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

Application of single-cell Raman-deuterium isotope probing to reveal the resistance of marine ammonia-oxidizing archaea SCM1 against common antibiotics

Antimicrobial resistance (AMR) in oceans poses a significant threat to human health through the seafood supply chain. Ammonia-oxidizing archaea (AOA) are important marine microorganisms and play a key role in the biogeochemical nitrogen cycle around the world. However, the AMR of marine AOA to aquicultural antibiotics is poorly explored. Here, Raman-deuterium isotope probing (Raman-DIP), a single-cell tool, was developed to reveal the AMR of a typical marine species of AOA, Nitrosopumilus maritimus (designated SCM1), against six antibiotics, including erythromycin, tetracycline, novobiocin, neomycin, bacitracin, and vancomycin. The D2O concentration (30% v/v) and culture period (9 days) were optimized for the precise detection of metabolic activity in SCM1 cells through Raman-DIP. The relative metabolic activity of SCM1 upon exposure to antibiotics was semi-quantitatively calculated based on single-cell Raman spectra. SCM1 exhibited high resistance to erythromycin, tetracycline, novobiocin, neomycin, and vancomycin, with minimum inhibitory concentration (MIC) values between 100 and 400 mg/L, while SCM1 is very sensitive to bacitracin (MIC: 0.8 mg/L). Notably, SCM1 cells were completely inactive under the metabolic activity minimum inhibitory concentration conditions (MA-MIC: 1.6-800 mg/L) for the six antibiotics. Further genomic analysis revealed the antibiotic resistance genes (ARGs) of SCM1, including 14 types categorized into 33 subtypes. This work increases our knowledge of the AMR of marine AOA by linking the resistant phenome to the genome, contributing to the risk assessment of AMR in the underexplored ocean environment. As antibiotic resistance in marine microorganisms is significantly affected by the concentration of antibiotics in coastal environments, we encourage more studies concentrating on both the phenotypic and genotypic antibiotic resistance of marine archaea. This may facilitate a comprehensive evaluation of the capacity of marine microorganisms to spread AMR and the implementation of suitable control measures to protect environmental safety and human health.

Micro-flow cell washing technique combined with single-cell Raman spectroscopy for rapid and automatic antimicrobial susceptibility test of pathogen in urine

Facing the rapid spread of antimicrobial resistance, methods based on single-cell Raman spectroscopy have proven their advances in reducing the turn-around time (TAT) of antimicrobial susceptibility tests (AST). However, the Raman-based methods are still hindered by the prolonged centrifugal cell washing procedure, which may require complex labor operation and induce high mechanical stress, resulting in a pretreatment time of over 1 h as well as a high cell-loss probability. In this study, we developed a micro-flow cell washing device and corresponding Raman-compatible washing chips, which were able to automatically remove the impurities in the samples, retain the bacterial cell and perform Raman spectra acquisition in situ. Results of washing the 5- and 10-μm polymethyl methacrylate (PMMA) microspheres showed that the novel technique achieved a successful removal of 99 % impurity and an 80 % particle retention rate after 6 to 10 cycles of washing. The micro-flow cell washing technique could complete the pretreatment for urine samples in a 96-well plate within 10 min, only taking 15 % of the handling time required by centrifugation. The AST profiles of urine sample spiked with E. coli 25922, E. faecalis 29212, and S. aureus 29213 obtained by the proposed Raman-based approach were found to be 100 % consistent with the results from broth micro-dilution while reducing the TAT to 3 h from several days which is required by the latter. Our study has demonstrated the micro-flow cell washing technique is a reliable, fast and compatible approach to replace centrifuge washing for sample pretreatment of Raman-AST and could be readily applied in clinical scenarios.

Sensing the Bactericidal and Bacteriostatic Antimicrobial Mode of Action Using Raman Deuterium Stable Isotope Probing (DSIP) in Escherichia coli

The mode of action of antibiotics can be broadly classified as bacteriostatic and bactericidal. The bacteriostatic mode leads to the arrested growth of the cells, while the bacteriocidal mode causes cell death. In this work, we report the applicability of deuterium stable isotope probing (DSIP) in combination with Raman spectroscopy (Raman DSIP) for discriminating the mode of action of antibiotics at the community level. Escherichia coli, a well-known model microbe, was used as an organism for the study. We optimized the concentration of deuterium oxide required for metabolic activity monitoring without compromising the microbial growth. Our findings suggest that changes in the intensity of the C-D band in the high-wavenumber region could serve as a quantifiable marker for determining the antibiotic mode of action. This can be used for early identification of the antibiotic's mode of action. Our results explore the new perspective that supports the utility of deuterium-based vibrational tags in the field of clinical spectroscopy. Understanding the antibiotic's mode of action on bacterial cells in a short and objective manner can significantly enhance the clinical management abilities of infectious diseases and may also help in personalized antimicrobial therapy.

