Hyperspectral microscope imaging methods for multiplex detection of Campylobacter

Campylobacter is an emerging zoonotic bacterial threat in the poultry industry. The current methods for the isolation and detection of Campylobacter are culture-based techniques with several selective agars designed to isolate Campylobacter colonies, which is time-consuming, labour intensive and has low sensitivity. Several immunological and molecular techniques such as enzyme-linked immunosorbent assay (ELISA) and Latex agglutination are commercially available for the detection and identification of Campylobacter. However, these methods demand more advanced instruments as well as specially trained experts. A hyperspectral microscope imaging (HMI) technique with the fluorescence in situ hybridisation (FISH) technique has the potential for multiplex foodborne pathogen detection. Using Alexa488 and Cy3 fluorophores, the HMI (450–800 nm) technique was able to identify Campylobacter jejuni stains with high sensitivity and specificity. In addition, HMI was able to classify six bacteria using scattering intensity from their spectra without a FISH fluorophore. Overall classification accuracy of quadratic discriminant analysis (QDA) method for six bacteria including Bifidobacter longum, Campylobacter jejuni, Clostridium perfringens, Enterobacter cloacae, Lactobacillus salivarius and Shigella flexneri using the HMI technique without fluorescent markers was approximately 88.6 % with pixel-wise classification

Nanotechnology for Foodborne Pathogenic Bacteria Detection

Bosoon Park, Lead Scientist at Richard B. Russell Research Center, USDA, Agricultural Research Service, presented a lecture at the Nano@Tech Meeting on November 11, 2008 at 12 noon in room 102 of the Microelectronics Research Center.Runtime: 42:18 minutesAmong several potentials of nanotechnology applications in food, development of nanoscale sensors for food safety and security measurement are emerging. A novel bio-functional nanosensor for foodborne pathogenic bacteria detection was developed using hetero-Au/Si nanorods. For the development of nanobiosensor, the protocol for bio-functional nanorod fabrication has been developed. The protocols include; 1) Silicon nanorod fabrication including substrate preparation and deposition control; 2) Surface oxidation including annealing and oxygen plasma process; 3) Nanoparticle coating onto the silicon using sputter coating system; 4) Biological dye immobilization including APTES treatment and incubation process; 5) Antibody conjugation with DSP pretreatment and antibody incubation followed by antigen/infected cells preparation; finally, 6) Antigen/infected cells detection by bio-functional nanorods. The Si nanorods were fabricated by glancing angle deposition (GLAD) method, and Au was sputtered onto the silicon nanorods. Alexa488-succinimide dye molecules were immobilized onto the annealed silicon nanorods thru the attachment between dye ester and primary amine supplied by 3-Aminopropyltriethoxysilane (APTES). Anti-Salmonella was conjugated to the Au via Dithiobis [succinimidylpropionate] (DSP) self-assembly monolayer (SAM). Due to the high aspect ratio nature of Si nanorods, hundreds or thousands of dye molecules attached to silicon nanorods enhanced fluorescence signals. These biologically functionalized nanorods can be used for nanobiosensor to detect foodborne pathogenic bacteria with fluorescent microscopic imaging. This new nanoscale sensing technology will be of great significance for food safety and security applications as well as biomedical diagnostics

High-Tech Tactic May Newly Expose Stealthy \u3ci\u3eSalmonella\u3c/i\u3e

At laboratories of the future, even the smallest quantity of Salmonella bacteria may be easily detected with a technology known as “SERS,” short for “surface-enhanced Raman scattering.” Agricultural engineer Bosoon Park, in the Agricultural Research Service’s Quality and Safety Assessment Research Unit in Athens, Georgia, is leading exploratory studies of this analytical technique’s potential for quick, easy, and reliable detection of Salmonella and other foodborne pathogens. According to the U.S. Centers for Disease Control and Prevention, Salmonella causes more than 1 million cases of illness in this country every year

