Single-cell rapid identification, in situ viability and vitality profiling, and genome-based source-tracking for probiotics products

Double difference accumulation SERS strategy for rapid separation and detection of probiotic Bacillus endospores and vegetative cells

Although probiotic Bacillus already has a large-scale market throughout the world, there is a lack of methods for rapid separation and detection of endospores and vegetative cells of probiotic Bacillus. In this study, a "double differential accumulation" SERS strategy was constructed by combining Fe3O4@AgNPs@Van and AuNPs-PA-COFs substrates. While realizing the rapid separation and enrichment of endospores and vegetative cells of probiotic Bacillus, the SERS signals form a "cliff-like" signal difference (10-fold), which was conducive to the rapid differentiation of endospores and vegetative cells. PCA and PLS-DA could completely visualize and classify the endospores and vegetative cells, with detection limits of less than 10 CFU/mL. In food matrix samples, the detection accuracy of "double differential accumulation" SERS strategy for endospores and vegetative cells ranged from 95.45 % to 98.52 %. It can protect the high-density culture of probiotic Bacillus and the safety monitoring of the industrialized production of related products.

Phylogeny-metabolism dual-directed single-cell genomics for dissecting and mining ecosystem function by FISH-scRACS-seq

Microbiome-wide association studies (MWASs) have uncovered microbial markers linked to ecosystem traits, but the mechanisms underlying their functions can remain elusive. This is largely due to challenges in validating their in situ metabolic activities and tracing such activities to individual genomes. Here, we introduced a phylogeny-metabolism dual-directed single-cell genomics approach called fluorescence-in situ-hybridization-guided single-cell Raman-activated sorting and sequencing (FISH-scRACS-seq). It directly localizes individual cells from target taxon via an FISH probe for marker organism, profiles their in situ metabolic functions via single-cell Raman spectra, sorts cells of target taxonomy and target metabolism, and produces indexed, high-coverage, and precisely-one-cell genomes. From cyclohexane-contaminated seawater, cells representing the MWAS-derived marker taxon of γ-Proteobacteria and that are actively degrading cyclohexane in situ were directly identified via FISH and Raman, respectively, then sorted and sequenced for one-cell full genomes. In such a Pseudoalteromonas fuliginea cell, we discovered a three-component cytochrome P450 system that can convert cyclohexane to cyclohexanol in vitro, representing a previously unknown group of cyclohexane-degrading enzymes and organisms. Therefore, by unveiling enzymes, pathways, genomes, and their in situ cellular functions specifically for those organisms with ecological relevance at one-cell resolution, FISH-scRACS-seq is a rational and generally applicable approach to dissecting and mining microbiota functions.

Flow Cytometry in Microbiology: A Review of the Current State in Microbiome Research, Probiotics, and Industrial Manufacturing

Flow cytometry (FC) is a versatile and powerful tool in microbiology, enabling precise analysis of single cells for a variety of applications, including the detection and quantification of bacteria, viruses, fungi, as well as algae, phytoplankton, and parasites. Its utility in assessing cell viability, metabolic activity, immune responses, and pathogen-host interactions makes it indispensable in both research and diagnostics. The analysis of microbiota (community of microorganisms) and microbiome (collective genomes of the microorganisms) has become essential for understanding the intricate role of microbial communities in health, disease, and physiological functions. FC offers a promising complement, providing rapid, cost-effective, and dynamic profiling of microbial communities, with the added ability to isolate and sort bacterial populations for further analysis. In the probiotic industry, FC facilitates fast, affordable, and versatile analyses, helping assess both probiotics and postbiotics. It also supports the study of bacterial viability under stress conditions, including gastric acid and bile, improving insight into probiotic survival and adhesion to the intestinal mucosa. Additionally, the integration of Machine Learning in microbiology research has transformative potential, improving data analysis and supporting advances in personalized medicine and probiotic formulations. Despite the need for further standardization, FC continues to evolve as a key tool in modern microbiology and clinical diagnostics.

