A flow cytometric assay to detect viability and persistence of Salmonella enterica subsp. enterica serotypes in nuclease-free water at 4 and 25°C.

Salmonella spp. is one of the most isolated microorganisms reported to be responsible for human foodborne diseases and death. Water constitutes a major reservoir where the Salmonella spp. can persist and go undetected when present in low numbers. In this study, we assessed the viability of 12 serotypes of Salmonella enterica subsp. enterica for 160 days in nuclease-free water at 4 and 25°C using flow cytometry and Tryptic Soy Agar (TSA) plate counts. The results show that all 12 serotypes remain viable after 160 days in distilled water using flow cytometry, whereas traditional plate counts failed to detect ten serotypes incubated at 25°C. Moreover, the findings demonstrate that 4°C constitutes a more favorable environment where Salmonella can remain viable for prolonged periods without nutrients. Under such conditions, however, Salmonella exhibits a higher susceptibility to all tested antibiotics and benzalkonium chloride (BZK). The pre-enrichment with Universal Pre-enrichment Broth (UP) and 1/10 × Tryptic Soy broth (1/10 × TSB) resuscitated all tested serotypes on TSA plates, nevertheless cell size decreased after 160 days. Furthermore, phenotype microarray (PM) analysis of S. Inverness and S. Enteritidis combined with principal component analysis (PCA) revealed an inter-individual variability in serotypes with their phenotype characteristics, and the impact of long-term storage at 4 and 25°C for 160 days in nuclease-free water. This study provides an insight to Salmonella spp. long-term survivability at different temperatures and highlights the need for powerful tools to detect this microorganism to reduce the risk of disease transmission of foodborne pathogens via nuclease-free water.

Specific Detection and Enumeration of Burkholderia cepacia Complex by Flow Cytometry Using a Fluorescence-Labeled Oligonucleotide Probe.

Burkholderia cepacia complex (BCC) contamination has resulted in recalls of non-sterile pharmaceutical products. The fast, sensitive, and specific detection of BCC is critical for ensuring the quality and safety of pharmaceutical products. In this study, a rapid flow cytometry-based detection method was developed using a fluorescence-labeled oligonucleotide Kef probe that specifically binds a KefB/KefC membrane protein sequence within BCC. Optimal conditions of a 1 nM Kef probe concentration at a 60 °C hybridization temperature for 30 min were determined and applied for the flow cytometry assay. The true-positive rate (sensitivity) and true-negative rate (specificity) of the Kef probe assay were 90% (18 positive out of 20 BCC species) and 88.9% (16 negative out of 18 non-BCC), respectively. The detection limit for B. cenocepacia AU1054 with the Kef probe flow cytometry assay in nuclease-free water was 1 CFU/mL. The average cell counts using the Kef probe assay from a concentration of 10 µg/mL chlorhexidine gluconate and 50 µg/mL benzalkonium chloride were similar to those of the RAPID-B total plate count (TPC). We demonstrate the potential of Kef probe flow cytometry as a more sensitive alternative to culture-based methods for detecting BCC in non-sterilized pharmaceutical raw materials and products with regards to water-based environments.

Draft Genome Sequence of Ralstonia pickettii Strain NCTR106, Isolated from Milk Carton Paperboard.

Ralstonia pickettii is an emerging global opportunistic pathogen. Here, we report the 5.3-Mbp draft genome sequence of R. pickettii NCTR106, isolated from milk carton paperboard obtained from a commercial paper mill. The genome sequence carries two beta-lactamase genes similar to those reported in R. pickettii isolates collected from a hospital.

A comparison of culture-based, real-time PCR, droplet digital PCR and flow cytometric methods for the detection of Burkholderia cepacia complex in nuclease-free water and antiseptics.

The presence of Burkholderia cepacia complex (BCC) strains has resulted in recalls of pharmaceutical products, since these opportunistic pathogens can cause serious infections. Rapid and sensitive diagnostic methods to detect BCC are crucial to determine contamination levels. We evaluated bacterial cultures, real-time PCR (qPCR), droplet digital PCR (ddPCR), and flow cytometry to detect BCC in nuclease-free water, in chlorhexidine gluconate (CHX) and benzalkonium chloride (BZK) solutions. Twenty BCC strains were each suspended (1, 10, 100, and 1000 CFU/ml) in autoclaved nuclease-free water, 10 µg/ml CHX, and 50 µg/ml BZK. Five replicates of each strain were tested at each concentration (20 strains × 4 concentrations × 5 replicates = 400 tests) to detect BCC using the aforementioned four methods. We demonstrated the potential of ddPCR and flow cytometry as more sensitive alternatives to culture-based methods to detect BCC in autoclaved nuclease-free water and antiseptics samples.

