Light-scattering sensor for real-time identification of Vibrio parahaemolyticus, Vibrio vulnificus and Vibrio cholerae colonies on solid agar plate

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.

Advances in Vibrio-related infection management: an integrated technology approach for aquaculture and human health

Vibrio species pose significant threats worldwide, causing mortalities in aquaculture and infections in humans. Global warming and the emergence of worldwide strains of Vibrio diseases are increasing day by day. Control of Vibrio species requires effective monitoring, diagnosis, and treatment strategies at the global scale. Despite current efforts based on chemical, biological, and mechanical means, Vibrio control management faces limitations due to complicated implementation processes. This review explores the intricacies and challenges of Vibrio-related diseases, including accurate and cost-effective diagnosis and effective control. The global burden due to emerging Vibrio species further complicates management strategies. We propose an innovative integrated technology model that harnesses cutting-edge technologies to address these obstacles. The proposed model incorporates advanced tools, such as biosensing technologies, the Internet of Things (IoT), remote sensing devices, cloud computing, and machine learning. This model offers invaluable insights and supports better decision-making by integrating real-time ecological data and biological phenotype signatures. A major advantage of our approach lies in leveraging cloud-based analytics programs, efficiently extracting meaningful information from vast and complex datasets. Collaborating with data and clinical professionals ensures logical and customized solutions tailored to each unique situation. Aquaculture biotechnology that prioritizes sustainability may have a large impact on human health and the seafood industry. Our review underscores the importance of adopting this model, revolutionizing the prognosis and management of Vibrio-related infections, even under complex circumstances. Furthermore, this model has promising implications for aquaculture and public health, addressing the United Nations Sustainable Development Goals and their development agenda.

Residual toxins on aquatic animals in the Pacific areas: Current findings and potential health effects

The Pacific Ocean is among the five largest and deepest oceans in the world. The area of the Pacific Ocean covers about 28 % of the Earth's surface. This is the habitat of many marine species, and its diversity is recognized as a fundamental element of Pacific culture and heritage. The ecosystems of aquatic animals are highly affected by climate change and by other factors. Residual toxins on aquatic animals can be categorized into two types based on origin: toxins of marine origin and toxins associated with human activity. Residual toxins have emerged as a global concern in recent years due to their frequent presence in aquatic environments. Furthermore, residual toxins in organisms living in the marine environment in the Pacific Ocean region also seriously affect food safety, food security, and especially human health. In this review we discuss important issues about residual toxins on aquatic animals in the Pacific areas specifically about the types of toxins that exist in marine animals, their contamination pathways in the Asia, Pacific region and the potential health effects for humans, the application of information technology and artificial intelligence in residual toxins on aquatic animal.

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.

Label-free 1D microfluidic dipstick counting of microbial colonies and bacteriophage plaques

Counting viable bacterial cells and functional bacteriophage is fundamental to microbiology underpinning research, surveillance, biopharmaceuticals and diagnostics. Colony forming unit (CFU) and plaque forming unit (PFU) counting still requires slow and laborious solid culture on agar in Petri dishes or plates. Here, we show that dip-stick microfluidic strips can be used without growth indicator dye for rapid and simple CFU ml^-1 and PFU ml^-1 measurement. We demonstrate for the first time that fluoropolymer microcapillaries combined with digital imaging allow bacteriophage plaques to be counted rapidly in a dip-and-test format. The microfluidic length scales offer a linear 1-dimensional alternative to a 2D solid agar medium surface, with colonies or plaques clearly visible as "dashes" or "gaps". An inexpensive open source darkfield biosensor system using Raspberry Pi imaging permits label-free detection and counting of colonies or plaques within 4-8 hours in a linear, liquid matrix within ∼200 μm inner diameter microcapillaries. We obtained full quantitative agreement between 1D microfluidic colony counting in dipsticks versus conventional 2D solid agar Petri dish plates for S. aureus and E. coli, and for T2 phage and phage K, but up to 6 times faster. Time-lapse darkfield imaging permitted detailed kinetic analysis of colony growth in the microcapillaries, providing new insight into microfluidic microbiology and colony growth, not possible with Petri dishes. Surprisingly, whilst E. coli colonies appeared earlier, subsequent colony expansion was faster along the microcapillaries for S. aureus. This may be explained by the microenvironment offered for 1D colony growth within microcapillaries, linked to a mass balance between nutrient (glucose) diffusion and bacterial growth kinetics. Counting individual colonies in liquid medium was not possible for motile strains that spread rapidly along the capillary, however inclusion of soft agar inhibited spreading, making this new simple dip-and-test counting method applicable to both motile and non-motile bacteria. Label-free dipstick colony and plaque counting has potential for many analytical microbial tasks, and the innovation of 1D colony counting has relevance to other microfluidic microbiology.

