Tracking production and interconversion of extra- and intra-cellular metabolites during beer fermentation by ramanomics

Cellular metabolic state and its heterogeneity are pivotal features that determine fermentation productivity, yet label-free monitoring has generally been difficult. Employing beer fermentation by Saccharomyces pastorianusas a model, we demonstrated that temporal sampling of ramanomes, the collection of spontaneous Single-Cell Raman Spectra (SCRS) from an isogenic population, provides rich insights into the profiles and inter-conversion of both intra- and extra-cellular metabolites. Among 43 extracellular metabolic phenotypes, ramanomes successfully modeled 19 of them, including the extracellular levels of four alcohols, four esters, four amino acids, two acids, and four mono- and di-saccharide substrates, plus the alcohol-to-ester ratio. Moreover, Intra-Ramanome Correlation Analysis (IRCA) revealed potential metabolic interactions in pairs of intracellular metabolites, extracellular metabolites, and medium substrates. Specifically, carbohydrates were the most active intracellular metabolites, while proteins significantly influenced alcohol and ester synthesis on Day 1 of fermentation. Additionally, both alcohols and esters showed negative correlations with extracellular amino acids and acids. The global-IRCN average degree, reflecting metabolic network complexity, increased over time and was positively correlated with extracellular levels of key products such as n-propanol and various esters, while negatively correlated with acetic acid and certain sugars. Therefore, by enabling non-destructive, label-free, and rapid modeling of both intra- and extracellular metabolite levels, ramanomics can find wide applications in process monitoring and control.