Raman Microscopic Analysis of Single Microbial Cells

Biofouling of reverse osmosis membranes: assessment by surface-enhanced Raman spectroscopy and microscopic imaging

The decline in the performance of spiral-wound reverse osmosis (SWRO) membranes is frequently due to biofouling. This study focus on qualitative and quantitative diagnosis of SWRO membrane biofouling. Bacterial counts on the different surfaces of the fouled membranes were carried out. Surface enhanced Raman spectroscopy (SERS) was performed to highlight clogging materials as well as their natures and identity. The topography of the fouled membranes and the structures of biofilms were visualized by fluorescence microscopy (FM) and scanning electron microscopy (SEM). The results indicated the presence of bacteria in the different SWRO membrane areas. Those strongly adhered were significantly higher than those weakly. It varied between 26 × 10⁵ and 262 × 10⁵ CFU m^-2. However, SERS mapping showed different fouling levels and the thickness of the fouling layer was 5 µm. Microscopic imaging revealed biotic and abiotic deposits. These data can together allow better management of the seawater desalination process.

In situ identification of environmental microorganisms with Raman spectroscopy

Microorganisms in natural environments are crucial in maintaining the material and energy cycle and the ecological balance of the environment. However, it is challenging to delineate environmental microbes' actual metabolic pathways and intraspecific heterogeneity because most microorganisms cannot be cultivated. Raman spectroscopy is a culture-independent technique that can collect molecular vibration profiles from cells. It can reveal the physiological and biochemical information at the single-cell level rapidly and non-destructively in situ. The first part of this review introduces the principles, advantages, progress, and analytical methods of Raman spectroscopy applied in environmental microbiology. The second part summarizes the applications of Raman spectroscopy combined with stable isotope probing (SIP), fluorescence in situ hybridization (FISH), Raman-activated cell sorting and genomic sequencing, and machine learning in microbiological studies. Finally, this review discusses expectations of Raman spectroscopy and future advances to be made in identifying microorganisms, especially for uncultured microorganisms.

Featuring few essential Raman spectroscopic signatures between heterogeneous cells

Here we demonstrate it is instructive to quantify cell Raman spectroscopy by sparse regularization. To be able to extract the specific spectral differences in a heterogeneous cell system with great spectroscopic similarities derived from many common biomolecular components, the maximum information entropy probability was proposed and exemplified by identifying normal lymphocytes from leukemia cells. The essential spectroscopic features were observed to locate at three Raman peaks whose spectral signatures were commensurate. The applicability of the extracted features was acknowledged by that the predicted identification accuracy of up to 93% was still achieved when only two peaks were loaded into decision tree model, which may provide the possibility of a clinically rapid hematological malignancy detection.

Clinical Perspective of Antimicrobial Resistance in Bacteria

Antimicrobial resistance (AMR) has become a global clinical problem in recent years. With the discovery of antibiotics, infections were not a deadly problem for clinicians as they used to be. However, worldwide AMR comes with the overuse/misuse of antibiotics and the spread of resistance is deteriorated by a multitude of mobile genetic elements and relevant resistant genes. This review provides an overview of the current situation, mechanism, epidemiology, detection methods and clinical treatment for antimicrobial resistant genes in clinical important bacteria including methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), penicillin-resistant Streptococcus pneumoniae (PRSP), extended-spectrum β-lactamase-producing Enterobacteriaceae, acquired AmpC β-lactamase-producing Enterobacteriaceae, carbapenemase-producing Enterobacteriaceae (CPE), multidrug-resistant (MDR) Acinetobacter baumannii and Pseudomonas aeruginosa.

A Review of Raman-Based Technologies for Bacterial Identification and Antimicrobial Susceptibility Testing

Antimicrobial resistance (AMR) is a global medical threat that seriously endangers human health. Rapid bacterial identification and antimicrobial susceptibility testing (AST) are key interventions to combat the spread and emergence of AMR. Although current clinical bacterial identification and AST provide comprehensive information, they are labor-intensive, complex, inaccurate, and slow (requiring several days, depending on the growth of pathogenic bacteria). Recently, Raman-based identification and AST technologies have played an increasingly important role in fighting AMR. This review summarizes major Raman-based techniques for bacterial identification and AST, including spontaneous Raman scattering, surface-enhanced Raman scattering (SERS), and coherent Raman scattering (CRS) imaging. Then, we discuss recent developments in rapid identification and AST methods based on Raman technology. Finally, we highlight the major challenges and potential future efforts to improve clinical outcomes through rapid bacterial identification and AST.

Label-Free Raman Microspectroscopy for Identifying Prokaryotic Virocells

Raman microspectroscopy has been used to thoroughly assess growth dynamics and heterogeneity of prokaryotic cells, yet little is known about how the chemistry of individual cells changes during infection with virulent viruses, resulting in so-called virocells. Here, we investigate biochemical changes of bacterial and archaeal cells of three different species in laboratory cultures before and after addition of their respective viruses using single-cell Raman microspectroscopy. By applying multivariate statistics, we identified significant differences in the spectra of single cells with/without addition of virulent dsRNA phage (phi6) for Pseudomonas syringae. A general ratio of wavenumbers that contributed the greatest differences in the recorded spectra was defined as an indicator for virocells. Based on reference spectra, this difference is likely attributable to an increase in nucleic acid versus protein ratio of virocells. This method also proved successful for identification of Bacillus subtilis cells infected with the double-stranded DNA (dsDNA) phage phi29, displaying a decrease in respective ratio, but failed for archaeal virocells (Methanosarcina mazei with the dsDNA methanosarcina spherical virus) due to autofluorescence. Multivariate and univariate analyses suggest that Raman spectral data of infected cells can also be used to explore the complex biology behind viral infections of bacteria. Using this method, we confirmed the previously described two-stage infection of P. syringae's phi6 and that infection of B. subtilis with phi29 results in a stress response within single cells. We conclude that Raman microspectroscopy is a promising tool for chemical identification of Gram-positive and Gram-negative virocells undergoing infection with virulent DNA or RNA viruses. IMPORTANCE Viruses are highly diverse biological entities shaping many ecosystems across Earth. However, understanding the infection of individual microbial cells and the related biochemical changes remains limited. Using Raman microspectroscopy in conjunction with univariate and multivariate statistics, we established a marker for identification of infected Gram-positive and Gram-negative bacteria. This nondestructive, label-free analytical method at single-cell resolution paves the way for future studies geared towards analyzing virus-host systems of prokaryotes to further understand the complex chemistry and function of virocells.

