Isolation and identification of bacteria by means of Raman spectroscopy

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.

Template bacteria-free fabrication of surface imprinted polymer-based biosensor for E. coli detection using photolithographic mimics: Hacking bacterial adhesion

As one class of molecular imprinted polymers (MIPs), surface imprinted polymer (SIP)-based biosensors show great potential in direct whole-bacteria detection. Micro-contact imprinting, that involves stamping the template bacteria immobilized on a substrate into a pre-polymerized polymer matrix, is the most straightforward and prominent method to obtain SIP-based biosensors. However, the major drawbacks of the method arise from the requirement for fresh template bacteria and often non-reproducible bacteria distribution on the stamp substrate. Herein, we developed a positive master stamp containing photolithographic mimics of the template bacteria (E. coli) enabling reproducible fabrication of biomimetic SIP-based biosensors without the need for the "real" bacteria cells. By using atomic force and scanning electron microscopy imaging techniques, respectively, the E. coli-capturing ability of the SIP samples was tested, and compared with non-imprinted polymer (NIP)-based samples and control SIP samples, in which the cavity geometry does not match with E. coli cells. It was revealed that the presence of the biomimetic E. coli imprints with a specifically designed geometry increases the sensor E. coli-capturing ability by an "imprinting factor" of about 3. These findings show the importance of geometry-guided physical recognition in bacterial detection using SIP-based biosensors. In addition, this imprinting strategy was employed to interdigitated electrodes and QCM (quartz crystal microbalance) chips. E. coli detection performance of the sensors was demonstrated with electrochemical impedance spectroscopy (EIS) and QCM measurements with dissipation monitoring technique (QCM-D).

RamanCluster: A deep clustering-based framework for unsupervised Raman spectral identification of pathogenic bacteria

Raman spectroscopy serves as a powerful and reliable tool for the characterization of pathogenic bacteria. The integration of Raman spectroscopy with artificial intelligence techniques to rapidly identify pathogenic bacteria has become paramount for expediting disease diagnosis. However, the development of prevailing supervised artificial intelligence algorithms is still constrained by costly and limited well-annotated Raman spectroscopy datasets. Furthermore, tackling various high-dimensional and intricate Raman spectra of pathogenic bacteria in the absence of annotations remains a formidable challenge. In this paper, we propose a concise and efficient deep clustering-based framework (RamanCluster) to achieve accurate and robust unsupervised Raman spectral identification of pathogenic bacteria without the need for any annotated data. RamanCluster is composed of a novel representation learning module and a machine learning-based clustering module, systematically enabling the extraction of robust discriminative representations and unsupervised Raman spectral identification of pathogenic bacteria. The extensive experimental results show that RamanCluster has achieved high accuracy on both Bacteria-4 and Bacteria-6, with ACC values of 77 % and 74.1 %, NMI values of 75 % and 73 %, as well as AMI values of 74.6 % and 72.6 %, respectively. Furthermore, compared with other state-of-the-art methods, RamanCluster exhibits the superior accuracy on handling various complicated pathogenic bacterial Raman spectroscopy datasets, including situations with strong noise and a wide variety of pathogenic bacterial species. Additionally, RamanCluster also demonstrates commendable robustness in these challenging scenarios. In short, RamanCluster has a promising prospect in accelerating the development of low-cost and widely applicable disease diagnosis in clinical medicine.

Non-invasive detection of regulatory T cells with Raman spectroscopy

Regulatory T cells (Tregs) are a type of lymphocyte that is key to maintaining immunological self-tolerance, with great potential for therapeutic applications. A long-standing challenge in the study of Tregs is that the only way they can be unambiguously identified is by using invasive intracellular markers. Practically, the purification of live Tregs is often compromised by other cell types since only surrogate surface markers can be used. We present here a non-invasive method based on Raman spectroscopy that can detect live unaltered Tregs by coupling optical detection with machine learning implemented with regularized logistic regression. We demonstrate the validity of this approach first on murine cells expressing a surface Foxp3 reporter, and then on peripheral blood human T cells. By including methods to account for sample purity, we could generate reliable models that can identify Tregs with an accuracy higher than 80%, which is already comparable with typical sorting purities achievable with standard methods that use proxy surface markers. We could also demonstrate that it is possible to reliably detect Tregs in fully independent donors that are not part of the model training, a key milestone for practical applications.

Fabrication of a Label-Free Immunosensor Using Surface-Engineered AuPt@GQD Core-Shell Nanocomposite for the Selective Detection of Trace Levels of Escherichia coli from Contaminated Food Samples

Fabrication of label-free immunosensors is highly necessitated due to their simplicity, cost-effectiveness, and robustness. Herein, we report the facile development of a label-free, direct, rapid, capacitive immunosensor for ultrasensitive and rapid recognition of trace levels of Escherichia coli from contaminated food samples. This was achieved using gold platinum core-shell nanoparticles loaded with graphene quantum dots (AuPt@GQDs) that were utilized as electrode modifiers. The incorporation of GQDs to the surface of AuPt core-shell nanoparticles was performed using the "greener" probe-sonication method. The electrochemical properties of AuPt@GQDs, determined using cyclic voltammetry and electrochemical impedance spectroscopy, suggested the optimized loading concentration of AuPt to be 0.05% in the core-shell nanocomposite to exhibit the highest current response. Furthermore, immobilization of anti-E. coli monoclonal antibodies (anti-E. coli mAb) onto the surface of modified electrodes was performed using amine coupling. The high specific binding of E. coli cells onto the surface of the immuno-electrode was measured as a direct function of change in transient capacitance with time that was measured at low and high frequencies. The resultant immunosensor (bovine serum albumin/anti-E. coli mAb/AuPt0.05@GQDs/FTO) demonstrated a detection range (5 to 4.5 × 103 cells/mL), with the detection limit as low as 1.5 × 102 cells/mL, and an excellent sensitivity ∼171,281.40 μF-1 mL cells-1 cm-2 without the use of any labels (R2-0.99). These findings were further verified using real sample analysis wherein the immuno-electrode demonstrated outstanding sensitivity, the highest noticed so far. More interestingly, the high resuability ∼48 weeks (RSD-5.92%) and excellent reproducibility in detection results (RSD ∼ 9.5%) testify its potential use in a clinical setting. The results reveal the usefulness of the surface-engineered AuPt@GQDs core-shell nanocomposite as an electrode modifier that can be used for the development of newer on-site monitoring devices to estimate trace levels of pathogens present as contaminants in food samples.

