Rapid Detection of Bacteria from Blood with Surface-Enhanced Raman Spectroscopy

Ultrafast and Ultrasensitive Bacterial Detection in Biofluids: Leveraging Resazurin as a Visible and Fluorescent Spectroscopic Marker

Here we report the application of chemometric analysis for modeling absorbance spectroscopy and fluorescence emission data from a resazurin-based assay targeting low-level bacterial detection in biofluids. Bacteria spiked samples were incubated with resazurin and absorbance and fluorescence data were collected at 30 min intervals. The absorbance data was subjected to Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) and compared with the univariate fluorescence spectroscopy approach. The analysis demonstrated the multidimensional nature of the absorbance data, highlighting the appearance of the resorufin peak at the 2 h time point with a low bacterial inoculum of 0.01 CFU mL-1 across all the samples tested-water, urine and serum. The PLSR models supported the PCA data and exhibited strong predictive capabilities for water (RC2 = 0.937, RCV2 = 0.934), urine (RC2 = 0.899, RCV2 = 0.880) and serum (RC2 = 0.985, RCV2 = 0.967). Conversely, fluorescence is contingent upon resorufin existence, necessitating a prolonged waiting period postincubation with resazurin to verify the presence of bacteria, especially when contamination levels are low. Given the substantial global impact of bacteria-related infections, this method detects bacteria at low concentrations precisely and rapidly, improving efficiency and adaptability for point-of-care settings, promising swift diagnosis of bacterial infections, environmental monitoring, or food-quality control.

A sensitive SERS-based assay technique for accurate detection of foodborne pathogens without interference

The accurate and sensitive detection of foodborne pathogens is critical for timely food quality supervision and human health. To address this issue, herein, we developed a simple and novel surface-enhanced Raman scattering (SERS) assay using p-mercaptobenzoic acid (MBN)-modified gold nanoparticles (Au NPs) and magnetic beads for interference-free detection of Escherichia coli (E. coli). This assay technique cleverly reduced silver ions (Ag+) on the surface of E. coli (bacteria@Ag NPs), and the functionalized magnetic beads (capture probes) captured and enriched bacteria@Ag NPs, forming the structure of the capture probes-bacteria@Ag NPs. Then, the capture probes-bacteria@Ag NPs were dissolved in the acidic medium, and the Ag NPs on the surface of E. coli was converted to Ag+ again. Due to the special coordination between Ag+ and MBN-modified Au NPs (functionalized Au NPs), the SERS intensity of MBN exhibited a positive correlation with the E. coli concentration, and the SERS detection assay of E. coli was established. The signal of the functionalized Au NPs located at 2228 cm-1 perfectly avoided the spectral overlap with coexisting materials in the Raman fingerprint region, which ensured the accuracy of the technique. The controlled aggregation of the functionalized Au NPs ensured the reproducibility and reliability of the detection system; the emergence of MBs greatly reduced the reaction time and made sure the operation was rapid, simple and portable. The limit of detection (LOD) for E. coli was as low as 10 cfu mL-1, and the detection assay was successfully applied for the detection of E. coli in bottled water and milk. As a sensitive and accurate analytical technique for the detection of pathogens, this SERS-based method has great potential to be applied in the field of food safety.

Spectroscopic analysis of bacterial photoreactivation

With the rise of bacterial infections and antibiotic resistance, spectroscopic devices originally developed for bacterial detection have shown promise to rapidly identify bacterial strains and determine the ratio of live to dead bacteria. However, the detection of the photoreactivated pathogens remains a critical concern. This study utilizes fluorescence and Raman spectroscopy to analyze bacterial responses to UV irradiation and subsequent photoreactivation. Our experimental results reveal limitations in fluorescence spectroscopy for detecting photoreactivated bacteria, as the intense fluorescence of tryptophan and tyrosine amino acids masks the fluorescence emitted by thymine molecules. Conversely, Raman spectroscopy proves more effective, showing a detectable decrease in band intensities of E. coli bacteria at 1248 and 1665 cm-1 after exposure to UVC radiation. Subsequent UVA irradiation results in the partial restoration of these band intensities, indicating DNA repair and bacterial photoreactivation. This enhanced understanding aims to improve the accuracy and effectiveness of these spectroscopic tools in clinical and environmental settings.

When surface-enhanced Raman spectroscopy meets complex biofluids: A new representation strategy for reliable and comprehensive characterization

BACKGROUND

Surface-enhanced Raman spectroscopy (SERS) has gained increasing importance in molecular detection due to its high specificity and sensitivity. Complex biofluids (e.g., cell lysates and serums) typically contain large numbers of different bio-molecules with various concentrations, making it extremely challenging to be reliably and comprehensively characterized via conventional single SERS spectra due to uncontrollable electromagnetic hot spots and irregular molecular motions. The traditional approach of directly reading out the single SERS spectra or calculating the average of multiple spectra is less likely to take advantage of the full information of complex biofluid systems.

RESULTS

Herein, we propose to construct a spectral set with unordered multiple SERS spectra as a novel representation strategy to characterize full molecular information of complex biofluids. This new SERS representation not only contains details from each single spectra but captures the temporal/spatial distribution characteristics. To address the ordering-independent property of traditional chemometric methods (e.g., the Euclidean distance and the Pearson correlation coefficient), we introduce Wasserstein distance (WD) to quantitatively and comprehensively assess the quality of spectral sets on biofluids. WD performs its superiority for the quantitative assessment of the spectral sets. Additionally, WD benefits from its independence of the ordering of spectra in a spectral set, which is undesirable for traditional chemometric methods. With experiments on cell lysates and human serums, we successfully achieve the verification for the reproducibility between parallel samples, the uniformity at different positions in the same sample, the repeatability from multiple tests at one location of the same sample, and the cardinality effect of the spectral set. SERS spectral sets also manage to distinguish different classes of human serums and achieve higher accuracy than the traditional prostate-specific antigen in prostate cancer classification.

SIGNIFICANCE

The proposed SERS spectral set is a robust representation approach in accessing full information of biological samples compared to relying on a single or averaged spectra in terms of reproducibility, uniformity, repeatability, and cardinality effect. The application of WD further demonstrates the effectiveness and robustness of spectral sets in characterizing complex biofluid samples, which extends and consolidates the role of SERS.

Fluorescent carbon dots for discriminating cell types: a review

Carbon dots (CDs) are quasi-spherical carbon nanoparticles with excellent photoluminescence, good biocompatibility, favorable photostability, and easily modifiable surfaces. CDs, serving as fluorescent probes, have emerged as an ideal tool for cellular differentiation owing to their outstanding luminescence performance and tunable surface properties. In this review, we summarize the recent research progress with CDs in the differentiation of cancer/normal cells, Gram-positive/Gram-negative bacteria, and live/dead cells, as well as the cellular differences used for differentiation. Additionally, we summarize the preparation methods, raw materials, and properties of the CDs used for cell discrimination. The differentiation mechanisms and the advantages or limitations of the differentiation methods are also introduced. Finally, we propose several research challenges in this field and future research directions that require extensive investigation. It is hoped that this review will help researchers in the design of new CDs as ideal fluorescent probes for realizing diverse cell differentiation applications.