Classification and Identification of Archaea Using Single-Cell Raman Ejection and Artificial Intelligence: Implications for Investigating Uncultivated Microorganisms

Archaea can produce special cellular components such as polyhydroxyalkanoates, carotenoids, rhodopsin, and ether lipids, which have valuable applications in medicine and green energy production. Most of the archaeal species are uncultivated, posing challenges to investigating their biomarker components and biochemical properties. In this study, we applied Raman spectroscopy to examine the biological characteristics of nine archaeal isolates, including halophilic archaea (Haloferax larsenii, Haloarcula argentinensis, Haloferax mediterranei, Halomicrobium mukohataei, Halomicrobium salinus, Halorussus sp., Natrinema gari), thermophilic archaea (Sulfolobus acidocaldarius), and marine group I (MGI) archaea (Nitrosopumilus maritimus). Linear discriminant analysis of the Raman spectra allowed visualization of significant separations among the nine archaeal isolates. Machine-learning classification models based on support vector machine achieved accuracies of 88-100% when classifying the nine archaeal species. The predicted results were validated by DNA sequencing analysis of cells isolated from the mixture by Raman-activated cell sorting. Raman spectra of uncultured archaea (MGII) were also obtained based on Raman spectroscopy and fluorescence in situ hybridization. The results combining multiple Raman-based techniques indicated that MGII may have the ability to produce lipids distinct from other archaeal species. Our study provides a valuable approach for investigating and classifying archaea, especially uncultured species, at the single-cell level.

Direct monitoring of single-cell response to biomaterials by Raman spectroscopy

There is continued focus on the development of new biomaterials and associated biological testing methods needed to reduce the time taken for their entry to clinical use. The application of Raman spectroscopy to the study of individual cells that have been in contact with biomaterials offers enhanced in vitro information in a potentially non-destructive testing regime. The work presented here reports the Raman spectral analysis of discreet U-2 OS bone cells after exposure to hydroxyapatite (HA) coated titanium (Ti) substrates in both the as-deposited and thermally annealed states. These data show that cells that were in contact with the bioactive HA surface for 7 days had spectral markers similar to those cultured on the Ti substrate control for the same period. However, the spectral features for those cells that were in contact with the annealed HA surface had indicators of significant differentiation at day 21 while cells on the as-deposited surface did not show these Raman changes until day 28. The cells adhered to pristine Ti control surface showed no spectral changes at any of the timepoints studied. The validity of these spectroscopic results has been confirmed using data from standard in vitro cell viability, adhesion, and proliferation assays over the same 28-day culture period. In this case, cell maturation was evidenced by the formation of natural bone apatite, which precipitated intracellularly for cells exposed to both types of HA-coated Ti at 21 and 28 days, respectively. The properties of the intracellular apatite were markedly different from that of the synthetic HA used to coat the Ti substrate with an average particle size of 230 nm, a crystalline-like shape and Ca/P ratio of 1.63 ± 0.5 as determined by SEM-EDX analysis. By comparison, the synthetic HA particles used as a control had an average size of 372 nm and were more-rounded in shape with a Ca/P ratio of 0.8 by XPS analysis and 1.28 by SEM-EDX analysis. This study shows that Raman spectroscopy can be employed to monitor single U-2 OS cell response to biomaterials that promote cell maturation towards de novo bone thereby offering a label-free in vitro testing method that allows for non-destructive analyses.

Single-Cell Technologies to Study Phenotypic Heterogeneity and Bacterial Persisters

Antibiotic persistence is a phenomenon in which rare cells of a clonal bacterial population can survive antibiotic doses that kill their kin, even though the entire population is genetically susceptible. With antibiotic treatment failure on the rise, there is growing interest in understanding the molecular mechanisms underlying bacterial phenotypic heterogeneity and antibiotic persistence. However, elucidating these rare cell states can be technically challenging. The advent of single-cell techniques has enabled us to observe and quantitatively investigate individual cells in complex, phenotypically heterogeneous populations. In this review, we will discuss current technologies for studying persister phenotypes, including fluorescent tags and biosensors used to elucidate cellular processes; advances in flow cytometry, mass spectrometry, Raman spectroscopy, and microfluidics that contribute high-throughput and high-content information; and next-generation sequencing for powerful insights into genetic and transcriptomic programs. We will further discuss existing knowledge gaps, cutting-edge technologies that can address them, and how advances in single-cell microbiology can potentially improve infectious disease treatment outcomes.

Using Stable Isotope Probing and Raman Microspectroscopy To Measure Growth Rates of Heterotrophic Bacteria

The suitability of stable isotope probing (SIP) and Raman microspectroscopy to measure growth rates of heterotrophic bacteria at the single-cell level was evaluated. Label assimilation into Escherichia coli biomass during growth on a complex ¹³C-labeled carbon source was monitored in time course experiments. ¹³C incorporation into various biomolecules was measured by spectral "red shifts" of Raman-scattered emissions. The ¹³C- and ¹²C-isotopologues of the amino acid phenylalanine (Phe) proved to be quantitatively accurate reporter molecules of cellular isotopic fractional abundances (f_cell). Values of f_cell determined by Raman microspectroscopy and independently by isotope ratio mass spectrometry (IRMS) over a range of isotopic enrichments were statistically indistinguishable. Progressive labeling of Phe in E. coli cells among a range of ¹³C/¹²C organic substrate admixtures occurred predictably through time. The relative isotopologue abundances of Phe determined by Raman spectral analysis enabled the accurate calculation of bacterial growth rates as confirmed independently by optical density (OD) measurements. The results demonstrate that combining SIP and Raman microspectroscopy can be a powerful tool for studying bacterial growth at the single-cell level on defined or complex organic ¹³C carbon sources, even in mixed microbial assemblages. IMPORTANCE Population growth dynamics and individual cell growth rates are the ultimate expressions of a microorganism's fitness under its environmental conditions, whether natural or engineered. Natural habitats and many industrial settings harbor complex microbial assemblages. Their heterogeneity in growth responses to existing and changing conditions is often difficult to grasp by standard methodologies. In this proof-of-concept study, we tested whether Raman microspectroscopy can reliably quantify the assimilation of isotopically labeled nutrients into E. coli cells and enable the determination of individual growth rates among heterotrophic bacteria. Raman-derived growth rate estimates were statistically indistinguishable from those derived by standard optical density measurements of the same cultures. Raman microspectroscopy can also be combined with methods for phylogenetic identification. We report the development of Raman-based techniques that enable researchers to directly link genetic identity to functional traits and rate measurements of single cells within mixed microbial assemblages, currently a major technical challenge in microbiological research.

