Combined negative dielectrophoresis with a flexible SERS platform as a novel strategy for rapid detection and identification of bacteria

Recent developments and applications of surface enhanced Raman scattering spectroscopy in safety detection of fruits and vegetables

This article reviewed the latest research progress of Surface-enhanced Raman Spectroscopy (SERS) in the security detection of fruits and vegetables in recent years, especially in three aspects: pesticide residues, microbial toxin contamination and harmful microorganism infection. The binding mechanism and application potential of SERS detection materials (including universal type and special type) and carrier materials (namely rigid and flexible materials) were discussed. Finally, the application prospect of SERS in fruit and vegetable safety detection was explored, and the problems to be solved and development trends were put forward. The poor stability and reproducibility of SERS substrates make it difficult for practical applications. It is necessary to continuously optimize SERS substrates and develop small and portable Raman spectroscopy analyzers. In the future, SERS technology is expected to play an important role in human health, food safety and economy.

High-Frequency Dielectrophoresis Reveals That Distinct Bio-Electric Signatures of Colorectal Cancer Cells Depend on Ploidy and Nuclear Volume

Aneuploidy, or an incorrect chromosome number, is ubiquitous among cancers. Whole-genome duplication, resulting in tetraploidy, often occurs during the evolution of aneuploid tumors. Cancers that evolve through a tetraploid intermediate tend to be highly aneuploid and are associated with poor patient prognosis. The identification and enrichment of tetraploid cells from mixed populations is necessary to understand the role these cells play in cancer progression. Dielectrophoresis (DEP), a label-free electrokinetic technique, can distinguish cells based on their intracellular properties when stimulated above 10 MHz, but DEP has not been shown to distinguish tetraploid and/or aneuploid cancer cells from mixed tumor cell populations. Here, we used high-frequency DEP to distinguish cell subpopulations that differ in ploidy and nuclear size under flow conditions. We used impedance analysis to quantify the level of voltage decay at high frequencies and its impact on the DEP force acting on the cell. High-frequency DEP distinguished diploid cells from tetraploid clones due to their size and intracellular composition at frequencies above 40 MHz. Our findings demonstrate that high-frequency DEP can be a useful tool for identifying and distinguishing subpopulations with nuclear differences to determine their roles in disease progression.

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.

How Surface-Enhanced Raman Spectroscopy Could Contribute to Medical Diagnoses

In the last decade, there has been a rapid increase in the number of surface-enhanced Raman scattering (SERS) spectroscopy applications in medical research. In this article we review some recent, and in our opinion, most interesting and promising applications of SERS spectroscopy in medical diagnostics, including those that permit multiplexing within the range important for clinical samples. We focus on the SERS-based detection of markers of various diseases (or those whose presence significantly increases the chance of developing a given disease), and on drug monitoring. We present selected examples of the SERS detection of particular fragments of DNA or RNA, or of bacteria, viruses, and disease-related proteins. We also describe a very promising and elegant ‘lab-on-chip’ approach used to carry out practical SERS measurements via a pad whose action is similar to that of a pregnancy test. The fundamental theoretical background of SERS spectroscopy, which should allow a better understanding of the operation of the sensors described, is also briefly outlined. We hope that this review article will be useful for researchers planning to enter this fascinating field.

Optically-assisted thermophoretic reversible assembly of colloidal particles and E. coli using graphene oxide microstructures

Optically-assisted large-scale assembly of nanoparticles have been of recent interest owing to their potential in applications to assemble and manipulate colloidal particles and biological entities. In the recent years, plasmonic heating has been the most popular mechanism to achieve temperature hotspots needed for extended assembly and aggregation. In this work, we present an alternative route to achieving strong thermal gradients that can lead to non-equilibrium transport and assembly of matter. We utilize the excellent photothermal properties of graphene oxide to form a large-scale assembly of silica beads. The formation of the assembly using this scheme is rapid and reversible. Our experiments show that it is possible to aggregate silica beads (average size 385 nm) by illuminating thin graphene oxide microplatelet by a 785 nm laser at low intensities of the order of 50-100 µW/µm2. We further extend the study to trapping and photoablation of E. coli bacteria using graphene oxide. We attribute this aggregation process to optically driven thermophoretic forces. This scheme of large-scale assembly is promising for the study of assembly of matter under non-equilibrium processes, rapid concentration tool for spectroscopic studies such as surface-enhanced Raman scattering and for biological applications.

SERS Based Lateral Flow Assay for Rapid and Ultrasensitive Quantification of Dual Laryngeal Squamous Cell Carcinoma-Related miRNA Biomarkers in Human Serum Using Pd-Au Core-Shell Nanorods and Catalytic Hairpin Assembly

Non-invasive early diagnosis is of great significant in disease pathologic development and subsequent medical treatments, and microRNA (miRNA) detection has attracted critical attention in early cancer screening and diagnosis. However, it was still a challenge to report an accurate and sensitive method for the detection of miRNA during cancer development, especially in the presence of its analogs that produce intense background noise. Herein, we developed a surface-enhanced Raman scattering (SERS)–based lateral flow assay (LFA) biosensor, assisted with catalytic hairpin assembly (CHA) amplification strategy, for the dynamic monitoring of miR-106b and miR-196b, associated with laryngeal squamous cell carcinoma (LSCC). In the presence of target miRNAs, two hairpin DNAs could self-assemble into double-stranded DNA, exposing the biotin molecules modified on the surface of palladium (Pd)–gold (Au) core–shell nanorods (Pd-AuNRs). Then, the biotin molecules could be captured by the streptavidin (SA), which was fixed on the test lines (T1 line and T2 line) beforehand. The core–shell spatial structures and aggregation Pd-AuNRs generated abundant active “hot spots” on the T line, significantly amplifying the SERS signals. Using this strategy, the limits of detections were low to aM level, and the selectivity, reproducibility, and uniformity of the proposed SERS-LFA biosensor were satisfactory. Finally, this rapid analysis strategy was successfully applied to quantitatively detect the target miRNAs in clinical serum obtained from healthy subjects and patients with LSCC at different stages. The results were consistent with the quantitative real-time PCR (qRT-PCR). Thus, the CHA-assisted SERS-LFA biosensor would become a promising alternative tool for miRNAs detection, which showed a tremendous clinical application prospect in diagnosing LSCC.

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.

Microscale electrokinetic-based analysis of intact cells and viruses

Miniaturized electrokinetic methods have proven to be robust platforms for the analysis and assessment of intact microorganisms, offering short response times and higher integration than their bench-scale counterparts. The present review article discusses three types of electrokinetic-based methodologies: electromigration or motion-based techniques, electrode-based electrokinetics, and insulator-based electrokinetics. The fundamentals of each type of methodology are discussed and relevant examples from recent reports are examined, to provide the reader with an overview of the state-of-the-art on the latest advancements on the analysis of intact cells and viruses with microscale electrokinetic techniques. The concluding remarks discuss the potential applications and future directions.