Raman Flow Cytometry and Its Biomedical Applications

Raman flow cytometry (RFC) uniquely integrates the "label-free" capability of Raman spectroscopy with the "high-throughput" attribute of traditional flow cytometry (FCM), offering exceptional performance in cell characterization and sorting. Unlike conventional FCM, RFC stands out for its elimination of the dependency on fluorescent labels, thereby reducing interference with the natural state of cells. Furthermore, it significantly enhances the detection information, providing a more comprehensive chemical fingerprint of cells. This review thoroughly discusses the fundamental principles and technological advantages of RFC and elaborates on its various applications in the biomedical field, from identifying and characterizing cancer cells for in vivo cancer detection and surveillance to sorting stem cells, paving the way for cell therapy, and identifying metabolic products of microbial cells, enabling the differentiation of microbial subgroups. Moreover, we delve into the current challenges and future directions regarding the improvement in sensitivity and throughput. This holds significant implications for the field of cell analysis, especially for the advancement of metabolomics.

Reverse stable isotope labelling with Raman spectroscopy for microbial proteomics

Global proteome changes in microbes affect the survival and overall production of commercially relevant metabolites through different bioprocesses. The existing methods to monitor proteome level changes are destructive in nature. Stable isotope probing (SIP) coupled with Raman spectroscopy is a relatively new approach for proteome analysis. However, applying this approach for monitoring changes in a large culture volume is not cost-effective. In this study, for the first time we are presenting a novel method of combining reverse SIP using 13 C-glucose and Deuterium to monitor the proteome changes through Raman spectroscopy. The findings of the study revealed visible changes (blue shifts) in proteome related peaks that can be used for monitoring proteome dynamics, that is, synthesis of nascent amino acids and its turnover with time in a non-destructive, cost-effective, and label-free manner.

Cost-Efficient Micro-Well Array-Based Colorimetric Antibiotic Susceptibility Testing (MacAST) for Bacteria from Culture or Community

Rapid and cost-efficient antibiotic susceptibility testing (AST) is key to timely prescription-oriented diagnosis and precision treatment. However, current AST methods have limitations in throughput or cost effectiveness, and are impractical for microbial communities. Here, we developed a high-throughput micro-well array-based colorimetric AST (macAST) system equipped with a self-developed smartphone application that could efficiently test sixteen combinations of bacteria strains and antibiotics, achieving comparable AST results based on resazurin metabolism assay. For community samples, we integrated immunomagnetic separation into the macAST (imacAST) system to specifically enrich the target cells before testing, which shortened bacterial isolation time from days to only 45 min and achieved AST of the target bacteria with a low concentration (~103 CFU/mL). This proof-of-concept study developed a high-throughput AST system with an at least ten-fold reduction in cost compared with a system equipped with a microscope or Raman spectrum. Based on colorimetric readout, the antimicrobial susceptibility of the bacteria from microbial communities can be delivered within 6 h, compared to days being required based on standard procedures, bypassing the need for precise instrumentation in therapy to combat bacterial antibiotic resistance in resource-limited settings.

2D Fluorinated Graphene Oxide (FGO)-Polyethyleneimine (PEI) Based 3D Porous Nanoplatform for Effective Removal of Forever Toxic Chemicals, Pharmaceutical Toxins, and Waterborne Pathogens from Environmental Water Samples

Although water is essential for life, as per the United Nations, around 2 billion people in this world lack access to safely managed drinking water services at home. Herein we report the development of a two-dimensional (2D) fluorinated graphene oxide (FGO) and polyethylenimine (PEI) based three-dimensional (3D) porous nanoplatform for the effective removal of polyfluoroalkyl substances (PFAS), pharmaceutical toxins, and waterborne pathogens from contaminated water. Experimental data show that the FGO-PEI based nanoplatform has an estimated adsorption capacity (qm) of ∼219 mg g-1 for perfluorononanoic acid (PFNA) and can be used for 99% removal of several short- and long-chain PFAS. A comparative PFNA capturing study using different types of nanoplatforms indicates that the qm value is in the order FGO-PEI > FGO > GO-PEI, which indicates that fluorophilic, electrostatic, and hydrophobic interactions play important roles for the removal of PFAS. Reported data show that the FGO-PEI based nanoplatform has a capability for 100% removal of moxifloxacin antibiotics with an estimated qm of ∼299 mg g-1. Furthermore, because the pore size of the nanoplatform is much smaller than the size of pathogens, it has a capability for 100% removal of Salmonella and Escherichia coli from water. Moreover, reported data show around 96% removal of PFAS, pharmaceutical toxins, and pathogens simultaneously from spiked river, lake, and tap water samples using the nanoplatform.