Prospects for multi-omics in the microbial ecology of water engineering

Advances in high-throughput sequencing technologies and bioinformatics approaches over almost the last three decades have substantially increased our ability to explore microorganisms and their functions - including those that have yet to be cultivated in pure isolation. Genome-resolved metagenomic approaches have enabled linking powerful functional predictions to specific taxonomical groups with increasing fidelity. Additionally, related developments in both whole community gene expression surveys and metabolite profiling have permitted for direct surveys of community-scale functions in specific environmental settings. These advances have allowed for a shift in microbiome science away from descriptive studies and towards mechanistic and predictive frameworks for designing and harnessing microbial communities for desired beneficial outcomes. Water engineers, microbiologists, and microbial ecologists studying activated sludge, anaerobic digestion, and drinking water distribution systems have applied various (meta)omics techniques for connecting microbial community dynamics and physiologies to overall process parameters and system performance. However, the rapid pace at which new omics-based approaches are developed can appear daunting to those looking to apply these state-of-the-art practices for the first time. Here, we review how modern genome-resolved metagenomic approaches have been applied to a variety of water engineering applications from lab-scale bioreactors to full-scale systems. We describe integrated omics analysis across engineered water systems and the foundations for pairing these insights with modeling approaches. Lastly, we summarize emerging omics-based technologies that we believe will be powerful tools for water engineering applications. Overall, we provide a framework for microbial ecologists specializing in water engineering to apply cutting-edge omics approaches to their research questions to achieve novel functional insights. Successful adoption of predictive frameworks in engineered water systems could enable more economically and environmentally sustainable bioprocesses as demand for water and energy resources increases.

Raman Stable Isotope Probing of Bacteria in Visible and Deep UV-Ranges

Raman stable isotope probing (Raman-SIP) is an excellent technique that can be used to access the overall metabolism of microorganisms. Recent studies have mainly used an excitation wavelength in the visible range to characterize isotopically labeled bacteria. In this work, we used UV resonance Raman spectroscopy (UVRR) to evaluate the spectral red-shifts caused by the uptake of isotopes (¹³C, ¹⁵N, ²H(D) and ¹⁸O) in E. coli cells. Moreover, we present a new approach based on the extraction of labeled DNA in combination with UVRR to identify metabolically active cells. The proof-of-principle study on E. coli revealed heterogeneities in the Raman features of both the bacterial cells and the extracted DNA after labeling with ¹³C, ¹⁵N, and D. The wavelength of choice for studying ¹⁸O- and deuterium-labeled cells is 532 nm is, while ¹³C-labeled cells can be investigated with visible and deep UV wavelengths. However, ¹⁵N-labeled cells are best studied at the excitation wavelength of 244 nm since nucleic acids are in resonance at this wavelength. These results highlight the potential of the presented approach to identify active bacterial cells. This work can serve as a basis for the development of new techniques for the rapid and efficient detection of active bacteria cells without the need for a cultivation step.

Resonance Raman Spectra for the In Situ Identification of Bacteria Strains and Their Inactivation Mechanism

The resonance Raman spectra of bacterial carotenoids have been employed to identify bacterial strains and their intensity changes as a function of ultraviolet (UV) radiation dose have been used to differentiate between live and dead bacteria. In addition, the resonance-enhanced Raman spectra enabled us to detect bacteria in water at much lower concentrations (∼10⁸ cells/mL) than normally detected spectroscopically. A handheld spectrometer capable of recording resonance Raman spectra in situ was designed, constructed, and was used to record the spectra. In addition to bacteria, the method presented in this paper may also be used to identify fungi, viruses, and plants, in situ, and detect infections within a very short period of time.

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.

In Vitro Anticancer Drug Sensitivity Sensing through Single-Cell Raman Spectroscopy

Traditional in vitro anticancer drug sensitivity testing at the population level suffers from lengthy procedures and high false positive rates. To overcome these defects, we built a confocal Raman microscopy sensing system and proposed a single-cell approach via Raman-deuterium isotope probing (Raman-DIP) as a rapid and reliable in vitro drug efficacy evaluation method. Raman-DIP detected the incorporation of deuterium into the cell, which correlated with the metabolic activity of the cell. The human non-small cell lung cancer cell line HCC827 and human breast cancer cell line MCF-7 were tested against eight different anticancer drugs. The metabolic activity of cancer cells could be detected as early as 12 h, independent of cell growth. Incubation of cells in 30% heavy water (D₂O) did not show any negative effect on cell viability. Compared with traditional methods, Raman-DIP could accurately determine the drug effect, meanwhile, it could reduce the testing period from 72–144 h to 48 h. Moreover, the heterogeneity of cells responding to anticancer drugs was observed at the single-cell level. This proof-of-concept study demonstrated the potential of Raman-DIP to be a reliable tool for cancer drug discovery and drug susceptibility testing.

Low Global Diversity of Candidatus Microthrix, a Troublesome Filamentous Organism in Full-Scale WWTPs

Candidatus Microthrix is one of the most common bulking filamentous microorganisms found in activated sludge wastewater treatment plants (WWTPs) across the globe. One species, Ca. M. parvicella, is frequently observed, but global genus diversity, as well as important aspects of its ecology and physiology, are still unknown. Here, we use the MiDAS ecosystem-specific 16S rRNA gene database in combination with amplicon sequencing of Danish and global WWTPs to investigate Ca. Microthrix spp. diversity, distribution, and factors affecting their global presence. Only two species were abundant across the world confirming low diversity of the genus: the dominant Ca. M. parvicella and an unknown species typically present along with Ca. M. parvicella, although usually in lower abundances. Both species were mostly found in Europe at low-to-moderate temperatures and their growth was favored in municipal WWTPs with advanced process designs. As no isolate is available for the novel species, we propose the name "Candidatus Microthrix subdominans." Ten high-quality metagenome-assembled genomes recovered from Danish WWTPs, including 6 representing the novel Ca. M. subdominans, demonstrated high genetic similarity between the two species with a likely preference for lipids, a putative capability to reduce nitrate and nitrite, and the potential to store lipids and poly-P. Ca. M. subdominans had a potentially more versatile metabolism including additional sugar transporters, higher oxygen tolerance, and the potential to use carbon monoxide as energy source. Newly designed fluorescence in situ hybridization probes revealed similar filamentous morphology for both species. Raman microspectroscopy was used to quantify the in situ levels of intracellular poly-P. Despite the observed similarities in their physiology (both by genomes and in situ), the two species showed different seasonal dynamics in Danish WWTPs through a 13-years survey, possibly indicating occupation of slightly different niches. The genomic information provides the basis for future research into in situ gene expression and regulation, while the new FISH probes provide a useful tool for further characterization in situ. This study is an important step toward understanding the ecology of Ca. Microthrix in WWTPs, which may eventually lead to optimization of control strategies for its growth in this ecosystem.

Raman 18 O-labeling of bacteria in visible and deep UV-ranges

Raman stable isotope labeling with ² H, ¹³ C or ¹⁵ N has been reported as an elegant approach to investigate cellular metabolic activity, which is of great importance to reveal the functions of microorganisms in native environments. A new strategy termed Raman ¹⁸ O-labeling was developed to probe the metabolic activity of bacteria. Raman ¹⁸ O-labeling refers to the combination of Raman microspectroscopy with ¹⁸ O-labeling using H₂¹⁸ O. At an excitation wavelength of 532 nm, the incorporation of ¹⁸ O into the amide I group of proteins and DNA/RNA bases was observed in Escherichia coli cells, while for an excitation wavelength electronically resonant with DNA or aromatic amino acid absorption at 244 nm ¹⁸ O assimilation was detected using chemometric tools rather than visual inspection. Raman ¹⁸ O-labeling at 532 nm combined with 2D correlation analysis confirmed the assimilation of ¹⁸ O in proteins and nucleic acids and revealed the growth strategy of E. coli cells; they underwent protein synthesis followed by nucleic acid synthesis. Independent cultural replicates at different incubation times corroborated the reproducibility of these results. The variations in spectral features of ¹⁸ O-labeled cells revealed changes in physiological information of cells. Hence, Raman ¹⁸ O-labeling could provide a powerful tool to identify metabolically active bacterial cells.

