Bartek Rajwa

Label-free detection of multiple bacterial pathogens using light-scattering sensor

Technologies for rapid detection and classification of bacterial pathogens are crucial for securing the food supply. This report describes a light-scattering sensor capable of real-time detection and identification of colonies of multiple pathogens without the need for a labeling reagent or biochemical processing. Bacterial colonies consisting of the progeny of a single parent cell scatter light at 635 nm to produce unique forward-scatter signatures. Zernike moment invariants and Haralick descriptors aid in feature extraction and construction of the scatter-signature image library. The method is able to distinguish bacterial cultures at the genus and species level for Listeria, Staphylococcus, Salmonella, Vibrio, and Escherichia with an accuracy of 90-99% for samples derived from food or experimentally infected animal. Varied amounts of exopolysaccharide produced by the bacteria causes changes in phase modulation distributions, resulting in strikingly different scatter signatures. With the aid of a robust database the method can potentially detect and identify any bacteria colony essentially instantaneously. Unlike other methods, it does not destroy the sample, but leaves it intact for other confirmatory testing, if needed, for forensic or outbreak investigations.

DPI induces mitochondrial superoxide-mediated apoptosis

The iodonium compounds diphenyleneiodonium (DPI) and diphenyliodonium (IDP) are well-known phagocyte NAD(P)H oxidase inhibitors. However, it has been shown that at high concentrations they can inhibit the mitochondrial respiratory chain as well. Since inhibition of the mitochondrial respiratory chain has been shown to induce superoxide production and apoptosis, we investigated the effect of iodonium compounds on mitochondria-derived superoxide and apoptosis. Mitochondrial superoxide production was measured on both cultured cells and isolated rat-heart submitochondrial particles. Mitochondria function was examined by monitoring mitochondrial membrane potential. Apoptotic pathways were studied by measuring cytochrome c release and caspase 3 activation. Apoptosis was characterized by detecting DNA fragmentation on agarose gel and measuring propidium iodide- (PI-) stained subdiploid cells using flow cytometry. Our results showed that DPI could induce mitochondrial superoxide production. The same concentration of DPI induced apoptosis by decreasing mitochondrial membrane potential and releasing cytochrome c. Addition of antioxidants or overexpression of MnSOD significantly reduced DPI-induced mitochondrial damage, cytochrome c release, caspase activation, and apoptosis. These observations suggest that DPI can induce apoptosis via induction of mitochondrial superoxide. DPI-induced mitochondrial superoxide production may prove to be a useful model to study the signaling pathways of mitochondrial superoxide.

Feature extraction from light-scatter patterns of Listeria colonies for identification and classification

Bacterial contamination by Listeria monocytogenes not only puts the public at risk, but also is costly for the food-processing industry. Traditional biochemical methods for pathogen identification require complicated sample preparation for reliable results. Optical scattering technology has been used for identification of bacterial cells in suspension, but with only limited success. Therefore, to improve the efficacy of the identification process using our novel imaging approach, we analyze bacterial colonies grown on solid surfaces. The work presented here demonstrates an application of computer-vision and pattern-recognition techniques to classify scatter patterns formed by Listeria colonies. Bacterial colonies are analyzed with a laser scatterometer. Features of circular scatter patterns formed by bacterial colonies illuminated by laser light are characterized using Zernike moment invariants. Principal component analysis and hierarchical clustering are performed on the results of feature extraction. Classification using linear discriminant analysis, partial least squares, and neural networks is capable of separating different strains of Listeria with a low error rate. The demonstrated system is also able to determine automatically the pathogenicity of bacteria on the basis of colony scatter patterns. We conclude that the obtained results are encouraging, and strongly suggest the feasibility of image-based biodetection systems.

Automated classification of bacterial particles in flow by multiangle scatter measurement and support vector machine classifier

Biological microparticles, including bacteria, scatter light in all directions when illuminated. The complex scatter pattern is dependent on particle size, shape, refraction index, density, and morphology. Commercial flow cytometers allow measurement of scattered light intensity at forward and perpendicular (side) angles (2° ≤ θ1 ≤ 20° and 70° ≤ θ2 ≤ 110°, respectively) with a speed varying from 10 to 10,000 particles per second. The choice of angle is dictated by the fact that scattered light in the forward region is primarily dependent on cell size and refractive index, whereas side‐scatter intensity is dependent on the granularity of cellular structures. However, these two‐parameter measurements cannot be used to separate populations of cells of similar shape, size, or structure. Hence, there have been several attempts in flow cytometry to measure the entire scatter patterns. The published concepts require the use of unique custom‐built flow cytometers and cannot be applied to existing instruments. It was also not clear how much information about patterns is really necessary to separate various populations of cells present in a given sample. The presented work demonstrates application of pattern‐recognition techniques to classify particles on the basis of their discrete scatter patterns collected at just five different angles, and accompanied by the measurement of axial light loss. The proposed approach can be potentially used with existing instruments because it requires only the addition of a compact enhanced scatter detector. An analytical model of scatter of laser beams by individual bacterial cells suspended in a fluid was used to determine the location of scatter sensors. Experimental results were used to train the support vector machine‐based pattern recognition system. It has been shown that information provided just by five angles of scatter and axial light loss can be sufficient to recognize various bacteria with 68–99% success rate.

