Active antibiotic resistome in soils unraveled by single-cell isotope probing and targeted metagenomics.

Antimicrobial resistance (AMR) in soils represents a serious risk to human health through the food chain and human-nature contact. However, the active antibiotic-resistant bacteria (ARB) residing in soils that primarily drive AMR dissemination are poorly explored. Here, single-cell Raman-D2O coupled with targeted metagenomics is developed as a culture-independent approach to phenotypically and genotypically profiling active ARB against clinical antibiotics in a wide range of soils. This method quantifies the prevalence (contamination degree) and activity (spread potential) of soil ARB and reveals a clear elevation with increasing anthropogenic activities such as farming and the creation of pollution, thereby constituting a factor that is critical for the assessment of AMR risks. Further targeted sorting and metagenomic sequencing of the most active soil ARB uncover several uncultured genera and a pathogenic strain. Furthermore, the underlying resistance genes, virulence factor genes, and associated mobile genetic elements (including plasmids, insertion sequences, and prophages) are fully deciphered at the single-cell level. This study advances our understanding of the soil active AMR repertoire by linking the resistant phenome to the genome. It will aid in the risk assessment of environmental AMR and guide the combat under the One Health framework.

An Isotope-Labeled Single-Cell Raman Spectroscopy Approach for Tracking the Physiological Evolution Trajectory of Bacteria toward Antibiotic Resistance.

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.

Machine Learning-Based Determination of Sampling Depth for Complex Environmental Systems: Case Study with Single-Cell Raman Spectroscopy Data in EBPR Systems.

Rapid progress in various advanced analytical methods, such as single-cell technologies, enable unprecedented and deeper understanding of microbial ecology beyond the resolution of conventional approaches. A major application challenge exists in the determination of sufficient sample size without sufficient prior knowledge of the community complexity and, the need to balance between statistical power and limited time or resources. This hinders the desired standardization and wider application of these technologies. Here, we proposed, tested and validated a computational sampling size assessment protocol taking advantage of a metric, named kernel divergence. This metric has two advantages: First, it directly compares data set-wise distributional differences with no requirements on human intervention or prior knowledge-based preclassification. Second, minimal assumptions in distribution and sample space are made in data processing to enhance its application domain. This enables test-verified appropriate handling of data sets with both linear and nonlinear relationships. The model was then validated in a case study with Single-cell Raman Spectroscopy (SCRS) phenotyping data sets from eight different enhanced biological phosphorus removal (EBPR) activated sludge communities located across North America. The model allows the determination of sufficient sampling size for any targeted or customized information capture capacity or resolution level. Promised by its flexibility and minimal restriction of input data types, the proposed method is expected to be a standardized approach for sampling size optimization, enabling more comparable and reproducible experiments and analysis on complex environmental samples. Finally, these advantages enable the extension of the capability to other single-cell technologies or environmental applications with data sets exhibiting continuous features.

Regulation and Characterization of Mutants of fixABCX in Rhizobium leguminosarum.

Symbiosis between Rhizobium leguminosarum and Pisum sativum requires tight control of redox balance in order to maintain respiration under the microaerobic conditions required for nitrogenase while still producing the eight electrons and sixteen molecules of ATP needed for nitrogen fixation. FixABCX, a cluster of electron transfer flavoproteins essential for nitrogen fixation, is encoded on the Sym plasmid (pRL10), immediately upstream of nifA, which encodes the general transcriptional regulator of nitrogen fixation. There is a symbiotically regulated NifA-dependent promoter upstream of fixA (PnifA1), as well as an additional basal constitutive promoter driving background expression of nifA (PnifA2). These were confirmed by 5'-end mapping of transcription start sites using differential RNA-seq. Complementation of polar fixAB and fixX mutants (Fix- strains) confirmed expression of nifA from PnifA1 in symbiosis. Electron microscopy combined with single-cell Raman microspectroscopy characterization of fixAB mutants revealed previously unknown heterogeneity in bacteroid morphology within a single nodule. Two morphotypes of mutant fixAB bacteroids were observed. One was larger than wild-type bacteroids and contained high levels of polyhydroxy-3-butyrate, a complex energy/reductant storage product. A second bacteroid phenotype was morphologically and compositionally different and resembled wild-type infection thread cells. From these two characteristic fixAB mutant bacteroid morphotypes, inferences can be drawn on the metabolism of wild-type nitrogen-fixing bacteroids.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Using Raman spectroscopy and chemometrics to identify the growth phase of Lactobacillus casei Zhang during batch culture at the single-cell level.

