E. Daniel Hirleman

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.

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.

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.

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.

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.

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.

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.

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.

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. Biotechnol. Bioeng. 2011; 108:637–644.

System automation for a bacterial colony detection and identification instrument via forward scattering

A system design and automation of a microbiological instrument that locates bacterial colonies and captures the forward-scattering signatures are presented. The proposed instrument integrates three major components: a colony locator, a forward scatterometer and a motion controller. The colony locator utilizes an off-axis light source to illuminate a Petri dish and an IEEE1394 camera to capture the diffusively scattered light to provide the number of bacterial colonies and two-dimensional coordinate information of the bacterial colonies with the help of a segmentation algorithm with region-growing. Then the Petri dish is automatically aligned with the respective centroid coordinate with a trajectory optimization method, such as the Traveling Salesman Algorithm. The forward scatterometer automatically computes the scattered laser beam from a monochromatic image sensor via quadrant intensity balancing and quantitatively determines the centeredness of the forward-scattering pattern. The final scattering signatures are stored to be analyzed to provide rapid identification and classification of the bacterial samples.