Rapid Mycobacterium abscessus antimicrobial susceptibility testing based on antibiotic treatment response mapping via Raman Microspectroscopy

OBJECTIVES

Antimicrobial susceptibility tests (ASTs) are pivotal tools for detecting and combating infections caused by multidrug-resistant rapidly growing mycobacteria (RGM) but are time-consuming and labor-intensive.

DESIGN

We used a Mycobacterium abscessus-based RGM model to develop a rapid (24-h) AST from the beginning of the strain culture, the Clinical Antimicrobials Susceptibility Test Ramanometry for RGM (CAST-R-RGM). The ASTs obtained for 21 clarithromycin (CLA)-treated and 18 linezolid (LZD)-treated RGM isolates.

RESULTS

CAST-R-RGM employs D2O-probed Raman microspectroscopy to monitor RGM metabolic activity, while also revealing bacterial antimicrobial drug resistance mechanisms. The results of clarithromycin (CLA)-treated and linezolid (LZD)-treated RGM isolates exhibited 90% and 83% categorical agreement, respectively, with conventional AST results of the same isolates. Furthermore, comparisons of time- and concentration-dependent Raman results between CLA- and LZD-treated RGM strains revealed distinct metabolic profiles after 48-h and 72-h drug treatments, despite similar profiles obtained for both drugs after 24-h treatments.

CONCLUSIONS

Ultimately, the rapid, accurate, and low-cost CAST-R-RGM assay offers advantages over conventional culture-based ASTs that warrant its use as a tool for improving patient treatment outcomes and revealing bacterial drug resistance mechanisms.

Rapid Antifungal Susceptibility Testing Based on Single-Cell Metabolism Analysis Using Stimulated Raman Scattering Imaging

Rapid antifungal susceptibility testing (AFST) is urgently needed in clinics to treat invasive fungal infections with the appropriate antifungal drugs and to slow the emergence of antifungal resistance. However, current AFST methods are time-consuming (24-48 h) due to the slow growth of fungal cells and the methods not being able to work directly for clinical samples. Here, we demonstrate rapid AFST by measuring the metabolism in single fungal cells using stimulated Raman scattering imaging and deuterium probing. Distinct metabolic responses were observed in Candida albicans to different classes of antifungal drugs: while the metabolism was inhibited by amphotericin B, it was stimulated by azoles (fluconazole and voriconazole) and micafungin. Accordingly, we propose metabolism change as a biomarker for rapid AFST. The results were obtained in 4 h with 100% categorical agreement with the gold standard broth microdilution test. In addition, a protocol was developed for direct AFST from positive blood cultures. This method overcomes the limitation of slow growth in conventional methods and has the potential for the rapid diagnosis of candidemia and other clinical fungal infections.

Raman-Based Antimicrobial Susceptibility Testing on Antibiotics of Last Resort

Background

Antibiotic resistance represents a serious global health challenge, particularly with the emergence of strains resistant to last-resort antibiotics such as tigecycline, polymyxin B, and vancomycin. Urgent measures are required to alleviate this situation. To facilitate the judicious use of antibiotics, rapid and precise antimicrobial susceptibility testing (AST) is essential. Heavy water (deuterium oxide, D2O)-labeled Raman spectroscopy has emerged as a promising time-saving tool for microbiological testing.

Methods

Deuterium incorporation and experimental conditions were examined to develop and apply a Raman-based AST method to evaluate the efficacy of last-resort antibiotics, including tigecycline, polymyxin B, and vancomycin, against Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Enterococcus faecium. Essential agreement and categorical agreement were used to assess the metabolism inactivation concentration based on Raman spectroscopy (R-MIC)-a new metric developed in this study-and minimum inhibitory concentration (MIC) determined via the traditional microdilution broth method. Spearman's rank correlation coefficient was employed to measure the association between R-MIC and MIC values.