Identification of colonies of cultured shellfish-associated Arcobacter species by Elastic Light Scatter Analysis

An increasing number of Arcobacter species (including several regarded as emerging human foodborne pathogens) have been isolated from shellfish, an important food commodity. A method to distinguish these species and render viable isolates for further analysis would benefit epidemiological and ecological studies. We describe a method based on Elastic Light Scatter analysis (ELSA) for the detection and discrimination of eleven shellfish-associated Arcobacter species. Although substantive differences in the growth rates of some taxa were seen, ELSA was able to differentiate all the species studied, apart from some strains of A. butzleri and A. cryaerophilus, which were nonetheless distinguished from all other species examined. ELSA appears to be a promising new approach for the detection and identification of Arcobacter species in shellfish and may also be applicable for studies in other foods and matrices.

Current State of Development of Biosensors and Their Application in Foodborne Pathogen Detection

ABSTRACT

Foodborne disease outbreaks continue to be a major public health and food safety concern. Testing products promptly can protect consumers from foodborne diseases by ensuring the safety of food before retail distribution. Fast, sensitive, and accurate detection tools are in great demand. Therefore, various approaches have been explored recently to find a more effective way to incorporate antibodies, oligonucleotides, phages, and mammalian cells as signal transducers and analyte recognition probes on biosensor platforms. The ultimate goal is to achieve high specificity and low detection limits (1 to 100 bacterial cells or piconanogram concentrations of toxins). Advancements in mammalian cell-based and bacteriophage-based sensors have produced sensors that detect low levels of pathogens and differentiate live from dead cells. Combinations of biotechnology platforms have increased the practical utility and application of biosensors for detection of foodborne pathogens. However, further rigorous testing of biosensors with complex food matrices is needed to ensure the utility of these sensors for point-of-care needs and outbreak investigations.

HIGHLIGHTS

Elastic Light Scatter Pattern Analysis for the Expedited Detection of Yersinia Species in Pork Mince: Proof of Concept

Isolation of the pathogens Yersinia enterocolitica and Yersinia pseudotuberculosis from foods typically rely on slow (10–21 day) “cold enrichment” protocols before confirmed results are obtained. We describe an approach that yields results in 39 h that combines an alternative enrichment method with culture on a non-selective medium, and subsequent identification of suspect colonies using elastic light scatter (ELS) analysis. A prototype database of ELS profiles from five Yersinia species and six other bacterial genera found in pork mince was established, and used to compare similar profiles of colonies obtained from enrichment cultures from pork mince samples seeded with representative strains of Y. enterocolitica and Y. pseudotuberculosis. The presumptive identification by ELS using computerised or visual analyses of 83/90 colonies in these experiments as the target species was confirmed by partial 16S rDNA sequencing. In addition to seeded cultures, our method recovered two naturally occurring Yersinia strains. Our results indicate that modified enrichment combined with ELS is a promising new approach for expedited detection of foodborne pathogenic yersiniae.

The diagnostic tools for viable but nonculturable pathogens in the food industry: Current status and future prospects

Viable but nonculturable (VBNC) microorganisms have been recognized as pathogenic contaminants in foods and environments. The failure of VBNC cells to form the visible colonies hinders the ability to use conventional media for their detection. Efficient and rapid detection of pathogens in the VBNC state is a prerequisite to ensure the food safety and public health. Despite their nonculturability, VBNC cells have distinct characteristics, such as morphology, metabolism, chemical composition, and gene and protein expression, that have been used as the basis for the development of abundant diagnostic tools. This review covers the current status and advances in various approaches for examining microorganisms in the VBNC state, including but not limited to the methodological aspects, advantages, and drawbacks of each technique. Existing methods, such as direct viable count, SYTO/PI dual staining, and propidium monoazide quantitative polymerase chain reaction (PCR), as well as some techniques with potential to be applied in the future, such as digital PCR, enhanced-surface Raman spectroscopy, and impedance-based techniques, are summarized in depth. Finally, future prospects for the one-step detection of VBNC bacteria are proposed and discussed. We believe that this review can provide more optional methods for researchers and promote the development of rapid, accurate detecting methods, and for inspectors, the diagnostic tools can provide data to undertake risk analysis of VBNC cells.

