Raman-activated sorting of antibiotic-resistant bacteria in human gut microbiota

Starting with screening strains to construct synthetic microbial communities (SynComs) for traditional food fermentation

With the elucidation of community structures and assembly mechanisms in various fermented foods, core communities that significantly influence or guide fermentation have been pinpointed and used for exogenous restructuring into synthetic microbial communities (SynComs). These SynComs simulate ecological systems or function as adjuncts or substitutes in starters, and their efficacy has been widely verified. However, screening and assembly are still the main limiting factors for implementing theoretic SynComs, as desired strains cannot be effectively obtained and integrated. To expand strain screening methods suitable for SynComs in food fermentation, this review summarizes the recent research trends in using SynComs to study community evolution or interaction and improve the quality of food fermentation, as well as the specific process of constructing synthetic communities. The potential for novel screening modalities based on genes, enzymes and metabolites in food microbial screening is discussed, along with the emphasis on strategies to optimize assembly for facilitating the development of synthetic communities.

Nanoplasmonic sensors for extracellular vesicles and bacterial membrane vesicles

Extracellular vesicles (EVs) are promising tools for the early diagnosis of diseases, and bacterial membrane vesicles (MVs) are especially important in health and environment monitoring. However, detecting EVs or bacterial MVs presents significant challenges for the clinical translation of EV-based diagnostics. In this Review, we provide a comprehensive discussion on the basics of nanoplasmonic sensing and emphasize recent developments in nanoplasmonics-based optical sensors to effectively identify EVs or bacterial MVs. We explore various nanoplasmonic sensors tailored for EV or bacterial MV detection, emphasizing the application of localized surface plasmon resonance through gold nanoparticles and their multimers. Additionally, we highlight advanced EV detection techniques based on surface plasmon polaritons using plasmonic thin film and nanopatterned structures. Furthermore, we evaluate the improved detection capability of surface-enhanced Raman spectroscopy in identifying and classifying these vesicles, aided by plasmonic nanostructures. Nanoplasmonic sensing techniques have remarkable precision and sensitivity, making them a potential tool for accurate EV detection in clinical applications, facilitating point-of-care molecular diagnostics. Finally, we summarize the challenges associated with nanoplasmonic EV or bacterial MV sensors and offer insights into potential future directions for this evolving field.

Raman-Activated Cell Ejection for Validating the Reliability of the Raman Fingerprint Database of Foodborne Pathogens

Raman spectroscopy for rapid identification of foodborne pathogens based on phenotype has attracted increasing attention, and the reliability of the Raman fingerprint database through genotypic determination is crucial. In the research, the classification model of four foodborne pathogens was established based on t-distributed stochastic neighbor embedding (t-SNE) and support vector machine (SVM); the recognition accuracy was 97.04%. The target bacteria named by the model were ejected through Raman-activated cell ejection (RACE), and then single-cell genomic DNA was amplified for species analysis. The accuracy of correct matches between the predicted phenotype and the actual genotype of the target cells was at least 83.3%. Furthermore, all anticipant sequencing results brought into correspondence with the species were predicted through the model. In sum, the Raman fingerprint database based on Raman spectroscopy combined with machine learning was reliable and promising in the field of rapid detection of foodborne pathogens.

Application of single-cell Raman-deuterium isotope probing to reveal the resistance of marine ammonia-oxidizing archaea SCM1 against common antibiotics

Antimicrobial resistance (AMR) in oceans poses a significant threat to human health through the seafood supply chain. Ammonia-oxidizing archaea (AOA) are important marine microorganisms and play a key role in the biogeochemical nitrogen cycle around the world. However, the AMR of marine AOA to aquicultural antibiotics is poorly explored. Here, Raman-deuterium isotope probing (Raman-DIP), a single-cell tool, was developed to reveal the AMR of a typical marine species of AOA, Nitrosopumilus maritimus (designated SCM1), against six antibiotics, including erythromycin, tetracycline, novobiocin, neomycin, bacitracin, and vancomycin. The D2O concentration (30% v/v) and culture period (9 days) were optimized for the precise detection of metabolic activity in SCM1 cells through Raman-DIP. The relative metabolic activity of SCM1 upon exposure to antibiotics was semi-quantitatively calculated based on single-cell Raman spectra. SCM1 exhibited high resistance to erythromycin, tetracycline, novobiocin, neomycin, and vancomycin, with minimum inhibitory concentration (MIC) values between 100 and 400 mg/L, while SCM1 is very sensitive to bacitracin (MIC: 0.8 mg/L). Notably, SCM1 cells were completely inactive under the metabolic activity minimum inhibitory concentration conditions (MA-MIC: 1.6-800 mg/L) for the six antibiotics. Further genomic analysis revealed the antibiotic resistance genes (ARGs) of SCM1, including 14 types categorized into 33 subtypes. This work increases our knowledge of the AMR of marine AOA by linking the resistant phenome to the genome, contributing to the risk assessment of AMR in the underexplored ocean environment. As antibiotic resistance in marine microorganisms is significantly affected by the concentration of antibiotics in coastal environments, we encourage more studies concentrating on both the phenotypic and genotypic antibiotic resistance of marine archaea. This may facilitate a comprehensive evaluation of the capacity of marine microorganisms to spread AMR and the implementation of suitable control measures to protect environmental safety and human health.

