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Facing the rapid spread of antimicrobial resistance, methods based on single-cell Raman spectroscopy have proven their advances in reducing the turn-around time (TAT) of antimicrobial susceptibility tests (AST). However, the Raman-based methods are still hindered by the prolonged centrifugal cell washing procedure, which may require complex labor operation and induce high mechanical stress, resulting in a pretreatment time of over 1 h as well as a high cell-loss probability. In this study, we developed a micro-flow cell washing device and corresponding Raman-compatible washing chips, which were able to automatically remove the impurities in the samples, retain the bacterial cell and perform Raman spectra acquisition in situ. Results of washing the 5- and 10-µm polymethyl methacrylate (PMMA) microspheres showed that the novel technique achieved a successful removal of 99 % impurity and an 80 % particle retention rate after 6 to 10 cycles of washing. The micro-flow cell washing technique could complete the pretreatment for urine samples in a 96-well plate within 10 min, only taking 15 % of the handling time required by centrifugation. The AST profiles of urine sample spiked with E. coli 25922, E. faecalis 29212, and S. aureus 29213 obtained by the proposed Raman-based approach were found to be 100 % consistent with the results from broth micro-dilution while reducing the TAT to 3 h from several days which is required by the latter. Our study has demonstrated the micro-flow cell washing technique is a reliable, fast and compatible approach to replace centrifuge washing for sample pretreatment of Raman-AST and could be readily applied in clinical scenarios.

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Ammonia is a prevalent aquatic pollutant that disrupts cellular functions and energy metabolism in fish, posing significant environmental and health threats. This research investigates the critical role of arginase 2 (ARG2) in mitigating ammonia toxicity in fish cells and its implications in adapting to nitrogen metabolism under high ammonia exposure. Through a CRISPR-Cas9 engineered ARG2 knockdown (KD) in the Epithelioma Papulosum Cyprini (EPC) cell line, we first investigated the biochemical responses of ARG2 KD and wild-type (WT) EPC cells to ammonia stress (NH4Cl treatment), showing diminished urea production and decreased cell viability in ARG2 KD cells. Subsequently, single-cell Raman spectroscopy analysis revealed that ARG2 KD cells exhibited profound metabolic shifts, including changes in protein, nucleic acids, lipid and sugar levels, showing the adjusting role of ARG2 in the balance of carbohydrate and nitrogen metabolism. Furthermore, the upregulated responses of various amino acids, such as glutamine, arginine, alanine, glutamic acid, glycine, histidine, phenylalanine and valine, in WT cells after NH4Cl treatment diminished in ARG2 KD cells except for the decrease in aspartic acid, indicating a switching effect of ARG2 in nitrogen metabolism under ammonia stress. This study highlights ARG2's essential role in ammonia detoxification and emphasizes ARG2's protective function and its importance in metabolism, shedding light on the adaptive mechanisms fish cells deploy against high ammonia environments. These insights contribute to deep understanding of aquatic organisms' molecular responses to environmental ammonia pollution, offering potential strategies for their protection.

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The consumption of cycloalkanes is prevalent in low-temperature marine environments, likely influenced by psychrophilic microorganisms. Despite their significance, the primary active species responsible for marine cycloalkane degradation remain largely unidentified due to cultivation challenges. In this study, we provide compelling evidence indicating that the uncultured genus C1-B045 of Gammaproteobacteria is a pivotal participant in cycloalkane decomposition within China's marginal seas. Notably, the relative abundance of C1-B045 surged from 15.9% in the methylcyclohexane (MCH)-consuming starter culture to as high as 97.5% in MCH-utilizing extinction cultures following successive dilution-to-extinction and incubation cycles. We used stable isotope probing, Raman-activated gravity-driven encapsulation, and 16 S rRNA gene sequencing to link cycloalkane-metabolizing phenotype to genotype at the single-cell level. By annotating key enzymes (e.g., alkane monooxygenase, cyclohexanone monooxygenase, and 6-hexanolactone hydrolase) involved in MCH metabolism within C1-B045's representative metagenome-assembled genome, we developed a putative MCH degradation pathway.

