Monitoring single-cell metabolic phenotyping changes of Daqu microbiota during fermentation by Raman spectroscopy

ABSTRACT Monitoring microbial community dynamics during natural food fermentation is essential for stabilizing fermentation processes and improving product flavor profiles. This study employed single-cell Raman spectroscopy to monitor the metabolic change of Daqu microbiota during fermentation. First, we developed an operational phenotypic unit (OPU)-based clustering method to process Raman spectral data of a nine-member microbial community. The clustering was validated by sorting out representative single cells from different OPUs and performing 16S/ITS region sequencing. Second, single-cell Raman spectroscopy was applied to the mixed Daqu sample throughout fermentation, identifying 10 OPUs and 54 sub-OPUs. Single cells from different OPUs were sorted and sequenced to obtain taxonomic annotation, which were correlated with the Raman spectral characteristics of each OPU. The OPU-based Raman spectroscopy method was successfully used to monitor the single-cell metabolic phenotypes of Daqu microbiota during fermentation, and taxonomic annotations revealed the succession of microbial community structure. This study presents a novel approach for monitoring the dynamics of metabolic phenotypes within Daqu microbial communities, offering a data-driven decision-making approach for subsequent production operations in multi-species fermentation processes. IMPORTANCE Microbial communities play pivotal roles in diverse industrial processes, yet monitoring their metabolic dynamics remains challenging. Current omics approaches, including metagenomics, transcriptomics, and mass spectrometry-based metabolomics, fail to establish direct genotype-phenotype linkages at the single-cell level and often require destructive sampling. This study introduces operational phenotypic units (OPUs) as a novel framework to bridge this gap by integrating single-cell Raman spectroscopy with sequencing technologies. Our method enables non-destructive, rapid monitoring of metabolic phenotypes while preserving cells for downstream analysis. By applying this approach to solid-state Daqu fermentation, we demonstrate its capability to track microbial community succession and identify functionally distinct populations in real time. This data-driven strategy offers significant potential for optimizing multi-species fermentation processes across food, environmental, and biotechnological industries, providing a powerful tool for microbial community management and process control.