Raman cell sorting for single-cell research

Cells constitute the fundamental units of living organisms. Investigating individual differences at the single-cell level facilitates an understanding of cell differentiation, development, gene expression, and cellular characteristics, unveiling the underlying laws governing life activities in depth. In recent years, the integration of single-cell manipulation and recognition technologies into detection and sorting systems has emerged as a powerful tool for advancing single-cell research. Raman cell sorting technology has garnered attention owing to its non-labeling, non-destructive detection features and the capability to analyze samples containing water. In addition, this technology can provide live cells for subsequent genomics analysis and gene sequencing. This paper emphasizes the importance of single-cell research, describes the single-cell research methods that currently exist, including single-cell manipulation and single-cell identification techniques, and highlights the advantages of Raman spectroscopy in the field of single-cell analysis by comparing it with the fluorescence-activated cell sorting (FACS) technique. It describes various existing Raman cell sorting techniques and introduces their respective advantages and disadvantages. The above techniques were compared and analyzed, considering a variety of factors. The current bottlenecks include weak single-cell spontaneous Raman signals and the requirement for a prolonged total cell exposure time, significantly constraining Raman cell sorting technology's detection speed, efficiency, and throughput. This paper provides an overview of current methods for enhancing weak spontaneous Raman signals and their associated advantages and disadvantages. Finally, the paper outlines the detailed information related to the Raman cell sorting technology mentioned in this paper and discusses the development trends and direction of Raman cell sorting.

Plasma-derived Extracellular Vesicles (EVs) as Biomarkers of Sepsis in Burn Patients via Label-free Raman Spectroscopy

Sepsis following burn trauma is a global complication with high mortality, with ~60% of burn patient deaths resulting from infectious complications. Sepsis diagnosis is complicated by confounding clinical manifestations of the burn injury, and current biomarkers markers lack the sensitivity and specificity required for prompt treatment. Circulating extracellular vesicles (EVs) from patient liquid biopsy as biomarkers of sepsis due to their release by pathogens from bacterial biofilms and roles in subsequent immune response. This study applies Raman spectroscopy to patient plasma derived EVs for rapid, sensitive, and specific detection of sepsis in burn patients, achieving 97.5% sensitivity and 90.0% specificity. Furthermore, spectral differences between septic and non-septic burn patient EVs could be traced to specific glycoconjugates of bacterial strains associated with sepsis morbidity. This work illustrates the potential application of EVs as biomarkers in clinical burn trauma care, and establishes Raman analysis as a fast, label-free method to specifically identify features of bacterial EVs relevant to infection amongst the host background.

Aerobic Anoxygenic Phototrophic Bacteria in the Marine Environments Revealed by Raman/Fluorescence-Guided Single-Cell Sorting and Targeted Metagenomics

