There is a need to determine the relationship between the material properties and the performance of 3D tissue scaffolds. Material properties of scaffolds currently used in FDA-approved devices have not been optimized; new devices face regulatory challenges and it costs $100 M to bring a new device from concept to market. Thus, the path to translation could be accelerated if the most important scaffold properties for tissue engineering could be identified. Whole genome microarrays measure 25,000 mRNAs while mass spectrometry based proteomics methods measure 4000 proteins. Applying omics measurements to biomaterials development can identify the molecular mechanisms that scaffolds employ to control stem cell fate. In this project, we will use omics approaches to measure stem cell expression signatures on scaffolds with widely varied properties. Scaffold treatments will be sorted by hierarchical cluster analysis, which will enable identification of the scaffold properties that are most influential in determining the expression signatures. Further, relevant controls will be included to identify the scaffold properties that yield desirable signatures. For instance, human bone marrow cells treated with osteogenic, adipogenic, or chondrogenic differentiation supplements will be measured and then scaffold properties that elicit expression patterns that are similar to the different supplements can be identified. This work will establish a new measurement paradigm for identifying the most important scaffold properties for directing stem cell differentiation and will provide scaffold measurement targets to accelerate the path to device translation.
Stem cell; Regenerative medicine; Polymer scaffold; 3D imaging; Genomics; Proteomics; Transcriptomics; Biomaterials; Tissue engineering;