A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR.

Area of Science:

• Biotechnology
• Analytical Chemistry
• Molecular Diagnostics

Background:

• Accurate protein quantification is crucial for disease diagnosis and personalized medicine.
• Current methods often require complex manual sample preparation, limiting point-of-care applications.
• Novel, automated solutions are needed for sensitive and rapid protein analysis.

Purpose of the Study:

• To develop a fully automated point-of-care device for sensitive protein quantification.
• To integrate aptamer-based detection with real-time polymerase chain reaction (qPCR) for enhanced sensitivity.
• To introduce a new algorithm (PeakFluo) for automated protein quantification and explore machine learning for clinical classification.

Main Methods:

• Development of a novel automated point-of-care device with a disposable cartridge for sample preparation.
• Integration of aptamer-based sensing with real-time polymerase chain reaction (qPCR) for DNA amplification and signal detection.
• Implementation of a new quantification algorithm (PeakFluo) and a convolutional neural network for sample classification based on qPCR data.

Main Results:

• The device enables fully automated, hands-free protein quantification with high sensitivity.
• The PeakFluo algorithm demonstrates improved linearity and earlier signal detection compared to commercial qPCR software.
• A convolutional neural network successfully classified samples into high/low protein concentration categories, showing potential for clinical applications like obesity patient stratification.

Conclusions:

• The developed point-of-care device offers a sensitive, automated solution for protein quantification.
• The system facilitates disease diagnostics and personalized medicine through rapid biomarker analysis.
• The integration of machine learning provides a novel approach for clinical decision support using quantitative biomarker data.