In silico prediction of transcription factor combinations that can rejuvenate cells without loss of cell identity

Since the discovery that cells are rejuvenated during the process of reprogramming into induced pluripotent stem cells (iPSCs), researchers have been trying to harness this rejuvenation for therapeutic purposes. A major challenge is that reversal of biological age via expression of the four transcription factors originally used for cellular reprogramming (collectively termed the Yamanaka factors) is accompanied by a loss of cell identity, which can lead to teratoma formation in vivo. Various strategies have been explored to try and mitigate this dedifferentiation risk, such as modifying the exact subset of Yamanaka factors used, adding additional factors, or restricting the amount of time that the factors are expressed. However, it seems likely that - if almost completely decoupling rejuvenation from dedifferentiation is even possible - it would involve using transcription factors other than the Yamanaka factors, which seem to represent too blunt an instrument. The present PhD project attempts to address this issue by using and modifying the bioinformatics tool CellOracle to predict combinations of transcription factors that could result in cellular rejuvenation without dedifferentiation. The first stage involves validating CellOracle's predictive power by comparing the tool's predictions with available iPSC and fibroblast experimental data on how perturbations to transcription factor expression affect cell identity. The second, main stage of the project involves adapting CellOracle to screen for transcription factors that restore youthful expression (while avoiding dedifferentiation) in at least one cell type, with several possible stretch goals that could be pursued upon success in this main stage.