Micro-Raman spectroscopic identification of bacterial cells of the genus Staphylococcus and dependence on their cultivation conditions

Determination of Raman spectrum under different culture conditions: preliminary research on bacterial fish pathogens

The intensive labour and time required for conventional methods to identify bacterial fish pathogens have revealed the need to develop alternative methods. Raman spectroscopy has been used in the rapid optical identification of bacterial pathogens in recent years as an alternative method in microbiology. Strains of bacterial fish pathogens (Vibrio anguillarum, Lactococcus garvieae and Yersinia ruckeri) that often cause infectious diseases in fish were here identified and analyzed in terms of their biochemical structures in different media and at different incubation times, and the data were specified by using Raman spectroscopy. The results demonstrated that Raman spectroscopy presents species-specific Raman spectra of each disease-causing bacteria and that it would be more appropriate to choose general microbiological media over selective media for routine studies. Additionally, it was found that species-specific band regions did not differ in 24- and 48-hour cultures, but there could be a difference in peak intensity which may lead to difficult characterization of spectrum. The current study, conducted for the first time with bacterial fish pathogens under different incubation conditions, is believed to provide a basis for the routine use of Raman spectroscopy for quick pathogen identification and the precise determination of the methodology for further research.

Chemical sensing of common microorganisms found in biopharmaceutical industries using MIR laser spectroscopy and multivariate analysis

Mid-infrared laser spectroscopy was used to investigate common bacteria encountered in biopharmaceutical industries. The study involved the detection of bacteria using quantum cascade laser spectroscopy coupled to a grazing angle probe (QCL-GAP). Substrates similar to surfaces commonly used in biopharmaceutical industries were used as support media for the samples. Reflectance measurements were assisted by Multivariate Analysis (MVA) to assemble a powerful spectroscopic technique with classification and identification resources. The species analyzed, Staphylococcus aureus, Staphylococcus epidermidis, and Micrococcus luteus, were used to challenge the technique's capability to discriminate from microorganisms of the same family. Principal Components Analysis and Partial Least Squares-Discriminant Analysis differentiated between the bacterial species, using QCL-GAP-MVA as the reference. Spectral differences in the bacterial membrane were used to determine if these microorganisms were present in the samples analyzed. Results herein provided effective discrimination for the bacteria under study with high sensitivity and specificity.

Discrimination of Genetic Biomarkers of Disease through Machine-Learning-Based Hypothesis Testing of Direct SERS Spectra of DNA and RNA

Cancer is globally a leading cause of death that would benefit from diagnostic approaches detecting it in its early stages. However, despite much research and investment, cancer early diagnosis is still underdeveloped. Owing to its high sensitivity, surface-enhanced Raman spectroscopy (SERS)-based detection of biomarkers has attracted growing interest in this area. Oligonucleotides are an important type of genetic biomarkers as their alterations can be linked to the disease prior to symptom onset. We propose a machine-learning (ML)-enabled framework to analyze complex direct SERS spectra of short, single-stranded DNA and RNA targets to identify relevant mutations occurring in genetic biomarkers, which are key disease indicators. First, by employing ad hoc-synthesized colloidal silver nanoparticles as SERS substrates, we analyze single-base mutations in ssDNA and RNA sequences using a direct SERS-sensing approach. Then, an ML-based hypothesis test is proposed to identify these changes and differentiate the mutated sequences from the corresponding native ones. Rooted in "functional data analysis," this ML approach fully leverages the rich information and dependencies within SERS spectral data for improved modeling and detection capability. Tested on a large set of DNA and RNA SERS data, including from miR-21 (a known cancer miRNA biomarker), our approach is shown to accurately differentiate SERS spectra obtained from different oligonucleotides, outperforming various data-driven methods across several performance metrics, including accuracy, sensitivity, specificity, and F1-scores. Hence, this work represents a step forward in the development of the combined use of SERS and ML as effective methods for disease diagnosis with real applicability in the clinic.

Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms

In a previous publication, we trained predictive models based on Raman bulk spectra of microorganisms placed on a silicon dioxide protected silver mirror slide to make predictions for new Raman spectra, unknown to the models, of microorganisms placed on a different substrate, namely stainless steel. Now we have combined large sections of this data and trained a convolutional neural network (CNN) to make predictions for single cell Raman spectra. We show that a database based on microbial bulk material is conditionally suited to make predictions for the same species in terms of single cells. Data of 13 different microorganisms (bacteria and yeasts) were used. Two of the 13 species could be identified 90% correctly and five other species 71%-88%. The six remaining species were correctly predicted by only 0%-49%. Especially stronger fluorescence in bulk material compared to single cells but also photodegradation of carotenoids are some effects that can complicate predictions for single cells based on bulk data. The results could be helpful in assessing universal Raman tools or databases.

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.

Combined effects of cold and acid on dual-species biofilms of Pseudomonas fluorescens and Listeria monocytogenes under simulated chilled beef processing conditions

Interactions across bacterial species boundaries are usually influenced by environmental stresses, yet little has been evaluated regarding multifactorial stresses on the fate of dual-species biofilm formation in food industry. In this study, the processing conditions of chilled beef were established as a combination of cold and acid stresses (4 °C and pH 5.4), with pH 7.0 or 25 °C serving as the controls, to investigate the interaction of dual-species biofilm between Pseudomonas fluorescens and Listeria monocytogenes. Dual-species biofilms significantly increased biofilm formation at 72 h under the condition of 25°C-pH7.0 and 25°C-pH5.4 (P < 0.05). Compared with mono-species biofilms, the cell numbers of L. monocytogenes in dual-species biofilms were lower at 25 °C (P < 0.05), however, the adherent cells of L. monocytogenes was higher in dual-species biofilms at 4 °C (P < 0.05). Furthermore, the amount of extracellular polysaccharides and proteins secreted by single P. fluorescens biofilms at 4 °C was more than three times than those at 25 °C. The surface-enhanced Raman spectroscopy further profiled the variability of extracellular polymeric substances (EPS) composition. Additionally, RT-qPCR results revealed an upregulation of biofilm-related and genes in co-culture species. It provides valuable insights into the strategies for removing mixed biofilms under diverse stressful conditions in practical food processing.

