Detecting and tracking nosocomial methicillin-resistant Staphylococcus aureus using a microfluidic SERS biosensor

Microfluidics for detection of food pathogens: recent trends and opportunities

Safe and healthy food is the fundamental right of every citizen. Problems caused by foodborne pathogens have always raised a threat to food safety and human health. Centers for Disease Control and Prevention (CDC) estimates that around 48 million people are affected by food intoxication, and 3000 people succumb to death. Hence, it is inevitable that an approach that is efficient, reliable, sensitive, and rapid approach that can replace the conventional analytical methods such as microbiological and biochemical methods, high throughput next-generation sequence (NGS), polymerase chain reaction (PCR), and enzyme-linked immunosorbent assay (ELISA), etc. Even though the accuracy of conventional methods is high, it is tedious; increased consumption of reagents/samples, false positives, and complex operations are the drawbacks of these methods. Microfluidic devices have shown remarkable advances in all branches of science. They serve as an alternative to conventional ways to overcome the abovementioned drawbacks. Furthermore, coupling microfluidics can improve the efficiency and accuracy of conventional methods such as surface plasma resonance, loop-mediated isothermal amplification, ELISA, and PCR. This article reviewed the progress of microfluidic devices in the last ten years in detecting foodborne pathogens. Microfluidic technology has opened the research gateway for developing low-cost, on-site, portable, and rapid assay devices. The article includes the application of microfluidic-based devices to identify critical food pathogens and briefly discusses the necessary research in this area.

Recent Advances in Bacterial Detection Using Surface-Enhanced Raman Scattering

Rapid identification of microorganisms with a high sensitivity and selectivity is of great interest in many fields, primarily in clinical diagnosis, environmental monitoring, and the food industry. For over the past decades, a surface-enhanced Raman scattering (SERS)-based detection platform has been extensively used for bacterial detection, and the effort has been extended to clinical, environmental, and food samples. In contrast to other approaches, such as enzyme-linked immunosorbent assays and polymerase chain reaction, SERS exhibits outstanding advantages of rapid detection, being culture-free, low cost, high sensitivity, and lack of water interference. This review aims to cover the development of SERS-based methods for bacterial detection with an emphasis on the source of the signal, techniques used to improve the limit of detection and specificity, and the application of SERS in high-throughput settings and complex samples. The challenges and advancements with the implementation of artificial intelligence (AI) are also discussed.

Microfluidic Electrochemical Integrated Sensor for Efficient and Sensitive Detection of Candida albicans

Traditional methods for the detection of pathogenic bacteria are time-consuming, less efficient, and sensitive, which affects infection control and bungles illness. Therefore, developing a method to remedy these problems is very important in the clinic to diagnose the pathogenic diseases and guide the rational use of antibiotics. Here, microfluidic electrochemical integrated sensor (MEIS) has been investigated, functionally for rapid, efficient separation and sensitive detection of pathogenic bacteria. Three-dimensional macroporous PDMS and Au nanotube-based electrode are successfully assembled into the modeling microchip, playing the functions of "3D chaotic flow separator" and "electrochemical detector," respectively. The 3D chaotic flow separator enhances the turbulence of the fluid, achieving an excellent bacteria capture efficiency. Meanwhile, the electrochemical detector provides a quantitative signal through enzyme-linked immunoelectrochemistry with improved sensitivity. The microfluidic electrochemical integrated sensor could successfully isolate Candida albicans (C. albicans) in the range of 30-3,000,000 CFU in the saliva matrix with over 95% capture efficiency and sensitively detect C. albicans in 1 h in oral saliva samples. The integrated device demonstrates great potential in the diagnosis of oral candidiasis and is also applicable in the detection of other pathogenic bacteria.

Surface-enhanced Raman spectroscopy for the characterization of bacterial pellets of Staphylococcus aureus infected by bacteriophage

Drug-resistant pathogenic bacteria are a major cause of infectious diseases in the world and they have become a major threat through the reduced efficacy of developed antibiotics. This issue can be addressed by using bacteriophages, which can kill lethal bacteria and prevent them from causing infections. Surface-enhanced Raman spectroscopy (SERS) is a promising technique for studying the degradation of infectious bacteria by the interaction of bacteriophages to break the vicious cycle of drug-resistant bacteria and help to develop chemotherapy-independent remedial strategies. The phage (viruses)-sensitive Staphylococcus aureus (S. aureus) bacteria are exposed to bacteriophages (Siphoviridae family) in the time frame from 0 min (control) to 50 minutes with intervals of 5 minutes and characterized by SERS using silver nanoparticles as SERS substrate. This allows us to explore the effects of the bacteriophages against lethal bacteria (S. aureus) at different time intervals. The differentiating SERS bands are observed at 575 (C-C skeletal mode), 620 (phenylalanine), 649 (tyrosine, guanine (ring breathing)), 657 (guanine (COO deformation)), 728-735 (adenine, glycosidic ring mode), 796 (tyrosine (C-N stretching)), 957 (C-N stretching (amide lipopolysaccharides)), 1096 (PO2 (nucleic acid)), 1113 (phenylalanine), 1249 (CH2 of amide III, N-H bending and C-O stretching (amide III)), 1273 (CH2, N-H, C-N, amide III), 1331 (C-N stretching mode of adenine), 1373 (in nucleic acids (ring breathing modes of the DNA/RNA bases)) and 1454 cm-1 (CH2 deformation of saturated lipids), indicating the degradation of bacteria and replication of bacteriophages. Multivariate data analysis was performed by employing principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to study the biochemical differences in the S. aureus bacteria infected by the bacteriophage. The SERS spectral data sets were successfully differentiated by PLS-DA with 94.47% sensitivity, 98.61% specificity, 94.44% precision, 98.88% accuracy and 81.06% area under the curve (AUC), which shows that at 50 min interval S. aureus bacteria is degraded by the replicating bacteriophages.