Isolation and purification of lactic acid bacteria and yeasts based on a multi-channel magnetic flow device and rapid qualitative and quantitative detection

Rapid isolation and identification of lactic acid bacteria and yeasts during fermentation is of great significance for quality control and regulation of fermented foods. In this study, we prepared a multi-channel magnetic flow device for rapid separation and purification of lactic acid bacteria and yeast, and based on SERS spectrum, we made rapid qualitative and quantitative analysis of Lactobacillus plantarum, Lactococcus lactis and Saccharomyces cerevisiae. The results showed that the synthesized Synthesized Fe3O4-Van antibiotic magnetic beads are paramagnetic; Fe3O4-Van antibiotic magnetic beads achieved capture efficiencies of more than 98.5 % for both L. plantarum and L. lactis at 102-104 CFU/mL, respectively. Separation and purification efficiency of single S. cerevisiae, L. plantarum and L. lactis by multi-channel magnetic flow device all reached more than 98 % with good isolation and purification results. The SERS spectra of the three microorganisms were classified and analyzed using linear discriminant analysis (LDA), and the accuracy of the established LDA model was 100 %, which completely differentiated the SERS spectra of the three microorganisms,and realized the qualitative identification of L. plantarum, L. lactis, and S. cerevisiae, and finally, quantitative model was established with the logarithmic values (lg C) of different concentrations of L. plantarum, L. lactis, and S. cerevisiae as the horizontal coordinates, and the Raman intensities at their strongest characteristic peaks of 512 cm-1, 1669 cm-1, and 1125 cm-1, respectively, were used as vertical coordinates to establish a quantitative model, with the lowest detection limit of 10 CFU/mL, and the digital quantification of lactic acid bacteria and yeast were achieved. It provided an effective means for real-time monitoring and tracking of the dynamics of lactic acid bacteria and yeast in the fermentation process and the quality control of fermented foods.

Rapid screening and identification strategy of lactic acid bacteria and yeasts based on Ramanomes technology and its application in fermented food

There is an urgent need for quick, sensitive, thorough, and low-cost preliminary screening and rapid identification method for probiotic resource development. Here, we constructed a culture-free, accurate and sensitive "separation-enrichment-detection" rapid evaluation and identification method of probiotics in fermented food based on Ramanomes technology, and established a Ramanome reference of lactic acid bacteria and yeasts by AgNPs nanostructure array (AgNPs NSA) chips with uniform "hot spots" and high signal enhancement ability as SERS substrates. Systematic cluster analysis could clearly distinguish among different genera of lactic acid bacteria and yeast, and could indicate the affinity and difference between lactic acid bacteria of the same genus and yeast of the same genus. The recognition accuracy of convolutive neural networks was 100 %, and the recognition sensitivity was less than 10 CFU/mL. We constructed a probiotic isolation and enrichment method by velocity gradient centrifugation and density gradient differential centrifugation, and the average recoveries of lactobacilli and yeasts were greater than 98 % in the practical application, and the accuracy of the verified classification was 100 %. In conclusion, this study has established a preliminary screening strategy of "screening before cultivating" for lactic acid bacteria and yeasts, which breaks through the principle limitation of the current traditional paradigm of "cultivating before screening". It can greatly improve the preliminary screening rate of probiotics in fermented foods, and provide a good technical support for the mining of probiotic resources from environmental or complex matrix samples.

Assessing the physiological properties of baker's yeast based on single-cell Raman spectrum technology

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.

Raman-Activated, Interactive Sorting of Isotope-Labeled Bacteria

Due to its high spatial resolution, Raman microspectroscopy allows for the analysis of single microbial cells. Since Raman spectroscopy analyzes the whole cell content, this method is phenotypic and can therefore be used to evaluate cellular changes. In particular, labeling with stable isotopes (SIPs) enables the versatile use and observation of different metabolic states in microbes. Nevertheless, static measurements can only analyze the present situation and do not allow for further downstream evaluations. Therefore, a combination of Raman analysis and cell sorting is necessary to provide the possibility for further research on selected bacteria in a sample. Here, a new microfluidic approach for Raman-activated continuous-flow sorting of bacteria using an optical setup for image-based particle sorting with synchronous acquisition and analysis of Raman spectra for making the sorting decision is demonstrated, showing that active cells can be successfully sorted by means of this microfluidic chip.