One-Cell Metabolic Phenotyping and Sequencing of Soil Microbiome by Raman-Activated Gravity-Driven Encapsulation (RAGE)

Soil harbors arguably the most metabolically and genetically heterogeneous microbiomes on Earth, yet establishing the link between metabolic functions and genome at the precisely one-cell level has been difficult. Here, for mock microbial communities and then for soil microbiota, we established a Raman-activated gravity-driven single-cell encapsulation and sequencing (RAGE-Seq) platform, which identifies, sorts, and sequences precisely one bacterial cell via its anabolic (incorporating D from heavy water) and physiological (carotenoid-containing) functions. We showed that (i) metabolically active cells from numerically rare soil taxa, such as Corynebacterium spp., Clostridium spp., Moraxella spp., Pantoea spp., and Pseudomonas spp., can be readily identified and sorted based on D₂O uptake, and their one-cell genome coverage can reach ∼93% to allow high-quality genome-wide metabolic reconstruction; (ii) similarly, carotenoid-containing cells such as Pantoea spp., Legionella spp., Massilia spp., Pseudomonas spp., and Pedobacter spp. were identified and one-cell genomes were generated for tracing the carotenoid-synthetic pathways; and (iii) carotenoid-producing cells can be either metabolically active or inert, suggesting culture-based approaches can miss many such cells. As a Raman-activated cell sorter (RACS) family member that can establish a metabolism-genome link at exactly one-cell resolution from soil, RAGE-Seq can help to precisely pinpoint “who is doing what” in complex ecosystems.

IMPORTANCE Soil is home to an enormous and complex microbiome that features arguably the highest genomic diversity and metabolic heterogeneity of cells on Earth. Their in situ metabolic activities drive many natural processes of pivotal ecological significance or underlie industrial production of numerous valuable bioactivities. However, pinpointing “who is doing what” in a soil microbiome, which consists of mainly yet-to-be-cultured species, has remained a major challenge. Here, for soil microbiota, we established a Raman-activated gravity-driven single-cell encapsulation and sequencing (RAGE-Seq) method, which identifies, sorts, and sequences at the resolution of precisely one microbial cell via its catabolic and anabolic functions. As a Raman-activated cell sorter (RACS) family member that can establish a metabolism-genome link at one-cell resolution from soil, RAGE-Seq can help to precisely pinpoint “who is doing what” in complex ecosystems.

Campylobacter jejuni Antimicrobial Resistance Profiles and Mechanisms Determined Using a Raman Spectroscopy-Based Metabolomic Approach

Rapid identification of antimicrobial resistance (AMR) profiles and mechanisms is critical for clinical management and drug development. However, the current AMR detection approaches take up to 48 h to obtain a result. Here, we demonstrate a Raman spectroscopy-based metabolomic approach to rapidly determine the AMR profile of Campylobacter jejuni, a major cause of foodborne gastroenteritis worldwide. C. jejuni isolates with susceptible and resistant traits to ampicillin and tetracycline were subjected to different antibiotic concentrations for 5 h, followed by Raman spectral collection and chemometric analysis (i.e., second-derivative transformation analysis, hierarchical clustering analysis [HCA], and principal-component analysis [PCA]). The MICs obtained by Raman-2nd derivative transformation agreed with the reference agar dilution method for all isolates. The AMR profile of C. jejuni was accurately classified by Raman-HCA after treating bacteria with antibiotics at clinical susceptible and resistant breakpoints. According to PCA loading plots, susceptible and resistant strains showed different Raman metabolomic patterns for antibiotics. Ampicillin-resistant isolates had distinctive Raman signatures of peptidoglycan, which is related to cell wall synthesis. The ratio of saturated to unsaturated fatty acids in the lipid membrane layer of ampicillin-resistant isolates was higher than in susceptible ones, indicating more rigid envelope structure under ampicillin treatment. In comparison, tetracycline-resistant isolates exhibited prominent Raman spectral features associated with proteins and nucleic acids, demonstrating more active protein synthesis than susceptible strains with the presence of tetracycline. Taken together, Raman spectroscopy is a powerful metabolic fingerprinting technique for simultaneously revealing the AMR profiles and mechanisms of foodborne pathogens. IMPORTANCE Metabolism plays the central role in bacteria to mediate the early response against antibiotics and demonstrate antimicrobial resistance (AMR). Understanding the whole-cell metabolite profiles gives rise to a more complete AMR mechanism insight. In this study, we have applied Raman spectroscopy and chemometrics to achieve a rapid, accurate, and easy-to-operate investigation of bacterial AMR profiles and mechanisms. Raman spectroscopy reduced the analysis time by an order of magnitude to obtain the same results achieved through traditional culture-based antimicrobial susceptibility approaches. It offers great benefits as a high-throughput screening method in food chain surveillance and clinical diagnostics. Meanwhile, the AMR mechanisms toward two representative antibiotic classes, namely, ampicillin and tetracycline, were revealed by Raman spectroscopy at the metabolome level. This approach is based on bacterial phenotypic responses to antibiotics, providing information complementary to that obtained by conventional genetic methods such as genome sequencing. The knowledge obtained from Raman metabolomic data can be used in drug discovery and pathogen intervention.

Rapid identification of pathogens by using surface-enhanced Raman spectroscopy and multi-scale convolutional neural network

Salmonella is a prevalent pathogen causing serious morbidity and mortality worldwide. There are over 2600 serovars of Salmonella. Among them, Salmonella Enteritidis, Salmonella Typhimurium, and Salmonella Paratyphi were reported to be the most common foodborne pathogenic serovars in the EU and China. In order to provide a more efficient approach to detect and distinguish these serovars, a new analytical method was developed by combining surface-enhanced Raman spectroscopy (SERS) with multi-scale convolutional neural network (CNN). We prepared 34-nm gold nanoparticles (AuNPs) as the label-free Raman substrate, measured 1854 SERS spectra of these three Salmonella serovars, and then proposed a multi-scale CNN model with three parallel CNNs to achieve multi-dimensional extraction of SERS spectral features. We observed the impact of the number of iterations and training samples on the recognition accuracy by changing the ratio of the number of the training and testing sets. By comparing the calculated data with experimental one, it was shown that our model could reach recognition accuracy more than 97%. These results indicate that it was not only feasible to combine SERS spectroscopy with multi-scale CNN for Salmonella serotype identification, but also for other pathogen species and serovar identifications.

