Raman Microscopic Analysis of Single Microbial Cells

Surface-enhanced confocal Raman microscopy to characterize esophageal cancer cell-derived extracellular vesicles and maternal cells

Exosomes, a subtype of extracellular vesicles, are increasingly recognized as promising biomarkers for human cancers. Rapid detection and classification of esophageal cancer-associated exosomes could significantly improve non-invasive screening for potential patients. This study aims to establish a label-free, direct surface-enhanced Raman scattering (SERS) method to capture characteristic molecular information from both esophageal cancer cells and their corresponding exosomes using confocal Raman microscopy. The results revealed distinct Raman spectra for esophageal cancer cells and their exosomes within the range of 500-1600 cm-1, with notable signal similarities observed at 506-622, 778-832, 1079-1098, and 1572-1630 cm-1. In contrast, significant differences were identified in Raman peaks related to nucleic acids (723, 654, 1354 cm-1) and proteins (998, 1028, 1354, 1560 cm-1). An orthogonal partial least squares discriminant analysis (OPLS-DA) model was utilized to discern subtle variations among these highly similar samples, achieving an accuracy rate of 100%. By comparing the spectral correlations between esophageal cancer cells and their exosomes, this study provides valuable insights into the molecular composition and cellular origins of exosomes. The findings demonstrate the potential of integrating SERS with OPLS-DA for the precise and rapid detection and monitoring of esophageal cancer through exosomal biomarkers, offering a powerful tool for diagnostic applications.

Clear as mud redefined: Tunable transparent mineral scaffolds for visualizing microbial processes below ground

Microbes inhabiting complex porous microenvironments in sediments and aquifers catalyze reactions that are critical to global biogeochemical cycles and ecosystem health. However, the opacity and complexity of porous sediment and rock matrices have considerably hindered the study of microbial processes occurring within these habitats. Here, we generated microbially compatible, optically transparent mineral scaffolds to visualize and investigate microbial colonization and activities occurring in these environments, in laboratory settings and in situ. Using inexpensive synthetic cryolite mineral, we produced optically transparent scaffolds mimicking the complex 3D structure of sediments and rocks by adapting a suspension-based, freeze-casting technique commonly used in materials science. Fine-tuning of parameters, such as freezing rate and choice of solvent, provided full control of pore size and architecture. The combined effects of scaffold porosity and structure on the movement of microbe-sized particles, tested using velocity tracking of fluorescent beads, showed diverse yet reproducible behaviors. The scaffolds we produced are compatible with epifluorescence microscopy, allowing the fluorescence-based identification of colonizing microbes by DNA-based staining and fluorescence in situ hybridization (FISH) to depths of 100 µm. Additionally, Raman spectroscopy analysis indicates minimal background signal in regions used for measuring deuterium and 13C enrichment in microorganisms, highlighting the potential to directly couple D2O or 13C stable isotope probing and Raman-FISH for quantifying microbial activity at the single-cell level. To demonstrate the relevance of cryolite scaffolds for environmental field studies, we visualized their colonization by diverse microorganisms within rhizosphere sediments of a coastal seagrass plant using epifluorescence microscopy. The tool presented here enables highly resolved, spatially explicit, and multimodal investigations into the distribution, activities, and interactions of underground microbes typically obscured within opaque geological materials until now.

Intelligent identification of foodborne pathogenic bacteria by self-transfer deep learning and ensemble prediction based on single-cell Raman spectrum

Foodborne pathogenic infections pose a significant threat to human health. Accurate detection of foodborne diseases is essential in preventing disease transmission. This study proposed an AI model for precisely identifying foodborne pathogenic bacteria based on single-cell Raman spectrum. Self-transfer deep learning and ensemble prediction algorithms had been incorporated into the model framework to improve training efficiency and predictive performance, significantly improving prediction results. Our model can identify simultaneously gram-negative and positive, genus, species of foodborne pathogenic bacteria with an accuracy over 99.99 %, as well as recognized strain with over 99.49 %. At all four classification levels, unprecedented excellent predictive performance had been achieved. This advancement holds practical significance for medical detection and diagnosis of foodborne diseases by reducing false negatives.

Raman spectroscopy for cell analysis: Retrospect and prospect

Cell analysis is crucial to contemporary biomedical research, as it plays a pivotal role in elucidating life processes and advancing disease diagnosis and treatment. Raman spectroscopy, harnessing distinctive molecular vibrational data, provides a non-destructive method for cell analysis. This review surveys the progress of Raman spectroscopy in cellular analysis, emphasizing its utility in identifying individual cells, monitoring biomolecules, and assessing intracellular environments. A significant focus is placed on the novel application of triple-bond molecules as Raman tags, which enhance imaging capabilities by creating a distinctive signature with minimal background noise. The summary of Raman spectroscopy studies provides a forward-looking perspective on its applications.

Unveiling bisphenol A-degrading bacteria in activated sludge through plating and 13C isotope labeled single-cell Raman spectroscopy

Bacteria play a crucial role in biodegradation of recalcitrant endocrine-disrupting compounds (EDCs), such as bisphenol A (BPA). However, in-situ identification of BPA-degrading bacteria remains technically challenging. Herein, we employed a conventional plating isolation (PI) and a new single cell Raman spectroscopy coupled with stable isotope probing (Raman-SIP) approach to enrich and identify BPA-degrading bacteria from activated sludge (AS). AS-inhabitant bacteria were exposed to either 12C-BPA or 13C-BPA as sole carbon source over three consecutive generations. While PI relies on colony proliferation on agar media, Raman-SIP enables identification of in situ BPA-degrading bacteria in a culture-independent way. The results showed that BPA dissipation correlated with increased bacterial growth. The uptake of 13C-BPA by single cells was verified by Raman spectra, suggesting occurrence of both metabolic and biosynthesis processes. This direct tracking of the fate of 13C-BPA within cells highlights the advantages of Raman-SIP over PI technique. PI isolated four BPA-degrading bacterial strains belonging to Comamonas, Pseudomonas, and Herbaspirillum genera. Meanwhile, Raman-SIP identified labeled cells belonging to Comamonas and Pseudomonas genera. Metagenomics of labeled cells revealed the presence of fifteen genes associated with benzene ring cleavage. This study provides a novel Raman-SIP approach for detecting and characterizing BPA-assimilating bacteria at a single cell level.