BACKGROUND: As microbial cultures are comprised of heterogeneous cells that differ according to their size and intracellular concentrations of DNA, proteins, and other constituents, the detailed identification and discrimination of the growth phases of bacterial populations in batch culture is challenging. Cell analysis is indispensable for quality control and cell enrichment. METHODS: In this paper, we report the results of our investigation on the use of single-cell Raman spectrometry (SCRS) for real-time analysis and prediction of cells in different growth phases during batch culture of Lactobacillus (L.) casei Zhang. A targeted analysis of defined cell growth phases at the level of the single cell, including lag phase, log phase, and stationary phase, was facilitated by SCRS. RESULTS: Spectral shifts were identified in different states of cell growth that reflect biochemical changes specific to each cell growth phase. Raman peaks associated with DNA and RNA displayed a decrease in intensity over time, whereas protein-specific and lipid-specific Raman vibrations increased at different rates. Furthermore, a supervised classification model (Random Forest) was used to specify the lag phase, log phase, and stationary phase of cells based on SCRS, and a mean sensitivity of 90.7% and mean specificity of 90.8% were achieved. In addition, the correct cell type was predicted at an accuracy of approximately 91.2%. CONCLUSIONS: To conclude, Raman spectroscopy allows label-free, continuous monitoring of cell growth, which may facilitate more accurate estimates of the growth states of lactic acid bacterial populations during fermented batch culture in industry.

Mining strategies for isolating plastic-degrading microorganisms.

Plastic waste is a growing global pollutant. Plastic degradation by microorganisms has captured attention as an earth-friendly tactic. Although the mechanisms of plastic degradation by bacteria, fungi, and algae have been explored over the past decade, a large knowledge gap still exists regarding the identification, sorting, and cultivation of efficient plastic degraders, primarily because of their uncultivability. Advances in sequencing techniques and bioinformatics have enabled the identification of microbial degraders and related enzymes and genes involved in plastic biodegradation. In this review, we provide an outline of the situation of plastic degradation and summarize the methods for effective microbial identification using multidisciplinary techniques such as multiomics, meta-analysis, and spectroscopy. This review introduces new strategies for controlling plastic pollution in an environmentally friendly manner. Using this information, highly efficient and colonizing plastic degraders can be mined via targeted sorting and cultivation. In addition, based on the recognized rules and plastic degraders, we can perform an in-depth analysis of the associated degradation mechanism, metabolic features, and interactions.

Micro-flow cell washing technique combined with single-cell Raman spectroscopy for rapid and automatic antimicrobial susceptibility test of pathogen in urine.

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.

Cycloalkane degradation by an uncultivated novel genus of Gammaproteobacteria derived from China's marginal seas.

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.

Rapid profiling of fish cell nitrogen metabolism with single-cell Raman spectroscopy: Unveiling enzyme's role in ammonia detoxification.

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.

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.

Live tracking metabolic networks and physiological responses within microbial assemblages at single-cell level.

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.

Comparative Analysis Using Raman Spectroscopy of the Cellular Constituents of Lacticaseibacillus paracasei Zhang in a Normal and Viable but Nonculturable State.

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.

An attempt to simultaneously quantify the polysaccharide, total lipid, protein and pigment in single Cyclotella cryptica cell by Raman spectroscopy.