Results

The Raman-based AST method achieved a 100% categorical agreement (92/92) with the traditional microdilution broth method within five hours, while the traditional method required approximately 24 h. The R-MIC values shared 68.5% (63/92) consistency with the MIC values. In addition, the R-MIC and MIC values were highly correlated (Spearman's r=0.96), resulting in an essential agreement of 100%.

Conclusion

Our optimized experimental method and conditions indicate that Raman-based AST holds great promise as a solution to overcome the time-consuming challenges of traditional AST methods.

Sensing of Antibiotic-Bacteria Interactions

Sensing of antibiotic-bacteria interactions is an important area of research that has gained significant attention in recent years. Antibiotic resistance is a major public health concern, and it is essential to develop new strategies for detecting and monitoring bacterial responses to antibiotics in order to maintain effective antibiotic development and antibacterial treatment. This review summarizes recent advances in sensing strategies for antibiotic-bacteria interactions, which are divided into two main parts: studies on the mechanism of action for sensitive bacteria and interrogation of the defense mechanisms for resistant ones. In conclusion, this review provides an overview of the present research landscape concerning antibiotic-bacteria interactions, emphasizing the potential for method adaptation and the integration of machine learning techniques in data analysis, which could potentially lead to a transformative impact on mechanistic studies within the field.

Single-cell rapid identification, in situ viability and vitality profiling, and genome-based source-tracking for probiotics products

Rapid expansion of the probiotics industry demands fast, sensitive, comprehensive, and low-cost strategies for quality assessment. Here, we introduce a culture-free, one-cell-resolution, phenome-genome-combined strategy called Single-Cell Identification, Viability and Vitality tests, and Source-tracking (SCIVVS). For each cell directly extracted from the product, the fingerprint region of D2O-probed single-cell Raman spectrum (SCRS) enables species-level identification with 93% accuracy, based on a reference SCRS database from 21 statutory probiotic species, whereas the C-D band accurately quantifies viability, metabolic vitality plus their intercellular heterogeneity. For source-tracking, single-cell Raman-activated Cell Sorting and Sequencing can proceed, producing indexed, precisely one-cell-based genome assemblies that can reach ~99.40% genome-wide coverage. Finally, we validated an integrated SCIVVS workflow with automated SCRS acquisition where the whole process except sequencing takes just 5 h. As it is >20-fold faster, >10-time cheaper, vitality-revealing, heterogeneity-resolving, and automation-prone, SCIVVS is a new technological and data framework for quality assessment of live-cell products.

Activated alkyne-enabled turn-on click bioconjugation with cascade signal amplification for ultrafast and high-throughput antibiotic screening

Antimicrobial susceptibility testing plays a pivotal role in the discovery of new antibiotics. However, the development of simple, sensitive, and rapid assessment approaches remains challenging. Herein, we report an activated alkyne-based cascade signal amplification strategy for ultrafast and high-throughput antibiotic screening. First of all, a novel water-soluble aggregation-induced emission (AIE) luminogen is synthesized, which contains an activated alkyne group to enable fluorescence turn-on and metal-free click bioconjugation under physiological conditions. Taking advantage of the in-house established method for bacterial lysis, a number of clickable biological substances (i.e., bacterial solutes and debris) are released from the bacterial bodies, which remarkably increases the quantity of analytes. By means of the activated alkyne-mediated turn-on click bioconjugation, the system fluorescence signal is significantly amplified due to the increased labeling sites as well as the AIE effect. Such a cascade signal amplification strategy efficiently improves the detection sensitivity and thus enables ultrafast antimicrobial susceptibility assessment. By integration with a microplate reader, this approach is further applied to high-throughput antibiotic screening.

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.

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.

Leveraging single-cell Raman spectroscopy and single-cell sorting for the detection and identification of yeast infections

