StratoLAMP: Label-free, multiplex digital loop-mediated isothermal amplification based on visual stratification of...

Area of Science:

• Biomedical diagnostics
• Molecular biology
• Digital assays

Background:

• Multiplex nucleic acid detection is crucial for biomedical applications.
• Current methods often rely on costly and complex fluorescent dyes or probes.
• There is a need for simpler, more cost-effective multiplex detection strategies.

Purpose of the Study:

• To introduce StratoLAMP, a novel label-free method for multiplex digital nucleic acid detection.
• To demonstrate the feasibility of using visual precipitate stratification and deep learning for nucleic acid quantification.
• To develop a cost-effective and simplified alternative to existing multiplex assays.

Main Methods:

• Developed StratoLAMP utilizing differential primer concentrations for varied precipitate yields.
• Employed deep learning image analysis for stratifying precipitates and identifying nucleic acid targets within droplets.
• Optimized amplification reagents and conditions for efficient precipitate generation.

Main Results:

• Achieved accurate, simultaneous quantification of two nucleic acid targets.
• Implemented a deep learning pipeline with 94.3% accuracy for droplet detection.
• Demonstrated StratoLAMP's effectiveness in label-free, multiplex nucleic acid assays.

Conclusions:

• StratoLAMP provides a unique, label-free approach for multiplex nucleic acid quantification.
• The method leverages visual precipitate stratification and deep learning, enhancing cost-effectiveness.
• StratoLAMP represents a significant advancement in digital nucleic acid detection technologies.