(437e) Yeast Intracellular Staining (yICS): Enabling Rapid Screening of High-Expressing Clones By Directly Quantifying Protein Expression at the Single-Cell Level

Despite great successes, metabolic engineering of microbes to produce chemical compounds with industrially relevant yield, titer, and productivity remains a challenge. The introduction of heterologous metabolic pathways often results in unbalanced fluxes that limit the yield of the desired product. Therefore identifying and relieving flux-controlling bottlenecks is essential. One such bottleneck has been identified when expressing the oxidoreductase _D-xylose assimilation pathway in Saccharomyces cerevisiae consisting of xylose reductase (XR), xylitol dehydrogenase (XDH), and xylulokinase (XK) enzymes. The accumulation of xylitol, an intermediate produced by XR and consumed by XDH, is a persistent problem and results in a significant reduction in ethanol yield. A potential solution is to introduce more copies of XDH into the yeast genome via CRISPR/Cas9 integration. However, high-copy integrants are relatively rare and require screening of the transformants using methods such as Western blot or activity assays, both of which are laborious, time consuming, and of low throughput.

To address this challenge, we developed yeast intracellular staining (yICS) using flow cytometry, which allows for the high-throughput, quantitative analysis of proteins in a metabolic pathway with single-cell resolution in a 96-well-plate format. Using yICS, rare clones expressing high levels of XDH were rapidly identified possessing greater than 10 copies of the XDH gene after a single round of CRISPR. One of the integrants was found to have a four-fold improvement in activity compared to the parent strain and a 22% increase in ethanol yield. We have also demonstrated that it is possible to do yICS of multiple proteins simultaneously, opening up numerous opportunities in metabolic engineering. For example pathway flux can now be balanced by simultaneously tuning the ratios of multiple enzymes, greatly expediting the clonal selection process. The yICS method developed here expands the metabolic engineering toolbox to enable quantitative, high-throughput analysis of multi-enzyme pathways at the single-cell level.