Integration of statistical modeling and high-content microscopy to systematically investigate cell–substrate interactions

Abstract Cell–substrate interactions are multifaceted, involving the integration of various physical and biochemical signals. The interactions among these microenvironmental factors cannot be facilely elucidated and quantified by conventional experimentation, and necessitate multifactorial strategies. Here we describe an approach that integrates statistical design and analysis of experiments with automated microscopy to systematically investigate the combinatorial effects of substrate-derived stimuli (substrate stiffness and matrix protein concentration) on mesenchymal stem cell (MSC) spreading, proliferation and osteogenic differentiation. C3H10T1/2 cells were grown on type I collagen- or fibronectin-coated polyacrylamide hydrogels with tunable mechanical properties. Experimental conditions, which were defined according to central composite design, consisted of specific permutations of substrate stiffness (3–144 kPa) and adhesion protein concentration (7–520 μg/mL). Spreading area, BrdU incorporation and Runx2 nuclear translocation were quantified using high-content microscopy and modeled as mathematical functions of substrate stiffness and protein concentration. The resulting response surfaces revealed distinct patterns of protein-specific, substrate stiffness-dependent modulation of MSC proliferation and differentiation, demonstrating the advantage of statistical modeling in the detection and description of higher-order cellular responses. In a broader context, this approach can be adapted to study other types of cell–material interactions and can facilitate the efficient screening and optimization of substrate properties for applications involving cell–material interfaces.