Single-cell Raman Microspectroscopy Research Articles

Modeling versatile and dynamic anaerobic metabolism for PAOs/GAOs competition using agent-based model and verification via single cell Raman Micro-spectroscopy

Side-stream enhanced biological phosphorus removal process (S2EBPR) has been demonstrated to improve performance stability and offers a suite of advantages compared to conventional EBPR design. Design and optimization of S2EBPR require modification of the current EBPR models that were not able to fully reflect the metabolic functions of and competition between the polyphosphate-accumulating organisms (PAOs) and glycogen-accumulating organisms (GAOs) under extended anaerobic conditions as in the S2EBPR conditions. In this study, we proposed and validated an improved model (iEBPR) for simulating PAO and GAO competition that incorporated heterogeneity and versatility in PAO sequential polymer usage, staged maintenance-decay, and glycolysis-TCA pathway shifts. The iEBPR model was first calibrated against bulk batch testing experiment data and proved to perform better than the previous EBPR model for predicting the soluble orthoP, ammonia, biomass glycogen, and PHA temporal profiles in a starvation batch testing under prolonged anaerobic conditions. We further validated the model with another independent set of anaerobic testing data that included high-resolution single-cell and specific population level intracellular polymer measurements acquired with single-cell Raman micro-spectroscopy technique. The model accurately predicted the temporal changes in the intracellular polymers at cellular and population levels within PAOs and GAOs, and further confirmed the proposed mechanism of sequential polymer utilization, and polymer availability-dependent and staged maintenance-decay in PAOs. These results indicate that under extended anaerobic phases as in S2EBPR, the PAOs may gain competitive advantages over GAOs due to the possession of multiple intracellular polymers and the adaptive switching of the anaerobic metabolic pathways that consequently lead to the later and slower decay in PAOs than GAOs. The iEBPR model can be applied to facilitate and optimize the design and operations of S2EBPR for more reliable nutrient removal and recovery from wastewater.

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.

Label-free differentiation of clinical E. coli and Klebsiella isolates with Raman spectroscopy.

Raman spectroscopy is a promising spectroscopic technique for microbiological diagnostics. In routine diagnostic, the differentiation of pathogens of the Enterobacteriaceae family remain challenging. In this study, Raman spectroscopy was applied for the differentiation of 24 clinical E. coli, Klebsiella pneumoniae and Klebsiella oxytoca isolates. Spectra were collected with two spectroscopic approaches: UV-Resonance Raman spectroscopy (UVRR) and single-cell Raman microspectroscopy with 532 nm excitation. A description of the different biochemical profiles provided by the different excitation wavelengths was performed followed by machine-learning models for the classification at the genus and species levels. UVRR was shown to outperform 532 nm excitation, enabling correct classification at the genus level of 23/24 isolates. Furthermore, for the first time, Klebsiella species were correctly classified at the species level with 92% accuracy, classifying all three K. oxytoca isolates correctly. These findings should guide future applicative studies, increasing the scope of Raman spectroscopy's suitability for clinical applications.

Effects of Candida albicans on the metabolic activity and drug resistance of Streptococcus mutans based on D2O-labeled single-cell Raman microspectroscopy.

To evaluate the susceptibility of Streptococcus mutans (S. mutans) to NaF and the effects of Candida albicans (C. albicans) metabolites on the metabolic activity and drug resistance of S. mutans. The minimum inhibitory concentration (MIC) and MIC based on metabolic activity (MIC-MA) of NaF against S. mutans were measured through the broth dilution test and D2O-labeled single-cell Raman microspectroscopy, respectively, to explore the inhibitory effect of NaF on the growth and metabolism of S. mutans. The effects of different concentrations of C. albicans supernatant on the metabolic activity of S. mutans were evaluated by using D2O-labeled single-cell Raman microspectroscopy. The correlation between C. albicans and S. mutans was explored throughassessing the drug resistance of S. mutans against NaF in a mixed system containing C. albicans supernatant by using D2O-labeled single-cell Raman microspectroscopy. The MIC and MIC-MA of NaF against S. mutans were 0.4 g·L-1 and 1.2 g·L-1, respectively. At the MIC level, S. mutans showed completely inhibited growth but retained high metabolic activity. Therefore, S. mutans may regain its virulence. At the MIC-MA level, the metabolic activity of S. mutans was inhibited only when the NaF concentration reached 3×MIC. At the concentration of OD600≥0.5, the C. albicans supernatant significantly promoted the metabolic activity of S. mutans at different time points (P<0.05). In the mixed system, the susceptibility of S. mutans to NaF decreased, and statistical differences at different time points were observed (P<0.05). The metabolic activity of S. mutans was inhibited completely only at the concentration of 4×MIC (1.6 g·L-1). D2O-labeled single-cell Raman microspectroscopy is suitable for evaluating the effects of drugs on bacterial metabolic activity. In the mixed system, the metabolites in the C. albicans supernatant at the concentration of OD600≥0.5 significantly promoted the metabolic activity of S. mutans and reducedthe susceptibility of S. mutans to NaF.