Rapid Flow Cytometry Detection of a Single Viable Escherichia coli O157:H7 Cell in Raw Spinach Using a Simplified Sample Preparation Technique.

Very low cell count detection of Escherichia coli O157:H7 in foods is critical, since an infective dose for this pathogen may be only 10 cells, and fewer still for vulnerable populations. A flow cytometer is able to detect and count individual cells of a target bacterium, in this case E. coli O157:H7. The challenge is to find the single cell in a complex matrix like raw spinach. To find that cell requires growing it as quickly as possible to a number sufficiently in excess of matrix background that identification is certain. The experimental design for this work was that of a U.S. Food and Drug Administration (FDA) In-House Level 3 validation executed in the technology's originating laboratory. Using non-selective enrichment broth, 6.5 h incubation at 42°C, centrifugation for target cell concentration, and a highly selective E. coli O157 fluorescent antibody tag, the cytometry method proved more sensitive than a reference regulatory method (p = 0.01) for detecting a single target cell, one E. coli O157:H7 cell, in 25 g of spinach. It counted that cell's daughters with at least 38× signal-to-noise ratio, analyzing 25 samples in total-time-to-results of 9 h.

Flow-Cytometry-Based Method to Detect Escherichia coli and Shigella Spp. Using 16S rRNA-Based Probe.

Detection of microbial contamination in foods before they go on to the market can help prevent the occurrence of foodborne illness outbreaks. Current methods for the detection of Escherichia coli are limited by time-consuming procedures, which include multiple culture incubation steps, and require several days to get results. This unit describes the development of an improved rapid flow-cytometry-based detection method that has greater sensitivity and specificity. This method requires less time-to-results (TTR) and can detect a small number of E. coli in the presence of large numbers of other bacteria. Clear step-by-step protocols for cell concentration determination, sample preparation, and flow cytometric analysis are provided. © 2017 by John Wiley & Sons, Inc.

3D-SDAR modeling of hERG potassium channel affinity: A case study in model design and toxicophore identification.

A dataset of 237 human Ether-à-go-go Related Gene (hERG) potassium channel inhibitors (180 of which were used for model building and validation, whereas 57 constituted the "true" external prediction set) collected from 22 literature sources was modeled by 3D-SDAR. To produce reliable and reproducible classification models for hERG blocking, the initial set of 180 chemicals was split into two subsets: a balanced modeling set consisting of 118 compounds and an unbalanced validation set comprised of 62 compounds. A PLS bagging-like algorithm written in Matlab was used to process the data and assign each compound to one of the two (hERG+ or hERG-) activity classes. The best predictive model evaluated on the basis of a fully randomized hold-out test set (comprising 20% of the modeling set) used 4 latent variables and a grid of 6ppm×6ppm×1Å in the C-C region, 6ppm×30ppm×1Å in the C-N region, and 30ppm×30ppm×1Å in the N-N region. An overall accuracy of 0.84 was obtained for both the hold-out test set and the validation set. Further, an external prediction set consisting of 57 drugs and drug derivatives was used to estimate the true predictive power of the reported 3D-SDAR model - a slight reduction of the overall accuracy down to 0.77 was observed. 3D-SDAR map of the most frequently occurring bins and their projection on the standard coordinate space of the chemical structures allowed identification of a three-center toxicophore composed of two aromatic rings and an amino group. A U test along the distance axis of the most frequently occurring 3D-SDAR bins was used to set the distance limits of the toxicophore. This toxicophore was found to be similar to an earlier reported phospholipidosis (PLD) toxicophore.

Alignment-independent technique for 3D QSAR analysis.