Foodborne Pathogens and Toxin Detection from Food Matrix with Nanotechnology

2012 Fall Meeting of the NANOFANS Forum. Presented on November 20, 2012 from 11 am-2 pm in the Marcus Nanotechnology Building (Rooms 1116-1118) on the Georgia Tech campus.Bosoon Park, PhD, is a member of the Faculty of Engineering at UGA and Research Scientist in the ARS Quality and Safety Assessment Research Unit, USDA, Athens, GA. His research interests are in the area of hyperspectral and real-time multispectral imaging and nanotechnology for food safety.Runtime: 45:33 minutesThe goal for the forum is to connect the medical/life sciences/biology and nanotechnology communities. The goal is to reach out researchers in the biomedical / life sciences areas and letting them know what nanotechnology can offer them in the advancement of their research

Unsupervised classification of individual foodborne bacteria from a mixture of bacteria cultures within a hyperspectral microscope image

Salmonella is a leading cause of foodborne illness. Traditional detection methods require lengthy incubation periods or expensive reagent kits. Hyperspectral microscope images (HMIs) have been previously investigated as a method for early and rapid detection of bacteria by using a spectral signature that is unique to the organism. Previous HMI use with bacteria has consisted of supervised classification with hypercubes collected for single culture images isolated from highly selective growth media. In order to move forward with HMI as a detection tool in the food industry, unsupervised classification of bacteria cells in mixed culture HMIs was investigated. Four foodborne bacteria cultures, S. Typhimurium (ST) E. coli (Ec), S. aureus (Sa) and L. innocua (Li) were combined in seven different culture combinations with HMIs collected between 450 nm and 800 nm. A k-means divisive cluster analysis (CA) was implemented and mixed culture image sets were found to contain between two and four clusters. CA cluster accuracy was obtained by assigning a dummy variable of the proposed CA classification, then carrying out a discriminant analysis. From the mixed culture HMIs, 700 bacteria cells were classified and accuracies were between 91.92% and 100%, with six of the seven HMI sets resulting in > 97% accuracies. A distance measure between clusters was applied to identify unknown clusters based on single culture reference samples of the four bacteria used. Results showed that the CA has potential for unsupervised classification of bacteria cells, but the distance metric was not an adequate method for identifying the unknown cluster based on reference spectra, potentially due to the collinearity amongst bacteria spectra

An Unsupervised Prediction Model for <i>Salmonella</i> Detection with Hyperspectral Microscopy: A Multi-Year Validation

Hyperspectral microscope images (HMIs) have been previously explored as a tool for the early and rapid detection of common foodborne pathogenic bacteria. A robust unsupervised classification approach to differentiate bacterial species with the potential for single cell sensitivity is needed for real-world application, in order to confirm the identity of pathogenic bacteria isolated from a food product. Here, a one-class soft independent modelling of class analogy (SIMCA) was used to determine if individual cells are Salmonella positive or negative. The model was constructed and validated with a spectral library built over five years, containing 13 Salmonella serotypes and 14 non-Salmonella foodborne pathogens. An image processing method designed to take less than one minute paired with the one-class Salmonella prediction algorithm resulted in an overall classification accuracy of 95.4%, with a Salmonella sensitivity of 0.97, and specificity of 0.92. SIMCA’s prediction accuracy was only achieved after a robust model incorporating multiple serotypes was established. These results demonstrate the potential for HMI as a sensitive and unsupervised presumptive screening method, moving towards the early (Salmonella from food matrices

