Rapid antibiotic susceptibility testing and species identification for mixed samples

Microfluidic technologies for advanced antimicrobial susceptibility testing

Antimicrobial resistance is getting serious and becoming a threat to public health worldwide. The improper and excessive use of antibiotics is responsible for this situation. The standard methods used in clinical laboratories, to diagnose bacterial infections, identify pathogens, and determine susceptibility profiles, are time-consuming and labor-intensive, leaving the empirical antimicrobial therapy as the only option for the first treatment. To prevent the situation from getting worse, evidence-based therapy should be given. The choosing of effective drugs requires powerful diagnostic tools to provide comprehensive information on infections. Recent progress in microfluidics is pushing infection diagnosis and antimicrobial susceptibility testing (AST) to be faster and easier. This review summarizes the recent development in microfluidic assays for rapid identification and AST in bacterial infections. Finally, we discuss the perspective of microfluidic-AST to develop the next-generation infection diagnosis technologies.

Inferring fungal growth rates from optical density data

Quantifying fungal growth underpins our ability to effectively treat severe fungal infections. Current methods quantify fungal growth rates from time-course morphology-specific data, such as hyphal length data. However, automated large-scale collection of such data lies beyond the scope of most clinical microbiology laboratories. In this paper, we propose a mathematical model of fungal growth to estimate morphology-specific growth rates from easy-to-collect, but indirect, optical density (OD600) data of Aspergillus fumigatus growth (filamentous fungus). Our method accounts for OD600 being an indirect measure by explicitly including the relationship between the indirect OD600 measurements and the calibrating true fungal growth in the model. Therefore, the method does not require de novo generation of calibration data. Our model outperformed reference models at fitting to and predicting OD600 growth curves and overcame observed discrepancies between morphology-specific rates inferred from OD600 versus directly measured data in reference models that did not include calibration.

Accurate and rapid antibiotic susceptibility testing using a machine learning-assisted nanomotion technology platform

Antimicrobial resistance (AMR) is a major public health threat, reducing treatment options for infected patients. AMR is promoted by a lack of access to rapid antibiotic susceptibility tests (ASTs). Accelerated ASTs can identify effective antibiotics for treatment in a timely and informed manner. We describe a rapid growth-independent phenotypic AST that uses a nanomotion technology platform to measure bacterial vibrations. Machine learning techniques are applied to analyze a large dataset encompassing 2762 individual nanomotion recordings from 1180 spiked positive blood culture samples covering 364 Escherichia coli and Klebsiella pneumoniae isolates exposed to cephalosporins and fluoroquinolones. The training performances of the different classification models achieve between 90.5 and 100% accuracy. Independent testing of the AST on 223 strains, including in clinical setting, correctly predict susceptibility and resistance with accuracies between 89.5% and 98.9%. The study shows the potential of this nanomotion platform for future bacterial phenotype delineation.

Metabolism-Triggered Plasmonic Nanosensor for Bacterial Detection and Antimicrobial Susceptibility Testing of Clinical Isolates

Antimicrobial resistance (AMR) is predicted to become the leading cause of death worldwide in the coming decades. Rapid and on-site antibiotic susceptibility testing (AST) is crucial for guiding appropriate antibiotic choices to combat AMR. With this in mind, we have designed a simple and efficient plasmonic nanosensor consisting of Cu2+ and cysteine-modified AuNP (Au/Cys) that utilizes the metabolic activity of bacteria toward Cu2+ for bacterial detection and AST. When Cu2+ is present, it induces the aggregation of Au/Cys. However, in the presence of bacteria, Cu2+ is metabolized to varying extents, resulting in distinct levels of aggregation. Moreover, the metabolic activity of bacteria can be influenced by their antibiotic susceptibility, allowing us to differentiate between susceptible and resistant strains through direct color changes from the Cu2+-Au/Cys platform over approximately 3 h. These color changes can be easily detected using naked-eye observation, smartphone analysis, or absorption readout. We have validated the platform using four clinical isolates and six types of antibiotics, demonstrating a clinical sensitivity and specificity of 95.8%. Given its simplicity, low cost, high speed, and high accuracy, the plasmonic nanosensor holds great potential for point-of-care detection of antibiotic susceptibility across various settings.

