Drug Encapsulation on Phosphoglycerolipids

Abstract:

Although Cancer is the major cause of death in many countries around the world, the efficacy of current standard treatments is suboptimal. The main drawbacks of conventional chemotherapy are its poor bioavailability, high-dose requirements, adverse side effects, low therapeutic indices, development of multiple drug resistance, and non-specific targeting. This was overcome by the application of nanotechnology to develop effective Nano sized drug delivery systems known commonly as Nanoparticles. These nanoparticles possess various advantages like high selectivity and allow a slow release of drugs, which will reduce systemic toxicity and also improve the bioavailability of drugs. Polymers, Lipids, Inorganic carriers, polymeric hydrogels and bio-macro-molecular scaffolds are the most versatile Nano carriers. However, natural occurring polymers are preferred over synthetic polymers due to their drug loading capacity, biocompatibility and generate less opsonization by the reticuloendothelial system. In addition to this, natural polymers produce less toxic end products after degradation. We have phospholipids, polysaccharides, proteins, and peptides as most promising formulations for natural polymers. Among them lipids are easy to study and are proved to be suitable drug delivery systems. Having known about the importance of mediating lipids in cell signalling, modulating and controlling major transducing steps of hormone signals, applying them for drug delivery would give better results. In this report, a detailed review on lipid-based drug delivery and how phosphoglycerolipids can enhance drug delivery is being produced.

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Keywords:

Phospholipids; Phosphoglycerolipids; Signalling; Drug Delivery; Breast cancer

Introduction:

Hormones are molecules produced by cells in response to environmental stresses and during development. The chemical natures of hormones are diverse. Some are related to adenine, others are terpenoids and some are related to phenol or oxidized fatty acid derivatives. Some plant hormones are volatile as ethylene and some jasmonates. All hormones act at extremely low concentrations. Their action gets initiated when they are recognized by a receptor, thereby triggering the intracellular transduction and amplifying the signal. Ultimately, this leads to cellular and organ responses[1].

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Hormone production, signaling and responses can affect vegetative growth, plant health and flowering, fruits and seed development, all aspects of high importance to the agriculture.

Major advances in hormone signalling pathways were achieved using genetics. But with Arabidopsis, where the focus is on the regulation of gene expression, the protein/protein interactions, the protein post-translational modifications and, recently, the role of noncoding RNAs. The classical studies of promoter analysis allowed the identification of ABA-responsive element motifs[2]; the first step is to locate the ABRE binding factors and their regulators. The isolation of ABA-insensitive mutants made it possible to show the importance of protein phosphorylation status in ABA responses[3]. These major discoveries and the ease of molecular biology tools using genes encoding proteins overshadowed the fact that lipids were discovered early on as signal mediators in hormone transduction[4]. Recent studies, however, have put the production and involvement of lipids in hormone signalling back in the limelight. Nevertheless, knowledge about lipids needs to be better integrated into some of the genetically defined hormone pathways, which is attempted here by checking the current knowledge about the role of phosphoglycerolipids in hormone signalling.

We will describe which specific lipid signaling pathways are activated or inhibited for each hormone signaling cascade which requires a phosphoglycerolipid pathway. Ideas on how these pathways and the molecules they produce, act and interact are discussed. The involvement of lipid mediators, particularly phosphatidic acid in the control of well- established hormone signal transducers such as protein phosphatases or NADPH oxidases is shown.

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Phosphoglycerolipids Structure and Biosynthesis:

A general phosphoglycerolipid consists of a glycerol backbone that is acylated on hydroxyls at positions sn-1 and sn-2. The hydroxyl group at the 3rd position can be esterified by a variety of alcohol derivatives ranging from myo-inositol or choline to ethanolamine or serine. These phosphoglycerolipids exhibit dual functions in cell membranes, with their fatty acid tail embedded in the membrane and their polar head group exposed to the cytosol displaying cell signalling functions[5]. The signalling activities of PIs, which soluble headgroup containing myo-inositol can be phosphorylated at different positions, giving rise to a broad spectrum of regulatory compounds[6, 7]. Particularly, the activity of PI-kinases produces biologically active plant PIs, which phosphorylate reversibly at different positions of the inositol headgroup. It is important that each kinase reaction is reflected by a PI- phosphatase catalyzing the exact opposite enzymatic step, ensuring the rapid conversion of different PI species. Phosphatidylinositol-4-phosphate is the most abundant PI in plant cells. In vivo 32Pi incorporation studies have shown a faster turnover of PtdIns (4, 5) P2 levels, which together with its low plasma membrane abundance characterizes this compound as a signaling molecule[6, 8]. In fact, this metabolite is produced by the activity of PtdIns 4 5-kinases (PIP5 K), whereas signal termination is achieved by activation of 5′-phosphatidyilinositol phosphatases. The close levels of PtdIns (4, 5) P2 at the plasma membrane are also controlled by the Phospholipase C (PLC), an enzyme that cleaves PtdIns (4, 5) P2 into inositol- trisphosphate (InsP3) and 1, 2. -diacylglycerol (DAG). DAG may be more phosphorylated by diacylglycerol kinases (DGK) forming phosphatidic acid (PA). In addition to PI- specific PLC, plants also encode non- specific PLC (NPC), which can use phosphatidylcholine (PtdCho) or phosphatidylethanolamine (PtdE) to produce DAG and the corresponding phospho-alcohol[9]. In summary, several kinases, phosphateses and lipases can metabolize several phosphoglycerolipids and produce separate pools of different phospholipid species. Close regulation of phospholipid catabolism is therefore crucial for determining adequate biological responses.

Role of Phosphoglycerolipids in Vascular Cell Differentiation:

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In higher plants, the vascular system has evolved to provide a wide range of signaling factors among distantly separated organs as an inter-organic communication network. The vascular system comprises of two functionally specialized conducting elements: Xylem, required for the transport of water and minerals, and Phloem, responsible for the reallocation of photosynthetic compounds. To be conductive, phloem and xylem cells undergo a drastic differentiation program, which involves the degradation of most of their organelles. Surprisingly, beyond the well-characterized factors, recent studies have reported the presence of phosphoglycerolipids and lipid-transport proteins in vascular exudates. This raises the question whether these compounds might act not only as membrane components but also as long-distance signaling factors[10]. In addition, Phosphoinositides (PIs) are known to act as constitutive signals that define organelle identity and regulate subcellular trafficking[11]. The ability of these lipids to synthesize, modify and hydrolyze quickly makes them suitable candidates to signal complex cellular processes such as vascular differentiation.

Materials and Methods:

The lipophilic organic dye Atto647N (fluorescence excitation and emission maxima at 645 nm and 670 nm, in aqueous solution; Atto-Tec, Siegen, Germany) is used as the fluorescence marker. Also synthesized different fluorescent phosphoglycerolipid and sphingolipid analogs.

Figure 3. Structures of the fluorescent label and the fluorescent lipid analogs with the structural elements of ceramide (or sphingosine) that may participate in hydrogen bonding marked in grey. The asterix denotes the position of the fluorescent label Atto647N.

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Mammalian PtK2 cells were prepared on standard glass coverslips by incorporating the fluorescent lipid analogs into the plasma membrane of the living cells via bovine serum albumin (BSA) complexes.

Molecular Membrane Dynamics in living cell:

The superior spatial resolution of Stimulated Emission Depletion (STED) Nanoscopy[12] can help us to get more details about molecular membrane dynamics in living cells[13-18], such as lipid-protein interactions[15, 19-23], which are not possible by the conventional far-field optical microscopy due to its limited spatial resolution. The application of fluorescence correlation spectroscopy (FCS)[24] in focal zones continuously adjusted to a diameter of 30 nm distinguishes between free and abnormal molecular scattering due, for example, to the transient binding of lipids to other membrane components, such as lipids and proteins. We compared STED-FCS data in the plasma membrane of living mammalian cells recorded on various fluorescent lipid analogs. The trapping characteristics of different fluorescent lipid analogues were described, different in the number of chains, the position of the fluorescent marker and the structure of the headgroup.