Deriving accurate molecular indicators of protein synthesis through Raman-based sparse classification

Raman spectroscopy has the ability to retrieve molecular information from live biological samples non-invasively through optical means. Coupled with machine learning, it is possible to use this large amount of information to create models that can predict the state of new samples. We study here linear models, whose separation coefficients can be used to interpret which bands are contributing to the discrimination, and compare the performance of principal component analysis coupled with linear discriminant analysis (PCA/LDA), with regularized logistic regression (Lasso). By applying these methods to single-cell measurements for the detection of macrophage activation, we found that PCA/LDA yields poorer performance in classification compared to Lasso, and underestimates the required sample size to reach stable models. Direct use of Lasso (without PCA) also yields more stable models, and provides sparse separation vectors that directly contain the Raman bands most relevant to classification. To further evaluate these sparse vectors, we apply Lasso to a well-defined case where protein synthesis is inhibited, and show that the separating features are consistent with RNA accumulation and protein levels depletion. Surprisingly, when features are selected purely in terms of their classification power (Lasso), they consist mostly of side bands, while typical strong Raman peaks are not present in the discrimination vector. We propose that this occurs because large Raman bands are representative of a wide variety of intracellular molecules and are therefore less suited for accurate classification.

Genome engineering of Nannochloropsis with hundred-kilobase fragment deletions by Cas9 cleavages

Industrial microalgae are promising photosynthetic cell factories, yet tools for large-scale targeted genome engineering are limited. Here for the model industrial oleaginous microalga Nannochloropsis oceanica, we established a method to precisely and serially delete large genome fragments of ~100 kb from its 30.01 Mb nuclear genome. We started by identifying the 'non-essential' chromosomal regions (i.e. low expression region or LER) based on minimal gene expression under N-replete and N-depleted conditions. The largest such LER (LER1) is ~98 kb in size, located near the telomere of the 502.09-kb-long Chromosome 30 (Chr 30). We deleted 81 kb and further distal and proximal deletions of up to 110 kb (21.9% of Chr 30) in LER1 by dual targeting the boundaries with the episome-based CRISPR/Cas9 system. The telomere-deletion mutants showed normal telomeres consisting of CCCTAA repeats, revealing telomere regeneration capability after losing the distal part of Chr 30. Interestingly, the deletions caused no significant alteration in growth, lipid production or photosynthesis (transcript-abundance change for < 3% genes under N depletion). We also achieved double-deletion of both LER1 and LER2 (from Chr 9) that total ~214 kb at maximum, which can result in slightly higher growth rate and biomass productivity than the wild-type. Therefore, loss of the large, yet 'non-essential' regions does not necessarily sacrifice important traits. Such serial targeted deletions of large genomic regions had not been previously reported in microalgae, and will accelerate crafting minimal genomes as chassis for photosynthetic production.

Unveiling Cancer Metabolism through Spontaneous and Coherent Raman Spectroscopy and Stable Isotope Probing

Metabolic reprogramming is a common hallmark in cancer. The high complexity and heterogeneity in cancer render it challenging for scientists to study cancer metabolism. Despite the recent advances in single-cell metabolomics based on mass spectrometry, the analysis of metabolites is still a destructive process, thus limiting in vivo investigations. Being label-free and nonperturbative, Raman spectroscopy offers intrinsic information for elucidating active biochemical processes at subcellular level. This review summarizes recent applications of Raman-based techniques, including spontaneous Raman spectroscopy and imaging, coherent Raman imaging, and Raman-stable isotope probing, in contribution to the molecular understanding of the complex biological processes in the disease. In addition, this review discusses possible future directions of Raman-based technologies in cancer research.

Opening up the Single-Cell Toolbox for Microbial Natural Products Research

The diverse microbes that produce natural products represent an important source of novel therapeutics, drug leads, and scientific tools. However, the vast majority have not been grown in axenic culture and are members of complex communities. While meta-'omic methods such as metagenomics, -transcriptomics, and -proteomics reveal collective molecular features of this "microbial dark matter", the study of individual microbiome members can be challenging. To address these limits, a number of techniques with single-bacterial resolution have been developed in the last decade and a half. While several of these are embraced by microbial ecologists, there has been less use by researchers interested in mining microbes for natural products. In this review, we discuss the available and emerging techniques for targeted single-cell analysis with a particular focus on applications to the discovery and study of natural products.

Raman spectroscopy combined with machine learning for rapid detection of food-borne pathogens at the single-cell level

Rapid detection of food-borne pathogens in early food contamination is a permanent topic to ensure food safety and prevent public health problems. Raman spectroscopy, a label-free, highly sensitive and dependable technology has attracted more and more attention in the field of diagnosing food-borne pathogens in recent years. In the research, 15,890 single-cell Raman spectra of 23 common strains from 7 genera were obtained at the single cell level. Then, the nonlinear features of raw data were extracted by kernel principal component analysis, and the individual bacterial cell was evaluated and discriminated at the serotype level through the decision tree algorithm. The results demonstrated that the average correct rate of prediction on independent test set was 86.23 ± 0.92% when all strains were recognized by only one model, but there were high misjudgment rates for certain strains. Therefore, the four-level classification models were introduced, and the different hierarchies of the identification models achieved accuracies in the range of 87.1%-95.8%, which realized the efficient prediction of strains at the serotype level. In summary, Raman spectroscopy combined with machine learning based on fingerprint difference was a prospective strategy for the rapid diagnosis of pathogenic bacteria.

Development overview of Raman-activated cell sorting devoted to bacterial detection at single-cell level

Understanding the metabolic interactions between bacteria in natural habitat at the single-cell level and the contribution of individual cell to their functions is essential for exploring the dark matter of uncultured bacteria. The combination of Raman-activated cell sorting (RACS) and single-cell Raman spectra (SCRS) with unique fingerprint characteristics makes it possible for research in the field of microbiology to enter the single cell era. This review presents an overview of current knowledge about the research progress of recognition and assessment of single bacterium cell based on RACS and further research perspectives. We first systematically summarize the label-free and non-destructive RACS strategies based on microfluidics, microdroplets, optical tweezers, and specially made substrates. The importance of RACS platforms in linking target cell genotype and phenotype is highlighted and the approaches mentioned in this paper for distinguishing single-cell phenotype include surface-enhanced Raman scattering (SERS), biomarkers, stable isotope probing (SIP), and machine learning. Finally, the prospects and challenges of RACS in exploring the world of unknown microorganisms are discussed. KEY POINTS: • Analysis of single bacteria is essential for further understanding of the microbiological world. • Raman-activated cell sorting (RACS) systems are significant protocol for characterizing phenotypes and genotypes of individual bacteria.