Assessing Efficacy of Clinical Disinfectants for Pathogenic Fungi by Single-Cell Raman Microspectroscopy.

Disinfectants are crucial for root canal therapy (RCT), as metabolism of canal-inhabiting microbes can cause refractory infections. To develop effective yet patient- and environment-friendly disinfectant formulations, we quantitatively assessed the metabolism-inhibiting effects of intracanal disinfectants via D2O-probed Single-Cell Raman Spectra (SCRS), using Candida albicans (C. albicans) as a pathogen model. For chlorhexidine gluconate (CHX), sodium hypochlorite (NaClO), and hydrogen peroxide (H2O2), at their MIC of 4, 168, and 60 μg/ml, respectively, despite the complete growth halt, metabolic activity of individual fungal cells was reduced on average by 0.4%, 93.9%, and 94.1% at 8 h, revealing a “nongrowing but metabolically active” (NGMA) state that may underlie potential refractory infections, particularly for CHX. In contrast, at their Metabolic Activity-based Minimum Inhibitory Concentrations (MIC-MA) of 8, 336, and 120 μg/ml, respectively, metabolic activity of all cells was completely halted throughout 8 h exposure. Moreover, combined use of NaClO+H2O2 (combination at 0.5× MIC-MA each) outperforms solo uses of CHX, NaClO, H2O2, or other binary combinations. Furthermore, dynamics of SCRS revealed distinct fungicidal mechanisms of CHX, NaClO, H2O2, and their pairwise combinations. MIC-MA is advantageous in critically assessing antifungal efficacy, and NaClO+H2O2 can potentially serve as a more efficient disinfectant formula for fungal pathogens.

Assessment of the antimicrobial effects of sodium hypochlorite on Enterococcus faecalis by D2O-labeled single-cell Raman micro-spectroscopy.

To systematically evaluate the feasibility of D2O-labeled single-cell Raman micro-spectroscopy in drug resistance research and test the susceptibility of Enterococcus faecalis (E. faecalis)to sodium hypochlorite. 1) The growth of E. faecalis in different doses of D2O and the regularity of D2O intake were evaluated through absorbance measurement and D2O-labeled single-cell Raman micro-spectroscopy to examine the universality of D2O-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 D2O-labeled single-cell raman micro-spectroscopy. 1) The growth of E. faecalis was not significantly inhibited by ≤40% D2O in the medium. E. faecalis could actively metabolize D2O 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 D2O 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. D2O-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.

Detection of multi-resistant clinical strains of E. coli with Raman spectroscopy

In recent years, we have seen a steady rise in the prevalence of antibiotic-resistant bacteria. This creates many challenges in treating patients who carry these infections, as well as stopping and preventing outbreaks. Identifying these resistant bacteria is critical for treatment decisions and epidemiological studies. However, current methods for identification of resistance either require long cultivation steps or expensive reagents. Raman spectroscopy has been shown in the past to enable the rapid identification of bacterial strains from single cells and cultures. In this study, Raman spectroscopy was applied for the differentiation of resistant and sensitive strains of Escherichia coli. Our focus was on clinical multi-resistant (extended-spectrum β-lactam and carbapenem-resistant) bacteria from hospital patients. The spectra were collected using both UV resonance Raman spectroscopy in bulk and single-cell Raman microspectroscopy, without exposure to antibiotics. We found resistant strains have a higher nucleic acid/protein ratio, and used the spectra to train a machine learning model that differentiates resistant and sensitive strains. In addition, we applied a majority of voting system to both improve the accuracy of our models and make them more applicable for a clinical setting. This method could allow rapid and accurate identification of antibiotic resistant bacteria, and thus improve public health.Graphical abstract

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.

