Most current knowledge on central carbon metabolism has been generated under the assumption of steady-state conditions. However, environments are highly dynamic, and organisms often experience variations in nutrient composition. This also applies to baker’s yeast, a reference model organism in biological research and a microbial cell factory in the biotechnology industry. Frequent and sudden changes in conditions promote an increase in non-genetic cell heterogeneity and may result in diverse metabolic responses. While this cell-to-cell metabolic heterogeneity is viewed as an evolutionary fitness benefit to unpredictable conditions, it is typically undesirable in industrial settings, as large-scale reactors characterized by poor mixing. To fully understand the emergence of such cell heterogeneity, quantitative single-cell tools are needed. So far, a detailed quantitative single-cell perspective has been lacking. In this study, we combined population and single-cell techniques to monitor yeast responses to carbon source transitions, including mRNA expression, protein abundances, and enzymatic activities.
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