Classification and prediction of Klebsiella pneumoniae strains with different MLST allelic profiles via SERS spectral analysis

The Gram-negative non-motile Klebsiella pneuomoniae is currently a major cause of hospital-acquired (HA) and community-acquired (CA) infections, leading to great public health concern globally, while rapid identification and accurate tracing of the pathogenic bacterium is essential in facilitating monitoring and controlling of K. pneumoniae outbreak and dissemination. Multi-locus sequence typing (MLST) is a commonly used typing approach with low cost that is able to distinguish bacterial isolates based on the allelic profiles of several housekeeping genes, despite low resolution and labor intensity of the method. Core-genome MLST scheme (cgMLST) is recently proposed to sub-type and monitor outbreaks of bacterial strains with high resolution and reliability, which uses hundreds or thousands of genes conserved in all or most members of the species. However, the method is complex and requires whole genome sequencing of bacterial strains with high costs. Therefore, it is urgently needed to develop novel methods with high resolution and low cost for bacterial typing. Surface enhanced Raman spectroscopy (SERS) is a rapid, sensitive and cheap method for bacterial identification. Previous studies confirmed that classification and prediction of bacterial strains via SERS spectral analysis correlated well with MLST typing results. However, there is currently no similar comparative analysis in K. pneumoniae strains. In this pilot study, 16 K. pneumoniae strains with different sequencing typings (STs) were selected and a phylogenetic tree was constructed based on core genome analysis. SERS spectra (N = 45/each strain) were generated for all the K. pneumoniae strains, which were then comparatively classified and predicted via six representative machine learning (ML) algorithms. According to the results, SERS technique coupled with the ML algorithm support vector machine (SVM) could achieve the highest accuracy (5-Fold Cross Validation = 100%) in terms of differentiating and predicting all the K. pneumoniae strains that were consistent to corresponding MLSTs. In sum, we show in this pilot study that the SERS-SVM based method is able to accurately predict K. pneumoniae MLST types, which has the application potential in clinical settings for tracing dissemination and controlling outbreak of K. pneumoniae in hospitals and communities with low costs and high rapidity.

Polymeric and Paper-Based Lab-on-a-Chip Devices in Food Safety: A Review

Food quality and safety are important to protect consumers from foodborne illnesses. Currently, laboratory scale analysis, which takes several days to complete, is the main way to ensure the absence of pathogenic microorganisms in a wide range of food products. However, new methods such as PCR, ELISA, or even accelerated plate culture tests have been proposed for the rapid detection of pathogens. Lab-on-chip (LOC) devices and microfluidics are miniaturized devices that can enable faster, easier, and at the point of interest analysis. Nowadays, methods such as PCR are often coupled with microfluidics, providing new LOC devices that can replace or complement the standard methods by offering highly sensitive, fast, and on-site analysis. This review's objective is to present an overview of recent advances in LOCs used for the identification of the most prevalent foodborne and waterborne pathogens that put consumer health at risk. In particular, the paper is organized as follows: first, we discuss the main fabrication methods of microfluidics as well as the most popular materials used, and then we present recent literature examples for LOCs used for the detection of pathogenic bacteria found in water and other food samples. In the final section, we summarize our findings and also provide our point of view on the challenges and opportunities in the field.

Rapid Prediction of Multidrug-Resistant Klebsiella pneumoniae through Deep Learning Analysis of SERS Spectra

Klebsiella pneumoniae is listed by the WHO as a priority pathogen of extreme importance that can cause serious consequences in clinical settings. Due to its increasing multidrug resistance all over the world, K. pneumoniae has the potential to cause extremely difficult-to-treat infections. Therefore, rapid and accurate identification of multidrug-resistant K. pneumoniae in clinical diagnosis is important for its prevention and infection control. However, the limitations of conventional and molecular methods significantly hindered the timely diagnosis of the pathogen. As a label-free, noninvasive, and low-cost method, surface-enhanced Raman scattering (SERS) spectroscopy has been extensively studied for its application potentials in the diagnosis of microbial pathogens. In this study, we isolated and cultured 121 K. pneumoniae strains from clinical samples with different drug resistance profiles, which included polymyxin-resistant K. pneumoniae (PRKP; n = 21), carbapenem-resistant K. pneumoniae, (CRKP; n = 50), and carbapenem-sensitive K. pneumoniae (CSKP; n = 50). For each strain, a total of 64 SERS spectra were generated for the enhancement of data reproducibility, which were then computationally analyzed via the convolutional neural network (CNN). According to the results, the deep learning model CNN plus attention mechanism could achieve a prediction accuracy as high as 99.46%, with robustness score of 5-fold cross-validation at 98.87%. Taken together, our results confirmed the accuracy and robustness of SERS spectroscopy in the prediction of drug resistance of K. pneumoniae strains with the assistance of deep learning algorithms, which successfully discriminated and predicted PRKP, CRKP, and CSKP strains. IMPORTANCE This study focuses on the simultaneous discrimination and prediction of Klebsiella pneumoniae strains with carbapenem-sensitive, carbapenem-resistant, and polymyxin-resistant phenotypes. The implementation of CNN plus an attention mechanism makes the highest prediction accuracy at 99.46%, which confirms the diagnostic potential of the combination of SERS spectroscopy with the deep learning algorithm for antibacterial susceptibility testing in clinical settings.

