- Original article
- Open Access
Diffusion and interaction in PEG-DA hydrogels
- Valentin Hagel^{1, 3},
- Tamás Haraszti^{1, 3} and
- Heike Boehm^{1, 2, 3}Email author
https://doi.org/10.1186/1559-4106-8-36
© Hagel et.al.; licensee Springer. 2013
- Received: 23 August 2013
- Accepted: 7 November 2013
- Published: 6 December 2013
Abstract
Polyethylenglycol (PEG) hydrogels are widely used as tuneable substrates for biological and technical applications due to their good biocompatibility and their high hydrophilicity. Here we compare the mesh size and diffusion characteristics of PEG hydrogels by analyzing the diffusion of solutes with different, well-defined sizes over long and short time scales. Interestingly, one can tune the mesh size and the density of the gel simply by changing the inital concentrations of the PEG-diacrylate (PEG-DA) polymer, which also enhances the solute uptake in equilibrium through the interaction with the PEG chains. This increased uptake can be characterized by an enhancement factor determined by partition ratio analysis. It increases linearly with the polymer volume fraction, but is not caused by immobilization inside the hydrogel as evident from FRAP measurements, thus rendering these hydrogels ideal materials for i.e. drug delivery applications.
Keywords
- Hydrodynamic Radius
- Fluorescence Recovery After Photobleaching
- Partition Ratio
- Hydrogel Sample
- Fluorescence Recovery After Photobleaching Experiment
1 Background
Hydrogels are water-swollen, cross-linked polymeric materials with a complex specific three-dimensional architecture. In the last decades they have gained considerable attention due to their applicability both as biological and as technical building blocks. In technical applications, hydrogels serve as basic building blocks for gel electrophoresis or size exclusion chromatography, etc. Moreover they are nowadays implementet as “intelligent” chemo-mechanical materials using them as stimuli-responsive sensor-actor systems [1] based on their high sensitivity to environmental conditions [2] such as pH [3, 4], temperature [5], solvent composition [3], light or pressure [2]. In biological contexts, hydrogels have become ideal model systems for tissue matrices due to their similarity to the extra-cellular matrix (ECM). They opened up new methods to study cell behavior such as migration on substrates with different stiffness [6] as well as in three dimensions [7]. Understanding the permability to various solutes such as growth factors, vitamins, etc. in these synthetic ECMs [8] not only enhances our basic understanding of ECMs, but can also be employed in various biomedical applications including the use of hydogels as drug- and cell carriers, artificial tissue scaffolds or growth factor release systems [9].
Therefore, understanding diffusion processes of macromolecules in hydrogel-based systems plays a key role in the design of new hydrogel-based materials. Controlling and predicting diffusion within biomedical materials, such as artificial implants or drug delivery systems, is a prerequisite for the design of feasible materials. However, diffusion within hydrogels is a very complex process due to the different interactions a molecule undergoes while diffusing through the hydrogel mesh. The physical models of diffusion are mainly based on either hydrodynamic effects, obstruction effects, the free volume theory or, in further advanced cases, on thermodynamic models [10]. Many of these models successfully describe diffusion processes under various circumstances and thus promote the understanding the complex diffusion properties within hydrogels. However, many controversies still remain and it seems that obstruction effects, hydrodynamic interactions and thermodynamic agitation should all be considered simultaneously in order to develop a broader understanding of diffusion in polymeric networks [10]. The limited ability of diffusion models to describe diffusion processes in a broad range of experimental situations is due to the many types of possible specific interactions between the polymer mesh and the diffusing species [11]. Such interactions include hydrogen bonding, electrostatic and van der Waals interactions as well as hydrophobic effects. Also, the influence of altered solvent properties such as structuring of solvent molecules by the polymer mesh expressed as an entropic effect [12] or as an increased ‘local viscosity’ of the solvent [13] are difficult to be incorporated quantitatively to construct exact diffusion models.
Among the synthetic hydrogels, polyethylene glycol (PEG) hydrogels belong to the most important materials. As they exhibit many advantageous material properties such as being transparent, deformable, biocompatible [14], and permeable to gases and nutrients [15], PEG hydrogels are widely used in biomedical applications including artificial tissue scaffolds, matrices for the controlled release of biomolecules [14], wound dressings [16], and contact lenses [17]. Thus, understanding diffusion mechanisms within PEG hydrogels and understanding them with respect to the polymeric structure is of great interest.
