Simple Ciprofloxacin Resistance Test and Determination of Minimal Inhibitory Concentration within 2 h Using Raman Spectroscopy

Phenotypic and genotypic perspectives on detection methods for bacterial antimicrobial resistance in a One Health context: research progress and prospects

The widespread spread of bacterial antimicrobial resistance (AMR) and multidrug-resistant bacteria poses a significant threat to global public health. Traditional methods for detecting bacterial AMR are simple, reproducible, and intuitive, requiring long time incubation and high labor intensity. To quickly identify and detect bacterial AMR is urgent for clinical treatment to reduce mortality rate, and many new methods and technologies were required to be developed. This review summarizes the current phenotypic and genotypic detection methods for bacterial AMR. Phenotypic detection methods mainly include antimicrobial susceptibility tests, while genotypic detection methods have higher sensitivity and specificity and can detect known or even unknown drug resistance genes. However, most of the current tests are either genotypic or phenotypic and rarely combined. Combining the advantages of phenotypic and genotypic methods, combined with the joint application of multiple rapid detection methods may be the trend for future AMR testing. Driven by rapid diagnostic technology, big data analysis, and artificial intelligence, detection methods of bacterial AMR are expected to constantly develop and innovate. Adopting rational detection methods and scientific data analysis can better address the challenges of bacterial AMR and ensure human health and social well-being.

"Magnet" Based on Activated Silver Nanoparticles Adsorbed Bacteria to Predict Refractory Apical Periodontitis Via Surface-Enhanced Raman Scattering

Refractory apical periodontitis (RAP) is an endodontic apical inflammatory disease caused by Enterococcus faecalis (E. faecalis). Bacterial detection using surface-enhanced Raman scattering (SERS) technology is a hot research topic, but the specific and direct detection of oral bacteria is a challenge, especially in real clinical samples. In this paper, we develop a novel SERS-based green platform for label-free detection of oral bacteria. The platform was built on silver nanoparticles with a two-step enhancement way using NaBH4 and sodium (Na+) to form "hot spots," which resulted in an enhanced SERS fingerprint of E. faecalis with fast, quantitative, lower-limit, reproducibility, and stability. In combination with machine learning, four different oral bacteria (E. faecalis, Porphyromonas gingivalis, Streptococcus mutans, and Escherichia coli) could be intelligently distinguished. The unlabeled detection method emphasized the specificity of E. faecalis in simulated saliva, serum, and even real samples from patients with clinical root periapical disease. In addition, the assay has been shown to be environmentally friendly and without secondary contamination through antimicrobial assays. The proposed label-free, rapid, safe, and green SERS detection strategy for oral bacteria provided an innovative solution for the early diagnosis and prevention of RAP and other perioral diseases.

Antimicrobial susceptibility testing using infrared attenuated total reflection (IR-ATR) spectroscopy to monitor metabolic activity

Fast and accurate detection of antimicrobial resistance in pathogens remains a challenge, and with the increase in antimicrobial resistance due to mis- and overuse of antibiotics, it has become an urgent public health problem. We demonstrate how infrared attenuated total reflection (IR-ATR) can be used as a simple method for assessment of bacterial susceptibility to antibiotics. This is achieved by monitoring the metabolic activities of bacterial cells via nutrient consumption and using this as an indicator of bacterial viability. Principal component analysis of the obtained spectra provides a tool for fast and simple discrimination of antimicrobial resistance in the acquired data. We demonstrate this concept using four bacterial strains and four different antibiotics, showing that the change in glucose concentration in the growth medium after 2 h, as monitored by IR-ATR, can be used as a spectroscopic diagnostic technique, to reduce detection time and to improve quality in the assessment of antimicrobial resistance in pathogens.

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

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

Accurate Prediction of Antimicrobial Susceptibility for Point-of-Care Testing of Urine in Less than 90 Minutes via iPRISM Cassettes

The extensive and improper use of antibiotics has led to a dramatic increase in the frequency of antibiotic resistance among human pathogens, complicating infectious disease treatments. In this work, a method for rapid antimicrobial susceptibility testing (AST) is presented using microstructured silicon diffraction gratings integrated into prototype devices, which enhance bacteria-surface interactions and promote bacterial colonization. The silicon microstructures act also as optical sensors for monitoring bacterial growth upon exposure to antibiotics in a real-time and label-free manner via intensity-based phase-shift reflectometric interference spectroscopic measurements (iPRISM). Rapid AST using clinical isolates of Escherichia coli (E. coli) from urine is established and the assay is applied directly on unprocessed urine samples from urinary tract infection patients. When coupled with a machine learning algorithm trained on clinical samples, the iPRISM AST is able to predict the resistance or susceptibility of a new clinical sample with an Area Under the Receiver Operating Characteristic curve (AUC) of ∼ 0.85 in 1 h, and AUC > 0.9 in 90 min, when compared to state-of-the-art automated AST methods used in the clinic while being an order of magnitude faster.