Molecular biochemistry is controlled by 3D phenomena but structure-activity models based on 3D descriptors are infrequently used for large data sets because of the computational overhead for determining molecular conformations. A diverse dataset of 146 androgen receptor binders was used to investigate how different methods for defining molecular conformations affect the performance of 3D-quantitative spectral data activity relationship models. Molecular conformations tested: (1) global minimum of molecules' potential energy surface; (2) alignment-to-templates using equal electronic and steric force field contributions; (3) alignment using contributions "Best-for-Each" template; (4) non-energy optimized, non-aligned (2D > 3D). Aggregate predictions from models were compared. Highest average coefficients of determination ranged from R Test (2) = 0.56 to 0.61. The best model using 2D > 3D (imported directly from ChemSpider) produced R Test (2) = 0.61. It was superior to energy-minimized and conformation-aligned models and was achieved in only 3-7 % of the time required using the other conformation strategies. Predictions averaged from models built on different conformations achieved a consensus R Test (2) = 0.65. The best 2D > 3D model was analyzed for underlying structure-activity relationships. For the compound strongest binding to the androgen receptor, 10 substructural features contributing to binding were flagged. Utility of 2D > 3D was compared for two other activity endpoints, each modeling a medium sized data set. Results suggested that large scale, accurate predictions using 2D > 3D SDAR descriptors may be produced for interactions involving endocrine system nuclear receptors and other data sets in which strongest activities are produced by fairly inflexible substrates.

Development of a Flow Cytometry-Based Method for Rapid Detection of Escherichia coli and Shigella Spp. Using an Oligonucleotide Probe.

Standard methods to detect Escherichia coli contamination in food use the polymerase chain reaction (PCR) and agar culture plates. These methods require multiple incubation steps and take a long time to results. An improved rapid flow-cytometry based detection method was developed, using a fluorescence-labeled oligonucleotide probe specifically binding a16S rRNA sequence. The method positively detected 51 E. coli isolates as well as 4 Shigella species. All 27 non-E. coli strains tested gave negative results. Comparison of the new genetic assay with a total plate count (TPC) assay and agar plate counting indicated similar sensitivity, agreement between cytometry cell and colony counts. This method can detect a small number of E.coli cells in the presence of large numbers of other bacteria. This method can be used for rapid, economical, and stable detection of E. coli and Shigella contamination in the food industry and other contexts.

Level 2 validation of a flow cytometric method for detection of Escherichia coli O157:H7 in raw spinach.

The Bacteriological Analytical Manual (BAM) method currently used by the United States Food and Drug Administration (FDA) to detect Escherichia coli O157:H7 in spinach was systematically compared to a new flow cytometry based method. This Food and Drug Administration (FDA) level 2 external laboratory validation study was designed to determine the latter method's sensitivity and speed for analysis of this pathogen in raw spinach. Detection of target cell inoculations with a low cell count is critical, since enterohemorrhagic strains of E. coli require an infective dose of as few as 10 cells (Schmid-Hempel and Frank, 2007). Although, according to the FDA, the infectious dose is unknown (Food and Drug Administration, 1993). Therefore, the inoculation level into the spinach, a total of 2.0±2.6 viable E. coli O157 cells, was specified to yield between 25% and 75% detection by the new method, out of 20 samples (10 positives and 10 negatives). This criterion was met in that the new method detected 60% of the nominally positive samples; the corresponding sensitivity of the reference method was 50%. For both methods the most likely explanation for false negatives was that no viable cells were actually introduced into the sample. In this validation study, the flow cytometry method was equal to the BAM in sensitivity and far superior in speed.

Computational identification of a phospholipidosis toxicophore using (13)C and (15)N NMR-distance based fingerprints.