Evaluation of poly(lactic acid) and ECOVIO based biocomposites loaded with antimicrobial sodium phosphate microparticles

Herein, biobased composite materials based on poly(lactic acid) (PLA) and poly(butylene adipate-co-terephthalate) (PBAT) as matrices, sodium hexametaphosphate microparticles (E452i, food additive microparticles, 1 and 5 wt%) as antimicrobial filler and acetyl tributyl citrate (ATBC, 15 wt%) as plasticizer, were developed for potential food packaging applications. Two set of composite films were obtained by melt-extrusion and compression molding, i) based on PLA matrix and ii) based on Ecovio® matrix (PLA/PBAT blend). Thermal characterization by thermogravimetric analysis and differential scanning calorimetry demonstrated that the incorporation of E452i particles improved thermal stability and crystallinity, while the mechanical test showed an increase in the Young's modulus. E452i particles also provide antimicrobial properties to the films against food-borne bacteria Listeria innocua and Staphylococcus aureus, with bacterial reduction percentages higher than 50 % in films with 5 wt% of particles. The films also preserved their disintegradability as demonstrated by an exhaustive characterization of the films under industrial composting conditions. Therefore, the results obtained in this work reveal the potential of these biocomposites as appropriated materials for antibacterial and compostable food packaging films.

A magnetic field augmented ultra-thin layer chromatography coupled surface enhanced Raman spectroscopy separation of hemozoin from bacterial mixture

Malaria is considered as one the most widespread disease with highest possibility of co-infection at all levels of the disease prognosis. Rapid detection and discrimination of malaria from other co-infections remains a challenge. Hemozoin is a metabolic biproduct of malaraia possessing paramagnetic property due to presence of iron at its centre. Here, we report a label free, rapid and highly sensitive magnetic field based ultra-thin layer chromatography (UTLC) coupled with surface enhanced Raman spectroscopy (SERS) technique for detection and separation of hemozoin from a bacterial mixture. Highly optimized silver nanorods chip fabricated using glancing angle deposition (GLAD) is explored for the UTLC-SERS separation. These chips possessing channel like characteristic and high surface to the volume ratio serve as excellent UTLC plates. The magnetic nature of hemozoin has been exploited for its separation from the mixture of P. aeruginosa (Gram-negative) and S. aureus (Gram-positive) by allocating a 0.6 T magnet over the UTLC flow setup. The solvent front migrated approximately to a distance of 13 mm from the sample point due to the magnetic environment. Spatially resolved SERS data was collected along the mobile phase and separation of mixture was confirmed. Further, staining of hemozoin, P. aeruginosa and S. aureus was done using methylene blue, acridine orange and rhodamine 6 G respectively. The separation was confirmed for the stained analytes. The present developed method provides plate height as low as 18 µm and hemozoin detection limit as <10 parasites/mL. Therefore, we establish a highly specific and sensitive technique capable of separating small amounts of bioanalytes, aiding in the removal of co-infections from the disease at a very early stage of infection.

Nano-Biotechnology for Bacteria Identification and Potent Anti-bacterial Properties: A Review of Current State of the Art

Sepsis is a critical disease caused by the abrupt increase of bacteria in human blood, which subsequently causes a cytokine storm. Early identification of bacteria is critical to treating a patient with proper antibiotics to avoid sepsis. However, conventional culture-based identification takes a long time. Polymerase chain reaction (PCR) is not so successful because of the complexity and similarity in the genome sequence of some bacterial species, making it difficult to design primers and thus less suitable for rapid bacterial identification. To address these issues, several new technologies have been developed. Recent advances in nanotechnology have shown great potential for fast and accurate bacterial identification. The most promising strategy in nanotechnology involves the use of nanoparticles, which has led to the advancement of highly specific and sensitive biosensors capable of detecting and identifying bacteria even at low concentrations in very little time. The primary drawback of conventional antibiotics is the potential for antimicrobial resistance, which can lead to the development of superbacteria, making them difficult to treat. The incorporation of diverse nanomaterials and designs of nanomaterials has been utilized to kill bacteria efficiently. Nanomaterials with distinct physicochemical properties, such as optical and magnetic properties, including plasmonic and magnetic nanoparticles, have been extensively studied for their potential to efficiently kill bacteria. In this review, we are emphasizing the recent advances in nano-biotechnologies for bacterial identification and anti-bacterial properties. The basic principles of new technologies, as well as their future challenges, have been discussed.

Understanding Raman Spectral Based Classifications with Convolutional Neural Networks Using Practical Examples of Fungal Spores and Carotenoid-Pigmented Microorganisms

Numerous publications showing that robust prediction models for microorganisms based on Raman micro-spectroscopy in combination with chemometric methods are feasible, often with very precise predictions. Advances in machine learning and easier accessibility to software make it increasingly easy for users to generate predictive models from complex data. However, the question regarding why those predictions are so accurate receives much less attention. In our work, we use Raman spectroscopic data of fungal spores and carotenoid-containing microorganisms to show that it is often not the position of the peaks or the subtle differences in the band ratios of the spectra, due to small differences in the chemical composition of the organisms, that allow accurate classification. Rather, it can be characteristic effects on the baselines of Raman spectra in biochemically similar microorganisms that can be enhanced by certain data pretreatment methods or even neutral-looking spectral regions can be of great importance for a convolutional neural network. Using a method called Gradient-weighted Class Activation Mapping, we attempt to peer into the black box of convolutional neural networks in microbiological applications and show which Raman spectral regions are responsible for accurate classification.