Unmasking Bacterial Identities: Exploiting Silver Nanoparticle 'Masks' for Enhanced Raman Scattering in the Rapid Discrimination of Diverse Bacterial Species and Antibiotic-Resistant Strains

Unraveling bacterial identity through Raman scattering techniques has been persistently challenging due to homogeneously amplified Raman signals across a wide variety of bacterial molecules, predominantly protein- or nucleic acid-mediated. In this study, we present an approach involving the use of silver nanoparticles to completely and uniformly "mask" adsorption on the surface of bacterial molecules through sodium borohydride and sodium chloride. This approach enables the acquisition of enhanced surface-enhanced Raman scattering (SERS) signals from all components on the bacterial surface, facilitating rapid, specific, and label-free bacterial identification. For the first time, we have characterized the identity of a bacterium, including its DNA, metabolites, and cell walls, enabling the accurate differentiation of various bacterial strains, even within the same species. In addition, we embarked on an exploration of the origin and variability patterns of the main characteristic peaks of Gram-positive and Gram-negative bacteria. Significantly, the SERS peak ratio was found to determine the inflection point of accelerated bacterial death upon treatment with antimicrobials. We further applied this platform to identify 15 unique clinical antibiotic-resistant bacterial strains, including five Escherichia coli strains in human urine, a first for Raman technology. This work has profound implications for prompt and accurate identification of bacteria, particularly antibiotic-resistant strains, thereby significantly enhancing clinical diagnostics and antimicrobial treatment strategies.

Progress in methods for the detection of viable Escherichia coli

Escherichia coli (E. coli) is a prevalent enteric bacterium and a necessary organism to monitor for food safety and environmental purposes. Developing efficient and specific methods is critical for detecting and monitoring viable E. coli due to its high prevalence. Conventional culture methods are often laborious and time-consuming, and they offer limited capability in detecting potentially harmful viable but non-culturable E. coli in the tested sample, which highlights the need for improved approaches. Hence, there is a growing demand for accurate and sensitive methods to determine the presence of viable E. coli. This paper scrutinizes various methods for detecting viable E. coli, including culture-based methods, molecular methods that target DNAs and RNAs, bacteriophage-based methods, biosensors, and other emerging technologies. The review serves as a guide for researchers seeking additional methodological options and aiding in the development of rapid and precise assays. Moving forward, it is anticipated that methods for detecting E. coli will become more stable and robust, ultimately contributing significantly to the improvement of food safety and public health.

Rapid detection of Pseudomonas syringae pv. actinidiae by electrochemical surface-enhanced Raman spectroscopy

Bacterial cancer caused by Pseudomonas syringae pv. actinidiae (Psa) is a major threat to kiwifruit in the world, and there is still a lack of effective control measures. The field of bacterial detection needs a fast, easy-to-use and sensitive identification platform. The current bacterial identification methods are lack of time efficiency, which brings problems to many sectors of society. Surface-enhanced Raman spectroscopy (SERS) and electrochemistry (EC) have been studied as possible candidates for bacterial detection because of their high sensitivity for the detection of biomolecules. In this work, SERS, EC and electrochemical surface-enhanced Raman spectroscopy (EC-SERS) were used for the first time to study the adsorption and EC behavior of Psa on the surface of nanostructured silver electrodes. Two different Raman spectra of a single analyte were obtained, and this dual detection was realized. Silver nanoparticles with iodide and calcium ions (Ag@ICNPs) were synthesized as SERS substrates significantly enhanced the characteristic signal peaks of Psa, and the limit of detection (LOD) is as low as 1.0 × 102 cfu/mL. Chemical imaging results show that the application of negative voltage can significantly improve the spectrum quality, showing a higher signal at -0.8 V, indicating that Psa molecules may have potential-induced reorientation on the electrode surface. Therefore, EC-SERS has the ability to greatly improve the SERS performance of bacteria in terms of peak intensity and spectral richness.

Key steps for improving bacterial SERS signals in complex samples: Separation, recognition, detection, and analysis

Rapid and reliable detection of pathogenic bacteria is absolutely essential for research in environmental science, food quality, and medical diagnostics. Surface-enhanced Raman spectroscopy (SERS), as an emerging spectroscopic technique, has the advantages of high sensitivity, good selectivity, rapid detection speed, and portable operation, which has been broadly used in the detection of pathogenic bacteria in different kinds of complex samples. However, the SERS detection method is also challenging in dealing with the detection difficulties of bacterial samples in complex matrices, such as interference from complex matrices, confusion of similar bacteria, and complexity of data processing. Therefore, researchers have developed some technologies to assist in SERS detection of bacteria, including both the front-end process of obtaining bacterial sample data and the back-end data processing process. The review summarizes the key steps for improving bacterial SERS signals in complex samples: separation, recognition, detection, and analysis, highlighting the principles of each step and the key roles for SERS pathogenic bacteria analysis, and the interconnectivity between each step. In addition, the current challenges in the practical application of SERS technology and the development trends are discussed. The purpose of this review is to deepen researchers' understanding of the various stages of using SERS technology to detect bacteria in complex sample matrices, and help them find new breakthroughs in different stages to facilitate the detection and control of bacteria in complex samples.

SERS combined with the difference in bacterial extracellular electron transfer ability to distinguish Shewanella

Shewanella plays an important role in geochemical cycle, biological corrosion, bioremediation and bioenergy. The development of methods for identifying Shewanella can provide technical support for its rapid screening, in-depth research into its extracellular respiratory mechanism and its application in ecological environment remediation. As a tool for microbial classification, identification and detection, Surface-enhanced Raman scattering (SERS) has high feasibility and application potential. In this work, bio-synthesized silver nanoparticles (AgNPs) were used as SERS substrates to effectively distinguish different types of Shewanella bacteria based on the difference in bacterial extracellular electron transfer (EET) ability. AgNPs were combined with the analyzed bacteria to prepare "Bacteria-AgNPs" SERS samples, which can strongly enhance the Raman signal of the target bacteria and reliably obtain spatial information of different molecular functional groups of each bacteria. Our developed approach can effectively distinguish between non-metal reducing and metal-reducing bacteria, and can further distinguish the three subspecies of Shewanella (Shewanella oneidensis MR-1, Shewanella decolorationis S12, and Shewanella putrefaciens SP200) at the genus and species level. The Raman signal enhancement is presumably caused by the excitation of local surface plasma (LSP) and the enhancement of surrounding electric field. Therefore, our developed method can achieve interspecific and intraspecies discrimination of bacteria. The proposed method can be extended to distinguish other metal-reducing bacteria, and the novel SERS active substrates can be developed for practical applications.

Evaluation of Raman spectroscopy combined with the gated recurrent unit serum detection method in early screening of gastrointestinal cancer

Gastric and colorectal cancers are significant causes of human mortality. Conventionally, the diagnosis of gastrointestinal tumors has been accomplished through image-based techniques, including endoscopic and biopsy procedures coupled with tissue staining. Most of these methods are invasive. In contrast, Raman spectroscopy has the advantages of being non-invasive and label-free and requiring no additional reagents, making it a potential tool for the detection of serum components. In this study, we collected Raman spectra of serum samples from patients with gastric cancer (n = 93) and colorectal cancer (n = 92) and from healthy individuals (n = 100). Analysis of Raman peak areas revealed that cancer patients had significantly higher peak areas at around 2923 cm-1 compared to normal individuals, which corresponded to the presence of lipids and proteins. We successfully achieved the early screening of gastrointestinal tumors using the improved gated recurrent unit (GRU) algorithm and traditional machine learning methods. The accuracy of identifying digestive tract tumors using different recognition models exceeds 84.72%, with support vector machine (SVM) and GRU achieving 100% accuracy. The use of GRU further demonstrated its ability to differentiate subtypes of gastric and colorectal cancers based on the degree of differentiation and stage, with a recognition accuracy exceeding 95%, which is challenging using traditional machine learning methods. Furthermore, our study revealed that principal component analysis (PCA) dimensionality reduction has a limited impact on the recognition results obtained using different recognition models.

