Since the discovery of induced pluripotent stem cells, a major goal for regenerative medicine has been to replace damaged or diseased tissues with patient-derived cells. In principle, it should be possible to generate functional tissues for clinical use through directed differentiation protocols that specifically alter the fate of pluripotent cells. Unfortunately, there are many challenges associated with directed differentiation. First, we do not fully understand all of the signaling mechanisms that participate in developmental regulatory pathways. We therefore cannot always identify the correct combination of molecular factors that will recapitulate these signals in a laboratory setting. Even if the required factors are known, it is unclear what doses, timing, or combinations of factors will produce an efficient differentiation. Most protocols are developed by trial-and-error; take years to optimize; and lead to incomplete, inefficient, or heterogeneous mixtures of the desired cell types. Clearly, there is a critical need for improving differentiation procedures if we expect to produce realistic cell-based therapies. In this essay, I propose a novel solution for controlling the fate of human stem cells. The approach, which we have pioneered in our lab, works by allowing a computational model to design the differentiation protocol. To build the model, we use time-lapse microscopy to monitor the expression of key developmental markers over the time course of differentiation. These data provide a mechanistic description of cellular fate decisions at single-cell resolution. To train the model, we then apply a series of systematic perturbations to differentiating cells, monitor the cells by live-cell microscopy, and integrate these new measurements into the working model. Finally, we use the model as a predictive tool by performing thousands of virtual experiments to identify a set of perturbations that are predicted to alter stem cell fate in a prescribed way. Model predictions are validated through single-cell transcription profiling, and these data are used to iteratively refine the model?s predictive power. As proof of principle, we will use this approach to produce precise combinations of human lung progenitor cells that could be used to treat pulmonary diseases such as pulmonary fibrosis, emphysema, or interstitial lung disease. In the near term, our approach will enable us to manipulate pluripotent cells to acquire a precise combination of differentiated cell fates?overcoming a major hurdle in stem cell therapy. In the long term, this approach could be used more generally to control the fate of other cell types including bacteria or cancer cells, and may help to design improved drug delivery protocols.
Computational models are used to predict many types of complex phenomena including storm tracking, stock prices, and professional sporting events; but their utility has not been exploited to develop stem cell therapies. The goal of this proposal is to use computational models to design new and efficient protocols for converting human stem cells into functionally differentiated cell types.
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