Rapid, Ultrasensitive, and Visual Detection of Pathogens Based on Cation Dye-Triggered Gold Nanoparticle Electrokinetic Agglutination Analysis

Rapid prescribing of the right antibiotic is the key to treat infectious diseases and decelerate the challenge of bacterial antibiotic resistance. Herein, by targeting the 16S rRNA of bacteria, we developed a cation dye-triggered electrokinetic gold nanoparticle (AuNP) agglutination (CD-TEAA) method, which is rapid, visual, ultrasensitive, culture-independent, and low in cost. The limit of detection (LOD) is as low as 1 CFU mL-1 Escherichia coli. The infection identifications of aseptic fluid samples (n = 11) and urine samples with a clinically suspected urinary tract infection (UTI, n = 78) were accomplished within 50 and 30 min for each sample, respectively. The antimicrobial susceptibility testing (AST) of UTI urine samples was achieved within 2.5 h. In ROC analysis of urine, the sensitivity and specificity were 100 and 96% for infection identification, and 100 and 98% for AST, respectively. Moreover, the overall cost of materials for each test is about US$0.69. Therefore, the CD-TEAA method is a superior approach to existing, time-consuming, and expensive methods, especially in less developed areas.

Microwell-enhanced optical rapid antibiotic susceptibility testing of single bacteria

Bacteria that are resistant to antibiotics present an increasing burden on healthcare. To address this emerging crisis, novel rapid antibiotic susceptibility testing (AST) methods are eagerly needed. Here, we present an optical AST technique that can determine the bacterial viability within 1 h down to a resolution of single bacteria. The method is based on measuring intensity fluctuations of a reflected laser focused on a bacterium in reflective microwells. Using numerical simulations, we show that both refraction and absorption of light by the bacterium contribute to the observed signal. By administering antibiotics that kill the bacteria, we show that the variance of the detected fluctuations vanishes within 1 h, indicating the potential of this technique for rapid sensing of bacterial antibiotic susceptibility. We envisage the use of this method for massively parallelizable AST tests and fast detection of drug-resistant pathogens.

Deep learning and single-cell phenotyping for rapid antimicrobial susceptibility detection in Escherichia coli

The rise of antimicrobial resistance (AMR) is one of the greatest public health challenges, already causing up to 1.2 million deaths annually and rising. Current culture-based turnaround times for bacterial identification in clinical samples and antimicrobial susceptibility testing (AST) are typically 18-24 h. We present a novel proof-of-concept methodological advance in susceptibility testing based on the deep-learning of single-cell specific morphological phenotypes directly associated with antimicrobial susceptibility in Escherichia coli. Our models can reliably (80% single-cell accuracy) classify untreated and treated susceptible cells for a lab-reference fully susceptible E. coli strain, across four antibiotics (ciprofloxacin, gentamicin, rifampicin and co-amoxiclav). For ciprofloxacin, we demonstrate our models reveal significant (p < 0.001) differences between bacterial cell populations affected and unaffected by antibiotic treatment, and show that given treatment with a fixed concentration of 10 mg/L over 30 min these phenotypic effects correlate with clinical susceptibility defined by established clinical breakpoints. Deploying our approach on cell populations from six E. coli strains obtained from human bloodstream infections with varying degrees of ciprofloxacin resistance and treated with a range of ciprofloxacin concentrations, we show single-cell phenotyping has the potential to provide equivalent information to growth-based AST assays, but in as little as 30 min.

Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy

Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For instance, identifying the species and its antibiotic susceptibility is vital for effective bacterial infection treatment. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to classify four species of bacteria relevant to human health. The classification is performed on living bacteria and does not require fixation or staining, meaning that the bacterial species can be determined as the bacteria reproduce in a microfluidic device, enabling parallel determination of susceptibility to antibiotics. We assess the performance of convolutional neural networks and vision transformers, where the best model attained a class-average accuracy exceeding 98%. Our successful proof-of-principle results suggest that the methods should be challenged with data covering more species and clinically relevant isolates for future clinical use.

A rapid screening platform for antibiotic susceptibility testing based on a simple colorimetric method

Rapid screening platforms for antibiotic susceptibility testing (AST) are important in inhibiting bacterial resistance in clinical practice. Herein, a rapid screening platform is reported for AST, which is based on nanofiber membrane enrichment bacteria-assisted cell counting Kit-8 (CCK8) colorimetry. The absorbance of CCK8 formazan has a linear relationship with the number of bacteria. The interference of antibiotics in the absorbance of CCK8 formazan could be eliminated by separating planktonic bacteria from the culture medium using nanofiber membranes. The total detection time is 7-9 h, using the new screening platform, which is significantly shorter than that with the traditional method, and the limit of detection of this method is 10 CFU mL-1. The evaluation results of antibiotic susceptibility are identical when using the new screening method and traditional methods. This method meets the definition of "rapid testing" for antibiotic susceptibility by most microbiologists. Furthermore, the new screening platform for antibiotic susceptibility testing ability in vitro was proved using E. coli in urine and blood, and S. aureus in wound fluid as practical samples. All the results showed that the new screening platform is a promising method for rapid antibiotic susceptibility testing in vitro.

