Nanoparticle Sensor for Detection of Pollutants in Real-Time and Real-World Conditions

Contamination of water and food with pathogenic E. coli poses a considerable health hazard to humans. Traditional bacterial culture technologies are the gold standard for detection, but can take several days for the results to become available. Alternatives to cultures are immunoassays and real time PCR-based analysis. However, PCR is often compromised by contaminants in real life samples and can require several preparation steps, including immunomagnetic separation and enrichment. Antibodies are the traditional high-affinity reagents used to capture pathogens for PCR and ELISA. However, polyclonal antibodies purified from immunized animals can vary from lot-to-lot and separation of ultrapure biomaterials in large quantities can be expensive. Monoclonal antibodies do not vary between production lots, but may not be effective against pathogens that are capable of antigenic variation. In addition, antibodies are environmentally labile under real world conditions, exhibiting poor shelf life and requiring refrigeration. Here, we propose a bio-based, but non-proteinaceious method of detecting E. coli in real-time in water that differentiates between strains of E. coli. This method does not require any sample preparation and it is a passive technology. It will send a signal as soon as it detects E. coli.

Each year many beaches are closed due to E. coli contamination. Additionally, in each of the last several years there have been wide scale recalls of food (beef, tomatoes, spinach) due to E. coli contamination causing consumers to become sick. The current protocol for E. coli detection in bodies of water involves taking grab samples at sampling sites, transporting these samples back to labs, and determining whether E. coli grows from the samples over a 1-3 day period followed by the determination of which E. coli strains are present. In the food processing industry, samples are analyzed in a similar manner, however sometimes the contamination is not detected until consumers fall ill. By the point of human illness, the contaminated food products may have been shipped to every state in the country and been processed into many different forms. Clearly, a real-time E. coli O157:H7 sensor would be an enormous benefit for time savings, cost savings, and the prevention of human illness. Here, we propose such a real-time sensor for pathogenic E. coli detection. The detector can distinguish between strains of E. coli and reports E. coli detection in real time as the E. coli is captured by the sensor.