Recent advances in surface enhanced Raman spectroscopy for bacterial pathogen identifications

BACKGROUND

The rapid and reliable detection of pathogenic bacteria at an early stage is a highly significant research field for public health. However, most traditional approaches for pathogen identification are time-consuming and labour-intensive, which may cause physicians making inappropriate treatment decisions based on an incomplete diagnosis of patients with unknown infections, leading to increased morbidity and mortality. Therefore, novel methods are constantly required to face the emerging challenges of bacterial detection and identification. In particular, Raman spectroscopy (RS) is becoming an attractive method for rapid and accurate detection of bacterial pathogens in recent years, among which the newly developed surface-enhanced Raman spectroscopy (SERS) shows the most promising potential.

AIM OF REVIEW

Recent advances in pathogen detection and diagnosis of bacterial infections were discussed with focuses on the development of the SERS approaches and its applications in complex clinical settings.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The current review describes bacterial classification using surface enhanced Raman spectroscopy (SERS) for developing a rapid and more accurate method for the identification of bacterial pathogens in clinical diagnosis. The initial part of this review gives a brief overview of the mechanism of SERS technology and development of the SERS approach to detect bacterial pathogens in complex samples. The development of the label-based and label-free SERS strategies and several novel SERS-compatible technologies in clinical applications, as well as the analytical procedures and examples of chemometric methods for SERS, are introduced. The computational challenges of pre-processing spectra and the highlights of the limitations and perspectives of the SERS technique are also discussed.Taken together, this systematic review provides an overall summary of the SERS technique and its application potential for direct bacterial diagnosis in clinical samples such as blood, urine and sputum, etc.

SERS-based rapid susceptibility testing of commonly administered antibiotics on clinically important bacteria species directly from blood culture of bacteremia patients

Bloodstream infections are a growing public health concern due to emerging pathogens and increasing antimicrobial resistance. Rapid antibiotic susceptibility testing (AST) is urgently needed for timely and optimized choice of antibiotics, but current methods require days to obtain results. Here, we present a general AST protocol based on surface-enhanced Raman scattering (SERS-AST) for bacteremia caused by eight clinically relevant Gram-positive and Gram-negative pathogens treated with seven commonly administered antibiotics. Our results show that the SERS-AST protocol achieves a high level of agreement (96% for Gram-positive and 97% for Gram-negative bacteria) with the widely deployed VITEK 2 diagnostic system. The protocol requires only five hours to complete per blood-culture sample, making it a rapid and effective alternative to conventional methods. Our findings provide a solid foundation for the SERS-AST protocol as a promising approach to optimize the choice of antibiotics for specific bacteremia patients. This novel protocol has the potential to improve patient outcomes and reduce the spread of antibiotic resistance.

Rapid separation and detection of Listeria monocytogenes with the combination of phage tail fiber protein and vancomycin-magnetic nanozyme

In this work, a lateral flow assay for Listeria monocytogenes was developed based on phage tail fiber protein (TFP) and triple-functional nanozyme probes with capture-separation-catalytic activity. Inspired by interaction between phage and bacteria, TFP of L. monocytogenes phage was immobilized on test line as capture molecule, which replaced traditional antibody and aptamer. After Gram-positive bacteria was captured and separated from samples by nanozyme probes modified with vancomycin (Van), TFP specifically recognized L. monocytogenes and overcame non-specific binding of Van. Special color reaction between Coomassie Brilliant Blue and bovine serum albumin which was an amplification carrier on probe was simply utilized as control zone to replace traditional control line. Relying on enzyme-like catalytic activity of nanozyme, this biosensor realized improved sensitivity and colorimetric quantitative detection with a detection limit of 10 CFU mL^-1. Analytic performance results suggested this TFP-based biosensor provided a portable, sensitive and specific strategy to detect pathogen.

Classification of pathogenic bacteria by Raman spectroscopy combined with variational auto-encoder and deep learning

Rapid and early identification of pathogens is critical to guide antibiotic therapy. Raman spectroscopy as a noninvasive diagnostic technique provides rapid and accurate detection of pathogens. Raman spectrum of single cells serves as the "fingerprint" of the cell, revealing its metabolic characteristics. Rapid identification of pathogens can be achieved by combining Raman spectroscopy and deep learning. Traditional classification techniques frequently require lots of data for training, which is time costing to collect Raman spectra. For trace samples and strains that are difficult to culture, it is difficult to provide an accurate classification model. In order to reduce the number of samples collected and improve the accuracy of the classification model, a new pathogen detection method integrating Raman spectroscopy, variational auto-encoder (VAE), and long short-term memory network (LSTM) is proposed in this paper. We collect the Raman signals of pathogens and input them to VAE for training. VAE will generate a large number of Raman spectral data that cannot be distinguished from the real spectrum, and the signal-to-noise ratio is higher than that of the real spectrum. These spectra are input into the LSTM together with the real spectrum for training, and a good classification model is obtained. The results of the experiments reveal that this method not only improves the average accuracy of pathogen classification to 96.9% but also reduces the number of Raman spectra collected from 1000 to 200. With this technology, the number of Raman spectra collected can be greatly reduced, so that strains that are difficult to culture or trace can be rapidly identified.

Luminescent Guests Encapsulated in Metal-Organic Frameworks for Portable Fluorescence Sensor and Visual Detection Applications: A Review

Metal-organic frameworks (MOFs) have excellent applicability in several fields and have significant structural advantages, due to their open pore structure, high porosity, large specific surface area, and easily modifiable and functionalized porous surface. In addition, a variety of luminescent guest (LG) species can be encapsulated in the pores of MOFs, giving MOFs a broader luminescent capability. The applications of a variety of LG@MOF sensors, constructed by doping MOFs with LGs such as lanthanide ions, carbon quantum dots, luminescent complexes, organic dyes, and metal nanoclusters, for fluorescence detection of various target analyses such as ions, biomarkers, pesticides, and preservatives are systematically introduced in this review. The development of these sensors for portable visual fluorescence sensing applications is then covered. Finally, the challenges that these sectors currently face, as well as the potential for future growth, are briefly discussed.

AIEgen-Based Nanomaterials for Bacterial Imaging and Antimicrobial Applications: Recent Advances and Perspectives

Microbial infections have always been a thorny problem. Multi-drug resistant (MDR) bacterial infections rendered the antibiotics commonly used in clinical treatment helpless. Nanomaterials based on aggregation-induced emission luminogens (AIEgens) recently made great progress in the fight against microbial infections. As a family of photosensitive antimicrobial materials, AIEgens enable the fluorescent tracing of microorganisms and the production of reactive oxygen (ROS) and/or heat upon light irradiation for photodynamic and photothermal treatments targeting microorganisms. The novel nanomaterials constructed by combining polymers, antibiotics, metal complexes, peptides, and other materials retain the excellent antimicrobial properties of AIEgens while giving other materials excellent properties, further enhancing the antimicrobial effect of the material. This paper reviews the research progress of AIEgen-based nanomaterials in the field of antimicrobial activity, focusing on the materials' preparation and their related antimicrobial strategies. Finally, it concludes with an outlook on some of the problems and challenges still facing the field.