Invasive fungal infection serves as a great threat to human health. Discrimination between fungal and bacterial infections at the earliest stage is vital for effective clinic practice; however, traditional culture-dependent microscopic diagnosis of fungal infection usually requires several days, meanwhile, culture-independent immunological and molecular methods are limited by the detectable type of pathogens and the issues with high false-positive rates. In this study, we proposed a novel culture-independent phenotyping method based on single-cell Raman spectroscopy for the rapid discrimination between fungal and bacterial infections. Three Raman biomarkers, including cytochrome c, peptidoglycan, and nucleic acid, were identified through hierarchical clustering analysis of Raman spectra across 12 types of most common yeast and bacterial pathogens. Compared to those of bacterial pathogens, the single cells of yeast pathogens demonstrated significantly stronger Raman peaks for cytochrome c, but weaker signals for peptidoglycan and nucleic acid. A two-step protocol combining the three biomarkers was established and able to differentiate fungal infections from bacterial infections with an overall accuracy of 94.9%. Our approach was also used to detect ten raw urinary tract infection samples. Successful identification of fungi was achieved within half an hour after sample obtainment. We further demonstrated the accurate fungal species taxonomy achieved with Raman-assisted cell ejection. Our findings demonstrate that Raman-based fungal identification is a novel, facile, reliable, and with a breadth of coverage approach, that has a great potential to be adopted in routine clinical practice to reduce the turn-around time of invasive fungal disease (IFD) diagnostics.

Mid-Infrared Photothermal-Fluorescence In Situ Hybridization for Functional Analysis and Genetic Identification of Single Cells

Simultaneous identification and metabolic analysis of microbes with single-cell resolution and high throughput are necessary to answer the question of "who eats what, when, and where" in complex microbial communities. Here, we present a mid-infrared photothermal-fluorescence in situ hybridization (MIP-FISH) platform that enables direct bridging of genotype and phenotype. Through multiple improvements of MIP imaging, the sensitive detection of isotopically labeled compounds incorporated into proteins of individual bacterial cells became possible, while simultaneous detection of FISH labeling with rRNA-targeted probes enabled the identification of the analyzed cells. In proof-of-concept experiments, we showed that the clear spectral red shift in the protein amide I region due to incorporation of ¹³C atoms originating from ¹³C-labeled glucose can be exploited by MIP-FISH to discriminate and identify ¹³C-labeled bacterial cells within a complex human gut microbiome sample. The presented methods open new opportunities for single-cell structure-function analyses for microbiology.

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.

Cultureless enumeration of live bacteria in urinary tract infection by single-cell Raman spectroscopy

Urinary tract infections (UTIs) are the most common outpatient infections. Obtaining the concentration of live pathogens in the sample is crucial for the treatment. Still, the enumeration depends on urine culture and plate counting, which requires days of turn-around time (TAT). Single-cell Raman spectra combined with deuterium isotope probing (Raman-DIP) has been proven to identify the metabolic-active bacteria with high accuracy but is not able to reveal the number of live pathogens due to bacteria replication during the Raman-DIP process. In this study, we established a new approach of using sodium acetate to inhibit the replication of the pathogen and applying Raman-DIP to identify the active single cells. By combining microscopic image stitching and recognition, we could further improve the efficiency of the new method. Validation of the new method on nine artificial urine samples indicated that the exact number of live pathogens obtained with Raman-DIP is consistent with plate-counting while shortening the TAT from 18 h to within 3 h, and the potential of applying Raman-DIP for pathogen enumeration in clinics is promising.

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.

Rapid Antimicrobial Susceptibility Testing Based on a Bio-Inspired Chemiluminescence Sensor

Indiscriminate usage of antibiotics has caused accelerating growth and global expansion of antimicrobial resistance. Therefore, rapid antimicrobial susceptibility testing (AST) for guiding antibiotic prescription and preventing the spread of antimicrobial resistance is in urgent need. Phenotypic AST is the clinical gold standard method; however, no phenotypic AST has realized a colony-to-answer at about 1 h by utilizing the chemiluminescence sensor to detect the enzyme expressed by bacteria. Inspired by the bubble formation in the mixture of Escherichia coli and H₂O₂, we demonstrate a strategy based on the chemiluminescence sensor for rapid AST. Compared with the gold standard methods, the values of AUC are 0.960 for E. coli and 0.950 for Staphylococcus aureus, close to 1, indicating superb diagnostic performance as an AST method. The whole process from colonies to answer is 55 min for E. coli and 70 min for S. aureus. The chemiluminescence readout is based on the common equipment in the laboratory of the hospital, which is conducive to follow-up clinical promotion. Our sensor promises great potential in rapid AST, facilitating antimicrobial stewardship.