Phylogeny-metabolism dual-directed single-cell genomics for dissecting and mining ecosystem function by FISH-scRACS-seq

Microbiome-wide association studies (MWASs) have uncovered microbial markers linked to ecosystem traits, but the mechanisms underlying their functions can remain elusive. This is largely due to challenges in validating their in situ metabolic activities and tracing such activities to individual genomes. Here, we introduced a phylogeny-metabolism dual-directed single-cell genomics approach called fluorescence-in situ-hybridization-guided single-cell Raman-activated sorting and sequencing (FISH-scRACS-seq). It directly localizes individual cells from target taxon via an FISH probe for marker organism, profiles their in situ metabolic functions via single-cell Raman spectra, sorts cells of target taxonomy and target metabolism, and produces indexed, high-coverage, and precisely-one-cell genomes. From cyclohexane-contaminated seawater, cells representing the MWAS-derived marker taxon of γ-Proteobacteria and that are actively degrading cyclohexane in situ were directly identified via FISH and Raman, respectively, then sorted and sequenced for one-cell full genomes. In such a Pseudoalteromonas fuliginea cell, we discovered a three-component cytochrome P450 system that can convert cyclohexane to cyclohexanol in vitro, representing a previously unknown group of cyclohexane-degrading enzymes and organisms. Therefore, by unveiling enzymes, pathways, genomes, and their in situ cellular functions specifically for those organisms with ecological relevance at one-cell resolution, FISH-scRACS-seq is a rational and generally applicable approach to dissecting and mining microbiota functions.

Single-cell Raman spectroscopy for rapid detection of bacteria in ballast water and UV254 treatment evaluation

The increasing global trade has facilitated the transfer of ship ballast water, which has emerged as a primary pathway for alien species invasion into marine ecosystems, posing significant threats to marine biodiversity. Addressing the technical challenges in rapid microorganism detection and treatment efficiency assessment, this study developed a confocal Raman microscopic imaging (CRMI) system integrated with a metal-insulator-metal (MIM) broadband surface-enhanced Raman scattering (SERS) chip, enabling efficient acquisition of single-cell Raman spectroscopy (SCRS). By incorporating machine learning algorithms, the system achieved precise identification of up to 10 bacterial types in ballast water, exhibiting remarkable performance metrics with average accuracy, sensitivity, specificity, and precision above 95.5 %, 95.5 %, 99.5 %, and 95.5 %, respectively. To evaluate the efficacy of ultraviolet (UV) treatment, a Raman spectroscopy-based approach combined with heavy water labeling was introduced to characterize the changes in bacterial single-cell metabolic activity under UV254 irradiation. Experimental results demonstrated that a 10-min UV254 exposure at an effective intensity of 2 mW/cm2 was sufficient to achieve complete bacterial sterilization for the specific ballast water used in our experiment. This study not only established an efficient and accurate method for rapid detection of mixed bacteria but also provided a novel perspective for assessing UV treatment effects. It holds significance and practical value for optimizing ship ballast water management strategies and safeguarding the safety of marine ecosystems.

On-Chip Stimulated Raman Scattering Imaging and Quantification of Molecular Diffusion in Aqueous Microfluidics

Numerous chemical reactions and most life processes occur in aqueous solutions, where the physical diffusion of small molecules plays a vital role, including solvent water molecules, solute biomolecules, and ions. Conventional methods of measuring diffusion coefficients are often limited by technical complexity, large sample consumption, or significant time cost. Here, we present an optical imaging method to study molecular diffusion by combining stimulated Raman scattering (SRS) microscopy with microfluidics: a "Y"-shaped microfluidic channel forming two laminar flows with a stable concentration gradient across the interface. SRS imaging of a specific molecule allows us to obtain a high-resolution chemical profile of the diffusion region at varying inspection locations and flow rates, which enables the extraction of diffusion coefficients using the convection-diffusion model. As a proof of concept, we measured diffusion coefficients of molecules including water, protein, and multiple ions, with a sample volume of less than 1 mL and a time cost of less than 10 min. Moreover, we demonstrated a high-resolution three-dimensional (3D) reconstruction of the diffusion patterns in the microfluidic channel. The high-speed microfluidic SRS platform holds the potential for quantitative measurements of molecular diffusion, chemical reaction, and fluidic dynamics at the liquid-liquid interfaces.

Study of interaction in dual-species biofilm of Candida glabrata and Klebsiella pneumoniae co-isolated from peripheral venous catheter using Raman characterization mapping and machine learning algorithms

Polymicrobial biofilm infections, especially associated with medical devices such as peripheral venous catheters, are challenging in clinical settings for treatment and management. In this study, we examined the mixed biofilm formed by Candida glabrata and Klebsiella pneumoniae, which were co-isolated from the same peripheral venous catheter. Our results revealed that C. glabrata can form mixed biofilms with K. pneumoniae in vitro on peripheral venous catheters and the bottom of microplate wells, as confirmed by scanning electron microscopy. Additionally, using Raman mapping, we showed the distribution of both species in mono- and dual-species biofilms and suggested the type of microbial interaction in this polymicrobial biofilm. Finally, with the assistance of appropriate machine learning (ML) algorithms, based on identified peaks of bacteria, yeast, catheter, and Microplate mapping spectra, we develop a dedicated Raman database to detect the presence of these elements in an unknown spectrum in the future.