Genetic algorithm-based semisupervised convolutional neural network for real-time monitoring of Escherichia coli fermentation of recombinant protein production using a Raman sensor

As a non-destructive sensing technique, Raman spectroscopy is often combined with regression models for real-time detection of key components in microbial cultivation processes. However, achieving accurate model predictions often requires a large amount of offline measurement data for training, which is both time-consuming and labor-intensive. In order to overcome the limitations of traditional models that rely on large datasets and complex spectral preprocessing, in addition to the difficulty of training models with limited samples, we have explored a genetic algorithm-based semi-supervised convolutional neural network (GA-SCNN). GA-SCNN integrates unsupervised process spectral labeling, feature extraction, regression prediction, and transfer learning. Using only an extremely small number of offline samples of the target protein, this framework can accurately predict protein concentration, which represents a significant challenge for other models. The effectiveness of the framework has been validated in a system of Escherichia coli expressing recombinant ProA5M protein. By utilizing the labeling technique of this framework, the available dataset for glucose, lactate, ammonium ions, and optical density at 600 nm (OD600) has been expanded from 52 samples to 1302 samples. Furthermore, by introducing a small component of offline detection data for recombinant proteins into the OD600 model through transfer learning, a model for target protein detection has been retrained, providing a new direction for the development of associated models. Comparative analysis with traditional algorithms demonstrates that the GA-SCNN framework exhibits good adaptability when there is no complex spectral preprocessing. Cross-validation results confirm the robustness and high accuracy of the framework, with the predicted values of the model highly consistent with the offline measurement results.

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.

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.

New clues for postbiotics to improve host health: a review from the perspective of function and mechanisms

Strain activity and stability severely limit the beneficial effects of probiotics in modulating host health. Postbiotics have emerged as a promising alternative as they can provide similar or even enhanced efficacy to probiotics, even under inactivated conditions. This review introduces the ingredients, preparation, and identification techniques of postbiotics, focusing on the comparison of the advantages and limitations between probiotics and postbiotics based on their mechanisms and applications. Inactivation treatment is the most significant difference between postbiotics and probiotics. We highlight the use of emerging technologies to inactivate probiotics, optimize process conditions to maintain the activity of postbiotics, or scale up their production. Postbiotics have high stability which can overcome unfavorable factors, such as easy inactivation and difficult colonization of probiotics after entering the intestine, and are rapidly activated, allowing continuous and rapid optimization of the intestinal microecological environment. They provide unique mechanisms, and multiple targets act on the gut-organ axis, co-providing new clues for the study of the biological functions of postbiotics. We summarize the mechanisms of action of inactivated lactic acid bacteria, highlighting that the NF-κB and MAPK pathways can be used as immune targeting pathways for postbiotic modulation of host health. Generally, we believe that as the classification, composition, and efficacy mechanism of postbiotics become clearer they will be more widely used in food, medicine, and other fields, greatly enriching the dimensions of food innovation. © 2024 Society of Chemical Industry.

Passive microfluidic devices for cell separation

The separation of specific cell populations is instrumental in gaining insights into cellular processes, elucidating disease mechanisms, and advancing applications in tissue engineering, regenerative medicine, diagnostics, and cell therapies. Microfluidic methods for cell separation have propelled the field forward, benefitting from miniaturization, advanced fabrication technologies, a profound understanding of fluid dynamics governing particle separation mechanisms, and a surge in interdisciplinary investigations focused on diverse applications. Cell separation methodologies can be categorized according to their underlying separation mechanisms. Passive microfluidic separation systems rely on channel structures and fluidic rheology, obviating the necessity for external force fields to facilitate label-free cell separation. These passive approaches offer a compelling combination of cost-effectiveness and scalability when compared to active methods that depend on external fields to manipulate cells. This review delves into the extensive utilization of passive microfluidic techniques for cell separation, encompassing various strategies such as filtration, sedimentation, adhesion-based techniques, pinched flow fractionation (PFF), deterministic lateral displacement (DLD), inertial microfluidics, hydrophoresis, viscoelastic microfluidics, and hybrid microfluidics. Besides, the review provides an in-depth discussion concerning cell types, separation markers, and the commercialization of these technologies. Subsequently, it outlines the current challenges faced in the field and presents a forward-looking perspective on potential future developments. This work hopes to aid in facilitating the dissemination of knowledge in cell separation, guiding future research, and informing practical applications across diverse scientific disciplines.

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.

Bioaerosols in the atmosphere: A comprehensive review on detection methods, concentration and influencing factors

In the past few decades, especially since the outbreak of the coronavirus disease (COVID-19), the effects of atmospheric bioaerosols on human health, the environment, and climate have received great attention. To evaluate the impacts of bioaerosols quantitatively, it is crucial to determine the types of bioaerosols in the atmosphere and their spatial-temporal distribution. We provide a concise summary of the online and offline observation strategies employed by the global research community to sample and analyze atmospheric bioaerosols. In addition, the quantitative distribution of bioaerosols is described by considering the atmospheric bioaerosols concentrations at various time scales (daily and seasonal changes, for example), under various weather, and different underlying surfaces. Finally, a comprehensive summary of the reasons for the spatiotemporal distribution of bioaerosols is discussed, including differences in emission sources, the impact process of meteorological factors and environmental factors. This review of information on the latest research progress contributes to the emergence of further observation strategies that determine the quantitative dynamics of public health and ecological effects of bioaerosols.