Deuterium-labeled Raman tracking of glucose accumulation and protein metabolic dynamics in Aspergillus nidulans hyphal tips

Filamentous fungi grow exclusively at their tips, where many growth-related fungal processes, such as enzyme secretion and invasion into host cells, take place. Hyphal tips are also a site of active metabolism. Understanding metabolic dynamics within the tip region is therefore important for biotechnology and medicine as well as for microbiology and ecology. However, methods that can track metabolic dynamics with sufficient spatial resolution and in a nondestructive manner are highly limited. Here we present time-lapse Raman imaging using a deuterium (D) tracer to study spatiotemporally varying metabolic activity within the hyphal tip of Aspergillus nidulans. By analyzing the carbon-deuterium (C-D) stretching Raman band with spectral deconvolution, we visualize glucose accumulation along the inner edge of the hyphal tip and synthesis of new proteins from the taken-up D-labeled glucose specifically at the central part of the apical region. Our results show that deuterium-labeled Raman imaging offers a broadly applicable platform for the study of metabolic dynamics in filamentous fungi and other relevant microorganisms in vivo.

Optofluidic Raman-activated cell sorting for targeted genome retrieval or cultivation of microbial cells with specific functions

Stable isotope labeling of microbial taxa of interest and their sorting provide an efficient and direct way to answer the question "who does what?" in complex microbial communities when coupled with fluorescence in situ hybridization or downstream 'omics' analyses. We have developed a platform for automated Raman-based sorting in which optical tweezers and microfluidics are used to sort individual cells of interest from microbial communities on the basis of their Raman spectra. This sorting of cells and their downstream DNA analysis, such as by mini-metagenomics or single-cell genomics, or cultivation permits a direct link to be made between the metabolic roles and the genomes of microbial cells within complex microbial communities, as well as targeted isolation of novel microbes with a specific physiology of interest. We describe a protocol from sample preparation through Raman-activated live cell sorting. Subsequent cultivation of sorted cells is described, whereas downstream DNA analysis involves well-established approaches with abundant methods available in the literature. Compared with manual sorting, this technique provides a substantially higher throughput (up to 500 cells per h). Furthermore, the platform has very high sorting accuracy (98.3 ± 1.7%) and is fully automated, thus avoiding user biases that might accompany manual sorting. We anticipate that this protocol will empower in particular environmental and host-associated microbiome research with a versatile tool to elucidate the metabolic contributions of microbial taxa within their complex communities. After a 1-d preparation of cells, sorting takes on the order of 4 h, depending on the number of cells required.

Detection of Penicillin G Produced by Penicillium chrysogenum with Raman Microspectroscopy and Multivariate Curve Resolution-Alternating Least-Squares Methods

Raman microspectroscopy is a minimally invasive technique that can identify molecules without labeling. In this study, we demonstrate the detection of penicillin G inside Penicillium chrysogenum KF425 fungal cells. Raman spectra acquired from the fungal cells had highly overlapped spectroscopic signatures and hence were analyzed with multivariate curve resolution by alternating least-squares (MCR-ALS) to extract the spectra of individual molecular constituents. In addition to detecting spatial distribution of multiple constituents such as proteins and lipids inside the fungal body, we could also observe the subcellular localization of penicillin G. This methodology has the potential to be employed in screening the production of bioactive compounds by microorganisms.

Transparent soil microcosms for live-cell imaging and non-destructive stable isotope probing of soil microorganisms

Microscale processes are critically important to soil ecology and biogeochemistry yet are difficult to study due to soil's opacity and complexity. To advance the study of soil processes, we constructed transparent soil microcosms that enable the visualization of microbes via fluorescence microscopy and the non-destructive measurement of microbial activity and carbon uptake in situ via Raman microspectroscopy. We assessed the polymer Nafion and the crystal cryolite as optically transparent soil substrates. We demonstrated that both substrates enable the growth, maintenance, and visualization of microbial cells in three dimensions over time, and are compatible with stable isotope probing using Raman. We applied this system to ascertain that after a dry-down/rewetting cycle, bacteria on and near dead fungal hyphae were more metabolically active than those far from hyphae. These data underscore the impact fungi have facilitating bacterial survival in fluctuating conditions and how these microcosms can yield insights into microscale microbial activities.

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 11.88 pM 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.

Rapid and sensitive identification of uropathogenic Escherichia coli using a surface-enhanced-Raman-scattering-based biochip

Rapid, selective and sensitive sensing of bacteria remains challenging. We report on a highly sensitive and reproducible surface-enhanced Raman spectroscopy (SERS)-based sensing approach for the detection of uropathogenic Escherichia coli (E. coli) bacteria in urine. The assay is based on the specific capture of the bacteria followed by interaction with cetyltrimethylammonium bromide (CTAB)-stabilised gold nanorods (Au NRS) as SERS markers. High sensitivity up to 10 CFU mL^-1 is achieved by optimizing the capture interface based on hydrogenated amorphous silicon a-Si:H thin films. The integration of CH₃O-PEG750 onto a-Si:H gives the sensing interface an efficient anti-fouling character, while covalent linkage of antibodies directed against the major type-1 fimbrial pilin FimA of the human pathogen E. coli results in the specific trapping of fimbriated E. coli onto the SERS substrate and their spectral fingerprint identification.

Rapid and label-free identification of different cancer types based on surface-enhanced Raman scattering profiles and multivariate statistical analysis

Rapid detection and classification of cancer cells with label-free and non-destructive methods are helpful for rapid screening of cancer patients in clinical settings. Here, surface-enhanced Raman scattering (SERS) was used for rapid, unlabeled, and non-destructive detection of seven different cell types, including human cancer cells and non-tumorous cells. Au nanoparticles were used as enhanced substrates and directly added to cell surfaces. The single cellular SERS signals could be easily and stably collected in several minutes, and the cells maintained structural integrity over one hour. Different types of cells had unique Raman phenotypes. By applying multivariate statistical analysis to the Raman phenotypes, the cancer cells and non-tumorous cells were accurately identified. The high sensitivity enabled this method to discriminate subtle molecular changes in different cell types, and the accuracy reached 81.2% with principal components analysis and linear discriminant analysis. The technique provided a rapid, unlabeled, and non-destructive method for the detection and identification of various cancer types.

Microbial single-cell omics: the crux of the matter

Abstract

Single-cell genomics and transcriptomics can provide reliable context for assembled genome fragments and gene expression activity on the level of individual prokaryotic genomes. These methods are rapidly emerging as an essential complement to cultivation-based, metagenomics, metatranscriptomics, and microbial community-focused research approaches by allowing direct access to information from individual microorganisms, even from deep-branching phylogenetic groups that currently lack cultured representatives. Their integration and binning with environmental ‘omics data already provides unprecedented insights into microbial diversity and metabolic potential, enabling us to provide information on individual organisms and the structure and dynamics of natural microbial populations in complex environments. This review highlights the pitfalls and recent advances in the field of single-cell omics and its importance in microbiological and biotechnological studies.

Key points

• Single-cell omics expands the tree of life through the discovery of novel organisms, genes, and metabolic pathways.

• Disadvantages of metagenome-assembled genomes are overcome by single-cell omics.

• Functional analysis of single cells explores the heterogeneity of gene expression.

• Technical challenges still limit this field, thus prompting new method developments.

