Abstract: Microbiota-driven fermentation is a global biomanufacturing process that often operates under extreme and fluctuating temperatures. To understand how such systems maintain productivity, this study investigated the Chinese fermentation starter high-temperature Daqu (HTD) as a model system. By combining metagenomics and Raman microspectroscopy, the analysis revealed a drastic decoupling between phylogenetic composition and metabolic activity, with only 10–32 % of yeast species detected by sequencing remaining metabolically active under heat stress. Raman-activated cell sorting and culture (RACS-Culture) recovered three yeasts that consistently maintained viability throughout HTD production: Pichia kudriavzevii, Wickerhamomyces anomalus, and Saccharomycopsis fibuligera. Mono-species and synthetic-community fermentation further revealed a sophisticated mechanism of temporal niche partitioning: in the moderate-temperature early and late stages, S. fibuligera and W. anomalus dominated substrate degradation and flavor precursor biosynthesis, respectively. However, as temperatures rose above 45 °C, both species exhibited low metabolic activity and survival rates. In contrast, only P. kudriavzevii sustained robust growth at this elevated temperature. Genomic analysis revealed a remarkable expansion of heat-resistance and cell-clustering–related genes of wos2 and FLO8 in P. kudriavzevii. These genetic characteristics underpin its enhanced viability, which enables the initially low-abundance species to thrive as a primary ethanol producer and ultimately establish numerical dominance. Thus, temporally overlaying single-cell metabolic vitality profiles onto the corresponding metagenomes can unravel novel functional species and reveal their ecological roles in a complex ecosystem.

Abstract: 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 pastorianus as 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.

Abstract: Phenotype-based screening remains a major bottleneck in the development of microbial cell factories. Here, we present a Digital Colony Picker (DCP), an AI-powered platform for automated, high-throughput screening and export of microbial clones based on growth and metabolic phenotypes at single-cell resolution, without agar or physical contact. Using a microfluidic chip comprising 16,000 addressable picoliter-scale microchambers, individual cells are compartmentalized, dynamically monitored by AI-driven image analysis, and selectively exported via laser-induced bubble technique. Applied to Zymomonas mobilis, DCP enabled en masse screening and identified a mutant with 19.7% increased lactate production and 77.0% enhanced growth under 30 g/L lactate stress. This phenotype was linked to overexpression of ZMOp39x027, a canonical outer membrane autotransporter that promotes lactate transport and cell proliferation under stress. DCP provides a multi-modal phenotyping solution with spatiotemporal precision and scalable throughput, offering a generalizable strategy for accelerated strain engineering and functional gene discovery.

Abstract: Researchers used high-throughput, label-free Raman flow cytometry (FlowRACS) to identify a lipid-rich Saccharomyces cerevisiae mutant. The mutant, with a 30.85% increase in lipid content (40.26%), offers a new pathway for the industrial production of palmitoleic acid, an omega-7 fatty acid with anti-inflammatory and metabolic benefits.

Abstract: Researchers developed a new platform that reduces the time for diagnosing bloodstream infections and determining effective antibiotics from 48-72 hours to just 12 hours. The system uses microfluidic chips for pathogen detection, identification, and antimicrobial susceptibility testing, with AI-assisted imaging to calculate antibiotic efficacy, showing promising results with spiked and cultured samples.

Abstract: Developed the IMSCA strategy to identify and cultivate robust in situ PAOs from environmental samples. Identified Micrococcus luteum MCI5–8 as a novel PAO with unique features: no anaerobic phosphate release, glycogen-based energy storage, and lack of denitrification. Demonstrated enhanced phosphorus removal efficiency from 45 % to 89 % in an AAO reactor upon introducing M. luteum MCI5–8. Provided a broadly applicable framework for functional microbial screening and rational ecosystem engineering in environmental and industrial contexts.