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Using big data approaches to develop cell therapies

An area where stem cell biology and medicine are combining effectively is the establishment of new cell therapies. However, current therapies are limited to a narrow set of cell types that can be isolated or created and expanded in vitro. Dr Owen Rackham discusses how utilising computational approaches will further enhance applications of stem-cell-derived therapies in the future.

Purple DNA strand on a blue to grey gradient background

For decades (or perhaps centuries) 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 realised in the clinic, but the breadth of cell types being used is still relatively narrow. Recent technological advances in the field have been focused on the safe and scalable manufacture of therapies. While these are revolutionary breakthroughs, the applications are largely limited to T cells, haematopoietic- 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.











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