Multiplex fatty acid imaging inside cells by Raman microscopy

Visualizing intracellular fatty acids (including free and esterified form) is very useful for understanding how and where such molecules are incorporated, stored, and metabolized within cells. However, techniques of imaging multiple intracellular fatty acids have been limited by their small size, making it difficult to label and track without changing their biological and biophysical characteristics. Here, we present a new method for simultaneously visualizing up to five atomically labeled intracellular fatty acid species. For this, we utilized the distinctive Raman spectra depending on the labeling patterns and created a new, extensible opensource software to perform by-pixel analysis of extracting original spectra from mixed ones. Our multiplex imaging method revealed that fatty acids with more double bonds tend to concentrate more efficiently at lipid droplets. This novel approach contributes to reveal not only the spatial dynamics of fatty acids, but also of any other metabolites inside cells.

Magnetic Nanomotor-Based Maneuverable SERS Probe

Surface-enhanced Raman spectroscopy (SERS) is a powerful sensing technique capable of capturing ultrasensitive fingerprint signal of analytes with extremely low concentration. However, conventional SERS probes are passive nanoparticles which are usually massively applied for biochemical sensing, lacking controllability and adaptability for precise and targeted sensing at a small scale. Herein, we report a "rod-like" magnetic nanomotor-based SERS probe (MNM-SP) that integrates a mobile and controllable platform of micro-/nanomotors with a SERS sensing technique. The "rod-like" structure is prepared by coating a thin layer of silica onto the self-assembled magnetic nanoparticles. Afterwards, SERS hotspots of silver nanoparticles (AgNPs) are decorated as detecting nanoprobes. The MNM-SPs can be navigated on-demand to avoid obstacles and target sensing sites by the guidance of an external gradient magnetic field. Through applying a rotating magnetic field, the MNM-SPs can actively rotate to efficiently stir and mix surrounding fluid and thus contact with analytes quickly for SERS sensing. Innovatively, we demonstrate the self-cleaning capability of the MNM-SPs which can be used to overcome the contamination problem of traditional single-use SERS probes. Furthermore, the MNM-SPs could precisely approach the targeted single cell and then enter into the cell by endocytosis. It is worth mentioning that by the effective mixing of intracellular biocomponents, much more informative Raman signals with improved signal-to-noise ratio can be captured after active rotation. Therefore, the demonstrated magnetically activated MNM-SPs that are endowed with SERS sensing capability pave way to the future development of smart sensing probes with maneuverability for biochemical analysis at the micro-/nanoscale.

Combination of an Artificial Intelligence Approach and Laser Tweezers Raman Spectroscopy for Microbial Identification

Raman spectroscopy is a nondestructive, label-free, highly specific approach that provides the chemical information on materials. Thus, it is suitable to be used as an effective analytical tool to characterize biological samples. Here we introduce a novel method that uses artificial intelligence to analyze biological Raman spectra and identify the microbes at a single-cell level. The combination of a framework of convolutional neural network (ConvNet) and Raman spectroscopy allows the extraction of the Raman spectral features of a single microbial cell and then categorizes cells according to their spectral features. As the proof of concept, we measured Raman spectra of 14 microbial species at a single-cell level and constructed an optimal ConvNet model using the Raman data. The average accuracy of classification by ConvNet is 95.64 ± 5.46%. Meanwhile, we introduced an occlusion-based Raman spectra feature extraction to visualize the weights of Raman features for distinguishing different species.

Chromosome-free bacterial cells are safe and programmable platforms for synthetic biology

A type of chromosome-free cell called SimCells (simple cells) has been generated from Escherichia coli, Pseudomonas putida, and Ralstonia eutropha. The removal of the native chromosomes of these bacteria was achieved by double-stranded breaks made by heterologous I-CeuI endonuclease and the degradation activity of endogenous nucleases. We have shown that the cellular machinery remained functional in these chromosome-free SimCells and was able to process various genetic circuits. This includes the glycolysis pathway (composed of 10 genes) and inducible genetic circuits. It was found that the glycolysis pathway significantly extended longevity of SimCells due to its ability to regenerate ATP and NADH/NADPH. The SimCells were able to continuously express synthetic genetic circuits for 10 d after chromosome removal. As a proof of principle, we demonstrated that SimCells can be used as a safe agent (as they cannot replicate) for bacterial therapy. SimCells were used to synthesize catechol (a potent anticancer drug) from salicylic acid to inhibit lung, brain, and soft-tissue cancer cells. SimCells represent a simplified synthetic biology chassis that can be programmed to manufacture and deliver products safely without interference from the host genome.

Rapid and accurate identification of marine microbes with single-cell Raman spectroscopy

Rapid and accurate identification of individual microorganisms, such as pathogenic or unculturable microbes, is significant in microbiology. In this work, rapid identification of marine microorganisms by single-cell Raman spectroscopy (scRS) using one-dimensional convolutional neural networks (1DCNN) was explored. Here, single-cell Raman spectra of ten species of marine actinomycetes, two species of non-marine actinomycetes and E. coli (as a reference) were individually collected. Several common classification algorithms in chemometrics, including linear discriminant analysis with principal component analysis and a support vector machine, were applied to evaluate the 1DCNN performance based on the raw and pre-processed Raman spectra. 1DCNN showed superior performance on the raw data in terms of its accuracy and recall rate compared with other classification algorithms. Our investigation demonstrated that the scRS-integrating advanced 1DCNN classification algorithm provided a rapid and accurate approach for identifying individual microorganisms without time-consuming cell culture and sophisticated or specific techniques, which could be a useful methodology for discriminating the microbes that cannot be cultured under normal conditions, especially for 'biological risk'-related emergencies.

Rapid detection of bacterial infection and viability assessment with high specificity and sensitivity using Raman microspectroscopy

Infectious diseases caused by bacteria still pose major diagnostic challenges in spite of the availability of various molecular approaches. Irrespective of the type of infection, rapid identification of the causative pathogen with a high degree of sensitivity and specificity is essential for initiating appropriate treatment. While existing methods like PCR possess high sensitivity, they are incapable of identifying the viability status of the pathogen and those which can, like culturing, are inherently slow. To overcome these limitations, we developed a diagnostic platform based on Raman microspectroscopy, capable of detecting biochemical signatures from a single bacterium for identification as well as viability assessment. The study also establishes a decontamination protocol for handling live pathogenic bacteria which does not affect identification and viability testing, showing applicability in the analysis of sputum samples containing pathogenic mycobacterial strains. The minimal sample processing along with multivariate analysis of spectroscopic signatures provides an interface for automatic classification, allowing the prediction of unknown samples by mapping signatures onto available datasets. Also, the novelty of the current work is the demonstration of simultaneous identification and viability assessment at a single bacterial level for pathogenic bacteria. Graphical abstract.