Compressive Raman imaging by combining scattering-projection interleaving with context-aware excitation

Exciting an object with a laser-focus array and randomly interleaving its scattering projection has been proved to be an effective strategy for speeding up Raman imaging. The so-called scattering interleaved Raman imaging (SIRI) method allows Raman hyperspectral imaging with a single snapshot and exhibits excellent reconstruction fidelity and signal-to-noise ratios (SNRs). Here, we show that the performance of SIRI is significantly improved when combined with context-aware excitation. The experiments on micro-plastics demonstrate that the restriction of Raman excitation within a smaller region of interest as guided by bright-field microscopy improves the signal intensity and the SNR, and it is surprising that the spectral resolution is also significantly improved. The context-aware SIRI method is successfully used for imaging of lipid-producing yeast cells, suggesting that it is a promising analytical tool for studying live cells or tissues.

Simulating a reference medium for determining bacterial growth in hospital wastewater for Raman spectroscopic investigation

Wastewater is a very complex and diverse medium, which despite low nutrient density still harbors bacteria. Especially the wastewater from hospitals contains a high germ load. However, wastewater is also very variable and differs not only from day to day, but also from house to house. Since wastewater is always changing and medium has an impact on Raman spectra of bacteria, it is necessary to find a surrogate material in which bacteria can be cultured to mimic a real hospital wastewater sample. In this study, we investigate two different artificial wastewaters for their abilities as a good alternative to real wastewater from the Jena University Hospital and to serve as a reference material for bacterial cultivation with subsequent Raman measurement. Each of the artificial wastewater on its own was not suitable to be used as a reference medium. Only the combination of the two simulated wastewaters achieved satisfactory results in the Raman spectroscopic identification of bacteria from real wastewater. These results could be used later in new experiments as a reference dataset to identify bacteria from real hospital wastewater samples.

Surface-enhanced Raman spectroscopy for characterization of supernatant samples of biofilm forming bacterial strains

Staphylococcus epidermidis is considered major cause of nosocomial infections. Its pathogenicity is mainly due to the ability to form biofilms on different surfaces, particularly indwelling medical devices. This bacterium consists of different strains consisting of non, medium and strong biofilm forming ones. Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical technique that can be used to detect and analyze biochemical composition of the supernatant samples of different strains of bacteria including non, medium and strong biofilm forming bacterial strains. SERS is a powerful technique for the robust, reliable, rapid detection and discrimination of bacteria in the form of characteristic SERS spectral features which can be used for detection and classification. SERS is used to differentiate three classes of bacteria with respect to their biofilm forming ability. Silver nanoparticles (Ag NPs) are used as SERS substrate and synthesized with chemical reduction method. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are used to discriminate SERS spectral data sets of non, medium and strong biofilm forming bacteria. PLS-DA analysis is a multivariate statistical technique that can be used to analyze data from bacterial sets.

Deep learning-based ultra-fast identification of Raman spectra with low signal-to-noise ratio

Ensuring the correct use of cell lines is crucial to obtaining reliable experimental results and avoiding unnecessary waste of resources. Raman spectroscopy has been confirmed to be able to identify cell lines, but the collection time is usually 10-30 s. In this study, we acquired Raman spectra of five cell lines with integration times of 0.1 and 8 s, respectively, and the average accuracy of using long-short memory neural network to identify the spectra of 0.1 s was 95%, and the average accuracy of identifying the spectra of 8 s was 99.8%. At the same time, we performed data enhancement of 0.1 s spectral data by real-valued non-volume preserving method, and the recognition average accuracy of long-short memory neural networks recognition of the enhanced spectral data was improved to 96.2%. With this method, we shorten the acquisition time of Raman spectra to 1/80 of the original one, which greatly improves the efficiency of cell identification.

Hybrid use of Raman spectroscopy and artificial neural networks to discriminate Mycobacterium bovis BCG and other Mycobacteriales

Even in the face of the COVID-19 pandemic, Tuberculosis (TB) continues to be a major public health problem and the 2nd biggest infectious cause of death worldwide. There is, therefore, an urgent need to develop effective TB diagnostic methods, which are cheap, portable, sensitive and specific. Raman spectroscopy is a potential spectroscopic technique for this purpose, however, so far, research efforts have focused primarily on the characterisation of Mycobacterium tuberculosis and other Mycobacteria, neglecting bacteria within the microbiome and thus, failing to consider the bigger picture. It is paramount to characterise relevant Mycobacteriales and develop suitable analytical tools to discriminate them from each other. Herein, through the combined use of Raman spectroscopy and the self-optimising Kohonen index network and further multivariate tools, we have successfully undertaken the spectral analysis of Mycobacterium bovis BCG, Corynebacterium glutamicum and Rhodoccocus erythropolis. This has led to development of a useful tool set, which can readily discern spectral differences between these three closely related bacteria as well as generate a unique spectral barcode for each species. Further optimisation and refinement of the developed method will enable its application to other bacteria inhabiting the microbiome and ultimately lead to advanced diagnostic technologies, which can save many lives.

Rapid identification of pathogens in blood serum via Raman tweezers in combination with advanced processing methods

Pathogenic microbes contribute to several major global diseases that kill millions of people every year. Bloodstream infections caused by these microbes are associated with high morbidity and mortality rates, which are among the most common causes of hospitalizations. The search for the "Holy Grail" in clinical diagnostic microbiology, a reliable, accurate, low cost, real-time, and easy-to-use diagnostic method, is one of the essential issues in clinical practice. These very critical conditions can be met by Raman tweezers in combination with advanced analysis methods. Here, we present a proof-of-concept study based on Raman tweezers combined with spectral mixture analysis that allows for the identification of microbial strains directly from human blood serum without user intervention, thus eliminating the influence of a data analyst.

