Bacterial Detection Research Articles

A self‐powered biosensor based on triboelectric nanogenerator for dual‐specificity bacterial detection

AbstractPathogenic and corrosive bacteria pose a significant risk to human health or economic well‐being. The specific, sensitive, and on‐site detection of these bacteria is thus of paramount significance but remains challenging. Taking inspiration from immunoassays with primary and secondary antibodies, we describe here a rational design of microbial sensor (MS) under a dual‐specificity recognition strategy using Pseudomonas aeruginosa (P. aeruginosa) as the detection model. In the MS, engineered aptamers are served as the primary recognition element, while polydopamine‐N‐acetyl‐D‐galactosamine (PDA‐Gal NAc) nanoparticles are employed as the secondary recognition element, which will also generate and amplify changes in the output voltage signal. To achieve self‐powering capability, the MS is constructed based on a triboelectric nanogenerator (TENG) with the specific aptamers immobilized on the TENG electrode surface. The as‐prepared MS‐TENG system exhibits good stability in output performance under external forces, and high specificity toward P. aeruginosa, with no cross‐reactivity observed. A linear relationship (R2 = 0.995) between the output voltage and P. aeruginosa concentration is established, with a limit of detection estimated at around 8.7 × 103 CFU mL−1. The utilization of PDA‐Gal NAc nanoparticles is found to play an important role in enhancing the specific and reliability of detection, and the underlying mechanisms are further clarified by computational simulations. In addition, the MS‐TENG integrates a wireless communication module, enabling real‐time monitoring of bacterial concentration on mobile devices. This work introduces a pioneering approach to designing self‐powered smart microbial sensors with high specificity, using a double recognition strategy applicable to various bacteria beyond P. aeruginosa.image

SERS immunoassay analysis of Escherichia coli and Staphylococcus aureus based on sandwich-structured complex probe and target-induced strand displacement.

Adirect and ultra-sensitive surface-enhanced Raman scattering (SERS) immunoassay method is introducedfor the detection of Escherichia coli and Staphylococcus aureus. This methodology is based on a sandwich-structured complex probe (SCP) mechanism, combined with target-induced strand displacement. Moreover, by leveraging the amplified SERS signal from gold nanoparticles (AuNPs) corresponding to an increase in bacterial count, we achieve quantitative determination. The SCP demonstrates remarkable specificity, sensitivity, and anti-interference capability in bacterial detection. The detection limits for both bacterial strains are as low as 10CFU/mL. In our selectivity tests, all peak intensities had standard deviations (n = 3) below 6%. Recoveries in normal human serum were 101-110% for E. coli and 96-101% for S. aureus. In milk, the recoveries were 102-105% for E. coli and 100-105% for S. aureus, respectively, demonstrating a high level of accuracy and resistance to interference. In addition, the SCP offers a dual-detection capability, enabling simultaneous diagnosis of multiple targets, which greatly simplifies the testing procedure. The findings underscore that this immunoassay platform fulfills the demand for rapid and precise pathogenic bacterial diagnosis, holding substantial potential for practical applications.

Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy

Periodontitis is a chronic inflammation of the periodontium caused by a persistent bacterial infection, resulting in destruction of the supporting structures of teeth. Analysis of microbial composition in saliva can inform periodontal status. Actinobacillus actinomycetemcomitans (Aa), Porphyromonas gingivalis (Pg), and Streptococcus mutans (Sm) are among reported periodontal pathogens, and were used as model systems in this study. Our atomic force microscopic (AFM) study revealed that these pathogens are biological nanorods with dimensions of 0.6–1.1 μm in length and 500–700 nm in width. Current bacterial detection methods often involve complex preparation steps and require labeled reporting motifs. Employing surface-enhanced Raman spectroscopy (SERS), we revealed cell-type specific Raman signatures of these pathogens for label-free detection. It overcame the complexity associated with spectral overlaps among different bacterial species, relying on high signal-to-noise ratio (SNR) spectra carefully collected from pure species samples. To enable simple, rapid, and multiplexed detection, we harnessed advanced machine learning techniques to establish predictive models based on a large set of raw spectra of each bacterial species and their mixtures. Using these models, given a raw spectrum collected from a bacterial suspension, simultaneous identification of all three species in the test sample was achieved at 95.6 % accuracy. This sensing modality can be applied to multiplex detection of a broader range and a larger set of periodontal pathogens, paving the way for hassle-free detection of oral bacteria in saliva with little to no sample preparation.

