It is increasingly recognized that material surface topography is able to evoke specific cellular responses, endowing materials with instructive properties that were formerly reserved for growth factors. This opens the window to improve upon, in a cost-effective manner, biological performance of any surface used in the human
body. Unfortunately, the interplay between surface topographies and cell behavior is complex and still incompletely understood. Rational approaches to search for bioactive surfaces will therefore omit previously unperceived interactions. Hence, in this projects, 2176 mathematically designed surface topologies were placed on chips of poly(lactic acid). Human mesenchymal stromal cells (hMSCs) were grown on the chips, and using high-content imaging and an analysis pipeline, surface effects on MSC proliferation and osteogenic differentiation were found. The analysis pipeline uses image processing and machine learning techniques to process raw chip images into results, such as best performing surfaces, surface parameters that play an important role in the tested biological process, and cell response predictors for new surfaces. Using robust statistics, quality measures and by exploiting surface similarity, we are able to significantly improve surface ranking consistency.
, Marcel Reinders