Machine learning-assisted single-cell Raman fingerprinting for in situ and nondestructive classification of prokaryotes

SummaryAccessing enormous uncultivated microorganisms (microbial dark matter) in various Earth environments requires accurate, nondestructive classification, and molecular understanding of the microorganisms in in situ and at the single-cell level. Here we demonstrate a combined approach of random forest (RF) machine learning and single-cell Raman microspectroscopy for accurate classification of phylogenetically diverse prokaryotes (three bacterial and three archaeal species from different phyla). Our RF classifier achieved a 98.8 ± 1.9% classification accuracy among the six species in pure populations and 98.4% for three species in an artificially mixed population. Feature importance scores against each wavenumber reveal that the presence of carotenoids and structure of membrane lipids play key roles in distinguishing the prokaryotic species. We also find unique Raman markers for an ammonia-oxidizing archaeon. Our approach with moderate data pretreatment and intuitive visualization of feature importance is easy to use for non-spectroscopists, and thus offers microbiologists a new single-cell tool for shedding light on microbial dark matter.

Single-cell Raman micro-spectroscopy for tracking of carotenoids in cyanobacteria exposed to Mn and Zn doped ferrite nanoparticles

Ferrite nanoparticles are increasingly produced and exploited as adsorbents for environmental pollutants. However, their impact on the aquatic microbiota such as cyanobacteria, are not yet investigated. Targeting the environmental monitoring context, in this paper we explored for the first time if any change in the carotenoid signal from cyanobacteria Coelomoron pussilum (AICB 1012) exposed to non-lethal doses of Mn and Zn doped ferrite nanoparticles (NPs) can be associated with the nano-aggression on single-cell level, using micro-Raman spectroscopy. UV–Vis absorption spectroscopy of the bulk culture and single-cell Raman microscopy showed that the carotenoid signal increases relative to the chlorophyll contribution upon exposure of the cells to the Mn-ferrite NPs throughout the 7 days of the experiment. The red-shift and broadening of the strongest carotenoid Raman band arising from (CC) stretching modes indicates the change of carotenoid profile towards increased amount of β-carotene in answer to the NPs stress. The increase of this band intensity relative to the fluorescence background was also observed in Zn-ferrite NPs treatment. Using a simplified and rapid sample preparation procedure, electron microscopy in both transmission and scanning modes, showed greater coverage of the cells by the stable colloidal AgNPs than by the magnetic ferrite NPs. The latter mostly clumped together rather than adhering to the cells. The combined single-cell micro-Raman tracking of physiological response of the unicellular photosynthetic microorganisms coupled with electron microscopy approach to visualise cell-NPs interaction and the extracellular polymeric substance secretion holds the promise for rapid assessment of the NPs-induced environmental stress acting on the unicellular organisms.

Granulocyte colony-stimulating factor promotes an aggressive phenotype of colon and breast cancer cells with biochemical changes investigated by single-cell Raman microspectroscopy and machine learning analysis.

