Advancing point-of-need bacteria detection using microfluidic paper-based analytical devices
Date
2018
Authors
Boehle, Katherine Elizabeth, author
Henry, Charles S., advisor
Krummel, Amber T., committee member
Geiss, Brian J., committee member
Ackerson, Christopher J., committee member
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Volume Title
Abstract
Bacteria are responsible for more hospitalizations and deaths than any other foodborne contaminant, making the detection of these pathogens of utmost importance. To further complicate bacteria detection, the overuse of antibiotics and genetic plasticity of bacteria has caused antimicrobial resistant (AMR) bacteria to become a more prevalent issue that threatens to be the number one cause of death worldwide by 2050 unless significant innovations are made. Although bacteria detection in the field is ideal, the current gold standards for detection require trained personnel and a central laboratory. The primary work in this dissertation acts to improve upon current bacteria detection methods by designing, developing, and optimizing inexpensive user-friendly tests that detect bacteria at the point-of-need without trained personnel or expensive equipment. These goals are accomplished using microfluidic paper-based analytical devices (μPADs), a growing field for point-of-need detection that have been used for a variety of analytes and applications. Using paper as a platform has allowed for the simple development of user-friendly devices because of their easily designed and modifiable material that typically costs <$0.01 USD per device and allows for multiple tests to be completed from one sample addition. Devices that will be described include colorimetric spot tests that detect common fecal indicator bacteria (FIB) species Escherichia coli and Enterococci spp. based on enzymes that are naturally produced by the bacteria. Utilizing these enzymes, a test was developed that turns from clear to yellow as an indication of live bacteria. These tests were successfully used in the detection of bacteria in food and water samples to demonstrate its efficacy in food safety applications. To improve specificity and sensitivity of bacteria detection, a second spot test was developed that utilizes immunomagnetic separation (IMS) and an enzymatic sandwich immunoassay in the detection of another common foodborne pathogen, Salmonella typhimurium. This assay was developed specifically for detecting pathogens in complex matrices, such as one of the most common causes of pathogen contamination: animal feces. Because AMR bacteria are becoming a more prevalent problem, devices were developed to specifically detect bacteria resistant to β-lactam antibiotics, the most common case of antimicrobial resistance observed in bacteria. The first generation of devices were developed to detect β-lactamase activity, an enzyme that facilitates resistance against β-lactam antibiotics. These devices were successful in detecting AMR in different species of bacteria isolated from environmental samples, and in the detection of AMR in sewage water. The second generation of devices enables detection of resistance against specific antibiotics through hydrolysis of the antibiotic and detecting a change in pH. Although not yet demonstrated, these devices will eventually be used to determine if bacteria are resistant against specific classes of β-lactam antibiotics, including a commonly used class of last resort antibiotics, carbapenems. Beyond bacteria detection, this dissertation also explores developing a field-ready device to identify falsified and substandard antibiotics. Because antibiotics are most commonly counterfeited in resource-limited settings, it is imperative to develop user-friendly point-of-need devices that can quantify the amount of active pharmaceutical ingredient in antibiotics. This was accomplished using enzyme competition, a method that had not been demonstrated paper-based devices. Finally, all devices that have been developed and optimized in this dissertation utilized colorimetric detection. While a user-friendly and easily implemented method of detection, it does suffer from drawbacks such as sensitivity and user subjectivity when using the devices. To eliminate subjectivity, a portable system using a Raspberry Pi computer and 3D-printed light box and device holder have been optimized. Although the system has been demonstrated by automatically analyzing images and calculating Michaelis-Menten enzyme kinetic values, this system has limitless possibilities in automatically analyzing colorimetric paper-based devices for truly objective colorimetric readouts and quantitative infield detection of pathogens or other analytes.
Description
Rights Access
Subject
bacteria detection
foodborne pathogens
antimicrobial resistant bacteria
paper-based analytical devices
counterfeit antibiotics