Fusion data from FT-IR and MALDI-TOF MS result in more accurate classification of specific microbiota

Microbes are usually present as a specific microbiota, and their classification remains a challenge. MALDI-TOF MS is particularly successful in library-based microbial identification at the species level as it analyzes the molecular weight of peptides and ribosomal proteins. FT-IR allows more accurate classification of bacteria at the subspecies level due to the high sensitivity, specificity and repeatability of FT-IR signals from bacteria, which is not achievable with MALDI-TOF MS. Previous studies have shown that more accurate identification results can be obtained by the fusion of FT-IR and MALDI-TOF MS spectral data. Here, we constructed 20 groups of model microbiota samples and used FT-IR, MALDI-TOF MS, and their fusion data to classify them. Hierarchical clustering analysis (HCA) showed that the classification accuracy of FT-IR, MALDI-TOF MS, and the fusion data was 85%, 90%, and 100%, respectively. These results indicate that both FT-IR and MALDI-TOF MS can effectively classify specific microbiota, and the fusion of their spectral data could improve the classification accuracy. The FT-IR and MALDI-TOF MS data fusion strategy may be a promising technology for specific microbiota classification.

Culture-Independent Multiplexed Detection of Drug-Resistant Bacteria Using Surface-Enhanced Raman Scattering

The rapid and accurate detection of bacteria resistance to β-lactam antibiotics is critical to inform optimal treatment and prevent overprescription of potent antibiotics. Here, we present a fast, culture-independent method for the detection of extended-spectrum β-lactamases (ESBLs) using surface-enhanced Raman scattering (SERS). The method uses Raman probes that release sulfur-based Raman active molecules in the presence of β-lactamases. The released thiol molecules can be captured by gold nanoparticles, leading to amplified Raman signals. A broad-spectrum cephalosporin probe R1G and an ESBL-specific probe R3G are designed to enable duplex detection of bacteria expressing broad-spectrum β-lactamases or ESBLs with a detection limit of 103 cfu/mL in 1 h incubation. Combined with a portable Raman microscope, our culturing-free SERS assay has reduced screening time to 1.5 h without compromising sensitivity and specificity.

Detection of apple fruit damages through Raman spectroscopy with cascade forest

Apple fruit damages seriously cause product and economic losses, infringe consumer rights and interests, and have harmful effects on human and livestock health. In this study, Raman spectroscopy (RS) and cascade forest (CForest) were adopted to determine apple fruit damages. First, the RS spectra of healthy, bruised, Rhizopus-infected, and Botrytis-infected apples were measured. Spectral changes and band attribution were analyzed. Different modeling methods were combined with various pre-processing and dimension reduction methods to construct recognition models. Among all models, CForest constructed with full spectra processed by Savitsky-Golay smoothing obtained the best performance with accuracies of 100%, 91.96%, and 92.80% in the training, validation, and test sets (ACC_TE). And the modeling time is reduced to 1/3 of the full-spectra model with a similar ACC_TE of 91.56% after principal component analysis. Overall, RS and CForest provided a non-destructive, rapid, and accurate identification of apple fruit damages and could be used in disease recognition and safety assurance of other fruits.

Antimicrobial Resistance Studies Using Raman Spectroscopy on Clinically Relevant Bacterial Strains

There has been a steep rise in the emergence of antibiotic-resistant bacteria in the past few years. A timely diagnosis can help in initiating appropriate antibiotic therapy. However, conventional techniques for diagnosing antibiotic resistance are time-consuming and labor-intensive. Therefore, we investigated the potential of Raman spectroscopy as a rapid surveillance technology for tracking the emergence of antibiotic resistance. In this study, we used Raman spectroscopy to differentiate clinical isolates of antibiotic-resistant and -sensitive bacteria of Escherichia coli, Acinetobacter baumannii, and Enterobacter species. The spectra were collected with or without exposure to various antibiotics (ciprofloxacin, gentamicin, meropenem, and nitrofurantoin), each having a distinct mechanism of action. Ciprofloxacin- and meropenem-treated sensitive strains showed a decrease in the intensity of Raman bands associated with DNA (667, 724, 785, 1378, 1480, and 1575 cm^-1) and proteins (640 and 1662 cm^-1), coupled with an increase in the intensity of lipid bands (891, 960, and 1445 cm^-1). Gentamicin- and nitrofurantoin-treated sensitive strains showed an increase in the intensity of nucleic acid bands (668, 724, 780, 810, 1378, 1480, and 1575 cm^-1) while a decrease in the intensity of protein bands (640, 1003, 1606, and 1662 cm^-1) and the lipid band (1445 cm^-1). The Raman spectral changes observed in the antibiotic-resistant strains were opposite to that of antibiotic-sensitive strains. The Raman spectral data correlated well with the antimicrobial susceptibility test results. The Raman spectral dataset was used for partial least-squares (PLS) analysis to validate the biomarkers obtained from the univariate analysis. Overall, this study showcases the potential of Raman spectroscopy for detecting antibiotic-resistant and -sensitive bacteria.

Rapid Identification of Pathogens Causing Bloodstream Infections by Raman Spectroscopy and Raman Tweezers

The search for the "Holy Grail" in clinical diagnostic microbiology-a reliable, accurate, low-cost, real-time, easy-to-use method-has brought up several methods with the potential to meet these criteria. One is Raman spectroscopy, an optical, nondestructive method based on the inelastic scattering of monochromatic light. The current study focuses on the possible use of Raman spectroscopy for identifying microbes causing severe, often life-threatening bloodstream infections. We included 305 microbial strains of 28 species acting as causative agents of bloodstream infections. Raman spectroscopy identified the strains from grown colonies, with 2.8% and 7% incorrectly identified strains using the support vector machine algorithm based on centered and uncentred principal-component analyses, respectively. We combined Raman spectroscopy with optical tweezers to speed up the process and captured and analyzed microbes directly from spiked human serum. The pilot study suggests that it is possible to capture individual microbial cells from human serum and characterize them by Raman spectroscopy with notable differences among different species. IMPORTANCE Bloodstream infections are among the most common causes of hospitalizations and are often life-threatening. To establish an effective therapy for a patient, the timely identification of the causative agent and characterization of its antimicrobial susceptibility and resistance profiles are essential. Therefore, our multidisciplinary team of microbiologists and physicists presents a method that reliably, rapidly, and inexpensively identifies pathogens causing bloodstream infections-Raman spectroscopy. We believe that it might become a valuable diagnostic tool in the future. Combined with optical trapping, it offers a new approach where the microorganisms are individually trapped in a noncontact way by optical tweezers and investigated by Raman spectroscopy directly in a liquid sample. Together with the automatic processing of measured Raman spectra and comparison with a database of microorganisms, it makes the whole identification process almost real time.