Next-generation physiology approaches to study microbiome function at single cell level

The function of cells in their native habitat often cannot be reliably predicted from genomic data or from physiology studies of isolates. Traditional experimental approaches to study the function of taxonomically and metabolically diverse microbiomes are limited by their destructive nature, low spatial resolution or low throughput. Recently developed technologies can offer new insights into cellular function in natural and human-made systems and how microorganisms interact with and shape the environments that they inhabit. In this Review, we provide an overview of these next-generation physiology approaches and discuss how the non-destructive analysis of cellular phenotypes, in combination with the separation of the target cells for downstream analyses, provide powerful new, complementary ways to study microbiome function. We anticipate that the widespread application of next-generation physiology approaches will transform the field of microbial ecology and dramatically improve our understanding of how microorganisms function in their native environment.

Rapid differentiation of Campylobacter jejuni cell wall mutants using Raman spectroscopy, SERS and mass spectrometry combined with chemometrics

SERS was developed for intercellular and intracellular analyses. Using a series of cell wall mutants in C. jejuni we show cell wall versus cytoplasm differences.

Discriminating Bacterial Phenotypes at the Population and Single-Cell Level: A Comparison of Flow Cytometry and Raman Spectroscopy Fingerprinting

Investigating phenotypic heterogeneity can help to better understand and manage microbial communities. However, characterizing phenotypic heterogeneity remains a challenge, as there is no standardized analysis framework. Several optical tools are available, such as flow cytometry and Raman spectroscopy, which describe optical properties of the individual cell. In this work, we compare Raman spectroscopy and flow cytometry to study phenotypic heterogeneity in bacterial populations. The growth stages of three replicate Escherichia coli populations were characterized using both technologies. Our findings show that flow cytometry detects and quantifies shifts in phenotypic heterogeneity at the population level due to its high-throughput nature. Raman spectroscopy, on the other hand, offers a much higher resolution at the single-cell level (i.e., more biochemical information is recorded). Therefore, it can identify distinct phenotypic populations when coupled with analyses tailored toward single-cell data. In addition, it provides information about biomolecules that are present, which can be linked to cell functionality. We propose a computational workflow to distinguish between bacterial phenotypic populations using Raman spectroscopy and validated this approach with an external data set. We recommend using flow cytometry to quantify phenotypic heterogeneity at the population level, and Raman spectroscopy to perform a more in-depth analysis of heterogeneity at the single-cell level. © 2019 International Society for Advancement of Cytometry.

Proteorhodopsin Overproduction Enhances the Long-Term Viability of Escherichia coli

Proteorhodopsin (PR) is part of a diverse, abundant, and widespread superfamily of photoreactive proteins, the microbial rhodopsins. PR, a light-driven proton pump, enhances the ability of the marine bacterium Vibrio strain AND4 to survive and recover from periods of starvation, and heterologously produced PR extends the viability of nutrient-limited Shewanella oneidensis. We show that heterologously produced PR enhances the viability of E. coli cultures over long periods of several weeks and use single-cell Raman spectroscopy (SCRS) to detect PR in 9-month-old cells. We identify a densely packed and consequently stabilized cell membrane as the likely basis for extended viability. Similar considerations are suggested to apply to marine bacteria, for which high PR levels represent a significant investment in scarce metabolic resources. PR-stabilized cell membranes in marine bacteria are proposed to keep a population viable during extended periods of light or nutrient limitation, until conditions improve.

Genes encoding the photoreactive protein proteorhodopsin (PR) have been found in a wide range of marine bacterial species, reflecting the significant contribution that PR makes to energy flux and carbon cycling in ocean ecosystems. PR can also confer advantages to enhance the ability of marine bacteria to survive periods of starvation. Here, we investigate the effect of heterologously produced PR on the viability of Escherichia coli. Quantitative mass spectrometry shows that E. coli, exogenously supplied with the retinal cofactor, assembles as many as 187,000 holo-PR molecules per cell, accounting for approximately 47% of the membrane area; even cells with no retinal synthesize ∼148,000 apo-PR molecules per cell. We show that populations of E. coli cells containing PR exhibit significantly extended viability over many weeks, and we use single-cell Raman spectroscopy (SCRS) to detect holo-PR in 9-month-old cells. SCRS shows that such cells, even incubated in the dark and therefore with inactive PR, maintain cellular levels of DNA and RNA and avoid deterioration of the cytoplasmic membrane, a likely basis for extended viability. The substantial proportion of the E. coli membrane required to accommodate high levels of PR likely fosters extensive intermolecular contacts, suggested to physically stabilize the cell membrane and impart a long-term benefit manifested as extended viability in the dark. We propose that marine bacteria could benefit similarly from a high PR content, with a stabilized cell membrane extending survival when those bacteria experience periods of severe nutrient or light limitation in the oceans.

IMPORTANCE Proteorhodopsin (PR) is part of a diverse, abundant, and widespread superfamily of photoreactive proteins, the microbial rhodopsins. PR, a light-driven proton pump, enhances the ability of the marine bacterium Vibrio strain AND4 to survive and recover from periods of starvation, and heterologously produced PR extends the viability of nutrient-limited Shewanella oneidensis. We show that heterologously produced PR enhances the viability of E. coli cultures over long periods of several weeks and use single-cell Raman spectroscopy (SCRS) to detect PR in 9-month-old cells. We identify a densely packed and consequently stabilized cell membrane as the likely basis for extended viability. Similar considerations are suggested to apply to marine bacteria, for which high PR levels represent a significant investment in scarce metabolic resources. PR-stabilized cell membranes in marine bacteria are proposed to keep a population viable during extended periods of light or nutrient limitation, until conditions improve.

Revealing the Metabolic Activity of Persisters in Mycobacteria by Single-Cell D2O Raman Imaging Spectroscopy

The metabolic activity of bacterial cells largely differentiates even within a clonal population. Such metabolic divergence among cells is thought to play an important role for phenotypic adaptation to ever-changing environmental conditions, such as antibiotic persistence. It has long been thought that persisters are in a state called dormancy, in which cells are metabolically inactive and do not grow. However, recent studies suggest that some types of persisters are not necessarily dormant, triggering a debate about the mechanisms of persisters. Here, we combined single-cell Raman imaging spectroscopy and D₂O labeling to analyze metabolic activities of bacterial persister cells. Metabolically active cells uptake deuterium through metabolic processes and give distinct C-D Raman bands, which are direct indicators of metabolic activity. Using this imaging method, we characterized the metabolic activity of Mycobacterium smegmatis, a fast-growing model for Mycobacterium tuberculosis. We found that persister cells of M. smegmatis show certain metabolic activity and active cell growth in the presence of the antibiotic rifampicin. Interestingly, persistence is not correlated with growth rate prior to antibiotic exposure. These results show that dormancy is not responsible for the persistence of M. smegmatis cells against rifampicin, suggesting that the mechanism of persistence largely varies depending on the type of antibiotics and bacteria. Our results successfully demonstrate the potential of our perfusion-based single-cell D₂O Raman imaging system for the analysis of the metabolic activity and growth of bacterial persister cells.