Aerobic anoxygenic phototrophic bacteria (AAPB) contribute profoundly to the global carbon cycle. However, most AAPB in marine environments are uncultured and at low abundance, hampering the recognition of their functions and molecular mechanisms. In this study, we developed a new culture-independent method to identify and sort AAPB using single-cell Raman/fluorescence spectroscopy. Characteristic Raman and fluorescent bands specific to bacteriochlorophyll a (Bchl a) in AAPB were determined by comparing multiple known AAPB with non-AAPB isolates. Using these spectroscopic biomarkers, AAPB in coastal seawater, pelagic seawater, and hydrothermal sediment samples were screened, sorted, and sequenced. 16S rRNA gene analysis and functional gene annotations of sorted cells revealed novel AAPB members and functional genes, including one species belonging to the genus Sphingomonas, two genera affiliated to classes Betaproteobacteria and Gammaproteobacteria, and function genes bchCDIX, pucC2, and pufL related to Bchl a biosynthesis and photosynthetic reaction center assembly. Metagenome-assembled genomes (MAGs) of sorted cells from pelagic seawater and deep-sea hydrothermal sediment belonged to Erythrobacter sanguineus that was considered as an AAPB and genus Sphingomonas, respectively. Moreover, multiple photosynthesis-related genes were annotated in both MAGs, and comparative genomic analysis revealed several exclusive genes involved in amino acid and inorganic ion metabolism and transport. This study employed a new single-cell spectroscopy method to detect AAPB, not only broadening the taxonomic and genetic contents of AAPB in marine environments but also revealing their genetic mechanisms at the single-genomic level.

Mining strategies for isolating plastic-degrading microorganisms

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

In Situ Discrimination and Cultivation of Active Degraders in Soils by Genome-Directed Cultivation Assisted by SIP-Raman-Activated Cell Sorting

The identification and in situ cultivation of functional yet uncultivable microorganisms are important to confirm inferences regarding their ecological functions. Here, we developed a new method that couples Raman-activated cell sorting (RACS), stable-isotope probing (SIP), and genome-directed cultivation (GDC)─namely, RACS-SIP-GDC─to identify, sort, and cultivate the active toluene degraders from a complex microbial community in petroleum-contaminated soil. Using SIP, we successfully identified the active toluene degrader Pigmentiphaga, the single cells of which were subsequently sorted and isolated by RACS. We further successfully assembled the genome of Pigmentiphaga based on the metagenomic sequencing of 13C-DNA and genomic sequencing of sorted cells, which was confirmed by gyrB gene comparison and average nucleotide identity determination. Additionally, the genotypes and phenotypes of this degrader were directly linked at the single-cell level, and its complete toluene metabolic pathways in petroleum-contaminated soil were reconstructed. Based on its unique metabolic properties uncovered by genome sequencing, we modified the traditional cultivation medium with antibiotics, amino acids, carbon sources, and growth factors (e.g., vitamins and metals), achieving the successful cultivation of RACS-sorted active degrader Pigmentiphaga sp. Our results implied that RACS-SIP-GDC is a state-of-the-art approach for the precise identification, targeted isolation, and cultivation of functional microbes from complex communities in natural habitats. RACS-SIP-GDC can be used to explore specific and targeted organic-pollution-degrading microorganisms at the single-cell level and provide new insights into their biodegradation mechanisms.

High-throughput single-cell sorting by stimulated Raman-activated cell ejection

Single-cell sorting is essential to explore cellular heterogeneity in biology and medicine. Recently developed Raman-activated cell sorting (RACS) circumvents the limitations of fluorescence-activated cell sorting, such as the cytotoxicity of labels. However, the sorting throughputs of all forms of RACS are limited by the intrinsically small cross-section of spontaneous Raman scattering. Here, we report a stimulated Raman-activated cell ejection (S-RACE) platform that enables high-throughput single-cell sorting based on high-resolution multi-channel stimulated Raman chemical imaging, in situ image decomposition, and laser-induced cell ejection. The performance of this platform was illustrated by sorting a mixture of 1 μm polymer beads, where 95% yield, 98% purity, and 14 events per second throughput were achieved. Notably, our platform allows live cell ejection, allowing for the growth of single colonies of bacteria and fungi after sorting. To further illustrate the chemical selectivity, lipid-rich Rhodotorula glutinis cells were successfully sorted from a mixture with Saccharomyces cerevisiae, confirmed by downstream quantitative PCR. Furthermore, by integrating a closed-loop feedback control circuit into the system, we realized real-time single-cell imaging and sorting, and applied this method to precisely eject regions of interest from a rat brain tissue section. The reported S-RACE platform opens exciting opportunities for a wide range of single-cell applications in biology and medicine.