Simple, Fast and Convenient Magnetic Bead-Based Sample Preparation for Detecting Viruses via Raman-Spectroscopy

We introduce a magnetic bead-based sample preparation scheme for enabling the Raman spectroscopic differentiation of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2)-positive and -negative samples. The beads were functionalized with the angiotensin-converting enzyme 2 (ACE2) receptor protein, which is used as a recognition element to selectively enrich SARS-CoV-2 on the surface of the magnetic beads. The subsequent Raman measurements directly enable discriminating SARS-CoV-2-positive and -negative samples. The proposed approach is also applicable for other virus species when the specific recognition element is exchanged. A series of Raman spectra were measured on three types of samples, namely SARS-CoV-2, Influenza A H1N1 virus and a negative control. For each sample type, eight independent replicates were considered. All of the spectra are dominated by the magnetic bead substrate and no obvious differences between the sample types are apparent. In order to address the subtle differences in the spectra, we calculated different correlation coefficients, namely the Pearson coefficient and the Normalized cross correlation coefficient. By comparing the correlation with the negative control, differentiating between SARS-CoV-2 and Influenza A virus is possible. This study provides a first step towards the detection and potential classification of different viruses with the use of conventional Raman spectroscopy.

Oxygen Sensor-Based Respirometry and the Landscape of Microbial Testing Methods as Applicable to Food and Beverage Matrices

The current status of microbiological testing methods for the determination of viable bacteria in complex sample matrices, such as food samples, is the focus of this review. Established methods for the enumeration of microorganisms, particularly, the 'gold standard' agar plating method for the determination of total aerobic viable counts (TVC), bioluminescent detection of total ATP, selective molecular methods (immunoassays, DNA/RNA amplification, sequencing) and instrumental methods (flow cytometry, Raman spectroscopy, mass spectrometry, calorimetry), are analyzed and compared with emerging oxygen sensor-based respirometry techniques. The basic principles of optical O₂ sensing and respirometry and the primary materials, detection modes and assay formats employed are described. The existing platforms for bacterial cell respirometry are then described, and examples of particular assays are provided, including the use of rapid TVC tests of food samples and swabs, the toxicological screening and profiling of cells and antimicrobial sterility testing. Overall, O₂ sensor-based respirometry and TVC assays have high application potential in the food industry and related areas. They detect viable bacteria via their growth and respiration; the assay is fast (time to result is 2-8 h and dependent on TVC load), operates with complex samples (crude homogenates of food samples) in a simple mix-and-measure format, has low set-up and instrumentation costs and is inexpensive and portable.

Detecting Pre-Analytically Delayed Blood Samples for Laboratory Diagnostics Using Raman Spectroscopy

In this proof-of-principle study, we systematically studied the potential of Raman spectroscopy for detecting pre-analytical delays in blood serum samples. Spectra from 330 samples from a liver cirrhosis cohort were acquired over the course of eight days, stored one day at room temperature, and stored subsequently at 4 °C. The spectra were then used to train Convolutional Neural Networks (CNN) to predict the delay to sample examination. We achieved 90% accuracy for binary classification of the serum samples in the groups "without delay" versus "delayed". Spectra recorded on the first day could be distinguished clearly from all subsequent measurements. Distinguishing between spectra taken in the range from the second to the last day seems to be possible as well, but currently, with an accuracy of approximately 70% only. Importantly, filtering out the fluorescent background significantly reduces the precision of detection.

Detection of 1-OHPyr in human urine using SERS with injection under wet liquid-liquid self-assembled films of β-CD-coated gold nanoparticles and deep learning

1-Hydroxypyrene (1-OHPyr), a typical hydroxylated polycyclic aromatic hydrocarbon (OH-PAH), has been commonly regarded as a urinary biomarker for assessing human exposure and health risks of PAHs. Herein, a fast and sensitive method was developed for the determination of 1-OHPyr in urine using surface-enhanced Raman spectroscopy (SERS) combined with deep learning (DL). After emulsification, urinary 1-OHPyr was separated using simple liquid-liquid extraction. Gold nanoparticles with β-cyclodextrin (β-CD@AuNPs) were synthesized, and homogeneous and ordered β-CD@AuNP films were prepared through a liquid-liquid interface self-assembly process. The separated 1-OHPyr was injected under wet assembled films for SERS detection. Concentration as low as 0.05 μg mL^-1 of 1-OHPyr in urine could still be detected, and the relative standard deviation was 5.5 %, and this was ascribed to the adsorption of β-CD and the high-probability contact between 1-OHPyr molecules and the nanogap of assembled films under the action of capillary force. Meanwhile, a convolutional neural network (CNN), a classical DL network architecture, was adopted to build the prediction model, and the model was further simplified by genetic algorithm (GA). CNN combined with a GA obtained optimized results with determination coefficient and a root mean square error of prediction sets of 0.9639 and 0.6327, respectively, outperforming other models. Overall, the proposed method achieves fast and accurate detection of 1-OHPyr in urine, improves the assessment human exposure to PAHs and is expected to have applications in the analysis of other OH-PAHs in complex environments.