Granulocyte colony-stimulating factor (G-CSF) is produced at high levels in several cancers and is directly linked with metastasis in gastrointestinal (GI) cancers. In order to further understand the alteration of molecular compositions and biochemical features triggered by G-CSF treatment at molecular and cell levels, we sought to investigate the long term treatment of G-CSF on colon and breast cancer cells measured by label-free, non-invasive single-cell Raman microspectroscopy. Raman spectrum captures the molecule-specific spectral signatures ("fingerprints") of different biomolecules presented on cells. In this work, mouse breast cancer line 4T1 and mouse colon cancer line CT26 were treated with G-CSF for 7 weeks and subsequently analyzed by machine learning based Raman spectroscopy and gene/cytokine expression. The principal component analysis (PCA) identified the Raman bands that most significantly changed between the control and G-CSF treated cells. Notably, here we proposed the concept of aggressiveness score, which can be derived from the posterior probability of linear discriminant analysis (LDA), for quantitative spectral analysis of tumorigenic cells. The aggressiveness score was effectively applied to analyze and differentiate the overall cell biochemical changes of G-CSF-treated two model cancer cells. All these tumorigenic progressions suggested by Raman analysis were confirmed by pro-tumorigenic cytokine and gene analysis. A high correlation between gene expression data and Raman spectra highlights that the machine learning based non-invasive Raman spectroscopy offers emerging and powerful tools to better understand the regulation mechanism of cytokines in the tumor microenvironment that could lead to the discovery of new targets for cancer therapy.

Machine Learning-Assisted Single-Cell Raman Fingerprinting for &lt;i&gt;in Situ&lt;i&gt; and Nondestructive Prokaryotic Classification in Complex Microbial Populations&lt;/i&gt;&lt;/i&gt;

Accessing enormous uncultivated microorganisms (known as microbial dark matter) in a variety of Earth environments requires accurate, nondestructive classification and molecular understanding of the microorganisms in situ and at the single-cell level, which are not feasible with the current genomics approaches. Here we demonstrate a combined approach of random forest (RF) machine learning and less-invasive, single-cell Raman microspectroscopy for accurate classification of phylogenetically diverse prokaryotes (three bacterial and three archaeal species from different phyla). Our RF classifier achieved 98.8±1.9% classification accuracy among the six species in pure populations and 98.4% for three species in an artificially mixed population. Feature importance scores against each wavenumber in the Raman spectrum reveal that, in addition to protein and DNA/RNA abundances, the presence of carotenoids and structure of membrane lipids play key roles in distinguishing the prokaryotic species. We also find unique Raman markers for an ammonia-oxidizing archaeon. Our approach with moderate data pretreatment and intuitive visualization of feature importance is easy to use for non-spectroscopists and thus offers microbiologists a new single-cell tool for shedding light on microbial dark matter.

Increased particle size of goethite enhances the antibacterial effect on human pathogen Escherichia coli O157:H7: A Raman spectroscopic study

The persistence of Escherichia coli O157:H7 in soil is one of the most common causes of the food-borne outbreaks. Nano-sized iron oxide minerals in soil, especially goethite, have been found to reduce bacterial viability, which helps to control the spread of human pathogens. However, little is known about the antibacterial effects of iron oxides with different particle sizes. Our result revealed that the micro-sized goethite exhibited a more effective antibacterial activity against E. coli O157:H7 than the nano-sized goethite. The underlying antibacterial mechanisms were further investigated via single-cell Raman microspectroscopy. The exposure to nano-sized goethite increased the levels of ribonucleoside-related substances, phenylalanine and adenosine 5′-triphosphate, while decreased those of glycogen, protein and lipopolysaccharide & outer membrane porins (LPS & OMPs). Meanwhile, micro-sized goethite triggered less variation in ribonucleoside-related substances and induced more reduction in LPS & OMPs. Therefore, the antibacterial effects of nano-sized goethite were mediated by both ROS-dependent RNA damage and cell membrane destruction, whereas micro-sized goethite induced severer membrane damage and less ROS-dependent oxidative stress. This work demonstrates the role of particle sizes in antibacterial effects of iron oxides and provides implications for the pathogen control in soil.

Elevated intracellular cyclic-di-GMP level in Shewanella oneidensis increases expression of c-type cytochromes.