The Development of Technology to Prevent, Diagnose, and Manage Antimicrobial Resistance in Healthcare-Associated Infections

There is a growing risk of antimicrobial resistance (AMR) having an adverse effect on the healthcare system, which results in higher healthcare costs, failed treatments and a higher death rate. A quick diagnostic test that can spot infections resistant to antibiotics is essential for antimicrobial stewardship so physicians and other healthcare professionals can begin treatment as soon as possible. Since the development of antibiotics in the last two decades, traditional, standard antimicrobial treatments have failed to treat healthcare-associated infections (HAIs). These results have led to the development of a variety of cutting-edge alternative methods to combat multidrug-resistant pathogens in healthcare settings. Here, we provide an overview of AMR as well as the technologies being developed to prevent, diagnose, and control healthcare-associated infections (HAIs). As a result of better cleaning and hygiene practices, resistance to bacteria can be reduced, and new, quick, and accurate instruments for diagnosing HAIs must be developed. In addition, we need to explore new therapeutic approaches to combat diseases caused by resistant bacteria. In conclusion, current infection control technologies will be crucial to managing multidrug-resistant infections effectively. As a result of vaccination, antibiotic usage will decrease and new resistance mechanisms will not develop.

Rapid detection of bacteria using gold nanoparticles in SERS with three different capping agents: Thioglucose, polyvinylpyrrolidone, and citrate

The increase in outbreaks of emerging and re-emerging bacterial infections over the last few decades calls for their rapid detection and treatment. Surface-enhanced Raman spectroscopy (SERS) is a technique that can be applied to develop fast screening systems for bacterial presence in biological samples. Optimizing the capping agents in nanoparticle synthesis is important because capping agents are responsible for controlling the morphological features and chemical properties of the nanoparticles that are essential for SERS. To the best of our knowledge, this paper is the first to study the application of gold nanoparticles capped with thioglucose and polyvinylpyrrolidone (PVP) in SERS detection of bacteria as an alternative to the citrate-capped gold nanoparticles that are often used in SERS detection of bacteria. Three different species of bacteria were used in this study: Cutibacterium acnes, Escherichia coli and Staphylococcus aureus (methicillin-sensitive and methicillin-resistant). This study demonstrates that the thioglucose, citrate both show good contribution in bacterial species identification and the thioglucose shows the best among the three capping agents in two types of S. aureus identification. Moreover, although PVP showed high Raman peaks in the SERS spectrum for each type of bacteria, it showed least contribution in identifying species and strains due to its low efficacy in producing responses from different nucleic acid components in the bacteria cells.

Electrochemical Quantification of Tobramycin Retention in Pseudomonas aeruginosa as Antimicrobial Susceptibility Indicator

The emergence and spread of bacterial resistance to antibiotics has developed into one of the most challenging threats to public health. Antibiotic susceptibility tests (ASTs) for bacterial infections are now essential, because they provide guidance for physicians in the selection of antibiotics, to which bacteria will respond. Most current AST methods require long periods of time, because of bacterial growth and incubation, leading to a prolonged and overuse of broad-spectrum antibiotics. Thus, there is a growing demand for methods and technologies that enable rapid antibiotic susceptibility assessment. Due to advantages related to cost-effectiveness, rapid response time and high sensitivity, electrochemical detection methods are promising analytical tools that can successfully quantify antibiotic uptake and retention in clinically relevant bacterial strains. This study presents the electroanalytical quantification of tobramycin (TOB) retention in susceptible and resistant bacterial strains of Pseudomonas aeruginosa. The electrochemical behavior of TOB was characterized by voltammetry, identifying redox potentials, the current dependence on pH conditions, and the detection limit at unmodified glassy carbon electrodes. The presented methodology was able to distinguish between susceptible and resistant bacterial strains, and is also capable of identifying varying degrees of resistance against TOB. The presented approach detects the immediate interaction of bacteria with an antibiotic, without the need of complex and cost-intense equipment related to genomic testing methods.

Nanotechnology Approaches for Rapid Detection and Theranostics of Antimicrobial Resistant Bacterial Infections

As declared by WHO, antimicrobial resistance (AMR) is a high priority issue with a pressing need to develop impactful technologies to curb it. The rampant and inappropriate use of antibiotics due to the lack of adequate and timely diagnosis is a leading cause behind AMR evolution. Unfortunately, populations with poor economic status and those residing in densely populated areas are the most affected ones, frequently leading to emergence of AMR pathogens. Classical approaches for AMR diagnostics like phenotypic methods, biochemical assays, and molecular techniques are cumbersome and resource-intensive and involve a long turnaround time to yield confirmatory results. In contrast, recent emergence of nanotechnology-assisted approaches helps to overcome challenges in classical approaches and offer simpler, more sensitive, faster, and more affordable solutions for AMR diagnostics. Nanomaterial platforms (metallic, quantum-dot, carbon-based, upconversion, etc.), nanoparticle-based rapid point-of-care platforms, nano-biosensors (optical, mechanical, electrochemical), microfluidic-assisted devices, and importantly, nanotheranostic devices for diagnostics with treatment of AMR infections are examples of rapidly growing nanotechnology approaches used for AMR management. This review comprehensively summarizes the past 10 years of research progress on nanotechnology approaches for AMR diagnostics and for estimating antimicrobial susceptibility against commonly used antibiotics. This review also highlights several bottlenecks in nanotechnology approaches that need to be addressed prior to considering their translation to clinics.

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.

Recent advances in antibiotic resistance diagnosis using SERS: focus on the “Big 5” challenges

SERS for antibiotic resistance diagnosis.