Raman Microscopic Analysis of Dry-Surface Biofilms on Clinically Relevant Materials

Moist/hydrated biofilms have been well-studied in the medical area, and their association with infections is widely recognized. In contrast, dry-surface biofilms (DSBs) on environmental surfaces in healthcare settings have received less attention. DSBs have been shown to be widespread on commonly used items in hospitals and to harbor bacterial pathogens that are known to cause healthcare-acquired infections (HAI). DSBs cannot be detected by routine surface swabbing or contact plates, and studies have shown DSBs to be less susceptible to cleaning/disinfection products. As DSBs are increasingly reported in the medical field, and there is a likelihood they also occur in food production and manufacturing areas, there is a growing demand for the rapid in situ detection of DSBs and the identification of pathogens within DSBs. Raman microspectroscopy allows users to obtain spatially resolved information about the chemical composition of biofilms, and to identify microbial species. In this study, we investigated Staphylococcus aureus mono-species DSB on polyvinylchloride blanks and stainless steel coupons, and dual-species (S. aureus/Bacillus licheniformis) DSB on steel coupons. We demonstrated that Raman microspectroscopy is not only suitable for identifying specific species, but it also enables the differentiation of vegetative cells from their sporulated form. Our findings provide the first step towards the rapid identification and characterization of the distribution and composition of DSBs on different surface areas.

Endophytes from blueberry (Vaccinium sp.) fruit: Characterization of yeast and bacteria via label-free surface-enhanced Raman spectroscopy (SERS)

Blueberries (Vaccinium sp.) are consumed all around the globe, however, their endophytic community has not been thoroughly researched, specifically their fruit endophytes. We aimed to isolate and analyze easily cultivable blueberry fruit endophytes to help in future research, concerning probiotic microorganisms. Twelve strains were isolated in this pilot study, genetically homologous with Staphylococcus hominis, Staphylococcus cohnii, Salmonella enterica, Leuconostoc mesenteroides, and [Candida] santamariae. To determine the molecular composition of these isolates we used label-free surface-enhanced Raman spectroscopy (SERS). To our knowledge, this is the first time that SERS spectra for L. mesenteroides and C. santamariae are presented, as well as the first report of Candida yeast, isolated specifically from blueberry fruits. Our findings suggest that the differences in tested yeast and bacteria SERS spectra and subsequent differentiation are facilitated by minor shifts in spectral peak positions as well as their intensities. Moreover, we used principal component and discriminant function analyses to differentiate chemotypes within our isolate group, proving the sensitivity of the technique and its usefulness to recognize different strains in plant-associated microbe samples, which will aid to streamline future studies in biofertilizers and biocontrol agents.

Discrimination of Stressed and Non-Stressed Food-Related Bacteria Using Raman-Microspectroscopy

As the identification of microorganisms becomes more significant in industry, so does the utilization of microspectroscopy and the development of effective chemometric models for data analysis and classification. Since only microorganisms cultivated under laboratory conditions can be identified, but they are exposed to a variety of stress factors, such as temperature differences, there is a demand for a method that can take these stress factors and the associated reactions of the bacteria into account. Therefore, bacterial stress reactions to lifetime conditions (regular treatment, 25 °C, HCl, 2-propanol, NaOH) and sampling conditions (cold sampling, desiccation, heat drying) were induced to explore the effects on Raman spectra in order to improve the chemometric models. As a result, in this study nine food-relevant bacteria were exposed to seven stress conditions in addition to routine cultivation as a control. Spectral alterations in lipids, polysaccharides, nucleic acids, and proteins were observed when compared to normal growth circumstances without stresses. Regardless of the involvement of several stress factors and storage times, a model for differentiating the analyzed microorganisms from genus down to strain level was developed. Classification of the independent training dataset at genus and species level for Escherichia coli and at strain level for the other food relevant microorganisms showed a classification rate of 97.6%.

Fiber Probe-Based Raman Spectroscopic Identification of Pathogenic Infection Microorganisms on Agar Plates

Rapid identification of microorganisms is clinically meaningful, and it helps to decelerate the spread of drug resistance and improve patient treatment. In this study, we present a rapid fiber probe-based Raman technique with an excitation wavelength of 785 nm, which is applied to classify and identify nine different species of microorganisms. The cost-effective fiber probe compresses the dimension of the system and provides a more reliable and stable database. All microorganisms were simply cultivated on Luria-Bertani (LB) agar, and Raman spectra were obtained directly from the microbial colonies with the fiber probe within 30 s. The classification model consists of principal component analysis (PCA) in combination with linear discriminant analysis (LDA) and was examined by applying leave-one-batch-out cross-validation (LOBOCV). This model achieved an accuracy of 98.9%. In addition, the validation and identification processes based on independent replicates achieved accuracies of 99.8% and 100%, respectively. The results demonstrated that fiber probe Raman spectroscopy in combination with chemometric analysis allowed a rapid classification and identification of microorganisms only with a normal culture. Therefore, it is promising especially for medical applications and could moreover be helpful to investigate and identify microorganisms rapidly in further studies.

An ensemble approach to the structure-function problem in microbial communities

The metabolic activity of microbial communities plays a primary role in the flow of essential nutrients throughout the biosphere. Molecular genetics has revealed the metabolic pathways that model organisms utilize to generate energy and biomass, but we understand little about how the metabolism of diverse, natural communities emerges from the collective action of its constituents. We propose that quantifying and mapping metabolic fluxes to sequencing measurements of genomic, taxonomic, or transcriptional variation across an ensemble of diverse communities, either in the laboratory or in the wild, can reveal low-dimensional descriptions of community structure that can explain or predict their emergent metabolic activity. We survey the types of communities for which this approach might be best suited, review the analytical techniques available for quantifying metabolite fluxes in communities, and discuss what types of data analysis approaches might be lucrative for learning the structure-function mapping in communities from these data.

