While one strategy for biomaterials is the development of highly-robust bio-inert surfaces, researchers in the Santore lab are designing surfaces for tuned dynamic adhesive response, at different lengthscales. These specialized interfaces will ultimately meet the needs for smart biomaterials that can manipulate cells by control of the initially adsorbed proteins. This would be useful, for instance, in tissue engineering. Researchers in the Santore group believe, however, that the ability to tune adhesion dynamics with specialized surface features will enable technologies beyond those with obvious bio-interface components. Control of molecular motion and dissipation will lead the way for smart lubricants and responsive adhesives in the broadest range of applications.
Our efforts to develop surfaces for targeted control of protein behavior focus on lengthscales of 20 nm or less. Here, manipulation of proteins will lead to control at the cellular level. Surfaces directly targeting adhesion on other lengthscales are expected to be equally important in the manipulation of virus particles, sensors, and even smart engineering adhesives or lubricants.
While conventional implant material design seeks bio-inert surfaces, the surfaces of tissue scaffolds must direct specific cellular responses. Here, adhesion proteins such as fibronectin and collagen determine the viability of cells in the artificial environment of the tissue scaffold, while more complex proteins and pathways govern cellular metabolism and specialization. In the Santore lab, we are developing nano-patterned surfaces with precise chemistries covering lengthscales on the order of single proteins. Thus by controlling protein adsorption and the biofunctionality of adsorbed proteins, we will, ultimately, manipulate living cells.
Patterning. A variety of templating and phase separation methods are employed in the Santore lab to achieve surface patterns with the desired length scales, spacing, and chemistry. The ultimate fabrication goals are to independently manipulate these three features. In many fabrication schemes, the domain size is related to that of the spacing, or phase separation on the surface of a material depends on the choice of chemistries, making lengthscales and chemistry inter-dependent. One method breaking the chemistry length-scale dependence is the templating approach in Figure 1. Also key to the successful manipulation of proteins is uniformity of surface patterns. Activities in the Santore lab target features with polydispersities below 1.2 (or, about 20%).
Protein Adsorption. Properly nano-patterned surfaces generated in our lab have been shown to dramatically affect protein adsorption. For templating methods such as that above, the trick is to prevent “leakage” of one chemical species into the regions intended for the second surface chemistry. When this is accomplished, individual proteins can be isolated on templated islands, in Figure 2.
Control of Adhesion Rates.
In biological systems, specialized surface receptors with different “binding” and “breaking” rate constants dictate the rates, selectivity, and performance of biological processes. For instance, at the site of an injury, neutrophils (a specific kind of white blood cell) leave the blood stream and “put on the brakes” to adhere to the surface of blood vessels at the site of an injury, despite the strong wall shear. This is accomplished by two families of receptors: selectins and integrins. The former first initiate weak surface adhesion, in which neutrophils actually roll over the vessel walls. Then the integrins cause the neutrophils to arrest at a particular point on the surface. Without the selectins to slow the cells down, the integrins would be ineffective to cause the neutrophils to arrest at the wound site. In the Santore lab, we hope to accomplish such control of adhesion rates in completely artificial systems, as a means of making smart adhesives and lubricants.
With protein-based receptors, the adhesion rate constants are determined by the receptor chemistry. Interactions between receptors and ligands typically involve both attractive and repulsive nano-meter scale interactions over a surface or energy landscape. In our artificial (non-protein-based) systems, we adopt a similar strategy of competing attractions and repulsions on different lengthscales, in Figure 3, to control adhesion.
Density of attractive patches. By varying the density of attractive patches relative to a repulsive matrix, researchers in the Santore lab manipulate the adhesion rates of micron-scale objects (in this case silica particles) interacting with a collecting surface. Figure 4 shows that in addition to controlled adhesion rate, there is a patch density below which adhesion does not occur.
Range of Adhesive Interactions. Some interfacial forces, such as those driven by electrostatic charge, can assume ranges varying several orders in magnitude. Researchers in the Santore lab find that in addition to the frequency of spatial heterogeneity on a surface, this interaction range is also important in determining the adhesion rate and critical density of surface features for adhesion.
• N. Kozlova and M. Santore, Langmuir, in preparation.
Current and Future Activities
Surfaces with tailored patterns and features will be a key enabling technology in many different fields. Beyond biomedical implants, diagnostics, and the obvious sensor applications, lies the use of these surfaces in devices for proteomic studies and manipulation. Here one requires protein separation and selective adhesion, with the additional capability of controlled inter-protein interactions and the ability to perform bioreactions that identify protein function. Surfaces developed for both protein manipulation and controlled adhesion dynamics of larger objects will find broad technological use. On the fundamental level, these patterned surfaces represent artificial (non-protein-based) receptor-ligand systems, motivating studies that parallel those of biological systems. Also on the fundamental level, these adhesion studies form the basis for interpreting dynamic aspects of pattern recognition.