Profiling antibiotic resistance in Escherichia coli strains displaying differential antibiotic susceptibilities using Raman spectroscopy

Correlation analysis between Raman spectral signature and transcriptomic features of carbapenem-resistant Klebsiella pneumoniae

The Raman microspectroscopy technology has been successfully applied to evaluate the molecular composition of living cells for identifying cell types and states, but the rationale behind it was not well investigated. In this study, we acquired single-cell Raman spectra (SCRS) of three Klebsiella pneumoniae (K. pneumoniae) strains with different Carbapenem resistant mechanisms and analyzed them with machine learning algorithm. Two carbapenem resistant Klebsiella pneumoniae (CRKP) strains can be successfully distinguished from susceptible strain and CRKP with KPC or IMP carbapenemases can be classified with an overall accuracy achieving 100 %. Furthermore, we performed a correlation analysis between transcriptome and Raman spectra, and found that Raman shifts such as 752 and 1039 cm-1 highly correlated with drug resistance genes expression and could be regarded as Raman biomarkers for CRKP with different mechanisms. The findings of the study provide a theoretical basis for identifying the relationship between Raman spectra and transcriptome of bacteria, as well as a novel method for rapid identification of CRKP and their carbapenemases types.

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.

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.

Antimicrobial Agent against Methicillin-Resistant Staphylococcus aureus Biofilm Monitored Using Raman Spectroscopy

The prevalence of antimicrobial-resistant bacteria has become a major challenge worldwide. Methicillin-resistant Staphylococcus aureus (MRSA)-a leading cause of infections-forms biofilms on polymeric medical devices and implants, increasing their resistance to antibiotics. Antibiotic administration before biofilm formation is crucial. Raman spectroscopy was used to assess MRSA biofilm development on solid culture media from 0 to 48 h. Biofilm formation was monitored by measuring DNA/RNA-associated Raman peaks and protein/lipid-associated peaks. The search for an antimicrobial agent against MRSA biofilm revealed that Eugenol was a promising candidate as it showed significant potential for breaking down biofilm. Eugenol was applied at different times to test the optimal time for inhibiting MRSA biofilms, and the Raman spectrum showed that the first 5 h of biofilm formation was the most antibiotic-sensitive time. This study investigated the performance of Raman spectroscopy coupled with principal component analysis (PCA) to identify planktonic bacteria from biofilm conglomerates. Raman analysis, microscopic observation, and quantification of the biofilm growth curve indicated early adhesion from 5 to 10 h of the incubation time. Therefore, Raman spectroscopy can help in monitoring biofilm formation on a solid culture medium and performing rapid antibiofilm assessments with new antibiotics during the early stages of the procedure.

Rapid identification and drug resistance screening of respiratory pathogens based on single-cell Raman spectroscopy

Respiratory infections rank fourth in the global economic burden of disease. Lower respiratory tract infections are the leading cause of death in low-income countries. The rapid identification of pathogens causing lower respiratory tract infections to help guide the use of antibiotics can reduce the mortality of patients with lower respiratory tract infections. Single-cell Raman spectroscopy is a "whole biological fingerprint" technique that can be used to identify microbial samples. It has the advantages of no marking and fast and non-destructive testing. In this study, single-cell Raman spectroscopy was used to collect spectral data of six respiratory tract pathogen isolates. The T-distributed stochastic neighbor embedding (t-SNE) isolation analysis algorithm was used to compare the differences between the six respiratory tract pathogens. The eXtreme Gradient Boosting (XGBoost) algorithm was used to establish a Raman phenotype database model. The classification accuracy of the isolated samples was 93-100%, and the classification accuracy of the clinical samples was more than 80%. Combined with heavy water labeling technology, the drug resistance of respiratory tract pathogens was determined. The study showed that single-cell Raman spectroscopy-D₂O (SCRS-D₂O) labeling could rapidly identify the drug resistance of respiratory tract pathogens within 2 h.