Quorum Quenching Enzyme (PF-1240) Capable to Degrade AHLs as a Candidate for Inhibiting Quorum Sensing in Food Spoilage Bacterium Hafnia alvei

Quorum sensing (QS) is widely present in microorganisms in marine aquatic products. Owing to the use of antibiotics, many spoilage bacteria in aquatic products are drug resistant. In order to slow down this evolutionary trend, the inhibition of spoilage phenotype of spoilage bacteria by interfering with QS has become a research hot spot in recent years. In this study, we found a new QS quenching enzyme, PF-1240; it was cloned and expressed in Pseudomonas fluorescens 08. Sequence alignment showed that its similarity with N-homoserine lactone (AHL) acylase QuiP protein of Pseudomonas fluorescens (Pf 0-1) was 78.4%. SDS-PAGE confirmed that the protein is a dimer composed of two subunits, which is similar to the structure of AHL acylases. The concentration of heterologous expression in Escherichia coli (DE3) was 26.64 μg/mL. Unlike most AHL acylases, PF-1240 can quench AHLs with different carbon chain lengths and inhibit the quorum sensing of the aquatic spoilage bacterium Hafnia alvei. It can significantly reduce the formation rate of biofilm of H. alvei to 44.4% and the yield of siderophores to 54%, inhibit the production of protease and lipase, and interfere with the motility of H. alvei. Through these corruption phenotypes, the specific application effect of PF-1240 can be further determined to provide a theoretical basis for its application in the preservation of practical aquatic products.

First preliminary study on identification of bacterial fish pathogens with Raman spectroscopy

Accurate and rapid determination of bacterial disease agents of fish is an important step for sustainable and efficient aquaculture production. In general, biochemical and molecular methods are used for pathogen detection but they are usually time-consuming and required qualified personnel. Recently spectroscopic methods are preferred in clinical and food microbiology and declared as a promising alternative method for pathogens diagnosis with many advantages. In this study, the significant spectra of three important bacterial fish pathogens (Lactococcus garvieae, Vibrio anguillarum and Yersinia ruckeri) were determined by Raman spectroscopy. The first data of the pathogens were obtained and the distinctive differences in polysaccharides, nucleic acids, fatty acids and amino acids were identified. This preliminary study aimed to be pioneer for further studies in aquaculture and veterinary microbiology toward developing an alternative method for routine identification.

Discrimination of bacteria using whole organism fingerprinting: the utility of modern physicochemical techniques for bacterial typing

This review compares and contrasts MALDI-MS, FT-IR spectroscopy and Raman spectroscopy for whole organism fingerprinting and bacterial typing.

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

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

Plasmonic and Electrostatic Interactions Enable Uniformly Enhanced Liquid Bacterial Surface-Enhanced Raman Scattering (SERS)

Surface-enhanced Raman spectroscopy (SERS) is a promising cellular identification and drug susceptibility testing platform, provided it can be performed in a controlled liquid environment that maintains cell viability. We investigate bacterial liquid-SERS, studying plasmonic and electrostatic interactions between gold nanorods and bacteria that enable uniformly enhanced SERS. We synthesize five nanorod sizes with longitudinal plasmon resonances ranging from 670 to 860 nm and characterize SERS signatures of Gram-negative Escherichia coli and Serratia marcescens and Gram-positive Staphylococcus aureus and Staphylococcus epidermidis bacteria in water. Varying the concentration of bacteria and nanorods, we achieve large-area SERS enhancement that is independent of nanorod resonance and bacteria type; however, bacteria with higher surface charge density exhibit significantly higher SERS signal. Using cryo-electron microscopy and zeta potential measurements, we show that the higher signal results from attraction between positively charged nanorods and negatively charged bacteria. Our robust liquid-SERS measurements provide a foundation for bacterial identification and drug testing in biological fluids.

Arcobacter Identification and Species Determination Using Raman Spectroscopy Combined with Neural Networks

Rapid and accurate identification of Arcobacter is of great importance because it is considered an emerging food- and waterborne pathogen and potential zoonotic agent. Raman spectroscopy can differentiate bacteria based on Raman scattering spectral patterns of whole cells in a fast, reagentless, and easy-to-use manner. We aimed to detect and discriminate Arcobacter bacteria at the species level using confocal micro-Raman spectroscopy (785 nm) coupled with neural networks. A total of 82 reference and field isolates of 18 Arcobacter species from clinical, environmental, and agri-food sources were included. We determined that the bacterial cultivation time and growth temperature did not significantly influence the Raman spectral reproducibility and discrimination capability. The genus Arcobacter could be successfully differentiated from the closely related genera Campylobacter and Helicobacter using principal-component analysis. For the identification of Arcobacter to the species level, an accuracy of 97.2% was achieved for all 18 Arcobacter species using Raman spectroscopy combined with a convolutional neural network (CNN). The predictive capability of Raman-CNN was further validated using an independent data set of 12 Arcobacter strains. Furthermore, a Raman spectroscopy-based fully connected artificial neural network (ANN) was constructed to determine the actual ratio of a specific Arcobacter species in a bacterial mixture ranging from 5% to 100% by biomass (regression coefficient >0.99). The application of both CNN and fully connected ANN improved the accuracy of Raman spectroscopy for bacterial species determination compared to the conventional chemometrics. This newly developed approach enables rapid identification and species determination of Arcobacter within an hour following cultivation.IMPORTANCE Rapid identification of bacterial pathogens is critical for developing an early warning system and performing epidemiological investigation. Arcobacter is an emerging foodborne pathogen and has become more important in recent decades. The incidence of Arcobacter species in the agro-ecosystem is probably underestimated mainly due to the limitation in the available detection and characterization techniques. Raman spectroscopy combined with machine learning can accurately identify Arcobacter at the species level in a rapid and reliable manner, providing a promising tool for epidemiological surveillance of this microbe in the agri-food chain. The knowledge elicited from this study has the potential to be used for routine bacterial screening and diagnostics by the government, food industry, and clinics.

Analysis of Bacteriophage-Host Interaction by Raman Tweezers

Bacteriophages, or "phages" for short, are viruses that replicate in bacteria. The therapeutic and biotechnological potential of phages and their lytic enzymes is of interest for their ability to selectively destroy pathogenic bacteria, including antibiotic-resistant strains. Introduction of phage preparations into medicine, biotechnology, and food industry requires a thorough characterization of phage-host interaction on a molecular level. We employed Raman tweezers to analyze the phage-host interaction of Staphylococcus aureus strain FS159 with a virulent phage JK2 (=812K1/420) of the Myoviridae family and a temperate phage 80α of the Siphoviridae family. We analyzed the timeline of phage-induced molecular changes in infected host cells. We reliably detected the presence of replicating phages in bacterial cells within 5 min after infection. Our results lay the foundations for building a Raman-based diagnostic instrument capable of real-time, in vivo, in situ, nondestructive characterization of the phage-host relationship on the level of individual cells, which has the potential of importantly contributing to the development of phage therapy and enzybiotics.