Our results show details of the observed transitional formation of molecular complexes. The diffusion characteristics of phosphoglycerolipids without hydroxylic head groups have shown weak interactions. The most powerful interactions with sphingolipid analogs showed cholesterol-assisted and cytoskeleton-dependent binding. The hydroxyl-containing ganglioside head group, galactosylceramide and phosphoinositol helped to bind, but in a much less cholesterol- and cytoskeleton-dependent manner. The observed anomalous diffusion indicates lipid- specific transient hydrogen bonding to other membrane molecules, such as proteins, and points to a distinct connectivity between the different lipids and other membrane constituents. This strong interaction differs from the cholesterol- dependent, liquid- ordered domain in model membranes. STED Nanoscopy offers far- field diffraction- unlimited spatial resolution by using stimulated emissions to inhibit fluorescence signaling of molecules everywhere outside small confined regions[25, 26]. STED- FCS directly revealed that, unlike fluorescent glycerophospholipid analogs, sphingolipid analogs were trapped in transient molecular complexes supported by cholesterol[27]. These STED- FCS data complemented previous diffraction- limited FCS data[18, 28] and were confirmed recently by fast single- molecule tracking experiments[29] and FCS measurements with a tip- based near- field optical microscope[30]. However, the dependency of observed lipid trapping on the molecular structure of the lipids and the influence of cholesterol and the cytoskeleton, remain to be clarified.

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STED nanoscopy allows fluorescence interrogation spots to be created in nanometric scales [25, 26]. STED nanoscopy is based on reversible inhibition of a marker fluorescence by stimulated emissions. The stimulated emissions are caused by STED light at a wavelength typically on the red edge of the emission spectrum. STED light irradiation with a distribution of focal intensity with one or more zeros in space inhibits the fluorescence of marker molecules everywhere except in subdiffraction- size regions around zeros. In our case, we superimposed the diffraction- limited fluorescence excitation spot on a doughnut- shaped intensity distribution of the STED light, giving an effective fluorescence spot of the size of the subdiffraction in the lateral directions. We were able to adjust the effective fluorescence spot dynamically by adjusting the STED beam intensity as shown in Fig. 4A plotting the diameter of the effective focal points against the PSTED beam power.

Figure 4.[31]STED-FCS reveals anomalous lipid diffusion in the plasma membrane of live mammalian cells. (A) The effective focal detection spots of the STED nanoscope (Eff.) were created by overlaying the diffraction-limited excitation spot (Exc.) with a doughnut-shaped focal intensity distribution of the STED laser (STED), turning fluorophores non-emissive everywhere but at the focal center: lateral scanning images of 80-nm gold beads (Exc. and STED) and 20-nm fluorescent beads (Eff.), scale bar 200 nm The focal diameter d (full width at half-maximum) was tuned by the power PSTED of the STED laser as calibrated by scanning 20-nm large fluorescent beads (black circles) or by measuring the focal transit time of fluorescent lipid analogs in supported lipid bilayers using FCS (open squares). (B) Confocal (d = 240 nm) and STED (d = 40 nm) FCS data of PE (red) and GM1 (gray) diffusion with fits using 1/α ≈ 1 (red lines) and >1.4 (blue line). All curves were normalized to amplitude 2. (C) Dependence of the focal transit time τD, the anomaly 1/α, and the apparent diffusion coefficient D of PE (black squares), SM (red circles), and GM1 (gray triangles) on the focal diameter d and area d² determined from FCS data recorded for increasing STED power. Black and red dotted lines indicate the dependence expected for free and trapped diffusion, respectively. Standard deviation of the mean was ∼10%.

We used FCS to study the diffusion of various membrane molecules through these STED nanoscopic spots. We have incorporated into the plasma membrane of living mammalian PtK2 cells different lipid analogs labeled with the lipophilic organic dye Atto647N by incubation with the following lipid BSA complexes: saturated phosphoglycerolipids phosphatidylethanolamine(DPPE, denoted by PE), phosphatidylcholine (DSPC, denoted by PC) and phosphatidylinositol (PI), unsaturated phosphatidylethanolamine (DOPE), saturated sphingolipids sphingomyelin (SM), ceramide (Cer), galactosylceramide (GalCer), and ceramide phosphoryl inositol (CPI), and the gangliosides GM1, GM2, and GM3. The lipid analogs differed in the headgroup structure, length and saturation of the acyl chains and the dye labeling position.