Identification of bacteria in mixed infection from urinary tract of patient's samples using Raman analysis of dried droplets

Urinary tract infections (UTI) are among the most frequent nosocomial infections. A fast identification of the pathogen and assignment of Gram type could help to prescribe most suitable treatments. Raman spectroscopy holds high potential for fast and reliable bacterial pathogens identification. While most studies so far have focused on individual pathogens or artificial mixtures, this contribution aims to translate the analysis to primary urine samples from patients with suspected UTIs. For this, we have included 59 primary urine samples out of which 29 were diagnosed as mixed infections. For Raman analysis, we first trained two classification models based on principal component analysis - linear discriminant analysis (PCA-LDA) with more than 3500 Raman spectra of 85 clinical isolates from 23 species in order to (1) identify the Gram type of the bacteria and (2) assign family membership to one of the six most abundant bacterial families in urinary tract infections (Enterobacteriaceae, Morganellaceae, Pseudomonadaceae, Enterococcaceae, Staphylococcaceae and Streptococcaceae). The classification models were applied to artificial mixtures of Gram positive and Gram negative bacteria to correctly predict mixed infections with an accuracy of 75%. Raman scans of dried droplets did not yet yield optimal classification results on family level. When translating the method to primary urine samples, we observed a strong bias towards Gram negative bacteria, on family level towards Morganellaceae, which reduced prediction accuracy. Spectral differences were observed between isolates grown on standard growth medium and bacteria of the same strain when characterized directly from the patient. Thus, improvement of the classification accuracy is expected with a larger data base containing also bacteria measured directly from the urine sample.

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.

Antibiotic Resistance Diagnosis in ESKAPE Pathogens-A Review on Proteomic Perspective

Antibiotic resistance has emerged as an imminent pandemic. Rapid diagnostic assays distinguish bacterial infections from other diseases and aid antimicrobial stewardship, therapy optimization, and epidemiological surveillance. Traditional methods typically have longer turn-around times for definitive results. On the other hand, proteomic studies have progressed constantly and improved both in qualitative and quantitative analysis. With a wide range of data sets made available in the public domain, the ability to interpret the data has considerably reduced the error rates. This review gives an insight on state-of-the-art proteomic techniques in diagnosing antibiotic resistance in ESKAPE pathogens with a future outlook for evading the "imminent pandemic".

Rapid determination of Proteus mirabilis susceptibility to antibiotics using infrared spectroscopy in tandem with random forest

Bacterial infections cause serious illnesses that are treated with antibiotics. Currently used methods for detecting bacterial antibiotic susceptibility consume 48-72 h, leading to overuse of antibiotics. Thus, many bacterial species have acquired resistance to a broad range of available antibiotics. There is an urgent need to develop efficient methods for rapid determination of bacterial susceptibility to antibiotics. The combination of machine learning and Fourier-transform infrared (FTIR) spectroscopy has generated a promising diagnostic approach in medicine and biology. Our main goal is to examine the potential of FTIR spectroscopy to determine the susceptibility of urinary tract infection-Proteus mirabilis to a specific range of antibiotics, within about 20 min after 24 h culture and identification. We measured the infrared spectra of 489 different P. mirabilis isolates and used random forest to analyze this spectral database. A classification success rate of ~84% was achieved in differentiating between the resistant and sensitive isolates based on their susceptibility to ceftazidime, ceftriaxone, cefuroxime, cefuroxime axetil, cephalexin, ciprofloxacin, gentamicin, and sulfamethoxazole antibiotics in a time span of 24 h instead of 48 h.

High-Throughput Antimicrobial Susceptibility Testing of Escherichia coli by Wide-Field Mid-Infrared Photothermal Imaging of Protein Synthesis

Antimicrobial resistance poses great threats to global health and economics. Current gold-standard antimicrobial susceptibility testing (AST) requires extensive culture time (36-72 h) to determine susceptibility. There is an urgent need for rapid AST methods to slow down antimicrobial resistance. Here, we present a rapid AST method based on wide-field mid-infrared photothermal imaging of protein synthesis from ¹³C-glucose in Escherichia coli. Our wide-field approach achieved metabolic imaging for hundreds of bacteria at the single-cell resolution within seconds. The perturbed microbial protein synthesis can be probed within 1 h after antibiotic treatment in E. coli cells. The susceptibility of antibiotics with various mechanisms of action has been probed through monitoring protein synthesis, which promises great potential of the proposed platform toward clinical translation.

Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy

Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h.

Antibiotic Susceptibility Testing with Raman Biosensing

Antibiotics guard us against bacterial infections and are among the most commonly used medicines. The immediate consequence of their large-scale production and prescription is the development of antibiotic resistance. Therefore, rapid detection of antibiotic susceptibility is required for efficient antimicrobial therapy. One of the promising methods for rapid antibiotic susceptibility testing is Raman spectroscopy. Raman spectroscopy combines fast and contactless acquisition of spectra with good selectivity towards bacterial cells. The antibiotic-induced changes in bacterial cell physiology are detected as distinct features in Raman spectra and can be associated with antibiotic susceptibility. Therefore, the Raman-based approach may be beneficial in designing therapy against multidrug-resistant infections. The surface-enhanced Raman spectroscopy (SERS) and resonance Raman spectroscopy (RRS) additionally provide excellent sensitivity. In this review, we present an analysis of the Raman spectroscopy-based optical biosensing approaches aimed at antibiotic susceptibility testing.

Phenotypic convergence of bacterial adaption to sub-lethal antibiotic treatment

Microorganisms can adapt quickly to changes in their environment, leading to various phenotypes. The dynamic for phenotypic plasticity caused by environmental variations has not yet been fully investigated. In this study, we analyzed the time-series of phenotypic changes in Staphylococcus cells during adaptive process to antibiotics stresses using flow cytometry and Raman spectroscopy. The nine antibiotics with four different mode of actions were treated in bacterial cells at a sub-lethal concentration to give adaptable stress. Although the growth rate initially varied depending on the type of antibiotic, most samples reached the maximum growth comparable to the control through the short-term adaptation after 24 h. The phenotypic diversity, which showed remarkable changes depending on antibiotic treatment, converged identical to the control over time. In addition, the phenotype with cellular biomolecules converted into a bacterial cell that enhance tolerance to antibiotic stress with increases in cytochrome and lipid. Our findings demonstrated that the convergence into the phenotypes that enhance antibiotic tolerance in a short period when treated with sub-lethal concentrations, and highlight the feasibility of phenotypic approaches in the advanced antibiotic treatment.