Scale-adaptive Deep Model for Bacterial Raman Spectra Identification

The combination of Raman spectroscopy and deep learning technology provides an automatic, rapid, and accurate scheme for the clinical diagnosis of pathogenic bacteria. However, the accuracy of existing deep learning methods is still limited because of the single and fixed scales of deep neural networks. We propose a deep neural network that can learn multi-scale features of Raman spectra by using the automatic combination of multi-receptive fields of convolutional layers. This model is based on the expert knowledge that the discrimination information of Raman spectra is composed of multi-scale spectral peaks. We enhance the interpretability of the model by visualizing the activated wavenumbers of the bacterial spectrum that can be used for reference in related work. Compared with existing state-of-the-art methods, the proposed method achieves higher accuracy and efficiency for bacterial identification on isolate-level, empiric-treatment-level, and antibiotic-resistance-level tasks. The clinical bacterial identification task requires significantly fewer patient samples to achieve similar accuracy. Therefore, this method has tremendous potential for the identification of clinical pathogenic bacteria, antibiotic susceptibility testing, and prescription guidance.

Nucleic acid amplification-based microfluidic approaches for antimicrobial susceptibility testing

Because of the global spread of antimicrobials, there is an urgent need to develop rapid and effective tools for antimicrobial susceptibility testing to help clinicians prescribe accurate and appropriate antibiotic doses sooner. The conventional methods for antimicrobial susceptibility testing are usually based on bacterial culture methods, which are time-consuming, complicated, and labor-intensive. Therefore, other approaches are needed to address these issues. Recently, microfluidic technology has gained significant attention in infection management due to its advantages including rapid detection, high sensitivity and specificity, highly automated assay, simplicity, low cost, and potential for point-of-care testing in low-resource areas. Microfluidic advances for antimicrobial susceptibility testing can be classified into phenotypic (usually culture-based) and genotypic tests. Genotypic antimicrobial susceptibility testing is the detection of resistant genes in a microorganism using methods such as nucleic acid amplification. This review (with 107 references) surveys the different forms of nucleic acid amplification-based microdevices used for genotypic antimicrobial susceptibility testing. The first section reviews the serious threat of antimicrobial-resistant microorganisms and the urgent need for fast check-ups. Next, several conventional antimicrobial susceptibility testing methods are discussed, and microfluidic technology as a promising candidate for rapid detection of antimicrobial-resistant microorganisms is briefly introduced. The next section highlights several advancements of microdevices, with an emphasis on their working principles and performance. The review concludes with the importance of fully integrated microdevices and a discussion on future perspectives.

Identification of methicillin-resistant Staphylococcus aureus bacteria using surface-enhanced Raman spectroscopy and machine learning techniques

To combat antibiotic resistance, it is extremely important to select the right antibiotic by performing rapid diagnosis of pathogens. Traditional techniques require complicated sample preparation and time-consuming processes which are not suitable for rapid diagnosis. To address this problem, we used surface-enhanced Raman spectroscopy combined with machine learning techniques for rapid identification of methicillin-resistant and methicillin-sensitive Gram-positive Staphylococcus aureus strains and Gram-negative Legionella pneumophila (control group). A total of 10 methicillin-resistant S. aureus (MRSA), 3 methicillin-sensitive S. aureus (MSSA) and 6 L. pneumophila isolates were used. The obtained spectra indicated high reproducibility and repeatability with a high signal to noise ratio. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and various supervised classification algorithms were used to discriminate both S. aureus strains and L. pneumophila. Although there were no noteworthy differences between MRSA and MSSA spectra when viewed with the naked eye, some peak intensity ratios such as 732/958, 732/1333, and 732/1450 proved that there could be a significant indicator showing the difference between them. The k-nearest neighbors (kNN) classification algorithm showed superior classification performance with 97.8% accuracy among the traditional classifiers including support vector machine (SVM), decision tree (DT), and naïve Bayes (NB). Our results indicate that SERS combined with machine learning can be used for the detection of antibiotic-resistant and susceptible bacteria and this technique is a very promising tool for clinical applications.

Rapid Methods for Antimicrobial Resistance Diagnostics

Antimicrobial resistance (AMR) is one of the most challenging threats in public health; thus, there is a growing demand for methods and technologies that enable rapid antimicrobial susceptibility testing (AST). The conventional methods and technologies addressing AMR diagnostics and AST employed in clinical microbiology are tedious, with high turnaround times (TAT), and are usually expensive. As a result, empirical antimicrobial therapies are prescribed leading to AMR spread, which in turn causes higher mortality rates and increased healthcare costs. This review describes the developments in current cutting-edge methods and technologies, organized by key enabling research domains, towards fighting the looming AMR menace by employing recent advances in AMR diagnostic tools. First, we summarize the conventional methods addressing AMR detection, surveillance, and AST. Thereafter, we examine more recent non-conventional methods and the advancements in each field, including whole genome sequencing (WGS), matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) spectrometry, Fourier transform infrared (FTIR) spectroscopy, and microfluidics technology. Following, we provide examples of commercially available diagnostic platforms for AST. Finally, perspectives on the implementation of emerging concepts towards developing paradigm-changing technologies and methodologies for AMR diagnostics are discussed.

SERS characterization of aggregated and isolated bacteria deposited on silver-based substrates

Surface-enhanced Raman scattering (SERS), based on the enhancement of the Raman signal of molecules positioned within a few nanometres from a structured metal surface, is ideally suited to provide bacterial-specific molecular fingerprints which can be used for analytical purposes. However, for some complex structures such as bacteria, the generation of reproducible SERS spectra is still a challenging task. Among the various factors influencing the SERS variability (such as the nature of SERS-active substrate, Raman parameters and bacterial specificity), we demonstrate in this study that the environment of Gram-positive and Gram-negative bacteria deposited on ultra-thin silver films also impacts the origin of the SERS spectra. In the case of densely packed bacteria, the obtained SERS signatures were either characteristic of the secretion of adenosine triphosphate for Staphylococcus aureus (S. aureus) or the cell wall and the pili/flagella for Escherichia coli (E. coli), allowing for an easy discrimination between the various strains. In the case of isolated bacteria, SERS mapping together with principal component analysis revealed some variabilities of the spectra as a function of the bacteria environment and the bactericidal effect of the silver. However, the variability does not preclude the SERS signatures of various E. coli strains to be discriminated.

