Parallelised online biomass monitoring in shake flasks enables efficient strain and carbon source dependent growth characterisation of Saccharomyces cerevisiae

Background: Baker’s yeast, Saccharomyces cerevisiae, as one of the most often used workhorses in biotechnology has been developed into a huge family of application optimised strains in the last decades. Increasing numbers of strains render their characterisation highly challenging, even with the simple methods of growth-based analytics. Here we present a new sensor system for the automated, non-invasive and parallelisable monitoring of biomass in continuously shaken shake flask cultures, called CGQ (“cell growth quantifier”). The CGQ implements a dynamic approach of backscattered light measurement, allowing for efficient and accurate growth-based strain characterisation, as exemplarily demonstrated for the four most commonly used laboratory and industrial yeast strains, BY4741, W303-1A, CEN.PK2-1C and Ethanol Red. Results: Growth experiments revealed distinct carbon source utilisation differences between the investigated S. cerevisiae strains. Phenomena such as diauxic shifts, morphological changes and oxygen limitations were clearly observable in the growth curves. A strictly monotonic non-linear correlation of OD600 and the CGQ’s backscattered light intensities was found, with strain-to-strain as well as growth-phase related differences. The CGQ measurements showed high resolution, sensitivity and smoothness even below an OD600 of 0.2 and were furthermore characterised by low background noise and signal drift in combination with high reproducibility. Conclusions: With the CGQ, shake flask fermentations can be automatically monitored regarding biomass and growth rates with high resolution and parallelisation. This makes the CGQ a valuable tool for growth-based strain characterisation and development. The exceptionally high resolution allows for the identification of distinct metabolic differences and shifts as well as for morphologic changes. Applications that will benefit from that kind of automatized biomass monitoring include, amongst many others, the characterization of deregulated native or integrated heterologous pathways, the fast detection of co-fermentation as well as the realisation of rational and growth-data driven evolutionary engineering approaches