Specific discrimination and efficient elimination of gram-positive bacteria by an aggregation-induced emission-active ruthenium (II) photosensitizer

The infections caused by Gram-positive bacteria (G⁺) have seriously endangered public heath due to their high morbidity and mortality. Therefore, it is urgent to develop a multifunctional system for selective recognition, imaging and efficient eradication of G⁺. Aggregation-induced emission materials have shown great promise for microbial detection and antimicrobial therapy. In this paper, a multifunctional ruthenium (II) polypyridine complex Ru2 with aggregation-induced emission (AIE) characteristic, was developed and used for selective discrimination and efficient extermination of G⁺ from other bacteria with unique selectivity. The selective G⁺ recognition benefited from the interaction between lipoteichoic acids (LTA) and Ru2. Accumulation of Ru2 on the G⁺ membrane turned on its AIE luminescence and allowed specific G⁺ staining. Meanwhile, Ru2 under light irradiation also possessed robust antibacterial activity for G⁺in vitro and in vivo antibacterial experiments. To the best of our knowledge, Ru2 is the first Ru-based AIEgen photosensitizer for simultaneous dual applications of G⁺ detection and treatment, and inspires the development of promising antibacterial agents in the future.

Use of Nanoparticle Decorated Surface-Enhanced Raman Scattering Active Sol-Gel Substrates for SALDI-MS Analysis

Spectroscopy and mass spectrometry techniques are sometimes combined into the same analytical workflow to leverage each technique's analytical benefits. This combined workflow is especially useful in forensic and medical contexts where samples are often precious in nature. Here, we adopt metal nanoparticle (NP) doped sol-gel substrates, initially developed for surface-enhanced Raman scattering (SERS) analysis, as surface-assisted laser desorption/ionization-mass spectrometry (SALDI-MS) substrates. Using dried blood and sample protocols previously developed for SERS analysis, we observe heme-related spectral features on both silver and gold NP substrates by SALDI-MS, demonstrating dual functionality for these orthogonal techniques. Modifying the dried blood extraction procedures also allows for the observation of blood triacylglycerols by SALDI-MS. This is the first demonstration of a SERS/SALDI-MS substrate based on a sol-gel scaffold and the first demonstration of a gold NP sol-gel substrate for SALDI-MS which features lower substrate-related SALDI-MS background compared to the silver substrate.

Optical Methods for Label-Free Detection of Bacteria

Pathogenic bacteria are the leading causes of food-borne and water-borne infections, and one of the most serious public threats. Traditional bacterial detection techniques, including plate culture, polymerase chain reaction, and enzyme-linked immunosorbent assay are time-consuming, while hindering precise therapy initiation. Thus, rapid detection of bacteria is of vital clinical importance in reducing the misuse of antibiotics. Among the most recently developed methods, the label-free optical approach is one of the most promising methods that is able to address this challenge due to its rapidity, simplicity, and relatively low-cost. This paper reviews optical methods such as surface-enhanced Raman scattering spectroscopy, surface plasmon resonance, and dark-field microscopic imaging techniques for the rapid detection of pathogenic bacteria in a label-free manner. The advantages and disadvantages of these label-free technologies for bacterial detection are summarized in order to promote their application for rapid bacterial detection in source-limited environments and for drug resistance assessments.

Ultrafast Determination of Antimicrobial Resistant Staphylococcus aureus Specifically Captured by Functionalized Magnetic Nanoclusters

Sepsis, the systemic response to infection, is a life-threatening situation for patients and leads to high mortality, especially when caused by antimicrobial resistant pathogens. Prompt diagnosis and identification of the pathogenic bacteria, including their antibiotic resistance, are highly desired to yield a timely decision for treatment. Here, we aim to develop a platform for rapid isolation and efficient identification of Staphylococcus aureus, the most frequently occurring pathogen in sepsis. A peptide (VPHNPGLISLQG, SA5-1), specifically binding to S. aureus, was conjugated to the PEGylated magnetic nanoclusters, successfully enabling the specific capture and enrichment of S. aureus from blood serum. Consequently, fast detection of the antimicrobial resistance of the collected S. aureus was achieved within 30 min using a novel luminescent probe. These magnetic nanoclusters manifest a promising diagnostic prospect to combat sepsis.

Ultrasensitive Fluorescence Lateral Flow Assay for Simultaneous Detection of Pseudomonas aeruginosa and Salmonella typhimurium via Wheat Germ Agglutinin-Functionalized Magnetic Quantum Dot Nanoprobe

Point-of-care testing methods for the rapid and sensitive screening of pathogenic bacteria are urgently needed because of the high number of outbreaks of microbial infections and foodborne diseases. In this study, we developed a highly sensitive and multiplex lateral flow assay (LFA) for the simultaneous detection of Pseudomonas aeruginosa and Salmonella typhimurium in complex samples by using wheat germ agglutinin (WGA)-modified magnetic quantum dots (Mag@QDs) as a universal detection nanoprobe. The Mag@QDs-WGA tag with a 200 nm Fe3O4 core and multiple QD-formed shell was introduced into the LFA biosensor for the universal capture of the two target bacteria and provided the dual amplification effect of fluorescence enhancement and magnetic enrichment for ultra-sensitivity detection. Meanwhile, two antibacterial antibodies were separately sprayed onto the two test lines of the LFA strip to ensure the specific identification of P. aeruginosa and S. typhimurium through one test. The proposed LFA exhibited excellent analytical performance, including high capture rate (>80%) to the target pathogens, low detection limit (<30 cells/mL), short testing time (<35 min), and good reproducibility (relative standard deviation < 10.4%). Given these merits, the Mag@QDs-WGA-based LFA has a great potential for the on-site and real-time diagnosis of bacterial samples.

Application of SERS in the Detection of Fungi, Bacteria and Viruses

In this review, we report the recent advances of SERS in fungi, bacteria, and viruses. Firstly, we briefly introduce the advantage of SERS over fluorescence on virus identification and detection. Secondly, we review the feasibility analysis of Raman/SERS spectrum analysis, identification, and fungal detection on SERS substrates of various nanostructures with a signal amplification mechanism. Thirdly, we focus on SERS spectra for nucleic acid, pathogens for the detection of viruses and bacteria, and furthermore introduce SERS-based microdevices, including SERS-based microfluidic devices, and three-dimensional nanostructured plasmonic substrates.

Culture-Independent Raman Spectroscopic Identification of Bacterial Pathogens from Clinical Samples Using Deep Transfer Learning

The rapid identification of bacterial pathogens in clinical samples like blood, urine, pus, and sputum is the need of the hour. Conventional bacterial identification methods like culturing and nucleic acid-based amplification have limitations like poor sensitivity, high cost, slow turnaround time, etc. Raman spectroscopy, a label-free and noninvasive technique, has overcome these drawbacks by providing rapid biochemical signatures from a single bacterium. Raman spectroscopy combined with chemometric methods has been used effectively to identify pathogens. However, a robust approach is needed to utilize Raman features for accurate classification while dealing with complex data sets such as spectra obtained from clinical isolates, showing high sample-to-sample heterogeneity. In this study, we have used Raman spectroscopy-based identification of pathogens from clinical isolates using a deep transfer learning approach at the single-cell level resolution. We have used the data-augmentation method to increase the volume of spectra needed for deep-learning analysis. Our ResNet model could specifically extract the spectral features of eight different pathogenic bacterial species with a 99.99% classification accuracy. The robustness of our model was validated on a set of blinded data sets, a mix of cultured and noncultured bacterial isolates of various origins and types. Our proposed ResNet model efficiently identified the pathogens from the blinded data set with high accuracy, providing a robust and rapid bacterial identification platform for clinical microbiology.

