Automated Electrical Detection of Proteins for Oral Squamous Cell Carcinoma in an Integrated Microfluidic Chip Using...

Area of Science:

• Biomarker Discovery
• Microfluidics
• Machine Learning

Background:

• Proteins serve as crucial biomarkers for disease prognosis and diagnosis.
• Understanding fundamental biological processes relies on protein analysis.
• Current methods for protein detection can be complex and time-consuming.

Purpose of the Study:

• To develop a fully automated, label-free method for electrical protein detection.
• To integrate multi-frequency impedance cytometry with a microfluidic chip for enhanced analysis.
• To create a programmable fluid control system using off-the-shelf components.

Main Methods:

• Utilized a custom polydimethylsiloxane (PDMS) microfluidic mixer with serpentine channels.
• Employed multi-frequency impedance cytometry for label-free electrical detection.
• Validated the mixing method using fluorescent sandwich immunoassay and compared with commercial mixers.
• Developed a machine learning-assisted system for automated protein quantification.

Main Results:

• Demonstrated robust mixing in the custom microfluidic chip.
• Successfully detected and quantified Interleukin-6 (IL-6) in solution with 96% accuracy.
• Confirmed the system's capability for biomarker detection relevant to oral squamous cell carcinoma (OSCC).

Conclusions:

• The developed system offers an accurate and automated approach for protein detection.
• The platform can be adapted for detecting various protein biomarkers with minor modifications.
• This technology holds potential for improved diagnostic tools and biological research.