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FCS analyses characteristic fluctuations in the fluorescence signal F(t) over the time t about an average value <F(t)> by calculating the second-order autocorrelation[13, 17, 18] function G(t_c).

δF(t) = <F(t)> – F(t)

G(t_c) = 1 + <δF(t) δF(t+t_c)> / <F(t)>²

Where, tc is the correlation lag time, and Triangular brackets indicate time averages.

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Fluctuations in the fluorescence signal are caused, for example, by typical variations in the concentration of fluorescent molecules that diffuse in and out of the effective detection area and by transitions into and out of the dark (triplet) state. Following earlier work, the auto-correlation function taking into account diffusion dynamics, the dark (triplet) state population and other kinetics causing changes in the fluorescence brightness can be approximated by,

G(t_c)=1 + (1/N) G_D(t_c) G_T(t_c) G_K(t_c)

where N is the particle number (i.e., the mean number of fluorescent molecules in the detection volume, which is proportional to the concentration divided by the measurement volume (or area for two-dimensional samples)).

Similar to G_D(t_c), the correlation term covering diffusion, G_T(t_c) is the correlation term covering the dark (triplet) state population (with the equilibrium fraction T_1eq of molecules in the dark triplet state, and the triplet correlation time τ_T, characterized by the triplet population and depopulation kinetics),

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G_T(t_c) = 1 + T_1eq/(1-T_1eq) exp(-t_c/τ_T)

And G_K(t_c) is an additional kinetic term with amplitude K and correlation time τ_K.

G_K(t_c) = 1 + K exp(-t_c/τ_K)

At the excitation intensities applied, T_1eq and τ_T of the Atto647N label were approximately 0.1 and 5 µs, respectively, and fixed throughout the analysis. We observed no dependence of values of T_1eq and τ_T on the STED power.  Furthermore, our analysis of all FCS data recorded for lipid dynamics of the Atto647N labeled lipids in living cells had to include an additional kinetic term with amplitude K = 0.05–0.1 and correlation time τ_K = 50–150 µs. This kinetic term was independent on the levels of excitation and STED light and might stem from the population of an additional dark state or conformational fluctuations of the dye-lipid system leading to changes in the fluorescence brightness. Its amplitude increased with hydrophobic environment as observed by measurements on model membranes at different humidity.

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The diffusion term includes the time it takes for a lipid to diffuse through the effective focal spot regarding the inhomogeneous intensity profile of the laser focus (or of the detection area) as well as possible heterogeneity in diffusion due to, for example, binding to less mobile or immobile membrane compounds. We applied two different approaches to parameterize deviations from normal diffusion.

Anomaly:

Deviations from normal Brownian diffusion may be analyzed using the model of anomalous subdiffusion[17]. Due to anomalous diffusion, the mean square displacement <r>² of a molecule’s diffusion is not linear with time, but follows a power law in time t,

<r>² = 4 D t^α

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Where,

D is the average diffusion coefficient and,

is the diffusion exponent.

Denoted anomaly throughout, the inverse (1/α) describes the degree of hindered diffusion. While diffusion is free for (1/α) = 1 and follows Brownian motion characterized by a constant D, (1/α) > 1 characterizes anomalous subdiffusion; the larger (1/α) the more hindered the diffusion.

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Figure 5. Dependence of the molecular trapping of SM on temperature. Diffusion coefficient D (left) and anomaly (1/α) (right) of the STED recordings (d ≈ 40 nm) of SM (white) and PE (black) for different sample temperatures T. Dashed lines highlight values of SM at 22° and (1/ α) = 1. While the apparent diffusion coefficient D of PE increased with temperature T, while that of SM stayed constant. The Arrhenius behavior confirmed the (almost) free diffusion characteristics of the PE lipids in the plasma membrane. In contrast, the insensitivity of the SM dynamics on T demonstrated that temperature did not influence the formation of the lipid complexes, at least not in the measured temperature range of 22 to 37° C.

