The rapidly expanding probiotic industry demands efficient methods for quality assessment of probiotic products. We previously established a microscopic imaging and Raman spectroscopy (miRS) based approach for rapid identification and tests for viability and vitality at single‐cell resolution via single‐cell Raman spectra (SCRS), yet its wider application is limited by the throughput of SCRS acquisition. Here, we employed a Raman flow cytometry (RFC) approach to accomplish this mission, which features a greatly simplified experimental workflow yet with >10‐fold higher throughput than miRS. At SCRS acquisition speed of one bacterial cell per second, RFC achieves much higher sampling depth, leading to higher accuracy of species classification than miRS in both reference ramanome database construction and quality assessment of probiotic products. Specifically, based on the fingerprint regions, the accuracy in classifying species and strains is notably improved, both for an isogenic population and for a multi‐strain probiotic product. Moreover, for probiotic products with highly biased compositions, the sensitivity of detecting and classifying low‐abundance members is order‐of‐magnitude higher. Furthermore, based on the fingerprint regions plus the C‐D band, D ₂ O‐probed RFC rapidly yields viable‐cell counts and quantifies single‐cell vitality in a species/strain‐resolved fashion yet without the need for fluorescence labeling, underscoring its strength over propidium monoazide (PMA)‐based fluorescence flow cytometry. Due to its speed, accuracy, sensitivity, rich information and ease of use, RFC is a powerful platform for quality assessment for probiotics and other live‐cell products.

With rapid progress in the yeast fermentation industry, a comprehensive commercial yeast quality assessment approach integrating efficiency, accuracy, sensitivity, and cost-effectiveness is required. In this study, a new yeast quality assessment method based on single-cell Raman technology was developed and contrasted with traditional methods. The findings demonstrated significant associations (Pearson correlation coefficient of 0.933 on average) between the two methods in measuring physiological indicators, including cell viability and intracellular trehalose content, demonstrating the credibility of the Raman method compared to the traditional method. Furthermore, the sensitivity of the Raman method in viable but non-culturable cells was higher in measuring yeast cell viability (17.9 % more sensitive). According to the accurate quantitative analysis of metabolic activity level (MAL) of yeast cells, the cell vitality was accurately quantified at population and single-cell levels, offering a more comprehensive assessment of yeast fermentation performance. Overall, the single-cell Raman method integrates credibility, feasibility, accuracy, and sensitivity in yeast quality assessment, offering a new technological framework for quality assessments of live-cell yeast products.

The construction of designer DNA sequences at the gene-to-pathway scale is a cornerstone of synthetic biology. In practice, however, oligonucleotide synthesis, fragment assembly, and error correction remain largely disconnected, with limited consideration of how upstream choices constrain downstream outcomes. This fragmentation imposes a ceiling on the fidelity, throughput, and scalability of gene construction, particularly for sequences in the tens-of-kilobases range. Existing automation has been developed separately for the front end, such as oligonucleotide synthesis and short fragment assembly, and for the back end, such as large-fragment concatenation, without bridging them into an error-aware pipeline. In this review, we argue that a truly integrated pipeline cannot be achieved simply by placing existing modules in sequence, but instead requires understanding the dependencies that couple synthesis, assembly, and fidelity control. We first trace how errors arise during oligonucleotide synthesis and propagate through hierarchical assembly, establishing that error correction is not optional but decisive for construction success. We then compare correction strategies and show that MutS-based enzymatic mismatch depletion offers superior correction efficiency and workflow compatibility over alternatives. Building on this, we argue that the emergence of enzymatic DNA synthesis, operating under aqueous conditions compatible with downstream enzymatic processes, creates an opportunity to restructure the gene construction pipeline. This opens the door to integrating oligonucleotide synthesis, assembly, and MutS-based error correction into a continuous automated workflow. We propose that such integration, rather than separate automation of individual stages, is the path toward high-fidelity, high-throughput, scalable construction of gene- and pathway-scale DNA sequences.

Sauce-flavor Baijiu owes its layered aroma to a diverse microbial consortium fermenting under extreme, open, solid-state conditions. High-throughput sequencing has revealed marked spatiotemporal heterogeneity in community composition, yet a fundamental composition-function disconnect persists: bulk omics average signals across heterogeneous micro-niches and cultivation recovers only a minor fraction of total diversity, leaving “who is doing what” unresolved. Closing this disconnect is a prerequisite for rational design of fermentation microbiomes and demands cell-resolved functional tools within an iterative engineering framework. This review proposes that Single-Cell Raman Spectroscopy (SCRS) and its derivatives, including D₂O-Raman activity mapping, scRACS-Seq phenotype-to-genome linkage, scRACS-Culture recovery of rare functional strains, and Intra-Ramanome Correlation Analysis (IRCA) predictive metabolic phenotyping, collectively provide a label-free, culture-independent toolkit suited to this role in solid-state matrices. We delineate how these single-cell insights feed each stage of a Design-Build-Test-Learn (DBTL) cycle, from phenotype-informed strain selection through consortium assembly and functional validation to data-driven iterative optimization. This convergence of cell-resolved functional dissection, synthetic ecology, and process analytical technologies establishes the foundation for advancing Sauce-flavor Baijiu from experience-dependent craftsmanship toward intelligent brewing.