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This study aimed to investigate the molecular composition of a viable but nonculturable (VBNC) state of a probiotic strain, Lacticaseibacillus paracasei Zhang (L. paracasei Zhang), using single-cell Raman spectroscopy (SCRS). Fluorescent microcopy with live/dead cell staining (propidium iodide and SYTO 9), plate counting, and scanning electron microscopy were used in combination to observe bacteria in an induced VBNC state. We induced the VBNC state by incubating the cells in de Man, Rogosa, and Sharpe broth (MRS) at 4 °C. Cells were sampled for subsequent analyses before VBNC induction, during it, and up to 220 days afterwards. We found that, after cold incubation for 220 days, the viable plate count was zero, but active cells could still be observed (as green fluorescent cells) under a fluorescence microscope, indicating that Lacticaseibacillus paracasei Zhang entered the VBNC state under these conditions. Scanning electron microscopy revealed the altered ultra-morphology of the VBNC cells, characterized by a shortened cell length and a wrinkled cell surface. Principal component analysis of the Raman spectra profiles revealed obvious differences in the intracellular biochemical constituents between normal and VBNC cells. Comparative analysis of the Raman spectra identified 12 main differential peaks between normal and VBNC cells, corresponding to carbohydrates, lipids, nucleic acids, and proteins. Our results suggested that there were obvious cellular structural intracellular macromolecular differences between normal and VBNC cells. During the induction of the VBNC state, the relative contents of carbohydrates (such as fructose), saturated fatty acids (such as palmitic acid), nucleic acid constituents, and some amino acids changed obviously, which could constitute a bacterial adaptive mechanism against adverse environmental conditions. Our study provides a theoretical basis for revealing the formation mechanism of a VBNC state in lactic acid bacteria.

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Microbial interactions impact the functioning of both natural and engineered systems, yet our ability to directly monitor these highly dynamic and spatially resolved interactions in living cells is very limited. Here, we developed a synergistic approach coupling single-cell Raman microspectroscopy with 15N2 and 13CO2 stable isotope probing in a microfluidic culture system (RMCS-SIP) for live tracking of the occurrence, rate, and physiological shift of metabolic interactions in active microbial assemblages. Quantitative and robust Raman biomarkers specific for N2 and CO2 fixation in both model and bloom-forming diazotrophic cyanobacteria were established and cross-validated. By designing a prototype microfluidic chip allowing simultaneous microbial cultivation and single-cell Raman acquisition, we achieved temporal tracking of both intercellular (between heterocyst and vegetative cells of cyanobacteria) and interspecies N and C metabolite exchange (from diazotroph to heterotroph). Moreover, single-cell N and C fixation and bidirectional transfer rate in living cells were quantified via SIP-induced characteristic Raman shifts. Remarkably, RMCS captured physiological responses of metabolically active cells to nutrient stimuli through comprehensive metabolic profiling, providing multimodal information on the evolution of microbial interactions and functions under fluctuating conditions. This noninvasive RMCS-SIP is an advantageous approach for live-cell imaging and represents an important advancement in the single-cell microbiology field. This platform can be extended for real-time tracking of a wide range of microbial interactions with single-cell resolution and advances the understanding and manipulation of microbial interactions for societal benefit.

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Understanding evolution of antibiotic resistance is vital for containing its global spread. Yet our ability to in situ track highly heterogeneous and dynamic evolution is very limited. Here, we present a new single-cell approach integrating D2 O-labeled Raman spectroscopy, advanced multivariate analysis, and genotypic profiling to in situ track physiological evolution trajectory toward resistance. Physiological diversification of individual cells from isogenic population with cyclic ampicillin treatment is captured. Advanced multivariate analysis of spectral changes classifies all individual cells into four subsets of sensitive, intrinsic tolerant, evolved tolerant and resistant. Remarkably, their dynamic shifts with evolution are depicted and spectral markers of each state are identified. Genotypic analysis validates the phenotypic shift and provides insights into the underlying genetic basis. The new platform advances rapid phenotyping resistance evolution and guides evolution control.