Processing and inspection of high-pressure microfluidics systems: A review

In the field of microfluidics, high-pressure microfluidics technology, which utilizes high driving pressure for microfluidic analysis, is an evolving technology. This technology combines microfluidics and pressurization, where the flow of fluid is controlled by means of high-pressure-driven devices greater than 10 MPa. This paper first reviews the existing high-pressure microfluidics systems and describes their components and applications. Then, it summarizes several materials used in the microfabrication of high-pressure microfluidics chips, reviewing their properties, processing methods, and bonding methods. In addition, advanced laser processing techniques for the microfabrication of high-pressure microfluidics chips are described. Last, the paper examines the analytical detection methods employed in high-pressure microfluidics systems, encompassing optical and electrochemical detection methods. The review of analytical detection methods shows the different functions and application scenarios of high-pressure microfluidics systems. In summary, this study provides an efficient and advanced microfluidics system, which can be widely used in chemical engineering, food industry, and environmental engineering under high pressure conditions.

Lighting the Path: Raman Spectroscopy's Journey Through the Microbial Maze

The rapid and precise identification of microorganisms is essential in environmental science, pharmaceuticals, food safety, and medical diagnostics. Raman spectroscopy, valued for its ability to provide detailed chemical and structural information, has gained significant traction in these fields, especially with the adoption of various excitation wavelengths and tailored optical setups. The choice of wavelength and setup in Raman spectroscopy is influenced by factors such as applicability, cost, and whether bulk or single-cell analysis is performed, each impacting sensitivity and specificity in bacterial detection. In this study, we investigate the potential of different excitation wavelengths for bacterial identification, utilizing a mock culture composed of six bacterial species: three Gram-positive (S. warneri, S. cohnii, and E. malodoratus) and three Gram-negative (P. stutzeri, K. terrigena, and E. coli). To improve bacterial classification, we applied machine learning models to analyze and extract unique spectral features from Raman data. The results indicate that the choice of excitation wavelength significantly influences the bacterial spectra obtained, thereby impacting the accuracy and effectiveness of the subsequent classification results.

Single-cell detection and linear discriminant analysis of bacterial Raman spectra in glass filter microholes

We report a study of micro-Raman spectroscopy towards an optimal approach for single cell measurements for the detection of bacteria by vibrational spectroscopy. The use of glass membrane filters was tested by microfiltration to separate individual bacterial cells. The glass membrane filters were applied to the study of Raman spectral classification analysis. This approach achieved the capture and individual detection of spiked bacterial cells. Linear discriminant analysis (LDA) of Raman spectra measured on glass membrane filters was successfully used to distinguish several bacterial species.

In Situ Raman Hyperspectral Analysis of Microbial Colonies for Secondary Metabolites Screening

Since the discovery of penicillin, a vast array of microbial antibiotics has been identified and applied in the medical field. Globally, the search for drug candidates via microbial screening is ongoing. Traditional screening methods, however, are time-consuming and require labor-intensive sample processing, significantly reducing throughput. This research introduces a Raman spectroscopy-based screening system tailored to the in situ analysis of microbial colonies on solid culture media. Employing multivariate curve resolution-alternating least-squares (MCR-ALS) for spectral decomposition, our approach reveals the production of secondary metabolites at the single colony level. We enhanced the microbial culture method, enabling direct, high signal-to-noise (S/N) ratio Raman spectroscopic measurements of colonies of Escherichia coli and actinomycetes species. Through semisupervised MCR analysis using the known spectra of actinorhodin and undecylprodigiosin as references, we accurately assessed the production of these compounds by Streptomyces coelicolor A3(2). Furthermore, we herein successfully detected the production of amphotericin B by Streptomyces nodosus, even in the absence of prior spectral information. This demonstrates the potential of our technique in the discovery of secondary metabolites. In addition to enabling the detection of the above-mentioned compounds, this analysis revealed the heterogeneity of the spatial distribution of their production in each colony. Our technique makes a significant contribution to the advancement of microbial screening, offering a rapid, efficient alternative to conventional methods and opening avenues for secondary metabolites discovery.

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

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

Developing fish oil emulsion gel enriched with Lentinula edodes single cell protein and its effect on controlling the growth of Acinetobacter baumannii

In this study, the characterization of fish oil (FO) emulsion gel (EGEL) containing single cell protein (SCP) produced from Lentinula edodes (L. edodes) and its potential inhibition against Acinetobacter baumannii (A. baumannii) were investigated. Oil extracted from the fish liver was emulsified with tween 80 and water, and then gelled using gelatin with the assistance of an ultrasonic homogenizer. The characteristics and surface analysis of SCP-EGEL were examined using FTIR (Fourier-transform infrared spectroscopy) and SEM (Scanning electron microscope). The particle size distribution and zeta potential of SCP-EGEL were measured using a Malvern Zetasizer. When SCP-EGEL was applied to the surface of the medium inoculated with A. baumannii, the inhibition zone (IZ) was 8.2 mm. An expansion of the IZ was observed (10.2 mm) when SCP-EGEL was applied to a fish skin (FS) surface prepared in the shape of a 6-mm diameter disc. In the SEM images, when SCP was added to lipo gel, the gel structure appeared flattened or swollen in some areas. The appearance of SCP cells being covered with gel gave the impression that they have a secondary wall. Therefore, the resulting complex can potentially be used as an additive in animal and human nutrition, in functional food coatings to suppress A. baumannii, and in fish feed to enrich it with protein.

