Laser optical sensor, a label-free on-plate Salmonella enterica colony detection tool.

UNLABELLED: 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 × 10(2) 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.

Development of an integrated optical analyzer for characterization of growth dynamics of bacterial colonies.

In order to understand the biophysics behind collective behavior of a bacterial colony, a confocal displacement meter was used to measure the profiles of the bacterial colonies, together with a custom built optical density circuits. The system delivered essential information related to the quantitative growth dynamics (height, diameter, aspect ratio, optical density) of the bacterial colony. For example, the aspect ratio of S. aureus was approximately two times higher than that of E. coli O157 : H7, while the OD of S. aureus was approximately 1/3 higher than that of E. coli O157 : H7.

Determine the quality of human embryonic stem colonies with laser light scattering patterns.

BACKGROUND: With the prompt developments of regenerative medicine, the potential clinical applications of human embryonic stem cells have attracted intense attention. However, the labor-intensive and complex manual cell selection processes required during embryonic stem cell culturing have seriously limited large-scale production and broad applications. Thus, availability of a label-free, non-invasive platform to replace the current cumbersome manual selection has become a critical need. RESULTS: A non-invasive, label-free, and time-efficient optical platform for determining the quality of human embryonic stem cell colonies was developed by analyzing the scattering signals from those stem cell colonies. Additionally, confocal microscopy revealed that the cell colony morphology and surface structures were correlated with the resulting characteristic light scattering patterns. Standard immunostaining assay (Oct-4) was also utilized to validate the quality-determination from this light scattering protocol. The platform developed here can therefore provide identification accuracy of up to 87% for colony determination. CONCLUSIONS: Our study here demonstrated that light scattering patterns can serve as a feasible alternative approach to replace conventional manual selection for human embryonic stem cell cultures.

Discrimination of real and virtual surfaces with sinusoidal and triangular gratings using the fingertip and stylus.

Two-interval two-alternative forced-choice discrimination experiments were conducted separately for sinusoidal and triangular textured surface gratings from which amplitude (i.e., height) discrimination thresholds were estimated. Participants (group sizes: n = 4 to 7) explored one of these texture types either by fingertip on real gratings (Finger real), by stylus on real gratings (Stylus real), or by stylus on virtual gratings (Stylus virtual). The real gratings were fabricated from stainless steel by an electrical discharge machining process while the virtual gratings were rendered via a programmable force-feedback device. All gratings had a 2.5-mm spatial period. On each trial, participants compared test gratings with 55, 60, 65, or 70 µm amplitudes against a 50-µm reference. The results indicate that discrimination thresholds did not differ significantly between sinusoidal and triangular gratings. With sinusoidal and triangular data combined, the average (mean + standard error) for the Stylus-real threshold (2.5 ± 0.2 µm) was significantly smaller (p <; 0.01) than that for the Stylus-virtual condition (4.9 ± 0.2 µm). Differences between the Finger-real threshold (3.8 ± 0.2 µm) and those from the other two conditions were not statistically significant. Further studies are needed to better understand the differences in perceptual cues resulting from interactions with real and virtual gratings.

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.

Development of a microbial high-throughput screening instrument based on elastic light scatter patterns.

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.

Application of sampling criterion on numerical diffraction from bacterial colonies.

Numerical diffraction from a bacterial colony was investigated from the viewpoint of applying the sampling criterion for both spatial and frequency domains. Once the morphology information of a bacterial colony was given, the maximum diffraction angle was estimated to reveal the minimum and maximum length of both the imaging and aperture domains. Scalar diffraction modeling was applied to estimate the diffraction pattern, which provided that two phase functions were contributing to the phase modulation: chirp and Gaussian phase functions. Optimal sampling intervals for both phase functions were investigated, and the effect of violating these conditions was demonstrated. Finally, the Fresnel approximation was compared to the angular spectrum method for accuracy and applicability, which then revealed that the Fresnel approximation was valid for both large imaging distances and longer wavelengths.

Label-free identification of bacterial microcolonies via elastic scattering.

Label-free microcolony identification via elastic light scattering was investigated for three different genera: Salmonella enterica serovar Montevideo, Listeria monocytogenes F4244, and Escherichia coli DH5α. Microcolonies were defined as bacterial colonies in their late-lag phase to early-exponential phase with the diameter range of 100-200 µm. To link biophysical characteristics with corresponding scattering patterns, a phase contrast microscope and a confocal displacement meter were used to measure the colony diameter and its 3D height profile. The results indicated that the growth characteristics of microcolonies were encoded in their morphologies which correlated to the characteristic diffraction patterns. Proposed methodology was able to classify three genera based on comprehensive phenotypic map which incorporated growth speed, ring count, and colony diameter. While the proposed method illustrated the possibility of discriminating microcolonies in their early growth stage, more thorough biophysical understanding is needed to expand the technology to other species.

