J.P. Robinson

Microfluidic Biochip for Impedance Spectroscopy of Biological Species

This paper describes the fabrication and characterization of a microelectronic device for the electrical interrogation and impedance spectroscopy of biological species. Key features of the device include an all top-side processing for the formation of fluidic channels, planar fluidic interface ports, integrated metal electrodes for impedance measurements, and a glass cover sealing the non-planar topography of the chip using spin-on-glass as an intermediate bonding layer. The total fluidic path volume in the device is on the order of 30 nl. Flow fields in the closed chip were mapped by particle image velocimetry. Electrical impedance measurements of suspensions of the live microorganism Listeria innocua injected into the chip demonstrate an easy method for detecting the viability of a few bacterial cells. By-products of the bacterial metabolism modify the ionic strength of a low conductivity suspension medium, significantly altering its electrical characteristics.

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.

Classification of Bacterial Contamination Using Image Processing and Distributed Computing

Disease outbreaks due to contaminated food are a major concern not only for the food-processing industry but also for the public at large. Techniques for automated detection and classification of microorganisms can be a great help in preventing outbreaks and maintaining the safety of the nations food supply. Identification and classification of foodborne pathogens using colony scatter patterns is a promising new label-free technique that utilizes image-analysis and machine-learning tools. However, the feature-extraction tools employed for this approach are computationally complex, and choosing the right combination of scatter-related features requires extensive testing with different feature combinations. In this study, we used computer clusters to speed up the feature-extraction process, which enables us to analyze the contribution of different scatter-based features to the overall classification accuracy. A set of 1000 scatter patterns representing ten different bacterial strains was used. Zernike and Chebyshev moments as well as Haralick texture features were computed from the available light-scatter patterns. The most promising features were first selected using Fishers discriminant analysis, and subsequently a support-vector-machine classifier with a linear kernel was used. With extensive testing, we were able to identify a small subset of features that produced the desired results in terms of classification accuracy and execution speed. The use of distributed computing for scatter-pattern analysis, feature extraction, and selection provides a feasible mechanism for large-scale deployment of a light scatter-based approach to bacterial classification.

State of the Art in Information Extraction and Quantitative Analysis for Multimodality Biomolecular Imaging

Rapid advances in optical instrumentation, high-speed cameras, and fluorescent probes have spurred tremendous growth in the volume of biomolecular imaging data. Various optical imaging modalities are used for probing biological systems in vivo and in vitro. These include traditional two-dimensional imaging, three-dimensional confocal imaging, time-lapse imaging, and multispectral imaging. Many applications require a combination of these imaging modalities, which gives rise to huge data sets. However, lack of powerful information extraction and quantitative analysis tools poses a major hindrance to exploiting the full potential of the information content of these data. In particular, automated extraction of semantic information from multimodality imaging data, crucial for understanding biological processes, poses unique challenges. Information extraction from large sets of biomolecular imaging data requires modeling at multiple levels of detail to allow not only quantitative analysis but also interpretation and extraction of high-level semantic information. In this paper, we survey the state of the art in the area of information extraction and automated analysis tools for in vivo and in vitro biomolecular imaging. The modeling and knowledge extraction for these data require sophisticated image processing and machine learning techniques, as well as formalisms for information extraction and knowledge management. Development of such tools has the potential to significantly improve biological discovery and drug development processes.

Using Scattering to Identify Bacterial Pathogens

New advances in elastic light scattering technology allow for faster and more accurate identification of bacteria. By using globally networked libraries of unique scattering patterns produced by bacterial colonies, researchers have developed an efficient method of identifying pathogens that has potential applications in food and water safety, health care and biodefense.

Quantitative Analysis of Inter-object Spatial Relationships in Biological Images

Study of spatial relations between biological objects is crucial for understanding biological processes. Monitoring drug or particle delivery inside cells and studying the dynamics of subcellular proteins are some of the examples. Biological applications have varying demands in terms of speed and accuracy. While accuracy may be the most important factor for small-scale biology, speed is also a concern for high-content/high-throughput screening applications. In this paper we present a variety of algorithms for inter-object spatial relations in two-and three-dimensional space. These algorithms provide trade-off between speed and accuracy, depending on the requirements of the application. Results for speed and accuracy are reported for real as well as synthetic data sets.