In situ investigation of acute exposure of graphene oxide on activated sludge: Biofilm characteristics, microbial activity and cytotoxicity

Graphene Oxide (GO) has wide applications in many fields which has caused a large expected quantity of the graphene-based wastes. It is necessary to understand the toxic effects of the GO on the activated sludge (AS) considering its inevitable discharge to the wastewater treatment plants as the ultimate repositories for these wastes. In this study, the acute exposures of the multilayer Nano-graphene oxide (MNGO) at different dosages were conducted in order to investigate its integrated effects on the formation of the biofilm, mature biofilm and the microbial activity of the activated sludge. Raman spectroscopy and laser scanning confocal microscopy (LSCM) were adopted for the in-situ characterization of the biofilm with the exposure of the MNGO. The results showed that the activated sludge was tolerable to the acute exposure of the less than 100 mg/L of the MNGO, especially for the mature biofilm, and only a subtle decrease was found in the size and thickness during the formation of the biofilm, while the amount of 300 mg/L of the MNGO caused the sever deterioration on the activated sludge system. The microbial metabolic activity, viability, and the biological removal of the nutrients were significantly affected with the more than 100 mg/L of the MNGO. It was also demonstrated by the microbial cytotoxicity tests that the increase in the exposure of the MNGO was related to the increase in the reactive oxygen species (ROS) and the damaging degree of the cell membrane.

Biochemical characterization of pathogenic bacterial species using Raman spectroscopy and discrimination model based on selected spectral features

This study aimed to evaluate the differences in the Raman spectra of nine clinical species of bacteria isolated from infections (three Gram-positive and six Gram-negative species), correlating the spectra with the chemical composition of each species and to develop a classification model through discriminant analysis to categorize each bacterial strain using the peaks with the most significant differences. Bacteria were cultured in Mueller Hinton agar and a sample of biomass was harvested and placed in an aluminum sample holder. A total of 475 spectra from 115 different strains were obtained through a dispersive Raman spectrometer (830 nm) with exposure time of 50 s. The intensities of the peaks were evaluated by one-way analysis of variance (ANOVA) and the peaks with significant differences were related to the differences in the biochemical composition of the strains. Discriminant analysis based on quadratic distance applied to the peaks with the most significant differences and partial least squares applied to the whole spectrum showed 89.5% and 90.1% of global accuracy, respectively, for classification of the spectra in all the groups. Raman spectroscopy could be a promising technique to identify spectral differences related to the biochemical content of pathogenic microorganisms and to provide a faster diagnosis of infectious diseases.

A Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species

Burkholderia (B.) mallei, the causative agent of glanders, and B. pseudomallei, the causative agent of melioidosis in humans and animals, are genetically closely related. The high infectious potential of both organisms, their serological cross-reactivity, and similar clinical symptoms in human and animals make the differentiation from each other and other Burkholderia species challenging. The increased resistance against many antibiotics implies the need for fast and robust identification methods. The use of Raman microspectroscopy in microbial diagnostic has the potential for rapid and reliable identification. Single bacterial cells are directly probed and a broad range of phenotypic information is recorded, which is subsequently analyzed by machine learning methods. Burkholderia were handled under biosafety level 1 (BSL 1) conditions after heat inactivation. The clusters of the spectral phenotypes and the diagnostic relevance of the Burkholderia spp. were considered for an advanced hierarchical machine learning approach. The strain panel for training involved 12 B. mallei, 13 B. pseudomallei and 11 other Burkholderia spp. type strains. The combination of top- and sub-level classifier identified the mallei-complex with high sensitivities (>95%). The reliable identification of unknown B. mallei and B. pseudomallei strains highlighted the robustness of the machine learning-based Raman spectroscopic assay.

In situ molecular vibration insights into the antibacterial behavior of silicon nitride bioceramic versus gram-negative Escherichia coli

Gram-negative bacteria represent a substantial fraction of pathogens responsible for periprosthetic infections. Given the increasing resistance of such bacteria to antibiotics, significant efforts are nowadays paid in developing new biomaterial surfaces, which offer resistance against bacterial adhesion and/or possess inherent antibacterial effects. Non-oxide silicon nitride (Si₃N₄) bioceramic in its polycrystalline form is a biomaterial with inherent antibacterial properties. Building upon previous phenomenological findings, the present study focuses on vibrational analyses of the metabolic response of Escherichia coli at the molecular level. A time-lapse study is conducted upon exposing the bacteria in vitro to Si₃N₄ bioceramic surfaces. A comparison is carried out with the as-cultured bacterial strain and with bacteria exposed to other commercially available biomaterials, namely, Ti-alloy (Ti6Al4V-ELI) and zirconia-toughened alumina (ZTA) oxide bioceramic tested under exactly the same experimental conditions. The metabolic pathways before and after exposure to different substrates were monitored by means of Raman and FTIR spectroscopies. Results indicated the development of significant osmotic stress in the bacterial strain and constant concentration decreases of its cellular compounds markers over time upon exposure to Si₃N₄. This ultimately led to bacterial lysis (also confirmed by conventional fluorescence microscopy assays). The main antibacterial effect was of chemical origin and driven by the elution of nitrogen ions from the Si₃N₄ surface, successively converted into ammonia (NH₃) or ammonium (NH₄)⁺ in aqueous solution, depending on environmental pH. The presence of these nitrogen species created osmotic stress in the cytoplasmic space. In answer to the osmotic stress, metabolic rates changed rapidly, the bacterial membrane was damaged, and lysis occurred almost completely within 48 h exposure. The antibacterial behavior exerted by the Si₃N₄ substrate on E. coli was more effective than that observed on the biomedical Ti6Al4V alloy. Conversely, no lysis but bacterial proliferation was recorded for E. coli exposed to ZTA bioceramic oxide substrates.