Raman-Activated, Interactive Sorting of Isotope-Labeled Bacteria

Due to its high spatial resolution, Raman microspectroscopy allows for the analysis of single microbial cells. Since Raman spectroscopy analyzes the whole cell content, this method is phenotypic and can therefore be used to evaluate cellular changes. In particular, labeling with stable isotopes (SIPs) enables the versatile use and observation of different metabolic states in microbes. Nevertheless, static measurements can only analyze the present situation and do not allow for further downstream evaluations. Therefore, a combination of Raman analysis and cell sorting is necessary to provide the possibility for further research on selected bacteria in a sample. Here, a new microfluidic approach for Raman-activated continuous-flow sorting of bacteria using an optical setup for image-based particle sorting with synchronous acquisition and analysis of Raman spectra for making the sorting decision is demonstrated, showing that active cells can be successfully sorted by means of this microfluidic chip.

Isotope labeled 3D-Raman confocal imaging and atomic force microscopy study on epithelial cells interacting with the fungus Candida albicans

The human pathogenic fungus Candida albicans damages epithelial cells during superficial infections. Here we use three-dimensional-sequential-confocal Raman spectroscopic imaging and atomic force microscopy to investigate the interaction of C. albicans wild type cells, the secreted C. albicans peptide toxin candidalysin and mutant cells lacking candidalysin with epithelial cells. The candidalysin is responsible for epithelial cell damage and exhibits in its deuterated form an identifiable Raman signal in a frequency region distinct from the cellular frequency region. Vibration modes at 2100-2200 cm-1 attributed to carbon‑deuterium bending and at 477 cm-1, attributed to the nitrogen‑deuterium out-of-plane bending, found around the nucleus, can be assigned to deuterated candidalysin. Atomic force microscopy visualized 100 nm deep lesions on the cell and force-distance curves indicate the higher adhesion on pore surrounding after incubation with candidalysin. Candidalysin targets the plasma membrane, but is also found inside of the cytosol of epithelial cells during C. albicans infection.

Symbiodiniaceae and Ruegeria sp. Co-Cultivation to Enhance Nutrient Exchanges in Coral Holobiont

The symbiotic relationship between corals and their associated microorganisms is crucial for the health of coral reef eco-environmental systems. Recently, there has been a growing interest in unraveling how the manipulation of symbiont nutrient cycling affects the stress tolerance in the holobiont of coral reefs. However, most studies have primarily focused on coral-Symbiodiniaceae-bacterial interactions as a whole, neglecting the interactions between Symbiodiniaceae and bacteria, which remain largely unexplored. In this study, we proposed a hypothesis that there exists an inner symbiotic loop of Symbiodiniaceae and bacteria within the coral symbiotic loop. We conducted experiments to demonstrate how metabolic exchanges between Symbiodiniaceae and bacteria facilitate the nutritional supply necessary for cellular growth. It was seen that the beneficial bacterium, Ruegeria sp., supplied a nitrogen source to the Symbiodiniaceae strain Durusdinium sp., allowing this dinoflagellate to thrive in a nitrogen-free medium. The Ruegeria sp.-Durusdinium sp. interaction was confirmed through 15N-stable isotope probing-single cell Raman spectroscopy, in which 15N infiltrated into the bacterial cells for intracellular metabolism, and eventually the labeled nitrogen source was traced within the macromolecules of Symbiodiniaceae cells. The investigation into Symbiodiniaceae loop interactions validates our hypothesis and contributes to a comprehensive understanding of the intricate coral holobiont. These findings have the potential to enhance the health of coral reefs in the face of global climate change.

A metabolic map and artificial intelligence-aided identification of nasopharyngeal carcinoma via a single-cell Raman platform

BACKGROUND

Nasopharyngeal carcinoma (NPC) is a complex cancer influenced by various factors. This study explores the use of single-cell Raman spectroscopy as a potential diagnostic tool for investigating biomolecular changes associated with NPC carcinogenesis.

METHODS

Seven NPC cell lines, one immortalised nasopharyngeal epithelial cell line, six nasopharyngeal mucosa tissues and seven NPC tissue samples were analysed by performing confocal Raman spectroscopic measurements and imaging. The single-cell Raman spectral dataset was used to quantify relevant biomolecules and build machine learning classification models. Metabolomic profiles were investigated using ultra-performance liquid chromatography-tandem mass spectrometer (UPLC-MS/MS).

RESULTS

By generating a metabolic map of seven NPC cell lines, we identified an interplay of altered metabolic processes involving nucleic acids, amino acids, lipids and sugars. The results from spatially resolved Raman maps and UPLC-MS/MS metabolomics were consistent, revealing an increase of unsaturated fatty acids in cancer cells, particularly in highly metastatic 5-8F and poorly differentiated CNE2 cells. The classification model achieved a nearly perfect classification when identifying NPC and non-NPC cells with an ROC-AUC of 0.99 and a value of 0.97 when identifying 13 tissue samples.

CONCLUSION

This study unveils a complex interplay of metabolic network and highlights the potential roles of unsaturated fatty acids in NPC progression and metastasis. This renders further research to provide deeper insights into NPC pathogenesis, identify new metabolic targets and improve the efficacy of targeted therapies in NPC. Artificial intelligence-aided analysis of single-cell Raman spectra has achieved high accuracies in the classification of both cancer cells and patient tissues, paving the way for a simple, less invasive and accurate diagnostic test.