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.

Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning

Early identification of pathogenic bacteria in food, water, and bodily fluids is very important and yet challenging, owing to sample complexities and large sample volumes that need to be rapidly screened. Existing screening methods based on plate counting or molecular analysis present various tradeoffs with regard to the detection time, accuracy/sensitivity, cost, and sample preparation complexity. Here, we present a computational live bacteria detection system that periodically captures coherent microscopy images of bacterial growth inside a 60-mm-diameter agar plate and analyses these time-lapsed holograms using deep neural networks for the rapid detection of bacterial growth and the classification of the corresponding species. The performance of our system was demonstrated by the rapid detection of Escherichia coli and total coliform bacteria (i.e., Klebsiella aerogenes and Klebsiella pneumoniae subsp. pneumoniae) in water samples, shortening the detection time by >12 h compared to the Environmental Protection Agency (EPA)-approved methods. Using the preincubation of samples in growth media, our system achieved a limit of detection (LOD) of ~1 colony forming unit (CFU)/L in ≤9 h of total test time. This platform is highly cost-effective (~$0.6/test) and has high-throughput with a scanning speed of 24 cm2/min over the entire plate surface, making it highly suitable for integration with the existing methods currently used for bacteria detection on agar plates. Powered by deep learning, this automated and cost-effective live bacteria detection platform can be transformative for a wide range of applications in microbiology by significantly reducing the detection time and automating the identification of colonies without labelling or the need for an expert.

Rapid discrimination of fungal species by the colony fingerprinting

The contamination of foods and beverages by fungi is a severe health hazard. The rapid identification of fungi species in contaminated goods is important to avoid further contamination. To this end, we developed a fungal discrimination method based on the bioimage informatics approach of colony fingerprinting. This method involves imaging and visualizing microbial colonies (referred to as colony fingerprints) using a lens-less imaging system. Subsequently, the quantitative image features were extracted as discriminative parameters and subjected to analysis using machine learning approaches. Colony fingerprinting has been previously found to be a promising approach to discriminate bacteria. In the present proof-of-concept study, we tested whether this method is also useful for fungal discrimination. As a result, 5 fungi belonging to the Aspergillus, Penicilium, Eurotium, Alternaria, and Fusarium genera were successfully discriminated based on the extracted parameters, including the number of hyphae and their branches, and their intensity distributions on the images. The discrimination of 6 closely-related Aspergillus spp. was also demonstrated using additional parameters. The cultivation time required to generate the fungal colonies with a sufficient size for colony fingerprinting was less than 48 h, shorter than those for other discrimination methods, including MALDI-TOF-MS. In addition, colony fingerprinting did not require any cumbersome pre-treatment steps prior to discrimination. Colony fingerprinting is promising for the rapid and easy discrimination of fungi for use in the ensuring the safety of food manufacturing.

Generalized spectral light scatter models of diverse bacterial colony morphologies

An optical forward-scatter model was generalized to encompass the diverse nature of bacterial colony morphologies and the spectral information. According to the model, the colony shape and the wavelength of incident light significantly affect the characteristics of a forward elastic-light-scattering pattern. To study the relationship between the colony morphology and the scattering pattern, three-dimensional colony models were generated in various morphologies. The propagation of light passing through the colony model was then simulated. In validation of the theoretical modeling, the scattering patterns of three bacterial genera, Staphylococcus, Exiguobacterium and Bacillus, which grow into colonies having convex, crateriform and flat elevations, respectively, were qualitatively compared to the simulated scattering patterns. The strong correlations observed between simulated and experimental patterns validated the scatter model. In addition, spectral effect on the scattering pattern was studied using the scatter model, and experimentally investigated using Staphylococcus, whose colony has circular form and convex elevation. Both simulation and experiment showed that changes in wavelength affected the overall pattern size and the number of rings.

