 could both be caused by reduction in lipid density while the increase 11


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Figure 2: Time dependent τd measured using confocal FCS measurements for a) L1P0, b) F and c) S phase of L1P1 membranes. The corresponding confocal time dependent fluorescence intensities are shown in d), e) and f) respectively τd is given in y - axis and real time scale is indicated in x - axis. Each data point in the figure corresponds to τd at a given time. tR and tF have been extracted by BS fit (Eqn 4) of respective data.

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in lipid τd for the F phase of L1P1 membranes could be caused by increase in lipid density. The fact that S and F phase shows opposite behavior upon incubation of the QDs suggests that there might be flow of lipids taking place amongst these phases induced by QD binding. Information on changes in lipid density can also be extracted from the amplitude (G(0)) of FCS correlation curves, where G(0) ∼

1 N

as shown in Figure S3.

Table 2 summarizes lipid diffusion coefficient, D, extracted from τd value before and after QD mediated lipid re-organization reached equilibrium. The extent of change in D is also lipid lipid represented using the parameter ρ ( = Daf ter / Dbef ore ). The changes in D values reflect the

corresponding changes in τd values reported earlier. We also observe that ρ depend on the membrane phase (Table 2) or QD concentration (Table S1 ). Table 2: Lipid diffusion D Sample

bef ore DLipid (µm2 /s)

af ter DLipid (µm2 /s)

L1P0 L1P1 - F L1P1 - S

3.6 ± 0.6 2.8 ± 0.4 1.5 ± 0.3

6.0 ± 1.0 1.6 ± 0.5 3.3 ± 0.4

ρ=

af ter DLipid bef ore DLipid

1.67 0.57 2.2

bef ore af ter DLipid and DLipid are the lipid diffusion values measured before and after addition of QDs respectively. Eqn (1) was employed in extracting diffusion values from τd

It is puzzling that lipid re-organization kinetics is significantly faster in the S phase in which lipid dynamics is relatively slower compared to the F phase. In order to explore this behavior further, we next discuss lipid dynamics in the STED mode within the F and S phase before and after QD incubation. We have shown recently that the dynamics in these two phases are significantly different 36 and shows evidence of nanoscale dynamic heterogeneity. Is the NP interaction sensitive to these nanoscale heterogeneity? In Figure 3, we show spot variation (sv)-FCS measurements in STED mode for the F and S phase of L1P1 samples. As observed earlier, we find that D is much higher in F phase compared to S phase, before NP incubation, which is consistent with confocal FCS measurements. However, a clear dynamical cross-over at ξ ∼ 125 nm occurs for the S phase which indicates presence of 13


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nanoscale heterogeneity and domains, as discussed earlier. 36 Since this phase contains more L0P1, one expects that these are caused by pockets of fluid phase (mostly containing L1P0) within a background of L0P1. The Def f values in the respective regimes as well as the values of t0 (Table 3) suggests this might be the case. The effect of QD addition is also significantly different in the two phases. The F phase data after QD incubation looks almost identical to that before incubation except for a large change in Def f (consistent with changes in Table 2). However, in the S phase the changes are much more subtle. For example, the value of ξ decreases to ∼ 115 nm suggesting that dynamical domains, responsible for the existence of this cross-over, shrinks due to QD interactions. More significantly, t0 value above ξ changes from -1.4 to 0.4 suggestive of a gel-to-fluid transition, on those length scales, while below ξ it changes from -0.3 to -1.3 indicative of stiffening (or gelling) of domains at the shorter length scales. While the long length scale fluidization is similar to confocal FCS observations, the short length scale stiffening was unexpected. However, this suggests that the QDs are sensitive to these nanoscale dynamic texturing and the lipid response in this dynamically textured phase is significantly length scale dependent. It has been reported earlier that NP binding onto multi-component membranes prefers the interface between two phases. 29 It is therefore possible that similar phenomena occurs within the S phase due to the presence of these nanoscale dynamical domains and also drives the considerably faster lipid response after QD incubation even though the lipid D is considerably slower in this phase compared to the F phase before QD incubation. We now proceed to understand how the binding and diffusion of the NPs depend on the composition and phase of the lipid bilayers. Charged NPs are known to bind in a nonspecific manner to single component lipid bilayers 24,26 and largely at the phase boundary in multi-component lipid membranes. 29,53 In Figure 4, we show typical images and time dependent intensity profiles of QDs for similar concentration of incubation of QDs onto untagged L1P0 and L1P1 bilayers. Few things can be clearly observed by comparing the respective images. For identical concentration and incubation time of QDs, the intensity and

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Figure 3: STED FCS results on F phase of L1P1 bilayer a) before, b) after QD addition and S phase of L1P1 c) before and d) after QD addition. ti0 where i - 1,2 - denotes region above and below the cross over length scale (ξ) respectively. Eqn (3) was used in fitting the data points.