Conventional and advanced detection techniques of foodborne pathogens: A comprehensive review

Foodborne pathogens are a major public health concern and have a significant economic impact globally. From harvesting to consumption stages, food is generally contaminated by viruses, parasites, and bacteria, which causes foodborne diseases such as hemorrhagic colitis, hemolytic uremic syndrome (HUS), typhoid, acute, gastroenteritis, diarrhea, and thrombotic thrombocytopenic purpura (TTP). Hence, early detection of foodborne pathogenic microbes is essential to ensure a safe food supply and to prevent foodborne diseases. The identification of foodborne pathogens is associated with conventional (e.g., culture-based, biochemical test-based, immunological-based, and nucleic acid-based methods) and advances (e.g., hybridization-based, array-based, spectroscopy-based, and biosensor-based process) techniques. For industrial food applications, detection methods could meet parameters such as accuracy level, efficiency, quickness, specificity, sensitivity, and non-labor intensive. This review provides an overview of conventional and advanced techniques used to detect foodborne pathogens over the years. Therefore, the scientific community, policymakers, and food and agriculture industries can choose an appropriate method for better results.

Recent Progress of Surface-Enhanced Raman Spectroscopy for Bacteria Detection

There are various pathogenic bacteria in the surrounding living environment, which not only pose a great threat to human health but also bring huge losses to economic development. Conventional methods for bacteria detection are usually time-consuming, complicated and labor-intensive, and cannot meet the growing demands for on-site and rapid analyses. Sensitive, rapid and effective methods for pathogenic bacteria detection are necessary for environmental monitoring, food safety and infectious bacteria diagnosis. Recently, benefiting from its advantages of rapidity and high sensitivity, surface-enhanced Raman spectroscopy (SERS) has attracted significant attention in the field of bacteria detection and identification as well as drug susceptibility testing. Here, we comprehensively reviewed the latest advances in SERS technology in the field of bacteria analysis. Firstly, the mechanism of SERS detection and the fabrication of the SERS substrate were briefly introduced. Secondly, the label-free SERS applied for the identification of bacteria species was summarized in detail. Thirdly, various SERS tags for the high-sensitivity detection of bacteria were also discussed. Moreover, we emphasized the application prospects of microfluidic SERS chips in antimicrobial susceptibility testing (AST). In the end, we gave an outlook on the future development and trends of SERS in point-of-care diagnoses of bacterial infections.

Multi-point scanning confocal Raman spectroscopy for accurate identification of microorganisms at the single-cell level

Raman spectroscopy has been widely used for microbial analysis due to its exceptional qualities as a rapid, simple, non-invasive, reproducible, and real-time monitoring tool. The Raman spectrum of a cell is a superposition of the spectral information of all biochemical components in the laser focus. In the case where the microbial size is larger than the laser spot size, the Raman spectrum measured from a single-point within a cell cannot capture all biochemical information due to the spatial heterogeneity of microorganisms. In this work, we have proposed a method for the accurate identification of microorganisms using multi-point scanning confocal Raman spectroscopy. Through an image recognition algorithm and the control of a high-precision motorized stage, Raman spectra can be integrated at one time to measure the multi-point biochemical information of microorganisms. This solves the problem that the measured single microbial cells are of different sizes, and the laser spot of the confocal Raman system is not easy to change. Here, the single-cell Raman spectra of three Escherichia coli and seven Lactobacillus species were measured separately. The commonly used supervised classification method, support vector machine (SVM), was applied to compare the data based on the single-point spectra and multi-point scanning spectra. Multi-point spectra showed superior performance in terms of their accuracy and recall rates compared with single-point spectra. The results show that multi-point scanning confocal Raman spectra can be used for more accurate species classification at different taxonomic levels, which is of great importance in species identification.

Nanogap traps for passive bacteria concentration and single-point confocal Raman spectroscopy

A microfluidic device enabling the isolation and concentration of bacteria for analysis by confocal Raman spectroscopy is presented. The glass-on-silicon device employs a tapered chamber surrounded by a 500 nm gap that serves to concentrate cells at the chamber apex during sample perfusion. The sub-micrometer gap retains bacteria by size exclusion while allowing smaller contaminants to pass unimpeded. Concentrating bacteria within the fixed volume enables the use of single-point confocal Raman detection for the rapid acquisition of spectral signatures for bacteria identification. The technology is evaluated for the analysis of E. cloacae, K. pneumoniae, and C. diphtheriae, with automated peak extraction yielding distinct spectral fingerprints for each pathogen at a concentration of 103 CFU/ml that compare favorably with spectra obtained from significantly higher concentration reference samples evaluated by conventional confocal Raman analysis. The nanogap technology offers a simple, robust, and passive approach to concentrating bacteria from dilute samples into well-defined optical detection volumes, enabling rapid and sensitive confocal Raman detection for label-free identification of focused cells.

Multimodal Raman spectroscopy and optical coherence tomography for biomedical analysis

Optical techniques hold great potential to detect and monitor disease states as they are a fast, non-invasive toolkit. Raman spectroscopy (RS) in particular is a powerful label-free method capable of quantifying the biomolecular content of tissues. Still, spontaneous Raman scattering lacks information about tissue morphology due to its inability to rapidly assess a large field of view. Optical Coherence Tomography (OCT) is an interferometric optical method capable of fast, depth-resolved imaging of tissue morphology, but lacks detailed molecular contrast. In many cases, pairing label-free techniques into multimodal systems allows for a more diverse field of applications. Integrating RS and OCT into a single instrument allows for both structural imaging and biochemical interrogation of tissues and therefore offers a more comprehensive means for clinical diagnosis. This review summarizes the efforts made to date toward combining spontaneous RS-OCT instrumentation for biomedical analysis, including insights into primary design considerations and data interpretation.

Use of polymers as wavenumber calibration standards in deep-UVRR

Deep-UV resonance Raman spectroscopy (UVRR) allows the classification of bacterial species with high accuracy and is a promising tool to be developed for clinical application. For this attempt, the optimization of the wavenumber calibration is required to correct the overtime changes of the Raman setup. In the present study, different polymers were investigated as potential calibration agents. The ones with many sharp bands within the spectral range 400-1900 cm^-1 were selected and used for wavenumber calibration of bacterial spectra. Classification models were built using a training cross-validation dataset that was then evaluated with an independent test dataset obtained after 4 months. Without calibration, the training cross-validation dataset provided an accuracy for differentiation above 99 % that dropped to 51.2 % after test evaluation. Applying the test evaluation with PET and Teflon calibration allowed correct assignment of all spectra of Gram-positive isolates. Calibration with PS and PEI leads to misclassifications that could be overcome with majority voting. Concerning the very closely related and similar in genome and cell biochemistry Enterobacteriaceae species, all spectra of the training cross-validation dataset were correctly classified but were misclassified in test evaluation. These results show the importance of selecting the most suitable calibration agent in the classification of bacterial species and help in the optimization of the deep-UVRR technique.