Highly Accurate Identification of Bacteria's Antibiotic Resistance Based on Raman Spectroscopy and U-Net Deep Learning Algorithms

Bacterial pathogens especially antibiotic-resistant ones are a public health concern worldwide. To oppose the morbidity and mortality associated with them, it is critical to select an appropriate antibiotic by performing a rapid bacterial diagnosis. Using a combination of Raman spectroscopy and deep learning algorithms to identify bacteria is a rapid and reliable method. Nevertheless, due to the loss of information during training a model, some deep learning algorithms suffer from low accuracy. Herein, we modify the U-Net architecture to fit our purpose of classifying the one-dimensional Raman spectra. The proposed U-Net model provides highly accurate identification of the 30 isolates of bacteria and yeast, empiric treatment groups, and antimicrobial resistance, thanks to its capability to concatenate and copy important features from the encoder layers to the decoder layers, thereby decreasing the data loss. The accuracies of the model for the 30-isolate level, empiric treatment level, and antimicrobial resistance level tasks are 86.3, 97.84, and 95%, respectively. The proposed deep learning model has a high potential for not only bacterial identification but also for other diagnostic purposes in the biomedical field.

Rapid Discrimination of Clinically Important Pathogens Through Machine Learning Analysis of Surface Enhanced Raman Spectra

With its low-cost, label-free and non-destructive features, Raman spectroscopy is becoming an attractive technique with high potential to discriminate the causative agent of bacterial infections and bacterial infections per se. However, it is challenging to achieve consistency and accuracy of Raman spectra from numerous bacterial species and phenotypes, which significantly hinders the practical application of the technique. In this study, we analyzed surfaced enhanced Raman spectra (SERS) through machine learning algorithms in order to discriminate bacterial pathogens quickly and accurately. Two unsupervised machine learning methods, K-means Clustering (K-Means) and Agglomerative Nesting (AGNES) were performed for clustering analysis. In addition, eight supervised machine learning methods were compared in terms of bacterial predictions via Raman spectra, which showed that convolutional neural network (CNN) achieved the best prediction accuracy (99.86%) with the highest area (0.9996) under receiver operating characteristic curve (ROC). In sum, machine learning methods can be potentially applied to classify and predict bacterial pathogens via Raman spectra at general level.

Rapid, Label-Free Prediction of Antibiotic Resistance in Salmonella typhimurium by Surface-Enhanced Raman Spectroscopy

The rapid identification of bacterial antibiotic susceptibility is pivotal to the rational administration of antibacterial drugs. In this study, cefotaxime (CTX)-derived resistance in Salmonella typhimurium (abbr. CTX^r-S. typhimurium) during 3 months of exposure was rapidly recorded using a portable Raman spectrometer. The molecular changes that occurred in the drug-resistant strains were sensitively monitored in whole cells by label-free surface-enhanced Raman scattering (SERS). Various degrees of resistant strains could be accurately discriminated by applying multivariate statistical analyses to bacterial SERS profiles. Minimum inhibitory concentration (MIC) values showed a positive linear correlation with the relative Raman intensities of I₉₉₀/I₁₃₄₈, and the R² reached 0.9962. The SERS results were consistent with the data obtained by MIC assays, mutant prevention concentration (MPC) determinations, and Kirby-Bauer antibiotic susceptibility tests (K-B tests). This preliminary proof-of-concept study indicates the high potential of the SERS method to supplement the time-consuming conventional method and help alleviate the challenges of antibiotic resistance in clinical therapy.

Machine learning analysis of SERS fingerprinting for the rapid determination of Mycobacterium tuberculosis infection and drug resistance

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Handheld Raman spectrometer is able to generate SERS spectra with sufficient quality for Mycobacterium tuberculosis detection.

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It is feasible to accurately discriminate Mtb-positive sputum from Mtb-negative sputum through SERS spectrometry.

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Pulmonary and extra-pulmonary Mtb strains were able to be accurately distinguished via SERS spectral analysis.

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Profiling of antibiotic resistance of Mtb strains was successfully achieved through machine learning analysis of SERS spectra.

Over the past decades, conventional methods and molecular assays have been developed for the detection of tuberculosis (TB). However, these techniques suffer limitations in the identification of Mycobacterium tuberculosis (Mtb), such as long turnaround time and low detection sensitivity, etc., not even mentioning the difficulty in discriminating antibiotics-resistant Mtb strains that cause great challenges in TB treatment and prevention. Thus, techniques with easy implementation for rapid diagnosis of Mtb infection are in high demand for routine TB diagnosis. Due to the label-free, low-cost and non-invasive features, surface enhanced Raman spectroscopy (SERS) has been extensively investigated for its potential in bacterial pathogen identification. However, at current stage, few studies have recruited handheld Raman spectrometer to discriminate sputum samples with or without Mtb, separate pulmonary Mtb strains from extra-pulmonary Mtb strains, or profile Mtb strains with different antibiotic resistance characteristics. In this study, we recruited a set of supervised machine learning algorithms to dissect different SERS spectra generated via a handheld Raman spectrometer with a focus on deep learning algorithms, through which sputum samples with or without Mtb strains were successfully differentiated (5-fold cross-validation accuracy = 94.32%). Meanwhile, Mtb strains isolated from pulmonary and extra-pulmonary samples were effectively separated (5-fold cross-validation accuracy = 99.86%). Moreover, Mtb strains with different drug-resistant profiles were also competently distinguished (5-fold cross-validation accuracy = 99.59%). Taken together, we concluded that, with the assistance of deep learning algorithms, handheld Raman spectrometer has a high application potential for rapid point-of-care diagnosis of Mtb infections in future.

AIEgens for Bacterial Imaging and Ablation

Accurate and sensitive diagnosis of pathogenic bacterial infection is a fundamental first step for correct bacteria management, helping to avoid the development of drug-resistant bacteria caused by the inappropriate use and overuse of antibiotics. Fluorescence probes as a promising visual tool can help identify pathogens rapidly and reliably. However, rigidly structured traditional fluorescence probes generally suffer from the drawback of aggregation-caused quenching (ACQ) effect, which greatly undermines their advantages with respect to sensitivity. Luminogens with aggregation-induced emission properties, namely AIEgens, can overcome the ACQ effect and certain AIEgen-based materials are capable of generating reactive oxygen species (ROS) in the aggregate states. Hence, they have become powerful tools for imaging and killing bacteria. This review summarizes the recent advances in AIEgens for the diagnosis and treatment of pathogen infections. Special attention has been paid to the molecular design, the application in bacterial imaging and ablation in vitro and in vivo, and the biocompatibility of AIEgens. Finally, the challenges and prospects are discussed in terms of using AIEgens to advance precision therapies for pathogen infections.

A review of surface-enhanced Raman spectroscopy in pathological processes

With the continuous growth of the human population and new challenges in the quality of life, it is more important than ever to diagnose diseases and pathologies with high accuracy, sensitivity and in different scenarios from medical implants to the operation room. Although conventional methods of diagnosis revolutionized healthcare, alternative analytical methods are making their way out of academic labs into clinics. In this regard, surface-enhanced Raman spectroscopy (SERS) developed immensely with its capability to achieve single-molecule sensitivity and high-specificity in the last two decades, and now it is well on its way to join the arsenal of physicians. This review discusses how SERS is becoming an essential tool for the clinical investigation of pathologies including inflammation, infections, necrosis/apoptosis, hypoxia, and tumors. We critically discuss the strategies reported so far in nanoparticle assembly, functionalization, non-metallic substrates, colloidal solutions and how these techniques improve SERS characteristics during pathology diagnoses like sensitivity, selectivity, and detection limit. Moreover, it is crucial to introduce the most recent developments and future perspectives of SERS as a biomedical analytical method. We finally discuss the challenges that remain as bottlenecks for a routine SERS implementation in the medical room from in vitro to in vivo applications. The review showcases the adaptability and versatility of SERS to resolve pathological processes by covering various experimental and analytical methods and the specific spectral features and analysis results achieved by these methods.