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.

Dynamic insights into increasing antibiotic resistance in Staphylococcus aureus by label-free SERS using a portable Raman spectrometer

Rapid and quantitative detection of bacterial antibiotic resistance is of great significance for the prevention and treatment of infections and understanding drug-resistant mechanism. In this study, label-free surface-enhanced Raman spectroscopy (SERS) technology was applied to dynamically explore oxacillin/cefazolin-derived resistance in Staphylococcus aureus using a portable Raman spectrometer. The results showed that S. aureus rapidly responded to oxacillin/cefazolin stimulation and gradually developed different degrees of drug resistance during the 21 days of exposure. The molecular changes that accumulated in the drug-resistant strains were sensitively recorded by SERS in a whole-cell manner. Principal components-linear discriminant analysis correctly distinguished various degrees of drug-resistant strains. The typical Raman peak intensities of I₇₃₄/I₈₆₇ showed a negative and non-linear correlation with the minimum inhibitory concentration (MIC). The correlation coefficient reached above 0.9. The target sites of oxacillin/cefazolin on S. aureus clearly reflected on SERS profiles. The results collected by SERS were further verified by other biological methods including the antibiotic susceptibility test, MIC determination, and PCR results. This study indicates that SERS technology provides a rapid and flexible alternative to current drug susceptibility testing, laying a foundation for qualitative and quantitative evaluation of drug resistance in clinical detection.

An antibiotic concentration gradient microfluidic device integrating surface-enhanced Raman spectroscopy for multiplex antimicrobial susceptibility testing

Antimicrobial susceptibility testing (AST) is a key measure in clinical microbiology laboratories to enable appropriate antimicrobial administration. During an AST, the determination of the minimum inhibitory concentration (MIC) is an important step in which the bacterial responses to an antibiotic at a series of concentrations obtained in separate bacterial growth chambers or sites are compared. However, the preparation of different antibiotic concentrations is time-consuming and labor-intensive. In this paper, we present a microfluidic device that generates a concentration gradient for antibiotics that is produced by diffusion in the laminar flow regime along a series of lateral microwells to encapsulate bacteria for antibiotic treatment. All the AST preparation steps (including bacterium loading, antibiotic concentration generation, buffer washing, and isolated bacterial growth with an antibiotic) can be performed in a single chip. The viable bacterial cells in each microwell after the antibiotic treatment are then quantified by their surface-enhanced Raman scattering (SERS) signals that are acquired after placing a uniform SERS-active substrate in contact with all the microwells. For proof-of-concept, we demonstrated the AST performance of this system on ampicillin (AMP)-susceptible and -resistant E. coli strains. Compared with the parameters for conventional AST methods, the AST procedure based on this chip requires only 20 μL of bacteria solution and 5 h of operation time. This result indicates that this integrated system can greatly shorten and simplify the tedious and labor-intensive procedures required for current standard AST methods.

Rapid Antimicrobial Susceptibility Testing by Stimulated Raman Scattering Imaging of Deuterium Incorporation in a Single Bacterium

To slow and prevent the spread of antimicrobial resistant infections, rapid antimicrobial susceptibility testing (AST) is in urgent need to quantitatively determine the antimicrobial effects on pathogens. It typically takes days to complete the AST by conventional methods based on the long-time culture, and they do not work directly for clinical samples. Here, we report a rapid AST method enabled by stimulated Raman scattering (SRS) imaging of deuterium oxide (D2O) metabolic incorporation. Metabolic incorporation of D2O into biomass and the metabolic activity inhibition upon exposure to antibiotics at the single bacterium level are monitored by SRS imaging. The single-cell metabolism inactivation concentration (SC-MIC) of bacteria upon exposure to antibiotics can be obtained after a total of 2.5 h of sample preparation and detection. Furthermore, this rapid AST method is directly applicable to bacterial samples in complex biological environments, such as urine or whole blood. SRS metabolic imaging of deuterium incorporation is transformative for rapid single-cell phenotypic AST in the clinic.

Detection of multi-resistant clinical strains of E. coli with Raman spectroscopy

In recent years, we have seen a steady rise in the prevalence of antibiotic-resistant bacteria. This creates many challenges in treating patients who carry these infections, as well as stopping and preventing outbreaks. Identifying these resistant bacteria is critical for treatment decisions and epidemiological studies. However, current methods for identification of resistance either require long cultivation steps or expensive reagents. Raman spectroscopy has been shown in the past to enable the rapid identification of bacterial strains from single cells and cultures. In this study, Raman spectroscopy was applied for the differentiation of resistant and sensitive strains of Escherichia coli. Our focus was on clinical multi-resistant (extended-spectrum β-lactam and carbapenem-resistant) bacteria from hospital patients. The spectra were collected using both UV resonance Raman spectroscopy in bulk and single-cell Raman microspectroscopy, without exposure to antibiotics. We found resistant strains have a higher nucleic acid/protein ratio, and used the spectra to train a machine learning model that differentiates resistant and sensitive strains. In addition, we applied a majority of voting system to both improve the accuracy of our models and make them more applicable for a clinical setting. This method could allow rapid and accurate identification of antibiotic resistant bacteria, and thus improve public health.

