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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study, Raman spectroscopy is used to characterize different formulations of active pharmaceutical ingredient (API) of Lornoxicam, a nonsteroidal anti‐inflammatory drug for qualitative and quantitative analyses. The Raman spectral characteristic peaks associated with API and excipients are identified, which increase or decrease as their concentrations change in different formulations. The spectral response was analyzed qualitatively and quantitatively by using chemometrics tools such as principal component analysis (PCA) model and partial least squares regression (PLSR) analysis model, respectively. The characteristic Raman spectral features associated with the different formulations of API of Lornoxicam were used to differentiate different groups of Raman spectral data using PCA. By using PLSR model, root mean square error of cross validation (RMSECV) was found to be 0.76 mg during the development of prediction model and according to the (R2) value, the goodness of the model was found to be 0.99. Furthermore, the concentration of API in the blind sample was measured. In comparison to the 15/35 mg (w/w), this concentration was projected to be 14.13/35 mg (w/w). Raman spectroscopy along with PCA and PLSR analyses is a useful tool for determining the concentration of Lornoxicam in pharmaceutical samples, both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

The hospital environment is a field with unique microbiological characteristics. Pathogens evolve and spread in different areas of the hospital affecting patients and staff. In addition, a constant circulation of pathogens between the hospital and the outer environment is ongoing. In this context, an extensive management is required in order to minimize the harmful effect of hospital flora on humans as well as the natural environment. Raman spectroscopy has been shown to be an effective tool for this purpose since it is applicable in a variety of biological samples ranging from the patient samples to the hospital's wastewater. It enables the detection of infection, bacterial species identification, antimicrobial resistance determination, epidemiological typing as well as infection control, mandatory for hospital management. The biggest advantages of this analytical method are the limited time and minimal resources required in its workflow. In the current review the Raman‐based analytical methods that have been developed over years in the field of microbiology are presented and their applicability in the different areas of the hospital environment is discussed. [ABSTRACT FROM AUTHOR]

Enterobacteriaceae are the leading cause of urinary tract infections, and include pathogens such as E. coli, K. pneumoniae and P. mirabilis. Due to their similarity, the correct identification of these pathogens is difficult and time-consuming. Raman spectroscopy has been demonstrated extensively as a tool for rapid microbiological differentiation. However, for pathogenic Enterobacteriaceae the application of Raman spectroscopy has been particularly challenging. In this study, two promising methods for Raman-based microbiological diagnostics were compared for differentiating Enterobacteriaceae. Spectra were collected from single-cells with Raman microspectroscopy and from colonies on agar with an NIR Raman fiber-probe. A comprehensive dataset of spectra from 8 different, clinically relevant, genera was collected. Visually, the spectra obtained from both methods presented little difference between the genera. For classification, single cell analysis yielded limited results, while the fiber-probe spectra enabled perfect classification of all 16 isolates. Moreover, the model was validated on new replicates and 15/16 strains were correctly identified (94% overall accuracy). This is the first study to focus on the closely related Enterobacteriaceae, who have previously been avoided or differentiated poorly. It shows how, with the correct spectroscopic setup, even challenging questions in clinical microbiology can be resolved with Raman spectroscopy, highlighting the method's potential for improving patient care. [ABSTRACT FROM AUTHOR]

Sterility testing is a laborious and slow process to detect contaminants present in drug products. Raman spectroscopy is a promising label-free tool to detect microorganisms and thus gaining relevance as a future alternative culture-free method for sterility testing in the pharmaceutical industry. However, reaching detection limits similar to standard procedures while keeping a high accuracy remains challenging, due to weak bacterial Raman signals. In this work, we show a new non-invasive approach focusing on detection of different bacteria in concentrations below 100 CFU per ml within drug product containers using Raman spectroscopy and multivariate data analysis. Even though Raman spectra from drug product with and without bacteria are similar, a partial least squared discriminant analysis (PLS-DA) model shows great performance to distinguish samples with bacterial contaminants in concentrations down to 10 CFU per ml. We used spiked samples with bacterial spores for model validation achieving a detection accuracy of 99%. Our results indicate the great potential of this rapid, and cost-effective approach to be used in quality control in the pharmaceutical industry. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

While Raman spectroscopy can provide label‐free discrimination between highly similar biological species, the discrimination is often marginal, and optimal use of spectral information is imperative. Here, we compare two machine learning models, an artificial neural network and a support vector machine, for discriminating between Raman spectra of 11 bacterial mutants of Escherichia coli MDS42. While we find that both models discriminate the 11 bacterial strains with similarly high accuracy, sensitivity and specificity, it is clear that the models form different class boundaries. By extracting strain‐specific (and function‐specific) spectral features utilized by the models, we find that both models utilize a small subset of high intensity peaks while separate subsets of lower intensity peaks are utilized by only one method or the other. This analysis highlights the need for methods to use the complete spectral information more effectively, beginning with a better understanding of the distinct information gained from each model. [ABSTRACT FROM AUTHOR]