Highly specific and sensitive detection of bacteria by dark-field light-scattering imaging based on bacteriophage-modified magnetoplasmonic nanoparticles

Abstract Bacterial infections pose significant global health concerns, necessitating precise and sensitive detection methods. This study introduces a multifunctional probe for bacterial detection. Bifunctional magnetoplasmonic nanoparticles (NPs) serve a dual purpose as carriers for magnetic separation (MS) and enhancers for light scattering of the target bacteria. We achieved exceptional selectivity at the bacterial species level by bioconjugating them with bacteriophages (phages). Optimal phage coverage was carefully determined to enhance the capture efficiency of the target bacterial cells. Additionally, we identified the ideal mixing ratios of the phage probe to bacteria, ensuring efficient MS and high light-scattering intensity. The use of the phage probe enabled the successful separation of captured target bacteria from other bacterial strains in the sample mixture. Bacterial detection was accomplished through dark-field light-scattering imaging, eliminating the need for additional labeling with other probes. This method showed a remarkable sensitivity, achieving a detection limit of ∼102 colony-forming units ml−1. This value is 2 orders of magnitude lower than that achieved using our previously reported technique using monofunctional plasmonic NPs. These phage probes hold promise for healthcare, biotech, and environmental monitoring owing to their adaptability to diverse bacterial species.

Advanced Bacterial Detection with SERS-Active Gold- and Silver-Coated Porous Silicon Nanowires

Advanced Bacterial Detection with SERS-Active Gold- and Silver-Coated Porous Silicon Nanowires

Applications of CRISPR-based Lateral Flow Assays in Medicine

Due to the constant development of medicine, CRISPR-based lateral flow assays (LFAs) have become a promising technique for detecting various diseases and pathogens. A lot of researches concerning with CRISPR-Cas have been done. Its performance would be enhanced when it was combined with LFAs. In this way, a simple, rapid, and cost-effective diagnostic test could be achieved. This work points out the applications of CRISPR-based LFAs in medicine, specifically in virus detection and bacterial detection. In terms of virus detection, this review focuses on detecting SARS-CoV-2, HBV, and EBV. In terms of bacterial detection, this review highlights the detection of Salmonella, Leptospira, and Staphylococcus aureus. These assays are all rapid, accurate, simple, time-saving, and cost-effective. Despite the advantages of CRISPR-based LFAs, there are also limitations and challenges that need to be addressed, which are mainly centered on the sensitivity and target range of current assays. To overcome those limitations, future directions should focus on areas such as multiplexing, integration with digital technologies, point-of-care testing in situations with limited resources, and the potential for CRISPR-based therapeutics.

One-pot rapid visual detection of E. coli O157:H7 by label-free AuNP-based plasmonic-aptasensor in water sample

Access to clean water for irrigation and drinking has long been a global concern. The need for fast, precise, and cost-effective methods to detect harmful bacteria like Enterohemorrhagic Escherichia coli (EHEC) serotype O157:H7 is high due to the potential for severe infectious diseases. Fortunately, recent research has led to developing and utilizing rapid bacterial detection methods. The creation of an aptamer-based biosensor (aptasensor) for the detection of E. coli O157:H7 using label-free aptamers and gold nanoparticles (AuNPs) is described in this study. The specific aptamers that can detect target bacteria are adsorbed on the surface of unmodified AuNPs to form the aptasensor. The detection is performed by target bacterium-induced aptasensor aggregation, which is associated with a red-to-purple color change under high-salt circumstances. We devised a quick and easy method for detecting bacteria using an anti-E. coli O157:H7 aptamer without the need for specialized equipment or pretreatment processes like cell lysis. The aptasensor could identify target bacteria with only as few as 250 colony-forming units (CFU)/ml in 15 min or less, and its specificity based on our test was 100%. This method not only provides a fast direct preparation process but also exhibits remarkable proficiency in promptly identifying the intended target with a heightened level of sensitivity and specificity. Therefore, it can serve as an intelligent tool for monitoring water reservoirs and preventing the transmission of infectious diseases associated with EHEC.

