Using big data approaches to develop cell therapies

For decades the approach in cell biology has remained relatively unchanged. We isolate cells and with our confined knowledge of their endogenous conditions, begin to experiment until we can sustain them in vitro. Once established, we can conduct further investigation to assess a cell’s response to different conditions, changes over time, or response to manipulation. This is especially true of stem cell biology, established from tireless efforts to incrementally improve culture conditions or differentiation protocols based on fragmented knowledge of developmental processes.

Despite this, the promise of stem-cell therapies is already being realized in the clinic, but the breadth of cell types being used is still relatively narrow, limited to T cells, hematopoietic- and pluripotent-stem cells (HSCs and PSCs), a small fraction in the grand heterogeneity of cell types. Consequently, the lack of cell source diversity prevents cell therapy from fulfilling its clinical potential, pointing to the need for new means to isolate or generate source cells. This article discusses how utilizing computational approaches will further enhance applications of stem-cell-derived therapies in the future.