Diagnostic Kit Detects Pathogenic Microorganisms Based on Electric Signatures

Abstract

Researchers at the University of Central Florida have developed a novel diagnostic kit for quickly identifying microorganisms and detecting live versus dead cells, based on their electrical activity. The invention can also be used to determine whether a cell is actively growing, drug-resistant or drug-susceptible. With such a comprehensive diagnostic tool, clinicians (plant pathologists, doctors and veterinarians) will be able to determine the stage of a disease and prescribe the most effective treatment strategy.

Compared to conventional testing methods, the invention is a significant improvement. Tests, such as Polymerase Chain Reaction (PCR), only enable clinicians to identify whether a pathogenic microorganism is present—not whether the cells are live or dead. Thus,  the tests lack the ability to gauge the stage of a microorganism’s growth and how best to fight a disease. Moreover, the tests are often inefficient and slow at providing results (requiring hours or days for processing).

Technical Details

The invention is a diagnostic kit for quickly detecting the presence of different pathogenic microorganisms, their viability and metabolic states. The detection mechanism identifies differences in the electric signatures of cells, both on their surface and their immediate micro-environment. Cells of both pathogenic and non-pathogenic microorganisms (such as bacteria, fungi and viruses) exhibit electrical activity as they interact with their surroundings and exchange ions across cell walls.

Specifically, the kit comprises a method and apparatus to detect the electrochemical response of a microorganism and to compare that response against a database for identification. The apparatus of the invention includes an interdigitated electrode (IDE), which detects morphological changes, and/or a microelectrode array (MEA), which captures electrophysiological measurements.

In an example use, a clinician takes a sample from an infected orange tree and uses the diagnostic kit to generate an electrophysiological and/or impedance signature of the sample. The measurements are then compared with the electrophysiological and/or impedance signatures stored in the system’s memory. Based on the sample results, the system indicates that the tree is infected with the American form of Huanglongbing (HLB), a citrus disease caused by the bacterium, Candidatus Liberibacter americanus. The kit also enables the clinician to distinguish between live and dead cells in the sample and to determine the stage of growth.

Benefit

Market Application