Surface-Enhanced Raman Scattering (SERS) in Microbiology: Illumination and Enhancement of the Microbial World

The applicability of the SERS technique in food contamination testing - The detailed spectroscopic, chemometric, genetic, and comparative analysis of food-borne Cronobacter spp. strains

Microorganisms assigned as Cronobacter are Gram-negative, facultatively anaerobic, bacteria widely distributed in nature, home environments, and hospitals. They can also be detected in foods, milk powder, and powdered infant formula (PIF). Additionally, as an opportunistic pathogen, Cronobacter may cause serious infections, sometimes leading to the death of neonates and infants. Thus, it is essential to test food products for the presence of Cronobacter spp. The currently used standard described in ISO 22964:2017 is a laborious method that could be easily replaced by surface-enhanced Raman scattering (SERS). Here, we demonstrate that SERS allows the identification of food-borne bacteria belonging to Cronobacter spp. based on their SERS spectra. For this purpose, twenty-six Cronobacter strains from different food samples were analyzed. Additionally, it was shown that it is possible to differentiate them from other closely related pathogens such as Salmonella enterica subsp. enterica, Escherichia coli, or Enterobacter spp. The SERS results were supported by principal component analysis (PCA), as well as and sequencing of 16S rRNA, rpoB and fusA genes. Last but not least, it was demonstrated that the cells of Cronobacter sakazakii may be easily separated from PIF using an appropriate filter, microfluidic chip, and dielectrophoresis (DEP) technique.

Comparative study of machine-and deep-learning based classification algorithms for biomedical Raman spectroscopy (RS): case study of RS based pathogenic microbe identification

One key aspect pushing the frontiers of biomedical RS is dedicated machine- or deep- learning (ML or DL) algorithms. Yet, systematic comparative study between ML and DL algorithms has not been conducted for biomedical RS, largely due to the limited availability of open-source and large Raman spectra dataset. Therefore we compared typical ML partial least square-discriminant analysis (PLS-DA) and DL one dimensional convolution neural network (1D-CNN) based pathogenic microbe identification on 12,000 Raman spectra from six species of microbe (i.e., K. aerogenes (Klebsiella aerogenes), C. albicans (Candida albicans), C. glabrata (Candida glabrata), Group A Strep. (Group A Streptococcus), E. coli1 (Escherichia coli1), E. coli2 (Escherichia coli2)) when 100%, 75%, 50% and 25% of the 12,000 Raman spectra were retained. The total Raman dataset was analyzed with 80% split for training and 20% for testing. The 100% retained testing dataset accuracy, area under curve (AUC) of the receiver operating characteristic (ROC) curve were 95.25% and 0.997 for 1D-CNN, which are higher than those (89.42% and 0.979) of PLS-DA. Yet, PLS-DA outperforms 1D-CNN for 75%, 50% and 25% retained testing dataset. The resultant accuracies and AUCs demonstrated the performance reliance of PLS-DA and 1D-CNN on Raman spectra number. Besides, both loadings on the latent variables of PLS-DA and the saliency maps of 1D-CNN largely captured Raman peaks arising from DNA and proteins with comparable interpretability. The results of the current work indicated that both ML and DL algorithms should be explored for application-wise Raman spectra identification to select whichever with higher accuracies and AUCs.

A Sensitive Fluorescence Analysis Method of Pathogenic Microorganisms Based on Silicon Photomultiplier

The monitoring of pathogenic microorganisms in water is important for public health and disease outbreaks prediction. Recently, optical detection techniques have drawn much attention due to the advantages of rapid response, security and high sensitivity. In this paper, a fluorescence spectrometer based on 375 nm exciting laser and the microchannel liquid sample flow technology is proposed. The 4 × 4 narrowband filter array was coupled to a Silicon Photomultiplier (SiPM) array with single-photon sensitivity. B500 fluorescent microspheres and Escherichia coli were used for performance evaluation of the spectrometer. As a result, it is feasible to use random particle counting method to detect the bacteria concentration level in water even low to several CFU/mL. In addition, based on Python tools and neural network algorithm models, the fluorescence spectra of different kinds of substances (biotic and abiotic) can be classified with an accuracy of more than 97%. The method was successfully applied to tap water samples. The results suggest that the proposed method is applicable for on-site bacteria detection.