Knowledge Extraction for High-Throughput Biological Imaging

We present a multilayered architecture and spatiotemporal models for searching, retrieving, and analyzing high-throughput biological imaging data. The analysis is divided into low-and high-level processing. At the lower level, we address issues like segmentation, tracking, and object recognition, and at the high level, we use finite state machine-and Petri-net-based models for spatiotemporal event recognition.

Application of wavelet denoising to improve compression efficiency while preserving integrity of digital micrographs

Modern microscopy methods require efficient image compression techniques owing to collection of up to thousands of images per experiment. Current irreversible techniques such as JPEG and JPEG2000 are not optimized to preserve the integrity of the scientific data as required by 21 CFR part 11. Therefore, to construct an irreversible, yet integrity‐preserving compression mechanism, we establish a model of noise as a function of signal in our imaging system. The noise is then removed with a wavelet shrinkage algorithm whose parameters are adapted to local image structure. We ascertain the integrity of the denoised images by measuring changes in spatial and intensity distributions of registered light in the biological images and estimating changes of the effective microscope MTF. We demonstrate that the proposed denoising procedure leads to a decrease in image file size when a reversible JPEG2000 coding is used and provides better fidelity than irreversible JPEG and JPEG2000 at the same compression ratio. We also demonstrate that denoising reduces image artefacts when used as a pre‐filtering step prior to irreversible image coding.

Rapid Detection and Classification of Bacterial Contamination Using Grid Computing

Bacterial contamination of food products is a serious public health problem that creates high costs for the food-processing industry. Rapid detection of bacterial pathogens is the key to avoiding disease outbreaks and costly product recalls associated with food-borne pathogens. Automated identification of pathogens using scatter patterns of bacterial colonies is a promising technique that uses image processing and machine learning approaches to extract features from forward-scatter patterns produced by irradiating bacterial colonies with red laser light. The feature vector used for this approach can consist of hundreds of features, and a sufficiently large number of training images is required for accurate classification. As most feature extraction algorithms have high computational cost, the feature extraction step becomes the bottleneck in the whole processing pipeline. Computational grid technologies provide a promising and economical solution to this problem. In this work we report the implementation of the laser-scatter-analysis technique on a computational grid. A set of more than 2000 images was used for training of classifiers. The invariant form of Zernike moments up to order 20, radial Chebyshev moments, and Haralick features were extracted. Linear discriminant analysis and support vector machine classifiers were used for classification. We report speed-ups achieved and the scalability of this approach for large sets of images and for higher-order moments. Laser-scatter-analysis technique combined with computational grid technology offers a feasible and economic solution for rapid and accurate detection and classification of bacterial contamination.

Multispectral imaging analysis: spectral deconvolution and applications in biology

Multispectral imaging has been in use for over half a century. Owing to advances in digital photographic technology, multispectral imaging is now used in settings ranging from clinical medicine to industrial quality control. Our efforts focus on the use of multispectral imaging coupled with spectral deconvolution for measurement of endogenous tissue fluorophores and for animal tissue analysis by multispectral fluorescence, absorbance, and reflectance data. Multispectral reflectance and fluorescence images may be useful in evaluation of pathology in histological samples. For example, current hematoxylin/eosin diagnosis limits spectral analysis to shades of red and blue/grey. It is possible to extract much more information using multispectral techniques. To collect this information, a series of filters or a device such as an acousto-optical tunable filter (AOTF) or liquid-crystal filter (LCF) can be used with a CCD camera, enabling collection of images at many more wavelengths than is possible with a simple filter wheel. In multispectral data processing the “unmixing” of reflectance or fluorescence data and analysis and the classification based upon these spectra is required for any classification. In addition to multispectral techniques, extraction of topological information may be possible by reflectance deconvolution or multiple-angle imaging, which could aid in accurate diagnosis of skin lesions or isolation of specific biological components in tissue. The goal of these studies is to develop spectral signatures that will provide us with specific and verifiable tissue structure/function information. In addition, relatively complex classification techniques must be developed so that the data are of use to the end user.