In FCS, anomalous diffusion is treated by introducing the anomalous diffusion exponent α into the term expressing the diffusion dynamics

G_D(t_c) = (1 + (t_c/τ_D)^α)^-1 ,

With, τ_D = d² / (8 ln2 D)

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Here, we only considered diffusion along the lateral direction, since the plasma membrane can be treated as a flat, two-dimensional sample. τ_D is the average transit time of a fluorophore with the apparent diffusion coefficient D through the focal detection area of diameter d. For free Brownian diffusion D is constant for different d and τ_D scales linearly with the focal area d². This is different for anomalous diffusion, where D varies with d² depending on the spatial and temporal characteristic of the process causing the anomaly (e.g., time span and size of a trap or dimension of an obstacle). In this case, D represents an average apparent value.

Figure 6. Dependence of the molecular dynamics of SM and PE on the excitation power P_exc: focal transit time τ_D (left) and anomaly (1/α) (right) determined from FCS data of STED recordings (d = 40 nm). Increasing P_exc results in enhanced photobleaching and thus in an apparent reduction of τ_D due to irreversible loss of fluorescence before leaving the focal area. Molecules that are dwelling longer in the focal area are more probably photobleached. Consequently, with increasing P_exc trapped SM lipids are much less observed than freely diffusing SM and PE transits, resulting in the decrease of 1/_α. We applied P_exc ≈ 10 µW in all of our experiments, giving values of τ_D and (1/α) highlighted by the grey and black lines.

Both, the intensity profile of the laser spot, which establishes the fluorescence emission profile (at the excitation intensities applied we can safely assume a linear dependency of the fluorescence emission on the laser intensity) and the detection profile of the microscope (given by the point-spread function of the microscope objective and the transmission function of the confocal pinhole) determine the spatial profile of the effective focal spot. Equation of G_D(t_c) assumes a Gaussian-intensity profile, which is a good approximation for confocal FCS experiments at low laser intensities. For very small focal spots created by STED, this assumption slightly deviated from the actual shape of the focal spots. Consequently, the analysis of the correlation data may show an anomaly artifact. However, this bias hardly influenced the correlation analysis in the case of hindered diffusion, because the focal passage was dominated by trapping, which for example resulted in values of 1/α   > 1.4. These values exceeded by far the artifact due to non-Gaussian spot profiles where 1/α ≈ 1.1 (as determined from measurements of purely freely diffusing lipids in SLBs).

Binding:

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A second approach is based on the fact that anomalous diffusion is caused by transient trapping due to binding of the diffusing molecule to a fixed or comparatively slow moving particle. Such reactions are described by the on/off kinetic of the binding process to an immobile complex in the diffusion path of the molecule with an effective encounter rate k_on and a dissociation rate k_off. If the trapping time 1/k_off is much longer than the average time the freely diffusing molecule would spend in the observation focus, the diffusion is reaction-dominated and we can describe the correlation function by

G_D (t_c )= (1− B)(1+t_c /τ_Dfree )⁻¹ + Bexp(−k_off t_c )

Where,

τ_Dfree is the average focal transit time for free diffusion (with free diffusion constant D_free) and,

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Β = k_off / (k_off+k_on)

is the fraction of bound molecules.

We usually fixed τ_Dfree to values

τ_Dfree = d²/(8 ln2 D_free)

Expected for a value of D_free that was estimated from measurements at large d.

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Concluding Remarks and Future Perspectives:

 

Dye dependence:

We found no significant dependence of the nanoscale trapping on the labeling conditions. The dynamics of the fluorescent lipid analogs remained unchanged when labeling with Atto647N at the head group or by replacing the acyl chain. We had previously observed consistent diffusion characteristics when applying another lipophilic marker or when labeling the head group with a very polar dye. However, using the very same polar dye for labeling at the water-lipid interface (i.e., by acyl chain replacement) had resulted in almost diminished trapping and a much faster diffusion, indicating a less tight membrane anchoring. Therefore, neither the label type nor the label position had a significant influence except if labeled with a polar dye at the acyl chain. This independence was surprising, because it revealed that only the polarity but not the bulkiness of the dye label introduced a bias on at least the observed trapping dynamics.

