Weight Differences-Based Multi-level Signal Profiling for Homogeneous and Ultrasensitive Intelligent Bioassays

Area of Science:

• Biomedical Engineering
• Analytical Chemistry
• Immunotechnology

Background:

• Current high-sensitivity immunoassays face challenges in balancing sensitivity with operational simplicity.
• Complex signal generation and sophisticated readout devices often hinder accessibility.

Purpose of the Study:

• To introduce Mata, a novel homogeneous intelligent analysis strategy for high-sensitivity immunoassays.
• To demonstrate a method that simplifies operations while maintaining or improving assay sensitivity.

Main Methods:

• Utilizing nanomagnetic labeling on polystyrene subunits for magnetically regulated kinetic signal expression.
• Employing a multi-level signal classifier with signal weight analysis and deep learning models for signal quantification.
• Performing analysis using standard bright-field optical imaging.

Main Results:

• Achieved ultra-high sensitivity for interleukin-6 detection at 0.61 pg/mL within 20 minutes.
• Demonstrated sensitivity comparable to digital immunoassays and over 22-fold improvement compared to chemiluminescence immunoassay.
• Reduced detection time by over 70% compared to conventional methods.

Conclusions:

• Mata offers a promising solution for high-sensitivity analysis with wide accessibility due to its simplified hardware requirements.
• The intelligent analysis strategy effectively balances high sensitivity with convenient operation.
• Homogeneous reaction and standard optical imaging enable broad applicability.