Raman spectroscopy-a tool for rapid differentiation among microbes causing urinary tract infections

Urinary tract infections belong to the most common infections in the world. Besides community-acquired infections, nosocomial infections pose a high risk especially for patients having indwelling catheters, undergoing urological surgeries or staying at hospital for prolonged time. They can be often complicated by antimicrobial resistance and/or biofilm formation. Therefore, a rapid diagnostic tool enabling timely identification of a causative agent and its susceptibility to antimicrobials is a need. Raman spectroscopy appears to be a suitable method that allows rapid differentiation among microbes and provides a space for further analyses, such as determination of capability of biofilm formation or antimicrobial susceptibility/resistance in tested strains. Our work here presents a possibility to differ among most common microbes causing urinary tract infections (belonging to 20 species). We tested 254 strains directly from colonies grown on Mueller-Hinton agar plates. The results show that it is possible to distinguish among the tested species using Raman spectroscopy, which proves its great potential for future use in clinical diagnostics. Moreover, we present here a pilot study of a real-time analysis and identification (in less than 10 min) of single microbial cells directly in urine employing optical tweezers combined with Raman spectroscopy.

Airy light-sheet Raman imaging

Light-sheet fluorescence microscopy has greatly improved the speed and overall photostability of optically sectioning cellular and multi-cellular specimens. Similar gains have also been conferred by light-sheet Raman imaging; these schemes, however, rely on diffraction limited Gaussian beams that hinder the uniformity and size of the imaging field-of-view, and, as such, the resulting throughput rates. Here, we demonstrate that a digitally scanned Airy beam increases the Raman imaging throughput rates by more than an order of magnitude than conventional diffraction-limited beams. Overall, this, spectrometer-less, approach enabled 3D imaging of microparticles with high contrast and 1 µm axial resolution at 300 msec integration times per plane and orders of magnitude lower irradiation density than coherent Raman imaging schemes. We detail the apparatus and its performance, as well as its compatibility with fluorescence light-sheet and quantitative-phase imaging towards rapid and low phototoxicity multimodal imaging.

Surface-enhanced Raman spectroscopy for bioanalysis and diagnosis

In recent years, bioanalytical surface-enhanced Raman spectroscopy (SERS) has blossomed into a fast-growing research area. Owing to its high sensitivity and outstanding multiplexing ability, SERS is an effective analytical technique that has excellent potential in bioanalysis and diagnosis, as demonstrated by its increasing applications in vivo. SERS allows the rapid detection of molecular species based on direct and indirect strategies. Because it benefits from the tunable surface properties of nanostructures, it finds a broad range of applications with clinical relevance, such as biological sensing, drug delivery and live cell imaging assays. Of particular interest are early-stage-cancer detection and the fast detection of pathogens. Here, we present a comprehensive survey of SERS-based assays, from basic considerations to bioanalytical applications. Our main focus is on SERS-based pathogen detection methods as point-of-care solutions for early bacterial infection detection and chronic disease diagnosis. Additionally, various promising in vivo applications of SERS are surveyed. Furthermore, we provide a brief outlook of recent endeavours and we discuss future prospects and limitations for SERS, as a reliable approach for rapid and sensitive bioanalysis and diagnosis.

Fast Antibiotic Susceptibility Testing via Raman Microspectrometry on Single Bacteria: An MRSA Case Study

Despite recent advances in molecular diagnostics, ultrafast determination of the antibiotic susceptibility phenotype of pathogenic microorganisms is still a major challenge of in vitro diagnostics (IVD) of infectious diseases. Raman microspectroscopy has been proposed as a means to achieve this goal. Previous studies have shown that susceptibility phenotyping could be done through Raman analysis of microbial cells, either in large clusters or down to the single-cell level in the case of Gram-negative rods. Gram-positive cocci such as Staphylococcus aureus pose several challenges due to their size and their different metabolic and chemical characteristics. Using a tailored automated single-cell Raman spectrometer and a previously proposed sample preparation protocol, we acquired and analyzed 9429 S. aureus single cells belonging to three cefoxitin-resistant strains and two susceptible strains during their incubation in the presence of various concentrations of cefoxitin. We observed an effect on S. aureus spectra that is weaker than what was detected on previous bacteria/drug combinations, with a higher cell-to-cell response variability and an important impact of incubation conditions on the phenotypic resistance of a given strain. Overall, the proposed protocol was able to correlate strains' phenotype with a specific modification of the spectra using majority votes. We, hence, confirm that our previous results on single-cell Raman antibiotic susceptibility testing can be extended to the S. aureus case and further clarify potential limitations and development requirements of this approach in the move toward industrial applications.

Campylobacter jejuni Antimicrobial Resistance Profiles and Mechanisms Determined Using a Raman Spectroscopy-Based Metabolomic Approach