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Microbial persisters are the featured tiny sub-population of microorganisms that are highly tolerant to multiple antimicrobials. Currently, studies on persisters remain a considerable challenge owing to technical limitations. Here, we explored the application of single-cell Raman spectroscopy (SCRS) in the investigation of persisters. Escherichia coli (ATCC 25922) cells were treated with a lethal dosage of ampicillin (100 µg/mL, 32 × MIC, 4 h) for the formation of persisters. The biochemical characters of E. coli and its persisters were assessed by SCRS, and their metabolic activities were labeled and measured with D2O-based single-cell Raman spectroscopy (D2O-Ramanometry). Notable differences in the intensity of Raman bands related to major cellular components and metabolites were observed between E. coli and its ampicillin-treated persisters. Based on their distinct Raman spectra, E. coli and its persister cells were classified into different projective zones through the principal component analysis and t-distributed stochastic neighbor embedding. According to the D2O absorption rate, E. coli persisters exhibited higher metabolic activities than those of untreated E. coli. Importantly, after the termination of ampicillin exposure, these persister cells showed a temporal pattern of D2O intake that was distinct from non-persister cells. To our knowledge, this is the first report on identifying E. coli persisters and assessing their metabolic activities through the integrated SCRS and D2O-Ramanometry approach. These novel findings enhance our understanding of the phenotypes and functionalities of microbial persister cells. Further investigations could be extended to other pathogens by disclosing microbial pathogenicity mechanisms for developing novel therapeutic strategies and approaches.

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The optimization of Raman instruments greatly expands our understanding of single-cell Raman spectroscopy. The improvement in the speed and sensitivity of the instrument and the implementation of advanced data mining methods help to reveal the complex chemical and biological information within the Raman spectral data. Here we introduce a new Matlab Graphical User-Friendly Interface (GUI), named "CELL IMAGE" for the analysis of cellular Raman spectroscopy data. The three main steps of data analysis embedded in the GUI include spectral processing, pattern recognition and model validation. Various well-known methods are available to the user of the GUI at each step of the analysis. Herein, a new subsampling optimization method is integrated into the GUI to estimate the minimum number of spectral collection points. The introduction of the signal-to-noise ratio (SNR) of the analyte in the binomial statistical model means the new subsampling model is more sophisticated and suitable for complicated Raman cell data. These embedded methods allow "CELL IMAGE" to transform spectral information into biological information, including single-cell visualization, cell classification and biomolecular/ drug quantification.

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Nosocomial infection by multi-drug resistance Elizabethkingia spp. is an emerging concern with severe clinical consequences, particularly in immunocompromised individuals and infants. Efficient control of this infection requires quick and reliable methods to determine the appropriate drugs for treatment. In this study, a total of 31 Elizabethkingia spp., including two standard strains (ATCC 13253 and FMS-007) and 29 clinical isolates obtained from hospitals in China were subjected to single cell Raman spectroscopy analysis coupled with deuterium probing (single cell Raman-DIP). The results demonstrated that single cell Raman-DIP could determine antimicrobial susceptibility of Elizabethkingia spp. in 4 h, only one third of the time required by standard broth microdilution method. The method could be integrated into current clinical protocol for sepsis and halve the report time. The study also confirmed that minocycline and levofloxacin are the first-line antimicrobials for Elizabethkingia spp. infection.