Stain-free Gram staining classification of pathogens via single-cell Raman spectroscopy combined with machine learning

Gram staining (GS) is one of the routine microbiological operations to classify bacteria based on the cell wall structure. Accurate GS classification of pathogens is of great significance since it helps correct administration of antimicrobial treatment. The laborious procedure and low sensitivity results related to conventional GS have resulted in reluctance among clinicians. In this study, we integrate confocal Raman spectroscopy and machine learning techniques to distinguish Gram-negative (GN) or Gram-positive (GP) bacteria. A single-cell Raman database including seven most common clinical pathogens (three GP strains and four GN strains) was constructed. Machine learning algorithms including the support-vector machine (SVM), k-nearest neighbors' algorithm (k-NN), gradient boosting machine (GBM), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) were trained to achieve the binary classification for GS. With such a relatively small database, the SVM model achieved the highest accuracy of 98.1%. The molecular signatures of GN and GP embedded in their Raman fingerprints were identified with hierarchical cluster analysis (HCA). The results indicated that Raman peaks for peptidoglycan and teichoic acid were the most significant factors that contributed to accurate classification. The Raman machine learning approach could greatly enhance the diagnosis of pathogenic infections.

Combinatorial screening SlipChip for rapid phenotypic antimicrobial susceptibility testing

Antimicrobial resistance (AMR) by bacteria is a serious global threat, and a rapid, high-throughput, and easy-to-use phenotypic antimicrobial susceptibility testing (AST) method is essential for making timely treatment decisions and controlling the spread of antibiotic resistant micro-organisms. Traditional culture-based methods are time-consuming, and their capability to screen against a large number of different conditions is limited; meanwhile genotypic based methods, including sequencing and PCR based methods, are constrained by rarely identified resistance genes and complicated resistance mechanisms. Here, a combinatorial-screening SlipChip (cs-SlipChip) containing 192 nanoliter-sized compartments is developed which can perform high-throughput phenotypic AST within three hours by monitoring the bacterial growth within nanoliter-sized droplets with bright-field imaging and analyzing the changes in bacterial number and morphology. The minimum inhibitory concentration (MIC) of Escherichia coli ATCC 25922 against four antibiotics (ampicillin, ciprofloxacin, ceftazidime, and nitrofurantoin) can be measured in one chip within 3 hours. Furthermore, five antibiotic-resistant E. coli strains were isolated from patients diagnosed with urinary tract infections (UTIs), and an individual isolate was tested using four antibiotics and eleven antibiotic combinations simultaneously with three different concentrations of each. The results from the cs-SlipChip agree with those of a VITEK 2 automated system. This cs-SlipChip provides a practical high-throughput and rapid phenotypic method for AST and can also be used to screen different chemicals and antibiotic combinations for the treatment of multiple antibiotic-resistant bacteria.

Rapid discrimination of glycogen particles originated from different eukaryotic organisms

There are many commercially available glycogen particles in the market due to their bioactive functions as food additive, drug carrier and natural moisturizer, etc. It would be beneficial to rapidly determine the origins of commercially-available glycogen particles, which could facilitate the establishment of quality control methodology for glycogen-containing products. With its non-destructive, label-free and low-cost features, surface enhanced Raman spectroscopy (SERS) is an attractive technique with high potential to discriminate chemical compounds in a rapid mode. In this study, we applied the combination of SERS technique and machine leaning algorithms on glycogen analysis, which successfully predicted the origins of glycogen particles from a variety of organisms with convolutional neural network (CNN) algorithm plus attention mechanism having the best computational performance (5-fold cross validation accuracy = 96.97 %). In sum, this is the first study focusing on the discrimination of commercial glycogen particles originated from different organisms, which holds the application potential in quality control of glycogen-containing products.

Rapid, automated, and reliable antimicrobial susceptibility test from positive blood culture by CAST-R

Antimicrobial susceptibility tests (ASTs) are pivotal in combating multidrug resistant pathogens, yet they can be time-consuming, labor-intensive, and unstable. Using the AST of tigecycline for sepsis as the main model, here we establish an automated system of Clinical Antimicrobials Susceptibility Test Ramanometry (CAST-R), based on D2O-probed Raman microspectroscopy. Featuring a liquid robot for sample pretreatment and a machine learning-based control scheme for data acquisition and quality control, the 3-h, automated CAST-R process accelerates AST by >10-fold, processes 96 paralleled antibiotic-exposure reactions, and produces high-quality Raman spectra. The Expedited Minimal Inhibitory Concentration via Metabolic Activity is proposed as a quantitative and broadly applicable parameter for metabolism-based AST, which shows 99% essential agreement and 93% categorical agreement with the broth microdilution method (BMD) when tested on 100 Acinetobacter baumannii isolates. Further tests on 26 clinically positive blood samples for eight antimicrobials, including tigecycline, meropenem, ceftazidime, ampicillin/sulbactam, oxacillin, clindamycin, vancomycin, and levofloxacin reveal 93% categorical agreement with BMD-based results. The automation, speed, reliability, and general applicability of CAST-R suggest its potential utility for guiding the clinical administration of antimicrobials.

