SCIENTIFIC ANAYSIS OF THE ASCOT CELL TRANSPLANT – FOCUS ON TRANSCRIPTOMICS AND PROTEOMICS

Abi Jones, Lauren Tierney, Helen McCarthy, Sally Roberts, Charlotte Hulme, Jan Herman Kuiper, Martyn Snow, Dan Tonge, Mandy Peffers (Liverpool University) and Karina Wright

Funded by Versus Arthritis and the Medical Research Council

Using cells obtained from the ASCOT trial, bioinformatics analysis on genes (transcriptomic) and proteins (proteomic) data will be performed. This represents a Versus Arthritis funded PhD fellowship that intends to identify a set of genes and proteins which can differentiate responders and non-responders to treatment, within the different cell therapy groups in the ASCOT trial.

By looking at the genes expressed in the chondrocytes and the bone marrow mesenchymal stromal cells (MSCs) the biological activity that is influenced by their transplantation can be determined and we can better understand the mechanism that the cells use to repair the cartilage and restore the function to the joint. The protein content in the same cells helps to confirm that the genes identified are resulting in proteins being made, as well as being easier to measure in a sample than genes in a clinical setting for future applications. We want to ultimately see if there are any proteins which can be used to predict outcome following treatment. The differences in both genes and protein in the ASCOT cells can potentially highlight specific biological pathways involved and expand our knowledge and understanding of the mechanisms responsible for the effects of each of the transplanted cell type(s). Our ultimate aim is to identify a molecular profile(s) that can help predict clinical efficacy.

These analyses will be undertaken using techniques called RNA sequencing for transcriptomic data (see picture insert), and label free proteomics for proteomic data. Both will be performed on cells taken at the time of implantation. These two analyses combined will create a very large dataset which will be interpreted using sophisticated data analysis techniques called bioinformatics. We will be able to identify thousands of genes and proteins from the dataset. The gene and protein signatures found through these analyses will compared with the clinical outcomes of the patients 12+ months on from the implantation surgery to see if there are any genes and/or proteins which can predict clinical outcomes. This can also help to refine or select which cell therapy option is best for clinical use in the future.