Rapid identification of antimicrobial resistance (AMR) profiles and mechanisms is critical for clinical management and drug development. However, the current AMR detection approaches take up to 48 h to obtain a result. Here, we demonstrate a Raman spectroscopy-based metabolomic approach to rapidly determine the AMR profile of Campylobacter jejuni, a major cause of foodborne gastroenteritis worldwide. C. jejuni isolates with susceptible and resistant traits to ampicillin and tetracycline were subjected to different antibiotic concentrations for 5 h, followed by Raman spectral collection and chemometric analysis (i.e., second-derivative transformation analysis, hierarchical clustering analysis [HCA], and principal-component analysis [PCA]). The MICs obtained by Raman-2nd derivative transformation agreed with the reference agar dilution method for all isolates. The AMR profile of C. jejuni was accurately classified by Raman-HCA after treating bacteria with antibiotics at clinical susceptible and resistant breakpoints. According to PCA loading plots, susceptible and resistant strains showed different Raman metabolomic patterns for antibiotics. Ampicillin-resistant isolates had distinctive Raman signatures of peptidoglycan, which is related to cell wall synthesis. The ratio of saturated to unsaturated fatty acids in the lipid membrane layer of ampicillin-resistant isolates was higher than in susceptible ones, indicating more rigid envelope structure under ampicillin treatment. In comparison, tetracycline-resistant isolates exhibited prominent Raman spectral features associated with proteins and nucleic acids, demonstrating more active protein synthesis than susceptible strains with the presence of tetracycline. Taken together, Raman spectroscopy is a powerful metabolic fingerprinting technique for simultaneously revealing the AMR profiles and mechanisms of foodborne pathogens. IMPORTANCE Metabolism plays the central role in bacteria to mediate the early response against antibiotics and demonstrate antimicrobial resistance (AMR). Understanding the whole-cell metabolite profiles gives rise to a more complete AMR mechanism insight. In this study, we have applied Raman spectroscopy and chemometrics to achieve a rapid, accurate, and easy-to-operate investigation of bacterial AMR profiles and mechanisms. Raman spectroscopy reduced the analysis time by an order of magnitude to obtain the same results achieved through traditional culture-based antimicrobial susceptibility approaches. It offers great benefits as a high-throughput screening method in food chain surveillance and clinical diagnostics. Meanwhile, the AMR mechanisms toward two representative antibiotic classes, namely, ampicillin and tetracycline, were revealed by Raman spectroscopy at the metabolome level. This approach is based on bacterial phenotypic responses to antibiotics, providing information complementary to that obtained by conventional genetic methods such as genome sequencing. The knowledge obtained from Raman metabolomic data can be used in drug discovery and pathogen intervention.

Assessment of bacterial viability by laser desorption ionization mass spectrometry for antimicrobial susceptibility testing

Bacterial infection poses a serious threat to human health worldwide. Rapid antimicrobial susceptibility testing (AST) is essential for the clinical treatment of bacterial infection patients. However, the traditional AST relies on bacteria culture, which is time-consuming and limits the analysis to culturable species. Herein, we present a laser desorption ionization (LDI) mass spectrometry-based method for rapid bacterial viability assessment and AST by tracing the redox of resazurin (RS) by viable bacteria. RS as well as its reduction product, fluorescent resorufin (RF), can be directly detected by LDI-MS in the absence of matrix. The intensity ratio between RF and RS can be used to assess the viability of bacteria in specimens. We have demonstrated the high efficiency of the method using different bacterial species, including K. pneumoniae, S. aureus, E. coli, and P. aeruginosa, and various antibiotic drugs, such as ciprofloxacin, ampicillin, tetracycline, oxytetracycline, ciprofloxacin and levofloxacin. Compared to traditional methods based on optical absorption, the current method is faster and more sensitive. Furthermore, we applied the method to bacterial viability detection and AST using human body fluid samples, i.e. serum and urine, demonstrating that it can screen rapidly appropriate antibiotic drugs for timely clinical treatment of infectious diseases. With the advantages of simplicity in methodology as well as sensitivity and speed in analysis, the current method holds the potential of clinical usages.

Development of a Fast Raman-Assisted Antibiotic Susceptibility Test (FRAST) for the Antibiotic Resistance Analysis of Clinical Urine and Blood Samples

Human health is at great risk due to the spreading of antimicrobial resistance (AMR). The lengthy procedure of conventional antimicrobial susceptibility testing (AST) usually requires a few days. We developed a fast Raman-assisted antibiotic susceptibility test (FRAST), which detects single bacterial metabolic activity in the presence of antibiotics, using Raman single-cell spectroscopy. It was found that single-cell Raman spectra (SCRS) would show a clear and distinguishable Raman band at the "silent zone" (2000-2300 cm^-1), due to the active incorporation of deuterium from heavy water (D₂O) by antibiotic-resistant bacteria. This pilot study has compared the FRAST and the conventional AST for six clinical standard quality controls (four Gram-negative and two Gram-positive bacteria strains) in response to 38 antibiotics. In total, 3200 treatments have been carried out and approximately 64 000 SCRS have been acquired for FRAST analysis. The result showed an overall agreement of 88.0% between the FRAST and the conventional AST assay. The gram-staining classification based on the linear discriminant analysis (LDA) model of SCRS was developed, seamlessly coupling with the FRAST to further reduce the turnaround time. We applied the FRAST to real clinical analysis for nine urinary infectious samples and three sepsis samples. The results were consistent with MALDI-TOF identification and the conventional AST. Under the optimal conditions, the "sample to report" of the FRAST could be reduced to 3 h for urine samples and 21 h for sepsis samples. The FRAST provides fast and reliable susceptibility tests, which could speed up microbiological analysis for clinical practice and facilitate antibiotic stewardship.

Recent Development of Rapid Antimicrobial Susceptibility Testing Methods through Metabolic Profiling of Bacteria

Due to the inappropriate use and overuse of antibiotics, the emergence and spread of antibiotic-resistant bacteria are increasing and have become a major threat to human health. A key factor in the treatment of bacterial infections and slowing down the emergence of antibiotic resistance is to perform antimicrobial susceptibility testing (AST) of infecting bacteria rapidly to prescribe appropriate drugs and reduce the use of broad-spectrum antibiotics. Current phenotypic AST methods based on the detection of bacterial growth are generally reliable but are too slow. There is an urgent need for new methods that can perform AST rapidly. Bacterial metabolism is a fast process, as bacterial cells double about every 20 to 30 min for fast-growing species. Moreover, bacterial metabolism has shown to be related to drug resistance, so a comparison of differences in microbial metabolic processes in the presence or absence of antimicrobials provides an alternative approach to traditional culture for faster AST. In this review, we summarize recent developments in rapid AST methods through metabolic profiling of bacteria under antibiotic treatment.