Compound Raman microscopy for rapid diagnosis and antimicrobial susceptibility testing of pathogenic bacteria in urine

Rapid identification and antimicrobial susceptibility testing (AST) of bacteria are key interventions to curb the spread and emergence of antimicrobial resistance. The current gold standard identification and AST methods provide comprehensive diagnostic information but often take 3 to 5 days. Here, a compound Raman microscopy (CRM), which integrates Raman spectroscopy and stimulated Raman scattering microscopy in one system, is presented and demonstrated for rapid identification and AST of pathogens in urine. We generated an extensive bacterial Raman spectral dataset and applied deep learning to identify common clinical bacterial pathogens. In addition, we employed stimulated Raman scattering microscopy to quantify bacterial metabolic activity to determine their antimicrobial susceptibility. For proof-of-concept, we demonstrated an integrated assay to diagnose urinary tract infection pathogens, S. aureus and E. coli. Notably, the CRM system has the unique ability to provide Gram-staining classification and AST results within ~3 h directly from urine samples and shows great potential for clinical applications.

Development and Automation of a Bacterial Biosensor to the Targeting of the Pollutants Toxic Effects by Portable Raman Spectrometer

Water quality monitoring requires a rapid and sensitive method that can detect multiple hazardous pollutants at trace levels. This study aims to develop a new generation of biosensors using a low-cost fiber-optic Raman device. An automatic measurement system was thus conceived, built and successfully tested with toxic substances of three different types: antibiotics, heavy metals and herbicides. Raman spectroscopy provides a multiparametric view of metabolic responses of biological organisms to these toxic agents through their spectral fingerprints. Spectral analysis identified the most susceptible macromolecules in an E. coli model strain, providing a way to determine specific toxic effects in microorganisms. The automation of Raman analysis reduces the number of spectra required per sample and the measurement time: for four samples, time was cut from 3 h to 35 min by using a multi-well sample holder without intervention from an operator. The correct classifications were, respectively, 99%, 82% and 93% for the different concentrations of norfloxacin, while the results were 85%, 93% and 81% for copper and 92%, 90% and 96% for 3,5-dichlorophenol at the three tested concentrations. The work initiated here advances the technology needed to use Raman spectroscopy coupled with bioassays so that together, they can advance field toxicological testing.

Single Cell Raman Spectroscopy Deuterium Isotope Probing for Rapid Antimicrobial Susceptibility Test of Elizabethkingia spp

Nosocomial infection by multi-drug resistance Elizabethkingia spp. is an emerging concern with severe clinical consequences, particularly in immunocompromised individuals and infants. Efficient control of this infection requires quick and reliable methods to determine the appropriate drugs for treatment. In this study, a total of 31 Elizabethkingia spp., including two standard strains (ATCC 13253 and FMS-007) and 29 clinical isolates obtained from hospitals in China were subjected to single cell Raman spectroscopy analysis coupled with deuterium probing (single cell Raman-DIP). The results demonstrated that single cell Raman-DIP could determine antimicrobial susceptibility of Elizabethkingia spp. in 4 h, only one third of the time required by standard broth microdilution method. The method could be integrated into current clinical protocol for sepsis and halve the report time. The study also confirmed that minocycline and levofloxacin are the first-line antimicrobials for Elizabethkingia spp. infection.

Metabolism-Triggered Colorimetric Sensor Array for Fingerprinting and Antibiotic Susceptibility Testing of Bacteria

The rapid identification and antibiotic susceptibility testing (AST) of bacteria would help us to accurately identify the infectious sources as well as guide the use of antibiotics, which are crucial for improving the survival rate and antimicrobial resistance. Herein, a colorimetric sensor array for bacteria fingerprinting was constructed with d-amino acid (d-AA)-modified gold nanoparticles (AuNPs) as probes (Au/d-AA). Bacteria can metabolize the d-AA, triggering the aggregation of AuNPs. Making use of different metabolic capabilities of bacteria toward different d-AA, eight kinds of bacteria including antibiotic-resistant bacteria and strains of the same bacterial species are successfully differentiated via learning the response patterns. Meanwhile, the sensor array also performs well in quantitative analysis of single bacterium and differentiation of bacteria mixtures. More interestingly, a rapid colorimetric AST approach has been developed based on the Au/d-AA nanoprobes by monitoring the d-AA metabolic activity of bacteria toward various antibiotic treatments. In this regard, the outlined work here would promote clinical practicability and facilitate antibiotic stewardship.