A numerical recipe for accurate image reconstruction from discrete orthogonal moments

Recursive procedures used for sequential calculations of polynomial basis coefficients in discrete orthogonal moments produce unreliable results for high moment orders as a result of error accumulation. This paper demonstrates accurate reconstruction of arbitrary-size images using full-order (orders as large as the image size) Tchebichef and Krawtchouk moments by calculating polynomial coefficients directly from their definition formulas in hypergeometric functions and by creating lookup tables of these coefficients off-line. An arbitrary precision calculator is used to achieve greater numerical range and precision than is possible with software using standard 64-bit IEEE floating-point arithmetic. This reconstruction scheme is content and noise independent.

Automated quantification and reconstruction of collagen matrix from 3D confocal datasets

The geometrical structure of fibrous extracellular matrix (ECM) impacts on its biological function. In this report, we demonstrate a new algorithm designed to extract quantitative structural information about individual collagen fibres (orientation, length and diameter) from 3D backscattered‐light confocal images of collagen gels. The computed quantitative data allowed us to create surface‐rendered 3D images of the investigated sample.

Light-scattering sensor for real-time identification of Vibrio parahaemolyticus, Vibrio vulnificus and Vibrio cholerae colonies on solid agar plate.

The three most common pathogenic species of Vibrio, Vibrio cholerae, Vibrio parahaemolyticus and Vibrio vulnificus, are of major concerns due to increased incidence of water‐ and seafood‐related outbreaks and illness worldwide. Current methods are lengthy and require biochemical and molecular confirmation. A novel label‐free forward light‐scattering sensor was developed to detect and identify colonies of these three pathogens in real time in the presence of other vibrios in food or water samples. Vibrio colonies grown on agar plates were illuminated by a 635 nm laser beam and scatter‐image signatures were acquired using a CCD (charge‐coupled device) camera in an automated BARDOT (BActerial Rapid Detection using Optical light‐scattering Technology) system. Although a limited number of Vibrio species was tested, each produced a unique light‐scattering signature that is consistent from colony to colony. Subsequently a pattern recognition system analysing the collected light‐scatter information provided classification in 1−2 min with an accuracy of 99%. The light‐scattering signatures were unaffected by subjecting the bacteria to physiological stressors: osmotic imbalance, acid, heat and recovery from a viable but non‐culturable state. Furthermore, employing a standard sample enrichment in alkaline peptone water for 6 h followed by plating on selective thiosulphate citrate bile salts sucrose agar at 30°C for ∼ 12 h, the light‐scattering sensor successfully detected V. cholerae, V. parahaemolyticus and V. vulnificus present in oyster or water samples in 18 h even in the presence of other vibrios or other bacteria, indicating the suitability of the sensor as a powerful screening tool for pathogens on agar plates.

Hyperspectral cytometry at the single-cell level using a 32-channel photodetector

Despite recent progress in cell‐analysis technology, rapid classification of cells remains a very difficult task. Among the techniques available, flow cytometry (FCM) is considered especially powerful, because it is able to perform multiparametric analyses of single biological particles at a high flow rate–up to several thousand particles per second. Moreover, FCM is nondestructive, and flow cytometric analysis can be performed on live cells. The current limit for simultaneously detectable fluorescence signals in FCM is around 8–15 depending upon the instrument. Obtaining multiparametric measurements is a very complex task, and the necessity for fluorescence spectral overlap compensation creates a number of additional difficulties to solve. Further, to obtain well‐separated single spectral bands a very complex set of optical filters is required. This study describes the key components and principles involved in building a next‐generation flow cytometer based on a 32‐channel PMT array detector, a phase‐volume holographic grating, and a fast electronic board. The system is capable of full‐spectral data collection and spectral analysis at the single‐cell level. As demonstrated using fluorescent microspheres and lymphocytes labeled with a cocktail of antibodies (CD45/FITC, CD4/PE, CD8/ECD, and CD3/Cy5), the presented technology is able to simultaneously collect 32 narrow bands of fluorescence from single particles flowing across the laser beam in <5 μs. These 32 discrete values provide a proxy of the full fluorescence emission spectrum for each single particle (cell). Advanced statistical analysis has then been performed to separate the various clusters of lymphocytes. The average spectrum computed for each cluster has been used to characterize the corresponding combination of antibodies, and thus identify the various lymphocytes subsets. The powerful data‐collection capabilities of this flow cytometer open up significant opportunities for advanced analytical approaches, including spectral unmixing and unsupervised or supervised classification.

