StratoLAMP-2: A Microfluidics-Free, Deep-Learning Platform for Multiplex Digital Molecular Diagnostics.

Area of Science:

• Biotechnology and Molecular Diagnostics
• Digital Nucleic Acid Testing
• Point-of-Care Technologies

Background:

• Accurate molecular diagnostics are crucial for disease detection and personalized medicine.
• Current digital nucleic acid testing often relies on microfluidics and fluorescence, limiting scalability and accessibility.
• There is a need for cost-effective, scalable, and accessible digital molecular diagnostic platforms.

Purpose of the Study:

• To develop a microfluidics-free, label-free digital nucleic acid quantification platform for multiplex molecular diagnostics.
• To enable absolute quantification of nucleic acids with enhanced accuracy and robustness.
• To provide a low-cost, scalable alternative for point-of-care molecular diagnostics.

Main Methods:

• Developed StratoLAMP-2, a platform using vortex-generated polydisperse droplets and magnesium pyrophosphate precipitate for visual readout.
• Utilized primer concentration-driven stratification of precipitate levels for target identity encoding.
• Employed a rule-based classification and deep learning approach for droplet analysis, combined with volume-aware Poisson modeling.

Main Results:

• Achieved accurate and robust multiplexed absolute quantification of nucleic acids over a broad dynamic range.
• Demonstrated superior accuracy and robustness in droplet segmentation, tracking, and classification using deep learning.
• The platform successfully distinguished between droplets containing Target 1, Target 2, or both.

Conclusions:

• StratoLAMP-2 offers a clinically relevant, low-cost, and scalable alternative to conventional digital molecular assays.
• The microfluidics-free, label-free approach enhances accessibility for decentralized and underserved environments.
• This technology paves the way for widespread adoption of point-of-care molecular diagnostics.