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

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

Antimicrobial Activity and Mechanisms of Punicalagin against Vibrio parahaemolyticus

This study sought to explore the antimicrobial activity of punicalagin against V. parahaemolyticus and its potential modes of action. V. parahaemolyticus ATCC 17802 and RIMD 2210633Sm were exposed to punicalagin, and the energy production, membrane potential, and envelope permeability, as well as the interaction with cell biomolecules, were measured using a variety of fluorescent probes combined with electrophoresis and Raman spectroscopy. Punicalagin treatment disrupted the envelope integrity and induced a decrease in intracellular ATP and pH. The uptake of 1-N-phenyl-naphtylamine (NPN) demonstrated that punicalagin weakened the outer membrane. Punicalagin damaged the cytoplasmic membrane, as indicated by the membrane depolarization and the leakage of intracellular potassium ions, proteins, and nucleic acids. Electronic microscopy observation visualized the cell damage caused by punicalagin. Further, gel electrophoresis coupled with the Raman spectrum assay revealed that punicalagin affected the protein expression of V. parahaemolyticus, and there was no effect on the integrity of genomic DNA. Therefore, the cell envelope and proteins of V. parahaemolyticus were the assailable targets of punicalagin treatment. These findings suggested that punicalagin may be promising as a natural bacteriostatic agent to control the growth of V. parahaemolyticus.

Raman micro-spectroscopy as a tool to study immunometabolism

In the past two decades, immunometabolism has emerged as a crucial field, unraveling the intricate molecular connections between cellular metabolism and immune function across various cell types, tissues, and diseases. This review explores the insights gained from studies using the emerging technology, Raman micro-spectroscopy, to investigate immunometabolism. Raman micro-spectroscopy provides an exciting opportunity to directly study metabolism at the single cell level where it can be combined with other Raman-based technologies and platforms such as single cell RNA sequencing. The review showcases applications of Raman micro-spectroscopy to study the immune system including cell identification, activation, and autoimmune disease diagnosis, offering a rapid, label-free, and minimally invasive analytical approach. The review spotlights three promising Raman technologies, Raman-activated cell sorting, Raman stable isotope probing, and Raman imaging. The synergy of Raman technologies with machine learning is poised to enhance the understanding of complex Raman phenotypes, enabling biomarker discovery and comprehensive investigations in immunometabolism. The review encourages further exploration of these evolving technologies in the rapidly advancing field of immunometabolism.

Combined Mutual Learning Net for Raman Spectral Microbial Strain Identification

Infectious diseases pose a significant threat to global health, yet traditional microbiological identification methods suffer from drawbacks, such as high costs and long processing times. Raman spectroscopy, a label-free and noninvasive technique, provides rich chemical information and has tremendous potential in fast microbial diagnoses. Here, we propose a novel Combined Mutual Learning Net that precisely identifies microbial subspecies. It demonstrated an average identification accuracy of 87.96% in an open-access data set with thirty microbial strains, representing a 5.76% improvement. 50% of the microbial subspecies accuracies were elevated by 1% to 46%, especially for E. coli 2 improved from 31% to 77%. Furthermore, it achieved a remarkable subspecies accuracy of 92.4% in the custom-built fiber-optical tweezers Raman spectroscopy system, which collects Raman spectra at a single-cell level. This advancement demonstrates the effectiveness of this method in microbial subspecies identification, offering a promising solution for microbiology diagnosis.

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

Rapid detection of six Oceanobacillus species in Daqu starter using single-cell Raman spectroscopy combined with machine learning

Many traditional fermented foods and beverages industries around the world request the addition of multi-species starter cultures. However, the microbial community in starter cultures is subject to fluctuations due to their exposure to an open environment during fermentation. A rapid detection approach to identify the microbial composition of starter culture is essential to ensure the quality of the final products. Here, we applied single-cell Raman spectroscopy (SCRS) combined with machine learning to monitor Oceanobacillus species in Daqu starter, which plays crucial roles in the process of Chinese baijiu. First, a total of six Oceanobacillus species (O. caeni, O. kimchii, O. iheyensis, O. sojae, O. oncorhynchi subsp. Oncorhynchi and O. profundus) were detected in 44 Daqu samples by amplicon sequencing and isolated by pure culture. Then, we created a reference database of these Oceanobacillus strains which correlated their taxonomic data and single-cell Raman spectra (SCRS). Based on the SCRS dataset, five machine-learning algorithms were used to classify Oceanobacillus strains, among which support vector machine (SVM) showed the highest rate of accuracy. For validation of SVM-based model, we employed a synthetic microbial community composed of varying proportions of Oceanobacillus species and demonstrated a remarkable accuracy, with a mean error was less than 1% between the predicted result and the expected value. The relative abundance of six different Oceanobacillus species during Daqu fermentation was predicted within 60 min using this method, and the reliability of the method was proved by correlating the Raman spectrum with the amplicon sequencing profiles by partial least squares regression. Our study provides a rapid, non-destructive and label-free approach for rapid identification of Oceanobacillus species in Daqu starter culture, contributing to real-time monitoring of fermentation process and ensuring high-quality products.

Dual-Stable-Isotope-Probed Raman microspectroscopy reveals the metabolic dynamic of Streptococcus mutans

Streptococcus mutans (S. mutans) is regarded as a cariogenic pathogen with the ability to metabolize sugars and form organic acids. However, its actual timely level of glucose consumption and cellular vitality in a polymicrobial culture system remains largely unknown. To tackle this challenge, we employed the S. mutans UA159 as a model and developed a dual-stable-isotope-probed Raman microspectroscopy method (Dual SIP-Raman) to simultaneously profile the general metabolic activity and glucose assimilative activity in situ. (i) Mono-SIP substrate feeding revealed that 0.5% 13C-glucose and 30% D2O were proper doses in the medium to obtain prominent and quantitative band shifts along with the 13C or D2O incorporation. In addition, the intensity of the 13C peak of phenylalanine (Phe) is proposed as a Raman-based biomarker for glucose utilization in a cell. (ii) The state of dual SIP substrate incorporation of 13C-glucose and D2O could be visualized by the corresponding spectral "red shifts" of Raman-scattered emissions; moreover, we also demonstrated that 13C/12C analysis was closely correlated with the C-D ratio. (iii) The application of the dual 13C-glucose and D2O feeding approach on a mock microbiota of S. mutans UA159 and C. albicans ATCC14053 revealed a stimulatory effect of fungus on both the glucose intake rate and general metabolic vitality of S. mutans UA159 (p < 0.05). Therefore, the 13C-glucose and D2O dual-feeding Raman Microspectroscopy approach is a valuable new tool for evaluating the glucose intake rate and general metabolic levels in situ, tracing the changing trend of the above metabolic activities, which is helpful to clarify the changes in the cariogenicity of oral microorganisms caused by the external environment at the single-cell level.