Application Prospects of FTIR Spectroscopy and CLSM to Monitor the Drugs Interaction with Bacteria Cells Localized in Macrophages for Diagnosis and Treatment Control of Respiratory Diseases

Visualization of the interaction of drugs with biological cells creates new approaches to improving the bioavailability, selectivity, and effectiveness of drugs. The use of CLSM and FTIR spectroscopy to study the interactions of antibacterial drugs with latent bacterial cells localized in macrophages create prospects to solve the problems of multidrug resistance (MDR) and severe cases. Here, the mechanism of rifampicin penetration into E. coli bacterial cells was studied by tracking the changes in the characteristic peaks of cell wall components and intracellular proteins. However, the effectiveness of the drug is determined not only by penetration, but also by efflux of the drugs molecules from the bacterial cells. Here, the efflux effect was studied and visualized using FTIR spectroscopy, as well as CLSM imaging. We have shown that because of efflux inhibition, eugenol acting as an adjuvant for rifampicin showed a significant (more than three times) increase in the antibiotic penetration and the maintenance of its intracellular concentration in E. coli (up to 72 h in a concentration of more than 2 μg/mL). In addition, optical methods have been applied to study the systems containing bacteria localized inside of macrophages (model of the latent form), where the availability of bacteria for antibiotics is reduced. Polyethylenimine grafted with cyclodextrin carrying trimannoside vector molecules was developed as a drug delivery system for macrophages. Such ligands were absorbed by CD206+ macrophages by 60-70% versus 10-15% for ligands with a non-specific galactose label. Owing to presence of ligands with trimannoside vectors, the increase in antibiotic concentration inside macrophages, and thus, its accumulation into dormant bacteria, is observed. In the future, the developed FTIR+CLSM techniques would be applicable for the diagnosis of bacterial infections and the adjustment of therapy strategies.

Raman spectroscopy as a promising diagnostic method for rheumatoid arthritis

Background: Diagnosis of rheumatoid arthritis (RA) basically relies on clinical symptoms and autoantibodies, especially anti-citrullinated protein antibodies (ACPAs) and rheumatoid factor (RF). However, the lack of autoantibodies is still a dilemma clinically in seronegative RA, especially in the early stage of the disease. This study aimed to provide a unique disease fingerprint with high diagnostic value to discriminate RA based on Raman spectroscopy. Methods: Raman spectroscopy provides a repertoire of biomolecules in serum from RA. Multivariate dimension-reducing methods and machine-learning algorithms were exploited to reveal the intrinsic differences and the potential discrimination power. The underlying differential biomolecules were retrieved by the assignment of Raman peaks. Moreover, the correlations between the spectral differences and RA patient's clinical and immunological manifestations were also analyzed. Results: RA patients exhibited unique Raman spectra characterized by biomolecular alterations during the disease progression. The discrimination power yielded 97.3% sensitivity and 94.8% specificity for RA diagnosis. In the recognition of ACPA-negative RA, the sensitivity and specificity also reached 95.6% and 92.8%, respectively. In particular, the differential Raman spectrum peaks of RA patients mainly represented lipids, amino acids, glycogen, and fatty acids. Further analysis showed that the different serum Raman spectra correlated with the clinical features of RA, including disease duration, RF, anticyclic citrullinated peptide antibodies (anti-CCPs), IgA, IgM, IgG, tender joint count, and swollen joint count (|rs| = 0.15-0.52, p < 0.05). Conclusions: Raman spectroscopy was revealed to be a promising diagnostic method for RA, especially for ACPA-negative patients.

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.

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.

Paper-based sensors for bacteria detection

The detection of pathogenic bacteria is essential to prevent and treat infections and to provide food security. Current gold-standard detection techniques, such as culture-based assays and polymerase chain reaction, are time-consuming and require centralized laboratories. Therefore, efforts have focused on developing point-of-care devices that are fast, cheap, portable and do not require specialized training. Paper-based analytical devices meet these criteria and are particularly suitable to deployment in low-resource settings. In this Review, we highlight paper-based analytical devices with substantial point-of-care applicability for bacteria detection and discuss challenges and opportunities for future development.

Building an ensemble learning model for gastric cancer cell line classification via rapid raman spectroscopy

Cell misuse and cross-contamination can affect the accuracy of cell research results and result in wasted time, manpower and material resources. Thus, cell line identification is important and necessary. At present, the commonly used cell line identification methods need cell staining and culturing. There is therefore a need to develop a new method for the rapid and automated identification of cell lines. Raman spectroscopy has become one of the emerging techniques in the field of microbial identification, with the advantages of being rapid and noninvasive and providing molecular information for biological samples, which is beneficial in the identification of cell lines. In this study, we built a library of Raman spectra for gastric mucosal epithelial cell lines GES-1 and gastric cancer cell lines, such as AGS, BGC-823, HGC-27, MKN-45, MKN-74 and SNU-16. Five spectral datasets were constructed using spectral data and included the full spectrum, fingerprint region, high-wavelength number region and Raman background of Raman spectra. A stacking ensemble learning model, SL-Raman, was built for different datasets, and gastric cancer cell identification was achieved. For the gastric cancer cells we studied, the differentiation accuracy of SL-Raman was 100% for one of the gastric cancer cells and 100% for six of the gastric cancer cells. Additionally, the separation accuracy for two gastric cancer cells with different degrees of differentiation was 100%. These results demonstrate that Raman spectroscopy combined with SL-Raman may be a new method for the rapid and accurate identification of gastric cancer. In addition, the accuracy of 94.38% for classifying Raman spectral background data using machine learning demonstrates that the Raman spectral background contains some useful spectral features. These data have been overlooked in previous studies.