Table 3: STED FCS results Phase F - before F - after S - before 1 S - before 2 S - after 1 S - after 2

t0 (ms) -

0.1 0.2 1.4 0.3 0.4 1.3

Def f ( µm2 /s )

ξ (nm )

5.8 1.0 1.4 2.3 1.8 0.9

122 115

ti0 where i - 1,2 - denotes region above and below the cross over ξ respectively. FCS diffusion law (Eqn 3) was used in extracting these values.

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hence density is significantly higher on the phase separated L1P1 bilayer as compared to the homogeneous fluid L1P0 bilayer. In addition, the density of QDs is significantly higher in the F phase compared to the S phase. To quantify this further, we show average intensities (or density) of QDs with elapsed time for both the bilayers and the respective phases. The enhanced adsorption in L1P1 bilayers in general compared to the L1P0 bilayers is quite evident. The difference could be mediated by enhanced adsorption due to heterogeneities in the L1P1 bilayers. Such preferential interaction at the interface between two phases 53 has been reported earlier for binding of NPs to multi-component membrane 29,54–56 . What is also interesting is that, while the adsorption appears higher and more homogeneous in the F phase it is lower but also very heterogeneous in the S phase. This suggests that the binding in F phase is non-specific and de - localized, as expected, while the binding in the S phase is localized and specific to the heterogeneity. In this regard, the STED-FCS data suggesting presence of nanoscale dynamic texture in the S phase is worth reiterating because there is an intimate connection between those heterogeneities and the nature of QD adsorption in the S phase. Presence of these nanoscale lipid dynamical heterogeneities thus seems to promote specific and heterogeneous binding within the S phase which also leads to much faster lipid response compared to the F phase (Figure 2 a-c). We have also quantified the nature of phase dependent binding in the membrane using x-ray reflectivity (XR) and AFM measurements as shown in Figure S8 - S11. Interestingly, it was observed that the height profiles of QD layer and hence corresponding penetration depth (or protrusion height) changes with the phase of membrane as shown in Table 4. We observe that cationic QDs penetrates more in fluid like L1P0 membranes and least in L0P1 membranes. Between the two phases of L1P1 membranes, penetration is more in F phase compared to the S phase. We next discuss the diffusive behavior of these QDs on the various bilayer membranes and phases which we have discussed here. Surprisingly, while there have been enormous amount of studies on nanoparticle interactions with cells and model membranes, very few studies

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Figure 4: Confocal images showing QD distribution obtained after 30 minutes of QD addition on a) L1P0 , b) L1P1 bilayers. c)Time dependent QD intensity line profiles on L1P0 and F & S phase of L1P1 bilayer extracted from the images in a),b). For comparison, confocal images of tagged L1P0 and L1P1 bilayers are given in Figure S7 to elucidate the nature of phases in pristine bilayers.

Table 4: QD penetration and diffusion in different cases SAMPLE L1P0 L1P1 - F L1P1 - S L0P1 ?

Protrusion (nm) 6 ± 1.6 6.7 ± 1.5 8.4 ± 1.3 9.0 ± 1.5

?

(before , after ∗ )

± ± ± ±

0.07 , 0.04 0.14 , 0.25 0.29 , 0.13 -

0.26 0.40 0.44 0.34

0.14 0.16 0.11 0.10

Penetration = 10 nm - Protrusion. Lipid values taken from Table 2

∗D

17

DQD DLipid

DQD (µm2 /s)


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report on their diffusion behavior 57,58 . On the other hand, diffusion of proteins and other molecules 59–63 on membranes have been widely studied over the last several years, but have remained quite controversial. Protein diffusion studies suggest that their diffusion is sensitive to both their lateral dimension and the membrane viscosity. However, no systematic study has been performed on how the extent of penetration of the proteins into the membrane determines their mobility. Figure 5 shows correlation curve measured from QD diffusion on the membrane. By fitting respective data to appropriate equations (Eqn. 1 in SI), the correlation time, τd and hence QD diffusivity, DQD could be obtained.

Figure 5: FCS correlation curve measured from QD diffusion in different lipid bilayers. Corresponding τd is indicated next to the sample label. Black solid line shows fit to a representative data for QDs on F phase of L1P1 bilayer. Table 4 summarizes the DQD values measured in different membranes and in respective phases as obtained from confocal FCS data. What is surprising is that the DQD for the QDs is smaller on the fluid-like L1P0 membrane, as compared to the L1P1 membranes, for