Label-free detection of nosocomial bacteria using a nanophotonic interferometric biosensor

Nosocomial infections are a major concern at the worldwide level. Early and accurate identification of nosocomial pathogens is crucial to provide timely and adequate treatment. A prompt response also prevents the progression of the infection to life-threatening conditions, such as septicemia or generalized bloodstream infection. We have implemented two highly sensitive methodologies using an ultrasensitive photonic biosensor based on a bimodal waveguide interferometer (BiMW) for the fast detection of Pseudomonas aeruginosa and methicillin-resistant Staphylococcus aureus (MRSA), two of the most prevalent bacteria associated with nosocomial infections. For that, we have developed a biofunctionalization strategy based on the use of a PEGylated silane (silane-PEG-COOH) which provides a highly resistant and bacteria-repelling surface, which is crucial to specifically detect each bacterium. Two different biosensor assays have been set under standard buffer conditions: one based on a specific direct immunoassay employing polyclonal antibodies for the detection of P. aeruginosa and another one employing aptamers for the direct detection of MRSA. The biosensor immunoassay for P. aeruginosa is fast (it only takes 12 min) and specific and has experimentally detected concentrations down to 800 cfu mL-1 (cfu: colony forming unit). The second one relies on the use of an aptamer that specifically detects penicillin-binding protein 2a (PBP2a), a protein only expressed in the MRSA mutant, providing a photonic biosensor with the ability to identify the resistant pathogen MRSA and differentiate it from methicillin-susceptible S. aureus (MSSA). Direct, label-free, and selective detection of whole MRSA bacteria has been achieved, making possible the direct detection of also 800 cfu mL-1. According to the signal-to-noise (S/N) ratio of the device, a theoretical limit of detection (LOD) of around 49 and 29 cfu mL-1 was estimated for P. aeruginosa and MRSA, respectively. Both results obtained under standard conditions reveal the great potential this interferometric biosensor device has as a versatile and specific tool for bacterial detection and quantification, providing a rapid method for the identification of nosocomial pathogens within the clinical requirements of sensitivity for the diagnosis of infections.

Rapid Pomegranate Juice Authentication Using a Simple Sample-to-Answer Hybrid Paper/Polymer-Based Lab-on-a-Chip Device

As a super fruit, pomegranate and its juice have attracted increased consumer demands during the past decades. Given the high production cost and market price, adulteration of pomegranate juice is highly likely to occur. To authenticate pomegranate juice and avoid the addition of cheaper fruit juices, such as apple and grape, an analytical method based on loop-mediated isothermal amplification (LAMP) was developed. This LAMP-based authentication method achieved highly sensitive (i.e., 10 pg for pomegranate DNA and 100 pg for grape and apple DNA) and specific detection of pomegranate, apple, and grape DNA present in fresh fruit juice. To further simplify the overall analysis, a hybrid paper/polymer-based lab-on-a-chip (LOC) platform was designed to integrate DNA extraction, LAMP reaction, and LAMP result visualization onto a single device. This LOC device was able to detect 2 μL of fresh pomegranate juice and 5 μL of fresh apple and grape juice. Using a homemade portable heating device, the overall analysis could be completed in ∼1 h in an almost instrument-free setting. The cost for each authentication test is estimated to be ∼4 USD and the reusable homemade portable heating device is ∼15 USD. This LAMP-based simple sample-to-answer hybrid paper/polymer-based LOC device has high potential to be adopted by government laboratories and the food industry to rapidly and routinely authenticate pomegranate juice even in a resource-limited environment.

Nanofibrillar cellulose/Au@Ag nanoparticle nanocomposite as a SERS substrate for detection of paraquat and thiram in lettuce

A nanocomposite based on nanofibrillar cellulose (NFC) coated with gold-silver (core-shell) nanoparticles (Au@Ag NPs) was developed as a novel surface-enhanced Raman spectroscopy (SERS) substrate. SERS performance of NFC/Au@Ag NP nanocomposite was tested by 4-mercaptobenzoic acid. The cellulose nanofibril network was a suitable platform that allowed Au@Ag NPs to be evenly distributed and stabilized over the substrate, providing more SERS hotspots for the measurement. Two pesticides, thiram and paraquat, were successfully detected either individually or as a mixture in lettuce by SERS coupled with the nanocomposite. Strong Raman scattering signals for both thiram and paraquat were obtained within a Raman shift range of 400-2000 cm-1 and a Raman intensity ~ 8 times higher than those acquired by NFC/Au NP nanocomposite. Characteristic peaks were clearly observable in all SERS spectra even at a low concentration of 10 μg/L of pesticides. Limit of detection values of 71 and 46 μg/L were obtained for thiram and paraquat, respectively. Satisfactory SERS performance, reproducibility, and sensitivity of NFC/Au@Ag NP nanocomposite validate its applicability for real-world analysis to monitor pesticides and other contaminants in complex food matrices within a short acquisition time. Graphical abstract.

