Differentiation of Rat Mesenchymal Stem Cells toward Osteogenic Lineage on Extracellular Matrix Protein Gradients

This report addresses the issue of optimizing extracellular matrix protein density required to support osteogenic lineage differentiation of mesenchymal stem cells (MSCs) by culturing MSCs on surface-bound density gradients of immobilized collagen type I (COL1) and osteopontin (OPN). A chemical surface gradient is prepared by tailoring the surface chemical composition from high hydroxyl groups to aldehyde groups using a diffusion-controlled plasma polymerization technique. Osteogenesis on the gradient surface is determined by immunofluorescence staining against Runx2 as an early marker and by staining of calcium phosphate deposits as a late stage differentiation marker. The Runx2 intensity and calcified area increase with increasing COL1 density up to a critical value corresponding to 124.2 ng cm^-2 , above which cell attachment and differentiation do not rise further, while this critical value for OPN is 19.0 ng cm^-2 . This gradient approach may facilitate the screening of an optimal biomolecule surface density on tissue-engineered scaffolds, implants, or tissue culture ware to obtain the desired cell response, and may generate opportunities for more cost-effective regenerative medicine.

Synergistic influence of collagen I and BMP 2 drives osteogenic differentiation of mesenchymal stem cells: A cell microarray analysis

Cell microarrays are a novel platform for the high throughput discovery of new biomaterials. By re-creating a multitude of cell microenvironments on a single slide, this approach can identify the optimal surface composition to drive a desired cell response. To systematically study the effects of molecular microenvironments on stem cell fate, we designed a cell microarray based on parallel exposure of mesenchymal stem cells (MSCs) to surface-immobilised collagen I (Coll I) and bone morphogenetic protein 2 (BMP 2). This was achieved by means of a reactive coating on a slide surface, enabling the covalent anchoring of Coll I and BMP 2 as microscale spots printed by a robotic contact printer. The surface between the printed protein spots was passivated using poly (ethylene glycol) bisamine 10,000Da (A-PEG). MSCs were then captured and cultured on array spots composed of binary mixtures of Coll I and BMP 2, followed by automated image acquisition and quantitative, multi-parameter analysis of cellular responses. Surface compositions that gave the highest osteogenic differentiation were determined using Runx2 expression and calcium deposition. Quantitative single cell analysis revealed subtle concentration-dependent effects of surface-immobilised proteins on the extent of osteogenic differentiation obscured using conventional analysis. In particular, the synergistic interaction of Coll I and BMP 2 in supporting osteogenic differentiation was confirmed. Our studies demonstrate the value of cell microarray platforms to decipher the combinatorial interactions at play in stem cell niche microenvironments.

Analysis of high-throughput screening reveals the effect of surface topographies on cellular morphology

Surface topographies of materials considerably impact cellular behavior as they have been shown to affect cell growth, provide cell guidance, and even induce cell differentiation. Consequently, for successful application in tissue engineering, the contact interface of biomaterials needs to be optimized to induce the required cell behavior. However, a rational design of biomaterial surfaces is severely hampered because knowledge is lacking on the underlying biological mechanisms. Therefore, we previously developed a high-throughput screening device (TopoChip) that measures cell responses to large libraries of parameterized topographical material surfaces. Here, we introduce a computational analysis of high-throughput materiome data to capture the relationship between the surface topographies of materials and cellular morphology. We apply robust statistical techniques to find surface topographies that best promote a certain specified cellular response. By augmenting surface screening with data-driven modeling, we determine which properties of the surface topographies influence the morphological properties of the cells. With this information, we build models that predict the cellular response to surface topographies that have not yet been measured. We analyze cellular morphology on 2176 surfaces, and find that the surface topography significantly affects various cellular properties, including the roundness and size of the nucleus, as well as the perimeter and orientation of the cells. Our learned models capture and accurately predict these relationships and reveal a spectrum of topographies that induce various levels of cellular morphologies. Taken together, this novel approach of high-throughput screening of materials and subsequent analysis opens up possibilities for a rational design of biomaterial surfaces.