In this work we used monodisperse, fluorescent dextrans of various hydrodynamic radii, to study diffusion within PEG hydrogels of different densities. Dextrans are branched, spherical polysaccharides of predefined controlled size widely used as neutral model diffusants [18–20]. Diffusion coefficients were measured using the fluorescence recovery after photobleaching (FRAP) on a setup which has been established for three-dimensional measurements in hydrogel samples. Furthermore, the detailed analysis of the equilibrium partition ratios is fundamental in understanding the physical-chemical nature of the interaction between solutes and polymer [21]. Complementing the FRAP analysis with partitioning analysis provides access to short and long term interactions separately. Further, partitioning analysis allows to probe the real mesh size of the hydrogels and to investigate their polymeric microstructure. Mesh sizes are also calculated based on the measurement of shear moduli and using the theory of rubber elasticity and compared with the outcome of the partitioning analysis.
2 Results and discussion
Charakteristica of the three different PEG-DA (20kDa) hydogels depending on the PEG-DA concentration in the preparation mixture
% PEG-DA (v/v) | Swelling | Polymer volume | Shear moduliG |
---|---|---|---|
ratioQ | fraction ϕ [10^{−2}] | [kPa] at 25°C | |
10 | 32.67±0.73 | 3.06±0.07 | 2.8±0.7 |
20 | 19.06±0.41 | 5.25±0.12 | 7.7±0.7 |
30 | 13.20±0.33 | 7.58±0.20 | 13.7±0.7 |
Estimated average and maximal mesh sizes ξ of the PED-DA (20kDa) hydrogels for each polymer volume fractions based on polymer concentration c , elastic moduls G and parameter s obtained in the partition ratio analysis
ϕ | <ξ> _{ c } [ nm] | <ξ> _{ G } [ nm] | <ξ> _{ s } [ nm] | ξ _{ max } [ nm] |
---|---|---|---|---|
0.03 | 6.70±0.45 | 4.80±0.57 | 4.26±0.16 | 16.3 |
0.05 | 5.60±0.38 | 3.81±0.58 | 2.07±0.07 | 9.2 |
0.08 | 4.96±0.33 | 3.44±0.17 | 1.32±0.07 | 6.8 |
Results of the fitting analyses of the diffusion coefficients with equation (4)
ϕ | α | β |
---|---|---|
0.03 | 7.8±0.9 | 0.12±0.02 |
0.05 | 7.8±0.3 | 0.10±0.01 |
0.08 | 6.9±0.2 | 0.09±0.01 |
In order to investigate the influence of the temperature on the diffusion behavior the same measurements were repeated at a second temperature, T =37°C. On the one hand the temperature influences the thermodynamic fluctuations of the polymer chains in the network that may influence the diffusion behavior, on the other hand the viscosity of water decreases by around 30% due to the temperature change allowing to investigate viscosity effects on the diffusion behavior. Figure 4 shows the diffusion measurements at both temperatures. It can be seen that the diffusion coefficients overlap for both temperatures within their error bars for all three swelling ratios indicating that neither polymer chain fluctuations nor unpredicted viscosity effects significantly influence the diffusion behavior in the measured temperature range.
3 Conclusions
It is well known that permeability as well as mechanical properties of radically cross-linked PEG hydrogels depend not only on the size of PEG-DA polymers, but also on the polymer concentration in the reaction mixture [26, 27]. Well defined fluorescent molecules employed in partition ratio analysis are ideally suited to determine the long time permeability properties of hydrogels. Including FRAP measurements enables an estimation of the amount of immobile fractions and short time scale behavior of the solutes. Where the average mesh sizes estimated from the swelling ratio and elastic moduli of the hydrogel correspond very well to values reported in the literature for PEG membranes [15] and our values estimated by partition ratio analysis. These average mesh sizes define the overall structure of the hydrogel. On the other hand, the distribution of mesh sizes and especially the maximal mesh size within the gel caused for example by network defects [27] enhances the ability of large molecules and proteins to diffuse through the hydrogel, another important feature for biological applications. Here, our partition ratio analysis indicates a broad mesh size distribution with maximal values up to four times the average. Independet of the distribution of mesh sizes, the large enhancement factor indicates an increased and unexpected capacity for the reversible uptake of hydrophilic molecules by PEG hydrogels. In combination with their high biocompatibility this explains the good suitability of PEG hydrogels in drug delivery applications. This work also nicely demonstrates the impact of slight changes in the fabrication protocol and the necessity to determine the diffusion characteristics of each set of hydrogels individually.
4 Methods
4.1 Preparation of PEG-DA hydrogels
Polyethyleneglycol-diacrylate macromer of 20 kDa molecular weight (PEG-20k-DA) were synthezised as described earlier [6] and mixed with degassed PBS (phosphate-buffered saline, p H = 7.4) to final concentrations of 10%, 20% and 30% (v/v). Photoinitiator 2-hydroxy-4’-(2-hydroxyethoxy)-2-methylpropiophenon (Sigma-Aldrich, Deisenhofen, Germany) was dissolved at a concentration of 10% (w/v) in 70% ethanol and added at a concentration of 1% (v/v) to the polymerization mixtures. The polymerization mixtures were vortexed and subsequently degassed by centrifugation (5 min, 4.4 rpm), then cast to glass molds consisting of a bottom glass plate and a top quartz glass plate separated by a distance of 0.4 mm. Photopolymerization was carried out by a VL-6.L UV-lamp (Vilber Lourmat, Eberhardzell, Germany) at 365 nm for 20 min. Before further use the hydrogels were swollen to equilibrium in PBS for at least 24 h.