Simultaneous detection of Salmonella enterica, Escherichia coli and Listeria monocytogenes in food using a light scattering sensor

AIM

To investigate the use of a light scattering sensor, BActerial Rapid Detection using Optical scattering Technology (BARDOT) coupled with a multipathogen selective medium, Salmonella, Escherichia and Listeria (SEL), for concurrent detection of the three major foodborne pathogens in a single assay.

METHODS AND RESULTS

BARDOT was used to detect and distinguish the three major pathogens, Salmonella enterica, Shiga toxin-producing Escherichia coli (STEC) and Listeria monocytogenes from food based on colony scatter signature patterns on SEL agar (SELA). Multiple strains of three test pathogens were grown on SELA, and BARDOT was used to generate colony scatter image libraries for inclusive (SEL Library) and exclusive (non-SEL Library) bacterial group. These pathogens were further differentiated using the SEL scatter image library. Raw chicken and hotdog samples were artificially inoculated with pathogens (100 CFU per 25 g each), and enriched in SEL broth at 37°C for 18 h and colonies were grown on SELA for 11-22 h before screening with BARDOT. The BARDOT sensor successfully detected and differentiated Salmonella, STEC and Listeria on SELA with high classification accuracy 92-98%, 91-98% and 83-98% positive predictive values (PPV) respectively; whereas the nontarget strains showed only 0-13% PPV. BARDOT-identified colonies were further confirmed by multiplex PCR targeting inlB gene of L. monocytogenes, stx2 of STEC and sefA of S. enterica serovar Enteritidis.

CONCLUSIONS

The results show that BARDOT coupled with SELA can efficiently screen for the presence of three major pathogens simultaneously in a test sample within 29-40 h.

SIGNIFICANCE AND IMPACT OF THE STUDY

This innovative SELA-BARDOT detection platform can reduce turnaround time and economic burden on food industries by offering a label-free, noninvasive on-plate multipathogen screening technology for reducing microbial food safety and public health concerns.

Rapid subtyping of pathogenic and nonpathogenic Vibrio parahaemolyticus by fourier transform infrared spectroscopy with chemometric analysis

Vibrio parahaemolyticus which naturally inhabits marine and estuarine environment represents pathogenic strains (virulence genes tdh or trh positive) and non-pathogenic strains (virulence genes negative). In this study, a rapid method for subtyping pathogenic and non-pathogenic V. parahaemolyticus was established using fourier transform infrared (FTIR) spectroscopy with chemometric analysis. This method targeted three strains of genotypes of V. parahaemolyticus including tdh positive, trh positive and virulence gene-negative (nonpathogenic) V. parahaemolyticus. The FTIR absorption spectra between 1800 and 900 cm^-1 highlighted the most distinctive variations and were the most useful for characterizing the three bacteria. The successful differentiation and identification of the three bacteria could be accomplished in less than 1 h by FTIR using principal component analysis (PCA), or another cluster model of hierarchical cluster analysis (HCA). The method was verified by analyzing spiked V. parahaemolyticus fish samples. Furthermore, all of ten clinical isolates of V. parahaemolyticus were identified as tdh-positive, none of the clinical isolates were trh-positive, and all of ten environmental isolates were identified as non-pathogenic by the subtyping method, which were confirmed by PCR assays. All data demonstrated that the newly established subtyping method by FTIR is practical, time-saving, labor-saving, specific and cost-effective, especially suitable for the basic laboratories of CDC and port quarantine departments to perform suiveillance and epidemiological traceability of pathogenic V. parahaemolyticus.

Detecting Biothreat Agents: From Current Diagnostics to Developing Sensor Technologies

