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.

Development of an Anti-canine PD-L1 Antibody and Caninized PD-L1 Mouse Model as Translational Research Tools for the Study of Immunotherapy in Humans.

Immune checkpoint blockade therapy, one of the most promising cancer immunotherapies, has shown remarkable clinical impact in multiple cancer types. Despite the recent success of immune checkpoint blockade therapy, however, the response rates in patients with cancer are limited (∼20%-40%). To improve the success of immune checkpoint blockade therapy, relevant preclinical animal models are essential for the development and testing of multiple combination approaches and strategies. Companion dogs naturally develop several types of cancer that in many respects resemble clinical cancer in human patients. Therefore, the canine studies of immuno-oncology drugs can generate knowledge that informs and prioritizes new immuno-oncology therapy in humans. The challenge has been, however, that immunotherapeutic antibodies targeting canine immune checkpoint molecules such as canine PD-L1 (cPD-L1) have not been commercially available. Here, we developed a new cPD-L1 antibody as an immuno-oncology drug and characterized its functional and biological properties in multiple assays. We also evaluated the therapeutic efficacy of cPD-L1 antibodies in our unique caninized PD-L1 mice. Together, these in vitro and in vivo data, which include an initial safety profile in laboratory dogs, support development of this cPD-L1 antibody as an immune checkpoint inhibitor for studies in dogs with naturally occurring cancer for translational research. Our new therapeutic antibody and caninized PD-L1 mouse model will be essential translational research tools in raising the success rate of immunotherapy in both dogs and humans. Significance: Our cPD-L1 antibody and unique caninized mouse model will be critical research tools to improve the efficacy of immune checkpoint blockade therapy in both dogs and humans. Furthermore, these tools will open new perspectives for immunotherapy applications in cancer as well as other autoimmune diseases that could benefit a diverse and broader patient population.

Rapid Food Authentication Using a Portable Laser-Induced Breakdown Spectroscopy System.

Laser-induced breakdown spectroscopy (LIBS) is an atomic-emission spectroscopy technique that employs a focused laser beam to produce microplasma. Although LIBS was designed for applications in the field of materials science, it has lately been proposed as a method for the compositional analysis of agricultural goods. We deployed commercial handheld LIBS equipment to illustrate the performance of this promising optical technology in the context of food authentication, as the growing incidence of food fraud necessitates the development of novel portable methods for detection. We focused on regional agricultural commodities such as European Alpine-style cheeses, coffee, spices, balsamic vinegar, and vanilla extracts. Liquid examples, including seven balsamic vinegar products and six representatives of vanilla extract, were measured on a nitrocellulose membrane. No sample preparation was required for solid foods, which consisted of seven brands of coffee beans, sixteen varieties of Alpine-style cheeses, and eight different spices. The pre-processed and standardized LIBS spectra were used to train and test the elastic net-regularized multinomial classifier. The performance of the portable and benchtop LIBS systems was compared and described. The results indicate that field-deployable, portable LIBS devices provide a robust, accurate, and simple-to-use platform for agricultural product verification that requires minimal sample preparation, if any.

Development of a Smartphone-Integrated Reflective Scatterometer for Bacterial Identification.

We present a smartphone-based bacterial colony phenotyping instrument using a reflective elastic light scattering (ELS) pattern and the resolving power of the new instrument. The reflectance-type device can acquire ELS patterns of colonies on highly opaque media as well as optically dense colonies. The novel instrument was built using a smartphone interface and a 532 nm diode laser, and these essential optical components made it a cost-effective and portable device. When a coherent and collimated light source illuminated a bacterial colony, a reflective ELS pattern was created on the screen and captured by the smartphone camera. The collected patterns whose shapes were determined by the colony morphology were then processed and analyzed to extract distinctive features for bacterial identification. For validation purposes, the reflective ELS patterns of five bacteria grown on opaque growth media were measured with the proposed instrument and utilized for the classification. Cross-validation was performed to evaluate the classification, and the result showed an accuracy above 94% for differentiating colonies of E. coli, K. pneumoniae, L. innocua, S. enteritidis, and S. aureus.

A Semi-Automated Method for Measuring Biofilm Accumulation on the Teeth Using Quantitative Light-Induced Fluorescence in Dogs and Cats.

