Fluorescent Biosensors for In Vivo Single-Cell Analysis of the Physiology of Saccharomyces Cerevisiae

Baker’s yeast Saccharomyces cerevisiae is a main protagonist of modern applied and industrial biotechnology due to its robustness, well-established cultivation requirements and, last but not least, its highly plastic and engineering-friendly genome. Despite the vast knowledge of this model organism, many aspects regarding the physiology of this yeast still remain to be uncovered - especially in the context of the modern (lignocellulose) biorefinery. This discrepancy calls for novel and rapid methods for screening of beneficial phenotypes, with down-stream applications ranging from genetic library screening and novel metabolic target discovery to systems biology and modelling. As such, in vivo biosensors have become a promising step towards closing the gap between the possibilities of metabolic engineering and the currently limited methods of phenotypical assessment.

To dissect the intracellular response of this yeast to its many contemporary biotechnological applications, we have in our lab developed a set of integrative biosensor cassettes based on the yeast enhanced Green Fluorescent Protein (yEGFP) gene [1]. By coupling this fluorescent gene to different endogenous yeast promoters, we have established a panel of non-intrusive biosensor strains that allows us to rapidly monitor the changes in physiology of S. cerevisiae to a multitude of industrially relevant conditions on a single-cell level by assessing the changes in the fluorescent phenotype of the engineered yeast over time. An example of a recent application of this biosensor system is a set of S. cerevisiae redox sensor strains designed to monitor the intracellular NADH/NAD⁺ ratio based on transcriptional regulation of the yeast GPD2 promoter [2]. In addition to this and to our recent advances in this field, we present a recently developed protocol for high-throughput, single-cell screening of fluorescent S. cerevisiae biosensor strains using flow cytometry that comes with an integrated in silico data analysis pipeline.

References:

[1] Cormack, B. P., Bertram, G., Egerton, M., Gow, N. A., Falkow, S., & Brown, A. J. (1997). Microbiology, 143(2), 303-311.

[2] Knudsen, J. D., Carlquist, M., & Gorwa-Grauslund, M. (2014). AMB Express, 4(1), 81.