Modified 3D-SDAR fingerprints combining (13)C and (15)N NMR chemical shifts augmented with inter-atomic distances were used to model the potential of chemicals to induce phospholipidosis (PLD). A curated dataset of 328 compounds (some of which were cationic amphiphilic drugs) was used to generate 3D-QSDAR models based on tessellations of the 3D-SDAR space with grids of different density. Composite PLS models averaging the aggregated predictions from 100 fully randomized individual models were generated. On each of the 100 runs, the activities of an external blind test set comprised of 294 proprietary chemicals were predicted and averaged to provide composite estimates of their PLD-inducing potentials (PLD+ if PLD is observed, otherwise PLD-). The best performing 3D-QSDAR model utilized a grid with a density of 8ppm×8ppm in the C-C region, 8ppm×20ppm in the C-N region and 20ppm×20ppm in the N-N region. The classification predictive performance parameters of this model evaluated on the basis of the external test set were as follows: accuracy=0.70, sensitivity=0.73 and specificity=0.66. A projection of the most frequently occurring bins on the standard coordinate space suggested a toxicophore composed of an aromatic ring with a centroid 3.5-7.5Å distant from an amino-group. The presence of a second aromatic ring separated by a 4-5Å spacer from the first ring and at a distance of between 5.5Å and 7Å from the amino-group was also associated with a PLD+ effect. These models provide comparable predictive performance to previously reported models for PLD with the added benefit of being based entirely on non-confidential, publicly available training data and with good predictive performance when tested in a rigorous, external validation exercise.

An integrated flow cytometry-based system for real-time, high sensitivity bacterial detection and identification.

Foodborne illnesses occur in both industrialized and developing countries, and may be increasing due to rapidly evolving food production practices. Yet some primary tools used to assess food safety are decades, if not centuries, old. To improve the time to result for food safety assessment a sensitive flow cytometer based system to detect microbial contamination was developed. By eliminating background fluorescence and improving signal to noise the assays accurately measure bacterial load or specifically identify pathogens. These assays provide results in minutes or, if sensitivity to one cell in a complex matrix is required, after several hours enrichment. Conventional assessments of food safety require 48 to 56 hours. The assays described within are linear over 5 orders of magnitude with results identical to culture plates, and report live and dead microorganisms. This system offers a powerful approach to real-time assessment of food safety, useful for industry self-monitoring and regulatory inspection.

Partial least square and k-nearest neighbor algorithms for improved 3D quantitative spectral data-activity relationship consensus modeling of acute toxicity.

A diverse set of 154 chemicals that included US Food and Drug Administration-regulated compounds tested for their aquatic toxicity in Daphnia magna were modeled by a 3-dimensional quantitative spectral data-activity relationship (3D-QSDAR). Two distinct algorithms, partial least squares (PLS) and Tanimoto similarity-based k-nearest neighbors (KNN), were used to process bin occupancy descriptor matrices obtained after tessellation of the 3D-QSDAR space into regularly sized bins. The performance of models utilizing bins ranging in size from 2 ppm × 2 ppm × 0.5 Å to 20 ppm × 20 ppm × 2.5 Å was explored. Rigorous quality-control criteria were imposed: 1) 100 randomized 20% hold-out test sets were generated and the average R(2) test of the respective models was used as a measure of their performance, and 2) a Y-scrambling procedure was used to identify chance correlations. A consensus between the best-performing composite PLS model using 0.5 Å × 14 ppm × 14 ppm bins and 10 latent variables (average R(2) test = 0.770) and the best composite KNN model using 0.5 Å × 8 ppm × 8 ppm and 2 neighbors (average R(2) test = 0.801) offered an improvement of about 7.5% (R(2) test consensus = 0.845). Projection of the most frequently occurring bins on the standard coordinate space indicated that the presence of a primary or secondary amino group-substituted aromatic systems-would result in an increased toxic effect in Daphnia. The presence of a second aromatic ring with highly electronegative substituents 5 Å to 7 Å apart from the first ring would lead to a further increase in toxicity.

Complementary PLS and KNN algorithms for improved 3D-QSDAR consensus modeling of AhR binding.

Multiple validation techniques (Y-scrambling, complete training/test set randomization, determination of the dependence of R2test on the number of randomization cycles, etc.) aimed to improve the reliability of the modeling process were utilized and their effect on the statistical parameters of the models was evaluated. A consensus partial least squares (PLS)-similarity based k-nearest neighbors (KNN) model utilizing 3D-SDAR (three dimensional spectral data-activity relationship) fingerprint descriptors for prediction of the log(1/EC50) values of a dataset of 94 aryl hydrocarbon receptor binders was developed. This consensus model was constructed from a PLS model utilizing 10 ppm x 10 ppm x 0.5 Å bins and 7 latent variables (R2test of 0.617), and a KNN model using 2 ppm x 2 ppm x 0.5 Å bins and 6 neighbors (R2test of 0.622). Compared to individual models, improvement in predictive performance of approximately 10.5% (R2test of 0.685) was observed. Further experiments indicated that this improvement is likely an outcome of the complementarity of the information contained in 3D-SDAR matrices of different granularity. For similarly sized data sets of Aryl hydrocarbon (AhR) binders the consensus KNN and PLS models compare favorably to earlier reports. The ability of 3D-QSDAR (three dimensional quantitative spectral data-activity relationship) to provide structural interpretation was illustrated by a projection of the most frequently occurring bins on the standard coordinate space, thus allowing identification of structural features related to toxicity.