Unraveling RNA contribution to the molecular origins of bacterial surface-enhanced Raman spectroscopy (SERS) signals

Surface-enhanced Raman spectroscopy (SERS) is widely utilized in bacterial analyses, with the dominant SERS peaks attributed to purine metabolites released during sample preparation. Although adenosine triphosphate (ATP) and nucleic acids are potential molecular origins of these metabolites, research on their exact contributions remains limited. This study explored purine metabolite release from E. coli and RNA integrity following various sample preparation methods. Standard water washing generated dominant SERS signals within 10 s, a duration shorter than the anticipated RNA half-lives under starvation. Evaluating RNA integrity indicated that the most abundant ribosomal RNA species remained intact for hours post-washing, whereas messenger RNA and transfer RNA species degraded gradually. This suggests that bacterial SERS signatures observed after the typical washing step could originate from only a small fraction of endogenous purine-containing molecules. In contrast, acid depurination led to degradation of most RNA species, releasing about 40 times more purine derivatives than water washing. Mild heating also instigated the RNA degradation and released more purine derivatives than water washing. Notably, differences were also evident in the dominant SERS signals following these treatments. This work provides insights into SERS-based studies of purine metabolites released by bacteria and future development of methodologies.

Single-cell measurement of microbial growth rate with Raman microspectroscopy

Rates of microbial growth are fundamental to understanding environmental geochemistry and ecology. However, measuring the heterogeneity of microbial activity at the single-cell level, especially within complex populations and environmental matrices, remains a forefront challenge. Stable isotope probing (SIP) is a method for assessing microbial growth and involves measuring the incorporation of an isotopic label into microbial biomass. Here, we assess Raman microspectroscopy as a SIP technique, specifically focusing on the measurement of deuterium (2H), a tracer of microbial biomass production. We correlatively measured cells grown in varying concentrations of deuterated water with both Raman spectroscopy and nanoscale secondary ion mass spectrometry (nanoSIMS), generating isotopic calibrations of microbial 2H. Relative to Raman, we find that nanoSIMS measurements of 2H are subject to substantial dilution due to rapid exchange of H during sample washing. We apply our Raman-derived calibration to a numerical model of microbial growth, explicitly parameterizing the factors controlling growth rate quantification and demonstrating that Raman-SIP can sensitively measure the growth of microorganisms with doubling times ranging from hours to years. The measurement of single-cell growth with Raman spectroscopy, a rapid, nondestructive technique, represents an important step toward application of single-cell analysis into complex sample matrices or cellular assemblages.

Machine Learning-Driven SERS Nanoendoscopy and Optophysiology

A frontier of analytical sciences is centered on the continuous measurement of molecules in or near cells, tissues, or organs, within the biological context in situ, where the molecular-level information is indicative of health status, therapeutic efficacy, and fundamental biochemical function of the host. Following the completion of the Human Genome Project, current research aims to link genes to functions of an organism and investigate how the environment modulates functional properties of organisms. New analytical methods have been developed to detect chemical changes with high spatial and temporal resolution, including minimally invasive surface-enhanced Raman scattering (SERS) nanofibers using the principles of endoscopy (SERS nanoendoscopy) or optical physiology (SERS optophysiology). Given the large spectral data sets generated from these experiments, SERS nanoendoscopy and optophysiology benefit from advances in data science and machine learning to extract chemical information from complex vibrational spectra measured by SERS. This review highlights new opportunities for intracellular, extracellular, and in vivo chemical measurements arising from the combination of SERS nanosensing and machine learning.

Bacterial discrimination by Fourier transform infrared spectroscopy, MALDI-mass spectrometry and whole-genome sequencing

Aim: Proof-of-concept study, highlighting the clinical diagnostic ability of FT-IR compared with MALDI-TOF MS, combined with WGS. Materials & methods: 104 pathogenic isolates of Neisseria meningitidis, Streptococcus pneumoniae, Streptococcus pyogenes and Staphylococcus aureus were analyzed. Results: Overall prediction accuracy was 99.6% in FT-IR and 95.8% in MALDI-TOF-MS. Analysis of N. meningitidis serogroups was superior in FT-IR compared with MALDI-TOF-MS. Phylogenetic relationship of S. pyogenes was similar by FT-IR and WGS, but not S. aureus or S. pneumoniae. Clinical severity was associated with the zinc ABC transporter and DNA repair genes in S. pneumoniae and cell wall proteins (biofilm formation, antibiotic and complement permeability) in S. aureus via WGS. Conclusion: FT-IR warrants further clinical evaluation as a promising diagnostic tool.