4.2 Swelling ratio
4.3 Dextrans
In general, FITC (fluorescein isothiocyanate) labeled dextrans are produced commercially only by limited hydrolysis and fractionation. In order to obtain FITC-dextran samples of narrow size distribution and over a broad range of hydrodynamic radii further fractionation is required. We purchased FITC-dextrans with a mean molecular weight of 4 and 150 kDa (FD-4, FD-150S, Sigma-Aldrich, Deisenhofen, Germany). Fractionation allowed us to span a range of hydrodynamic radii between 0.9 and 12.2 nm (FD-150S dextrans have high polydispersity originally). This process was carried out by FPLC (fast protein liquid chromatography) fractioning on a HiPrep 26/60 Sephacryl S-300 high resolution column (GE Healthcare, Munich, Germany). FITC-dextrans were dissolved in PBS at a concentration of 50 mg/ml, sterilized by syringe filtration (Rotilabo syringe filter, 0.22 μ m, Carl Roth GmbH, Karlsruhe, Germany) and degassed under vacuum for 30 min. After loading the column, the flow speed was set to 1.3 ml/min. Elution of FITC-dextran from the column was monitored at 280 nm and collected in fractions of 10/20 ml (FD-150S/FD-4). Each fraction was desalted by dialysis (3 repetitions of 4 h exposure to 2 l water baths of double distilled water using dialysis membranes of 1000 Da molecular weight cut-off (Spectra/Por Dialysis Membrane MWCO 1000, Spectrum Laboratories Inc., USA)) and subsequently freeze-dried (lyophilization). The hydrodynamic radii of the FITC dextrans in each of the fractions was determined by measurement of the diffusion coefficients using FRAP (fluorescence recovery after photobleaching, see section Translational diffusion measurements measurements) and using the Stokes-Einstein relation. One of the fractions from the center of the distribution of the FD-150 polymer was fractionated a second time and analyzed for the size distribution of a typical fraction. Thus we measured the polydispersity index (PDI) to be 1.009 indicating that we obtained monodisperse FITC-dextran fractions.
4.4 Measurement of partition ratios
Equilibrium partition ratios K _{ i } = c/c _{0}, with i representing a specific dextran, c the concentration of molecules in the hydrogel phase at equilibrium partitioning, and c _{0} the concentration in the bulk solution, were measured by incubating hydrogel samples in FITC-dextran solution (0.5 m g/m l, PBS) for 5 days at 37°C.
with I and I _{0} the fluorescence intensity of the hydrogel sample and the bulk solution after subtraction of dark counts, d and d _{0} the thickness of the hydrogel sample and the bulk solution. Chambers for the bulk solution were made by two coverslips separated by double sided adhesive tape spacers of 0.4 mm thickness. Hydrogel samples were dried using paper tissue (Precision wipes), placed on a coverslip, surrounded by a ring of medium viscous silicone oil (Baysilone-Paste, GE Bayer Silicones, Germany) and capped by a second coverslip. The exact thickness of the equilibrium swollen hydrogels was measured on a rheometer (Kinexus, Malvern, UK) lowering the upper plate down to the gel contact point by setting the normal force to a very low value (0.1 - 0.2 N). Linearity of intensity vs. sample thickness in the measured range was verified measuring the signal of bulk solution samples of various thicknesses (see Supporting Information). Furthermore, we checked the linearity of the intensity vs. FITC-dextran concentration in the range of the analyzed intensities.
4.5 Translational diffusion measurements
To determine the diffusion coefficients based on FRAP experiments, we applied the method of Tsay and Jacobson, which is based on the spatial Fourier analysis of the fluorescence recovery images [28]. This method was shown to be applicable to thick hydrogel samples [29] and we adapted it to our setup. In contrast to conventional FRAP methods (e.g. direct photometric analysis [30]), this method is not sensitive to the generally confounding optical effects caused by out-of-focus light and scattering and absorption by the sample, thereby allowing to obtain accurate results from diffusion measurements in three-dimensional samples [29].