Controllable design of a nano-bio aptasensing interface based on tetrahedral framework nucleic acids in an integrated microfluidic platform

The limited reaction time and sample volume in the confined space of microfluidic devices give considerable importance to the development of more effective biosensing interfaces. Herein, the self-assembling of tetrahedral framework nucleic acids (FNAs) with controllable size on the interface of the microfluidic microchannels is studied. Compared with macroscopic turbulence control on traditional micro-structured microfluidic surface, the novel FNA-engineered microfluidic interface successfully constructs a 3D reaction space at nanoscale by raising DNA probes away from the surface. This FNA interface dramatically improves the reaction kinetics during molecular recognition due to extremely ordered orientation, configuration and density of DNA probes on the surface. Finally, the FNA-engineered interface is applied in a novel multi-functional microfluidic platform, towards a "one-stop" assay of Escherichia coli O157: H7 (E. coli O157: H7), integrating capture, release, enrichment, cell culture and antimicrobial susceptibility testing (AST). With the FNA-aptamer probe, we achieved an enhanced bacterial detecting efficiency (10 CFU/mL) plus excellent selectivity and precision. The appicability was strongly demonstrated when the biosensor was successfully applied in real samples, including the analysis of antibiotic susceptibility and minimum inhibitory concentration (MIC) of E. coli O157: H7 among different antibiotics. The application of FNA interface will open a wide avenue for the development of microfluidic biosensors for other pathogenic microorganisms or circulating tumor cells (CTC) simply by changing the aptamers.

Bacterial phenotype dependency from CO2 measured by Raman spectroscopy

In recent years, Raman spectroscopy has become an established method to study medical, biological or environmental samples. Since Raman spectroscopy is a phenotypic method, many parameters can influence the spectra. One of these parameters is the concentration of CO₂, as this never remains stable in nature, but always adjusts itself in a dynamic equilibrium. So, it is obvious that the concentration of CO₂ cannot be controlled but it might have a big impact on the bacteria and bacterial composition in medical samples. When using a phenotypic method like Raman spectroscopy it is also important to know the influence of CO₂ to the dataset. To investigate the influence of CO₂ towards Raman spectra we cultivated E. coli at different concentration of CO₂ since this bacterium is able to switch metabolism from aerobic to microaerophilic conditions. After applying statistic methods small changes in the spectra became visible and it was even possible to observe the change of metabolism in this species according to the concentration of CO₂.

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.

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

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.

Deep Learning Analysis of Vibrational Spectra of Bacterial Lysate for Rapid Antimicrobial Susceptibility Testing

Rapid antimicrobial susceptibility testing (AST) is an integral tool to mitigate the unnecessary use of powerful and broad-spectrum antibiotics that leads to the proliferation of multi-drug-resistant bacteria. Using a sensor platform composed of surface-enhanced Raman scattering (SERS) sensors with control of nanogap chemistry and machine learning algorithms for analysis of complex spectral data, bacteria metabolic profiles post antibiotic exposure are correlated with susceptibility. Deep neural network models are able to discriminate the responses of Escherichia coli and Pseudomonas aeruginosa to antibiotics from untreated cells in SERS data in 10 min after antibiotic exposure with greater than 99% accuracy. Deep learning analysis is also able to differentiate responses from untreated cells with antibiotic dosages up to 10-fold lower than the minimum inhibitory concentration observed in conventional growth assays. In addition, analysis of SERS data using a generative model, a variational autoencoder, identifies spectral features in the P. aeruginosa lysate data associated with antibiotic efficacy. From this insight, a combinatorial dataset of metabolites is selected to extend the latent space of the variational autoencoder. This culture-free dataset dramatically improves classification accuracy to select effective antibiotic treatment in 30 min. Unsupervised Bayesian Gaussian mixture analysis achieves 99.3% accuracy in discriminating between susceptible versus resistant to antibiotic cultures in SERS using the extended latent space. Discriminative and generative models rapidly provide high classification accuracy with small sets of labeled data, which enormously reduces the amount of time needed to validate phenotypic AST with conventional growth assays. Thus, this work outlines a promising approach toward practical rapid AST.

Rapid Determination of Antimicrobial Susceptibility by Stimulated Raman Scattering Imaging of D2O Metabolic Incorporation in a Single Bacterium

Rapid antimicrobial susceptibility testing (AST) is urgently needed for treating infections with appropriate antibiotics and slowing down the emergence of antibiotic-resistant bacteria. Here, a phenotypic platform that rapidly produces AST results by femtosecond stimulated Raman scattering imaging of deuterium oxide (D2O) metabolism is reported. Metabolic incorporation of D2O into biomass in a single bacterium and the metabolic response to antibiotics are probed in as short as 10 min after culture in 70% D2O medium, the fastest among current technologies. Single-cell metabolism inactivation concentration (SC-MIC) is obtained in less than 2.5 h from colony to results. The SC-MIC results of 37 sets of bacterial isolate samples, which include 8 major bacterial species and 14 different antibiotics often encountered in clinic, are validated by standard minimal inhibitory concentration blindly measured via broth microdilution. Toward clinical translation, stimulated Raman scattering imaging of D2O metabolic incorporation and SC-MIC determination after 1 h antibiotic treatment and 30 min mixture of D2O and antibiotics incubation of bacteria in urine or whole blood is demonstrated.

Automated and rapid identification of multidrug resistant Escherichia coli against the lead drugs of acylureidopenicillins, cephalosporins, and fluoroquinolones using specific Raman marker bands

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.