Detection of SARS-CoV-2 N protein using AgNPs-modified aligned silicon nanowires BioSERS chip

The SARS-CoV-2 (COVID-19) pandemic had a strong impact on societies and economies worldwide and tests for high-performance detection of SARS-CoV-2 biomarkers are still needed for potential future outbreaks of the disease. In this paper, we present the different steps for the design of an aptamer-based surface-enhanced Raman scattering (BioSERS) sensing chip capable of detecting the coronavirus nucleocapsid protein (N protein) in spiked phosphate-buffered solutions and real samples of human blood serum. Optimization of the preparation steps in terms of the aptamer concentration used for the functionalization of the silver nanoparticles, time for affixing the aptamer, incubation time with target protein, and insulation of the silver active surface with cysteamine, led to a sensitive BioSERS chip, which was able to detect the N protein in the range from 1 to 75 ng mL-1 in spiked phosphate-buffered solutions with a detection limit of 1 ng mL-1 within 30 min. Furthermore, the BioSERS chip was used to detect the target protein in scarcely spiked human serum. This study demonstrates the possibility of a clinical application that can improve the detection limit and accuracy of the currently commercialized SARS-CoV-2 immunodiagnostic kit. Additionally, the system is modular and can be applied to detect other proteins by only changing the aptamer.

Opportunities in optical and electrical single-cell technologies to study microbial ecosystems

New techniques are revolutionizing single-cell research, allowing us to study microbes at unprecedented scales and in unparalleled depth. This review highlights the state-of-the-art technologies in single-cell analysis in microbial ecology applications, with particular attention to both optical tools, i.e., specialized use of flow cytometry and Raman spectroscopy and emerging electrical techniques. The objectives of this review include showcasing the diversity of single-cell optical approaches for studying microbiological phenomena, highlighting successful applications in understanding microbial systems, discussing emerging techniques, and encouraging the combination of established and novel approaches to address research questions. The review aims to answer key questions such as how single-cell approaches have advanced our understanding of individual and interacting cells, how they have been used to study uncultured microbes, which new analysis tools will become widespread, and how they contribute to our knowledge of ecological interactions.

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

Rapid identification of salmonella serovars by using Raman spectroscopy and machine learning algorithm

A widespread and escalating public health problem worldwide is foodborne illness, and foodborne Salmonella infection is one of the most common causes of human illness.For the three most pathogenic Salmonella serotypes, Raman spectroscopy was employed to acquire spectral data.As machine learning offers high efficiency and accuracy, we have chosen the convolutional neural network(CNN), which is suitable for solving multi-classification problems, to do in-depth mining and analysis of Raman spectral data.To optimize the instrument parameters, we compared three laser wavelengths: 532, 638, and 785 nm.Ultimately, the 532 nm wavelength was chosen as the most effective for detecting Salmonella.A pre-processing step is necessary to remove interference from the background noise of the Raman spectrum.Our study compared the effects of five spectral preprocessing methods, Savitzky-Golay smoothing (SG), Multivariate Scatter Correction (MSC), Standard Normal Variate (SNV), and Hilbert Transform (HT), on the predictive power of CNN models.Accuracy(ACC), Precision, Recall, and F1-score 4 machine learning evaluation indicators are used to evaluate the model performance under different preprocessing methods.In the results, SG combined with SNV was found to be the most accurate spectral pre-processing method for predicting Salmonella serotypes using Raman spectroscopy, achieving an accuracy of 98.7% for the training set and over 98.5% for the test set in CNN model.Pre-processing spectral data using this method yields higher accuracy than other methods.As a conclusion, the results of this study demonstrate that Raman spectroscopy when used in conjunction with a convolutional neural network model enables the rapid identification of three Salmonella serotypes at the single-cell level, and that the model has a great deal of potential for distinguishing between different serotypes of pathogenic bacteria and closely related bacterial species.This is vital to preventing outbreaks of foodborne illness and the spread of foodborne pathogens.

Rapid detection of bacteria using gold nanoparticles in SERS with three different capping agents: Thioglucose, polyvinylpyrrolidone, and citrate

The increase in outbreaks of emerging and re-emerging bacterial infections over the last few decades calls for their rapid detection and treatment. Surface-enhanced Raman spectroscopy (SERS) is a technique that can be applied to develop fast screening systems for bacterial presence in biological samples. Optimizing the capping agents in nanoparticle synthesis is important because capping agents are responsible for controlling the morphological features and chemical properties of the nanoparticles that are essential for SERS. To the best of our knowledge, this paper is the first to study the application of gold nanoparticles capped with thioglucose and polyvinylpyrrolidone (PVP) in SERS detection of bacteria as an alternative to the citrate-capped gold nanoparticles that are often used in SERS detection of bacteria. Three different species of bacteria were used in this study: Cutibacterium acnes, Escherichia coli and Staphylococcus aureus (methicillin-sensitive and methicillin-resistant). This study demonstrates that the thioglucose, citrate both show good contribution in bacterial species identification and the thioglucose shows the best among the three capping agents in two types of S. aureus identification. Moreover, although PVP showed high Raman peaks in the SERS spectrum for each type of bacteria, it showed least contribution in identifying species and strains due to its low efficacy in producing responses from different nucleic acid components in the bacteria cells.