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Polyphosphate (polyP) accumulating organisms (PAOs) are the key agent to perform enhanced biological phosphorus removal (EBPR) activity, and intracellular polyP plays a key role in this process. Potential associations between EBPR performance and the polyP structure have been suggested, but are yet to be extensively investigated, mainly due to the lack of established methods for polyP characterization in the EBPR system. In this study, we explored and demonstrated that single-cell Raman spectroscopy (SCRS) can be employed for characterizing intracellular polyPs of PAOs in complex environmental samples such as EBPR systems. The results, for the first time, revealed distinct distribution patterns of polyP length (as Raman peak position) in PAOs in lab-scale EBPR reactors that were dominated with different PAO types, as well as among different full-scale EBPR systems with varying configurations. Furthermore, SCRS revealed distinctive polyP composition/features among PAO phenotypic sub-groups, which are likely associated with phylogenetic and/or phenotypic diversity in EBPR communities, highlighting the possible resolving power of SCRS at the microdiversity level. To validate the observed polyP length variations via SCRS, we also performed and compared bulk polyP length characteristics in EBPR biomass using conventional polyacrylamide gel electrophoresis (PAGE) and solution 31P nuclear magnetic resonance (31P-NMR) methods. The results are consistent with the SCRS findings and confirmed the variations in the polyP lengths among different EBPR systems. Compared to conventional methods, SCRS exhibited advantages as compared to conventional methods, including the ability to characterize in situ the intracellular polyPs at subcellular resolution in a label-free and non-destructive way, and the capability to capture subtle and detailed biochemical fingerprints of cells for phenotypic classification. SCRS also has recognized limitations in comparison with 31P-NMR and PAGE, such as the inability to quantitatively detect the average polyP chain length and its distribution. The results provided initial evidence for the potential of SCRS-enabled polyP characterization as an alternative and complementary microbial community phenotyping method to facilitate the phenotype-function (performance) relationship deduction in EBPR systems.

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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.

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Microorganisms play a key role in driving the global element (C, N, H, P, and S) cycling. However, the function and activity of environmental microbes remain largely elusive because the vast majority of them are yet uncultured. Recent achievements in single cell stable isotope-labeled Raman spectroscopy enable direct investigation of function and activity of individual microbes in complex environmental communities. Here, this protocol describes a workflow to investigate environmental microbes in soil and water by combining 15N, 2D, and 13C stable isotope labeling with different single-cell Raman techniques, including normal Raman, resonance Raman (RR), and surface-enhanced Raman spectroscopy (SERS). Their applications in investigating functional bacteria driving the N and C cycles, and metabolically active cells are described.

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IMPACT STATEMENT: This study is the first to investigate and confirm the effectiveness of single-cell Raman spectroscopy (SCRM), in its ability to discriminate between dental pulp stem cells (DPSCs) with contrasting proliferative and differentiation capabilities. The findings show that SCRM can rapidly and noninvasively distinguish and identify DPSC subpopulations in vitro with superior proliferative and multipotency properties, versus lesser quality DPSCs, thereby overcoming the significant heterogeneity issues surrounding DPSC ex vivo expansion and differentiation capabilities. Such findings support further SCRM assessment for the selective screening/isolation of superior quality DPSCs from whole dental pulp tissues, for more effective in vitro evaluation and therapy development.

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BACKGROUND: Current approaches for quantification of major energy-storage forms in microalgae, including starch, protein and lipids, generally require cell cultivation to collect biomass followed by tedious and time-consuming analytical procedures. Thus, label-free, non-destructive and simultaneous quantification of such macromolecules at single-cell resolution is highly desirable in microalgal feedstock development and bioprocess control. RESULTS: Here, we established a method based on single-cell Raman spectra (SCRS) that simultaneously quantifies the contents of starch, protein, triacylglycerol (TAG) and lipid unsaturation degree in individual Chlamydomonas reinhardtii cells. Measurement accuracy for the contents based on full SCRS spectrum each reached 96.86-99.24%, all significantly higher than single peak-based models. However, accuracy and reliability of measurement are dependent on the number of cells sampled, thus a formal mathematical framework was proposed and validated to rationally define "minimal sampling depth" for a given state of cellular population. Furthermore, a barcode consisting of 13 marker Raman peaks was proposed to characterize the temporal dynamics of these energy-storage products, which revealed that the average contents of starch and TAG increased, while their heterogeneity indices decreased, with those of protein being exactly the opposite. Finally, our method is widely applicable, as measurements among cells from liquid suspension culture, wet paste and frozen dried powder all exhibited excellent consistency. CONCLUSIONS: When sampled at proper depth, SCRS can serve as a quantitative and generally applicable tool for characterization and screening of strains and bioprocesses based on the profile of energy-storage macromolecules and their among-cell heterogeneity.