Identification and Antimicrobial Susceptibility Testing of Campylobacter Using a Microfluidic Lab-on-a-Chip Device

Campylobacter spp. have been recognized as major foodborne pathogens worldwide. An increasing frequency of antibiotic-resistant pathogens, including Campylobacter spp., have been identified to transmit from food products to humans and cause severe threats to public health. To better mitigate the antibiotic resistance crisis, rapid detection methods are required to provide timely antimicrobial resistance surveillance data for agri-food systems. Herein, we developed a polymer-based microfluidic device for the identification and antimicrobial susceptibility testing (AST) of Campylobacter spp. An array of bacterial incubation chambers were created in the microfluidic device, where chromogenic medium and antibiotics were loaded. The growth of Campylobacter spp. was visualized by color change due to chromogenic reactions. This platform achieved 100% specificity for Campylobacter identification. Sensitive detection of multiple Campylobacter species (C. jejuni, C. coli, and C. lari) was obtained in artificially contaminated milk and poultry meat, with detection limits down to 1 × 102 CFU/ml and 1 × 104 CFU/25 g, respectively. On-chip AST determined Campylobacter antibiotic susceptibilities by the lowest concentration of antibiotics that can inhibit bacterial growth (i.e., no color change observed). High coincidences (91% to 100%) of on-chip AST and the conventional agar dilution method were achieved against several clinically important antibiotics. For a presumptive colony, on-chip identification and AST were completed in parallel within 24 h, whereas standard methods, including biochemical assays and traditional culture-based AST, take several days for multiple sequential steps. In conclusion, this lab-on-a-chip device can achieve rapid and reliable detection of antibiotic-resistant Campylobacter spp.IMPORTANCE Increasing concerns of antibiotic-resistant Campylobacter spp. with regard to public health emphasize the importance of efficient and fast detection. This study described the timely identification and antimicrobial susceptibility testing of Campylobacter spp. by using a microfluidic device. Our developed method not only reduced the total analysis time, but it also simplified food sample preparation and chip operation for end users. Due to the miniaturized size of the lab-on-a-chip platform, the detection was achieved by using up to 1,000 times less of the reagents than with standard reference methods, making it a competitive approach for rapid screening and surveillance study in food industries. In addition, multiple clinically important Campylobacter species (C. jejuni, C. coli, and C. lari) could be tested by our device. This device has potential for wide application in food safety management and clinical diagnostics, especially in resource-limited regions.

Rapid and specific duplex detection of methicillin-resistant Staphylococcus aureus genes by surface-enhanced Raman spectroscopy

Methicillin-resistant Staphylococcus aureus (MRSA) is considered to be one of the important hospital-acquired pathogens. MRSA is also commonly associated with hospital-acquired infections and mortality. Quantitative and precise detection of MRSA is essential for rapid diagnosis and subsequent effective disease management strategies. We herein developed a highly specific method for rapid MRSA detection that combines surface-enhanced Raman spectroscopy (SERS) nanotags and polymerase chain reaction (PCR). SERS provided the sensitivity and spectral multiplexing capability while PCR provided the specificity required for the assay. The method was tested by the simultaneous detection of two MRSA specific genes (mecA and femA) amplified from genomic DNA isolated from clinical specimens. Magnetic isolation and rapid duplex detection were performed to obtain a detectable signal down to 10⁴ input copies within 80 min. This demonstrated the potential of the SERS-PCR based approach for the accurate identification of MRSA at an early-diagnosis stage. This study also provides an alternative approach to the existing methods for detecting clinical targets without compromising sensitivity and selectivity, and with minimal handling steps. We thus believe that this approach will find a broad application in clinical applications.

Microfluidics as an Emerging Platform for Tackling Antimicrobial Resistance (AMR): A Review

Background:

Antimicrobial resistance (AMR) occurs when microbes become resistant to antibiotics causing complications and limited treatment options. AMR is more significant where antibiotics use is excessive or abusive and the strains of bacteria become resistant to antibiotic treatments. Current technologies for bacteria and its resistant strains identification and antimicrobial susceptibility testing (AST) are mostly central-lab based in hospitals, which normally take days to weeks to get results. These tools and procedures are expensive, laborious and skills based. There is an ever-increasing demand for developing point-of-care (POC) diagnostics tools for rapid and near patient AMR testing. Microfluidics, an important and fundamental technique to develop POC devices, has been utilized to tackle AMR in healthcare. This review mainly focuses on the current development in the field of microfluidics for rapid AMR testing.

Method:

Due to the limitations of conventional AMR techniques, microfluidic-based platforms have been developed for better understandings of bacterial resistance, smart AST and minimum inhibitory concentration (MIC) testing tools and development of new drugs. This review aims to summarize the recent development of AST and MIC testing tools in different formats of microfluidics technology.

Results:

Various microfluidics devices have been developed to combat AMR. Miniaturization and integration of different tools has been attempted to produce handheld or standalone devices for rapid AMR testing using different formats of microfluidics technology such as active microfluidics, droplet microfluidics, paper microfluidics and capillary-driven microfluidics.

Conclusion:

Current conventional AMR detection technologies provide time-consuming, costly, labor-intensive and central lab-based solutions, limiting their applications. Microfluidics has been developed for decades and the technology has emerged as a powerful tool for POC diagnostics of antimicrobial resistance in healthcare providing, simple, robust, cost-effective and portable diagnostics. The success has been reported in research articles; however, the potential of microfluidics technology in tackling AMR has not been fully achieved in clinical settings.

Biosynthetically grown dendritic silver nanostructures for visible Surface Enhanced Resonance Raman Spectroscopy (v-SERRS)

A simple approach to achieve high SERS enhancement for bio-analyte detection at visible wavelength through a resonance Raman (RR) effect has been proposed in this study.

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.