Lung Cancer: Spectral and Numerical Differentiation among Benign and Malignant Pleural Effusions Based on the Surface-Enhanced Raman Spectroscopy

We present here that the surface-enhanced Raman spectroscopy (SERS) technique in conjunction with the partial least squares analysis is as a potential tool for the differentiation of pleural effusion in the course of the cancerous disease and a tool for faster diagnosis of lung cancer. Pleural effusion occurs mainly in cancer patients due to the spread of the tumor, usually caused by lung cancer. Furthermore, it can also be initiated by non-neoplastic diseases, such as chronic inflammatory infection (the most common reason for histopathological examination of the exudate). The correlation between pleural effusion induced by tumor and non-cancerous diseases were found using surface-enhanced Raman spectroscopy combined with principal component regression (PCR) and partial least squares (PLS) multivariate analysis method. The PCR predicts 96% variance for the division of neoplastic and non-neoplastic samples in 13 principal components while PLS 95% in only 10 factors. Similarly, when analyzing the SERS data to differentiate the type of tumor (squamous cell vs. adenocarcinoma), PLS gives more satisfactory results. This is evidenced by the calculated values of the root mean square errors of calibration and prediction but also the coefficients of calibration determination and prediction (R2C = 0.9570 and R2C = 0.7968), which are more robust and rugged compared to those calculated for PCR. In addition, the relationship between cancerous and non-cancerous samples in the dependence on the gender of the studied patients is presented.

Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study

In clinical settings, rapid and accurate diagnosis of antibiotic resistance is essential for the efficient treatment of bacterial infections. Conventional methods for antibiotic resistance testing are time consuming, while molecular methods such as PCR-based testing might not accurately reflect phenotypic resistance. Thus, fast and accurate methods for the analysis of bacterial antibiotic resistance are in high demand for clinical applications. In this pilot study, we isolated 7 carbapenem-sensitive Klebsiella pneumoniae (CSKP) strains and 8 carbapenem-resistant Klebsiella pneumoniae (CRKP) strains from clinical samples. Surface-enhanced Raman spectroscopy (SERS) as a label-free and noninvasive method was employed for discriminating CSKP strains from CRKP strains through computational analysis. Eight supervised machine learning algorithms were applied for sample analysis. According to the results, all supervised machine learning methods could successfully predict carbapenem sensitivity and resistance in K. pneumoniae, with a convolutional neural network (CNN) algorithm on top of all other methods. Taken together, this pilot study confirmed the application potentials of surface-enhanced Raman spectroscopy in fast and accurate discrimination of Klebsiella pneumoniae strains with different antibiotic resistance profiles. IMPORTANCE With the low-cost, label-free, and nondestructive features, Raman spectroscopy is becoming an attractive technique with great potential to discriminate bacterial infections. In this pilot study, we analyzed surfaced-enhanced Raman spectroscopy (SERS) spectra via supervised machine learning algorithms, through which we confirmed the application potentials of the SERS technique in rapid and accurate discrimination of Klebsiella pneumoniae strains with different antibiotic resistance profiles.

Label-Free Multiphoton Imaging of Microbes in Root, Mineral, and Soil Matrices with Time-Gated Coherent Raman and Fluorescence Lifetime Imaging

Imaging biogeochemical interactions in complex microbial systems─such as those at the soil-root interface─is crucial to studies of climate, agriculture, and environmental health but complicated by the three-dimensional (3D) juxtaposition of materials with a wide range of optical properties. We developed a label-free multiphoton nonlinear imaging approach to provide contrast and chemical information for soil microorganisms in roots and minerals with epi-illumination by simultaneously imaging two-photon excitation fluorescence (TPEF), coherent anti-Stokes Raman scattering (CARS), second-harmonic generation (SHG), and sum-frequency mixing (SFM). We used fluorescence lifetime imaging (FLIM) and time gating to correct CARS for the autofluorescence background native to soil particles and fungal hyphae (TG-CARS) using time-correlated single-photon counting (TCSPC). We combined TPEF, TG-CARS, and FLIM to maximize image contrast for live fungi and bacteria in roots and soil matrices without fluorescence labeling. Using this instrument, we imaged symbiotic arbuscular mycorrhizal fungi (AMF) structures within unstained plant roots in 3D to 60 μm depth. High-quality imaging was possible at up to 30 μm depth in a clay particle matrix and at 15 μm in complex soil preparation. TG-CARS allowed us to identify previously unknown lipid droplets in the symbiotic fungus, Serendipita bescii. We also visualized unstained putative bacteria associated with the roots of Brachypodium distachyon in a soil microcosm. Our results show that this multimodal approach holds significant promise for rhizosphere and soil science research.