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which the viscosity is expected to be higher (hence lipid diffusivity should be lower) than the L1P0 membranes. In addition we also observe that although the DQD on L0P1 (DPPC) membranes are slightly lower than L1P1 membranes it is, surprisingly, higher than that on L1P0 membranes. We also find that the ratio DQD /DLipid increases with increasing XDP P C . This ratio has been shown to be a constant in case of proteins diffusing in membrane with different phase / chain length 41 and this behavior is expected because protein (or any other membrane embedded object) diffusivity should scale with the viscosity of the embedding medium. In other words D of a membrane bound biomolecule or NP is expected to decrease with decreasing lipid diffusivity. However, we observe anomalous enhancement of diffusion of NPs with increasing XDP P C which should increase the average diffusion coefficient of the membrane lipids. How does one explain the anomalous diffusivity of QDs on the various membranes and on different phases on the same multi-component membrane? Recently, a simulation study suggested similar enhancement in Fickian diffusion of a uniformly charged NP mediated by membrane fluctuations 57 . We believe something similar could be responsible for the observed enhanced diffusion especially when one compares the difference between the S and F phase of the L1P1 membranes. We have discussed earlier about the presence of larger dynamical and structural heterogeneity in S phase and faster membrane re - organization on QD binding. Such fluctuations, is likely to be responsible for promoting faster QD diffusion as suggested by the simulation work by Chen et al 57 . This work also signifies that surface reconstruction and fluctuation of membrane invalidates SD model that assumes incompressible fluid theory 64 . However, this does not explain the anomaly in DQD values on L1P0 and L0P1 (DQD on L1P0 membranes > DQD on L0P1 membranes) which are both homogeneous and single component and such dynamic or structural heterogeneity is unlikely to influence the enhanced DQD on L0P1 membranes. Incidentally, L0P1 membranes are expected to be in the gel-phase at the measured temperatures (measured confocal FCS did not decay within the measured time indicating significantly lower lipid diffusivity in L0P1 membranes compared to L0P1

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or L1P1 membranes) with high membrane viscosity and hence DQD on these membranes should be considerably smaller than that on L0P1. In order to understand this QD diffusion anomaly, we try to explore the existence of some correlation between the observed DQD values and the extent of penetration of the QDs in the respective membranes. As Table 4 suggests, the QD penetration is more in membranes for which the membrane viscosity is expected to be lower (or lipid diffusivity is higher). While there are several models 64,65 of diffusion of objects embedded in membranes (bilayer or monolayer) only the model of Fischer et al 66 deals with situations where the diffusing object is partially embedded in the membrane and protrudes out into the surrounding fluid medium. The situation with the QDs on the various membranes is similar to the configurations discussed by Fischer et al. However, it is non-trivial to extract a reliable membrane viscosity from our measured QD diffusion data using a relevant form of the Fischer model as well as the experimentally obtained penetration depth of the QDs. Nevertheless, it is clear that both for the S phase of L1P1 membranes as well as for L0P1 membranes, DQD is unexpectedly higher while at the same time the penetration depth is also smaller compared to the other phase or lower viscosity membranes. In fact, in both these cases the QDs do not penetrate beyond the top leaflet of the bilayer membrane as shown in Figure S12. This suggests that in the S phase of L1P1 and on the L0P1 membranes the QDs might be, effectively, sampling a viscosity which is that due to the monolayer. Viscosity of lipid monolayers at the air-water interface have been estimated to be almost 10-100 times smaller than that of typical bilayer viscosity 67 . While the monolayer viscosity in a supported bilayer membrane depends on the inter-leaflet frictional coupling 68–70 it is expected that the leaflet away from a substrate might have lower viscosity. In fact our earlier studies 39 revealed slightly higher lipid diffusivity for the leaflet away from the substrate as compared to the one near substrate. Similar behavior is also expected in real cell membranes due to the asymmetric nature of the medium inside and outside the cell. While in most studies of protein diffusion in membranes this aspect of membrane penetration is neglected our studies reveal that this is quite important in the

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context of understanding NP diffusion. More importantly, several integral and peripheral membrane proteins 71 have protruding parts from the embedding membranes and several instances of diffusion of pore forming toxins are known to also have significant parts of their structure protruding away from the cell membrane 38,72,73 . Our study suggests that in all such cases using the Saffman-Delbruck model or its variants which do not consider this aspect can clearly lead to incorrect estimates of protein diffusivity which in turn is crucial for the properties and functionalities of these membrane bound proteins.

Conclusion In conclusion our confocal and STED FCS studies reveal several subtle aspects of lipid and charged NP diffusion on model single and multi-component lipid bilayer membranes. The important role of nanoscale dynamical heterogeneity in determining binding and anomalous enhanced diffusion of these charged NPs is revealed in experiments. The dependence of binding on lipid phase and nanoscale dynamic heterogeneity is particularly important in understanding NP uptake mechanisms, especially those related to raft-mediated uptake, in cell membranes. Further, we also demonstrate that NP diffusion is dependent significantly on both lateral heterogeneity as well as on membrane penetration. These results have significant impact on our understanding of not only NP binding and uptake as well as diffusion in biomembranes but has very strong implications for uptake and diffusion of other biomolecules, like proteins, on cell membranes.

Associated Content Supporting Information Preparation of bilayers, Modeling FCS data, AFM and XR data are provided.

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Acknowledgement The authors thank the Department of Science and Technology, India, for the financial support through a special project(DST-IRHPA). Authors also thank DST and Saha Institute of Nuclear Physics, India, for facilitating the experiments at the Indian Beamline, Photon Factory, KEK, Japan and Arka Bikash Dey, Nafisa Begam and Nimmi Das A for their assistance during XR experiment at the beamline.

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45x25mm (300 x 300 DPI)


Nanoscale Heterogeneities Drive Enhanced Binding and Anomalous

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