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.

Analysis of Raman Spectra by Using Deep Learning Methods in the Identification of Marine Pathogens

The need for efficient and accurate identification of pathogens in seafood and the environment has become increasingly urgent, given the current global pandemic. Traditional methods are not only time consuming but also lead to sample wastage. Here, we have proposed two new methods that involve Raman spectroscopy combined with a long short-term memory (LSTM) neural network and compared them with a method using a normal convolutional neural network (CNN). We used eight strains isolated from the marine organism Urechis unicinctus, including four kinds of pathogens. After the models were configured and trained, the LSTM methods that we proposed achieved average isolation-level accuracies exceeding 94%, not only meeting the requirement for identification but also indicating that the proposed methods were faster and more accurate than the normal CNN models. Finally, through a computational approach, we designed a loss function to explore the mechanism reflected by the Raman data, finding the Raman segments that most likely exhibited the characteristics of nucleic acids. These novel experimental results provide insights for developing additional deep learning methods to accurately analyze complex Raman data.

Applications of Raman Spectroscopy in Bacterial Infections: Principles, Advantages, and Shortcomings

Infectious diseases caused by bacterial pathogens are important public issues. In addition, due to the overuse of antibiotics, many multidrug-resistant bacterial pathogens have been widely encountered in clinical settings. Thus, the fast identification of bacteria pathogens and profiling of antibiotic resistance could greatly facilitate the precise treatment strategy of infectious diseases. So far, many conventional and molecular methods, both manual or automatized, have been developed for in vitro diagnostics, which have been proven to be accurate, reliable, and time efficient. Although Raman spectroscopy (RS) is an established technique in various fields such as geochemistry and material science, it is still considered as an emerging tool in research and diagnosis of infectious diseases. Based on current studies, it is too early to claim that RS may provide practical guidelines for microbiologists and clinicians because there is still a gap between basic research and clinical implementation. However, due to the promising prospects of label-free detection and noninvasive identification of bacterial infections and antibiotic resistance in several single steps, it is necessary to have an overview of the technique in terms of its strong points and shortcomings. Thus, in this review, we went through recent studies of RS in the field of infectious diseases, highlighting the application potentials of the technique and also current challenges that prevent its real-world applications.

Understanding the role of the gut in undernutrition: what can technology tell us?

Gut function remains largely underinvestigated in undernutrition, despite its critical role in essential nutrient digestion, absorption and assimilation. In areas of high enteropathogen burden, alterations in gut barrier function and subsequent inflammatory effects are observable but remain poorly characterised. Environmental enteropathy (EE)-a condition that affects both gut morphology and function and is characterised by blunted villi, inflammation and increased permeability-is thought to play a role in impaired linear growth (stunting) and severe acute malnutrition. However, the lack of tools to quantitatively characterise gut functional capacity has hampered both our understanding of gut pathogenesis in undernutrition and evaluation of gut-targeted therapies to accelerate nutritional recovery. Here we survey the technology landscape for potential solutions to improve assessment of gut function, focussing on devices that could be deployed at point-of-care in low-income and middle-income countries (LMICs). We assess the potential for technological innovation to assess gut morphology, function, barrier integrity and immune response in undernutrition, and highlight the approaches that are currently most suitable for deployment and development. This article focuses on EE and undernutrition in LMICs, but many of these technologies may also become useful in monitoring of other gut pathologies.

Challenges of SERS technology as a non-nucleic acid or -antigen detection method for SARS-CoV-2 virus and its variants

The COVID-19 pandemic has caused a significant burden since December 2019 that has negatively impacted the global economy owing to the fact that the SARS-CoV-2 virus is fast-transmitting and highly contagious. Efforts have been taken to minimize the impact through strict screening measures in country borders in order to isolate potential virus carriers. Effective fast-screening methods are thus needed to identify infected individuals. The standard diagnostic methods for screening SARS-CoV-2 virus have always been to perform nucleic acid-based and serological tests. However, with each having drawbacks on producing false results at very early or later stage after symptoms onset, supplementary techniques are needed to back up these tests. Surface-enhanced Raman spectroscopy (SERS) as a detection technique has continuously advanced throughout the years in terms of sensitivity and capability to detect ultralow concentration of analytes ranging from single molecule to pathogens, to present as a highly potential alternative to known sensing methods. SERS technology as a candidate for an alternative and supplementary diagnostic method for the viral envelope of SARS-CoV-2 virus is presented, comparing its pros and cons to the standard methods and what other aspects it could offer that the other methods are not capable of. Factors that contribute to the detection effectivity of SERS is also discussed to show the advantages and limitations of this technique. Despite its promising capabilities, challenges like sources of SARS-CoV-2 virus and its variations, reliable SERS spectra, mass production of SERS-active substrates, and compliance to regulations for wide-scale testing scenario are highlighted.

Challenges of SERS technology as a non-nucleic acid or -antigen detection method for SARS-CoV-2 virus and its variants

The COVID-19 pandemic has caused a significant burden since December 2019 that has negatively impacted the global economy owing to the fact that the SARS-CoV-2 virus is fast-transmitting and highly contagious. Efforts have been taken to minimize the impact through strict screening measures in country borders in order to isolate potential virus carriers. Effective fast-screening methods are thus needed to identify infected individuals. The standard diagnostic methods for screening SARS-CoV-2 virus have always been to perform nucleic acid-based and serological tests. However, with each having drawbacks on producing false results at very early or later stage after symptoms onset, supplementary techniques are needed to back up these tests. Surface-enhanced Raman spectroscopy (SERS) as a detection technique has continuously advanced throughout the years in terms of sensitivity and capability to detect ultralow concentration of analytes ranging from single molecule to pathogens, to present as a highly potential alternative to known sensing methods. SERS technology as a candidate for an alternative and supplementary diagnostic method for the viral envelope of SARS-CoV-2 virus is presented, comparing its pros and cons to the standard methods and what other aspects it could offer that the other methods are not capable of. Factors that contribute to the detection effectivity of SERS is also discussed to show the advantages and limitations of this technique. Despite its promising capabilities, challenges like sources of SARS-CoV-2 virus and its variations, reliable SERS spectra, mass production of SERS-active substrates, and compliance to regulations for wide-scale testing scenario are highlighted.

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.

Mode of Action of Disinfection Chemicals on the Bacterial Spore Structure and Their Raman Spectra

Contamination of toxic spore-forming bacteria is problematic since spores can survive a plethora of disinfection chemicals and it is hard to rapidly detect if the disinfection chemical has inactivated the spores. Thus, robust decontamination strategies and reliable detection methods to identify dead from viable spores are critical. In this work, we investigate the chemical changes of Bacillus thuringiensis spores treated with sporicidal agents such as chlorine dioxide, peracetic acid, and sodium hypochlorite using laser tweezers Raman spectroscopy. We also image treated spores using SEM and TEM to verify if we can correlate structural changes in the spores with changes to their Raman spectra. We found that over 30 min, chlorine dioxide did not change the Raman spectrum or the spore structure, peracetic acid showed a time-dependent decrease in the characteristic DNA/DPA peaks and ∼20% of the spores were degraded and collapsed, and spores treated with sodium hypochlorite showed an abrupt drop in DNA and DPA peaks within 20 min and some structural damage to the exosporium. Structural changes appeared in spores after 10 min, compared to the inactivation time of the spores, which is less than a minute. We conclude that vibrational spectroscopy provides powerful means to detect changes in spores but it might be problematic to identify if spores are live or dead after a decontamination procedure.