Label-free biosensing of Salmonella enterica serovars at single-cell level

Abstract Background The emerging nanotechnologies have greatly facilitated the development of label-free biosensors. The atomic force microscopy (AFM) has been used to study the molecular mechanism of the reactions for protein and aptamers. The surface plasmon resonance (SPR) have been used in fast detections of various pathogens such as bacteria. This study used both AFM and SPR to investigate the complex reactions between aptamers and outer membrane proteins (OMPs) on the surface of S. typhimurium. Results Two DNA aptamers were used for the label-free detections of S. typhimurium by AFM and SPR. The aptamers have specific binding affinities to the OMPs of S. typhimurium. At single-molecule level, the high resolution AFM topography and recognition images distinguished the OMPs on the bacteria surface, which is the first time the location of individual outer membrane protein have been determined on Salmonella surface. E. coli in the control experiments didn’t generate recognition signals, which proved the specificity of these two aptamers to S. typhimurium. The off-rate values for the interactions of these two aptamers to the OMPs were estimated as 5.2 × 10−3 and 7.4 × 10−3 s−1, respectively, by the AFM dynamic force microscopy (DFS). The force and extension values form DFS measurements were used to distinguish the two aptamers. The surface membrane model was proposed to explain the complex correlations among force and extension values. Next, these two aptamers were used in the bulk solution detections of S. typhimurium. The gold chips in SPR experiments were modified with carboxymethylated-dextran (CD), followed by aptamers immobilization, to reduce the non-specific binding signals. The limit of detection (LOD) was determined as 3 × 104 CFU mL−1. Conclusions The AFM single-molecule study revealed detailed information about the unbinding force and extension of the aptamer in complex biological reactions. The careful analysis of the experimental results provide better understanding of the molecular mechanism of OMPs reactions. The single-molecule measurements are helpful in evaluating the specificity of binding reagents, such as aptamers, in bulk solution detections. The protocols used in the SPR detections can be expanded into the label-free detections of other bacterial pathogens

Characterizing Hyperspectral Microscope Imagery for Classification of Blueberry Firmness with Deep Learning Methods

Firmness is an important quality indicator of blueberries. Firmness loss (or softening) of postharvest blueberries has posed a challenge in its shelf-life quality control and can be delineated with its microstructural changes. To investigate spatial and spectral characteristics of microstructures based on firmness, hyperspectral microscope imaging (HMI) was employed for this study. The mesocarp area with 20× magnification of blueberries was selectively imaged with a Fabry–Perot interferometer HMI system of 400–1000 nm wavelengths, resulting in 281 hypercubes of parenchyma cells in a resolution of 968 × 608 × 300 pixels. After properly processing each hypercube of parenchyma cells in a blueberry, the cell image with different firmness was examined based on parenchyma cell shape, cell wall segment, cell-to-cell adhesion, and size of intercellular spaces. Spectral cell characteristics of firmness were also sought based on the spectral profile of cell walls with different image preprocessing methods. The study found that softer blueberries (1.96–3.92 N) had more irregular cell shapes, lost cell-to-cell adhesion, loosened and round cell wall segments, large intercellular spaces, and cell wall colors that were more red than the firm blueberries (6.86–8.83 N). Even though berry-to-berry (or image-to-image) variations of the characteristics turned out large, the deep learning model with spatial and spectral features of blueberry cells demonstrated the potential for blueberry firmness classification with Matthew’s correlation coefficient of 73.4% and accuracy of 85% for test set

High-Resolution Single-Molecule Recognition Imaging of the Molecular Details of Ricin–Aptamer Interaction

We studied the molecular details of DNA aptamer–ricin interactions. The toxic protein ricin molecules were immobilized on a Au(111) surface using a <i>N</i>-hydroxysuccinimide (NHS) ester to specifically react with lysine residues located on the ricin B chains. A single ricin molecule was visualized in situ using the AFM tip modified with an antiricin aptamer. Computer simulation was used to illustrate the protein and aptamer structures, the single-molecule ricin images on a Au(111) surface, and the binding conformations of ricin–aptamer and ricin–antibody complexes. The various ricin conformations on a Au(111) surface were caused by the different lysine residues reacting with the NHS ester. It was also observed that most of the binding sites for aptamer and antibody on the A chains of ricin molecules were not interfered by the immobilization reaction. The different locations of the ricin binding sites to aptamer and antibody were also distinguished by AFM recognition images and interpreted by simulations