Automated multi-scale cascade of parallel stirred-tank bioreactors for fast protein expression studies

Abstract

Automation, parallelization and autonomous operation of standard lab equipment, usually applied for manual bioprocess development, is considered as the key for reduction of bioprocess development time and costs. An automated bioreactor system with 4 stirred-tank bioreactors on a L-scale was combined with a custom-made biomass transfer system to distribute the cell suspensions produced on the L-scale into 48 parallel stirred-tank bioreactors on a mL-scale. Afterwards parallel protein expression studies automated by a liquid handling system with integrated fluorescence reader were performed. Isopropyl β-D-1-thiogalactopyranoside-induced (IPTG) expression of the red fluorescence protein mCherry was studied as an example of using fed-batch processes with recombinant Escherichia coli. In a first automated study, IPTG concentrations were varied in 48 parallel fed-batch processes with E. coli cells produced at a growth rate of 0.1 h−1 on an L-scale and transferred automatically to the mL-scale. The mCherry expression rate increased with increasing inducer concentration until the highest protein expression rate was observed at > 9 μM IPTG. In a second automated study, the growth rate of E. coli was varied between 0.1−0.2 h-1 in parallelly-operated stirred-tank bioreactors on a L-scale. The cells were automatically transferred and distributed into the stirred-tank bioreactors on a mL-scale and the concentration of the inducer IPTG was varied as before in parallel fed-batch processes. An increased growth rate during the production of the recombinant E. coli cells and/or higher cell densities during protein expression resulted in the increased IPTG concentrations necessary to achieve identical expression rates compared to a growth rate of 0.1 h−1 with the exception of very low inducer concentrations and inducer concentrations in excess. The new automated multi-scale cascade of parallel stirred-tank bioreactors should easily be applicable for performing fast optimisation studies with other microbial production systems and will have the potential to reduce bioprocess development time and staff assignment considerably.