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

Identifying antibiotic-resistant strains via cell sorting and elastic-light-scatter phenotyping

The proliferation and dissemination of antimicrobial-resistant bacteria is an increasingly global challenge and is attributed mainly to the excessive or improper use of antibiotics. Currently, the gold-standard phenotypic methodology for detecting resistant strains is agar plating, which is a time-consuming process that involves multiple subculturing steps. Genotypic analysis techniques are fast, but they require pure starting samples and cannot differentiate between viable and non-viable organisms. Thus, there is a need to develop a better method to identify and prevent the spread of antimicrobial resistance. This work presents a novel method for detecting and identifying antibiotic-resistant strains by combining a cell sorter for bacterial detection and an elastic-light-scattering method for bacterial classification. The cell sorter was equipped with safety mechanisms for handling pathogenic organisms and enabled precise placement of individual bacteria onto an agar plate. The patterning was performed on an antibiotic-gradient plate, where the growth of colonies in sections with high antibiotic concentrations confirmed the presence of a resistant strain. The antibiotic-gradient plate was also tested with an elastic-light-scattering device where each colony's unique colony scatter pattern was recorded and classified using machine learning for rapid identification of bacteria. Sorting and patterning bacteria on an antibiotic-gradient plate using a cell sorter reduced the number of subculturing steps and allowed direct qualitative binary detection of resistant strains. Elastic-light-scattering technology is a rapid, label-free, and non-destructive method that permits instantaneous classification of pathogenic strains based on the unique bacterial colony scatter pattern. KEY POINTS: • Individual bacteria cells are placed on gradient agar plates by a cell sorter • Laser-light scatter patterns are used to recognize antibiotic-resistant organisms • Scatter patterns formed by colonies correspond to AMR-associated phenotypes.

Spectral analysis and sorting of microbial organisms using a spectral sorter

This chapter discusses the problems related to the application of conventional flow cytometers to microbiology. To address some of those limitations, the concept of spectral flow cytometry is introduced and the advantages over conventional flow cytometry for bacterial sorting are presented. We demonstrate by using ThermoFisher's Bigfoot spectral sorter where the spectral signatures of different stains for staining bacteria are demonstrated with an example of performing unmixing on spectral datasets. In addition to the Bigfoot's spectral analysis, the special biosafety features of this instrument are discussed. Utilizing these biosafety features, the sorting and patterning at the single cell level is optimized using non-pathogenic bacteria. Finally, the chapter is concluded by presenting a novel, label free, non-destructive, and rapid phenotypic method called Elastic Light Scattering (ELS) technology for identification of the patterned bacterial cells based on their unique colony scatter patterns.

Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns

The elastic light-scatter (ELS) technique, which detects and discriminates microbial organisms based on the light-scatter pattern of their colonies, has demonstrated excellent classification accuracy in pathogen screening tasks. The implementation of the multispectral approach has brought further advantages and motivated the design and validation of a hyperspectral elastic light-scatter phenotyping instrument (HESPI). The newly developed instrument consists of a supercontinuum (SC) laser and an acousto-optic tunable filter (AOTF). The use of these two components provided a broad spectrum of excitation light and a rapid selection of the wavelength of interest, which enables the collection of multiple spectral patterns for each colony instead of relying on single band analysis. The performance was validated by classifying microflora of green-leafed vegetables using the hyperspectral ELS patterns of the bacterial colonies. The accuracy ranged from 88.7% to 93.2% when the classification was performed with the scattering pattern created at a wavelength within the 473-709 nm region. When all of the hyperspectral ELS patterns were used, owing to the vastly increased size of the data, feature reduction and selection algorithms were utilized to enhance the robustness and ultimately lessen the complexity of the data collection. A new classification model with the feature reduction process improved the overall classification rate to 95.9%.

