Gradient substrate assembly for quantifying cellular response to biomaterials.

Using quantitative fluorescence microscopy in conjunction with a method of gradient substrate assembly established in their group, the authors were able to introduce and measure reproducible changes in cellular morphology and cell density by manipulating polymer grafting density. The mechanism behind this change in cellular behavior was explained by a semiempirical, geometric model that describes the effect of the spatial distribution of the polymer on protein attachment. A 10-fold increase in graft density of poly(2-hydroxyethyl methacrylate) [PHEMA] along the surface of a gradient sample, preexposed to bovine fibronectin, caused a change in the size of fibroblasts on the surface (i.e., cell spreading) from (1238 +/- 704) to (377 +/- 216) microm(2). The results were in quantitative agreement with those obtained on three separate gradient samples. Both cellular response and fibronectin adsorption (as measured via ellipsometry) were found to vary sigmoidally with graft density of PHEMA, demonstrating the high degree of correlation between the two phenomena. A simple, rigid-disk model accounting for the surface coverage of PHEMA was able to predict the amount of adsorbed fibronectin with a correlation coefficient of 0.97. Maximal cell adhesion and cell spreading were found to occur at fibronectin surface densities of 50 and 100 ng/cm(2), respectively. The results demonstrate the role of gradient substrate assembly as a method for quantifying the relationship between protein and cellular response to technologically relevant polymeric materials.

Tertiary structure changes in albumin upon surface adsorption observed via fourier transform infrared spectroscopy.

A nondestructive Fourier transform infrared (FTIR) spectroscopy assay, amenable to exploring a wide range of proteins and polymers, is used to measure changes in the tertiary structure of bovine serum albumin (BSA) adsorbed to three surfaces: gold, polystyrene (PS), and poly(D,L-lactic acid) (PDLLA). Tertiary structural analysis is important because typical secondary structural analysis (FTIR and CD) is not always sensitive enough to distinguish between the sometimes subtle protein structural changes caused by adsorption. The polymers are spin-coated onto a gold surface, exposed to protein, and then immersed in a deuterated buffer solution to probe the protein's tertiary structure before the sample is removed from its aqueous environment. Infrared band intensities, related to the exchange of amide hydrogen for deuterium (HDX), as a function of the immersion time in deuterated buffer, are used to determine the extent of amide solvent exposure. Analysis of the results in terms of a single exponential decay shows that enough amides undergo a measurable amount of exchange in 60 min to quantify relative changes in BSA solvent exposure on different surfaces. In addition, substantial fractions undergo HDX at a rate too fast or too slow to be followed with our experimental protocol. The proportions of these quickly and slowly exchanging amide groups also provide information about relative changes in the BSA structure on different surfaces. Adsorption was found to increase the extent of HDX over that observed for BSA in solution, consistent with surface-induced unfolding and a loss of tertiary structure. Changes in HDX were found to be more sensitive to which surface was absorbing the protein than the typical FTIR secondary structural analysis obtained from fitting the amide I band. HDX was greatest for BSA adsorbed to the surface of PDLLA and least in the case of BSA adsorbed to gold, which indicates the greatest and least degree of unfolding, respectively.

Local thickness and anisotropy approaches to characterize pore size distribution of three-dimensional porous networks.

Two image-analysis approaches for pore size distribution (PSD) of porous media are proposed. The methods are based on the skeleton representation of a porous object. One approach gives the local thickness of the pore object to represent the pore size corresponding to a lower limit of PSD. The other gives the pore size taking into account the anisotropy of pore object and corresponds to an upper limit of PSD. These two approaches can be incorporated into a computer program without computationally intensive and complex mathematical operations. In this study, these two approaches are applied to a two-dimensional (2D) synthetic image and 3D natural images of tissue scaffolds with various porosities and tortuosities. The scaffolds were prepared by removing the water-soluble poly(ethylene oxide) (PEO) component of the polycaprolactone (PCL)/PEO blend, leaving a porous PCL scaffold. Extracting quantitative PSD information for materials with an interconnected porous network rather than discrete voids (such as tissue scaffolds) is inevitably subjective without a universally accepted definition of "pore size." Therefore, the proposed lower and upper limits of PSD can come into play when considering mass transfer and scaffold surface area for cell-matrix interaction.

Time-dependent effects of pre-aging polymer films in cell culture medium on cell adhesion and spreading.

We have tested the hypothesis that cell adhesion and spreading on polymer films are influenced by the amount of time that the polymer films are pre-aged in cell culture medium. Cell adhesion and spreading were assessed after a 6-h culture on poly(D,L-lactic acid) (PDLLA) films that had been pre-aged in cell culture medium for 30 min, 1, 3 or 7 d. Cell adhesion and spread area were enhanced as the duration of pre-aging PDLLA films in cell culture medium was increased. Materials characterization showed that the hydrophobicity and surface morphology of the PDLLA films changed with increasing length of pre-aging time. These results suggest that cell adhesion and spreading are sensitive to the time-dependent changes in PDLLA hydrophobicity and surface morphology that occur during exposure of the polymer to cell medium for different lengths of time. These results demonstrate that cell response to a degradable, biomedical polymer can change as a function of the amount of time that the polymer is exposed to physiological medium.

Using surrogate modeling in the prediction of fibrinogen adsorption onto polymer surfaces.

We present a Surrogate (semiempirical) Model for prediction of protein adsorption onto the surfaces of biodegradable polymers that have been designed for tissue engineering applications. The protein used in these studies, fibrinogen, is known to play a key role in blood clotting. Therefore, fibrinogen adsorption dictates the performance of implants exposed to blood. The Surrogate Model combines molecular modeling, machine learning and an Artificial Neural Network. This novel approach includes an accounting for experimental error using a Monte Carlo analysis. Briefly, measurements of human fibrinogen adsorption were obtained for 45 polymers. A total of 106 molecular descriptors were generated for each polymer. Of these, 102 descriptors were computed using the Molecular Operating Environment (MOE) software based upon the polymer chemical structures, two represented different monomer types, and two were measured experimentally. The Surrogate Model was developed in two stages. In the first stage, the three descriptors with the highest correlation to adsorption were determined by calculating the information gain of each descriptor. Here a Monte Carlo approach enabled a direct assessment of the effect of the experimental uncertainty on the results. The three highest-ranking descriptors, defined as those with the highest information gain for the sample set, were then selected as the input variables for the second stage, an Artificial Neural Network (ANN) to predict fibrinogen adsorption. The ANN was trained using one-half of the experimental data set (the training set) selected at random. The effect of experimental error on predictive capability was again explored using a Monte Carlo analysis. The accuracy of the ANN was assessed by comparison of the predicted values for fibrinogen adsorption with the experimental data for the remaining polymers (the validation set). The mean value of the Pearson correlation coefficient for the validation data sets was 0.54 +/- 0.12. The average root-mean-square (relative) error in prediction for the validation data sets is 38%. This is an order of magnitude less than the range of experimental values (i.e., 366%) and compares favorably with the average percent relative standard deviation of the experimental measurements (i.e., 17.9%). The effects of each of the user-defined parameters in the ANN were explored. None were observed to have a significant effect on the results. Thus, the Surrogate Model can be used to accurately and unambiguously identify polymers whose fibrinogen absorption is at the limits of the range (i.e., low or high) which is an essential requirement for assessing polymers for regenerative tissue applications.