Oral health conditions (eg, plaque, calculus, gingivitis) cause morbidity and pain in companion animals. Thus, developing technologies that can ameliorate the accumulation of oral biofilm, a critical factor in the progression of these conditions, is vital. Quantitative light-induced fluorescence (QLF) is a method to quantify oral substrate accumulation, and therefore, it can assess biofilm attenuation of different products. New software has recently been developed that automates aspects of the procedure. However, few QLF studies in companion animals have been performed. QLF was used to collect digital images of oral substrate accumulation on the teeth of dogs and cats to demonstrate the ability of QLF to discriminate between foods known to differentially inhibit oral substrate accumulation. Images were taken as a function of time and diet. Software developed by the Cytometry Laboratory, Purdue University quantified biofilm coverage. Intra- and intergrader reproducibility was also assessed, as was a comparison of the results of the QLF software with those of an experienced grader using undisclosed coverage-only metrics similar to those used for the Logan and Boyce index. Quantification of oral substrate accumulation using QLF-derived images demonstrated the ability to distinguish between dental diets known to differentially inhibit oral biofilm accumulation. Little variance in intra- and intergrader reproducibility was observed, and the comparison between the experienced Logan and Boyce grader and the QLF software yielded a concordance correlation coefficient of 0.89 (95% CI = 0.84, 0.92). These results show that QLF is a useful tool that allows the semi-automated quantification of the accumulation of oral biofilm in companion animals.

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.

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.

Detection of E. coli labeled with metal-conjugated antibodies using lateral-flow assay and laser-induced breakdown spectroscopy.

This study explores the adoption of laser-induced breakdown spectroscopy (LIBS) for the analysis of lateral-flow immunoassays (LFIAs). Gold (Au) nanoparticles are standard biomolecular labels among LFIAs, typically detected via colorimetric means. A wide diversity of lanthanide-complexed polymers (LCPs) are also used as immunoassay labels but are inapt for LFIAs due to lab-bound detection instrumentation. This is the first study to show the capability of LIBS to transition LCPs into the realm of LFIAs, and one of the few to apply LIBS to biomolecular label detection in complete immunoassays. Initially, an in-house LIBS system was optimized to detect an Au standard through a process of line selection across acquisition delay times, followed by determining limit of detection (LOD). The optimized LIBS system was applied to Au-labeled Escherichia coli detection on a commercial LFIA; comparison with colorimetric detection yielded similar LODs (1.03E4 and 8.890E3 CFU/mL respectively). Optimization was repeated with lanthanide standards to determine if they were viable alternatives to Au labels. It was found that europium (Eu) and ytterbium (Yb) may be more favorable biomolecular labels than Au. To test whether Eu-complexed polymers conjugated to antibodies could be used as labels in LFIAs, the conjugates were successfully applied to E. coli detection in a modified commercial LFIA. The results suggest interesting opportunities for creating highly multiplexed LFIAs. Multiplexed, sensitive, portable, and rapid LIBS detection of biomolecules concentrated and labeled on LFIAs is highly relevant for applications like food safety, where in-field food contaminant detection is critical. Graphical abstract.

A Portable Spark-Induced Breakdown Spectroscopic (SIBS) Instrument and its Analytical Performance.

A compact spark-induced plasma spectroscopic device was developed to detect elements used in a variety of applications. The system consists of a spark generator connected to tungsten electrodes, a custom-built delay generator, and two spectrometers that together cover the ultraviolet visible (UV-Vis) range (214-631 nm). The system was evaluated by qualitatively and quantitatively sampling copper standards. Prominent spectral peaks were identified using the NIST database for atomic emissions. The effectiveness of the proposed system was also tested with a lanthanide sample (gadolinium) and provided qualitative identification of the characteristic peaks. A semi-quantitative measurement for silicon and gold was performed using variable amounts of each particulate. Silica microbeads in solution were applied to paper wafers, while gold nanoparticles were sputter-coated onto silicon wafers. Results showed a positive correlation between the intensity of the signal and the concentration of each type of particulate. The variation of signal intensity was investigated to determine the repeatability, and the coefficient of variation was lowered from 60% to 25% after averaging measurements of multiple ablations per observation.

Alternatives to current flow cytometry data analysis for clinical and research studies.