A Review of Raman-Based Technologies for Bacterial Identification and Antimicrobial Susceptibility Testing

Antimicrobial resistance (AMR) is a global medical threat that seriously endangers human health. Rapid bacterial identification and antimicrobial susceptibility testing (AST) are key interventions to combat the spread and emergence of AMR. Although current clinical bacterial identification and AST provide comprehensive information, they are labor-intensive, complex, inaccurate, and slow (requiring several days, depending on the growth of pathogenic bacteria). Recently, Raman-based identification and AST technologies have played an increasingly important role in fighting AMR. This review summarizes major Raman-based techniques for bacterial identification and AST, including spontaneous Raman scattering, surface-enhanced Raman scattering (SERS), and coherent Raman scattering (CRS) imaging. Then, we discuss recent developments in rapid identification and AST methods based on Raman technology. Finally, we highlight the major challenges and potential future efforts to improve clinical outcomes through rapid bacterial identification and AST.

High-Speed Diagnosis of Bacterial Pathogens at the Single Cell Level by Raman Microspectroscopy with Machine Learning Filters and Denoising Autoencoders

Accurate and rapid identification of infectious bacteria is important in medicine. Raman microspectroscopy holds great promise in performing label-free identification at the single-cell level. However, due to the naturally weak Raman signal, it is a challenge to build extensive databases and achieve both accurate and fast identification. Here, we used signal-to-noise ratio (SNR) as a standard indicator for Raman data quality and performed bacterial identification using 11, 141 single-cell Raman spectra from nine bacterial strains. Subsequently, using two machine learning methods, a simple filter, and a neural network-based denoising autoencoder (DAE), we demonstrated 92% (simple filter using 1 s/cell spectra) and 84% (DAE using 0.1 s/cell spectra) identification accuracy. Our machine learning-aided Raman analysis paves the way for high-speed Raman microspectroscopic clinical diagnostics.

Single-Cell Technologies to Study Phenotypic Heterogeneity and Bacterial Persisters

Antibiotic persistence is a phenomenon in which rare cells of a clonal bacterial population can survive antibiotic doses that kill their kin, even though the entire population is genetically susceptible. With antibiotic treatment failure on the rise, there is growing interest in understanding the molecular mechanisms underlying bacterial phenotypic heterogeneity and antibiotic persistence. However, elucidating these rare cell states can be technically challenging. The advent of single-cell techniques has enabled us to observe and quantitatively investigate individual cells in complex, phenotypically heterogeneous populations. In this review, we will discuss current technologies for studying persister phenotypes, including fluorescent tags and biosensors used to elucidate cellular processes; advances in flow cytometry, mass spectrometry, Raman spectroscopy, and microfluidics that contribute high-throughput and high-content information; and next-generation sequencing for powerful insights into genetic and transcriptomic programs. We will further discuss existing knowledge gaps, cutting-edge technologies that can address them, and how advances in single-cell microbiology can potentially improve infectious disease treatment outcomes.

In Vitro Anticancer Drug Sensitivity Sensing through Single-Cell Raman Spectroscopy

Traditional in vitro anticancer drug sensitivity testing at the population level suffers from lengthy procedures and high false positive rates. To overcome these defects, we built a confocal Raman microscopy sensing system and proposed a single-cell approach via Raman-deuterium isotope probing (Raman-DIP) as a rapid and reliable in vitro drug efficacy evaluation method. Raman-DIP detected the incorporation of deuterium into the cell, which correlated with the metabolic activity of the cell. The human non-small cell lung cancer cell line HCC827 and human breast cancer cell line MCF-7 were tested against eight different anticancer drugs. The metabolic activity of cancer cells could be detected as early as 12 h, independent of cell growth. Incubation of cells in 30% heavy water (D₂O) did not show any negative effect on cell viability. Compared with traditional methods, Raman-DIP could accurately determine the drug effect, meanwhile, it could reduce the testing period from 72–144 h to 48 h. Moreover, the heterogeneity of cells responding to anticancer drugs was observed at the single-cell level. This proof-of-concept study demonstrated the potential of Raman-DIP to be a reliable tool for cancer drug discovery and drug susceptibility testing.