Under-oil open microfluidic systems for rapid phenotypic antimicrobial susceptibility testing

Antimicrobial susceptibility testing (AST) remains the cornerstone of effective antimicrobial selection and optimization in patients. Despite recent advances in rapid pathogen identification and resistance marker detection with molecular diagnostics (e.g., qPCR, MALDI-TOF MS), phenotypic (i.e., microbial culture-based) AST methods - the gold standard in hospitals/clinics - remain relatively unchanged over the last few decades. Microfluidics-based phenotypic AST has been growing fast in recent years, aiming for rapid (i.e., turnaround time <8 h), high-throughput, and automated species identification, resistance detection, and antibiotics screening. In this pilot study, we describe the application of a multi-liquid-phase open microfluidic system, named under-oil open microfluidic systems (UOMS), to achieve a rapid phenotypic AST. UOMS provides an open microfluidics-based solution for rapid phenotypic AST (UOMS-AST) by implementing and recording a pathogen's antimicrobial activity in micro-volume testing units under an oil overlay. UOMS-AST allows free physical access (e.g., by standard pipetting) to the system and label-free, single-cell resolution optical access. UOMS-AST can accurately and rapidly determine antimicrobial activities [including susceptibility/resistance breakpoint and minimum inhibitory concentration (MIC)] from nominal sample/bacterial cells in a system aligned with clinical laboratory standards where open systems and optical microscopy are predominantly adopted. Further, we combine UOMS-AST with a cloud lab data analytic technique for real-time image analysis and report generation to provide a rapid (<4 h) sample-to-report turnaround time, shedding light on its utility as a versatile (e.g., low-resource setting and manual laboratory operation, or high-throughput automated system) phenotypic AST platform for hospital/clinic use.

Study of Laser Light Scattering Methods in Rapid Viability Assessment of Microorganisms under Antibiotics Exposure for Adaptation in Lab-on-A-Chip Format

The antibiotic resistance (ABR) problem is becoming increasingly disturbing and it is important to implement express methods of ABR testing to allow operative antibiotic therapy decisions. The application of laser light scattering (LLS) in microbiological analysis for express ABR testing of microorganisms has been considered. The ways of miniaturization of laser light scattering for creating the bases of their integration into microbiological laboratory-on-a-chip (MLOC) for clinical express diagnostics have been analysed. The advantage of miniaturization in the context of clinical express analysis realization problems are investigated. A system of parallel measuring cells and illumination, enabling simultaneous testing of a group of antibiotics, was tested by splitting a laser beam with a two-dimensional collimator prepared of nanoporous anodic aluminum oxide. It has been demonstrated that the application of LLS methods, providing high concentration and mass sensitivity as well as a miniaturization potential, is an effective approach in the development of new generation diagnostic instruments. The studies have demonstrated the ability of methods to register effects of antibiotics on microbiological samples within 10 min. The following microorganisms were used in the study: Escherichia coli M-17, Lactobacillus plantarum, Bifidobacterium bifidum, Stenotrophomonas maltophilia.

Microfluidic Chip for Detection of Drug Resistance at the Single-cell Level

Drug-resistant bacterial strains seriously threaten human health. Rapid screening of antibiotics is urgently required to improve clinical treatment. Conventional methods of antimicrobial susceptibility testing rely on turbidimetry that is evident only after several days of incubation. The lengthy time of the assay can delay clinical treatment. Here, we proposed a single-cell level rapid system based on a microfluidic chip. The detection period of 30 min to 2 h was significantly shorter than the conventional turbidity-based method. To promote detection efficiency, 16 independent channels were designed, permitting the simultaneous screening of 16 drugs in the microfluidic chip. Prepositioning of drugs in the chip permitted prolonged transportation and storage. This may allow for the widespread use of the novel system, particularly in the regions where medical facilities are scarce. The growth curves were reported rapidly through a custom code in Matlab after tracking and photographing the bacteria during microscopy examination. The capability of the proposed system was validated by antimicrobial susceptibility testing trials with standard strains. The system provides a potentially useful detection tool for drug-resistant bacteria.