BME PhD Thesis Proposal 

Date: Tuesday, November 10, 2020 

Time: 3:00PM 

BlueJeans link: https://bluejeans.com/643029310

Meeting ID: 643 029 310 

Advisors: Dr. Nick Willett and Dr. Levi Wood 

Committee Members: 

Dr. Rebecca Levit

Dr. Peng Qiu

Dr. Melissa Kinney (University of Wisconsin)

Dr. Ron June (Montana State University)

Title: Engineering a predictive platform of human Mesenchymal Stromal Cell therapeutic efficacy in Osteoarthritis

Abstract: Osteoarthritis (OA) is the most prevalent chronic disease of the joints and leads to degeneration of articular cartilage surfaces. To date there are no FDA approved disease modifying drugs available to treat OA. Mesenchymal stromal cells (MSC) offer a promising treatment strategy for OA due to the regenerative and immunomodulatory capacity these cells possess. While these cellular therapeutics have shown promise as a treatment for OA, effective translation has been limited by numerous factors ranging from high variability and heterogeneity of MSCs to poor understanding of critical quality and potency attributes. In preliminary studies we have demonstrated that human MSC (hMSC) donor heterogeneity effects therapeutic efficacy in OA. Furthermore, we have shown that therapeutic outcomes in OA significantly correlate with hMSC secretion of chemokine-ligand 1 (CXCL1;GRO), granulocyte-macrophage colony-stimulating factor (GM-CSF), interleukin 4 (IL-4), and platelet derived growth factor alpha (PDGF-AA); all of these factors are downstream of the mitogen activated protein kinase (MAPK). These results motivate our overall objective in this thesis to engineer a predictive platform of hMSC therapeutic efficacy in treating OA that combines an in vitro simulated OA microenvironment with therapeutic outcomes in vivo. The overarching hypothesis is that a profile of critical hMSC paracrine factors can be identified and used to predict performance of these cells as OA therapies. This hypothesis will be assessed through four specific aims: (1) investigate the effects of hMSC paracrine action in OA, (2) evaluate hMSC donor heterogeneity to identify key hMSC cytokines and metabolites that correlate with therapeutic efficacy in treating OA, (3) engineer a computational model to identify predictive signatures for the assessment of hMSC therapeutic outcomes in OA, and (4) identify hMSC mechanisms driving secretion of critical factors that yield therapeutic efficacy in OA. Upon completion, this thesis will establish critical knowledge of hMSCs as therapeutics for use in OA and provide a framework for a predictive platform of MSC therapeutics for OA treatment in the clinic.