Label-free, non-invasive light scattering sensor for rapid screening of Bacillus colonies

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.

Line image sensor-based colony fingerprinting system for rapid pathogenic bacteria identification

The rapid identification of pathogenic bacteria is crucial across various industries, including food or beverage manufacturing. Bacterial microcolony image-based classification has emerged as a promising approach to expedite identification, automate inspections, and reduce costs. However, conventional imaging methods have significant practical limitations, namely low throughput caused by the limited imaging range and slow imaging speed. To address these challenges, we developed an imaging system based on a line image sensor for rapid and wide-field imaging compared to existing colony imaging methods. This system can image a standard Petri dish (92 mm in diameter) completely within 22 s, successfully acquiring bacterial microcolony images. This process yielded a set of discrimination parameters termed as colony fingerprints, which were employed for machine learning. We demonstrated the performance of our system by identifying Staphylococcus aureus in food products using a machine learning model trained on a colony fingerprint dataset of 15 species from 9 genera, including foodborne pathogens. While conventional mass spectrometry-based methods require 24 h of incubation, our colony fingerprinting approach achieved 96% accuracy in just 10 h of incubation. Line image sensor offer high imaging speeds and scalability, allowing for swift and straightforward microbiological testing, eliminating the need for specialized expertise and overcoming the limitations of conventional methods. This innovation marks a transformative shift in industrial applications.

Progress and Challenge of Sensors for Dairy Food Safety Monitoring

One of the most consumed foods is milk and milk products, and guaranteeing the suitability of these products is one of the major concerns in our society. This has led to the development of numerous sensors to enhance quality controls in the food chain. However, this is not a simple task, because it is necessary to establish the parameters to be analyzed and often, not only one compound is responsible for food contamination or degradation. To attempt to address this problem, a multiplex analysis together with a non-directed (e.g., general parameters such as pH) analysis are the most relevant alternatives to identifying the safety of dairy food. In recent years, the use of new technologies in the development of devices/platforms with optical or electrochemical signals has accelerated and intensified the pursuit of systems that provide a simple, rapid, cost-effective, and/or multiparametric response to the presence of contaminants, markers of various diseases, and/or indicators of safety levels. However, achieving the simultaneous determination of two or more analytes in situ, in a single measurement, and in real time, using only one working 'real sensor', remains one of the most daunting challenges, primarily due to the complexity of the sample matrix. To address these requirements, different approaches have been explored. The state of the art on food safety sensors will be summarized in this review including optical, electrochemical, and other sensor-based detection methods such as magnetoelastic or mass-based sensors.

Machine learning identification of Pseudomonas aeruginosa strains from colony image data

When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shapes, edges, textures, and degrees of opacity and color. For over one hundred years, researchers have used these morphology cues to classify bacteria and guide more targeted treatment of pathogens. Advances in genome sequencing technology have revolutionized our ability to classify bacterial isolates and while genomic methods are in the ascendancy, morphological characterization of bacterial species has made a resurgence due to increased computing capacities and widespread application of machine learning tools. In this paper, we revisit the topic of colony morphotype on the within-species scale and apply concepts from image processing, computer vision, and deep learning to a dataset of 69 environmental and clinical Pseudomonas aeruginosa strains. We find that colony morphology and complexity under common laboratory conditions is a robust, repeatable phenotype on the level of individual strains, and therefore forms a potential basis for strain classification. We then use a deep convolutional neural network approach with a combination of data augmentation and transfer learning to overcome the typical data starvation problem in biological applications of deep learning. Using a train/validation/test split, our results achieve an average validation accuracy of 92.9% and an average test accuracy of 90.7% for the classification of individual strains. These results indicate that bacterial strains have characteristic visual 'fingerprints' that can serve as the basis of classification on a sub-species level. Our work illustrates the potential of image-based classification of bacterial pathogens and highlights the potential to use similar approaches to predict medically relevant strain characteristics like antibiotic resistance and virulence from colony data.

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 21st 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.

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

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.

Postharvest Monitoring of Tomato Ripening Using the Dynamic Laser Speckle

The dynamic laser speckle (biospeckle) method was tested as a potential tool for the assessment and monitoring of the maturity stage of tomatoes. Two tomato cultivars—Admiro and Starbuck—were tested. The process of climacteric maturation of tomatoes was monitored during a shelf life storage experiment. The biospeckle phenomena were captured using 640 nm and 830 nm laser light wavelength, and analysed using two activity descriptors based on biospeckle pattern decorrelation—C4 and ε. The well-established optical parameters of tomatoes skin were used as a reference method (luminosity, a*/b*, chroma). Both methods were tested with respect to their prediction capabilities of the maturity and destructive indicators of tomatoes—firmness, chlorophyll and carotenoids content. The statistical significance of the tested relationships were investigated by means of linear regression models. The climacteric maturation of tomato fruit was associated with an increase in biospckle activity. Compared to the 830 nm laser wavelength the biospeckle activity measured at 640 nm enabled more accurate predictions of firmness, chlorophyll and carotenoids content. At 640 nm laser wavelength both activity descriptors (C4 and ε) provided similar results, while at 830 nm the ε showed slightly better performance. The linear regression models showed that biospeckle activity descriptors had a higher correlation with chlorophyll and carotenoids content than the a*/b* ratio and luminosity. The results for chroma were comparable with the results for both biospeckle activity indicators. The biospeckle method showed very good results in terms of maturation monitoring and the prediction of the maturity indices of tomatoes, proving the possibility of practical implementation of this method for the determination of the maturity stage of tomatoes.

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.

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.

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.