Innovative Bacterial Colony Detection: Leveraging Multi-Feature Selection with the Improved Salp Swarm Algorithm.

In this paper, we introduce a new and advanced multi-feature selection method for bacterial classification that uses the salp swarm algorithm (SSA). We improve the SSA's performance by using opposition-based learning (OBL) and a local search algorithm (LSA). The proposed method has three main stages, which automate the categorization of bacteria based on their unique characteristics. The method uses a multi-feature selection approach augmented by an enhanced version of the SSA. The enhancements include using OBL to increase population diversity during the search process and LSA to address local optimization problems. The improved salp swarm algorithm (ISSA) is designed to optimize multi-feature selection by increasing the number of selected features and improving classification accuracy. We compare the ISSA's performance to that of several other algorithms on ten different test datasets. The results show that the ISSA outperforms the other algorithms in terms of classification accuracy on three datasets with 19 features, achieving an accuracy of 73.75%. Additionally, the ISSA excels at determining the optimal number of features and producing a better fit value, with a classification error rate of 0.249. Therefore, the ISSA method is expected to make a significant contribution to solving feature selection problems in bacterial analysis.

KLF2 Regulates NLRP3 Inflammasome Priming and Activation in Murine Myeloid Cells

Introduction: Krüppel-like factor-2 (KLF2) is a critical nodal transcription factor and a tonic repressor of myeloid cell activation. Loss of KLF2 is associated with a pro-inflammatory and pro-thrombotic state. Mice deficient in myeloid-KLF2 (MK2KO) experience significantly higher mortality from lipopolysaccharide (LPS), and have increased serum and tissue levels of pro-inflammatory cytokines such as IL-1β. IL-1β is a product of NLRP3 inflammasome priming and activation, which has been implicated in numerous inflammatory and immune-mediated diseases. Neutrophils are the first responders of the innate immune system and are responsible for bacterial antigen detection. We thus aimed to elucidate the role of KLF2 in the regulation of NLRP3 inflammasome activation and subsequent IL-1β release in the neutrophil compartment. Methods: 8-week old adult MK2KO ( Lyz2Cre/ Klf2fl/fl) and control ( Lyz2Cre) mice were used for obtaining neutrophils from the bone marrow. Neutrophils were plated in RPMI-1640 media and stimulated with 10 ng/mL of LPS for 1 hour (h) and 2 h, followed by nigericin for 30 minutes. LPS acts as a priming agent for NLRP3 inflammasome assembly, and nigericin is a potassium ionophore which enables the activation step and subsequent IL-1β release. Supernatant was used for IL-1β ELISA and cell lysate was used for protein extraction and Western blotting to quantify NLRP3 protein expression. GraphPad Prism was used for statistical analyses. Results: Neutrophils from both MK2KO and Cre mice have significantly increased production of IL-1β when stimulated with LPS followed by nigericin (Figure 1), and loss of KLF2 leads to significantly increased IL-1B release from neutrophils. MK2KO neutrophils also have significantly more NLRP3 protein expression as compared to Cre neutrophils at baseline, and in response to LPS and nigericin (Figure 2). This increase in NLRP3 protein expression was time dependent and dose-dependent (Figure 2). Conclusion: KLF2 negatively regulates the transcriptional expression of NLRP3 and loss of KLF2 leads to increased NLRP3 inflammasome priming, at baseline and in response to LPS. Loss of KLF2 also affects the second step of the NLRP3 inflammasome pathway - the activation step, leading to increased IL-1β release in neutrophils in adult mice. Ongoing studies are examining the mechanisms underlying KLF2 control of NLRP3 transcription and other aspects of inflammasome activation. Inflammasome activation has been implicated in sepsis-related mortality due to the pro-inflammatory cytokine storm, and thus modulating KLF2 might help in developing novel targets to reduce this burden of mortality.