For the FRAP measurements the samples were placed on the stage of an inverted microscope (Olympus IX 70) of a Delta Vision System (Applied Precision, Isaquah, USA) equipped with a mercury arc lamp (Mercury Short Arc, HBO, Osram) providing epi-illumination and a Cool Snap HQ CCD camera (Photometrics, Tucson, USA). The illumination profile of the mercury arc lamp was adjusted to be as homogeneous as possible over the entire field of view (1024 × 1024 pixel). Bleaching during recovery was minimized by closing a shutter in between the acquisition of images. Several prebleaching images were acquired in order to correct for the nonuniform illumination profile. The excitation filter (490/20 nm) and the emission filter (528/38 nm) were selected for the use of fluorescein. Photobleaching was carried out by a short laser pulse of 30 ms duration provided by an optically pumped solid-state laser (OPSL, Sapphire 488/20, Coherent Inc., Santa Clara, USA) operated at 488 nm in the TEM00-mode which yields a radially symmetric, approximately Gaussian intensity profile. The laser power was set to 90% of the maximum output power (20 mW). The laser source module is coupled to an optical fiber guiding the laser light to the microscope setup. The beam profile is expanded by coupling of the output fiber coupler to a telescope (beam expander), and thereafter focused close to the back focal plane of the objective (10×, NA=1.2) in order to generate a cylindrically beam shape in the specimen. Low spatial resolution was chosen as optimal to avoid measuring effects arising from local inhomogeneities.
Recovery images were stored at a rate which was adapted to the time span of the recovery process by the control PC of the microscope (softWoRx Imaging Workstation, Applied Precision, Isaquah, USA). Each FRAP image sequence was converted to 16-bit.tiff images and exported for Fourier transform analysis implemented in MATLAB 2009b.
and allows to determine D by fitting an exponential decay curve (non-linear least-squares curve-fitting algorithm) to the decay of Fourier amplitudes obtained by discrete Fourier transformation of each intensity profile I _{ c o r r }(x,y,t) of the recovery sequence. For fitting analysis we used all Fourier frequencies for which u,v≤2, corresponding to the five lowest frequency values (see Supporting Information for further explanation). Higher Fourier components were excluded as the corresponding decay amplitudes were prone to scattering originating from noise fluctuations in the images. Correct adjustment of the FRAP analysis setup was verified by checking the overlap of the Fourier decay coefficients for all of the five lowest spatial frequencies with a test solution and, further, by measuring diffusion coefficients for three different FITC-dextrans in a series of water-glycerol mixtures of known viscosity and comparing the results to the diffusion coefficients obtained theoretically using the Stokes-Einstein relation (see Supporting Information). We also ensured that the results of the experiments were independent of the sample thickness in the range used and of the concentration in the measured concentration range.
4.6 Rheometry
Rheometry was performed on a rotational rheometer (Kinexus, Malvern Instruments, UK). For each concentration of PEG-DA three different samples were measured to determine G ^{′} and G ^{″}. For all of the gels, the complex shear modulus G ^{∗}, where G ^{∗}=G ^{′}+i G ^{″} and , was dominated by G ^{′} as the viscous contribution G ^{″} was negligible. In detail, a parallel plate geometry of 20 mm diameter (PP 20) and a solvent trap was used. Dried hydrogel cylinders were fixed between the plates by a normal force set to 0.2 N. For all hydrogel concentrations, an initial amplitude sweep with an oscillatory shear strain of increasing amplitude at a constant frequency of 1 Hz was performed to determine the range of linear response. Based on this test, G was measured at an amplitude set within the linear elastic regime at constant frequency set to 1 Hz.
5 Appendix
5.1 Mesh size estimation based on the polymerization protocol
where N _{ A } is Avogadro’s number.
5.2 Mesh size calculation from the elastic modulus
where C _{ n } is a characteristic constant, l is a monomer size, usually C _{ n }≈4 and l≈1.54 n m for PEG.
where N denotes the number of monomers in the gel, estimated from the mass of the polymer in the gel, based on the density ρ, the volume fraction ϕ or the swelling ration Q of the polymer and the molar mass M _{ r } of the monomer. N _{ r } is the number of polymer segments, which is related to the number of crosslinking points ρ _{ x } V by a factor x. This factor depends on the type of the network: for polymers where only two chains are bound together x=2, while for a cubic mesh where each node is connected to 6 neighbors, x=3.
Here we neglect the modification factors relating the length of the polymer chain in its free state versus in the gel, which is usually close to 1 [32].
5.3 Results of fitting equation 1
The results of the fitting analyses of the partition ratio with equation (1)
ϕ | A | s[nm ^{ −1 } ] | R ^{ 2 } |
---|---|---|---|
0.03 | 2.9 | 0.70 | 0.993 |
0.05 | 7.8 | 1.45 | 0.995 |
0.08 | 23.1 | 2.28 | 0.990 |
Declarations
Acknowledgements
We like to thank Tobias Hofmann for helpfull discussions and Christine Mollenhauer, Radka Koelz and Yvonne Schoen for their expert technical assistance. We gratefully acknowledge Joachim Spatz for his generous support and the Max Planck Society for funding this research as well as the “bioinspired material synthesis” program of the Baden-Württemberg Stiftung.
Authors’ Affiliations
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