Algorithm-driven optimization of cryopreservation protocols for transfusion model cell types including Jurkat cells and mesenchymal stem cells

This investigation describes the use of a differential evolution (DE) algorithm to optimize cryopreservation solution compositions and cooling rates for specific cell types. Jurkat cells (a lymphocyte model cell type) and mesenchymal stem cells (MSCs) were combined with non-DMSO solutions at concentrations dictated by a DE algorithm. The cells were then frozen in 96-well plates at DE algorithm-dictated cooling rates in the range 0.5–10°C/min. The DE algorithm was iterated until convergence resulted in identification of an optimum solution composition and cooling rate, which occurred within six to nine generations (seven to 10 experiments) for both cell types. The optimal composition for cryopreserving Jurkat cells included 300 mm trehalose, 10% glycerol and 0.01% ectoine (TGE) at 10°C/min. The optimal composition for cryopreserving MSCs included 300 mm ethylene glycol, 1 mm taurine and 1% ectoine (SEGA) at 1°C/min. High-throughput concentration studies verified the optimum identified by the DE algorithm. Vial freezing experiments showed that experimental solutions of TGE at 10°C/min resulted in significantly higher viability for Jurkat cells than DMSO at 1°C/min, while experimental solutions of SEGA at 10°C/min resulted in significantly higher recovery for MSCs than DMSO at 1°C/min; these results were solution- and cell type-specific. Implementation of the DE algorithm permits optimization of multicomponent freezing solutions in a rational, accelerated fashion. This technique can be applied to optimize freezing conditions, which vary by cell type, with significantly fewer experiments than traditional methods.