Wash-free FISH of bacterial ribosomal RNAs by benzo[a]pyrene-modified oligonucleotides

Rapid and reliable bacterial detection methods are essential for appropriate antibiotic treatment of infectious diseases. Fluorescence in situ hybridization (FISH) is one of the most promising detection methods for the culture-independent detection of bacteria. However, standard FISH assays involve stringent washing procedures that result in poor reproducibility of assay results. Therefore, reliable FISH assays require fluorescent oligonucleotides that produce strong fluorescence in the presence of complementary RNAs. Here, we report the synthesis and photophysical properties of OMCbpy fluorescent oligonucleotides, which contain a 2′-O-benzo[a]pyrenylmethylcytidine (Cbpy) unit in the middle of each 2′-O-methyl RNA sequence. Fluorescence studies showed that the fluorescence emission of OMCbpy was enhanced up to 39-fold in the presence of complementary RNAs under slightly acidic conditions. Fluorescence microscopy analysis revealed that wash-free FISH with OMCbpy allows for detection of 16S ribosomal RNAs in fixed Escherichia coli (E. coli) cells with high reproducibility.

Optical Sensors for Bacterial Detection

Analytical devices for bacterial detection are an integral part of modern laboratory medicine, as they permit the early diagnosis of diseases and their timely treatment. Therefore, special attention is directed to the development of and improvements in monitoring and diagnostic methods, including biosensor-based ones. A promising direction in the development of bacterial detection methods is optical sensor systems based on colorimetric and fluorescence techniques, the surface plasmon resonance, and the measurement of orientational effects. This review shows the detecting capabilities of these systems and the promise of electro-optical analysis for bacterial detection. It also discusses the advantages and disadvantages of optical sensor systems and the prospects for their further improvement.

Evaluation of 10 Different Pipelines for Bacterial Single-Nucleotide Variant Detection

Abstract Bacterial genome sequencing is a powerful technique for studying the genetic diversity and evolution of microbial populations. However, the detection of genomic variants from sequencing data is challenging due to the presence of contamination, sequencing errors and multiple strains within the same species. Several bioinformatics tools have been developed to address these issues, but their performance and accuracy have not been systematically evaluated. In this study, we compared 10 variant detection pipelines using 18 simulated and 17 real datasets of high-throughput sequences from a bundle of representative bacteria. We assessed the sensitivity of each pipeline under different conditions of coverage, simulation and strain diversity. We also demonstrated the application of these tools to identify consistent mutations in a 30-time repeated sequencing dataset of Staphylococcus hominis. We found that HaplotypeCaller, but not Mutect2, from the GATK tool set showed the best performance in terms of accuracy and robustness. CFSAN and Snippy performed not as well in several simulated and real sequencing datasets. Our results provided a comprehensive benchmark and guidance for choosing the optimal variant detection pipeline for high-throughput bacterial genome sequencing data.

Electrocatalytic aptasensor for bacterial detection exploiting ferricyanide reduction by methylene blue on mixed PEG/aptamer monolayers

Pathogen-triggered infections are the most severe global threat to human health, and to provide their timely treatment and prevention, robust methods for rapid and reliable identification of pathogenic microorganisms are required. Here, we have developed a fast and inexpensive electrocatalytic aptamer assay enabling specific and ultrasensitive detection of E. coli. E. coli, a biomarker of environmental contamination and infections, was captured on the mixed aptamer/thiolated PEG self-assembled monolayers formed on electrochemically pre-treated gold screen-printed electrodes (SPE). Signals from aptamer - E. coli binding were amplified by electrocatalytic reduction of ferricyanide mediated by methylene blue (MB) adsorbed on bacterial and aptamer surfaces. PEG operated as an antifouling agent and inhibited direct (not MB-mediated) discharge of ferricyanide. The assay allowed from 10 to 1000 CFU mL−1E. coli detection in 30 min, with no interference from B. subtilis, in buffer and artificial urine samples. This electrocatalytic approach is fast, specific, sensitive, and can be used directly in in-field and point-of-care applications for analysis of bacteria in human environment.

Simultaneous analysis of seven 16S rRNA hypervariable gene regions increases efficiency in marine bacterial diversity detection.