Surface enhanced Raman scattering for the multiplexed detection of pathogenic microorganisms: towards point-of-use applications

Surface enhanced Raman scattering (SERS) is a technique that demonstrates a number of advantages for the rapid, specific and sensitive detection of pathogenic microorganisms. In this review, an overview of label-free and label-based SERS approaches, including microfluidics, nucleic acid detection and immunoassays, for the multiplexed detection of pathogenic bacteria and viruses from the last decade will be discussed, as well as their transition into promising point-of-use detection technologies in industrial and medical settings.

Differentiation of Closely Related Oak-Associated Gram-Negative Bacteria by Label-Free Surface Enhanced Raman Spectroscopy (SERS)

Due to the harmful effects of chemical fertilizers and pesticides, the need for an eco-friendly solution to improve soil fertility has become a necessity, thus microbial biofertilizer research is on the rise. Plant endophytic bacteria inhabiting internal tissues represent a novel niche for research into new biofertilizer strains. However, the number of species and strains that need to be differentiated and identified to facilitate faster screening in future plant-bacteria interaction studies, is enormous. Surface enhanced Raman spectroscopy (SERS) may provide a platform for bacterial discrimination and identification, which, compared with the traditional methods, is relatively rapid, uncomplicated and ensures high specificity. In this study, we attempted to differentiate 18 bacterial isolates from two oaks via morphological, physiological, biochemical tests and SERS spectra analysis. Previous 16S rRNA gene fragment sequencing showed that three isolates belong to Paenibacillus, 3-to Pantoea and 12-to Pseudomonas genera. Additional tests were not able to further sort these bacteria into strain-specific groups. However, the obtained label-free SERS bacterial spectra along with the high-accuracy principal component (PCA) and discriminant function analyses (DFA) demonstrated the possibility to differentiate these bacteria into variant strains. Furthermore, we collected information about the biochemical characteristics of selected isolates. The results of this study suggest a promising application of SERS in combination with PCA/DFA as a rapid, non-expensive and sensitive method for the detection and identification of plant-associated bacteria.

Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species

Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

Wide-Range, Rapid, and Specific Identification of Pathogenic Bacteria by Surface-Enhanced Raman Spectroscopy

Sensitive, selective, rapid, and label-free detection of pathogenic bacteria with high generality is of great importance for clinical diagnosis, biosecurity, and public health. However, most traditional approaches, such as microbial cultures, are time-consuming and laborious. To circumvent these problems, surface-enhanced Raman spectroscopy (SERS) appears to be a powerful technique to characterize bacteria at the single-cell level. Here, by SERS, we report a strategy for the rapid and specific detection of 22 strains of common pathogenic bacteria. A novel and high-quality silver nanorod SERS substrate, prepared by the facile interface self-assembly method, was utilized to acquire the chemical fingerprint information of pathogens with improved sensitivity. We also applied the mathematical analysis methods, such as the t-test and receiver operating characteristic method, to determine the Raman features of these 22 strains and demonstrate the clear identification of most bacteria (20 strains) from the rest and also the reliability of this SERS sensor. This rapid and specific strategy for wide-range bacterial detection offers significant advantages over existing approaches and sets the base for automated and onsite detection of pathogenic bacteria in a complex real-life situation.

Plasmodiophora brassicae-Triggered Cell Enlargement and Loss of Cellular Integrity in Root Systems Are Mediated by Pectin Demethylation

Gall formation on the belowground parts of plants infected with Plasmodiophora brassicae is the result of extensive host cellular reprogramming. The development of these structures is a consequence of increased cell proliferation followed by massive enlargement of cells colonized with the pathogen. Drastic changes in cellular growth patterns create local deformities in the roots and hypocotyl giving rise to mechanical tensions within the tissue of these organs. Host cell wall extensibility and recomposition accompany the growth of the gall and influence pathogen spread and also pathogen life cycle progression. Demethylation of pectin within the extracellular matrix may play an important role in P. brassicae-driven hypertrophy of host underground organs. Through proteomic analysis of the cell wall, we identified proteins accumulating in the galls developing on the underground parts of Arabidopsis thaliana plants infected with P. brassicae. One of the key proteins identified was the pectin methylesterase (PME18); we further characterized its expression and conducted functional and anatomic studies in the knockout mutant and used Raman spectroscopy to study the status of pectin in P. brassicae-infected galls. We found that late stages of gall formation are accompanied with increased levels of PME18. We have also shown that the massive enlargement of cells colonized with P. brassicae coincides with decreases in pectin methylation. In pme18-2 knockout mutants, P. brassicae could still induce demethylation; however, the galls in this line were smaller and cellular expansion was less pronounced. Alteration in pectin demethylation in the host resulted in changes in pathogen distribution and slowed down disease progression. To conclude, P. brassicae-driven host organ hypertrophy observed during clubroot disease is accompanied by pectin demethylation in the extracellular matrix. The pathogen hijacks endogenous host mechanisms involved in cell wall loosening to create an optimal cellular environment for completion of its life cycle and eventual release of resting spores facilitated by degradation of demethylated pectin polymers.