Identification of pathogens and detection of antibiotic susceptibility at single-cell resolution by Raman spectroscopy combined with machine learning

Rapid, accurate, and label-free detection of pathogenic bacteria and antibiotic resistance at single-cell resolution is a technological challenge for clinical diagnosis. Overcoming the cumbersome culture process of pathogenic bacteria and time-consuming antibiotic susceptibility assays will significantly benefit early diagnosis and optimize the use of antibiotics in clinics. Raman spectroscopy can collect molecular fingerprints of pathogenic bacteria in a label-free and culture-independent manner, which is suitable for pathogen diagnosis at single-cell resolution. Here, we report a method based on Raman spectroscopy combined with machine learning to rapidly and accurately identify pathogenic bacteria and detect antibiotic resistance at single-cell resolution. Our results show that the average accuracy of identification of 12 species of common pathogenic bacteria by the machine learning method is 90.73 ± 9.72%. Antibiotic-sensitive and antibiotic-resistant strains of Acinetobacter baumannii isolated from hospital patients were distinguished with 99.92 ± 0.06% accuracy using the machine learning model. Meanwhile, we found that sensitive strains had a higher nucleic acid/protein ratio and antibiotic-resistant strains possessed abundant amide II structures in proteins. This study suggests that Raman spectroscopy is a promising method for rapidly identifying pathogens and detecting their antibiotic susceptibility.

SERS-based antibiotic susceptibility testing: Towards point-of-care clinical diagnosis

Emerging antibiotic resistant bacteria constitute one of the biggest threats to public health. Surface-enhanced Raman scattering (SERS) is highly promising for detecting such bacteria and for antibiotic susceptibility testing (AST). SERS is fast, non-destructive (can probe living cells) and it is technologically flexible (readily integrated with robotics and machine learning algorithms). However, in order to integrate into efficient point-of-care (PoC) devices and to effectively replace the current culture-based methods, it needs to overcome the challenges of reliability, cost and complexity. Recently, significant progress has been made with the emergence of both new questions and new promising directions of research and technological development. This article brings together insights from several representative SERS-based AST studies and approaches oriented towards clinical PoC biosensing. It aims to serve as a reference source that can guide progress towards PoC routines for identifying antibiotic resistant pathogens. In turn, such identification would help to trace the origin of sporadic infections, in order to prevent outbreaks and to design effective medical treatment and preventive procedures.

Comparison of Different Label-Free Raman Spectroscopy Approaches for the Discrimination of Clinical MRSA and MSSA Isolates

Methicillin-resistant Staphylococcus aureus (MRSA) is classified as one of the priority pathogens that threaten human health. Resistance detection with conventional microbiological methods takes several days, forcing physicians to administer empirical antimicrobial treatment that is not always appropriate. A need exists for a rapid, accurate, and cost-effective method that allows targeted antimicrobial therapy in limited time. In this pilot study, we investigate the efficacy of three different label-free Raman spectroscopic approaches to differentiate methicillin-resistant and -susceptible clinical isolates of S. aureus (MSSA). Single-cell analysis using 532 nm excitation was shown to be the most suitable approach since it captures information on the overall biochemical composition of the bacteria, predicting 87.5% of the strains correctly. UV resonance Raman microspectroscopy provided a balanced accuracy of 62.5% and was not sensitive enough in discriminating MRSA from MSSA. Excitation of 785 nm directly on the petri dish provided a balanced accuracy of 87.5%. However, the difference between the strains was derived from the dominant staphyloxanthin bands in the MRSA, a cell component not associated with the presence of methicillin resistance. This is the first step toward the development of label-free Raman spectroscopy for the discrimination of MRSA and MSSA using single-cell analysis with 532 nm excitation. IMPORTANCE Label-free Raman spectra capture the high chemical complexity of bacterial cells. Many different Raman approaches have been developed using different excitation wavelength and cell analysis methods. This study highlights the major importance of selecting the most suitable Raman approach, capable of providing spectral features that can be associated with the cell mechanism under investigation. It is shown that the approach of choice for differentiating MRSA from MSSA should be single-cell analysis with 532 nm excitation since it captures the difference in the overall biochemical composition. These results should be taken into consideration in future studies aiming for the development of label-free Raman spectroscopy as a clinical analytical tool for antimicrobial resistance determination.

