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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. Singlecell 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. [ABSTRACT FROM AUTHOR]

The role of human microbiota in cancer initiation and progression is recognized in recent years. In order to investigate the interactions between cancer cells and microbes, a systematic analysis using various emerging techniques is required. Owing to the label-free, non-invasive and molecular fingerprinting characteristics, vibrational spectroscopy is uniquely suited to decode and understand the relationship and interactions between cancer and the microbiota at the molecular level. In this review, we first provide a quick overview of the fundamentals of vibrational spectroscopic techniques, namely Raman and infrared spectroscopy. Next, we discuss the emerging evidence underscoring utilities of these spectroscopic techniques to study cancer or microbes separately, and share our perspective on how vibrational spectroscopy can be employed at the intersection of the two fields. Finally, we envision the potential opportunities in exploiting vibrational spectroscopy not only in basic cancer-microbiome research but also in its clinical translation, and discuss the challenges in the bench to bedside translation. [ABSTRACT FROM AUTHOR]

Characterizing the metabolic network and dynamics of Viable But Nonculturable (VBNC) bacteria is crucial for their identification and control. In this study, single-cell Raman spectroscopy was used to explore the metabolic intricacies of VBNC P. aeruginosa induced by chlorine disinfection over 24 h. VBNC P. aeruginosa exhibited significant reductions of 75.98 %, 84.26 % and 81.07 % in carbohydrate, protein and nucleic acid Raman peaks, signaling decreased metabolic activity in these crucial components. Intriguingly, lipid metabolism in VBNC P. aeruginosa remained robust, indicating a potential adaptation to environmental stress. Furthermore, this study analyzed Raman characteristic peaks to examine metabolic conversion in these bacteria. VBNC P. aeruginosa displayed distinct phase-specific metabolic conversion patterns compared to culturable ones. These findings shed light on the temporal dynamics of metabolic processes, such as lipid-carbohydrate conversion, which appeared to be ongoing throughout the incubation period in VBNC P. aeruginosa. Compared to the culturable state, VBNC P. aeruginosa exhibited reduced isotopic assimilation rates for carbon, nitrogen and hydrogen by 40.57 %, 44.62 % and 39.45 %, indicating decreased substrate uptake for biomolecular synthesis. Additionally, the metabolic heterogeneity index of the VBNC bacterial population increased by 37.28 %, with this heterogeneity displaying a progressive increase with the duration of the culture. This observation suggests VBNC P. aeruginosa differentiation into distinct subpopulations, believed crucial for survival in challenging environments. These findings reveal the metabolic dynamics of VBNC state bacteria, providing insights into their adaptive strategies and implications for environmental health risk assessment and precise control technologies. [Display omitted] • Metabolic conversion network of VBNC bacteria was unveiled at single-cell level. • Lipids are the core link of the VBNC bacterial metabolic conversion network. • VBNC bacteria's metabolic dynamics are characterized by isotope assimilation ratios. • IRCA reveals phase-specific metabolic patterns of culturable not VBNC bacteria. • VBNC bacteria display significant phenotypic heterogeneity and metabolic stability. [ABSTRACT FROM AUTHOR]

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ABSTRACT To reveal the dynamic features of cellular systems, such as the correlation among phenotypes, a time or condition series set of samples is typically required. Here, we propose intra-ramanome correlation analysis (IRCA) to achieve this goal from just one snapshot of an isogenic population, via pairwise correlation among the cells of the thousands of Raman peaks in single-cell Raman spectra (SCRS), i.e., by taking advantage of the intrinsic metabolic heterogeneity among individual cells. For example, IRCA of Chlamydomonas reinhardtii under nitrogen depletion revealed metabolite conversions at each time point plus their temporal dynamics, such as protein-to-starch conversion followed by starch-to-triacylglycerol (TAG) conversion, and conversion of membrane lipids to TAG. Such among-cell correlations in SCRS vanished when the starch-biosynthesis pathway was knocked out yet were fully restored by genetic complementation. Extension of IRCA to 64 microalgal, fungal, and bacterial ramanomes suggests the IRCA-derived metabolite conversion network as an intrinsic metabolic signature of isogenic cellular population that is reliable, species-resolved, and state-sensitive. The high-throughput, low cost, excellent scalability, and general extendibility of IRCA suggest its broad applications. IMPORTANCE Each isogenic population of cells is characterized by many phenotypes, which change with time and condition. Correlations among such phenotypes are fundamental to system function, yet revelation of such links typically requires multiple samples. Here, we showed that, by exploiting the intrinsic metabolic heterogeneity among individual cells, such interphenotype correlations can be unveiled via just one snapshot of an isogenic cellular population. Specifically, a network of potential metabolite conversions can be reconstructed using intra-ramanome correlation analysis (IRCA), by pairwise correlation of the thousands of Raman peaks or combination of peaks among single-cell Raman spectra sampled from just one instance of the cellular population. The ability to rapidly and noninvasively reveal intermetabolite conversions from just one snapshot of one sample should usher in many new opportunities in functional profiling of cellular systems.