Culture-free identification of fast-growing cyanobacteria cells by Raman-activated gravity-driven encapsulation and sequencing

By directly converting solar energy and carbon dioxide into biobased products, cyanobacteria are promising chassis for photosynthetic biosynthesis. To make cyanobacterial photosynthetic biosynthesis technology economically feasible on industrial scales, exploring and engineering cyanobacterial chassis and cell factories with fast growth rates and carbon fixation activities facing environmental stresses are of great significance. To simplify and accelerate the screening for fast-growing cyanobacteria strains, a method called Individual Cyanobacteria Vitality Tests and Screening (iCyanVS) was established. We show that the 13C incorporation ratio of carotenoids can be used to measure differences in cell growth and carbon fixation rates in individual cyanobacterial cells of distinct genotypes that differ in growth rates in bulk cultivations, thus greatly accelerating the process screening for fastest-growing cells. The feasibility of this approach is further demonstrated by phenotypically and then genotypically identifying individual cyanobacterial cells with higher salt tolerance from an artificial mutant library via Raman-activated gravity-driven encapsulation and sequencing. Therefore, this method should find broad applications in growth rate or carbon intake rate based screening of cyanobacteria and other photosynthetic cell factories.

Reviews of bio-orthogonal probes in bioscience by stimulated Raman scattering microscopy

Stimulated Raman scattering (SRS) microscopy, is a nonlinear optical imaging method for visualizing chemical content based on molecular vibrational bonds, with high sensitivity, resolution, speed, and specificity. In the current review, we provided a comprehensive and critical review of the most recent developments in the field of SRS in combination with bio-orthogonal Raman tags or labels in bioscience. Firstly, we introduced the fundamentals of SRS microscopy and the theory principle of bio-orthogonal Raman tags. In particular, present the applications of each kind of bio-orthogonal Raman tags, including heavy water (D2O), stable isotope probes (SIP), and triple-bonds tags. And shared our vision for the remaining challenges, research needs, and potential future breakthroughs for SRS technology lastly. We envision that the advanced SRS imaging and analysis will be a major force in future biological discovery.

Asymmetric split-ring plasmonic nanostructures for the optical sensing of Escherichia coli

Strategies for in-liquid micro-organism detection are crucial for the clinical and pharmaceutical industries. While Raman spectroscopy is a promising label-free technique for micro-organism detection, it remains challenging due to the weak bacterial Raman signals. In this work, we exploit the unique electromagnetic properties of metamaterials to identify bacterial components in liquid using an array of Fano-resonant metamolecules. This Fano-enhanced Raman scattering (FERS) platform is designed to exhibit a Fano resonance close to the protein amide group fingerprint around 6030 nm. Raman signatures of Escherichia coli were recorded at several locations on the metamaterial under off-resonance laser excitation at 530 nm, where the photodamage effect is minimized. As the sizes of the Escherichia coli are comparable to the micro-gaps i.e, 0.41 µm, of the metamaterials, its local immobilisation leads to an increase in the Raman sensitivity. We also observed that the time-dependent FERS signal related to bacterial amide peaks increased during the bacteria's mid-exponential phase while it decreased during the stationary phase. This work provides a new set of opportunities for developing ultrasensitive FERS platforms suitable for large-scale applications and could be particularly useful for diagnostics and environmental studies at off-resonance excitation.

Single-Cell Level Raman Molecular Profiling Reveals the Classification of Growth Phases of Chaetoceros tenuissimus

Harmful algal blooms (HABs) are a natural phenomenon caused by outbreaks of algae, resulting in serious problems for aquatic ecosystems and the coastal environment. Chaetoceros tenuissimus (C. tenuissimus) is one of the diatoms responsible for HABs. The growth curve of C. tenuissimus can be observed from beginning to end of HABs: therefore, detailed analysis is necessary to characterize each growth phase of C. tenuissimus. It is important to examine the phenotype of each diatom cell individually, as they display heterogeneity even in the same growth phase. Raman spectroscopy is a label-free technique to elucidate biomolecular profiles and spatial information at the cellular level. Multivariate data analysis (MVA) is an efficient method for the analysis of complicated Raman spectra, to identify molecular features. Here, we utilized Raman microspectroscopy to identify the molecular information of each diatom cell, at the single-cell level. The MVA, together with a support vector machine, which is a machine learning technique, allowed the classification of proliferating and nonproliferating cells. The classification includes polyunsaturated fatty acids such as linoleic acid, eicosapentaenoic acid, and docosahexaenoic acid. This study indicated that Raman spectroscopy is an appropriate technique to examine C. tenuissimus at the single-cell level, providing relevant data to assess the correlation between the molecular details obtained from the Raman analysis, at each growth phase.