The Role of Raman Spectroscopy Within Quantitative Metabolomics

Ninety-four years have passed since the discovery of the Raman effect, and there are currently more than 25 different types of Raman-based techniques. The past two decades have witnessed the blossoming of Raman spectroscopy as a powerful physicochemical technique with broad applications within the life sciences. In this review, we critique the use of Raman spectroscopy as a tool for quantitative metabolomics. We overview recent developments of Raman spectroscopy for identification and quantification of disease biomarkers in liquid biopsies, with a focus on the recent advances within surface-enhanced Raman scattering-based methods. Ultimately, we discuss the applications of imaging modalities based on Raman scattering as label-free methods to study the abundance and distribution of biomolecules in cells and tissues, including mammalian, algal, and bacterial cells.

Metabolism in action: stable isotope probing using vibrational spectroscopy and SIMS reveals kinetic and metabolic flux of key substrates

Microbial communities play essential functions which drive various ecosystems supporting animal and aquatic life. However, linking bacteria with specific metabolic functions is difficult, since microbial communities consist of numerous and phylogenetically diverse microbes. Stable isotope probing (SIP) combined with single-cell tools has emerged as a novel culture-independent strategy for unravelling microbial metabolic roles and intertwined interactions in complex communities. In this study, we applied Raman and Fourier-transform infrared (FT-IR) spectroscopies, secondary ion mass spectrometry (SIMS) with SIP to probe the rate of ¹³C incorporation in Escherichia coli at 37 and 25 °C. Our results indicate quantitative enrichment and flow of ¹³C into E. coli at various time points. Multivariate and univariate analyses of Raman and FT-IR data demonstrated distinctive ¹³C concentration-dependent trends that were due to vibrational bands shifting to lower frequencies and these shifts were a result of incubation time and metabolic rate. SIMS results were in complete agreement with the spectroscopy findings, and confirmed the detected levels of ¹³C incorporation into microbial biomass at the investigated conditions. Having established that FT-IR and Raman spectroscopy with SIP can measure metabolism kinetics in this simple system, we have applied the kinetics concept to study the metabolism of phenol by Pseudomonas putida and metabolic interactions within a two-species consortia with E. coli that could not degrade phenol. Raman spectroscopy combined with SIP identified quantitative shifts in P. putida due to temporal assimilation of phenol. Although E. coli was unable to grow on phenol, in co-culture with P. putida, general metabolic probing using deuterated water for SIP revealed that E. coli displayed increasing metabolic activity, presumably due to cross feeding from metabolites generated by P. putida. This study clearly demonstrates that Raman and FT-IR combined with SIP provide rapid and sensitive detection of carbon incorporation rates and microbial interactions. These novel findings may guide the identification of primary substrate consumers in complex microbial communities in situ, which is a key step towards the characterisation of novel genes, enzymes and metabolic flux analysis in microbial consortia.

Pathogens electrogenicity as a tool for in-situ metabolic activity monitoring and drug assessment in biofilms

Concerns regarding increased antibiotic resistance arising from the emergent properties of biofilms have spurred interest in the discovery of novel antibiotic agents and techniques to directly estimate metabolic activity in biofilms. Although a number of methods have been developed to quantify biofilm formation, real-time quantitative assessment of metabolic activity in label-free biofilms remains a challenge. Production of electrical current via extracellular electron transport (EET) has recently been found in pathogens and appears to correlate with their metabolic activity. Accordingly, monitoring the production of electrical currents as an indicator of cellular metabolic activity in biofilms represents a new direction for research aiming to assess and screen the effects of antimicrobials on biofilm activity. In this article, we reviewed EET-capable pathogens and the methods to monitor biofilm activity to discuss advantages of using the capability of pathogens to produce electrical currents and effective combination of these methods. Moreover, we discussed EET mechanisms by pathogenic and environmental bacteria and open questions for the physiological roles of EET in pathogen's biofilm. The present limitations and possible future directions of in situ biofilm metabolic activity assessment for large-scale screening of antimicrobials are also discussed.