Microfluidics and Surface-Enhanced Raman Spectroscopy: A Perfect Match for New Analytical Tools

In this perspective article, we emphasize the combination of Surface-Enhanced Raman Spectroscopy (SERS) and Microfluidic devices. SERS approaches have been widely studied and used for multiple applications including trace molecules detection, in situ analysis of biological samples and monitoring or, all of them with good results, however still with limitations of the technique, for example regarding with improved precision and reproducibility. These implications can be overcome by microfluidic approaches. The resulting coupling Microfluidics - SERS (MF-SERS) has recently gained increasing attention by creating thundering opportunities for the analytical field. For this purpose, we introduce some of the strategies developed to implement SERS within microfluidic reactor along with a brief overview of the most recent MF-SERS applications for biology, health and environmental concerns. Eventually, we will discuss future research opportunities of such systems.

Recent Advances in Droplet-based Microfluidic Technologies for Biochemistry and Molecular Biology

Recently, droplet-based microfluidic systems have been widely used in various biochemical and molecular biological assays. Since this platform technique allows manipulation of large amounts of data and also provides absolute accuracy in comparison to conventional bioanalytical approaches, over the last decade a range of basic biochemical and molecular biological operations have been transferred to drop-based microfluidic formats. In this review, we introduce recent advances and examples of droplet-based microfluidic techniques that have been applied in biochemistry and molecular biology research including genomics, proteomics and cellomics. Their advantages and weaknesses in various applications are also comprehensively discussed here. The purpose of this review is to provide a new point of view and current status in droplet-based microfluidics to biochemists and molecular biologists. We hope that this review will accelerate communications between researchers who are working in droplet-based microfluidics, biochemistry and molecular biology.

Recent Advancement in the Surface-Enhanced Raman Spectroscopy-Based Biosensors for Infectious Disease Diagnosis

Diagnosis is the key component in disease elimination to improve global health. However, there is a tremendous need for diagnostic innovation for neglected tropical diseases that largely consist of mosquito-borne infections and bacterial infections. Early diagnosis of these infectious diseases is critical but challenging because the biomarkers are present at low concentrations, demanding bioanalytical techniques that can deliver high sensitivity with ensured specificity. Owing to the plasmonic nanomaterials-enabled high detection sensitivities, even up to single molecules, surface-enhanced Raman spectroscopy (SERS) has gained attention as an optical analytical tool for early disease biomarker detection. In this mini-review, we highlight the SERS-based assay development tailored to detect key types of biomarkers for mosquito-borne and bacterial infections. We discuss in detail the variations of SERS-based techniques that have developed to afford qualitative and quantitative disease biomarker detection in a more accurate, affordable, and field-transferable manner. Current and emerging challenges in the advancement of SERS-based technologies from the proof-of-concept phase to the point-of-care phase are also briefly discussed.

Surface-enhanced Raman spectroscopy of microorganisms: limitations and applicability on the single-cell level

Detection and characterization of microorganisms is essential for both clinical diagnostics and environmental studies. An emerging technique to analyse microbes at single-cell resolution is surface-enhanced Raman spectroscopy (surface-enhanced Raman scattering: SERS). Optimised SERS procedures enable fast analytical read-outs with specific molecular information, providing insight into the chemical composition of microbiological samples. Knowledge about the origin of microbial SERS signals and parameter(s) affecting their occurrence, intensity and/or reproducibility is crucial for reliable SERS-based analyses. In this work, we explore the feasibility and limitations of the SERS approach for characterizing microbial cells and investigate the applicability of SERS for single-cell sorting as well as for three-dimensional visualization of microbial communities. Analyses of six different microbial species (an archaeon, two Gram-positive bacteria, three Gram-negative bacteria) showed that for several of these organisms distinct features in their SERS spectra were lacking. As additional confounding factor, the physiological conditions of the cells (as influenced by e.g., storage conditions or deuterium-labelling) were systematically addressed, for which we conclude that the respective SERS signal at the single-cell level is strongly influenced by the metabolic activity of the analysed cells. While this finding complicates the interpretation of SERS data, it may on the other hand enable probing of the metabolic state of individual cells within microbial populations of interest.

Detection and Characterization of Antibiotic-Resistant Bacteria Using Surface-Enhanced Raman Spectroscopy

This mini-review summarizes the most recent progress concerning the use of surface-enhanced Raman spectroscopy (SERS) for the detection and characterization of antibiotic-resistant bacteria. We first discussed the design and synthesis of various types of nanomaterials that can be used as the SERS-active substrates for biosensing trace levels of antibiotic-resistant bacteria. We then reviewed the tandem-SERS strategy of integrating a separation element/platform with SERS sensing to achieve the detection of antibiotic-resistant bacteria in the environmental, agri-food, and clinical samples. Finally, we demonstrated the application of using SERS to investigate bacterial antibiotic resistance and susceptibility as well as the working mechanism of antibiotics based on spectral fingerprinting of the whole cells.

Paper-Based Surface-Enhanced Raman Spectroscopy for Diagnosing Prenatal Diseases in Women

We report the development of a surface-enhanced Raman spectroscopy sensor chip by decorating gold nanoparticles (AuNPs) on ZnO nanorod (ZnO NR) arrays vertically grown on cellulose paper (C). We show that these chips can enhance the Raman signal by 1.25 × 10⁷ with an excellent reproducibility of <6%. We show that we can measure trace amounts of human amniotic fluids of patients with subclinical intra-amniotic infection (IAI) and preterm delivery (PTD) using the chip in combination with a multivariate statistics-derived machine-learning-trained bioclassification method. We can detect the presence of prenatal diseases and identify the types of diseases from amniotic fluids with >92% clinical sensitivity and specificity. Our technology has the potential to be used for the early detection of prenatal diseases and can be adapted for point-of-care applications.