Biofilm‐related infections occur quite frequently in hospital settings and require rapid diagnostic identification as they are recalcitrant to antibiotic therapy and make special treatment necessary. One of the standard microbiological in vitro tests is the crystal violet test. It indirectly determines the amount of biofilm by measuring the optical density (OD) of the crystal violet‐stained biofilm matrix and cells. However, this test is quite time‐consuming, as it requires bacterial cultivation up to several days. In this study, we correlate fast Raman spectroscopic read‐out of clinical Staphylococcus aureus isolates from 47 patients with different disease background with their biofilm‐forming characteristics. Included were low (OD < 10), medium (OD ≥ 10 and ≤20), and high (OD > 20) biofilm performers as determined by the crystal violet test. Raman spectroscopic analysis of the bacteria revealed most spectral differences between high and low biofilm performers in the fingerprint region between 750 and 1150 cm−1. Using partial least square regression (PLSR) analysis on the Raman spectra involving the three categories of biofilm formation, it was possible to obtain a slight linear correlation of the Raman spectra with the biofilm OD values. The PLSR loading coefficient highlighted spectral differences between high and low biofilm performers for Raman bands that represent nucleic acids, carbohydrates, and proteins. Our results point to a possible application of Raman spectroscopy as a fast prediction tool for biofilm formation of bacterial strains directly after isolation from the infected patient. This could help clinicians make timely and adapted therapeutic decision in future. [ABSTRACT FROM AUTHOR]

The rapid identification of antibiotic resistant bacteria is important for public health. In the environment, bacteria are exposed to sub‐inhibitory antibiotic concentrations which has implications in the generation of multi‐drug resistant strains. To better understand these issues, Raman spectroscopy was employed coupled with partial least squares‐discriminant analysis to profile Escherichia coli strains treated with sub‐inhibitory concentrations of antibiotics. Clear differences were observed between cells treated with bacteriostatic (tetracycline and rifampicin) and bactericidal (ampicillin, ciprofloxacin, and ceftriaxone) antibiotics for 6 hr: First, atomic force microscopy revealed that bactericidal antibiotics cause extensive cell elongation whereas short filaments are observed with bacteriostatic antibiotics. Second, Raman spectral analysis revealed that bactericidal antibiotics lower nucleic acid to protein (I812/I830) and nucleic acid to lipid ratios (I1483/I1452) whereas the opposite is seen with bacteriostatic antibiotics. Third, the protein to lipid ratio (I2936/I2885 and I2936/I2850) is a Raman stress signature common to both the classes. These signatures were validated using two mutants, Δlon and ΔacrB, that exhibit relatively high and low resistance towards antibiotics, respectively. In addition, these spectral markers correlated with the emergence of phenotypic antibiotic resistance. Overall, this study demonstrates the efficacy of Raman spectroscopy to identify resistance in bacteria to sub‐lethal concentrations of antibiotics. [ABSTRACT FROM AUTHOR]

A revolutionary avenue for vibrational imaging with super‐multiplexing capability can be seen in the recent development of Raman‐active bioortogonal tags or labels. These tags and isotopic labels represent groups of chemically inert and small modifications, which can be introduced to any biomolecule of interest and then supplied to single cells or entire organisms. Recent developments in the field of spontaneous Raman spectroscopy and stimulated Raman spectroscopy in combination with targeted imaging of biomolecules within living systems are the main focus of this review. After having introduced common strategies for bioorthogonal labeling, we present applications thereof for profiling of resistance patterns in bacterial cells, investigations of pharmaceutical drug‐cell interactions in eukaryotic cells and cancer diagnosis in whole tissue samples. Ultimately, this approach proves to be a flexible and robust tool for in vivo imaging on several length scales and provides comparable information as fluorescence‐based imaging without the need of bulky fluorescent tags. [ABSTRACT FROM AUTHOR]

