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Updated: Jun 9, 2026

Bacterial Detection & Identification Using Electrochemical Sensors

Bacterial Detection & Identification Using Electrochemical Sensors

Published on: April 23, 2013

Single-Cell Electrochemical Aptasensor Array.

Shuo Li¹, Yannick Coffinier², Chann Lagadec³

¹IIS, LIMMS/CNRS-IIS IRL2820, The Univ. of Tokyo 4-6-1 Komaba, Meguro-ku 153-8505, Tokyo, Japan.

|July 11, 2023

Summary

This summary is machine-generated.

This study introduces a novel single-cell electrochemical aptasensor array using nanopillar technology. This breakthrough enables precise detection of cancer cells for improved early diagnosis and therapy monitoring.

Keywords:

EpCAM aptasensors bioelectrochemistry nanopillars single-cell

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Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

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Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

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Published on: September 7, 2018

Area of Science:

Biomedical Engineering
Nanotechnology
Electrochemistry

Background:

Electrochemical sensors show promise for detecting cancer cells, but scaling to single-cell arrays remains challenging.
Existing technologies struggle with the high-throughput analysis required for clinical applications.

Purpose of the Study:

To develop and demonstrate a scalable single-cell electrochemical aptasensor array.
To utilize nanopillar array technology for enhanced single-cell detection and analysis.
To target epithelial cell adhesion molecule (EpCAM) using redox-labeled aptamers.

Main Methods:

Integration of nanopillar array technology with microwells for single-cell trapping.
Development of aptasensors with redox-labeled aptamers targeting EpCAM.
Electrochemical detection of single target cells captured on the sensor surface.

Main Results:

Successful implementation of a single-cell electrochemical aptasensor array.
Demonstration of precise detection and analysis of single target cells.
Validation of nanopillar technology for scalable bioelectrochemical sensing.

Conclusions:

The developed single-cell aptasensor array overcomes previous scaling limitations.
This technology offers new possibilities for large-scale implementation in clinical settings.
Enables statistical analysis for early cancer diagnosis and therapy monitoring.