Petri-plate, bacteria, and laser optical scattering sensor

Classical microbiology has paved the path forward for the development of modern biotechnology and microbial biosensing platforms. Microbial culturing and isolation using the Petri plate revolutionized the field of microbiology. In 1887, Julius Richard Petri invented possibly the most important tool in microbiology, the Petri plate, which continues to have a profound impact not only on reliably isolating, identifying, and studying microorganisms but also manipulating a microbe to study gene expression, virulence properties, antibiotic resistance, and production of drugs, enzymes, and foods. Before the recent advances in gene sequencing, microbial identification for diagnosis relied upon the hierarchal testing of a pure culture isolate. Direct detection and identification of isolated bacterial colonies on a Petri plate with a sensing device has the potential for revolutionizing further development in microbiology including gene sequencing, pathogenicity study, antibiotic susceptibility testing , and for characterizing industrially beneficial traits. An optical scattering sensor designated BARDOT (bacterial rapid detection using optical scattering technology) that uses a red-diode laser, developed at the beginning of the 21^st century at Purdue University, some 220 years after the Petri-plate discovery can identify and study bacteria directly on the plate as a diagnostic tool akin to Raman scattering and hyperspectral imaging systems for application in clinical and food microbiology laboratories.

Metallic and Metal Oxides Nanoparticles for Sensing Food Pathogens—An Overview of Recent Findings and Future Prospects

Nowadays, special importance is given to quality control and food safety. Food quality currently creates significant problems for the industry and implicitly for consumers and society. The effects materialize in economic losses, alterations of the quality and organoleptic properties of the commercial products, and, last but not least, they constitute risk factors for the consumer’s health. In this context, the development of analytical systems for the rapid determination of the sanitary quality of food products by detecting possible pathogenic microorganisms (such as Escherichia coli or Salmonella due to the important digestive disorders that they can cause in many consumers) is of major importance. Using efficient and environmentally friendly detection systems for identification of various pathogens that modify food matrices and turn them into food waste faster will also improve agri-food quality throughout the food chain. This paper reviews the use of metal nanoparticles used to obtain bio nanosensors for the purpose mentioned above. Metallic nanoparticles (Au, Ag, etc.) and their oxides can be synthesized by several methods, such as chemical, physical, physico-chemical, and biological, each bringing advantages and disadvantages in their use for developing nanosensors. In the “green chemistry” approach, a particular importance is given to the metal nanoparticles obtained by phytosynthesis. This method can lead to the development of good quality nanoparticles, at the same time being able to use secondary metabolites from vegetal wastes, as such providing a circular economy character. Considering these aspects, the use of phytosynthesized nanoparticles in other biosensing applications is also presented as a glimpse of their potential, which should be further explored.

On the classification of simple and complex biological images using Krawtchouk moments and Generalized pseudo-Zernike moments: a case study with fly wing images and breast cancer mammograms

In image analysis, orthogonal moments are useful mathematical transformations for creating new features from digital images. Moreover, orthogonal moment invariants produce image features that are resistant to translation, rotation, and scaling operations. Here, we show the result of a case study in biological image analysis to help researchers judge the potential efficacy of image features derived from orthogonal moments in a machine learning context. In taxonomic classification of forensically important flies from the Sarcophagidae and the Calliphoridae family (n = 74), we found the GUIDE random forests model was able to completely classify samples from 15 different species correctly based on Krawtchouk moment invariant features generated from fly wing images, with zero out-of-bag error probability. For the more challenging problem of classifying breast masses based solely on digital mammograms from the CBIS-DDSM database (n = 1,151), we found that image features generated from the Generalized pseudo-Zernike moments and the Krawtchouk moments only enabled the GUIDE kernel model to achieve modest classification performance. However, using the predicted probability of malignancy from GUIDE as a feature together with five expert features resulted in a reasonably good model that has mean sensitivity of 85%, mean specificity of 61%, and mean accuracy of 70%. We conclude that orthogonal moments have high potential as informative image features in taxonomic classification problems where the patterns of biological variations are not overly complex. For more complicated and heterogeneous patterns of biological variations such as those present in medical images, relying on orthogonal moments alone to reach strong classification performance is unrealistic, but integrating prediction result using them with carefully selected expert features may still produce reasonably good prediction models.

Combining vancomycin-modified gold nanorod arrays and colloidal nanoparticles as a sandwich model for the discrimination of Gram-positive bacteria and their detection via surface-enhanced Raman spectroscopy (SERS)

This study reports the development of a highly sensitive antibiotic-based discrimination and sensor platform for the detection of Gram-positive bacteria through surface-enhanced Raman spectroscopy (SERS).