Raman spectroscopy reveals distinct differences between two closely related bacterial strains, Mycobacterium indicus pranii and Mycobacterium intracellulare

A common technique used to differentiate bacterial species and to determine evolutionary relationships is sequencing their 16S ribosomal RNA genes. However, this method fails when organisms exhibit high similarity in these sequences. Two such strains that have identical 16S rRNA sequences are Mycobacterium indicus pranii (MIP) and Mycobacterium intracellulare. MIP is of significance as it is used as an adjuvant for protection against tuberculosis and leprosy; in addition, it shows potent anti-cancer activity. On the other hand, M. intracellulare is an opportunistic pathogen and causes severe respiratory infections in AIDS patients. It is important to differentiate these two bacterial species as they co-exist in immuno-compromised individuals. To unambiguously distinguish these two closely related bacterial strains, we employed Raman and resonance Raman spectroscopy in conjunction with multivariate statistical tools. Phenotypic profiling for these bacterial species was performed in a kinetic manner. Differences were observed in the mycolic acid profile and carotenoid pigments to show that MIP is biochemically distinct from M. intracellulare. Resonance Raman studies confirmed that carotenoids were produced by both MIP as well as M. intracellulare, though the latter produced higher amounts. Overall, this study demonstrates the potential of Raman spectroscopy in differentiating two closely related mycobacterial strains. Graphical abstract.

Effect of growth media and phase on Raman spectra and discrimination of mycobacteria

When developing a Raman spectral library to identify bacteria, differences between laboratory and real world conditions must be considered. For example, culturing bacteria in laboratory settings is performed under conditions for ideal bacteria growth. In contrast, culture conditions in the human body may differ and may not support optimized bacterial growth. To address these differences, researchers have studied the effect of conditions such as growth media and phase on Raman spectra. However, the majority of these studies focused on Gram-positive or Gram-negative bacteria. This article focuses on the influence of growth media and phase on Raman spectra and discrimination of mycobacteria, an acid-fast genus. Results showed that spectral differences from growth phase and media can be distinguished by spectral observation and multivariate analysis. Results were comparable to those found for other types of bacteria, such as Gram-positive and Gram-negative. In addition, the influence of growth phase and media had a significant impact on machine learning models and their resulting classification accuracy. This study highlights the need for machine learning models and their associated spectral libraries to account for various growth parameters and stages to further the transition of Raman spectral analysis of bacteria from laboratory to clinical settings.

Rapid screening of colistin-resistant Escherichia coli, Acinetobacter baumannii and Pseudomonas aeruginosa by the use of Raman spectroscopy and hierarchical cluster analysis

Colistin is recognized as the last therapeutic option for multidrug-resistant Gram-negative bacteria infection. In addition, bacterial resistance to colistin could be transmitted between different species through plasmid-mediated mcr-1 gene transfer. Therefore, rapid screening of colistin-resistant isolates will play a key role in controlling the spread of resistance and improving patient outcomes. We developed a rapid method for the detection of colistin-resistance in Escherichia coli, Acinetobacter baumannii, and Pseudomonas aeruginosa bacteria based on Raman spectroscopy and hierarchical cluster analysis. Bacteria were incubated with and without colistin using CAMHB as the liquid culture medium. They were then centrifuged and dried on a glass slide. Five Raman spectra of each of the samples were recorded and analyzed by the hierarchical cluster analysis method to determine whether the bacteria were resistant. To evaluate this method, 123 clinical bacterial isolates (42 isolates of E. coli, 41 isolates of A. baumannii and 40 isolates of P. aeruginosa) were tested. The detection sensitivity and specificity were 90.9% and 91.1%, respectively, compared with the reference broth microdilution method. The screening is easy to perform and can be completed in 1.5 h, suggesting that it holds great potential to be an initial screening method in countries and areas where colistin becomes the last resort antibiotic.

Isolation of intact bacteria from blood by selective cell lysis in a microfluidic porous silica monolith

Rapid and efficient isolation of bacteria from complex biological matrices is necessary for effective pathogen identification in emerging single-cell diagnostics. Here, we demonstrate the isolation of intact and viable bacteria from whole blood through the selective lysis of blood cells during flow through a porous silica monolith. Efficient mechanical hemolysis is achieved while providing passage of intact and viable bacteria through the monoliths, allowing size-based isolation of bacteria to be performed following selective lysis. A process for synthesizing large quantities of discrete capillary-bound monolith elements and millimeter-scale monolith bricks is described, together with the seamless integration of individual monoliths into microfluidic chips. The impact of monolith morphology, geometry, and flow conditions on cell lysis is explored, and flow regimes are identified wherein robust selective blood cell lysis and intact bacteria passage are achieved for multiple gram-negative and gram-positive bacteria. The technique is shown to enable rapid sample preparation and bacteria analysis by single-cell Raman spectrometry. The selective lysis technique presents a unique sample preparation step supporting rapid and culture-free analysis of bacteria for the point of care.

In vitro discrimination and classification of Microbial Flora of Poultry using two dispersive Raman spectrometers (microscope and Portable Fiber-Optic systems) in tandem with chemometric analysis

Discrimination and classification of eight strains related to meat spoilage and pathogenic microorganisms commonly found in poultry meat were successfully carried out using two dispersive Raman spectrometers (Microscope and Portable Fiber-Optic systems) in combination with chemometric methods. Principal components analysis (PCA) and multi-class support vector machines (MC-SVM) were applied to develop discrimination and classification models. These models were certified using validation data sets which were successfully assigned to the correct bacterial species and even to the right strain. The discrimination of bacteria down to the strain level was performed for the pre-processed spectral data using a 3-stage model based on PCA. The spectral features and differences among the species on which the discrimination was based were clarified through PCA loadings. In MC-SVM the pre-processed spectral data was subjected to PCA and utilized to build a classification model. When using the first two components, the accuracy of the MC-SVM model was 97.64% and 93.23% for the validation data collected by the Raman Microscope and the Portable Fiber-Optic Raman system, respectively. The accuracy reached 100% for the validation data by using the first eight and ten PC's from the data collected by Raman Microscope and by Portable Fiber-Optic Raman system, respectively. The results reflect the strong discriminative power and the high performance of the developed models, the suitability of the pre-processing method used in this study and that the low accuracy of the Portable Fiber-Optic Raman system does not adversely affect the discriminative power of the developed models.