Although a fundamental understanding of the pathogenicity of most biothreat agents has been elucidated and available treatments have increased substantially over the past decades, they still represent a significant public health threat in this age of (bio)terrorism, indiscriminate warfare, pollution, climate change, unchecked population growth, and globalization. The key step to almost all prevention, protection, prophylaxis, post-exposure treatment, and mitigation of any bioagent is early detection. Here, we review available methods for detecting bioagents including pathogenic bacteria and viruses along with their toxins. An introduction placing this subject in the historical context of previous naturally occurring outbreaks and efforts to weaponize selected agents is first provided along with definitions and relevant considerations. An overview of the detection technologies that find use in this endeavor along with how they provide data or transduce signal within a sensing configuration follows. Current "gold" standards for biothreat detection/diagnostics along with a listing of relevant FDA approved in vitro diagnostic devices is then discussed to provide an overview of the current state of the art. Given the 2014 outbreak of Ebola virus in Western Africa and the recent 2016 spread of Zika virus in the Americas, discussion of what constitutes a public health emergency and how new in vitro diagnostic devices are authorized for emergency use in the U.S. are also included. The majority of the Review is then subdivided around the sensing of bacterial, viral, and toxin biothreats with each including an overview of the major agents in that class, a detailed cross-section of different sensing methods in development based on assay format or analytical technique, and some discussion of related microfluidic lab-on-a-chip/point-of-care devices. Finally, an outlook is given on how this field will develop from the perspective of the biosensing technology itself and the new emerging threats they may face.

Colony Fingerprint-Based Discrimination of Staphylococcus species with Machine Learning Approaches

Detection and discrimination of bacteria are crucial in a wide range of industries, including clinical testing, and food and beverage production. Staphylococcus species cause various diseases, and are frequently detected in clinical specimens and food products. In particular, S. aureus is well known to be the most pathogenic species. Conventional phenotypic and genotypic methods for discrimination of Staphylococcus spp. are time-consuming and labor-intensive. To address this issue, in the present study, we applied a novel discrimination methodology called colony fingerprinting. Colony fingerprinting discriminates bacterial species based on the multivariate analysis of the images of microcolonies (referred to as colony fingerprints) with a size of up to 250 μm in diameter. The colony fingerprints were obtained via a lens-less imaging system. Profiling of the colony fingerprints of five Staphylococcus spp. (S. aureus, S. epidermidis, S. haemolyticus, S. saprophyticus, and S. simulans) revealed that the central regions of the colony fingerprints showed species-specific patterns. We developed 14 discriminative parameters, some of which highlight the features of the central regions, and analyzed them by several machine learning approaches. As a result, artificial neural network (ANN), support vector machine (SVM), and random forest (RF) showed high performance for discrimination of theses bacteria. Bacterial discrimination by colony fingerprinting can be performed within 11 h, on average, and therefore can cut discrimination time in half compared to conventional methods. Moreover, we also successfully demonstrated discrimination of S. aureus in a mixed culture with Pseudomonas aeruginosa. These results suggest that colony fingerprinting is useful for discrimination of Staphylococcus spp.

Microfluidic multi-angle laser scattering system for rapid and label-free detection of waterborne parasites

A microfluidic laser scattering system for rapid and label-free detection of single waterborne parasites in microfluidic flows was designed, fabricated and demonstrated. The key novelty of the system lies in the integration of functional modules involving pre-concentration, on-chip laser scattering detection, and pattern recognition. The silicon-based pre-concentration chip can concentrate 10 ml reagent water sample spiked with protozoa (oo)cysts into a volume of 200 μl in ~30 minutes. The concentrated sample is further channeled into the on-chip laser scattering detection module at a flow rate of 10 μl/min, which can collect the multi-angle scattering pattern of single flowing microparticles. The Zernike moment features of scattering patterns are extracted using principal component analysis, and classification of scattering patterns are performed using the linear discriminator analysis algorithm. As a result, Cryptosporidium parvum oocysts and Giardia lamblia cysts spiked in ~10 ml reagent water can be enumerated and identified within an hour without labeling, with a mean recovery efficiency of ~73% and average accuracies of 96%, 97%, 97% and 98% at concentrations of 10, 50, 100, 300 (oo)cysts per 10 ml water, respectively. We believe that this compact microfluidic laser scattering system has potential for rapid and label-free water quality monitoring in field and resource-limited environments.

Light Emitting Marker for Robust Vision-Based On-The-Spot Bacterial Growth Detection

Simple methods using the striped pattern paper marker and FFT (fast Fourier transformation) have been proposed as alternatives to measuring the optical density for determining the level of bacterial growth. The marker-based method can be easily automated, but due to image-processing-base of the method, the presence of light or the color of the culture broth can disturb the detection process. This paper proposes a modified version of marker-FFT-based growth detection that uses a light emitting diode (LED) array as a marker. Since the marker itself can emit the light, the measurements can be performed even when there is no light source or the bacteria are cultured in a large volume of darkly colored broth. In addition, an LED marker can function as a region of interest (ROI) indicator in the image. We expect that the proposed LED-based marker system will allow more robust growth detection compared to conventional methods.