Environmental DNA sequencing is the gold standard to reveal microbial community structures. In most applications, a one-fragment PCR approach is applied to amplify a taxonomic marker gene, usually a hypervariable region of the 16S rRNA gene. We used a new reverse complement (RC)-PCR-based assay that amplifies seven out of the nine hypervariable regions of the 16S rRNA gene, to interrogate bacterial communities in sediment samples collected from different coastal marine sites with an impact gradient. In parallel, we employed a traditional one-fragment analysis of the hypervariable V3-V4 region to investigate whether the RC-PCR reveals more of the 'unseen' diversity obtained by the one-fragment approach. As a benchmark for the full deck of diversity, we subjected the samples to PCR-free metagenomic sequencing. None of the two PCR-based approaches recorded the full taxonomic repertoire obtained from the metagenomics datasets. However, the RC-PCR approach detected 2.8 times more bacterial genera compared to the near-saturation sequenced V3-V4 samples. RC-PCR is an ideal compromise between the standard one-fragment approach and metagenomics sequencing and may guide future environmental sequencing studies, in which bacterial diversity is a central subject.

Pseudomonas aeruginosa imprinted polydopamine@graphene-coated pencil graphite electrode for selective bacterial detection

In this study, molecularly imprinted polydopamine films were prepared on graphene oxide-modified graphite electrodes by chemical oxidation of dopamine in the presence of template molecules of P. aeruginosa. The electrodes were electrochemically characterized by cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and differential pulse voltammetry (DPV). The electrodes were also chemically characterized by Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). According to the results, films were successfully deposited on the electrodes and displayed increased electrical conductivity in combination with graphene oxide. After template removal, selective cavities for P. aeruginosa were exposed. The analytical performances of the electrodes were tested using DPV in the concentration range of 102 – 108 CFU/mL. Meanwhile, the limit of detection (LOD) and limit of quantification (LOQ) were calculated as 1.85 CFU/mL, and 6.50 CFU/mL, respectively. The sensor was also highly selective against P. aeruginosa in comparison to Escherichia coli, Staphylococcus aureus, and Bacillus subtilis being evaluated as potential interfering competitors.

Cervical HPV Infections, Sexually Transmitted Bacterial Pathogens and Cytology Findings-A Molecular Epidemiology Study.

Prevalent cervical HPV infection and high-risk HPV persistence consequences have been extensively investigated in the literature; nevertheless, any causative interrelations of other sexually transmitted bacterial infections (STIs) with cervical HPV infection have not yet been fully elucidated. This study aimed to investigate the possible association of STIs with cervical cytology aberrations and HPV genotyping results in a representative sample of predominantly young Greek women. Liquid-based cytology and molecular detection for bacterial STIs and HPV as well as extended HPV genotyping were simultaneously assessed in cervical samples from 2256 individuals visiting several urban outpatient Gynecology Departments for well-woman visits or cervical screening throughout a 20-month period. All specimens were centrally processed with validated molecular assays. The mean age of the studied women was 37.0 ± 11.7 years; 722 women (33.30%) tested positive for STI (mean age 34.23 ± 10.87 years). A higher mean age (38.34 ± 11.83 years (p < 0.05)) was associated with negative STI testing. Chlamydia trachomatis was detected in 59 individuals (8.2%), Mycoplasma hominis in 156 (21.6%), Mycoplasma genitalium in 14 (1.9%), and Ureaplasma spp. in 555 (76.9%); infections with two bacterial pathogens were identified in 73 samples (10.1%). Cervical HPV was detected in 357 out of 1385 samples with a valid HPV typing result (25.8%). The mean age of HPV-positive women was 32.0 ± 8.4 years; individuals testing HPV-negative were slightly older (N = 1028): 34.4 ± 9.2 (p < 0.05). Among the 1371 individuals with valid results both for bacterial STIs and cervical HPV detection, women with an HPV-positive sample were more likely to harbor an STI (OR: 2.69, 95% CI 2.10-3.46, p < 0.05). Interestingly, bacterial STI positivity illustrated significant heterogeneity between NILM and LSIL cases, with 28.88% of NILM and 46.33% of LSIL cases harboring an STI, respectively (p < 0.05). In brief, in a population with a high prevalence for STIs, especially Ureaplasma spp., an association was documented between bacterial pathogen detection and cervical HPV infection, as well as abnormal cytology; these findings merit further investigation.

Isothermal microcalorimetry improves accuracy and time to bacterial detection of periprosthetic joint infection after total joint arthroplasty.