Hierarchical deep convolutional neural networks combine spectral and spatial information for highly accurate Raman-microscopy-based cytopathology

Hierarchical variants of so-called deep convolutional neural networks (DCNNs) have facilitated breakthrough results for numerous pattern recognition tasks in recent years. We assess the potential of these novel whole-image classifiers for Raman-microscopy-based cytopathology. Conceptually, DCNNs facilitate a flexible combination of spectral and spatial information for classifying cellular images as healthy or cancer-affected cells. As we demonstrate, this conceptual advantage translates into practice, where DCNNs exceed the accuracy of both conventional classifiers based on pixel spectra as well as classifiers based on morphological features extracted from Raman microscopic images. Remarkably, accuracies exceeding those of all previously proposed classifiers are obtained while using only a small fraction of the spectral information provided by the dataset. Overall, our results indicate a high potential for DCNNs in medical applications of not just Raman, but also infrared microscopy.

Drug-Resistant Staphylococcus aureus Strains Reveal Distinct Biochemical Features with Raman Microspectroscopy

Staphylococcus aureus ( S. aureus) is a leading cause of hospital-acquired infections, such as bacteremia, pneumonia, and endocarditis. Treatment of these infections can be challenging since strains of S. aureus, such as methicillin-resistant S. aureus (MRSA), have evolved resistance to antimicrobials. Current methods to identify infectious agents in hospital environments often rely on time-consuming, multistep culturing techniques to distinguish problematic strains (i.e., antimicrobial resistant variants) of a particular bacterial species. Therefore, a need exists for a rapid, label-free technique to identify drug-resistant bacterial strains to guide proper antibiotic treatment. Here, our findings demonstrate the ability to characterize and identify microbes at the subspecies level using Raman microspectroscopy, which probes the vibrational modes of molecules to provide a biochemical "fingerprint". This technique can distinguish between different isolates of species such as Streptococcus agalactiae and S. aureus. To determine the ability of this analytical approach to detect drug-resistant bacteria, isogenic variants of S. aureus including the comparison of strains lacking or expressing antibiotic resistance determinants were evaluated. Spectral variations observed may be associated with biochemical components such as amino acids, carotenoids, and lipids. Mutants lacking carotenoid production were distinguished from wild-type S. aureus and other strain variants. Furthermore, spectral biomarkers of S. aureus isogenic bacterial strains were identified. These results demonstrate the feasibility of Raman microspectroscopy for distinguishing between various genetically distinct forms of a single bacterial species in situ. This is important for detecting antibiotic-resistant strains of bacteria and indicates the potential for future identification of other multidrug resistant pathogens with this technique.

Paper-Based Surface-Enhanced Raman Spectroscopy for Diagnosing Prenatal Diseases in Women

We report the development of a surface-enhanced Raman spectroscopy sensor chip by decorating gold nanoparticles (AuNPs) on ZnO nanorod (ZnO NR) arrays vertically grown on cellulose paper (C). We show that these chips can enhance the Raman signal by 1.25 × 10⁷ with an excellent reproducibility of <6%. We show that we can measure trace amounts of human amniotic fluids of patients with subclinical intra-amniotic infection (IAI) and preterm delivery (PTD) using the chip in combination with a multivariate statistics-derived machine-learning-trained bioclassification method. We can detect the presence of prenatal diseases and identify the types of diseases from amniotic fluids with >92% clinical sensitivity and specificity. Our technology has the potential to be used for the early detection of prenatal diseases and can be adapted for point-of-care applications.

Identification of natural product compounds as quorum sensing inhibitors in Pseudomonas fluorescens P07 through virtual screening

Pseudomonas fluorescens, a Gram-negative psychrotrophic bacteria, is the main microorganism causing spoilage of chilled raw milk and aquatic products. Quorum sensing (QS) widely exists in bacteria to monitor their population densities and regulate numerous physiological activities, such as the secretion of siderophores, swarming motility and biofilm formation. Thus, searching for quorum sensing inhibitors (QSIs) may be another promising way to control the deterioration of food caused by P. fluorescens. Here, we screened a traditional Chinese medicine (TCM) database to discover potential QSIs with lesser toxicity. The gene sequences of LuxI- and LuxR-type proteins of P. fluorescens P07 were obtained through whole-genome sequencing. In addition, the protein structures built by homology modelling were used as targets to screen for QSIs. Twenty-one compounds with a dock score greater than 6 were purchased and tested by biosensor strains (Chromobacterium violaceum CV026 and Agrobacterium tumefaciens A136). The results showed that 10 of the compounds were determined as hits (hit rate: 66.67%). Benzyl alcohol, rhodinyl formate and houttuynine were effective QSIs. The impact of the most active compound (benzyl alcohol) on the phenotypes of P. fluorescens P07, including swimming and swarming motility, production of extracellular enzymes and siderophores, N-acylhomoserine lactone (AHLs) content and biofilm formation were determined. The inhibitory mechanism of benzyl alcohol on the QS system of P. fluorescens P07 is further discussed. This study reveals the feasibility of searching for novel QSIs through virtual screening.

Microfluidic Cultivation and Laser Tweezers Raman Spectroscopy of E. coli under Antibiotic Stress

Analyzing the cells in various body fluids can greatly deepen the understanding of the mechanisms governing the cellular physiology. Due to the variability of physiological and metabolic states, it is important to be able to perform such studies on individual cells. Therefore, we developed an optofluidic system in which we precisely manipulated and monitored individual cells of Escherichia coli. We tested optical micromanipulation in a microfluidic chamber chip by transferring individual bacteria into the chambers. We then subjected the cells in the chambers to antibiotic cefotaxime and we observed the changes by using time-lapse microscopy. Separately, we used laser tweezers Raman spectroscopy (LTRS) in a different micro-chamber chip to manipulate and analyze individual cefotaxime-treated E. coli cells. Additionally, we performed conventional Raman micro-spectroscopic measurements of E. coli cells in a micro-chamber. We found observable changes in the cellular morphology (cell elongation) and in Raman spectra, which were consistent with other recently published observations. The principal component analysis (PCA) of Raman data distinguished between the cefotaxime treated cells and control. We tested the capabilities of the optofluidic system and found it to be a reliable and versatile solution for this class of microbiological experiments.