Development of a multispectral light-scatter sensor for bacterial colonies

We report a multispectral elastic-light-scatter instrument that can simultaneously detect three-wavelength scatter patterns and associated optical densities from individual bacterial colonies, overcoming the limits of the single-wavelength predecessor. Absorption measurements on liquid bacterial samples revealed that the spectroscopic information can indeed contribute to sample differentiability. New optical components, including a pellicle beam splitter and an optical cage system, were utilized for robust acquisition of multispectral images. Four different genera and seven shiga toxin producing E. coli serovars were analyzed; the acquired images showed differences in scattering characteristics among the tested organisms. In addition, colony-based spectral optical-density information was also collected. The optical model, which was developed using diffraction theory, correctly predicted wavelength-related differences in scatter patterns, and was matched with the experimental results. Scatter-pattern classification was performed using pseudo-Zernike (GPZ) polynomials/moments by combining the features collected at all three wavelengths and selecting the best features via a random-forest method. The data demonstrate that the selected features provide better classification rates than the same number of features from any single wavelength. Three wavelength-merged scatter pattern from E. coli.

Colony fingerprint for discrimination of microbial species based on lensless imaging of microcolonies

Detection and identification of microbial species are crucial in a wide range of industries, including production of beverages, foods, cosmetics, and pharmaceuticals. Traditionally, colony formation and its morphological analysis (e.g., size, shape, and color) with a naked eye have been employed for this purpose. However, such a conventional method is time consuming, labor intensive, and not very reproducible. To overcome these problems, we propose a novel method that detects microcolonies (diameter 10–500 μm) using a lensless imaging system. When comparing colony images of five microorganisms from different genera (Escherichia coli, Salmonella enterica, Pseudomonas aeruginosa, Staphylococcus aureus, and Candida albicans), the images showed obvious different features. Being closely related species, St. aureus and St. epidermidis resembled each other, but the imaging analysis could extract substantial information (colony fingerprints) including the morphological and physiological features, and linear discriminant analysis of the colony fingerprints distinguished these two species with 100% of accuracy. Because this system may offer many advantages such as high-throughput testing, lower costs, more compact equipment, and ease of automation, it holds promise for microbial detection and identification in various academic and industrial areas.

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.

Virulence Gene-Associated Mutant Bacterial Colonies Generate Differentiating Two-Dimensional Laser Scatter Fingerprints

In this study, we investigated whether a laser scatterometer designated BARDOT (bacterial rapid detection using optical scattering technology) could be used to directly screen colonies of Listeria monocytogenes, a model pathogen, with mutations in several known virulence genes, including the genes encoding Listeria adhesion protein (LAP; lap mutant), internalin A (ΔinlA strain), and an accessory secretory protein (ΔsecA2 strain). Here we show that the scatter patterns of lap mutant, ΔinlA, and ΔsecA2 colonies were markedly different from that of the wild type (WT), with >95% positive predictive values (PPVs), whereas for the complemented mutant strains, scatter patterns were restored to that of the WT. The scatter image library successfully distinguished the lap mutant and ΔinlA mutant strains from the WT in mixed-culture experiments, including a coinfection study using the Caco-2 cell line. Among the biophysical parameters examined, the colony height and optical density did not reveal any discernible differences between the mutant and WT strains. We also found that differential LAP expression in L. monocytogenes serotype 4b strains also affected the scatter patterns of the colonies. The results from this study suggest that BARDOT can be used to screen and enumerate mutant strains separately from the WT based on differential colony scatter patterns.

IMPORTANCE

In studies of microbial pathogenesis, virulence-encoding genes are routinely disrupted by deletion or insertion to create mutant strains. Screening of mutant strains is an arduous process involving plating on selective growth media, replica plating, colony hybridization, DNA isolation, and PCR or immunoassays. We applied a noninvasive laser scatterometer to differentiate mutant bacterial colonies from WT colonies based on forward optical scatter patterns. This study demonstrates that BARDOT can be used as a novel, label-free, real-time tool to aid researchers in screening virulence gene-associated mutant colonies during microbial pathogenesis, coinfection, and genetic manipulation studies.