Advances in Optical Detection of Human-Associated Pathogenic Bacteria

Bacterial infection is a global burden that results in numerous hospital visits and deaths annually. The rise of multi-drug resistant bacteria has dramatically increased this burden. Therefore, there is a clinical need to detect and identify bacteria rapidly and accurately in their native state or a culture-free environment. Current diagnostic techniques lack speed and effectiveness in detecting bacteria that are culture-negative, as well as options for in vivo detection. The optical detection of bacteria offers the potential to overcome these obstacles by providing various platforms that can detect bacteria rapidly, with minimum sample preparation, and, in some cases, culture-free directly from patient fluids or even in vivo. These modalities include infrared, Raman, and fluorescence spectroscopy, along with optical coherence tomography, interference, polarization, and laser speckle. However, these techniques are not without their own set of limitations. This review summarizes the strengths and weaknesses of utilizing each of these optical tools for rapid bacteria detection and identification.

Advances in Chemical and Biological Methods to Identify Microorganisms-From Past to Present

Fast detection and identification of microorganisms is a challenging and significant feature from industry to medicine. Standard approaches are known to be very time-consuming and labor-intensive (e.g., culture media and biochemical tests). Conversely, screening techniques demand a quick and low-cost grouping of bacterial/fungal isolates and current analysis call for broad reports of microorganisms, involving the application of molecular techniques (e.g., 16S ribosomal RNA gene sequencing based on polymerase chain reaction). The goal of this review is to present the past and the present methods of detection and identification of microorganisms, and to discuss their advantages and their limitations.

Integrated multi-channel optical system for bacteria characterization and its potential use for monitoring of environmental bacteria

The potential use of a novel multichannel optical system towards fast and non-destructive bacteria identification and its application for environmental bacteria characterisation on the strain level is presented. It is the first attempt to use the proposed optical method to study various bacteria species (Gram-negative, Gram-positive) commonly present in the environment. The novel configuration of the optical system enables multichannel examination of bacterial colonies and provides additional functionality such as registration of two-dimensional (2D) distribution of monochromatic transmission coefficient of examined colonies, what can be used as a novel optical signature for bacteria characterization. Performed statistical analysis indicates that it is possible to identify representatives of environmental soil bacteria on the species level with the 98.51% accuracy and in case of two strains of Rahnella aquatilis bacteria on the strain level with the 98.8% accuracy. The proposed method is an alternative to the currently used preliminary bacteria examination in environment safety control with the advantage of being fast, reliable, non-destructive and requiring minimal sample preparation.

Disposable all-printed electronic biosensor for instantaneous detection and classification of pathogens

A novel disposable all-printed electronic biosensor is proposed for a fast detection and classification of bacteria. This biosensor is applied to classify three types of popular pathogens: Salmonella typhimurium, and the Escherichia coli strains JM109 and DH5-α. The proposed sensor consists of inter-digital silver electrodes fabricated through an inkjet material printer and silver nanowires uniformly decorated on the electrodes through the electrohydrodynamic technique on a polyamide based polyethylene terephthalate substrate. The best sensitivity of the proposed sensor is achieved at 200 µm teeth spaces of the inter-digital electrodes along the density of the silver nanowires at 30 × 10³/mm². The biosensor operates on ±2.5 V and gives the impedance value against each bacteria type in 8 min after sample injection. The sample data are measured through an impedance analyzer and analyzed through pattern recognition methods such as linear discriminate analysis, maximum likelihood, and back propagation artificial neural network to classify each type of bacteria. A perfect classification and cross-validation is achieved by using the unique fingerprints extracted from the proposed biosensor through all the applied classifiers. The overall experimental results demonstrate that the proposed disposable all-printed biosensor is applicable for the rapid detection and classification of pathogens.

Reflected scatterometry for noninvasive interrogation of bacterial colonies

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.

Current status and future prospects of using advanced computer-based methods to study bacterial colonial morphology

Despite the advancement of recent molecular technologies, culturing is still considered the gold standard for microbial sample analysis. Here we review three different bacterial colony-based screening modalities that provide significant information beyond the simple shape and color of the colony. The plate imaging technique provides numeration and quantitative spectral reflectance information for each colony, while Raman spectroscopic analysis of bacteria colonies relates the Raman-shifted peaks to specific chemical bonding. Finally, the elastic-light-scatter technique provides a volumetric interaction of the whole colony through laser-bacteria interactions, instantly capturing the morphological traits of the colony and allowing quantitative classifications.