Discovering the unknown: detection of emerging pathogens using a label-free light-scattering system

A recently introduced technique for pathogen recognition called BARDOT (BActeria Rapid Detection using Optical scattering Technology) belongs to the broad class of optical sensors and relies on forward‐scatter phenotyping (FSP). The specificity of FSP derives from the morphological information that bacterial material encodes on a coherent optical wavefront passing through the colony. The system collects elastically scattered light patterns that, given a constant environment, are unique to each bacterial species and serovar. The notable similarity between FSP technology and spectroscopies is their reliance on statistical machine learning to perform recognition. Currently used methods utilize traditional supervised techniques which assume completeness of training libraries. However, this restrictive assumption is known to be false for most experimental conditions, resulting in unsatisfactory levels of accuracy, poor specificity, and consequently limited overall performance for biodetection and classification tasks. The presented work demonstrates application of the BARDOT system to classify bacteria belonging to the Salmonella class in a nonexhaustive framework, that is, without full knowledge about all the possible classes that can be encountered. Our study uses a Bayesian approach to learning with a nonexhaustive training dataset to allow for the automated detection of unknown bacterial classes.

Laser Optical Sensor, a Label-Free On-Plate Salmonella enterica Colony Detection Tool

ABSTRACT We investigated the application capabilities of a laser optical sensor, BARDOT (bacterial rapid detection using optical scatter technology) to generate differentiating scatter patterns for the 20 most frequently reported serovars of Salmonella enterica. Initially, the study tested the classification ability of BARDOT by using six Salmonella serovars grown on brain heart infusion, brilliant green, xylose lysine deoxycholate, and xylose lysine tergitol 4 (XLT4) agar plates. Highly accurate discrimination (95.9%) was obtained by using scatter signatures collected from colonies grown on XLT4. Further verification used a total of 36 serovars (the top 20 plus 16) comprising 123 strains with classification precision levels of 88 to 100%. The similarities between the optical phenotypes of strains analyzed by BARDOT were in general agreement with the genotypes analyzed by pulsed-field gel electrophoresis (PFGE). BARDOT was evaluated for the real-time detection and identification of Salmonella colonies grown from inoculated (1.2 × 102 CFU/30 g) peanut butter, chicken breast, and spinach or from naturally contaminated meat. After a sequential enrichment in buffered peptone water and modified Rappaport Vassiliadis broth for 4 h each, followed by growth on XLT4 (~16 h), BARDOT detected S. Typhimurium with 84% accuracy in 24 h, returning results comparable to those of the USDA Food Safety and Inspection Service method, which requires ~72 h. BARDOT also detected Salmonella (90 to 100% accuracy) in the presence of background microbiota from naturally contaminated meat, verified by 16S rRNA sequencing and PFGE. Prolonged residence (28 days) of Salmonella in peanut butter did not affect the bacterial ability to form colonies with consistent optical phenotypes. This study shows BARDOT’s potential for nondestructive and high-throughput detection of Salmonella in food samples. IMPORTANCE High-throughput screening of food products for pathogens would have a significant impact on the reduction of food-borne hazards. A laser optical sensor was developed to screen pathogen colonies on an agar plate instantly without damaging the colonies; this method aids in early pathogen detection by the classical microbiological culture-based method. Here we demonstrate that this sensor was able to detect the 36 Salmonella serovars tested, including the top 20 serovars, and to identify isolates of the top 8 Salmonella serovars. Furthermore, it can detect Salmonella in food samples in the presence of background microbiota in 24 h, whereas the standard USDA Food Safety and Inspection Service method requires about 72 h. High-throughput screening of food products for pathogens would have a significant impact on the reduction of food-borne hazards. A laser optical sensor was developed to screen pathogen colonies on an agar plate instantly without damaging the colonies; this method aids in early pathogen detection by the classical microbiological culture-based method. Here we demonstrate that this sensor was able to detect the 36 Salmonella serovars tested, including the top 20 serovars, and to identify isolates of the top 8 Salmonella serovars. Furthermore, it can detect Salmonella in food samples in the presence of background microbiota in 24 h, whereas the standard USDA Food Safety and Inspection Service method requires about 72 h.