Optical scatter patterns facilitate rapid differentiation of Enterobacteriaceae on CHROMagar™ Orientation medium

Enterobacteriaceae family comprised pathogens and commensals and has a significant impact on food safety and public health. Enterobacteriaceae is often enumerated and presumptively identified on chromogenic media, such as CHROMagar(TM) Orientation medium based on colony profile; however, classification is highly arbitrary, and some could not be differentiated due to similar chromogen production. Here, we investigated the ability of the laser optical sensor, BARDOT (bacterial rapid detection using optical scattering technology) for rapid screening and differentiation of colonies of the major bacterial genera from Enterobacteriaceae on CHROMagar(TM) Orientation. A total of 36 strains representing 12 genera and 15 species were used to generate colony scatter image library that comprised 1683 scatter images. This library was used to differentiate mixed cultures of Enterobacteriaceae family - Klebsiella pneumoniae, Enterobacter spp., Citrobacter freundii and Serratia marcescens (KECS group); Proteus mirabilis, Morganella morganii and Providencia rettgeri (PMP group); and non-Enterobacteriaceae family: Pseudomonas aeruginosa, Acinetobacter spp. and Staphylococcus aureus (PAS group) - and data show high accuracy (83-100%) for intra-group classification of colonies in 10-22 h or even before visible production of chromogens. BARDOT successfully differentiated the major genera, including the ones that do not produce visually distinguishable chromogens on CHROMagar(TM) Orientation, providing a label-free, real-time on-plate colony screening tool for Enterobacteriaceae.

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.

Does bacteriology laboratory automation reduce time to results and increase quality management?

Due to reductions in financial and human resources, many microbiological laboratories have merged to build very large clinical microbiology laboratories, which allow the use of fully automated laboratory instruments. For clinical chemistry and haematology, automation has reduced the time to results and improved the management of laboratory quality. The aim of this review was to examine whether fully automated laboratory instruments for microbiology can reduce time to results and impact quality management. This study focused on solutions that are currently available, including the BD Kiestra™ Work Cell Automation and Total Lab Automation and the Copan WASPLab(®).

Novel PCR Assays Complement Laser Biosensor-Based Method and Facilitate Listeria Species Detection from Food

The goal of this study was to develop the Listeria species-specific PCR assays based on a house-keeping gene (lmo1634) encoding alcohol acetaldehyde dehydrogenase (Aad), previously designated as Listeria adhesion protein (LAP), and compare results with a label-free light scattering sensor, BARDOT (bacterial rapid detection using optical scattering technology). PCR primer sets targeting the lap genes from the species of Listeria sensu stricto were designed and tested with 47 Listeria and 8 non-Listeria strains. The resulting PCR primer sets detected either all species of Listeria sensu stricto or individual L. innocua, L. ivanovii and L. seeligeri, L. welshimeri, and L. marthii without producing any amplified products from other bacteria tested. The PCR assays with Listeria sensu stricto-specific primers also successfully detected all species of Listeria sensu stricto and/or Listeria innocua from mixed culture-inoculated food samples, and each bacterium in food was verified by using the light scattering sensor that generated unique scatter signature for each species of Listeria tested. The PCR assays based on the house-keeping gene aad (lap) can be used for detection of either all species of Listeria sensu stricto or certain individual Listeria species in a mixture from food with a detection limit of about 10⁴ CFU/mL.

Streptomycin Induced Stress Response in Salmonella enterica Serovar Typhimurium Shows Distinct Colony Scatter Signature

