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A microbial high-throughput screening (HTS) system was developed that enabled high-speed combinatorial studies directly on bacterial colonies. The system consists of a forward scatterometer for elastic light scatter (ELS) detection, a plate transporter for sample handling, and a robotic incubator for automatic incubation. To minimize the ELS pattern-capturing time, a new calibration plate and correction algorithms were both designed, which dramatically reduced correction steps during acquisition of the circularly symmetric ELS patterns. Integration of three different control software programs was implemented, and the performance of the system was demonstrated with single-species detection for library generation and with time-resolved measurement for understanding ELS colony growth correlation, using Escherichia coli and Listeria. An in-house colony-tracking module enabled researchers to easily understand the time-dependent variation of the ELS from identical colony, which enabled further analysis in other biochemical experiments. The microbial HTS system provided an average scan time of 4.9 s per colony and the capability of automatically collecting more than 4000 ELS patterns within a 7-h time span., (© 2012 American Institute of Physics)

We describe a model-based instrument design combined with a statistical classification approach for the development and realization of high speed cell classification systems based on light scatter. In our work, angular light scatter from cells of four bacterial species of interest, Bacillus subtilis, Escherichia coli, Listeria innocua, and Enterococcus faecalis, was modeled using the discrete dipole approximation. We then optimized a scattering detector array design subject to some hardware constraints, configured the instrument, and gathered experimental data from the relevant bacterial cells. Using these models and experiments, it is shown that optimization using a nominal bacteria model (i.e., using a representative size and refractive index) is insufficient for classification of most bacteria in realistic applications. Hence the computational predictions were constituted in the form of scattering-data-vector distributions that accounted for expected variability in the physical properties between individual bacteria within the four species. After the detectors were optimized using the numerical results, they were used to measure scatter from both the known control samples and unknown bacterial cells. A multivariate statistical method based on a support vector machine (SVM) was used to classify the bacteria species based on light scatter signatures. In our final instrument, we realized correct classification of B. subtilis in the presence of E. coli,L. innocua, and E. faecalis using SVM at 99.1%, 99.6%, and 98.5%, respectively, in the optimal detector array configuration. For comparison, the corresponding values for another set of angles were only 69.9%, 71.7%, and 70.2% using SVM, and more importantly, this improved performance is consistent with classification predictions.

We investigate the relationship of incubation time and forward-scattering signature for bacterial colonies grown on solid nutrient surfaces. The aim of this research is to understand the colony growth characteristics and the corresponding evolution of the scattering patterns for a variety of pathogenic bacteria relevant to food safety. In particular, we characterized time-varying macroscopic and microscopic morphological properties of the growing colonies and modeled their optical properties in terms of two-dimensional (2-D) amplitude and phase modulation distributions. These distributions, in turn, serve as input to scalar diffraction theory, which is, in turn, used to predict forward-scattering signatures. For the present work, three different species of Listeria were considered: Listeria innocua, Listeria ivanovii, and Listeria monocytogenes. The baseline experiments involved the growth of cultures on brain heart infusion (BHI) agar and the capture of scatter images every 6 h over a total incubation period of 42 h. The micro- and macroscopic morphologies of the colonies were studied by phase contrast microscopy. Growth curves, represented by colony diameter as a function of time, were compared with the measured time-evolution of the scattering signatures.

A model for forward scattering from bacterial colonies is presented. The colonies of interest consist of approximately 10(12) - 10(13) individual bacteria densely packed in a configuration several millimeters in diameter and approximately 0.1-0.2 mm in thickness. The model is based on scalar diffraction theory and accounts for amplitude and phase modulation created by three macroscopic properties of the colonies: phase modulation due to the surface topography, phase modulation due to the radial structure observed from some strains and species, and diffraction from the outline of the colony. Phase contrast and confocal microscopy were performed to provide quantitative information on the shape and internal structure of the colonies. The computed results showed excellent agreement with the experimental scattering data for three different Listeria species: Listeria innocua, Listeria ivanovii, and Listeria monocytogenes. The results provide a physical explanation for the unique and distinctive scattering signatures produced by colonies of closely related Listeria species and support the efficacy of forward scattering for rapid detection and classification of pathogens without tagging.

We demonstrate here the development of a non-invasive optical forward-scattering system, called 'scatterometer' for rapid identification of bacterial colonies. The system is based on the concept that variations in refractive indices and size, relative to the arrangement of cells in bacterial colonies growing on a semi-solid agar surface will generate different forward-scattering patterns. A 1.2-1.5mm colony size for a 1mm laser beam and brain heart infusion agar as substrate were used as fixed variables. The current study is focused on exploring identification of Listeria monocytogenes and other Listeria species exploiting the known differences in their phenotypic characters. Using diffraction theory, we could model the scattering patterns and explain the appearance of radial spokes and the rings seen in the scattering images of L. monocytogenes. Further, we have also demonstrated development of a suitable software for the extraction of the features (scalar values) calculated from images of the scattering patterns using Zernike moment invariants and principal component analysis and were grouped using K-means clustering. We achieved 91-100% accuracy in detecting different species. It was also observed that substrate variations affect the scattering patterns of Listeria. Finally, a database was constructed based on the scattering patterns from 108 different strains belonging to six species of Listeria. The overall system proved to be simple, non-invasive and virtually reagent-less and has the potential for automated user-friendly application for detection and differentiation of L. monocytogenes and other Listeria species colonies grown on agar plates within 5-10 min analysis time.

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.

A phenotyping of bacterial colonies on agar plates using forward-scattering diffraction-pattern analysis provided promising classification of several different bacteria such as Salmonella, Vibrio, Listeria, and E. coli. Since the technique is based on forward-scattering phenomena, light transmittance of both the colony and the medium is critical to ensure quality data. However, numerous microorganisms and their growth media allow only limited light penetration and render the forward-scattering measurement a challenging task. For example, yeast, Lactobacillus, mold, and several soil bacteria form colorful and dense colonies that obstruct most of the incoming light passing through them. Moreover, blood agar, which is widely utilized in the clinical field, completely blocks the incident coherent light source used in forward scatterometry. We present a newly designed reflection scatterometer and validation of the resolving power of the instrument. The reflectance-type instrument can acquire backward elastic scatter patterns for both highly opaque media and colonies and has been tested with three different bacterial genera grown on blood agar plates. Cross-validation results show a classification rate above 90% for four genera. [ABSTRACT FROM AUTHOR]

Lymphocyte origin hybridoma Ped-2E9 cell-based cytotoxicity assay can detect virulent Listeria or Bacillus species, and its application in a cell-based biosensor for onsite use would be very attractive. However, maintaining enough viable cells on a sensor platform for a prolonged duration is a challenging task. In this study, key factors affecting the survival and growth of Ped-2E9 cells under modified conditions were investigated. When the Ped-2E9 cells were grown in media containing 5% fetal bovine serum in sealed tubes without any replenishment of nutrients or exogenous CO2 supply, a large portion of the cells remained viable for 6 to 7 days and cells entered into G0/G1 resting phase. The media pH change was negligible and no cell death was observed in the first 4 days, then cells sequentially underwent apoptotic (fourth day onward) phase until day 7 after which a majority was dead. Subsequent cytotoxicity testing of 3- to 7-day stored Ped-2E9 cells sensitively detected virulent Listeria and Bacillus species. These data strongly suggest that Ped-2E9 cells can be maintained in viable state for 6 days in a sealed tube mimicking the environment in a potential sensor device for onsite use without the need for expensive cell culture facilities. [ABSTRACT FROM AUTHOR]