Photobleaching with phloxine B sensitizer to reduce food matrix interference for detection of Escherichia coli serotype O157:H7 in fresh spinach by flow cytometry.

A flow cytometric method (RAPID-B™) with detection sensitivity of one viable cell of Escherichia coli serotype O157:H7 in fresh spinach (Spinacia oleracea) was developed and evaluated. The major impediment to achieving this performance was mistaking autofluorescing spinach particles for tagged target cells. Following a 5 h non-selective enrichment, artificially inoculated samples were photobleached, using phloxine B as a photosensitizer. Samples were centrifuged at high speed to concentrate target cells, then gradient centrifuged to separate them from matrix debris. In external laboratory experiments, RAPID-B and the reference method both correctly detected E. coli O157:H7 at inoculations of ca. 15 cells. In a follow-up study, after 4 cell inoculations of positives and 6 h enrichment, RAPID-B correctly identified 92% of 25 samples. The RAPID-B method limit of detection (LOD) was one cell in 25 g. It proved superior to the reference method (which incorporated real time-PCR, selective enrichment, and culture plating elements) in accuracy and speed.

¹³C NMR-distance matrix descriptors: optimal abstract 3D space granularity for predicting estrogen binding.

An improved three-dimensional quantitative spectral data-activity relationship (3D-QSDAR) methodology was used to build and validate models relating the activity of 130 estrogen receptor binders to specific structural features. In 3D-QSDAR, each compound is represented by a unique fingerprint constructed from (13)C chemical shift pairs and associated interatomic distances. Grids of different granularity can be used to partition the abstract fingerprint space into congruent "bins" for which the optimal size was previously unexplored. For this purpose, the endocrine disruptor knowledge base data were used to generate 50 3D-QSDAR models with bins ranging in size from 2 ppm × 2 ppm × 0.5 Å to 20 ppm × 20 ppm × 2.5 Å, each of which was validated using 100 training/test set partitions. Best average predictivity in terms of R(2)test was achieved at 10 ppm ×10 ppm × Z Å (Z = 0.5, ..., 2.5 Å). It was hypothesized that this optimum depends on the chemical shifts' estimation error (±4.13 ppm) and the precision of the calculated interatomic distances. The highest ranked bins from partial least-squares weights were found to be associated with structural features known to be essential for binding to the estrogen receptor.

Modeling chemical interaction profiles: I. Spectral data-activity relationship and structure-activity relationship models for inhibitors and non-inhibitors of cytochrome P450 CYP3A4 and CYP2D6 isozymes.

An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals--drugs, pesticides, and environmental pollutants--interact at the level of metabolic biotransformations mediated by cytochrome P450 (CYP) enzymes. In the present work, spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches were used to develop machine-learning classifiers of inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference pharmaceutical compounds whose interactions have been deduced from clinical data, and 100 additional chemicals that were used to evaluate model performance in an external validation (EV) test. SDAR is an innovative modeling approach that relies on discriminant analysis applied to binned nuclear magnetic resonance (NMR) spectral descriptors. In the present work, both 1D ¹³C and 1D ¹5N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D ¹³C-NMR and ¹5N-NMR spectra caused an increase in the tenfold cross-validation (CV) performance in terms of both the rate of correct classification and sensitivity. The results of SDAR modeling were verified using SAR. For SAR modeling, a decision forest approach involving from 6 to 17 Mold2 descriptors in a tree was used. Average rates of correct classification of SDAR and SAR models in a hundred CV tests were 60% and 61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4, and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data. Based on unique NMR structural descriptors, the new SDAR modeling method complements the existing SAR techniques, providing an independent estimator that can increase confidence in a structure-activity assessment. When modeling was applied to hazardous environmental chemicals, it was found that up to 20% of them may be substrates and up to 10% of them may be inhibitors of the CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for the environmental health branch of the public health service to extrapolate to hazardous chemicals directly from human clinical data. Therefore, the pharmacological and environmental health branches are both expected to benefit from these reported models.