Comparison to other cells:

The observed dynamical characteristics were not specific for the PtK2 cells. For example, we observed a similar difference in the dynamics of SM and PE and a similar dependence on cholesterol in other cells such as the human HeLa cell line (Fig. 7). Although the trapping of SM was similar, diffusion of PE and SM in HeLa cells was slightly slower

(D_conf ≈ 0.3 µm²/s) than in PtK2 cells (D_conf ≈ 0.5 µm²/s) and we determined lower on- and off-rates of the transient complex (k_on, k_off ≈ 30 s^-1 compared to k_on, k_off ≈ 70 s^-1 in PtK2 cells).

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Figure 7. [31]Dependence of the nanoscale trapping on various parameters. Apparent diffusion coefficient D (upper panel) determined from confocal (d = 240 nm, grey bars) and STED-FCS recordings (d = 40 nm, black bars), ratio D_STED/D_conf (middle panel) and recovery R (lower panel) for PE, SM and in some cases GM1: measurements in live HeLa cells (+COase: cholesterol depletion by COase), treatment by Zaragozic acid (ZA, lowering of the cholesterol level), Cytochalasin (Cyt, blocks the actin polymerization), Jasplakinolide (Jaspl, disruption of the actin filaments), and Nocodazol (Noc, blocking of the microtubule network polymerization, recov: recovery of the microtubule network after the drug removal), and Myriocin (Myr, reduction of the endogenous sphingomyelin).

 

Cholesterol dependence:

We used the drug Zaragozic acid (ZA) to further highlight the dependence on cholesterol. ZA partially reduces the cellular cholesterol content by acting as an inhibitor of sterol synthesis. Fig. 7 depicts our results obtained for SM and GM1 on PtK2 cells following ZA treatment, rendering a partial abolishment of complex formation for SM and hardly any influence on GM1, similar to COase treatment.

Cytoskeleton dependence:

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Fig. 7 also confirms the dependence of the SM dynamics on the cellular cytoskeleton applying other drugs. Similar to Latrunculin B, treatments with Cytochalasin, which blocks the polymerization of actin, or with Jasplakinolide, which was reported to disrupt actin filaments in vivo, partially reduced the trapping strength of SM. A similar reduction was observed upon treatment by Nocodazole, which interferes with the polymerization of the microtubule network. Strikingly, trapping was recovered to its original extend after removal of the drug.

Sphingomyelin reduction:

At last, we investigated the changes in nanoscale membrane dynamics of SM and GM1 upon addition of Myriocin, which is supposed to inhibit sphingosine biosynthesis (with no effect on cholesterol) and we thus expect a lowering of endogenous sphingomyelin and other sphingolipids in the plasma membrane to a certain extent. While trapping of SM was reduced that of GM1 remained almost unchanged.

Technologies that extrapolate results from diffraction- limited measurements may not be accurate.

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24. Verdaasdonk, J.S., J. Lawrimore, and K. Bloom, Chapter 19 – Determining absolute protein numbers by quantitative fluorescence microscopy, in Methods in Cell Biology, J.C. Waters and T. Wittman, Editors. 2014, Academic Press. p. 347-365.

25. Hell, S.W., Microscopy and its focal switch. Nature Methods, 2009. 6(1): p. 24-32.

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27. Eggeling, C., et al., Direct observation of the nanoscale dynamics of membrane lipids in a living cell. Nature, 2009. 457(7233): p. 1159-1162.

28. Lenne, P.F., et al., Dynamic molecular confinement in the plasma membrane by microdomains and the cytoskeleton meshwork. EMBO Journal, 2006. 25(14): p. 3245-3256.

29. Sahl, S.J., et al., Fast molecular tracking maps nanoscale dynamics of plasma membrane lipids. Proceedings of the National Academy of Sciences of the United States of America, 2010. 107(15): p. 6829-6834.

30. Manzo, C., T.S. Van Zanten, and M.F. Garcia-Parajo, Nanoscale fluorescence correlation spectroscopy on intact living cell membranes with NSOM probes. Biophysical Journal, 2011. 100(2): p. L08-L10.

31. Mueller, V., et al., STED Nanoscopy Reveals Molecular Details of Cholesterol- and Cytoskeleton-Modulated Lipid Interactions in Living Cells. Biophysical Journal, 2011. 101(7): p. 1651-1660.

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