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

BACKGROUND

At present, the conventional methods for determining photosynthetic products of microalgae are usually based on a large number of cell mass to reach the measurement baseline, and the result can only reveal the average state at the population level, which is not feasible for large-scale and rapid screening of specific phenotypes from a large number of potential microalgae mutants. In recent years, single-cell Raman spectra (SCRS) has been proved to be able to rapidly and simultaneously quantify the biochemical components of microalgae. However, this method has not been reported to analyze the biochemical components of Cyclotella cryptica (C. cryptica). Thus, SCRS was first attempt to determine these four biochemical components in this diatom.

RESULTS

The method based on SCRS was established to simultaneously quantify the contents of polysaccharide, total lipids, protein and Chl-a in C. cryptica, with thirteen Raman bands were found to be the main marker bands for the diatom components. Moreover, Partial Least Square Regression (PLSR) models based on full spectrum can reliably predict these four cellular components, with Pearson correlation coefficient for these components reached 0.949, 0.904, 0.801 and 0.917, respectively. Finally, based on SCRS data of one isogenic sample, the pairwise correlation and dynamic transformation process of these components can be analyzed by Intra-ramanome Correlation Analysis (IRCA), and the results showed silicon starvation could promote the carbon in C. cryptica cells to flow from protein and pigment metabolism to polysaccharide and lipid metabolism.

CONCLUSIONS

First, method for the simultaneous quantification of the polysaccharide, total lipid, protein and pigment in single C. cryptica cell are established. Second, the instant interconversion of intracellular components was constructed through IRCA, which is based on data set of one isogenic population and more precision and timeliness. Finally, total results indicated that silicon deficiency could promote the carbon in C. cryptica cells to flow from protein and pigment metabolism to polysaccharide and lipid metabolism.

Monitoring of microbial proteome dynamics using Raman stable isotope probing

Abnormal protein kinetics could be a cause of several diseases associated with essential life processes. An accurate understanding of protein dynamics and turnover is essential for developing diagnostic or therapeutic tools to monitor these changes. Raman spectroscopy in combination with stable isotope probes (SIP) such as carbon-13, and deuterium has been a breakthrough in the qualitative and quantitative study of various metabolites. In this work, we are reporting the utility of Raman-SIP for monitoring dynamic changes in the proteome at the community level. We have used ¹³ C-labeled glucose as the only carbon source in the medium and verified its incorporation in the microbial biomass in a time-dependent manner. A visible redshift in the Raman spectral vibrations of major biomolecules such as nucleic acids, phenylalanine, tyrosine, amide I, and amide III were observed. Temporal changes in the intensity of these bands demonstrating the feasibility of protein turnover monitoring were also verified. Kanamycin, a protein synthesis inhibitor was used to assess the feasibility of identifying effects on protein turnover in the cells. Successful application of this work can provide an alternate/adjunct tool for monitoring proteome-level changes in an objective and nondestructive manner.

Classification of deep-sea cold seep bacteria by transformer combined with Raman spectroscopy

Raman spectroscopy is a rapid analysis method of biological samples without labeling and destruction. At present, the commonly used Raman spectrum classification models include CNN, RNN, etc. The transformer has not been used for Raman spectrum identification. This paper introduces a new method of transformer combined with Raman spectroscopy to identify deep-sea cold seep microorganisms at the single-cell level. We collected the Raman spectra of eight cold seep bacteria, each of which has at least 500 spectra for the training of transformer model. We compare the transformer classification model with other deep learning classification models. The experimental results show that this method can improve the accuracy of microbial classification. Our average isolation level accuracy is more than 97%.

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.

Leveraging single-cell Raman spectroscopy and single-cell sorting for the detection and identification of yeast infections

Invasive fungal infection serves as a great threat to human health. Discrimination between fungal and bacterial infections at the earliest stage is vital for effective clinic practice; however, traditional culture-dependent microscopic diagnosis of fungal infection usually requires several days, meanwhile, culture-independent immunological and molecular methods are limited by the detectable type of pathogens and the issues with high false-positive rates. In this study, we proposed a novel culture-independent phenotyping method based on single-cell Raman spectroscopy for the rapid discrimination between fungal and bacterial infections. Three Raman biomarkers, including cytochrome c, peptidoglycan, and nucleic acid, were identified through hierarchical clustering analysis of Raman spectra across 12 types of most common yeast and bacterial pathogens. Compared to those of bacterial pathogens, the single cells of yeast pathogens demonstrated significantly stronger Raman peaks for cytochrome c, but weaker signals for peptidoglycan and nucleic acid. A two-step protocol combining the three biomarkers was established and able to differentiate fungal infections from bacterial infections with an overall accuracy of 94.9%. Our approach was also used to detect ten raw urinary tract infection samples. Successful identification of fungi was achieved within half an hour after sample obtainment. We further demonstrated the accurate fungal species taxonomy achieved with Raman-assisted cell ejection. Our findings demonstrate that Raman-based fungal identification is a novel, facile, reliable, and with a breadth of coverage approach, that has a great potential to be adopted in routine clinical practice to reduce the turn-around time of invasive fungal disease (IFD) diagnostics.

Long-term label-free assessments of individual bacteria using three-dimensional quantitative phase imaging and hydrogel-based immobilization

Three-dimensional (3D) quantitative phase imaging (QPI) enables long-term label-free tomographic imaging and quantitative analysis of live individual bacteria. However, the Brownian motion or motility of bacteria in a liquid medium produces motion artifacts during 3D measurements and hinders precise cell imaging and analysis. Meanwhile, existing cell immobilization methods produce noisy backgrounds and even alter cellular physiology. Here, we introduce a protocol that utilizes hydrogels for high-quality 3D QPI of live bacteria maintaining bacterial physiology. We demonstrate long-term high-resolution quantitative imaging and analysis of individual bacteria, including measuring the biophysical parameters of bacteria and responses to antibiotic treatments.

