(679e) Parsing Stem Cell Behaviors on Complex Biomaterials Via High Content Imaging and Modeling | AIChE

(679e) Parsing Stem Cell Behaviors on Complex Biomaterials Via High Content Imaging and Modeling


Treiser, M. D. - Presenter, Rutgers, The State University of New Jersey
Yang, E. - Presenter, Rutgers University
Androulakis, I. - Presenter, Rutgers University, the State University of New Jersey
Kohn, J. - Presenter, Rutgers University and The New Jersey Center for Biomaterials
Chen, C. S. - Presenter, University of Pennsylvania School of Engineering and Applied Science
Moghe, P. V. - Presenter, Rutgers University

The role of synthetic biomaterials on long-term stem cell differentiation is poorly understood and thus stem cell regenerative materials are difficult to rationally design. Recently, it has been demonstrated that the cytoskeletal proteins, particularly actin, are strong mediators of human mesenchymal stem cell (hMSC) differentiation, with early cell shape having large consequences on longer-term functions such as differentiation. As the cytoskeleton mediates the outside-in stem cell signaling, we sought to "profile" the effects of biomaterials based on the analysis of material induced changes in intracellular, quantitative descriptors of the cytoskeleton of stem cells. The ultimate goal is to predict long-term stem cell fates and lineage commitment based on the signature trends of early intracellular cytoskeletal organization correlated to biomaterial chemistry. Human mesenchymal stem cells were sub-cultured in 50:50 mixed osteogenic and adipogenic induction media on various polymeric biomaterial substrates. The substrates were spin-coated films from a combinatorially varied composition of a family of tyrosine-derived polycarbonates, (varied components included poly(ethylene glycol), anionic carboxylate groups, and a stiffening agent for radio-opacity, iodine) and from a family of combinatorially derived polymethacrylates. After 24 hours of culture, the cells were stained fluorescently for their actin cytoskeleton and high magnification (63X) and high resolution tile scan images spanning approximately 5 mm x 4.5 mm were produced using confocal imaging on a Leica TCS SP2 system. Utilizing these images, over 50 morphometric descriptors of actin were quantified for each substrate. Variations in the descriptor values were compared across different substrates and the role of material composition on stem cell descriptors was identified. Through multi-dimensional scaling (MDS) based modeling, cell populations were identified that best correlated with stem cell differentiation toward osteogenic vs. adipogenic lineages. This study highlights the possibility of using a combination of high content imaging and materials informatics toward predicting longer-term stem cell differentiation outcomes on complex biomaterials.