Scientists at Nara Institute of Science and Technology in Japan have come up with a method to rapidly determine the antibiotic susceptibility of a bacterial sample, such as a patient sample from a non-healing infected wound. The technique is based on impedance cytometry, which involves a high-throughput single cell analysis of the bacterial cells. The impedance system measures the dielectric properties of the cells as they flow through the device, and it can assess up to 1000 cells per minute.
Using machine learning to determine the differences in the dielectric properties between samples that have been treated with antibiotics and untreated samples let the researchers identify the susceptibility of the bacteria to a given antibiotic in as little as two hours after treatment.
Antibiotic-resistant bacteria are becoming more common, and the consequences for our healthcare will be profound. Routine surgical procedures could become fraught with risk and simple infections could progress into more serious issues without any available drugs to combat them. However, thankfully, we are not at this stage quite yet, and we still have time to prolong the utility of our existing antibiotic arsenal. In this context, using the correct antibiotic drug is important to achieve the desired treatment outcome, and also to reduce the likelihood that resistance develops further
Getting a readout on the antibiotic susceptibility of the bacteria causing problems for a particular patient is best completed quickly so that the patient can avail of the correct treatment as soon as possible. However, current approaches to achieve this can take too long. “Oftentimes susceptibility results are needed much faster than conventional tests can deliver them,” said Yaxiaer Yalikun, a researcher involved in the study. “To address this, we developed a technology that can meet this need.”
The new technology involves using impedance cytometry to measure the dielectric properties of the bacteria. These properties will change quite quickly on contact with an antibiotic that the bacteria are susceptible to. The researchers split the bacterial sample in two, and treat one of these samples with an antibiotic before separately analyzing treated bacteria and untreated controls. Then, a machine learning algorithm learns the characteristics of the untreated bacteria, and determines if there is anything different with the treated cells.
“Although there was a misidentification error of less than 10% in our work, there was a clear discrimination between susceptible and resistant cells within 2 hours of antibiotic treatment,” said Yoichiroh Hosokawa, another researcher involved in the study.
Study in journal Sensors and Actuators: B. Chemical: Machine learning-based impedance system for real-time recognition of antibiotic-susceptible bacteria with parallel cytometry