Label-free identification of individual bacteria using Fourier transform light scattering

Rapid identification of bacterial species is crucial in medicine and food hygiene. In order to achieve rapid and label-free identification of bacterial species at the single bacterium level, we propose and experimentally demonstrate an optical method based on Fourier transform light scattering (FTLS) measurements and statistical classification. For individual rod-shaped bacteria belonging to four bacterial species (Listeria monocytogenes, Escherichia coli, Lactobacillus casei, and Bacillus subtilis), two-dimensional angle-resolved light scattering maps are precisely measured using FTLS technique. The scattering maps are then systematically analyzed, employing statistical classification in order to extract the unique fingerprint patterns for each species, so that a new unidentified bacterium can be identified by a single light scattering measurement. The single-bacterial and label-free nature of our method suggests wide applicability for rapid point-of-care bacterial diagnosis.

Classification of foodborne pathogens using near infrared (NIR) laser scatter imaging system with multivariate calibration

An optical sensor system, namely NIR laser scatter imaging system, was developed for rapid and noninvasive classification of foodborne pathogens. This developed system was used for images acquisition. The current study is focused on exploring the potential of this system combined with multivariate calibrations in classifying three categories of popular bacteria. Initially, normalization and Zernike moments extraction were performed, and the resultant translation, scale and rotation invariances were applied as the characteristic variables for subsequent discriminant analysis. Both linear (LDA, KNN and PLSDA) and nonlinear (BPANN, SVM and OSELM) pattern recognition methods were employed comparatively for modeling, and optimized by cross validation. Experimental results showed that the performances of nonlinear tools were superior to those of linear tools, especially for OSELM model with 95% discrimination rate in the prediction set. The overall results showed that it is extremely feasible for rapid and noninvasive classifying foodborne pathogens using this developed system combined with appropriate multivariate calibration.

Light scattering sensor for direct identification of colonies of Escherichia coli serogroups O26, O45, O103, O111, O121, O145 and O157

Background

Shiga-toxin producing Escherichia coli (STEC) have emerged as important foodborne pathogens, among which seven serogroups (O26, O45, O103, O111, O121, O145, O157) are most frequently implicated in human infection. The aim was to determine if a light scattering sensor can be used to rapidly identify the colonies of STEC serogroups on selective agar plates.

Methodology/Principal Findings

Initially, a total of 37 STEC strains representing seven serovars were grown on four different selective agar media, including sorbitol MacConkey (SMAC), Rainbow Agar O157, BBL CHROMagarO157, and R&F E. coli O157:H7, as well as nonselective Brain Heart Infusion agar. The colonies were scanned by an automated light scattering sensor, known as BARDOT (BActerial Rapid Detection using Optical scattering Technology), to acquire scatter patterns of STEC serogroups, and the scatter patterns were analyzed using an image classifier. Among all of the selective media tested, both SMAC and Rainbow provided the best differentiation results allowing multi-class classification of all serovars with an average accuracy of more than 90% after 10–12 h of growth, even though the colony appearance was indistinguishable at that early stage of growth. SMAC was chosen for exhaustive scatter image library development, and 36 additional strains of O157:H7 and 11 non-O157 serovars were examined, with each serogroup producing unique differential scatter patterns. Colony scatter images were also tested with samples derived from pure and mixed cultures, as well as experimentally inoculated food samples. BARDOT accurately detected O157 and O26 serovars from a mixed culture and also from inoculated lettuce and ground beef (10-h broth enrichment +12-h on-plate incubation) in the presence of natural background microbiota in less than 24 h.

Conclusions

BARDOT could potentially be used as a screening tool during isolation of the most important STEC serovars on selective agar plates from food samples in less than 24 h.

Nano/micro and spectroscopic approaches to food pathogen detection

Despite continuing research efforts, timely and simple pathogen detection with a high degree of sensitivity and specificity remains an elusive goal. Given the recent explosion of sensor technologies, significant strides have been made in addressing the various nuances of this important global challenge that affects not only the food industry but also human health. In this review, we provide a summary of the various ongoing efforts in pathogen detection and sample preparation in areas related to Fourier transform infrared and Raman spectroscopy, light scattering, phage display, micro/nanodevices, and nanoparticle biosensors. We also discuss the advantages and potential limitations of the detection methods and suggest next steps for further consideration.