Flow cytometry has well-established methods for data analysis based on traditional data collection techniques. These techniques typically involved manual insertion of tube samples into an instrument that, historically, could only measure 1-3 colors. The field has since evolved to incorporate new technologies for faster and highly automated sample preparation and data collection. For example, the use of microwell plates on benchtop instruments is now a standard on virtually every new instrument, and so users can easily accumulate multiple data sets quickly. Further, because the user must carefully define the layout of the plate, this information is already defined when considering the analytical process, expanding the opportunities for automated analysis. Advances in multi-parametric data collection, as demonstrated by the development of hyperspectral flow-cytometry, 20-40 color polychromatic flow cytometry, and mass cytometry (CyTOF), are game-changing. As data and assay complexity increase, so too does the complexity of data analysis. Complex data analysis is already a challenge to traditional flow cytometry software. New methods for reviewing large and complex data sets can provide rapid insight into processes difficult to define without more advanced analytical tools. In settings such as clinical labs where rapid and accurate data analysis is a priority, rapid, efficient and intuitive software is needed. This paper outlines opportunities for analysis of complex data sets using examples of multiplexed bead-based assays, drug screens and cell cycle analysis - any of which could become integrated into the clinical environment.

A statistical modeling approach to computer-aided quantification of dental biofilm.

Biofilm is a formation of microbial material on tooth substrata. Several methods to quantify dental biofilm coverage have recently been reported in the literature, but at best they provide a semiautomated approach to quantification with significant input from a human grader that comes with the grader's bias of what is foreground, background, biofilm, and tooth. Additionally,human assessment indices limit the resolution of the quantification scale; most commercial scales use five levels of quantification for biofilm coverage (0%, 25%, 50%, 75%, and 100%). On the other hand, current state-of-the-art techniques in automatic plaque quantification fail to make their way into practical applications owing to their inability to incorporate human input to handle misclassifications. This paper proposes a new interactive method for biofilm quantification in Quantitative light-induced fluorescence(QLF) images of canine teeth that is independent of the perceptual bias of the grader. The method partitions a QLF image into segments of uniform texture and intensity called superpixels; every superpixel is statistically modeled as a realization of a single 2-D Gaussian Markov random field (GMRF) whose parameters are estimated; the superpixel is then assigned to one of three classes (background, biofilm, tooth substratum) based on the training set of data. The quantification results show a high degree of consistency and precision. At the same time, the proposed method gives pathologists full control to postprocess the automatic quantification by flipping misclassified superpixels to a different state (background,tooth, biofilm) with a single click, providing greater usability than simply marking the boundaries of biofilm and tooth as done by current state-of-the-art methods.

Hyperspectral cytometry.

Hyperspectral cytometry is an emerging technology for single-cell analysis that combines ultrafast optical spectroscopy and flow cytometry. Spectral cytometry systems utilize diffraction gratings or prism-based monochromators to disperse fluorescence signals from multiple labels (organic dyes, nanoparticles, or fluorescent proteins) present in each analyzed bioparticle onto linear detector arrays such as multianode photomultipliers or charge-coupled device sensors. The resultant data, consisting of a series of characterizing every analyzed cell, are not compensated by employing the traditional cytometry approach, but rather are spectrally unmixed utilizing algorithms such as constrained Poisson regression or non-negative matrix factorization. Although implementations of spectral cytometry were envisioned as early as the 1980s, only recently has the development of highly sensitive photomultiplier tube arrays led to design and construction of functional prototypes and subsequently to introduction of commercially available systems. This chapter summarizes the historical efforts and work in the field of spectral cytometry performed at Purdue University Cytometry Laboratories and describes the technology developed by Sony Corporation that resulted in release of the first commercial spectral cytometry system-the Sony SP6800. A brief introduction to spectral data analysis is also provided, with emphasis on the differences between traditional polychromatic and spectral cytometry approaches.

BiofilmQuant: a computer-assisted tool for dental biofilm quantification.

Dental biofilm is the deposition of microbial material over a tooth substratum. Several methods have recently been reported in the literature for biofilm quantification; however, at best they provide a barely automated solution requiring significant input needed from the human expert. On the contrary, state-of-the-art automatic biofilm methods fail to make their way into clinical practice because of the lack of effective mechanism to incorporate human input to handle praxis or misclassified regions. Manual delineation, the current gold standard, is time consuming and subject to expert bias. In this paper, we introduce a new semi-automated software tool, BiofilmQuant, for dental biofilm quantification in quantitative light-induced fluorescence (QLF) images. The software uses a robust statistical modeling approach to automatically segment the QLF image into three classes (background, biofilm, and tooth substratum) based on the training data. This initial segmentation has shown a high degree of consistency and precision on more than 200 test QLF dental scans. Further, the proposed software provides the clinicians full control to fix any misclassified areas using a single click. In addition, BiofilmQuant also provides a complete solution for the longitudinal quantitative analysis of biofilm of the full set of teeth, providing greater ease of usability.

