Rapid, simple, inexpensive, accurate, and sensitive point-of-care (POC) detection of viral pathogens in bodily fluids is a vital component of controlling the spread of infectious diseases. The predominant laboratory-based methods for sample processing and nucleic acid detection face limitations that prevent them from gaining wide adoption for POC applications in low-resource settings and self-testing scenarios. Here, we report the design and characterization of an integrated system for rapid sample-to-answer detection of a viral pathogen in a droplet of whole blood comprised of a 2-stage microfluidic cartridge for sample processing and nucleic acid amplification, and a clip-on detection instrument that interfaces with the image sensor of a smartphone. The cartridge is designed to release viral RNA from Zika virus in whole blood using chemical lysis, followed by mixing with the assay buffer for performing reverse-transcriptase loop-mediated isothermal amplification (RT-LAMP) reactions in six parallel microfluidic compartments. The battery-powered handheld detection instrument uniformly heats the compartments from below, and an array of LEDs illuminates from above, while the generation of fluorescent reporters in the compartments is kinetically monitored by collecting a series of smartphone images. We characterize the assay time and detection limits for detecting Zika RNA and gamma ray-deactivated Zika virus spiked into buffer and whole blood and compare the performance of the same assay when conducted in conventional PCR tubes. Our approach for kinetic monitoring of the fluorescence-generating process in the microfluidic compartments enables spatial analysis of early fluorescent “bloom” events for positive samples, in an approach called “Spatial LAMP” (S-LAMP). We show that S-LAMP image analysis reduces the time required to designate an assay as a positive test, compared to conventional analysis of the average fluorescent intensity of the entire compartment. S-LAMP enables the RT-LAMP process to be as short as 22 minutes, resulting in a total sample-to-answer time in the range of 17–32 minutes to distinguish positive from negative samples, while demonstrating a viral RNA detection as low as 2.70 × 10 2 copies per μl, and a gamma-irradiated virus of 10 3 virus particles in a single 12.5 μl droplet blood sample.
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Advances in Foodborne Pathogen Analysis
As the world population has grown, new demands on the production of foods have been met by increased efficiencies in production, from planting and harvesting to processing, packaging and distribution to retail locations. These efficiencies enable rapid intranational and global dissemination of foods, providing longer “face time” for products on retail shelves and allowing consumers to make healthy dietary choices year-round. However, our food production capabilities have outpaced the capacity of traditional detection methods to ensure our foods are safe. Traditional methods for culture-based detection and characterization of microorganisms are time-, labor- and, in some instances, space- and infrastructure-intensive, and are therefore not compatible with current (or future) production and processing realities. New and versatile detection methods requiring fewer overall resources (time, labor, space, equipment, cost, etc.) are needed to transform the throughput and safety dimensions of the food industry. Access to new, user-friendly, and point-of-care testing technologies may help expand the use and ease of testing, allowing stakeholders to leverage the data obtained to reduce their operating risk and health risks to the public. The papers in this Special Issue on “Advances in Foodborne Pathogen Analysis” address critical issues in rapid pathogen analysis, including preanalytical sample preparation, portable and field-capable test methods, the prevalence of antibiotic resistance in zoonotic pathogens and non-bacterial pathogens, such as viruses and protozoa.