Selective cultivation and rapid detection of Staphylococcus aureus by computer vision

In this paper, we developed a selective growth medium and a more rapid detection method based on computer vision for selective isolation and identification of Staphylococcus aureus from foods. The selective medium consisted of tryptic soy broth basal medium, 3 inhibitors (NaCl, K2 TeO3 , and phenethyl alcohol), and 2 accelerators (sodium pyruvate and glycine). After 4 h of selective cultivation, bacterial detection was accomplished using computer vision. The total analysis time was 5 h. Compared to the Baird-Parker plate count method, which requires 4 to 5 d, this new detection method offers great time savings. Moreover, our novel method had a correlation coefficient of greater than 0.998 when compared with the Baird-Parker plate count method. The detection range for S. aureus was 10 to 10(7) CFU/mL.

PRACTICAL APPLICATION

Our new, rapid detection method for microorganisms in foods has great potential for routine food safety control and microbiological detection applications.

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

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.

Optical forward-scattering for identification of bacteria within microcolonies

The development of methods for the rapid identification of pathogenic bacteria is a major step towards accelerated clinical diagnosis of infectious diseases and efficient food and water safety control. Methods for identification of bacterial colonies on gelified nutrient broth have the potential to bring an attractive solution, combining simple optical instrumentation, no need for sample preparation or labelling, in a non-destructive process. Here, we studied the possibility of discriminating different bacterial species at a very early stage of growth (6 h of incubation at 37 °C), on thin layers of agar media (1 mm of Tryptic Soy Agar), using light forward-scattering and learning algorithms (Bayes Network, Continuous Naive Bayes, Sequential Minimal Optimisation). A first database of more than 1,000 scatterograms acquired on 7 gram-negative strains yielded a recognition rate of nearly 80%, after only 6 h of incubation. We investigated also the prospect of identifying different strains from a same species through forward scattering. We discriminated, thus, four strains of Escherichia coli with a recognition rate reaching 82%. Finally, we show the discrimination of two species of coagulase-negative Staphylococci (S. haemolyticus and S. cohnii), on a commercial selective pre-poured medium used in clinical diagnosis (ChromID MRSA, bioMérieux), without opening lids during the scatterogram acquisition. This shows the potential of this method--non-invasive, preventing cross-contaminations and requiring minimal dish handling--to provide early clinically-relevant information in the context of fully automated microbiology labs.

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.

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 the presented work 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 (SVM) 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.

Portable bacterial identification system based on elastic light scatter patterns

Background

Conventional diagnosis and identification of bacteria requires shipment of samples to a laboratory for genetic and biochemical analysis. This process can take days and imposes significant delay to action in situations where timely intervention can save lives and reduce associated costs. To enable faster response to an outbreak, a low-cost, small-footprint, portable microbial-identification instrument using forward scatterometry has been developed.

Results

This device, weighing 9 lb and measuring 12 × 6 × 10.5 in., utilizes elastic light scatter (ELS) patterns to accurately capture bacterial colony characteristics and delivers the classification results via wireless access. The overall system consists of two CCD cameras, one rotational and one translational stage, and a 635-nm laser diode. Various software algorithms such as Hough transform, 2-D geometric moments, and the traveling salesman problem (TSP) have been implemented to provide colony count and circularity, centering process, and minimized travel time among colonies.

Conclusions

Experiments were conducted with four bacteria genera using pure and mixed plate and as proof of principle a field test was conducted in four different locations where the average classification rate ranged between 95 and 100%.

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.

Scar tissue classification using nonlinear optical microscopy and discriminant analysis

This paper addresses the scar tissue maturation process that occurs stepwise, and calls for reliable classification. The structure of collagen imaged by nonlinear optical microscopy (NLOM) in post-burn hypertrophic and mature scar, as well as in normal skin, appeared to distinguish these maturation steps. However, it was a discrimination analysis, demonstrated here, that automated and quantified the scar tissue maturation process. The achieved scar classification accuracy was as high as 96%. The combination of NLOM and discrimination analysis is believed to be instrumental in gaining insight into the scar formation, for express diagnosis of scar and surgery planning.

Image processing and classification algorithm for yeast cell morphology in a microfluidic chip

The study of yeast cell morphology requires consistent identification of cell cycle phases based on cell bud size. A computer-based image processing algorithm is designed to automatically classify microscopic images of yeast cells in a microfluidic channel environment. The images were enhanced to reduce background noise, and a robust segmentation algorithm is developed to extract geometrical features including compactness, axis ratio, and bud size. The features are then used for classification, and the accuracy of various machine-learning classifiers is compared. The linear support vector machine, distance-based classification, and k-nearest-neighbor algorithm were the classifiers used in this experiment. The performance of the system under various illumination and focusing conditions were also tested. The results suggest it is possible to automatically classify yeast cells based on their morphological characteristics with noisy and low-contrast images.