Toward rapid infectious disease diagnosis with advances in surface-enhanced Raman spectroscopy

In a pandemic era, rapid infectious disease diagnosis is essential. Surface-enhanced Raman spectroscopy (SERS) promises sensitive and specific diagnosis including rapid point-of-care detection and drug susceptibility testing. SERS utilizes inelastic light scattering arising from the interaction of incident photons with molecular vibrations, enhanced by orders of magnitude with resonant metallic or dielectric nanostructures. While SERS provides a spectral fingerprint of the sample, clinical translation is lagged due to challenges in consistency of spectral enhancement, complexity in spectral interpretation, insufficient specificity and sensitivity, and inefficient workflow from patient sample collection to spectral acquisition. Here, we highlight the recent, complementary advances that address these shortcomings, including (1) design of label-free SERS substrates and data processing algorithms that improve spectral signal and interpretability, essential for broad pathogen screening assays; (2) development of new capture and affinity agents, such as aptamers and polymers, critical for determining the presence or absence of particular pathogens; and (3) microfluidic and bioprinting platforms for efficient clinical sample processing. We also describe the development of low-cost, point-of-care, optical SERS hardware. Our paper focuses on SERS for viral and bacterial detection, in hopes of accelerating infectious disease diagnosis, monitoring, and vaccine development. With advances in SERS substrates, machine learning, and microfluidics and bioprinting, the specificity, sensitivity, and speed of SERS can be readily translated from laboratory bench to patient bedside, accelerating point-of-care diagnosis, personalized medicine, and precision health.

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

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

A Review of Graphene-Based Surface Plasmon Resonance and Surface-Enhanced Raman Scattering Biosensors: Current Status and Future Prospects

The surface plasmon resonance (SPR) biosensor has become a powerful analytical tool for investigating biomolecular interactions. There are several methods to excite surface plasmon, such as coupling with prisms, fiber optics, grating, nanoparticles, etc. The challenge in developing this type of biosensor is to increase its sensitivity. In relation to this, graphene is one of the materials that is widely studied because of its unique properties. In several studies, this material has been proven theoretically and experimentally to increase the sensitivity of SPR. This paper discusses the current development of a graphene-based SPR biosensor for various excitation methods. The discussion begins with a discussion regarding the properties of graphene in general and its use in biosensors. Simulation and experimental results of several excitation methods are presented. Furthermore, the discussion regarding the SPR biosensor is expanded by providing a review regarding graphene-based Surface-Enhanced Raman Scattering (SERS) biosensor to provide an overview of the development of materials in the biosensor in the future.

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.

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

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

Present and Future of Surface-Enhanced Raman Scattering

The discovery of the enhancement of Raman scattering by molecules adsorbed on nanostructured metal surfaces is a landmark in the history of spectroscopic and analytical techniques. Significant experimental and theoretical effort has been directed toward understanding the surface-enhanced Raman scattering (SERS) effect and demonstrating its potential in various types of ultrasensitive sensing applications in a wide variety of fields. In the 45 years since its discovery, SERS has blossomed into a rich area of research and technology, but additional efforts are still needed before it can be routinely used analytically and in commercial products. In this Review, prominent authors from around the world joined together to summarize the state of the art in understanding and using SERS and to predict what can be expected in the near future in terms of research, applications, and technological development. This Review is dedicated to SERS pioneer and our coauthor, the late Prof. Richard Van Duyne, whom we lost during the preparation of this article.

Hybrid plasmonic grating slot waveguide with high field enhancement for an on-chip surface-enhanced Raman scattering sensor

We design and theoretically investigate a surface-enhanced Raman scattering (SERS) sensor based on the hybrid plasmonic grating slot waveguide. The sensor is formed by combining a dielectric deep slot waveguide and a metallic grating slot waveguide. The proposed sensor exhibits a high field enhancement with a maximum enhancement factor of 7580.9 at the wavelength of 785 nm, revealing that the electric field in such hybrid plasmonic grating slot waveguide can be extremely strengthened. To better characterize the performance of the sensor in the SERS application, the total normalized volumetric enhancement factor (TNVEF) is proposed, which is determined by both the |E|⁴-approximation-based volumetric field enhancement and Raman scattered light collection efficiency. The TNVEF is utilized to characterize the influences of the structural parameters on the sensor and further optimize the sensing structure. Such on-chip SERS sensor can be integrated with a micro-laser and a micro-multiplexer on a photonic platform to realize an all-integrated on-chip SERS detection system.