Chemical Bleaching to Minimize Fluorescence Interference in Raman Spectroscopic Measurements for Sulfonated Polystyrene Solutions

Auto-fluorescence is a significant challenge for Raman spectroscopic analyses. Since fluorescence is a much stronger phenomenon than Raman scattering, even trace fluorescent impurities can overwhelm the Raman signal. Strategies to minimize fluorescence interference in Raman measurements include either an instrumental-based approach or treatment of the sample itself to minimize fluorescence. Efforts focused on sample-based treatments to reduce fluorescence interferences have generally focused on sample purification and photobleaching methodologies. In this work, we present a sample treatment approach based upon chemical bleaching to remove fluorescence from Raman measurements of aqueous solutions of sulfonated polystyrene (SPS). Synthetic batches of SPS are characterized by a wide variation in fluorescence from minimum to a catastrophic level, which greatly limits the use of Raman spectroscopy. We systematically investigate the efficacy of various sample-based treatments of the SPS samples. An important acceptance criterion is that the procedure effectively and reliably removes fluorescence without damaging the SPS component. The chemical bleaching, which involves the addition of hydrogen peroxide and incubation at 60 ℃, is found to be highly effective. The parameters affecting the bleaching efficacy are studied, including temperature, hydrogen peroxide dosage, and bleaching time. Classification models are then developed based on the drastically diverse fluorescence background levels in Raman spectra of SPS to help optimize bleaching time for each specific sample. This work serves as an example of using chemical bleaching to remove fluorescence, which is inexpensive and readily available. It can facilitate a broader use of Raman spectroscopy as a quantitative qualitative control method in industrial settings.

A WS2-gold nanoparticle heterostructure-based novel SERS platform for the rapid identification of antibiotic-resistant pathogens

The emergence of antibiotic-resistant bacteria is the biggest threat to our society. The rapid discovery of drug resistant bacteria is very urgently needed to guide antibiotic treatment development. The current manuscript reports the design of a 2D-0D heterostructure-based surface enhanced Raman spectroscopy (SERS) platform, which has the capability for the rapid identification of the multidrug resistant strain of Salmonella DT104. Details of the synthesis and characterization of the heterostructure SERS platform using a two dimensional (2D) WS₂ transition metal dichalcogenide (TMD) and zero dimensional (0D) plasmonic gold nanoparticles (GNPs) have been reported. The current manuscript reveals that the 2D-0D heterostructure-based SERS platform exhibits extremely high Raman enhancement capabilities. Using Rh-6G and 4-ATP probe molecules, we determined that the SERS sensitivity is in the range of ∼10^-10 to 10^-11 M, several orders of magnitude higher than 2D-TMD on its own (10^-3 M) or 0D-GNPs on their own (∼10^-6 to 10^-7 M). Experimental and theoretical finite-difference time-domain (FDTD) simulation data indicate that the synergistic effect of an electromagnetic mechanism (EM) and a chemical mechanism (CM) on the heterostructure is responsible for the excellent SERS enhancement observed. Notably, the experimental data reported here show that the heterostructure-based SERS has the ability to separate a multidrug resistance strain from a normal strain of Salmonella by monitoring the antibiotic-pathogen interaction within 90 minutes, even at a concentration of 100 CFU mL^-1.

Raman-activated sorting of antibiotic-resistant bacteria in human gut microbiota

The antibiotic‐resistant bacteria (ARB) and antibiotic‐resistant genes (ARGs) in human gut microbiota have significant impact on human health. While high throughput metagenomic sequencing reveals genotypes of microbial communities, the functionality, phenotype and heterogeneity of human gut microbiota are still elusive. In this study, we applied Raman microscopy and deuterium isotope probing (Raman–DIP) to detect metabolic active ARB (MA‐ARB) in situ at the single‐cell level in human gut microbiota from two healthy adults. We analysed the relative abundances of MA‐ARB under different concentrations of amoxicillin, cephalexin, tetracycline, florfenicol and vancomycin. To establish the link between phenotypes and genotypes of the MA‐ARB, Raman‐activated cell sorting (RACS) was used to sort MA‐ARB from human gut microbiota, and mini‐metagenomic DNA of the sorted bacteria was amplified, sequenced and analysed. The sorted MA‐ARB and their associated ARGs were identified. Our results suggest a strong relation between ARB in human gut microbiota and personal medical history. This study demonstrates that the toolkit of Raman–DIP, RACS and DNA sequencing can be useful to unravel both phenotypes and genotypes of ARB in human gut microbiota at the single‐cell level.

Genome Sequence of Escherichia coli KI683, Isolated from a Urosepsis Patient

Escherichia coli KI683 was isolated from blood of a patient who developed septicemia as a complication of a urinary tract infection. Genome sequencing resulted in three contigs with a total genome size of 5,243,173 bp encoding 5,143 genes.

Escherichia coli KI683 was isolated from blood of a patient who developed septicemia as a complication of a urinary tract infection. Genome sequencing resulted in three contigs with a total genome size of 5,243,173 bp encoding 5,143 genes.

Rapid detection of bacterial infection and viability assessment with high specificity and sensitivity using Raman microspectroscopy

Infectious diseases caused by bacteria still pose major diagnostic challenges in spite of the availability of various molecular approaches. Irrespective of the type of infection, rapid identification of the causative pathogen with a high degree of sensitivity and specificity is essential for initiating appropriate treatment. While existing methods like PCR possess high sensitivity, they are incapable of identifying the viability status of the pathogen and those which can, like culturing, are inherently slow. To overcome these limitations, we developed a diagnostic platform based on Raman microspectroscopy, capable of detecting biochemical signatures from a single bacterium for identification as well as viability assessment. The study also establishes a decontamination protocol for handling live pathogenic bacteria which does not affect identification and viability testing, showing applicability in the analysis of sputum samples containing pathogenic mycobacterial strains. The minimal sample processing along with multivariate analysis of spectroscopic signatures provides an interface for automatic classification, allowing the prediction of unknown samples by mapping signatures onto available datasets. Also, the novelty of the current work is the demonstration of simultaneous identification and viability assessment at a single bacterial level for pathogenic bacteria. Graphical abstract.

Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning

Raman optical spectroscopy promises label-free bacterial detection, identification, and antibiotic susceptibility testing in a single step. However, achieving clinically relevant speeds and accuracies remains challenging due to weak Raman signal from bacterial cells and numerous bacterial species and phenotypes. Here we generate an extensive dataset of bacterial Raman spectra and apply deep learning approaches to accurately identify 30 common bacterial pathogens. Even on low signal-to-noise spectra, we achieve average isolate-level accuracies exceeding 82% and antibiotic treatment identification accuracies of 97.0±0.3%. We also show that this approach distinguishes between methicillin-resistant and -susceptible isolates of Staphylococcus aureus (MRSA and MSSA) with 89±0.1% accuracy. We validate our results on clinical isolates from 50 patients. Using just 10 bacterial spectra from each patient isolate, we achieve treatment identification accuracies of 99.7%. Our approach has potential for culture-free pathogen identification and antibiotic susceptibility testing, and could be readily extended for diagnostics on blood, urine, and sputum.

The use of Raman spectroscopy for pathogen identification is hampered by the weak Raman signal and phenotypic diversity of bacterial cells. Here the authors generate an extensive dataset of bacterial Raman spectra and apply deep learning to identify common bacterial pathogens and predict antibiotic treatment from noisy Raman spectra.

Thermal illumination limits in 3D Raman microscopy: A comparison of different sample illumination strategies to obtain maximum imaging speed

Confocal Raman microscopy is a powerful tool for material science and biomedical research. However, the low Raman scattering cross-section limits the working speed, which reduces the applicability for large and sensitive samples. Here, we discuss the fundamental physical limits of Raman spectroscopy with respect to signal-to-noise, sample load and how to achieve maximal imaging speed. For this, we develop a simple model to describe arbitrary far field light microscopes and their thermal influence on the sample. This model is used to compare the practical applicability of point- and line-confocal microscopes as well as wide-field-, light sheet- and light line illumination, for the measurement of 3D biological samples. The parallelization degree of the illumination can positively affect the imaging speed as long as it is not limited by thermal sample heating. In case of heat build-up inside the sample, the advantages of parallelization can be lost due to the required attenuation of excitation and the working speed can drop below that of a sequential method. We show that for point like illumination, the exposure time is thermally not as critical for the sample as the irradiance, while for volume like illumination, the exposure time and irradiance result in the same thermal effect. The results of our theoretical study are experimentally confirmed and suggest new concepts of Raman microscopy, thus extending its applicability. The developed model can be applied to Raman imaging as well as to other modes (e.g. two- or three- photon imaging, STED, PALM/STORM, MINFLUX) where thermal effects impose a practical limit due to the high irradiance required.

Rapid Antibiotic Susceptibility Testing of Pathogenic Bacteria Using Heavy-Water-Labeled Single-Cell Raman Spectroscopy in Clinical Samples

Speeding up antibiotic susceptibility testing (AST) is urgently needed in clincial settings to guide fast and tailored antibiotic prescription before treatment. It remains a big challenge to achieve a sample-to-AST answer within a half working day directly from a clinical sample. Here we develop single-cell Raman spectroscopy coupled with heavy water labeling (Raman-D₂O) as a rapid activity-based AST approach directly applicable for clinical urine samples. By rapidly transferring (15 min) bacteria in clinical urine for AST, the total assay time from receiving urine to binary susceptibility/resistance (S/R) readout was shortened to only 2.5 h. Moreover, by overcoming the nonsynchronous responses between microbial activity and microbial growth, together with setting a new S/R cutoff value based on relative C-D ratios, S/R of both pathogenic isolates and three clinical urines against antibiotics of different action mechanisms determined by Raman-D₂O were all consistent with the slow standard AST assay used in clincial settings. This work promotes clinical practicability and faciliates antibiotic stewardship.

Recent Advances in the Race to Design a Rapid Diagnostic Test for Antimicrobial Resistance

Even with advances in antibiotic therapies, bacterial infections persistently plague society and have amounted to one of the most prevalent issues in healthcare today. Moreover, the improper and excessive administration of antibiotics has led to resistance of many pathogens to prescribed therapies, rendering such antibiotics ineffective against infections. While the identification and detection of bacteria in a patient's sample is critical for point-of-care diagnostics and in a clinical setting, the consequent determination of the correct antibiotic for a patient-tailored therapy is equally crucial. As a result, many recent research efforts have been focused on the development of sensors and systems that correctly guide a physician to the best antibiotic to prescribe for an infection, which can in turn, significantly reduce the instances of antibiotic resistance and the evolution of bacteria "superbugs." This review details the advantages and shortcomings of the recent advances (focusing from 2016 and onward) made in the developments of antimicrobial susceptibility testing (AST) measurements. Detection of antibiotic resistance by genomic AST techniques relies on the prediction of antibiotic resistance via extracted bacterial DNA content, while phenotypic determinations typically track physiological changes in cells and/or populations exposed to antibiotics. Regardless of the method used for AST, factors such as cost, scalability, and assay time need to be weighed into their design. With all of the expansive innovation in the field, which technology and sensing systems demonstrate the potential to detect antimicrobial resistance in a clinical setting?