Sunday, November 20, 2022

Impedance Cytometry for Rapid Antibiotic Susceptibility Testing

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.


Tao Tang, Xun Liu, Yapeng Yuan, Ryota Kiya, Tianlong Zhang, Yang Yang, Shiro Suetsugu, Yoichi Yamazaki, Nobutoshi Ota, Koki Yamamoto, Hironari Kamikubo, Yo Tanaka, Ming Li, Yoichiroh Hosokawa, Yaxiaer Yalikun. Machine learning-based impedance system for real-time recognition of antibiotic-susceptible bacteria with parallel cytometry. Sensors and Actuators B: Chemical. Volume 374. 2023.


Impedance cytometry has enabled label-free and fast antibiotic susceptibility testing of bacterial single cells. Here, a machine learning-based impedance system is provided to score the phenotypic response of bacterial single cells to antibiotic treatment, with a high throughput of more than one thousand cells per min. In contrast to other impedance systems, an online training method on reference particles is provided, as the parallel impedance cytometry can distinguish reference particles from target particles, and label reference and target particles as the training and test set, respectively, in real time. Experiments with polystyrene beads of two different sizes (3 and 4.5 µm) confirm the functionality and stability of the system. Additionally, antibiotic-treated Escherichia coli cells are measured every two hours during the six-hour drug treatment. All results successfully show the capability of real-time characterizing the change in dielectric properties of individual cells, recognizing single susceptible cells, as well as analyzing the proportion of susceptible cells within heterogeneous populations in real time. As the intelligent impedance system can perform all impedance-based characterization and recognition of particles in real time, it can free operators from the post-processing and data interpretation.

A Simple Label-Free Method Reveals Bacterial Growth Dynamics and Antibiotic Action in Real-Time

Scientists have published a paper in Nature that describes a simple label-free method that reveals bacterial growth dynamics and antibiotic action in real-time. They discuss a patented technology which utilizes an innovative combination of laser light scattering, locked signal and integrating detection space. The methodology, named scattered light integrated collection (SLIC), provides a very sensitive way to detect microorganisms at low concentrations allowing us to follow their growth in real-time and to study the impact of different stresses on their growth dynamics. The paper may be accessed by clicking here.


Hammond, R.J.H., Falconer, K., Powell, T. et al. A simple label-free method reveals bacterial growth dynamics and antibiotic action in real-time. Sci Rep 12, 19393 (2022). 


Understanding the response of bacteria to environmental stress is hampered by the relative insensitivity of methods to detect growth. This means studies of antibiotic resistance and other physiological methods often take 24 h or longer. We developed and tested a scattered light and detection system (SLIC) to address this challenge, establishing the limit of detection, and time to positive detection of the growth of small inocula. We compared the light-scattering of bacteria grown in varying high and low nutrient liquid medium and the growth dynamics of two closely related organisms. Scattering data was modelled using Gompertz and Broken Stick equations. Bacteria were also exposed meropenem, gentamicin and cefoxitin at a range of concentrations and light scattering of the liquid culture was captured in real-time. We established the limit of detection for SLIC to be between 10 and 100 cfu mL−1 in a volume of 1–2 mL. Quantitative measurement of the different nutrient effects on bacteria were obtained in less than four hours and it was possible to distinguish differences in the growth dynamics of Klebsiella pneumoniae 1705 possessing the BlaKPC betalactamase vs. strain 1706 very rapidly. There was a dose dependent difference in the speed of action of each antibiotic tested at supra-MIC concentrations. The lethal effect of gentamicin and lytic effect of meropenem, and slow bactericidal effect of cefoxitin were demonstrated in real time. Significantly, strains that were sensitive to antibiotics could be identified in seconds. This research demonstrates the critical importance of improving the sensitivity of bacterial detection. This results in more rapid assessment of susceptibility and the ability to capture a wealth of data on the growth dynamics of bacteria. The rapid rate at which killing occurs at supra-MIC concentrations, an important finding that needs to be incorporated into pharmacokinetic and pharmacodynamic models. Importantly, enhanced sensitivity of bacterial detection opens the possibility of susceptibility results being reportable clinically in a few minutes, as we have demonstrated.

Rapid Diagnostic Testing Highly Accurate for Ebola Virus Disease

Two rapid diagnostic testing methods were found to have high sensitivity and specificity for diagnosing Ebola virus disease (EVD), according to study findings published in Clinical Microbiology and Infection.

Researchers conducted a systematic review of the literature to analyze the diagnostic accuracy of rapid diagnostic tests for EVD. Publication databases were searched from inception through May 2021, and the review included only diagnostic accuracy studies conducted among a live patient population with confirmed or suspected EVD.

Among 1054 studies identified, 15 were included in the review. Of the included studies, 10 assessed lateral flow-based testing and 5 assessed polymerase chain reaction (PCR)-based testing for the rapid diagnosis of EVD. Reverse transcription (RT)-PCR testing was used as the reference test in all included studies, with variations in regard to the specific assay used.

Lateral flow-based rapid testing demonstrated and overall estimated sensitivity of 86.0% (95% CI, 86.0%-86.2%) and a specificity of 97% (95% CI, 96.1%-97.9%) for diagnosing EVD. The lowest reported sensitivity associated with lateral flow testing was 62% (95% CI, 53%-73%). The researchers found significant variation in the specificity (range, 73%-100%) of lateral flow tests used across all studies. There were 2 studies that reported a sensitivity of 100% for lateral flow testing, both of which used whole blood samples obtained from patients that were tested either at the point of care or in a laboratory.

Compared with RT-PCR testing, rapid PCR testing was highly accurate for diagnosing EVD, with a sensitivity of 96.2% (95% CI, 92.4%-98.1%) and a specificity of 96.8% (95% CI, 95.3%-97.9%).

Rapid diagnostic tests were found to be highly accurate for diagnosing EVD across a range of specimen types, including whole blood, plasma, and buccal swabs.

Limitations were the inclusion of some studies with high selection bias, the lack of data on cycle threshold counts, and potential specimen degradation among studies that evaluated specimens several years following collection.

According to the researchers, “Our findings support the use of RDTs [rapid diagnostic tests] as a ‘rule in’ test to expedite treatment and vaccination” in patients with suspected EVD.


Dagens AB, Rojek A, Sigfrid L, Plüddemann A. The diagnostic accuracy of rapid diagnostic tests for Ebola virus disease: a systematic review. Clin Microbiol Infect. Published online September 23. doi:10.1016/j.cmi.2022.09.014