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Deep Learning-Enabled Microfluidic Digital PCR Platform for Efficient Seven-Color Quantification
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DOI：10.1039/D6AN00369A.png)
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Abstract
En 中文
Digital PCR (dPCR); as a high-sensitivity technology for absolute nucleic acid quantification; holds significant value in biomedical research and environmental monitoring. However; current platforms still face challenges in multiplex fluorescence detection and rapid; high-precision droplet imaging. Moreover; the detection process is time-consuming (2-3 hours) and involves high costs. In this study; an integrated micro-droplet digital PCR (ddPCR) detection and analysis system was developed; featuring a droplet-based microfluidic chip; a high-precision thermal cycling module; and a seven-color filter-wheel-based imaging system (ATTO425 to CY7) to facilitate a seamless workflow from droplet generation to multiplex imaging. To address the challenges of identifying and segmenting massive droplets in complex fluorescence backgrounds; this paper proposes a detection method based on the You Only Look Once version 5 (YOLOv5) deep learning architecture. By integrating global coordinate remapping and sliding-window detection; the system enables rapid processing of ultra-high-resolution images (2; 448 × 10; 000 pixels). The end-to-end analysis pipeline achieved 99.8% overall accuracy in under 800 ms. Consequently; the total detection cycle for the full digital PCR process has been successfully reduced to under one hour. Furthermore; full-process validation experiments demonstrated excellent linearity across all fluorescence channels; with R2 values exceeding 0.999; and a coefficient of variation (CV) for quantitative repeatability of less than 2% across various concentrations. These results verify the system's precision; stability; and reproducibility. The developed system significantly enhances the throughput and accuracy of ddPCR detection; and the proposed algorithm further advances the practical application of deep learning in digital PCR image analysis.
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1.6W
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