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

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 all 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) oil mesenchymal stem cell (MSC) spreading, proliferation and osteogenic differentiation C3H10T1/2 cells were grown oil type I collagen- or fibronectin-coated polyacrylamide hydrogels with tunable mechanical properties Experimental which were defined according to central composite design. consisted of specific permutations conditions. of substrate stiffness (3-144 kPa) and adhesion protein concentration (7-520 mu g/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 call be adapted to study other types of cell-material interactions and call facilitate the efficient screening and optimization of substrate properties for applications involving cell-material interfaces (C) 2009 Elsevier Ltd All rights reserved