DOI: 10.1128/aem.01612-26 ISSN: 0099-2240
Monitoring single-cell metabolic phenotyping changes of
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microbiota during fermentation by Raman spectroscopy
Lei Xu, Peng Liang, Yun Wang, Xiao-Juan Zhang, Li-Juan Chai, Hong-Yu Xu, Song-Tao Wang, Su-Yi Zhang, Cai-Hong Shen, Zhen-Ming Lu, Wei E. Huang, Zheng-Hong Xu 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
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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
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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
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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
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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
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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.