Recent advances in dual recognition based surface enhanced Raman scattering for pathogenic bacteria detection: A review

Rapid and reliable detection of pathogenic bacteria at the early stage represents a highly topical research area for food safety and public health. Although culture based method is the gold standard method for bacteria detection, recent techniques have promoted the development of alternative methods, such as surface enhanced Raman scattering (SERS). SERS provides additional advantages of high speed, simultaneous detection and characterization, multiplex analysis, and comparatively low cost. However, conventional SERS methods for bacteria detection are facing limitations of low sensitivity, susceptible to matrix interference, and poor accuracy. In recent years, specific detection of pathogenic bacteria with dual recognition based SERS methods has attracted increasing attentions. These methods include two steps recognition of target bacteria, and integrate the functions of target separation and detection. Considering their merits of excellent specificity, ultrahigh sensitivity, multiplex detection capability, and potential for on-site applications, these methods are promising alternatives for rapid and reliable detection of pathogenic bacteria. Herein, this review aims to summarize the recent advances in dual recognition based SERS methods for specific detection of pathogenic bacteria. Their advantages and limitations are discussed, and further perspectives are tentatively given. This review provides new insights into the application of SERS as a reliable tool for pathogenic bacteria detection.

Toward rapid infectious disease diagnosis with advances in surface-enhanced Raman spectroscopy

In a pandemic era, rapid infectious disease diagnosis is essential. Surface-enhanced Raman spectroscopy (SERS) promises sensitive and specific diagnosis including rapid point-of-care detection and drug susceptibility testing. SERS utilizes inelastic light scattering arising from the interaction of incident photons with molecular vibrations, enhanced by orders of magnitude with resonant metallic or dielectric nanostructures. While SERS provides a spectral fingerprint of the sample, clinical translation is lagged due to challenges in consistency of spectral enhancement, complexity in spectral interpretation, insufficient specificity and sensitivity, and inefficient workflow from patient sample collection to spectral acquisition. Here, we highlight the recent, complementary advances that address these shortcomings, including (1) design of label-free SERS substrates and data processing algorithms that improve spectral signal and interpretability, essential for broad pathogen screening assays; (2) development of new capture and affinity agents, such as aptamers and polymers, critical for determining the presence or absence of particular pathogens; and (3) microfluidic and bioprinting platforms for efficient clinical sample processing. We also describe the development of low-cost, point-of-care, optical SERS hardware. Our paper focuses on SERS for viral and bacterial detection, in hopes of accelerating infectious disease diagnosis, monitoring, and vaccine development. With advances in SERS substrates, machine learning, and microfluidics and bioprinting, the specificity, sensitivity, and speed of SERS can be readily translated from laboratory bench to patient bedside, accelerating point-of-care diagnosis, personalized medicine, and precision health.

Lasting Tracking and Rapid Discrimination of Live Gram-Positive Bacteria by Peptidoglycan-Targeting Carbon Quantum Dots

Selective discrimination and lasting tracking of live bacteria are primary steps for microbiology research and treatment of bacterial infection. However, conventional detection methods, such as the gold standard of Gram staining, are being challenged under actual test conditions. Herein, we provided a novel method, namely, three excitation peaks and single-color emission carbon quantum dots (T-SCQDs) for the rapid (5 min) peptidoglycan-targeting discrimination of Gram-positive bacteria and lasting tracking (24 h) through one-step staining. Bacterial viability testing indicates that T-SCQDs can achieve nondestructive identification of Gram-positive bacteria within 50-500 μg mL^-1. Interestingly, the fluorescence imaging system suggests that T-SCQDs can also selectively distinguish the type of colonies based on fluorescence intensity. Furthermore, T-SCQDs were successfully used to visually distinguish Gram-positive bacteria from the microbial environment of A549 cells by confocal fluorescence microscopy. These properties endow T-SCQDs with excellent functions for the diagnosis of infection and other biological applications.

Bacteria Detection: From Powerful SERS to Its Advanced Compatible Techniques

The rapid, highly sensitive, and accurate detection of bacteria is the focus of various fields, especially food safety and public health. Surface-enhanced Raman spectroscopy (SERS), with the advantages of being fast, sensitive, and nondestructive, can be used to directly obtain molecular fingerprint information, as well as for the on-line qualitative analysis of multicomponent samples. It has therefore become an effective technique for bacterial detection. Within this progress report, advances in the detection of bacteria using SERS and other compatible techniques are discussed in order to summarize its development in recent years. First, the enhancement principle and mechanism of SERS technology are briefly overviewed. The second part is devoted to a label-free strategy for the detection of bacterial cells and bacterial metabolites. In this section, important considerations that must be made to improve bacterial SERS signals are discussed. Then, the label-based SERS strategy involves the design strategy of SERS tags, the immunomagnetic separation of SERS tags, and the capture of bacteria from solution and dye-labeled SERS primers. In the third part, several novel SERS compatible technologies and applications in clinical and food safety are introduced. In the final part, the results achieved are summarized and future perspectives are proposed.

Label-free surface enhanced Raman scattering spectroscopy for discrimination and detection of dominant apple spoilage fungus

Fungal infection is one of the main causes of apple corruption. The main dominant spoilage fungi in causing apple spoilage are storage mainly include Penicillium Paecilomyces paecilomyces (P. paecilomyces), penicillium chrysanthemum (P. chrysogenum), expanded Penicillium expansum (P. expansum), Aspergillus niger (Asp. niger) and Alternaria. In this study, surface-enhanced Raman spectroscopy (SERS) based on gold nanorod (AuNRs) substrate method was developed to collect and examine the Raman fingerprints of dominant apple spoilage fungus spores. Standard normal variable (SNV) was used to pretreat the obtained spectra to improve signal-to-noise ratio. Principal component analysis (PCA) was applied to extract useful spectral information. Linear discriminant analysis (LDA) and non-linear pattern recognition methods including K nearest neighbor (KNN), Support vector machine (SVM) and back propagation artificial neural networks (BPANN) were used to identify fungal species. As the comparison of modeling results shown, the BPANN model established based on the characteristic spectra variables have achieved the satisfactory result with discrimination accuracy of 98.23%; while the PCA-LDA model built using principal component variables achieved the best distinguish result with discrimination accuracy of 98.31%. It was concluded that SERS has the potential to be an inexpensive, rapid and effective method to detect and identify fungal species.

Advances in Optical Detection of Human-Associated Pathogenic Bacteria

Bacterial infection is a global burden that results in numerous hospital visits and deaths annually. The rise of multi-drug resistant bacteria has dramatically increased this burden. Therefore, there is a clinical need to detect and identify bacteria rapidly and accurately in their native state or a culture-free environment. Current diagnostic techniques lack speed and effectiveness in detecting bacteria that are culture-negative, as well as options for in vivo detection. The optical detection of bacteria offers the potential to overcome these obstacles by providing various platforms that can detect bacteria rapidly, with minimum sample preparation, and, in some cases, culture-free directly from patient fluids or even in vivo. These modalities include infrared, Raman, and fluorescence spectroscopy, along with optical coherence tomography, interference, polarization, and laser speckle. However, these techniques are not without their own set of limitations. This review summarizes the strengths and weaknesses of utilizing each of these optical tools for rapid bacteria detection and identification.