Herbicides 2,4-Dichlorophenoxy Acetic Acid and Glyphosate Induce Distinct Biochemical Changes in E. coli during Phenotypic Antibiotic Resistance: A Raman Spectroscopic Study

Antibiotic resistance is a major global health concern. The increased use of herbicides may lead to multiple antibiotic resistance in bacteria. Conventional techniques for diagnosing antibiotic resistance are laborious, time-intensive, expensive, and lack information about antibiotic susceptibility. On the other hand, Raman spectroscopy is a rapid, label-free, noninvasive alternative to traditional techniques to detect antibiotic resistance. In this study, two popular herbicides 2,4-dichlorophenoxy acetic acid (2,4-D) and N-(phosphonomethyl)glycine (glyphosate) were used to study their effects on the emergence of antibiotic resistance. The Escherichia coli wild-type (WT) MG1655 strain and two isogenic mutants, Δlon and ΔacrB, were used together with Raman spectroscopy. The WT E. coli is sensitive to antibiotics, but exposure to both herbicides induces antibiotic resistance. Using an excitation wavelength of 785 nm, the intensity ratios (e.g., I₇₄₀/I₇₈₅, I₇₄₀/I₁₀₀₃, I₁₄₈₀/I₁₄₄₅, I₂₉₃₄/I₂₈₆₈, and I₂₉₃₄/I₂₈₄₅) were identified as biomarkers to study the induction of antibiotic resistance in bacteria but not NaCl-mediated stress. Using an excitation wavelength of 633 nm, the peak intensity at 740 cm^-1 assigned to cytochrome bd decreases under antibiotic stress but increases upon exposure to both herbicides and antibiotics, indicating the development of resistance. Thus, this study can be applied to monitor antibiotic resistance using Raman spectroscopy.

Single-cell Raman spectroscopy identifies Escherichia coli persisters and reveals their enhanced metabolic activities

Microbial persisters are the featured tiny sub-population of microorganisms that are highly tolerant to multiple antimicrobials. Currently, studies on persisters remain a considerable challenge owing to technical limitations. Here, we explored the application of single-cell Raman spectroscopy (SCRS) in the investigation of persisters. Escherichia coli (ATCC 25922) cells were treated with a lethal dosage of ampicillin (100 μg/mL, 32 × MIC, 4 h) for the formation of persisters. The biochemical characters of E. coli and its persisters were assessed by SCRS, and their metabolic activities were labeled and measured with D₂O-based single-cell Raman spectroscopy (D₂O-Ramanometry). Notable differences in the intensity of Raman bands related to major cellular components and metabolites were observed between E. coli and its ampicillin-treated persisters. Based on their distinct Raman spectra, E. coli and its persister cells were classified into different projective zones through the principal component analysis and t-distributed stochastic neighbor embedding. According to the D₂O absorption rate, E. coli persisters exhibited higher metabolic activities than those of untreated E. coli. Importantly, after the termination of ampicillin exposure, these persister cells showed a temporal pattern of D₂O intake that was distinct from non-persister cells. To our knowledge, this is the first report on identifying E. coli persisters and assessing their metabolic activities through the integrated SCRS and D₂O-Ramanometry approach. These novel findings enhance our understanding of the phenotypes and functionalities of microbial persister cells. Further investigations could be extended to other pathogens by disclosing microbial pathogenicity mechanisms for developing novel therapeutic strategies and approaches.

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%.

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.

Monitoring the mode of action of synthetic and natural biocides against Aeromonas hydrophila by Raman spectroscopy and chemometrics

Abstract

We have investigated the mode of action of synthetic biocides, (2-(thiocyanomethylthio) benzothiazole(TCMTB), dichlorophen, (commonly used in leather industry for preservation) and natural biocides, oregano and eucalyptus oils, on Aeromonas hydrophila using Raman spectroscopy in collaboration with multivariate analysis and 2D correlation spectroscopy to evaluate whether Raman spectra acquired contained valuable information to study the action of biocides on bacterial cells. The growth of A. hydrophila in clear and outer edge zone of inhibition differ in their reaction with different biocides, which allows us to highlight the differences as a characteristic of two kinds of bacteria. Such classification helps identify oregano oil as the most effective biocide by altering clear and outer edge zone of bacteria. Standard disk diffusion assay method was used for screening biocide bacteria interactions and later analysed by Raman spectroscopy. The paper also presents the introduction of TCMTB and oregano oil into leather processing stages to examine and determine the antimicrobial effect as an application to real-world setting. Therefore, we conclude that Raman spectroscopy with appropriate computational tools constitutes a powerful approach for screening biocides, which provide solutions to all the industries using biocides including leather industry, considering the potentially harmful effect of biocides to humans and the environment.

Graphical abstract