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.

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.

Cytomics and nanobioengineering

The finding that an individual's genome differs as much as by many million variants from that of the human reference assembly diminished the great enthusiasm that every disease could be predicted based on nucleotide polymorphisms. Even individual cells of an organ may be specifically equipped to perform specific tasks and that the information of individual cells in a cell system is key information to understand function or dysfunction. Therefore, cytomics received great attention during the last years as it allows to quantitatively and qualitatively analyzing great number of individual cells, cell constituents, and of their intracellular and functional interactions in a cellular system and also giving the concept of analysis of these data.Exhaustive data extraction from multiparametric assays and multiple tests are the prerequisite for prediction of drug toxicity. Cytomics, as novel approach for unsupervised data analysis give a chance to find the most predictive parameters, which describe best the toxicity of a chemical. Cytomics is intrinsically connected to drug development and drug discovery.Focused on small structures, nanobioengineering is the ideal partner of cytomics, the systems biological discipline for cell population analysis. Realizing the idea "from the molecule to the patient" develops and offers chemical compounds, proteins, and other biomolecules, cells as well as tissues as instruments and products for a wide variety of biotechnological and biomedical applications.The integrative nanobioengineering combining different disciplines of nanotechnology will promote the development of innovative therapies and diagnostic methods. It can improve the precision of the measurements with focus on single cell analysis. By nanobioengineering and whole body imaging techniques, cytomics covers the field from molecules through bacterial cells, eukaryotic tissues, and organs to small animal live analysis. Toxicological testing and medical drug development are currently strongly broadening. It harbors the promise to substantially impact on various fields of biomedicine, drug discovery, and predictive medicine.As the number of scientific data is rising exponentially, new data analysis tools and strategies like cytomics and nanobioengineering take a lead and get closer to application. Bionanoengineering may strongly support the quantitative data supply, thus strengthening the rational for cytomics approach.

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.

Biosensors and bio-based methods for the separation and detection of foodborne pathogens

The safety of our food supply is always a major concern to consumers, food producers, and regulatory agencies. A safer food supply improves consumer confidence and brings economic stability. The safety of foods from farm-to-fork through the supply chain continuum must be established to protect consumers from debilitating, sometimes fatal episodes of pathogen outbreaks. The implementation of preventive strategies like hazard analysis critical control points (HACCP) assures safety but its full utility will not be realized unless supportive tools are fully developed. Rapid, sensitive, and accurate detection methods are such essential tools that, when integrated with HACCP, will improve safety of products. Traditional microbiological methods are powerful, error-proof, and dependable but these lengthy, cumbersome methods are often ineffective because they are not compatible with the speed at which the products are manufactured and the short shelf life of products. Automation in detection methods is highly desirable, but is not achievable with traditional methods. Therefore, biosensor-based tools offer the most promising solutions and address some of the modern-day needs for fast and sensitive detection of pathogens in real time or near real time. The application of several biosensor tools belonging to the categories of optical, electrochemical, and mass-based tools for detection of foodborne pathogens is reviewed in this chapter. Ironically, geometric growth in biosensor technology is fueled by the imminent threat of bioterrorism through food, water, and air and by the funding through various governmental agencies.

SEMANTIC ANALYSIS OF BIOLOGICAL IMAGING DATA: CHALLENGES AND OPPORTUNITIES

Microscopic imaging is one of the most common techniques for investigating biological systems. In recent years there has been a tremendous growth in the volume of biological imaging data owing to rapid advances in optical instrumentation, high-speed cameras and fluorescent probes. Powerful semantic analysis tools are required to exploit the full potential of the information content of these data. Semantic analysis of multi-modality imaging data, however, poses unique challenges. In this paper we outline the state-of-the-art in this area along with the challenges facing this domain. Information extraction from biological imaging data requires modeling at multiple levels of detail. While some applications require only quantitative analysis at the level of cells and subcellular objects, others require modeling of spatial and temporal changes associated with dynamic biological processes. Modeling of biological data at different levels of detail allows not only quantitative analysis but also the extraction of high-level semantics. Development of powerful image interpretation and semantic analysis tools has the potential to significantly help in understanding biological processes, which in turn will result in improvements in drug development and healthcare.