A Raman‐based, strain‐independent, semi‐automated method is presented that allows the rapid (<3 hours) determination of antibiotic susceptibility of bacterial pathogens isolated from clinical samples. Applying a priori knowledge about the mode of action of the respective antibiotic, we identified characteristic Raman marker bands in the spectrum and calculated batch‐wise weighted sum scores from standardized Raman intensity differences between spectra of antibiotic exposed and nonexposed samples of the same strains. The lead substances for three relevant antibiotic classes (fluoroquinolone ciprofloxacin, third‐generation cephalosporin cefotaxime, ureidopenicillin piperacillin) against multidrug‐resistant Gram‐negative bacteria (MRGN) revealed a high sensitivity and specificity for the susceptibility testing of two Escherichia coli laboratory strains and 12 clinical isolates. The method benefits from the parallel incubation of control and treated samples, which reduces the variance due to alterations in cultivation conditions and the standardization of differences between batches leading to long‐term comparability of Raman measurements. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the use of Raman spectroscopy for characterization of antimicrobial photodynamic therapy (APDT) with methylene blue (MB) to Staphylococcus epidermidis. Results showed that APDT with MB (0.5, 1.5, and 3 μM) revealed significant reduction in the viability of the microorganism. Raman spectra from the treated microorganism seem to be affected by APDT compared with their respective controls, and the highest effect was observed when MB used at 3 μM. The results obtained in this study showed that APDT can have an effect on Raman spectra of bacteria. During the last roughly 15 years, Raman spectroscopy has obtained extensive recognition as a mature analytical technique for rapid and non‐invasive characterization of microorganisms. The use of Raman spectroscopy for characterization of antimicrobial photodynamic therapy against Staphylococcus epidermidis is presented and discussed. [ABSTRACT FROM AUTHOR]

In times of rising antibiotic resistances, there is a high need for fast, sensitive and specific methods to determine antibiotic susceptibilities of bacterial pathogens. Here, we present an integrated microfluidic device in which bacteria from diluted suspensions are captured in well-defined regions using on-chip dielectrophoresis and further analyzed in a label-free and non-destructive manner using Raman spectroscopy. Minimal sample preparation and automated sample processing ensure safe handling of infectious material with minimal hands-on time for the operator. Clinical applicability of the presented device is demonstrated by antibiotic susceptibility testing of Escherichia coli towards the commonly prescribed second generation fluoroquinolone ciprofloxacin. Ciprofloxacin resistant E. coli were differentiated from sensitive E. coli with high accuracy within roughly three hours total analysis time paving the way for future point-of-care devices. Spectral changes leading to the discrimination between sensitive and resistant bacteria are in excellent agreement with expected metabolic changes in the bacteria due to the mode of action of the drug. The robustness of the method was confirmed with experiments involving different chip devices with different designs, both electrode as well as microfluidics design, and material. Furthermore, general applicability was demonstrated with different operators over an extended time period of half a year. [ABSTRACT FROM AUTHOR]

In this study, Raman microspectroscopy has been utilized to identify mycobacteria to the species level. Because of the slow growth of mycobacteria, the per se cultivation-independent Raman microspectroscopy emerges as a perfect tool for a rapid on-the-spot mycobacterial diagnostic test. Special focus was laid upon the identification of Mycobacterium tuberculosis complex (MTC) strains, as the main causative agent of pulmonary tuberculosis worldwide, and the differentiation between pathogenic and commensal nontuberculous mycobacteria (NTM). Overall the proposed model considers 26 different mycobacteria species as well as antibiotic susceptible and resistant strains. More than 8800 Raman spectra of single bacterial cells constituted a spectral library, which was the foundation for a two-level classification system including three support vector machines. Our model allowed the discrimination of MTC samples in an independent validation dataset with an accuracy of 94% and could serve as a basis to further improve Raman microscopy as a first-line diagnostic point-of-care tool for the confirmation of tuberculosis disease. [ABSTRACT FROM AUTHOR]

For the screening purposes urine is an especially attractive biofluid, since it offers easy and noninvasive sample collection and provides a snapshot of the whole metabolic status of the organism, which may change under different pathological conditions. Raman spectroscopy (RS) has the potential to monitor these changes and utilize them for disease diagnostics. The current study utilizes mouse models aiming to compare the feasibility of the urine based RS combined with chemometrics for diagnosing kidney diseases directly influencing urine composition and respiratory tract diseases having no direct connection to urine formation. The diagnostic models for included diseases were built using principal component analysis with linear discriminant analysis and validated with a leave‐one‐mouse‐out cross‐validation approach. Considering kidney disorders, the accuracy of 100% was obtained in discrimination between sick and healthy mice, as well as between two different kidney diseases. For asthma and invasive pulmonary aspergillosis achieved accuracies were noticeably lower, being, respectively, 77.27% and 78.57%. In conclusion, our results suggest that RS of urine samples not only provides a solution for a rapid, sensitive and noninvasive diagnosis of kidney disorders, but also holds some promises for the screening of nonurinary tract diseases. [ABSTRACT FROM AUTHOR]