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., Competing Interests: CS, JD, YJ, and PZ were employed by the company Qingdao Single-Cell Biotechnology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ren, Zheng, Wang, Qu, Sun, Song, Ding, Ji, Wang, Zhu and Cheng.)

Objectives: To systematically evaluate the feasibility of D 2 O-labeled single-cell Raman micro-spectroscopy in drug resistance research and test the susceptibility of Enterococcus faecalis ( E. faecalis )to sodium hypochlorite., Methods: 1) The growth of E. faecalis in different doses of D 2 O and the regularity of D 2 O intake were evaluated through absorbance measurement and D 2 O-labeled single-cell Raman micro-spectroscopy to examine the universality of D 2 O-labeled single-cell Raman micro-spectroscopy in bacterial resistance research. 2) Broth dilution method and absorbance measurement were performed to determine the minimum inhibitory concentration (MIC) of NaClO against E. faecalis and the MIC based on metabolic activity (MIC-MA) in vitro via D 2 O-labeled single-cell raman micro-spectroscopy., Results: 1) The growth of E. faecalis was not significantly inhibited by ≤40% D 2 O in the medium. E. faecalis could actively metabolize D 2 O and exhibit a C-D ratio in specific areas of Raman micro-spectroscopy results. The C-D ratio of E. faecalis at the stationary phase was positively correlated with D 2 O concentration. 2) The MIC and MIC-MA of NaClO against E. faecalis were 0.45 and 0.9 g·L -1 , respectively. The concentration of MIC-MA was twice that of MIC., Conclusions: D 2 O-labeled single-cell Raman micro-spectroscopy is important in screening antimicrobial agents and evaluating the efficacy of antimicrobial agents. It is suitable for evaluating the effect of drugs on bacterial metabolic activities. NaClO showed an effective antimicrobial activity against E. faecalis . E. faecalis ceased propagation yet remained highly metabolically active when it was exposed to NaClO at the MIC level. The metabolic activity of most cells was inhibited only when they were exposed to NaClO at the MIC-MA level.

To reveal the dynamic features of cellular systems, such as the correlation among phenotypes, a time or condition series set of samples is typically required. Here, we propose intra-ramanome correlation analysis (IRCA) to achieve this goal from just one snapshot of an isogenic population, via pairwise correlation among the cells of the thousands of Raman peaks in single-cell Raman spectra (SCRS), i.e., by taking advantage of the intrinsic metabolic heterogeneity among individual cells. For example, IRCA of Chlamydomonas reinhardtii under nitrogen depletion revealed metabolite conversions at each time point plus their temporal dynamics, such as protein-to-starch conversion followed by starch-to-triacylglycerol (TAG) conversion, and conversion of membrane lipids to TAG. Such among-cell correlations in SCRS vanished when the starch-biosynthesis pathway was knocked out yet were fully restored by genetic complementation. Extension of IRCA to 64 microalgal, fungal, and bacterial ramanomes suggests the IRCA-derived metabolite conversion network as an intrinsic metabolic signature of isogenic cellular population that is reliable, species-resolved, and state-sensitive. The high-throughput, low cost, excellent scalability, and general extendibility of IRCA suggest its broad applications. IMPORTANCE Each isogenic population of cells is characterized by many phenotypes, which change with time and condition. Correlations among such phenotypes are fundamental to system function, yet revelation of such links typically requires multiple samples. Here, we showed that, by exploiting the intrinsic metabolic heterogeneity among individual cells, such interphenotype correlations can be unveiled via just one snapshot of an isogenic cellular population. Specifically, a network of potential metabolite conversions can be reconstructed using intra-ramanome correlation analysis (IRCA), by pairwise correlation of the thousands of Raman peaks or combination of peaks among single-cell Raman spectra sampled from just one instance of the cellular population. The ability to rapidly and noninvasively reveal intermetabolite conversions from just one snapshot of one sample should usher in many new opportunities in functional profiling of cellular systems.