Isolation and Cultivation of Human Gut Microorganisms: A Review

Microbial resources from the human gut may find use in various applications, such as empirical research on the microbiome, the development of probiotic products, and bacteriotherapy. Due to the development of "culturomics", the number of pure bacterial cultures obtained from the human gut has significantly increased since 2012. However, there is still a considerable number of human gut microbes to be isolated and cultured. Thus, to improve the efficiency of obtaining microbial resources from the human gut, some constraints of the current methods, such as labor burden, culture condition, and microbial targetability, still need to be optimized. Here, we overview the general knowledge and recent development of culturomics for human gut microorganisms. Furthermore, we discuss the optimization of several parts of culturomics including sample collection, sample processing, isolation, and cultivation, which may improve the current strategies.

Metabolic Reprogramming in Colon Cancer Cells Persistently Infected with Newcastle Disease Virus

Newcastle disease virus (NDV) is an oncolytic agent against various types of mammalian cancers. As with all cancer therapies, the development of cancer resistance, both innate and acquired, is becoming a challenge. In this study, we investigated persistently NDV-infected Caco-2 colon cancer cells, designated as virus-resistant (VR) Caco-2 cells, which were then able to resist NDV-mediated oncolysis. We applied single-cell Raman spectroscopy, combined with deuterium isotope probing (Raman-DIP) techniques, to investigate the metabolic adaptations and dynamics in VR Caco-2 cells. A linear discriminant analysis (LDA) model demonstrated excellent performance in differentiating VR Caco-2 from Caco-2 cells at single-cell level. By comparing the metabolic profiles in a time-resolved manner, the de novo synthesis of proteins and lipids was found upregulated, along with decreased DNA synthesis in VR Caco-2. The results suggest that VR Caco-2 cells might reprogram their metabolism and divert energy from proliferation to protein synthesis and lipidic modulation. The ability to identify and characterise single resistant cells among a population of cancer cells would help develop a deeper understanding of the resistance mechanisms and better tactics for developing effective cancer treatment.

Vibrational spectroscopy for decoding cancer microbiota interactions: Current evidence and future perspective

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

Revealing CO2-Fixing SAR11 Bacteria in the Ocean by Raman-Based Single-Cell Metabolic Profiling and Genomics

The majority of marine microbes remain uncultured, which hinders the identification and mining of CO2-fixing genes, pathways, and chassis from the oceans. Here, we investigated CO2-fixing microbes in seawater from the euphotic zone of the Yellow Sea of China by detecting and tracking their 13C-bicarbonate (13C-HCO3-) intake via single-cell Raman spectra (SCRS) analysis. The target cells were then isolated by Raman-activated Gravity-driven Encapsulation (RAGE), and their genomes were amplified and sequenced at one-cell resolution. The single-cell metabolism, phenotype and genome are consistent. We identified a not-yet-cultured Pelagibacter spp., which actively assimilates 13C-HCO3-, and also possesses most of the genes encoding enzymes of the Calvin-Benson cycle for CO2 fixation, a complete gene set for a rhodopsin-based light-harvesting system, and the full genes necessary for carotenoid synthesis. The four proteorhodopsin (PR) genes identified in the Pelagibacter spp. were confirmed by heterologous expression in E. coli. These results suggest that hitherto uncultured Pelagibacter spp. uses light-powered metabolism to contribute to global carbon cycling.

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

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

Integrated identification of growth pattern and taxon of bacterium in gut microbiota via confocal fluorescence imaging-oriented single-cell sequencing

Despite the fast progress in our understanding of the complex functions of gut microbiota, it is still challenging to directly investigate the in vivo microbial activities and processes on an individual cell basis. To gain knowledge of the indigenous growth/division patterns of the diverse mouse gut bacteria with a relatively high throughput, here, we propose an integrative strategy, which combines the use of fluorescent probe labeling, confocal imaging with single-cell sorting, and sequencing. Mouse gut bacteria sequentially labeled by two fluorescent d-amino acid probes in vivo were first imaged by confocal microscopy to visualize their growth patterns, which can be unveiled by the distribution of the two fluorescence signals on each bacterium. Bacterial cells of interest on the imaging slide were then sorted using a laser ejection equipment, and the collected cells were then sequenced individually to identify their taxa. Our strategy allows integrated acquirement of the growth pattern knowledge of a variety of gut bacteria and their genomic information on a single-cell basis, which should also have great potential in studying many other complex bacterial systems.