BACKGROUND: At present, the conventional methods for determining photosynthetic products of microalgae are usually based on a large number of cell mass to reach the measurement baseline, and the result can only reveal the average state at the population level, which is not feasible for large-scale and rapid screening of specific phenotypes from a large number of potential microalgae mutants. In recent years, single-cell Raman spectra (SCRS) has been proved to be able to rapidly and simultaneously quantify the biochemical components of microalgae. However, this method has not been reported to analyze the biochemical components of Cyclotella cryptica (C. cryptica). Thus, SCRS was first attempt to determine these four biochemical components in this diatom. RESULTS: The method based on SCRS was established to simultaneously quantify the contents of polysaccharide, total lipids, protein and Chl-a in C. cryptica, with thirteen Raman bands were found to be the main marker bands for the diatom components. Moreover, Partial Least Square Regression (PLSR) models based on full spectrum can reliably predict these four cellular components, with Pearson correlation coefficient for these components reached 0.949, 0.904, 0.801 and 0.917, respectively. Finally, based on SCRS data of one isogenic sample, the pairwise correlation and dynamic transformation process of these components can be analyzed by Intra-ramanome Correlation Analysis (IRCA), and the results showed silicon starvation could promote the carbon in C. cryptica cells to flow from protein and pigment metabolism to polysaccharide and lipid metabolism. CONCLUSIONS: First, method for the simultaneous quantification of the polysaccharide, total lipid, protein and pigment in single C. cryptica cell are established. Second, the instant interconversion of intracellular components was constructed through IRCA, which is based on data set of one isogenic population and more precision and timeliness. Finally, total results indicated that silicon deficiency could promote the carbon in C. cryptica cells to flow from protein and pigment metabolism to polysaccharide and lipid metabolism.

Single-cell Raman and functional gene analyses reveal microbial P solubilization in agriculture waste-modified soils.

Application of agricultural waste such as rapeseed meal (RM) is regarded as a sustainable way to improve soil phosphorus (P) availability by direct nutrient supply and stimulation of native phosphate-solubilizing microorganisms (PSMs) in soils. However, exploration of the in situ microbial P solubilizing function in soils remains a challenge. Here, by applying both phenotype-based single-cell Raman with D2O labeling (Raman-D2O) and genotype-based high-throughput chips targeting carbon, nitrogen and P (CNP) functional genes, the effect of RM application on microbial P solubilization in three typical farmland soils was investigated. The abundances of PSMs increased in two alkaline soils after RM application identified by single-cell Raman D2O. RM application reduced the diversity of bacterial communities and increased the abundance of a few bacteria with reported P solubilization function. Genotypic analysis indicated that RM addition generally increased the relative abundance of CNP functional genes. A correlation analysis of the abundance of active PSMs with the abundance of soil microbes or functional genes was carried out to decipher the linkage between the phenotype and genotype of PSMs. Myxococcota and C degradation genes were found to potentially contribute to the enhanced microbial P release following RM application. This work provides important new insights into the in situ function of soil PSMs. It will lead to better harnessing of agricultural waste to mobilize soil legacy P and mitigate the P crisis.

Single-cell Raman spectroscopy identifies Escherichia coli persisters and reveals their enhanced metabolic activities.

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.

A User-Friendly Platform for Single-Cell Raman Spectroscopy Analysis.

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.

Single-Cell Photothermal Analysis Induced by MoS2 Nanoparticles by Raman Spectroscopy.

Two-dimensional nanomaterials, such as MoS2 nanosheets, have been attracting increasing attention in cancer diagnosis and treatment, thanks to their peculiar physical and chemical properties. Although the mechanisms which regulate the interaction between these nanomaterials and cells are not yet completely understood, many studies have proved their efficient use in the photothermal treatment of cancer, and the response to MoS2 nanosheets at the single-cell level is less investigated. Clearly, this information can help in shedding light on the subtle cellular mechanisms ruling the interaction of this 2D material with cells and, eventually, to its cytotoxicity. In this study, we use confocal micro-Raman spectroscopy to reconstruct the thermal map of single cells targeted with MoS2 under continuous laser irradiation. The experiment is performed by analyzing the water O-H stretching band around 3,400 cm-1 whose tetrahedral structure is sensitive to the molecular environment and temperature. Compared to fluorescence-based approaches, this Raman-based strategy for temperature measurement does not suffer fluorophore instability, which can be significant under continuous laser irradiation. We demonstrate that irradiation of human breast cancer MCF7 cells targeted with MoS2 nanosheets causes a relevant photothermal effect, which is particularly high in the presence of MoS2 nanosheet aggregates. Laser-induced heating is strongly localized near such particles which, in turn, tend to accumulate near the cytoplasmic membrane. Globally, our experimental outcomes are expected to be important for tuning the nanosheet fabrication process.

Single Cell Raman Spectroscopy Deuterium Isotope Probing for Rapid Antimicrobial Susceptibility Test of Elizabethkingia spp.

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.

Intracellular polyphosphate length characterization in polyphosphate accumulating microorganisms (PAOs): Implications in PAO phenotypic diversity and enhanced biological phosphorus removal performance.

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.

Intra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Population of Cells.

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.