Electrochemically active biofilms are capable of exchanging electrons with solid electron acceptors and have many energy and environmental applications such as bioelectricity generation and environmental remediation. The performance of electrochemically active biofilms is usually dependent on c-type cytochromes, while biofilm development is controlled by a signal cascade mediated by the intracellular secondary messenger bis-(3'-5') cyclic dimeric guanosine monophosphate (c-di-GMP). However, it is unclear whether there are any links between the c-di-GMP regulatory system and the expression of c-type cytochromes. In this study, we constructed a S. oneidensis MR-1 strain with a higher cytoplasmic c-di-GMP level by constitutively expressing a c-di-GMP synthase and it exhibited expected c-di-GMP-influenced traits, such as lowered motility and increased biofilm formation. Compared to MR-1 wild-type strain, the high c-di-GMP strain had a higher Fe(III) reduction rate (21.58 vs 11.88pM of Fe(III)/h cell) and greater expression of genes that code for the proteins involved in the Mtr pathway, including CymA, MtrA, MtrB, MtrC and OmcA. Furthermore, single-cell Raman microspectroscopy (SCRM) revealed a great increase of c-type cytochromes in the high c-di-GMP strain as compared to MR-1 wild-type strain. Our results reveal for the first time that the c-di-GMP regulation system indirectly or directly positively regulates the expression of cytochromes involved in the extracellular electron transport (EET) in S.oneidensis, which would help to understand the regulatory mechanism of c-di-GMP on electricity production in bacteria.

A single-cell Raman-based platform to identify developmental stages of human pluripotent stem cell-derived neurons

Stem cells with the capability to self-renew and differentiate into multiple cell derivatives provide platforms for drug screening and promising treatment options for a wide variety of neural diseases. Nevertheless, clinical applications of stem cells have been hindered partly owing to a lack of standardized techniques to characterize cell molecular profiles noninvasively and comprehensively. Here, we demonstrate that a label-free and noninvasive single-cell Raman microspectroscopy (SCRM) platform was able to identify neural cell lineages derived from clinically relevant human induced pluripotent stem cells (hiPSCs). By analyzing the intrinsic biochemical profiles of single cells at a large scale (8,774 Raman spectra in total), iPSCs and iPSC-derived neural cells can be distinguished by their intrinsic phenotypic Raman spectra. We identified a Raman biomarker from glycogen to distinguish iPSCs from their neural derivatives, and the result was verified by the conventional glycogen detection assays. Further analysis with a machine learning classification model, utilizing t-distributed stochastic neighbor embedding (t-SNE)-enhanced ensemble stacking, clearly categorized hiPSCs in different developmental stages with 97.5% accuracy. The present study demonstrates the capability of the SCRM-based platform to monitor cell development using high content screening with a noninvasive and label-free approach. This platform as well as our identified biomarker could be extensible to other cell types and can potentially have a high impact on neural stem cell therapy.

Correction: A new approach to find biomarkers in chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) by single-cell Raman micro-spectroscopy

Correction for 'A new approach to find biomarkers in chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) by single-cell Raman micro-spectroscopy' by Jiabao Xu et al., Analyst, 2019, 144, 913-920.

A new approach to find biomarkers in chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) by single-cell Raman micro-spectroscopy.

Chronic fatigue syndrome (CFS), also called myalgic encephalomyelitis (ME), is a debilitating disorder characterized by physical and mental exhaustion. Mitochondrial and energetic dysfunction has been investigated in CFS patients due to a hallmark relationship with fatigue; however, no consistent conclusion has yet been achieved. Single-cell Raman spectra (SCRS) are label-free biochemical profiles, indicating phenotypic fingerprints of single cells. In this study, we applied a new approach using single-cell Raman microspectroscopy (SCRM) to examine ρ0 cells that lack mitochondrial DNA (mtDNA), and peripheral blood mononuclear cells (PBMCs) from CFS patients and healthy controls. The experimental results show that Raman bands associated with phenylalanine in ρ0 cells and CFS patient PBMCs were significantly higher than those of the wild-type model and healthy controls. As similar changes were observed in the ρ0 cell model with a known deficiency in the mitochondrial respiratory chain as well as in CFS patients, our results suggest that the increase in cellular phenylalanine may be related to mitochondrial/energetic dysfunction in both systems. Interestingly, phenylalanine can be used as a potential biomarker for the diagnosis of CFS by SCRM. A machine learning classification model achieved an accuracy rate of 98% correctly assigning Raman spectra to either the CFS group or the control group. SCRM combined with a machine learning algorithm therefore has the potential to become a diagnostic tool for CFS.