Monitoring metabolic reactions in Staphylococcus epidermidis exposed to silicon nitride using in situ time-lapse Raman spectroscopy

Staphylococcus epidermidis (S. epidermidis) is one of the leading nosocomial pathogens, particularly associated with periprosthetic infections of biomedical implants. Silicon nitride (Si3N4), a nonoxide biomaterial widely used in spinal implants, has shown bacteriostatic effects against both gram-positive and gram-negative bacteria; however, the physicochemical interactions between Si3N4 and bacteria yet remain conspicuously unexplored. In situ time-lapse Raman spectroscopic experiments were conducted by exposing S. epidermidis for 12, 24, and 48 h to Si3N4 substrates to understand the evolution of bacterial metabolism and to elucidate the ceramics antimicrobial behavior. The Raman probe captured an initial metabolic response of the bacteria to the adverse chemistry of the Si3N4 surface, which included peroxidation of membrane phospholipids and protein structural modifications to adjust for survivorship. However, beyond 24 h of exposure, the Raman signals representing DNA, lipids, proteins, and carbohydrates showed clear fingerprints of bacterial lysis. Bands related to biofilm formation completely disappeared or underwent drastically reduced intensity. Bacterial lysis was confirmed by conventional fluorescence microscopy methods. Spectroscopic experiments suggested that a pH change at the Si3N4's surface induced variations in the membrane structure and D-alanylation of teichoic acids in its peptidoglycan layer. Concurrent stimulation of peptidoglycan hydrolase (i.e., an enzyme involved with autolysis) ultimately led to membrane degradation and cellular death. An additional finding was that modulating the Si3N4 surface by increasing the population of amine groups improved the efficiency of the substrate against S. epidermidis, thus suggesting that optimization of the near-surface (alkaline) conditions may be a viable approach to bacterial reduction.

Rapid identification of staphylococci by Raman spectroscopy

Clinical treatment of the infections caused by various staphylococcal species differ depending on the actual cause of infection. Therefore, it is necessary to develop a fast and reliable method for identification of staphylococci. Raman spectroscopy is an optical method used in multiple scientific fields. Recent studies showed that the method has a potential for use in microbiological research, too. Our work here shows a possibility to identify staphylococci by Raman spectroscopy. We present a method that enables almost 100% successful identification of 16 of the clinically most important staphylococcal species directly from bacterial colonies grown on a Mueller-Hinton agar plate. We obtained characteristic Raman spectra of 277 staphylococcal strains belonging to 16 species from a 24-hour culture of each strain grown on the Mueller-Hinton agar plate using the Raman instrument. The results show that it is possible to distinguish among the tested species using Raman spectroscopy and therefore it has a great potential for use in routine clinical diagnostics.

Detection of molecular changes induced by antibiotics in Escherichia coli using vibrational spectroscopy

This study aimed to test Raman (400-1800cm^-1) and Infra-red (1900-500cm^-1) spectroscopies followed by statistical analysis (principal component analysis) to detect molecular changes induced by antibiotics (ampicillin, cefotaxime - cell wall synthesis inhibitors, tetracycline - protein synthesis inhibitor, ciprofloxacin - DNA synthesis inhibitor) against Escherichia coli TOP10. In case of ampicillin and cefotaxime, a decrease in protein bands in both Raman (1240, 1660cm^-1), and IR spectra (1230, 1530, 1630cm^-1), and an increase in carbohydrate bands (1150, 1020cm^-1) in IR spectra were observed. Tetracycline addition caused an increase in nucleic acid bands (775, 1478, 1578cm^-1), a sharp decrease in phenylalanine (995cm^-1) in Raman spectra and the amide I and amide II bands (1630, 1530cm^-1) in IR spectra, an increase in DNA in both Raman (1083cm^-1) and IR spectra (1080cm^-1). Regarding ciprofloxacin, an increase in nucleic acids (775, 1478, 1578cm^-1) in Raman spectra and in protein bands (1230, 1520, 1630cm^-1), in DNA (1080cm^-1) in IR spectra were detected. Clear discrimination of antibiotic-treated samples compared to the control was recorded, showing that Raman and IR spectroscopies, coupled to principal component analysis for data, could be used to detect molecular modifications in bacteria exposed to different classes of antibiotics. These findings contribute to the understanding of the mechanisms of action of antibiotics in bacteria.

Removal of Foodborne Pathogen Biofilms by Acidic Electrolyzed Water

Biofilms, which are complex microbial communities embedded in the protective extracellular polymeric substances (EPS), are difficult to remove in food production facilities. In this study, the use of acidic electrolyzed water (AEW) to remove foodborne pathogen biofilms was evaluated. We used a green fluorescent protein-tagged Escherichia coli for monitoring the efficiency of AEW for removing biofilms, where under the optimal treatment conditions, the fluorescent signal of cells in the biofilm disappeared rapidly and the population of biofilm cells was reduced by more than 67%. Additionally, AEW triggered EPS disruption, as indicated by the deformation of the carbohydrate C-O-C bond and deformation of the aromatic rings in the amino acids tyrosine and phenylalanine. These deformations were identified by EPS chemical analysis and Raman spectroscopic analysis. Scanning electron microscopy (SEM) images confirmed that the breakup and detachment of biofilm were enhanced after AEW treatment. Further, AEW also eradicated biofilms formed by both Gram-negative bacteria (Vibrio parahaemolyticus) and Gram-positive bacteria (Listeria monocytogenes) and was observed to inactivate the detached cells which are a potential source of secondary pollution. This study demonstrates that AEW could be a reliable foodborne pathogen biofilm disrupter and an eco-friendly alternative to sanitizers traditionally used in the food industry.