Biphenyl-degrading Bacteria Isolation with Laser Induced Visualized Ejection Separation Technology and Traditional Colony Sorting

In this work, biphenyl was used as carbon source to enrich microorganisms from polychlorinated biphenyls (PCBs)-contaminated paddy soil samples, and the taxonomic structures in both of the soil samples and the fourth-generation enrichments were examined with high-throughput sequencing. Single cells were isolated from the enrichments via single cell sorting technology named Laser Induced Visualized Ejection Separation Technology (LIVEST) and also traditional single colony sorting, and the genera of the isolates were identified using 16S rRNA sequencing. The results from high-throughput sequencing present that enrichment from generation to generation can considerably change the microbial community. Comparing the two sorting methods, the LIVEST is more time-saving and cell-targeted for microbial resource exploration. Based on the further verification of biphenyl degradation, it was found that some strains belonging to genera Macrococcus, Aerococcus and Metabacillus are capable in degrading biphenyl, which have not been reported yet.

Single-cell metagenomics and metagenomics approaches reveal extracellular electron transfer of psychrophilic electroactive biofilms

Revealing the metabolic functions and the extracellular electron transfer (EET) process of psychrophilic electroactive biofilms (PEB) is important for understanding the functional adaptability of electroactive bacteria (EAB) to low temperatures. In this study, single cell sorting (SCS)-based metagenomics sequencing was used to characterize the composition and function of the PEB. Biofilm microbiome analysis showed that Geobacter, with a relative abundance of 62.64%, dominated the PEB enriched in the bioelectrochemical system (BES) at 4 °C. Both the metagenome (MAG) and single-cell metagenome (SCM) revealed that there were no obvious metabolic differences between the original biofilm and the sorted single cells. The presence of genes associated with type IV pilus, c-type cytochromes, and riboflavin indicated that the EET potential was maintained in the PEB at low temperatures. These results suggested that SCM provides an alternative approach to reconstruct the metabolic functions of uncultured and slow-growing EAB.

Single-cell sorting of microalgae and identification of optimal conditions by using response surface methodology coupled with life-cycle approaches

Response surface methodology (RSM) has been widely used to identify optimal conditions for environmental microorganisms to maximize degrading pollutants and accumulating biomass. However, to date, environmental impact and economic cost have rarely been considered. In this study, a single cell of microalgae Chlorella sorokiniana ZM-5 was sorted, and its enrichment was carried out for the first time. The optimized conditions by RSM for achieving the highest COD, TN, TP removal and 352.61 mg/g lipid production were 24 h light time, 4.3:1C/N, 7.2 pH, and 30 °C temperature, respectively. Life-cycle approaches were then carried out upon this illustrative case, and the results indicated that the implementation of the above optimal conditions could reduce the total environmental impact by 48.0% and the total economic impact by 10.2%. This study showed the feasibility of applying life-cycle approaches to examine the optimal conditions of a biological process in terms of minimizing environmental impact and economic costs.

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.

Opportunities and challenges of using metagenomic data to bring uncultured microbes into cultivation

Although there is now an extensive understanding of the diversity of microbial life on earth through culture-independent metagenomic DNA sequence analyses, the isolation and cultivation of microbes remains critical to directly study them and confirm their metabolic and physiological functions, and their ecological roles. The majority of environmental microbes are as yet uncultured however; therefore, bringing these rare or poorly characterized groups into culture is a priority to further understand microbiome functions. Moreover, cultivated isolates may find utility in a range of applications, such as new probiotics, biocontrol agents, and agents for industrial processes. The growing abundance of metagenomic and meta-transcriptomic sequence information from a wide range of environments provides more opportunities to guide the isolation and cultivation of microbes of interest. In this paper, we discuss a range of successful methodologies and applications that have underpinned recent metagenome-guided isolation and cultivation of microbe efforts. These approaches include determining specific culture conditions to enrich for taxa of interest, to more complex strategies that specifically target the capture of microbial species through antibody engineering and genome editing strategies. With the greater degree of genomic information now available from uncultivated members, such as via metagenome-assembled genomes, the theoretical understanding of their cultivation requirements will enable greater possibilities to capture these and ultimately gain a more comprehensive understanding of the microbiomes. Video Abstract.