High-throughput secondary screening at the single-cell level.

We have developed an automated system for drug screening using a single-cell-multiple functional response technology. The approach uses a semiautomated preparatory system, high-speed sample collection, and a unique analytical tool that provides instantaneous results for compound dilutions using 384-well plates. The combination of automation and rapid robotic sampling increases quality control and robustness. High-speed flow cytometry is used to collect single-cell results together with a newly defined analytical tool for extraction of IC(50) curves for multiple assays per cell. The principal advantage is the extreme speed of sample collection, with results from a 384-well plate being completed for both collection and data processing in less than 10 min. Using this approach, it is possible to extract detailed drug response information in a highly controlled fashion. The data are based on single-cell results, not populations. With simultaneous assays for different functions, it is possible to gain a more detailed understanding of each drug/compound interaction. Combined with integrated advanced data processing directly from raw data files, the process from sampling to analytical results is highly intuitive. Direct PubMed links allow review of drug structure and comparisons with similar compounds.

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%.

Computational analysis of high-throughput flow cytometry data.

INTRODUCTION: Flow cytometry has been around for over 40 years, but only recently has the opportunity arisen to move into the high-throughput domain. The technology is now available and is highly competitive with imaging tools under the right conditions. Flow cytometry has, however, been a technology that has focused on its unique ability to study single cells and appropriate analytical tools are readily available to handle this traditional role of the technology. AREAS COVERED: Expansion of flow cytometry to a high-throughput (HT) and high-content technology requires both advances in hardware and analytical tools. The historical perspective of flow cytometry operation as well as how the field has changed and what the key changes have been discussed. The authors provide a background and compelling arguments for moving toward HT flow, where there are many innovative opportunities. With alternative approaches now available for flow cytometry, there will be a considerable number of new applications. These opportunities show strong capability for drug screening and functional studies with cells in suspension. EXPERT OPINION: There is no doubt that HT flow is a rich technology awaiting acceptance by the pharmaceutical community. It can provide a powerful phenotypic analytical toolset that has the capacity to change many current approaches to HT screening. The previous restrictions on the technology, based on its reduced capacity for sample throughput, are no longer a major issue. Overcoming this barrier has transformed a mature technology into one that can focus on systems biology questions not previously considered possible.

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.

Hyperspectral cytometry at the single-cell level using a 32-channel photodetector.

Despite recent progress in cell-analysis technology, rapid classification of cells remains a very difficult task. Among the techniques available, flow cytometry (FCM) is considered especially powerful, because it is able to perform multiparametric analyses of single biological particles at a high flow rate-up to several thousand particles per second. Moreover, FCM is nondestructive, and flow cytometric analysis can be performed on live cells. The current limit for simultaneously detectable fluorescence signals in FCM is around 8-15 depending upon the instrument. Obtaining multiparametric measurements is a very complex task, and the necessity for fluorescence spectral overlap compensation creates a number of additional difficulties to solve. Further, to obtain well-separated single spectral bands a very complex set of optical filters is required. This study describes the key components and principles involved in building a next-generation flow cytometer based on a 32-channel PMT array detector, a phase-volume holographic grating, and a fast electronic board. The system is capable of full-spectral data collection and spectral analysis at the single-cell level. As demonstrated using fluorescent microspheres and lymphocytes labeled with a cocktail of antibodies (CD45/FITC, CD4/PE, CD8/ECD, and CD3/Cy5), the presented technology is able to simultaneously collect 32 narrow bands of fluorescence from single particles flowing across the laser beam in <5 µs. These 32 discrete values provide a proxy of the full fluorescence emission spectrum for each single particle (cell). Advanced statistical analysis has then been performed to separate the various clusters of lymphocytes. The average spectrum computed for each cluster has been used to characterize the corresponding combination of antibodies, and thus identify the various lymphocytes subsets. The powerful data-collection capabilities of this flow cytometer open up significant opportunities for advanced analytical approaches, including spectral unmixing and unsupervised or supervised classification.

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.