Isothermal microcalorimetry (IMC) was evaluated compared to conventional cultures to determine the clinical performance for diagnosing periprosthetic joint infection (PJI) of hip/knee replacements. We prospectively collected three to five deep tissue samples per patient from 152 patients undergoing conversion or revision hip/knee arthroplasty from July 2020 to November 2022. Cultures and IMC for each sample were compared for concordance, median time to detection (TTD), and diagnostic performance based on 2013 Musculoskeletal Infection Society criteria. Secondary analyses involved patients on antibiotics at sampling. The 152 total patients had 592 tissue samples (mean 3.9 ± 0.3) with sample concordance between cultures and IMC of 90%. IMC demonstrated a sensitivity of 83%, specificity of 100%, negative predictive value (NPV) of 89%, and positive predictive value (PPV) of 100% for PJI. Cultures resulted in 69% sensitivity, 100% specificity, 81% NPV, and 100% PPV. The accuracy of IMC was 93% compared to 87% for cultures (P < 0.001). The median TTD of PJI by cultures was 51 (21-410) hours compared to 10 (0.5-148) hours for IMC (P < 0.001). For 39 patients on chronic antibiotics, sensitivity in PJI detection was 93%, specificity 100%, NPV 85%, and PPV 100% by IMC compared to 79% sensitivity, 100% specificity, 65% NPV, and 100% PPV for cultures. The accuracy was 95% for IMC compared to 85% for cultures (P < 0.001) with median TTD of 12 (0.5-127) hours compared to 52 (21-174) hours (P < 0.001). Utilizing IMC for PJI detection improves TTD by nearly 2 days while improving diagnostic accuracy compared to cultures, particularly in patients on chronic antibiotics.

Detection and discrimination of pathogenic bacteria with nanomaterials-based optical biosensors: A review.

Pathogenic bacteria can pose a great threat to food safety and human health. It is therefore imperative to develop a rapid, portable, and sensitive determination and discrimination method for pathogenic bacteria. Over the past few years, various nanomaterials (NMs) have been employed as desirable nanoprobes because they possess extraordinary properties that can be used for optical signal enabled detection and identification of bacteria. By means of modification, NMs can, depending on different mechanisms, sense targets directly or indirectly, which then provides an essential support for the detection and differentiation of pathogenic bacteria. In this review, recent application of NMs-based optical biosensors for food safety bacterial detection and discrimination is performed, mainly in but not limited to noble metal NMs, fluorescent NMs, and point-of-care testing (POCT). This review also focuses on future trends in bacterial detection and discrimination, and machine learning in performing intelligent rapid detection and multiple accurate identification of bacteria.

Detection of respiratory pathogenic bacterial nucleic acid detection by Loop-mediated Isothermal Amplification in patients with bacterial pulmonary infections

ObjectiveNucleic acid testing can accurately and rapidly identify the presence of pathogenic bacteria. In this study, we analyzed respiratory pathogenic bacteria nucleic acids by LAMP (Loop-mediated isothermal amplification) to clarify the clinical application in patients with bacterial pulmonary infections. MethodsClinical data and specimens were collected from 99 patients with bacterial pulmonary infections from June 2021 to April 2023. We compared the differences between nucleic acid detection of LAMP and sputum culture. The correlation between inflammation manifestations of pulmonary imaging and the nucleic acid detection of LAMP was compared and analyzed. And the relationship between LAMP and blood inflammatory markers were analyzed. ResultsThe positive rate of LAMP using sputum specimens was significantly higher than that of sputum culture (P < 0.05). Pathogenic bacteria in sputum samples are more likely to be detected by LAMP in patients with inflammatory on lung imaging examination. The coincidence rate of elevated PCT and CRP expression with positive LAMP results were 83.87 % and 88.71 %, respectively. Moreover, PCT, CRP and WBC were significantly higher in LAMP positive group than those in negative group (P < 0.05). ConclusionNucleic acid testing of sputum specimens for pathogenic bacteria by LAMP on the basis of imaging examination can provide a rapid and accurate experimental basis for clinical diagnosis of bacterial pulmonary infections.

Near Infrared Spectroscopy for Bacterial Detection in the Dairy Industry

Near Infrared Spectroscopy for Bacterial Detection in the Dairy Industry