On the sensitivity of forward scattering patterns from bacterial colonies to media composition.

Morphology of colonies is important for taxonomy and diagnostics in microbiology where the response to environmental factors is sensitive enough to support discrimination. In this research, we analyzed the forward scattering patterns of individual Escherichia coli K12 colonies when agar hardness and nutrition levels were varied from the control sample. As the agar concentration increased from 1.2% to 1.8%, the diameter of the forward scattering patterns also increased for the same experimental condition which reflects that the colony thickness at the apex is greater for increased agar concentrations. Regarding nutrition, increasing dextrose resulted in smaller mean colony diameters while the mean diameters of the colonies were proportional to the yeast extract concentration up to 0.5%. The result reveals that ±0.3% agar concentration from the control sample is sufficient to create variations in the scattering patterns. For nutrition -0.25% of yeast extract showed significant variations while +0.25% from control sample showed minimal variations.

Modeling light propagation through bacterial colonies and its correlation with forward scattering patterns.

Bacterial colonies play an important role in the isolation and identification of bacterial species, and plating on a petri dish is still regarded as the gold standard for confirming the cause of an outbreak situation. A bacterial colony consists of millions of densely packed individual bacteria along with matrices such as extracellular materials. When a laser is directed through a colony, complicated structures encode their characteristic signatures, which results in unique forward scattering patterns. We investigate the connection between the morphological parameters of a bacterial colony and corresponding forward scattering patterns to understand bacterial growth morphology. A colony elevation is modeled with a Gaussian profile, which is defined with two critical parameters: center thickness and diameter. Then, applying the scalar diffraction theory, we compute an amplitude modulation via light attenuation from multiple layers of bacteria while a phase modulation is computed from the colony profile. Computational results indicate that center thickness plays a critical role in the total number of diffraction rings while the magnitude of the slope of a colony determines the maximum diffraction angle. Experimental validation is performed by capturing the scattering patterns, monitoring colony diameters via phase contrast microscope, and acquiring the colony profiles via confocal displacement meter.

A Machine-Learning Approach to Detecting Unknown Bacterial Serovars.

Technologies for rapid detection of bacterial pathogens are crucial for securing the food supply. A light-scattering sensor recently developed for real-time identification of multiple colonies has shown great promise for distinguishing bacteria cultures. The classification approach currently used with this system relies on supervised learning. For accurate classification of bacterial pathogens, the training library should be exhaustive, i.e., should consist of samples of all possible pathogens. Yet, the sheer number of existing bacterial serovars and more importantly the effect of their high mutation rate would not allow for a practical and manageable training. In this study, we propose a Bayesian approach to learning with a nonexhaustive training dataset for automated detection of unmatched bacterial serovars, i.e., serovars for which no samples exist in the training library. The main contribution of our work is the Wishart conjugate priors defined over class distributions. This allows us to employ the prior information obtained from known classes to make inferences about unknown classes as well. By this means, we identify new classes of informational value and dynamically update the training dataset with these classes to make it increasingly more representative of the sample population. This results in a classifier with improved predictive performance for future samples. We evaluated our approach on a 28-class bacteria dataset and also on the benchmark 26-class letter recognition dataset for further validation. The proposed approach is compared against state-of-the-art involving density-based approaches and support vector domain description, as well as a recently introduced Bayesian approach based on simulated classes.

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.

Application of the discrete dipole approximation for dipoles embedded in film.

An implementation of the discrete dipole approximation for dipoles embedded in film on substrate is derived. It is capable of predicting the scattering response from various types of subsurface features such as trenches and contact-vias. An arbitrarily shaped subsurface feature is modeled with dipoles inside the film material on top of a substrate. Relative polarizability, direct interactions, and reflection interactions are derived and applied to construct a system of equations that are solved with an iterative method. The far-field scattering response is computed from the dipole moment solution of the system matrix with the help of the reciprocity theorem. The validity of the proposed method is compared with that of other existing theories, and the effect of film structure on far-field scattering is shown with high-aspect-ratio cylindrical contact-via models.

Analysis of time-resolved scattering from macroscale bacterial colonies.

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.

High speed classification of individual bacterial cells using a model-based light scatter system and multivariate statistics.

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.

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 degrees

Biophysical modeling of forward scattering from bacterial colonies using scalar diffraction theory.

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.

Optical forward-scattering for detection of Listeria monocytogenes and other Listeria species.

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.

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.