Toward Better Understanding of EBPR Systems via Linking Raman-Based Phenotypic Profiling with Phylogenetic Diversity.

This study reports a proof-of concept study to demonstrate the novel approach of phenotyping microbial communities in enhanced biological phosphorus removal (EBPR) systems using single cell Raman microspectroscopy and link it with phylogentic structures. We use hierarchical clustering analysis (HCA) of single-cell Raman spectral fingerprints and intracellular polymer signatures to separate and classify the functionally relevant populations in EBPR systems, namely polyphosphate accumulating organisms (PAOs) and glycogen accumulating organisms (GAOs), as well as other microbial populations. We then investigated the link between Raman-based community phenotyping and 16S rRNA gene-based phylogenetic characterization of four lab-scale EBPR systems with varying solid retention time (SRT) to gain insights into possible genotype-function relationships. Combined and simultaneous phylogenetic and phenotypic evaluation of EBPR ecosystems revealed SRT-dependent phylogenetic and phenotypic characteristics of the PAOs and GAOs, and their association with EBPR performance. The phenotypic diversity and plasticity of PAO populations, which otherwise could not be obtained with phylogenetic analysis alone, showed complex but potentially crucial association with EBPR process stability.

Tracking active groundwater microbes with D2 O labelling to understand their ecosystem function.

Microbial activity is key in understanding the contribution of microbial communities to ecosystem functions. Metabolic labelling with heavy water (D2 O) leads to the formation of carbon-deuterium bonds in active microorganisms. We illustrated how D2 O labelling allows monitoring of metabolic activity combined with a functional characterization of active populations in complex microbial communities. First, we demonstrated by single cell Raman microspectroscopy that all measured bacterial cells from groundwater isolates growing in complex medium with D2 O were labelled. Next, we conducted a labelling approach with the total groundwater microbiome in D2 O amended microcosms. Deuterium was incorporated in most measured cells, indicating metabolic activity in the oligotrophic groundwater. Moreover, we spiked the groundwater microbiome with organic model compounds. We discovered that heterotrophs assimilating veratric acid, a lignin derivative, showed higher labelling than heterotrophs assimilating methylamine, a degradation product of biomass. This difference can be explained by dilution of the deuterium through hydrogen from the organic compounds. Metaproteomics identified Sphingomonadaceae and Microbacteriaceae as key players in veratric acid degradation, and the metabolic pathways employed. Methylamine, in contrast, stimulated various proteobacterial genera. We propose this combined approach of Raman microspectroscopy and metaproteomics for elucidating the complex metabolic response of microbial populations to different stimuli.

Raman Deuterium Isotope Probing Reveals Microbial Metabolism at the Single-Cell Level.

We illustrate that single-cell Raman microspectroscopy, coupled with deuterium isotope probing (Raman-DIP), provides a culture-independent and nondestructive approach to probe metabolic pathways of carbon substrates at the single-cell level. We found a distinguishable C-D vibration band at 2070-2300 cm-1 in single-cell Raman spectra (SCRS) when Escherichia coli used deuterated glucose and Pseudomonas sp. used deuterated naphthalene as sole carbon sources. The intensity of the C-D band is proportional to the extent of deuteration in the carbon source, and as little as 5% deuteration can be distinguished by analysis of SCRS. It suggests that Raman-DIP could be used to semiquantitatively and sensitively indicate the metabolism of deuterated carbon source in microbes. A lower lipid conversion rate of deuterated naphthalene compared to that of deuterated glucose was observed, presumably owing to different anabolic pathways and membrane alteration. Apart from the C-D band shift from C-H, SCRS also reveal several isotopic shifts of the phenylalanine band, of which the positions correlate well with a computational model. A reduction in phenylalanine deuteration in Pseudomonas sp. compared to that in E. coli is due to the dilution effect of different pathways of phenylalanine biosynthesis in Pseudomonas sp. Collectively, we demonstrate that Raman-DIP can not only indicate metabolic activity using deuterated carbon sources but also reveal different metabolic pathways by analyzing SCRS. By harnessing such low-cost and versatile deuterated substrates, Raman-DIP has the potential to probe a wide range of metabolic pathways and functions at the single-cell level.