Multi-element stable isotope Raman microspectroscopy of bacterial carotenoids unravels rare signal shift patterns and single-cell phenotypic heterogeneity

Investigation of bacteria with D-carotenoids reveals unique Raman signatures, inclusive unexpected blue-shift. Simultaneous monitoring of 13C & D of carotenoids provides complementary information on cell growth and metabolic activity, respectively.

Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy

Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h.

Bio-physical mechanisms of dehydrating membranes of Acinetobacter baumannii linked to drought-resistance

Acinetobacter baumanni, is an opportunistic nosocomial multi-drug resistant bacterium, which represents a threat for human health. This pathogen is able to persist in intensive care units thanks to its extraordinary resistance towards dehydration, whose mechanisms are unknown and enable it to easily spread through surfaces, contaminating also medical devices. In this article we reveal, with a multimodal approach, based on μ-R Spectroscopy, Gas Chromatography coupled to Mass Spectroscopy, Atomic Force Microscopy and Fluorescence Recovery After Photobleaching, the bio-physical mechanisms that the membrane of two A. baumannii strains undergoes during dehydration. Showing a substantial decoupling of the phase transition from liquid crystalline to gel phase from evidence of cell lysis. Such decoupling may be the core of the resistance of A. baumannii against dehydration and highlights the different ability to resist to drought between strains.

Surface-enhanced Raman spectroscopy combined with stable isotope probing to assess the metabolic activity of Escherichia coli cells in chicken carcass wash water

Rapid evaluation of the metabolic activity of microorganisms is crucial in the assessment of the disinfection ability of various antimicrobial agents in the food industry. In this study, surface-enhanced Raman spectroscopy combined with isotope probing was employed for the analysis of the disinfection of single bacterial cells in the chicken carcass wash water. The Raman signals from single Escherichia coli O157:H7 cells were enhanced by in situ synthesis of silver nanoparticles. The ΔCD of the cells grown in presence of 0.5% hydrogen peroxide and 50 ppm chlorine was 5.86 ± 1.86% and 5.1 ± 2.3%, respectively, which showed significant reduction compared with cells grown in the absence of disinfecting agents (19.86 ± 2.51%) after 2 h of incubation. The study proved that the proposed method had the potential to assess the metabolic activity of microorganisms in other food products and optimize the disinfection process.

Hyperglycemia and cancer in human lung carcinoma by means of Raman spectroscopy and imaging

Raman spectroscopy and Raman imaging were used to identify the biochemical and structural features of human cancer lung cells (CCL-185) and the cancer cells supplemented with glucose and deuterated glucose at normal and hyperglycemia conditions. We found that isotope substitution of glucose by deuterated glucose allows to separate de novo lipid synthesis from exogenous uptake of lipids obtained from the diet. We demonstrated that glucose is largely utilized for de novo lipid synthesis. Our results provide a direct evidence that high level of glucose decreases the metabolism via oxidative phosphorylation in mitochondria in cancer cells and shifts the metabolism to glycolysis via Warburg effect. It suggests that hyperglycemia is a factor that may contribute to a more malignant phenotype of cancer cells by inhibition of oxidative phosphorylation and apoptosis.

Stain-free Gram staining classification of pathogens via single-cell Raman spectroscopy combined with machine learning

Gram staining (GS) is one of the routine microbiological operations to classify bacteria based on the cell wall structure. Accurate GS classification of pathogens is of great significance since it helps correct administration of antimicrobial treatment. The laborious procedure and low sensitivity results related to conventional GS have resulted in reluctance among clinicians. In this study, we integrate confocal Raman spectroscopy and machine learning techniques to distinguish Gram-negative (GN) or Gram-positive (GP) bacteria. A single-cell Raman database including seven most common clinical pathogens (three GP strains and four GN strains) was constructed. Machine learning algorithms including the support-vector machine (SVM), k-nearest neighbors' algorithm (k-NN), gradient boosting machine (GBM), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) were trained to achieve the binary classification for GS. With such a relatively small database, the SVM model achieved the highest accuracy of 98.1%. The molecular signatures of GN and GP embedded in their Raman fingerprints were identified with hierarchical cluster analysis (HCA). The results indicated that Raman peaks for peptidoglycan and teichoic acid were the most significant factors that contributed to accurate classification. The Raman machine learning approach could greatly enhance the diagnosis of pathogenic infections.

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.

Culture-Independent Raman Spectroscopic Identification of Bacterial Pathogens from Clinical Samples Using Deep Transfer Learning

The rapid identification of bacterial pathogens in clinical samples like blood, urine, pus, and sputum is the need of the hour. Conventional bacterial identification methods like culturing and nucleic acid-based amplification have limitations like poor sensitivity, high cost, slow turnaround time, etc. Raman spectroscopy, a label-free and noninvasive technique, has overcome these drawbacks by providing rapid biochemical signatures from a single bacterium. Raman spectroscopy combined with chemometric methods has been used effectively to identify pathogens. However, a robust approach is needed to utilize Raman features for accurate classification while dealing with complex data sets such as spectra obtained from clinical isolates, showing high sample-to-sample heterogeneity. In this study, we have used Raman spectroscopy-based identification of pathogens from clinical isolates using a deep transfer learning approach at the single-cell level resolution. We have used the data-augmentation method to increase the volume of spectra needed for deep-learning analysis. Our ResNet model could specifically extract the spectral features of eight different pathogenic bacterial species with a 99.99% classification accuracy. The robustness of our model was validated on a set of blinded data sets, a mix of cultured and noncultured bacterial isolates of various origins and types. Our proposed ResNet model efficiently identified the pathogens from the blinded data set with high accuracy, providing a robust and rapid bacterial identification platform for clinical microbiology.