Nanomaterials meet surface-enhanced Raman scattering towards enhanced clinical diagnosis: a review

Surface-enhanced Raman scattering (SERS) is a very promising tool for the direct detection of biomarkers for the diagnosis of i.e., cancer and pathogens. Yet, current SERS strategies are hampered by non-specific interactions with co-existing substances in the biological matrices and the difficulties of obtaining molecular fingerprint information from the complex vibrational spectrum. Raman signal enhancement is necessary, along with convenient surface modification and machine-based learning to address the former issues. This review aims to describe recent advances and prospects in SERS-based approaches for cancer and pathogens diagnosis. First, direct SERS strategies for key biomarker sensing, including the use of substrates such as plasmonic, semiconductor structures, and 3D order nanostructures for signal enhancement will be discussed. Secondly, we will illustrate recent advances for indirect diagnosis using active nanomaterials, Raman reporters, and specific capture elements as SERS tags. Thirdly, critical challenges for translating the potential of the SERS sensing techniques into clinical applications via machine learning and portable instrumentation will be described. The unique nature and integrated sensing capabilities of SERS provide great promise for early cancer diagnosis or fast pathogens detection, reducing sanitary costs but most importantly allowing disease prevention and decreasing mortality rates.

Three-Dimensional Surface-Enhanced Raman Scattering Platform with Hotspots Built by a Nano-mower for Rapid Detection of MRSA

Methicillin-resistant Staphylococcus aureus (MRSA) has become one of the greatest threats to human health due to its strong drug resistance, wide distribution range, and high infection rate. Rapid identification of MRSA strains is very important for accurate diagnosis and early treatment of MRSA infections. Here, we introduced an Exo III-assisted nanomotor mower to build 3D hotspots for rapid detection of MRSA by surface-enhanced Raman scattering (SERS). As the bacteria bound to the aptamer, two trigger chains were released from the double-stranded structure, and the nano-mowers were activated by opening a hairpin probe on gold nanoparticles (AuNPs). With the continued cleavage of Exo III and cyclic release of the trigger chain, multiple hairpin DNAs on the AuNPs were cleaved to increase the motor power. The resulting nano-mower continued slicing protective DNA from larger AuNPs, exposing the AuNPs. Without the protection of DNA, Mg²⁺ in the buffer induced spontaneous aggregation of the AuNPs, and a large number of hotspots were formed for SERS measurements. Under optimal conditions, MRSA can be detected within 40 min, and the concentration of MRSA showed a good linear relationship with the SERS intensity at 1342 cm^-1, with a limit of detection as low as 1 CFU/mL and a wide linear range (10⁰ to 10⁷ CFU/mL). This strategy creates a rapid bacterial detection method that performs well on actual samples utilizing portable Raman spectroscopy instruments, with potential applications in food detection, water detection, clinical treatment, and diagnosis.

Effects of the cone angle on the SERS detection sensitivity of tapered fiber probes

In this paper, we investigate the effects of taper angle on the SERS detection sensitivity using tapered fiber probes with single-layer uniform gold spherical nanoparticles (GSNs). We show that the photothermal damage caused by excessive excitation laser power is the main factor that restricts the improvement of detection sensitivity of tapered fiber probes. Only when the cone angle is appropriate can a balance be achieved between increasing the excitation laser power and suppression of the transmission and scattering losses of the nanoparticles on the tapered fiber surface, thereby obtaining the best SERS detection sensitivity. Furthermore, the optimal cone angle depends on the complex refractive index of the equivalent composite dielectric (ECD) layer containing GSNs. For three SERS fiber probes with different ECD layers, the optimal cone angles measured are between 11-13°.

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.

Growth Kinetics Monitoring of Gram-Negative Pathogenic Microbes Using Raman Spectroscopy

Optical density based measurements are routinely performed to monitor the growth of microbes. These measurements solely depend upon the number of cells and do not provide any information about the changes in the biochemical milieu or biological status. An objective information about these parameters is essential for evaluation of novel therapies and for maximizing the metabolite production. In the present study, we have applied Raman spectroscopy to monitor growth kinetics of three different pathogenic Gram-negative microbes Escherichia coli, Pseudomonas aeruginosa, and Acinetobacter baumannii. Spectral measurements were performed under 532 nm excitation with 5 seconds of exposure time. Spectral features suggest temporal changes in the "peptide" and "nucleic acid" content of cells under different growth stages. Using principal component analysis (PCA), successful discrimination between growth phases was also achieved. Overall, the findings are supportive of the prospective adoption of Raman based approaches for monitoring microbial growth.

Accurate and fast identification of minimally prepared bacteria phenotypes using Raman spectroscopy assisted by machine learning

The worldwide increase of antimicrobial resistance (AMR) is a serious threat to human health. To avert the spread of AMR, fast reliable diagnostics tools that facilitate optimal antibiotic stewardship are an unmet need. In this regard, Raman spectroscopy promises rapid label- and culture-free identification and antimicrobial susceptibility testing (AST) in a single step. However, even though many Raman-based bacteria-identification and AST studies have demonstrated impressive results, some shortcomings must be addressed. To bridge the gap between proof-of-concept studies and clinical application, we have developed machine learning techniques in combination with a novel data-augmentation algorithm, for fast identification of minimally prepared bacteria phenotypes and the distinctions of methicillin-resistant (MR) from methicillin-susceptible (MS) bacteria. For this we have implemented a spectral transformer model for hyper-spectral Raman images of bacteria. We show that our model outperforms the standard convolutional neural network models on a multitude of classification problems, both in terms of accuracy and in terms of training time. We attain more than 96% classification accuracy on a dataset consisting of 15 different classes and 95.6% classification accuracy for six MR-MS bacteria species. More importantly, our results are obtained using only fast and easy-to-produce training and test data.

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

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

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

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

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%.