We investigated the streptomycin-induced stress response in Salmonella enterica serovars with a laser optical sensor, BARDOT (bacterial rapid detection using optical scattering technology). Initially, the top 20 S. enterica serovars were screened for their response to streptomycin at 100 μg/mL. All, but four S. enterica serovars were resistant to streptomycin. The MIC of streptomycin-sensitive serovars (Enteritidis, Muenchen, Mississippi, and Schwarzengrund) varied from 12.5 to 50 μg/mL, while streptomycin-resistant serovar (Typhimurium) from 125–250 μg/mL. Two streptomycin-sensitive serovars (Enteritidis and Mississippi) were grown on brain heart infusion (BHI) agar plates containing sub-inhibitory concentration of streptomycin (1.25–5 μg/mL) and a streptomycin-resistant serovar (Typhimurium) was grown on BHI containing 25–50 μg/mL of streptomycin and the colonies (1.2 ± 0.1 mm diameter) were scanned using BARDOT. Data show substantial qualitative and quantitative differences in the colony scatter patterns of Salmonella grown in the presence of streptomycin than the colonies grown in absence of antibiotic. Mass-spectrometry identified overexpression of chaperonin GroEL, which possibly contributed to the observed differences in the colony scatter patterns. Quantitative RT-PCR and immunoassay confirmed streptomycin-induced GroEL expression while, aminoglycoside adenylyltransferase (aadA), aminoglycoside efflux pump (aep), multidrug resistance subunit acrA, and ribosomal protein S12 (rpsL), involved in streptomycin resistance, were unaltered. The study highlights suitability of the BARDOT as a non-invasive, label-free tool for investigating stress response in Salmonella in conjunction with the molecular and immunoassay methods.

One day to one hour: how quickly can foodborne pathogens be detected?

Foodborne pathogens pose serious public health risks. Rapid, accurate technologies to detect a low number of target cells (1 cell/25-325 g sample) and microbial toxins are in demand in order to assess product safety in hours to up to 1 day. Varied pathogen loads and the complexity of food present a major challenge. Current culture methods, while accurate, are lengthy. New methods, using brief culturing and detection kits (antibody based, nucleic acid amplification or nano/biosensors) or a culture-independent approach coupled with nucleic acid amplification, traditionally used for viruses/parasites, can be used to obtain results in hours. A strategic approach involving two-step, rapid, high-throughput screening to rule out negatives followed by a confirmatory test could accomplish product testing in 1 h to 1 day.

The pathogenesis, detection, and prevention of Vibrio parahaemolyticus

Vibrio parahaemolyticus, a Gram-negative motile bacterium that inhabits marine and estuarine environments throughout the world, is a major food-borne pathogen that causes life-threatening diseases in humans after the consumption of raw or undercooked seafood. The global occurrence of V. parahaemolyticus accentuates the importance of investigating its virulence factors and their effects on the human host. This review describes the virulence factors of V. parahaemolyticus reported to date, including hemolysin, urease, two type III secretion systems and two type VI secretion systems, which both cause both cytotoxicity in cultured cells and enterotoxicity in animal models. We describe various types of detection methods, based on virulence factors, that are used for quantitative detection of V. parahaemolyticus in seafood. We also discuss some useful preventive measures and therapeutic strategies for the diseases mediated by V. parahaemolyticus, which can reduce, to some extent, the damage to humans and aquatic animals attributable to V. parahaemolyticus. This review extends our understanding of the pathogenic mechanisms of V. parahaemolyticus mediated by virulence factors and the diseases it causes in its human host. It should provide new insights for the diagnosis, treatment, and prevention of V. parahaemolyticus infection.

Laser-induced speckle scatter patterns in Bacillus colonies

Label-free bacterial colony phenotyping technology called BARDOT (Bacterial Rapid Detection using Optical scattering Technology) provided successful classification of several different bacteria at the genus, species, and serovar level. Recent experiments with colonies of Bacillus species provided strikingly different characteristics of elastic light scatter (ELS) patterns, which were comprised of random speckles compared to other bacteria, which are dominated by concentric rings and spokes. Since this laser-based optical sensor interrogates the whole volume of the colony, 3-D information of micro- and macro-structures are all encoded in the far-field scatter patterns. Here, we present a theoretical model explaining the underlying mechanism of the speckle formation by the colonies from Bacillus species. Except for Bacillus polymyxa, all Bacillus spp. produced random bright spots on the imaging plane, which presumably dependent on the cellular and molecular organization and content within the colony. Our scatter model-based analysis revealed that colony spread resulting in variable surface roughness can modify the wavefront of the scatter field. As the center diameter of the Bacillus spp. colony grew from 500 to 900 μm, average speckles area decreased two-fold and the number of small speckles increased seven-fold. In conclusion, as Bacillus colony grows, the average speckle size in the scatter pattern decreases and the number of smaller speckle increases due to the swarming growth characteristics of bacteria within the colony.

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.

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.