Recent advances in surface-enhanced Raman scattering-based microdevices for point-of-care diagnosis of viruses and bacteria

This minireview reports the recent advances in surface-enhanced Raman scattering (SERS)-based assay devices for the diagnosis of infectious diseases. SERS-based detection methods have shown promise in overcoming the low sensitivity and multiplex detection problems inherent to fluorescence detection. Therefore, it is interesting to investigate the current status, challenges, and applications associated with SERS-based microdevices for the point-of-care (POC) diagnosis of infectious diseases. The majority of this review highlights three different types of microdevices, namely microfluidic channels, lateral flow assay strips, and three-dimensional nanostructured substrates. Furthermore, the integration of portable Raman spectrophotometry with microdevices provides an ideal platform for the diagnosis of various infectious diseases in the field. Integrated SERS-based assay systems also enable measurements in minimal sample volumes and at low analyte concentrations of viral or bacterial samples. A significant number of studies using the SERS-based assay system have been performed recently to realize POC diagnostics, especially under resource-limited conditions. This portable SERS sensor is expected to be a next-generation POC assay system that could overcome the limitations of current fluorescence-based assay systems. This minireview summarizes recent advances in the development of SERS-based microdevices for the diagnosis of infectious diseases. Lastly, challenges to overcome and future perspectives are discussed.

Plasmonic and Electrostatic Interactions Enable Uniformly Enhanced Liquid Bacterial Surface-Enhanced Raman Scattering (SERS)

Surface-enhanced Raman spectroscopy (SERS) is a promising cellular identification and drug susceptibility testing platform, provided it can be performed in a controlled liquid environment that maintains cell viability. We investigate bacterial liquid-SERS, studying plasmonic and electrostatic interactions between gold nanorods and bacteria that enable uniformly enhanced SERS. We synthesize five nanorod sizes with longitudinal plasmon resonances ranging from 670 to 860 nm and characterize SERS signatures of Gram-negative Escherichia coli and Serratia marcescens and Gram-positive Staphylococcus aureus and Staphylococcus epidermidis bacteria in water. Varying the concentration of bacteria and nanorods, we achieve large-area SERS enhancement that is independent of nanorod resonance and bacteria type; however, bacteria with higher surface charge density exhibit significantly higher SERS signal. Using cryo-electron microscopy and zeta potential measurements, we show that the higher signal results from attraction between positively charged nanorods and negatively charged bacteria. Our robust liquid-SERS measurements provide a foundation for bacterial identification and drug testing in biological fluids.

Recent evolutions of machine learning applications in clinical laboratory medicine

Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational power, and the widespread accessibility of open source tools. Nevertheless, only a handful of ML-based products are currently commercially available for routine clinical laboratory practice. In this review, we start with an introduction to ML by providing an overview of the ML landscape, its general workflow, and the most commonly used algorithms for clinical laboratory applications. Furthermore, we aim to illustrate recent evolutions (2018 to mid-2020) of the techniques used in the clinical laboratory setting and discuss the associated challenges and opportunities. In the field of clinical chemistry, the reviewed applications of ML algorithms include quality review of lab results, automated urine sediment analysis, disease or outcome prediction from routine laboratory parameters, and interpretation of complex biochemical data. In the hematology subdiscipline, we discuss the concepts of automated blood film reporting and malaria diagnosis. At last, we handle a broad range of clinical microbiology applications, such as the reduction of diagnostic workload by laboratory automation, the detection and identification of clinically relevant microorganisms, and the detection of antimicrobial resistance.

Plasmon-coupled microcavity aptasensors for visual and ultra-sensitive simultaneous detection of Staphylococcus aureus and Escherichia coli

Septicemia and bacteremia are serious infections in the bloodstream. Thus, time-saving and ultra-sensitive pathogenic bacteria detection is highly required. Herein, we constructed gold nanoparticle-modified polystyrene microspheres (Au/PS) as plasmon-coupled microcavities to realize simultaneous detection of Staphylococcus aureus and Escherichia coli based on a fluorescence and surface-enhanced Raman spectroscopy (SERS) dual-mode method. Fluorescence imaging, serving as a means for assistant validation and rapid screening, was carried out to achieve qualitative and semi-quantitative determination, which gave us visual information of the existence and distribution of the target bacteria. Meanwhile, SERS test was conducted to realize ultra-sensitive quantitative detection. The evanescent wave aroused from total internal reflection in PS microcavities coupled with the localized electromagnetic field from surface plasmons of gold nanoparticles to improve light-matter interaction synergistically, leading to an enhancement factor of 2.25 × 10¹¹ for SERS sensing. The whole measurement was carried out in a typical sandwich assay of "capture probe-target bacteria-signal probe." As a result, calibrated concentration response curves demonstrated the sensitive quantitative detection with the limit of detection (LOD) of 3 cfu/mL for S. aureus and 2 cfu/mL for E. coli. This rapid, ultra-sensitive, and visual sensing method was further developed for dual-bacteria detection in the whole blood samples.

Improvement of Surface-Enhanced Raman Scattering Method for Single Bacterial Cell Analysis

Surface-enhanced Raman scattering (SERS) is a useful tool for label-free analysis of bacteria at the single cell level. However, low reproducibility limits the use of SERS. In this study, for the sake of sensitive and reproducible Raman spectra, we optimized the methods for preparing silver nanoparticles (AgNPs) and depositing AgNPs onto a cell surface. We found that fast dropwise addition of AgNO₃ into the reductant produced smaller and more stable AgNPs, with an average diameter of 45 ± 4 nm. Compared with that observed after simply mixing the bacterial cells with AgNPs, the SERS signal was significantly improved after centrifugation. To optimize the SERS enhancement method, the centrifugal force, method for preparing AgNPs, concentration of AgNPs, ionic strength of the solution used to suspend the cells, and density of the cells were chosen as impact factors and optimized through orthogonal experiments. Finally, the improved method could generate sensitive and reproducible SERS spectra from single Escherichia coli cells, and the SERS signals primarily arose from the cell envelope. We further verified that this optimal method was feasible for the detection of low to 25% incorporation of ¹³C isotopes by the cells and the discrimination of different bacterial species. Our work provides an improved method for generating sensitive and reproducible SERS spectra.

Rapid antibiotic susceptibility testing of bacteria from patients' blood via assaying bacterial metabolic response with surface-enhanced Raman spectroscopy

Blood stream infection is one of the major public health issues characterized with high cost and high mortality. Timely effective antibiotics usage to control infection is crucial for patients’ survival. The standard microbiological diagnosis of infection however can last days. The delay in accurate antibiotic therapy would lead to not only poor clinical outcomes, but also to a rise in antibiotic resistance due to widespread use of empirical broad-spectrum antibiotics. An important measure to tackle this problem is fast determination of bacterial antibiotic susceptibility to optimize antibiotic treatment. We show that a protocol based on surface-enhanced Raman spectroscopy can obtain consistent antibiotic susceptibility test results from clinical blood-culture samples within four hours. The characteristic spectral signatures of the obtained spectra of Staphylococcus aureus and Escherichia coli—prototypic Gram-positive and Gram-negative bacteria—became prominent after an effective pretreatment procedure removed strong interferences from blood constituents. Using them as the biomarkers of bacterial metabolic responses to antibiotics, the protocol reported the susceptibility profiles of tested drugs against these two bacteria acquired from patients’ blood with high specificity, sensitivity and speed.