Identifying the Active Phenanthrene Degraders and Characterizing Their Metabolic Activities at the Single-Cell Level by the Combination of Magnetic-Nanoparticle-Mediated Isolation, Stable-Isotope Probing, and Raman-Activated Cell Sorting (MMI-SIP-RACS)

Magnetic-nanoparticle-mediated isolation coupled with stable-isotope probing (MMI-SIP) is a cultivation-independent higher-resolution approach for isolating active degraders in their natural habitats. However, it addresses the community level and cannot directly link the microbial identities, phenotypes, and in situ functions of the active degraders at the single-cell level within complex microbial communities. Here, we used ¹³C-labeled phenanthrene as the target and developed a new method coupling MMI-SIP and Raman-activated cell sorting (RACS), namely, MMI-SIP-RACS, to identify the active phenanthrene-degrading bacterial cells from polycyclic aromatic hydrocarbon (PAH)-contaminated wastewater. MMI-SIP-RACS significantly enriched the active phenanthrene degraders and successfully isolated the representative single cells. Amplicon sequencing analysis by SIP, ¹³C shift of the single cell in Raman spectra, and the 16S rRNA gene from single cell sequencing via RACS confirmed that Novosphingobium was the active phenanthrene degrader. Additionally, MMI-SIP-RACS reconstructed the phenanthrene metabolic pathway and genes of Novosphingobium, including two novel genes encoding phenanthrene dioxygenase and naphthalene dioxygenase. Our findings suggested that MMI-SIP-RACS is a powerful method to efficiently and precisely isolate active PAH degraders from complex microbial communities and directly link their identities to functions at the single-cell level.

Rapid, Label-Free Prediction of Antibiotic Resistance in Salmonella typhimurium by Surface-Enhanced Raman Spectroscopy

The rapid identification of bacterial antibiotic susceptibility is pivotal to the rational administration of antibacterial drugs. In this study, cefotaxime (CTX)-derived resistance in Salmonella typhimurium (abbr. CTX^r-S. typhimurium) during 3 months of exposure was rapidly recorded using a portable Raman spectrometer. The molecular changes that occurred in the drug-resistant strains were sensitively monitored in whole cells by label-free surface-enhanced Raman scattering (SERS). Various degrees of resistant strains could be accurately discriminated by applying multivariate statistical analyses to bacterial SERS profiles. Minimum inhibitory concentration (MIC) values showed a positive linear correlation with the relative Raman intensities of I₉₉₀/I₁₃₄₈, and the R² reached 0.9962. The SERS results were consistent with the data obtained by MIC assays, mutant prevention concentration (MPC) determinations, and Kirby-Bauer antibiotic susceptibility tests (K-B tests). This preliminary proof-of-concept study indicates the high potential of the SERS method to supplement the time-consuming conventional method and help alleviate the challenges of antibiotic resistance in clinical therapy.

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.

Isolating and culturing of single microbial cells by laser ejection sorting technology

Single cell isolation and cultivation play an important role in studying physiology, gene expression and functions of microorganisms. A series of single-cell isolation technologies have been developed, among which single-cell ejection technology is one of the most promising. Single cell ejection technology has applied Laser Induced Forward Transfer Technique (LIFT) to isolate bacteria but the viability (or recovery rate) of cells after sorting has not been clarified in the current research progress. In this work, to keep the cells alive as much as possible, we propose a three-layer LIFT system (top layer: 25-nm aluminum film; second layer: 3 μm agar media; third layer: liquid containing bacterial) for the isolation and cultivation of single Gram-negative (E. coli), Gram-positive (Lactobacillus rhamnosus GG, LGG), and eukaryotic microorganisms (Saccharomyces cerevisiae). The experiment results showed that the average survival rates for ejected pure single cells were 63% for Saccharomyces cerevisiae, 22% for E. coli DH5α, and 74% for LGG. In addition, we successfully isolated and cultured the GFP expressing E. coli JM109 from the mixture containing complex communities of soil bacteria by fluorescence signal. The average survival rate of E. coli JM109 was demonstrated to be 25.3%. In this study, the isolated and cultured single colonies were further confirmed by colony PCR and sequencing. Such precise sorting and cultivation technique of live single microbial cells could be coupled with other microscopic approaches to isolate single microorganisms with specific functions, revealing their roles in the natural community. Importance We developed a laser induced forward transfer (LIFT) technology to accurately isolate single live microbial cells. The cultivation recovery rates of the ejected single cells were 63% for Saccharomyces cerevisiae, 22% for E. coli DH5α, and 74% for Lactobacillus rhamnosus GG (LGG). Coupled LIFT with fluorescent microscope, we demonstrated that single cells of GFP expressing E. coli JM109 were sorted according to fluorescence signal from a complex community of soil bacteria, and subsequently cultured with 25% cultivation recovery rate. This single cell live sorting technology could isolate single microbes with specific functions, revealing their roles in the natural community.