Surface-Enhanced Raman Scattering for Rapid Detection and Characterization of Antibiotic-Resistant Bacteria

As the prevalence of antibiotic-resistant bacteria continues to rise, biosensing technologies are needed to enable rapid diagnosis of bacterial infections. Furthermore, understanding the unique biochemistry of resistance mechanisms can facilitate the development of next generation therapeutics. Surface-enhanced Raman scattering (SERS) offers a potential solution to real-time diagnostic technologies, as well as a route to fundamental, mechanistic studies. In the current review, SERS-based approaches to the detection and characterization of antibiotic-resistant bacteria are covered. The commonly used nanomaterials (nanoparticles and nanostructured surfaces) and surface modifications (antibodies, aptamers, reporters, etc.) for SERS bacterial detection and differentiation are discussed first, and followed by a review of SERS-based detection of antibiotic-resistant bacteria from environmental/food processing and clinical sources. Antibiotic susceptibility testing and minimum inhibitory concentration testing with SERS are then summarized. Finally, recent developments of SERS-based chemical imaging/mapping of bacteria are reviewed.

A Graphene-Silver Nanoparticle-Silicon Sandwich SERS Chip for Quantitative Detection of Molecules and Capture, Discrimination, and Inactivation of Bacteria

There currently exists increasing concerns on the development of a kind of high-performance SERS platform, which is suitable for sensing applications ranging from the molecular to cellular (e.g., bacteria) level. Herein, we develop a novel kind of universal SERS chip, made of graphene (G)-silver nanoparticle (AgNP)-silicon (Si) sandwich nanohybrids (G@AgNPs@Si), in which AgNPs are in situ grown on a silicon wafer through hydrofluoric acid-etching-assisted chemical reduction, followed by coating with single-layer graphene via a polymer-protective etching method. The resultant chip features a strong, stable, reproducible surface-enhanced Raman scattering (SERS) effect and reliable quantitative capability. By virtues of these merits, the G@AgNPs@Si platform is capable for not only molecular detection and quantification but also cellular analysis in real systems. As a proof-of-concept application, the chip allows ultrahigh sensitive and reliable detection of adenosine triphosphate (ATP), with a detection limit of ∼1 pM. In addition, the chip, serving as a novel multifunctional platform, enables simultaneous capture, discrimination, and inactivation of bacteria. Typically, the bacterial capture efficiency is 54% at 10⁸ CFU mL^-1 bacteria, and the antibacterial rate reaches 93% after 24 h of treatment. Of particular note, Escherichia coli and Staphylococcus aureus spiked into blood can be readily distinguished via the chip, suggesting its high potential for clinical applications.

Detection of Antibiotics and Evaluation of Antibacterial Activity with Screen-Printed Electrodes

This review provides a brief overview of the fabrication and properties of screen-printed electrodes and details the different opportunities to apply them for the detection of antibiotics, detection of bacteria and antibiotic susceptibility. Among the alternative approaches to costly chromatographic or ELISA methods for antibiotics detection and to lengthy culture methods for bacteria detection, electrochemical biosensors based on screen-printed electrodes present some distinctive advantages. Chemical and (bio)sensors for the detection of antibiotics and assays coupling detection with screen-printed electrodes with immunomagnetic separation are described. With regards to detection of bacteria, the emphasis is placed on applications targeting viable bacterial cells. While the electrochemical sensors and biosensors face many challenges before replacing standard analysis methods, the potential of screen-printed electrodes is increasingly exploited and more applications are anticipated to advance towards commercial analytical tools.

Microfluidics: innovative approaches for rapid diagnosis of antibiotic-resistant bacteria

The emergence of antibiotic-resistant bacteria has become a major global health concern. Rapid and accurate diagnostic strategies to determine the antibiotic susceptibility profile prior to antibiotic prescription and treatment are critical to control drug resistance. The standard diagnostic procedures for the detection of antibiotic-resistant bacteria, which rely mostly on phenotypic characterization, are time consuming, insensitive and often require skilled personnel, making them unsuitable for point-of-care (POC) diagnosis. Various molecular techniques have therefore been implemented to help speed up the process and increase sensitivity. Over the past decade, microfluidic technology has gained great momentum in medical diagnosis as a series of fluid handling steps in a laboratory can be simplified and miniaturized on to a small platform, allowing marked reduction of sample amount, high portability and tremendous possibility for integration with other detection technologies. These advantages render the microfluidic system a great candidate to be developed into an easy-to-use sample-to-answer POC diagnosis suitable for application in remote clinical settings. This review provides an overview of the current development of microfluidic technologies for the nucleic acid based and phenotypic-based detections of antibiotic resistance.

Optical Biosensing of Bacteria and Bacterial Communities

Bacterial sensing is important for understanding the numerous roles bacteria play in nature and in technology, understanding and managing bacterial populations, detecting pathogenic bacterial infections, and preventing the outbreak of illness. Current analytical challenges in bacterial sensing center on the dilemma of rapidly acquiring quantitative information about bacteria with high detection efficiency, sensitivity, and specificity, while operating within a reasonable budget and optimizing the use of ancillary tools, such as multivariate statistics. This review starts from a general description of bacterial sensing methods and challenges, and then focuses on bacterial characterization using optical methods including Raman spectroscopy and imaging, infrared spectroscopy, fluorescence spectroscopy and imaging, and plasmonics, including both extended and localized surface plasmon resonance spectroscopy. The advantages and drawbacks of each method in relation to the others are discussed, as are their applications. A particularly promising direction in bacterial sensing lies in combining multiple approaches to achieve multiplex analysis, and examples where this has been achieved are highlighted.