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Related Experiment Video

Updated: Dec 23, 2025

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Published on: April 7, 2023

Electronic classification of barcoded particles for multiplexed detection using supervised machine learning analysis.

Jianye Sui¹, Pengfei Xie¹, Zhongtian Lin¹

¹Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, 08854, USA.

|April 22, 2020

Summary

This summary is machine-generated.

This study introduces electronic barcoding of micro-particles for multiplex biomarker analysis in wearable biosensors. This novel nano-electronic barcoding method enables reliable, portable diagnostics and real-time health monitoring.

Keywords:

Biomarker Biosensor Electrical impedance Electronically barcoded particles Support vector machine

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Last Updated: Dec 23, 2025

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

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Area of Science:

Biomedical Engineering
Materials Science
Nanotechnology

Background:

Wearable biosensors are crucial for health applications, requiring multiplex biomarker analysis for enhanced reliability.
Current methods for multiplexing in biosensors face limitations in sensitivity and portability.
Electronic barcoding of micro-particles offers a promising alternative for advanced biosensing capabilities.

Purpose of the Study:

To develop and evaluate a novel nano-electronic barcoding technique for micro-particles.
To enable multiplex biomarker analysis for wearable biosensing platforms.
To demonstrate the feasibility of electronic barcoding for portable point-of-care diagnostics.

Main Methods:

Fabrication of nine distinct barcoded micro-particles using atomic layer deposition to create tunable oxide layers.
Characterization of particle barcodes by analyzing their unique frequency-dependent impedance signatures.
Classification of barcoded particles using multi-frequency impedance cytometry and supervised machine learning algorithms.

Main Results:

Successfully fabricated micro-particles with distinct electronic barcodes based on oxide layer properties.
Demonstrated accurate classification of the fabricated barcoded particles using impedance cytometry and machine learning.
Validated the potential for a one-to-one correspondence between particle barcodes and target biomarkers.

Conclusions:

Nano-electronic barcoding of micro-particles is a viable method for multiplex biomarker detection.
This technology supports the development of compact, electronic readout platforms for wearable biosensors.
The approach holds significant promise for advancing portable diagnostics and real-time health monitoring.