Rapid identification of lactic acid bacteria at species/subspecies level via ensemble learning of Ramanomes

Rapid and accurate identification of lactic acid bacteria (LAB) species would greatly improve the screening rate for functional LAB. Although many conventional and molecular methods have proven efficient and reliable, LAB identification using these methods has generally been slow and tedious. Single-cell Raman spectroscopy (SCRS) provides the phenotypic profile of a single cell and can be performed by Raman spectroscopy (which directly detects vibrations of chemical bonds through inelastic scattering by a laser light) using an individual live cell. Recently, owing to its affordability, non-invasiveness, and label-free features, the Ramanome has emerged as a potential technique for fast bacterial detection. Here, we established a reference Ramanome database consisting of SCRS data from 1,650 cells from nine LAB species/subspecies and conducted further analysis using machine learning approaches, which have high efficiency and accuracy. We chose the ensemble meta-classifier (EMC), which is suitable for solving multi-classification problems, to perform in-depth mining and analysis of the Ramanome data. To optimize the accuracy and efficiency of the machine learning algorithm, we compared nine classifiers: LDA, SVM, RF, XGBoost, KNN, PLS-DA, CNN, LSTM, and EMC. EMC achieved the highest average prediction accuracy of 97.3% for recognizing LAB at the species/subspecies level. In summary, Ramanomes, with the integration of EMC, have promising potential for fast LAB species/subspecies identification in laboratories and may thus be further developed and sharpened for the direct identification and prediction of LAB species from fermented food.

Bifidobacterium species viability in dairy-based probiotic foods: challenges and innovative approaches for accurate viability determination and monitoring of probiotic functionality

Bifidobacterium species are essential members of a healthy human gut microbiota. Their presence in the gut is associated with numerous health outcomes such as protection against gastrointestinal tract infections, inflammation, and metabolic diseases. Regular intake of Bifidobacterium in foods is a sustainable way of maintaining the health benefits associated with its use as a probiotic. Owing to their global acceptance, fermented dairy products (particularly yogurt) are considered the ideal probiotic carrier foods. As envisioned in the definition of probiotics as "live organisms," the therapeutic functionalities of Bifidobacterium spp. depend on maintaining their viability in the foods up to the point of consumption. However, sustaining Bifidobacterium spp. viability during the manufacture and shelf-life of fermented dairy products remains challenging. Hence, this paper discusses the significance of viability as a prerequisite for Bifidobacterium spp. probiotic functionality. The paper focuses on the stress factors that influence Bifidobacterium spp. viability during the manufacture and shelf life of yogurt as an archetypical fermented dairy product that is widely accepted as a delivery vehicle for probiotics. It further expounds the Bifidobacterium spp. physiological and genetic stress response mechanisms as well as the methods for viability retention in yogurt, such as microencapsulation, use of oxygen scavenging lactic acid bacterial strains, and stress-protective agents. The report also explores the topic of viability determination as a critical factor in probiotic quality assurance, wherein, the limitations of culture-based enumeration methods, the challenges of species and strain resolution in the presence of lactic acid bacterial starter and probiotic species are discussed. Finally, new developments and potential applications of next-generation viability determination methods such as flow cytometry, propidium monoazide-quantitative polymerase chain reaction (PMA-qPCR), next-generation sequencing, and single-cell Raman spectroscopy (SCRS) methods are examined.

Culture-free identification of fast-growing cyanobacteria cells by Raman-activated gravity-driven encapsulation and sequencing

By directly converting solar energy and carbon dioxide into biobased products, cyanobacteria are promising chassis for photosynthetic biosynthesis. To make cyanobacterial photosynthetic biosynthesis technology economically feasible on industrial scales, exploring and engineering cyanobacterial chassis and cell factories with fast growth rates and carbon fixation activities facing environmental stresses are of great significance. To simplify and accelerate the screening for fast-growing cyanobacteria strains, a method called Individual Cyanobacteria Vitality Tests and Screening (iCyanVS) was established. We show that the 13C incorporation ratio of carotenoids can be used to measure differences in cell growth and carbon fixation rates in individual cyanobacterial cells of distinct genotypes that differ in growth rates in bulk cultivations, thus greatly accelerating the process screening for fastest-growing cells. The feasibility of this approach is further demonstrated by phenotypically and then genotypically identifying individual cyanobacterial cells with higher salt tolerance from an artificial mutant library via Raman-activated gravity-driven encapsulation and sequencing. Therefore, this method should find broad applications in growth rate or carbon intake rate based screening of cyanobacteria and other photosynthetic cell factories.