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- Award ID(s):
- 1826801
- NSF-PAR ID:
- 10227903
- Date Published:
- Journal Name:
- Foods
- Volume:
- 9
- Issue:
- 11
- ISSN:
- 2304-8158
- Page Range / eLocation ID:
- 1635
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Antimicrobial resistance (AMR) is arguably one of the major health and economic challenges in our society. A key aspect of tackling AMR is rapid and accurate detection of the emergence and spread of AMR in food animal production, which requires routine AMR surveillance. However, AMR detection can be expensive and time-consuming considering the growth rate of the bacteria and the most commonly used analytical procedures, such as Minimum Inhibitory Concentration (MIC) testing. To mitigate this issue, we utilized machine learning to predict the future AMR burden of bacterial pathogens. We collected pathogen and antimicrobial data from >600 farms in the United States from 2010 to 2021 to generate AMR time series data. Our prediction focused on five bacterial pathogens (
Escherichia coli, Streptococcus suis, Salmonella sp., Pasteurella multocida , andBordetella bronchiseptica ). We found that Seasonal Auto-Regressive Integrated Moving Average (SARIMA) outperformed five baselines, including Auto-Regressive Moving Average (ARMA) and Auto-Regressive Integrated Moving Average (ARIMA). We hope this study provides valuable tools to predict the AMR burden not only of the pathogens assessed in this study but also of other bacterial pathogens. -
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Abstract Molecular technologies have revolutionized the field of wildlife disease ecology, allowing the detection of outbreaks, novel pathogens, and invasive strains. In particular, metabarcoding approaches, defined here as tools used to amplify and sequence universal barcodes from a single sample (e.g., 16S rRNA for bacteria, ITS for fungi, 18S rRNA for eukaryotes), are expanding our traditional view of host–pathogen dynamics by integrating microbial interactions that modulate disease outcome. Here, I provide an analysis from the perspective of the field of amphibian disease ecology, where the emergence of multi-host pathogens has caused global declines and species extinctions. I reanalyzed an experimental mesocosm dataset to infer the functional profiles of the skin microbiomes of coqui frogs (Eleutherodactylus coqui), an amphibian species that is consistently found infected with the fungal pathogen Batrachochytrium dendrobatidis and has high turnover of skin bacteria driven by seasonal shifts. I found that the metabolic activities of microbiomes operate at different capacities depending on the season. Global enrichment of predicted functions was more prominent during the warm-wet season, indicating that microbiomes during the cool-dry season were either depauperate, resistant to new bacterial colonization, or that their functional space was more saturated. These findings suggest important avenues to investigate how microbes regulate population growth and contribute to host physiological processes. Overall, this study highlights the current challenges and future opportunities in the application of metabarcoding to investigate the causes and consequences of disease in wild systems.
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Discovery of new strains of bacteria that inhibit pathogen growth can facilitate improvements in biocontrol and probiotic strategies. Traditional, plate-based co-culture approaches that probe microbial interactions can impede this discovery as these methods are inherently low-throughput, labor-intensive, and qualitative. We report a second-generation, photo-addressable microwell device, developed to iteratively screen interactions between candidate biocontrol agents existing in bacterial strain libraries and pathogens under increasing pathogen pressure. Microwells (0.6 pl volume) provide unique co-culture sites between library strains and pathogens at controlled cellular ratios. During sequential screening iterations, library strains are challenged against increasing numbers of pathogens to quantitatively identify microwells containing strains inhibiting the highest numbers of pathogens. Ring-patterned 365 nm light is then used to ablate a photodegradable hydrogel membrane and sequentially release inhibitory strains from the device for recovery. Pathogen inhibition with each recovered strain is validated, followed by whole genome sequencing. To demonstrate the rapid nature of this approach, the device was used to screen a 293-membered biovar 1 agrobacterial strain library for strains inhibitory to the plant pathogen Agrobacterium tumefaciens sp. 15955. One iterative screen revealed nine new inhibitory strains. For comparison, plate-based methods did not uncover any inhibitory strains from the library (n = 30 plates). The novel pathogen-challenge screening mode developed here enables rapid selection and recovery of strains that effectively suppress pathogen growth from bacterial strain libraries, expanding this microwell technology platform toward rapid, cost-effective, and scalable screening for probiotics, biocontrol agents, and inhibitory molecules that can protect against known or emerging pathogens.
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Hyperspectral imaging (HSI) is a spectroscopic technique which captures images at a high contrast over a wide range of wavelengths to show pixel specific composition. Traditional uses of HSI include: satellite imagery, food distribution quality control and digital archaeological reconstruction. Our lab has focused on developing applications of HSI fluorescence imaging systems to study molecule-specific detection for rapid cell signaling events or real-time endoscopic screening. Previously, we have developed a prototype spectral light source, using our modified imaging technique, excitationscanning hyperspectral imaging (HIFEX), coupled to a commercial colonoscope for feasibility testing. The 16 wavelength LED array was combined, using a multi-branched solid light guide, to couple to the scope’s optical input. The prototype acquired a spectral scan at near video-rate speeds (~8 fps). The prototype could operate at very rapid wavelength switch speeds, limited to the on/off rates of the LEDs (~10 μs), but imaging speed was limited due to optical transmission losses (~98%) through the solid light guide. Here we present a continuation of our previous work in performing an in-depth analysis of the solid light guide to optimize the optical intensity throughput. The parameters evaluated include: LED intensity input, geometry (branch curvature and combination) and light propagation using outer claddings. Simulations were conducted using a Monte Carlo ray tracing software (TracePro). Results show that transmission within the branched light guide may be optimized through LED focusing lenses, bend radii and smooth tangential branch merges. Future work will test a new fabricated light guide from the optimized model framework.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/foods9111635