Modeling chemical interaction profiles: II. Molecular docking, spectral data-activity relationship, and structure-activity relationship models for potent and weak inhibitors of cytochrome P450 CYP3A4 isozyme.

Polypharmacy increasingly has become a topic of public health concern, particularly as the U.S. population ages. Drug labels often contain insufficient information to enable the clinician to safely use multiple drugs. Because many of the drugs are bio-transformed by cytochrome P450 (CYP) enzymes, inhibition of CYP activity has long been associated with potentially adverse health effects. In an attempt to reduce the uncertainty pertaining to CYP-mediated drug-drug/chemical interactions, an interagency collaborative group developed a consensus approach to prioritizing information concerning CYP inhibition. The consensus involved computational molecular docking, spectral data-activity relationship (SDAR), and structure-activity relationship (SAR) models that addressed the clinical potency of CYP inhibition. The models were built upon chemicals that were categorized as either potent or weak inhibitors of the CYP3A4 isozyme. The categorization was carried out using information from clinical trials because currently available in vitro high-throughput screening data were not fully representative of the in vivo potency of inhibition. During categorization it was found that compounds, which break the Lipinski rule of five by molecular weight, were about twice more likely to be inhibitors of CYP3A4 compared to those, which obey the rule. Similarly, among inhibitors that break the rule, potent inhibitors were 2-3 times more frequent. The molecular docking classification relied on logistic regression, by which the docking scores from different docking algorithms, CYP3A4 three-dimensional structures, and binding sites on them were combined in a unified probabilistic model. The SDAR models employed a multiple linear regression approach applied to binned 1D ¹³C-NMR and 1D ¹5N-NMR spectral descriptors. Structure-based and physical-chemical descriptors were used as the basis for developing SAR models by the decision forest method. Thirty-three potent inhibitors and 88 weak inhibitors of CYP3A4 were used to train the models. Using these models, a synthetic majority rules consensus classifier was implemented, while the confidence of estimation was assigned following the percent agreement strategy. The classifier was applied to a testing set of 120 inhibitors not included in the development of the models. Five compounds of the test set, including known strong inhibitors dalfopristin and tioconazole, were classified as probable potent inhibitors of CYP3A4. Other known strong inhibitors, such as lopinavir, oltipraz, quercetin, raloxifene, and troglitazone, were among 18 compounds classified as plausible potent inhibitors of CYP3A4. The consensus estimation of inhibition potency is expected to aid in the nomination of pharmaceuticals, dietary supplements, environmental pollutants, and occupational and other chemicals for in-depth evaluation of the CYP3A4 inhibitory activity. It may serve also as an estimate of chemical interactions via CYP3A4 metabolic pharmacokinetic pathways occurring through polypharmacy and nutritional and environmental exposures to chemical mixtures.

Reduction of food matrix interference by a combination of sample preparation and multi-dimensional gating techniques to facilitate rapid, high sensitivity analysis for Escherichia coli serotype O157 by flow cytometry.

Escherichia coli serotype O157 strains, which may be found in foods, often produce enterohemorrhagic toxins. The research goal was to facilitate rapid, sensitive detection in foods of E. coli serotype O157 by flow cytometry. Sample preparation methods were developed for potential use in 15 foods. Combined with multi-dimensional gating, these methods decreased time-to-results (TTR) for determination of low-level contamination. They mitigated the effects of interfering food components, concentrated cells for analysis without growth or, when necessary, used short-term incubation. The results showed qualitative analysis that was equivalent to culture plating in accuracy and superior in sensitivity and speed. Preparation time was 10-30 min per sample and detection took 3-4 min. Culture growth, if required, took an additional 4-6 h. A protocol for raw spinach analysis, using 4 h culture incubation, was 94% correct with one false negative for a low level inoculation. Its projected limit-of-detection (LOD) was 1 viable cell per 25 g of spinach, based on an average of 28 counts detected after growth and an estimated counts-to-threshold (C/T) ratio of 1.3. The results suggested potential uses for regulatory screening and food industry process control.