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.

Detection and Classification of Multi-Type Cells by Using Confocal Raman Spectroscopy

Tumor cells circulating in the peripheral blood are the prime cause of cancer metastasis and death, thus the identification and discrimination of these rare cells are crucial in the diagnostic of cancer. As a label-free detection method without invasion, Raman spectroscopy has already been indicated as a promising method for cell identification. This study uses a confocal Raman spectrometer with 532 nm laser excitation to obtain the Raman spectrum of living cells from the kidney, liver, lung, skin, and breast. Multivariate statistical methods are applied to classify the Raman spectra of these cells. The results validate that these cells can be distinguished from each other. Among the models built to predict unknown cell types, the quadratic discriminant analysis model had the highest accuracy. The demonstrated analysis model, based on the Raman spectrum of cells, is propitious and has great potential in the field of biomedical for classifying circulating tumor cells in the future.

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.

Mini-metagenome analysis of psychrophilic electroactive biofilms based on single cell sorting

Understanding the metabolic function of psychrophilic electroactive bacteria is important for the investigation of extracellular electron transfer (EET) mechanisms under low temperatures (4-15 °C). In this study, Raman activated cell ejection coupled high throughput sequencing was used to accurately generate a mini-metagenome of psychrophilic bacterial community. Hierarchical cluster analysis of the Raman spectrum could accurately select the target Geobacter cluster. The high relative abundance of the membrane transport functional genes ftsEX in the biofilm community indicated an adaptation to reduced temperature, which aided survival of the electroactive bacteria under low temperature. The basal metabolism such as citrate cycle and glycolytic pathway maintained the electron pool for the EET process. The identification of iron (III) transport system genes in high abundance indicated their presence in an active metabolic reaction for potential electron transfer process. It showed the potential involvement c-type cytochromes (coxA and cox1) activity in EET. These results indicated that psychrophilic Geobacter had effective EET mediated by c-type cytochromes at low temperatures.

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.

Phenotypic Tracking of Antibiotic Resistance Spread via Transformation from Environment to Clinic by Reverse D2O Single-Cell Raman Probing

The rapid spread of antibiotic resistance threatens our fight against bacterial infections. Environments are an abundant reservoir of potentially transferable resistance to pathogens. However, the trajectory of antibiotic resistance genes (ARGs) spreading from environment to clinic and the associated risk remain poorly understood. Here, single-cell Raman spectroscopy combined with reverse D₂O labeling (Raman-rD₂O) was developed as a sensitive and rapid phenotypic tool to track the spread of plasmid-borne ARGs from soil to clinical bacteria via transformation. Based on the activity of bacteria in assimilating H to substitute prelabeled D under antibiotic treatment, Raman-rD₂O sensitively discerned a small minority of phenotypically resistant transformants from a large pool of recipient cells. Its single-cell level detection greatly facilitated the direct calculation of spread efficiency. Raman-rD₂O was further employed to study the transfer of complex soil resistant plasmids to pathogenic bacteria. Soil plasmid ARG-dependent transformability against five clinically relevant antibiotics was revealed and used to assess the spreading risk of different soil ARGs, i.e., ampicillin > cefradine and ciprofloxacin > meropenem and vancomycin. The developed single-cell phenotypic method can track the fate and risk of environmental ARGs to pathogenic bacteria and may guide developing new strategies to prevent the spread of high-risk ARGs.

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

We developed a label-free and noninvasive single-cell Raman microspectroscopy (SCRM)-based platform to identify neural cell lineages derived from clinically relevant human induced pluripotent stem cells (hiPSCs). Through large-scale Raman spectral analysis, we can distinguish hiPSCs and hiPSC-derived neural cells using their intrinsic biochemical profile. We identified glycogen as a Raman biomarker for neuronal differentiation and validated the results using conventional glycogen detection assays. The parameters obtained from SCRM were processed by a novel machine learning method based on t-distributed stochastic neighbor embedding (t-SNE)-enhanced ensemble stacking, enabling highly accurate and robust cell classification. The platform and the proposed biomarker should also be applicable to other cell types and can shed light on developmental biology and glycogen metabolism disorders.

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.