We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
ZeptoMetrix an Antylia scientific company

Download Mobile App

Parallel Impedance Cytometry Screens Bacterial Cells in Real Time

By LabMedica International staff writers
Posted on 25 Oct 2022
Print article
Image: Schematic of the intelligent impedance system, consisting of a parallel impedance cytometry and a machine learning-based detection system (Photo courtesy of Yaxiaer Yalikun, PhD)
Image: Schematic of the intelligent impedance system, consisting of a parallel impedance cytometry and a machine learning-based detection system (Photo courtesy of Yaxiaer Yalikun, PhD)

Antibiotic-resistant infections are responsible for killing over a million people worldwide every year. Central to managing resistant infections is quickly identifying an appropriate treatment to which the infective bacteria are susceptible. Significant time is needed to determine the drug susceptibility profile of a bacterial infection.

Impedance cytometry measures the dielectric properties of individual cells with high throughput, over a thousand cells per minute. Because the electrical readout of a bacterium corresponds to its physical response to an antibiotic, one has a straightforward means of determining whether the antibiotic works against the bacteria.

Scientists at the Nara Institute of Science and Technology (Ikoma, Japan) developed a novel impedance cytometry method that simultaneously analyses the test and reference particles in separate channels, creating easily analyzable separate datasets. This cytometry had nanoscale sensitivity, allowing for detection of even minute physical changes in bacterial cells. The group designed a machine learning tool to analyze the impedance cytometry data. Because the new cytometry method splits the test and reference datasets, the machine learning tool could automatically label the reference dataset as the “learning” dataset and use it to learn the characteristics of an untreated bacterium. By real-time comparison with antibiotic-treated cells, the tool can identify whether the bacteria are susceptible to the drug and can even identify what proportion of bacterial cells are resistant in a mixed-resistance population.

Target objects can be detected even when benchmarked against similar objects. Parallel dual microchannels allow the simultaneous detection of reference and target particles in two separate microchannels, without the premixing of reference and target suspensions. The impedance pulses of both can appear separately on the opposite sides of the same time series, which have been verified via simulation and experimental results. Raw impedance signals can easily distinguish target particles from reference ones. Polystyrene beads with different sizes ranging from nano- to microscale (e.g., 500, 750 nm, 1, 2, 3, and 4.5 μm) confirm the nanosensitivity of the system. In addition, the detection of antibiotic-treated Escherichia coli cells demonstrates that the system can be used for the quantitative assessment of the dielectric properties of individual cells, as well as for the proportion of susceptible cells.

Yoichiroh Hosokawa, PhD, an Assistant Professor and a senior author of the study, said, “Although there was a misidentification error of less than 10% in our work, there was a clear discrimination between susceptible and resistant cells within two hours of antibiotic treatment.”

The authors concluded that their findings indicate that the parallel impedance cytometry can greatly facilitate the measurement and calibration of the impedances of various particles or cells and provide a means to compare their dielectric properties. The study was published on October 6, 2022 in the journal ACS Sensors.

Related Links:
Nara Institute of Science and Technology

Platinum Member
COVID-19 Rapid Test
OSOM COVID-19 Antigen Rapid Test
Magnetic Bead Separation Modules
Complement 3 (C3) Test
GPP-100 C3 Kit
Gold Member
Real-time PCR System
GentierX3 Series

Print article


Clinical Chemistry

view channel
Image: The 3D printed miniature ionizer is a key component of a mass spectrometer (Photo courtesy of MIT)

3D Printed Point-Of-Care Mass Spectrometer Outperforms State-Of-The-Art Models

Mass spectrometry is a precise technique for identifying the chemical components of a sample and has significant potential for monitoring chronic illness health states, such as measuring hormone levels... Read more


view channel
Image: The CAPILLARYS 3 DBS devices have received U.S. FDA 510(k) clearance (Photo courtesy of Sebia)

Next Generation Instrument Screens for Hemoglobin Disorders in Newborns

Hemoglobinopathies, the most widespread inherited conditions globally, affect about 7% of the population as carriers, with 2.7% of newborns being born with these conditions. The spectrum of clinical manifestations... Read more


view channel
Image: The AI predictive model identifies the most potent cancer killing immune cells for use in immunotherapies (Photo courtesy of Shutterstock)

AI Predicts Tumor-Killing Cells with High Accuracy

Cellular immunotherapy involves extracting immune cells from a patient's tumor, potentially enhancing their cancer-fighting capabilities through engineering, and then expanding and reintroducing them into the body.... Read more


view channel
Image: The T-SPOT.TB test is now paired with the Auto-Pure 2400 liquid handling platform for accurate TB testing (Photo courtesy of Shutterstock)

Integrated Solution Ushers New Era of Automated Tuberculosis Testing

Tuberculosis (TB) is responsible for 1.3 million deaths every year, positioning it as one of the top killers globally due to a single infectious agent. In 2022, around 10.6 million people were diagnosed... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.