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

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

Active Surface Hydrophobicity Switching and Dynamic Interfacial Trapping of Microbial Cells by Metal Nanoparticles for Preconcentration and In-Plane Optical Detection

The surface hydrophobicity of a microbial cell is known to be one of the important factors in its adhesion to an interface. To date, such property has been altered by either genetic modification or external pH, temperature, and nutrient control. Here we report a new strategy to engineer a microbial cell surface and discover the unique dynamic trapping of hydrophilic cells at an air/water interface via hydrophobicity switching. We demonstrate the surface transformation and hydrophobicity switching of Escherichia coli (E. coli) by metal nanoparticles. By employing real-time dark-field imaging, we directly observe that hydrophobic gold nanoparticle-coated E. coli, unlike its naked counterpart, is irreversibly trapped at the air/water interface because of elevated hydrophobicity. We show that our surface transformation method and resulting dynamic interfacial trapping can be generally extended to Gram-positive bateria, Gram-negative bacteria, and fungi. As the dynamic interfacial trapping allows the preconcentration of microbial cells, high intensity of scattering light, in-plane focusing, and near-field enhancement, we are able to directly quantify E. coli as low as 1.0 × 10³ cells/ml by using a smartphone with an image analyzer. We also establish the identification of different microbial cells by the characteristic Raman transitions directly measured from the interfacially trapped cells.

Microfluidics and Surface-Enhanced Raman Spectroscopy: A Perfect Match for New Analytical Tools

In this perspective article, we emphasize the combination of Surface-Enhanced Raman Spectroscopy (SERS) and Microfluidic devices. SERS approaches have been widely studied and used for multiple applications including trace molecules detection, in situ analysis of biological samples and monitoring or, all of them with good results, however still with limitations of the technique, for example regarding with improved precision and reproducibility. These implications can be overcome by microfluidic approaches. The resulting coupling Microfluidics - SERS (MF-SERS) has recently gained increasing attention by creating thundering opportunities for the analytical field. For this purpose, we introduce some of the strategies developed to implement SERS within microfluidic reactor along with a brief overview of the most recent MF-SERS applications for biology, health and environmental concerns. Eventually, we will discuss future research opportunities of such systems.

Identifying yeasts using surface enhanced Raman spectroscopy

The molecular fingerprints of yeasts Saccharomyces cerevisiae, Dekkera bruxellensis, and Wickerhamomyces anomalus (former name Pichia anomala) have been examined using surface-enhanced Raman spectroscopy (SERS) and helium ion microscopy (HIM). The SERS spectra obtained from cell cultures (lysate and non-treated cells) distinguish between these very closely related fungal species. Highly SERS active silver nano-particles suitable for detecting complex biomolecules were fabricated using a simple synthesis route. The yeast samples mixed with aggregated Ag nanoparticles yielded highly enhanced and reproducible Raman signal owing to the high density of the hot spots at the junctions of two or more Ag nanoparticles and enabled to differentiate the three species based on their unique features (spectral fingerprint). We also collected SERS spectra of the three yeast species in beer medium to demonstrate the potential of the method for industrial application. These findings demonstrate the great potential of SERS for detection and identification of fungi species based on the biochemical compositions, even in a chemically complex sample.

Monitoring Radiotherapeutic Response in Prostate Cancer Patients Using High Throughput FTIR Spectroscopy of Liquid Biopsies

Radiation therapy (RT) is used to treat approximately 50% of all cancer patients. However, RT causes a wide range of adverse late effects that can affect a patient's quality of life. There are currently no predictive assays in clinical use to identify patients at risk of normal tissue radiation toxicity. This study aimed to investigate the potential of Fourier transform infrared (FTIR) spectroscopy for monitoring radiotherapeutic response. Blood plasma was acquired from 53 prostate cancer patients at five different time points: prior to treatment, after hormone treatment, at the end of radiotherapy, two months post radiotherapy and eight months post radiotherapy. FTIR spectra were recorded from plasma samples at all time points and the data was analysed using MATLAB software. Discrimination was observed between spectra recorded at baseline versus follow up time points, as well as between spectra from patients showing minimal and severe acute and late toxicity using principal component analysis. A partial least squares discriminant analysis model achieved sensitivity and specificity rates ranging from 80% to 99%. This technology may have potential to monitor radiotherapeutic response in prostate cancer patients using non-invasive blood plasma samples and could lead to individualised patient radiotherapy.

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.

Detection and Characterization of Antibiotic-Resistant Bacteria Using Surface-Enhanced Raman Spectroscopy

This mini-review summarizes the most recent progress concerning the use of surface-enhanced Raman spectroscopy (SERS) for the detection and characterization of antibiotic-resistant bacteria. We first discussed the design and synthesis of various types of nanomaterials that can be used as the SERS-active substrates for biosensing trace levels of antibiotic-resistant bacteria. We then reviewed the tandem-SERS strategy of integrating a separation element/platform with SERS sensing to achieve the detection of antibiotic-resistant bacteria in the environmental, agri-food, and clinical samples. Finally, we demonstrated the application of using SERS to investigate bacterial antibiotic resistance and susceptibility as well as the working mechanism of antibiotics based on spectral fingerprinting of the whole cells.